Vegetation Change in Katsina State, Nigeria: Influence of Local Perceptions and Land Use Land Cover Dynamics

Vegetation Change in Katsina State, Nigeria: Influence of Local Perceptions and Land Use Land Cover Dynamics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Vegetation Change in Katsina State, Nigeria: Influence of Local Perceptions and Land Use Land Cover Dynamics Mohammad Hadi Ahmad, Abdullahi Jibrin, Abubakar Ahmed, Mohd Yusoff Ishak, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2402739/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Changing vegetation affects microclimates, groundwater tables, desertification, and biodiversity at the landscape level. The objective of this study is to assess the land cover dynamics and local perception of the influence of land use on vegetation change in Katsina State, Nigeria. Remote sensing and Geographic Information System (GIS)-based analysis, key informant interviews, and a semi-structured questionnaire covering 400 households were used to examine the driving forces behind vegetation change across Katsina State. As a result of the household survey, 86.5% (n = 400) of respondents reported a decline in vegetation in the study area, aligning with the Land Use Land Cover analysis phase of the study. The key drivers behind the observed vegetation depletion in the study area include firewood collection, charcoal production, and population growth. There has been an increasing awareness that education has emerged as one of the most significant socioeconomic factors influencing respondents' perceptions of these drivers. In spite of this, the unsustainable vegetation changes observed in this study have a negative impact on rural livelihoods and the management of natural resources in rural areas. This study recommends the implementation of sustainable land use policies that promote land-use practises that support economic growth and development. vegetation change local perception land use land cover Nigeria Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction As a result of climate change, land use, and habitat loss, global vegetation has changed dramatically over the past 18,000 years (Mottl et al. 2021 ; Li et al. 2022 ). However, the magnitude and patterns of vegetation change are poorly understood at the global scale (Chapin 2003 ; Mottl et al. 2021 ). The term LULC change refers to the transformation of the natural environment or wilderness into a built environment, such as fields, pastures, industrialization, settlement, agricultural practices, etc (Abbas et al. 2010 ). It has been demonstrated that LULC changes have significant impacts on the environment at the local, regional, and global levels (Kilic et al. 2006 ; Yaro and Abdulrashid 2017 ; da Silva et al. 2021 ). LULC change is a cause and consequence of global environmental change (Song et al. 2018 ). There is no doubt that anthropogenic activities have the potential to cause long-term effects on the loss of habitat in different regions of the world based on the structure and composition of vegetation across different land uses. The Sahel, for example, may experience seasonal changes in vegetation distribution due to the variability of annual precipitation (Jibrin and Jaiyeoba 2013 ). Changes in vegetation cover influences climate through biogeochemical, anthropogenic, and biophysical processes (Duveiller et al. 2018 ). As a result of LULC, global vegetation cover has changed over the last decade. There have been shifts in physiognomic vegetation characteristics, attributed to natural and anthropogenic factors, especially in arid and semi-arid Africa (Vanacker et al. 2005 ; Wittig et al. 2007 ). Deforestation is one of the biggest impacts of land use on land cover, especially in Africa (Kouassi et al. 2021 ). Nigeria loses about 350,000–400,000 hectares of vegetation a year due to LULC, human activities, and climate change (Akpu et al. 2017 ). As a result of LULC, Nigeria lost 21% of its forests between 1990 and 2005 (Aju et al. 2015 ). Cropland is increasing at an average of 554,657 hectares per year, while the forested area is diminishing at a rate of 105,865 hectares per year in Nigeria (Ogar et al. 2016 ). Much vegetation has been deliberately removed for infrastructural development, fuel wood, mineral exploration, expansion of settlements, and other LULC (Akpu et al. 2017 ; Akpu et al. 2017 ). For example, 65 of 560 tree species in Nigeria face extinction, while many others are at different stages of risk (Soule et al. 2016 ). Yaro and Abdulrashid ( 2017 ) emphasized that built-up areas and farmland dominate the LULC in Katsina state, Nigeria, at the expense of vegetation In most developing countries, like Nigeria, vegetation cover is regarded as an open-access resource (Osemeobo 1991 ), which can therefore be overexploited in the present with no regard for the needs of future generations (Klee et al. 2000 ; Hula 2010 ; Kankara 2013 ). There is a decline in plant diversity in the dry forests of Nigeria, which are important sources of fruit, food, and medicinal resources and are under threat from climate change (AbdulHakim et al. 2017 ). Additionally, natural and anthropogenic threats and direct and indirect consequences of socio-economic development have contributed to the destruction of vegetation in all the ecological zones in Nigeria (Ishaya et al. 2008 ). It is well known that tropical deforestation is one of the major causes of environmental change in Nigeria, and many of the completed LULC studies have focused on tropical deforestation (e.g., (Mengistu Bahir et al. 2007 ; Olokeogun et al. 2014 ; Elijah et al. 2019 ; Olorunfemi et al. 2020 ; Njoku and Tenenbaum 2022 ). There are still a lot of places outside Nigeria's tropical forests where documentation of changes in LULC is a challenge (Yaro and Abdulrashid 2017 ). Much of the available research on LULC change in Nigeria focused on forest and sub-humid zones located in the southern parts of the country (Yaro and Abdulrashid 2017 ). The objective of this study is to assess the influence of local perception and land use land cover dynamics on vegetation change in Katsina State, Nigeria. In addition to planning, geographers, environmentalists, and policymakers, remote sensing and GIS are crucial for mapping and detecting changes in LULC (Abbas et al. 2010 ). Accordingly, monitoring vegetation change and attribution analyses have become an essential part of environmental research globally (Buitenwerf et al. 2015 ; Li et al. 2022 ). The factors that influence land use and vegetation cover change in Katsina are not well understood. Indeed, few gray studies have focused attention on LULC change at a small scale at local government or district levels in Katsina state (e.g., (Abbas et al. 2010 ; Yaro and Abdulrashid 2017 ; Idris et al. 2019 ). The study area in the Sahelian part of Nigeria faces rainfall variability and population growth (Abbas et al. 2010 ), which increase pressure on natural vegetation (Musa and Kabuga 2018 ). This study provides hands-on information for policymakers in planning LULC for present and future generations and preserving the vegetation and the environment. Materials And Methods The study area Katsina state is located in the Northwestern part of Nigeria (Tukur and Akobundu 2014), geographically lies between latitude 110 07’ 49” and 130 22’ 57” North of the equator and longitude 60 52’ 03” and 90 09’ 02” East of the Greenwich meridian (Fig. 1 a). Katsina state has an estimated population of 5,801,587 (Federal Republic of Nigeria 2009 ) and covers an area of about 23,938 square kilometers (Ahmadu et al. 2022 ). The state covers three agroecological zones; the Sahel, Sudan, and the Northern Guinea Savanna with Savanna and wet soils (Fig. 1 b). The climate is tropical wet and dry, with semi-arid steppe types coded AW and BS (Umar et al. 2021 ). Annual rainfall in Katsina state ranges between 350 to 1000 mm, and temperature between 290C to 310C (Idris et al. 2019 ). The rainfall pattern in the zone is characterized by high inter-annual variability in spatial and temporal dimensions that frequently cause severe and widespread droughts (Oladipo 1993). The rainy season is between April and October when the prevailing wind is from the Southwest (Ohunakin et al. 2011 ). The dry season lasts from November to March when the prevailing wind is from the Sahara, known as the Harmattan (Umar 2016 ). The study area has four distinct seasons: hot and dry weather between February and May. From June to October, the rainy or Damina season begins, with over 90% of the annual rainfall; the Cool Dry Season (Kaka) is the harvest season between October and November, with less than 8% of the annual rainfall (Ahmad and Daura 2019 ). Between November and February, the dry air from the north brings no rainfall, but the transported harmattan dust is deposited and replenishes soil nutrients. A high level of dust circulates during the day and a low level of chilly air at night during this period (Ahmad and Daura 2019 ). Soil texture and major crops in the study area is presented in Fig. 1 c and 1 d. Reconnaissance survey This was the first activity carried out in the area for this research. It made the researcher acquainted with the study area. During this activity, observation was made on the nature and distribution of vegetation in the area to assess vegetation change driven by climatic indices and land use land cover in the study area. The field visit is necessary to identify areas going through serious vegetation changes and to identify key informants who know the study locations. These include people using vegetation resources for their livelihood, such as charcoal sellers, farmers, livestock production, etc. Type and sources of data The data types include Landsat Multispectral scanner (MSS), Thematic Mapper ™ 1999, Enhanced Thematic Mapper (ETM), and Operational Land Imager (OLI) as well as Sentinel 2A (2019) (see Table 1 ). These data were sourced from Global land cover facility of the University of Maryland. Population data were obtained from the National Bureau of Statistics, Nigeria, website ( www.nigerianstat.gov.ng/download ) to postulate the relationship between population, human activities such as wood extraction, infrastructure extension, and farming intensity. Table 1 Satellite images with their acquisition dates, resolution, and cloud cover Satellite ID Sensor ID Path/Row Image Acquisition Date Spatial Resolution Cloud Cover Landsat 4 MSS 188/51 1989-12-13 30m 9.00 Landsat 4 TM 189/51 1989-03-17 30m 5.20 Landsat 4 TM 189/52 1989-03-23 30m 0.00 Landsat 5 TM 188/51 1999-02-13 30m 4.23 Landsat 5 TM 189/51 1999-03-27 30m 3.20 Landsat 5 TM 189/52 1999-02-18 30m 0.00 Landsat 7 ETM 188/51 2009-11-17 30m 5.20 Landsat 7 ETM 189/51 2009-11-23 30m 10.0 Landsat 7 ETM 189/52 2009-11-02 30m 9.30 Landsat 8 OLI/TIRS 188/51 2019-11-12 30m 0.00 Landsat 8 OLI/TIRS 189/51 2019-11-18 30m 0.00 Landsat 8 OLI/TIRS 189/52 2019-11-29 30m 0.00 Kappa statistics between 0.61–0.80 are often considered substantial, and values > 0.81 are almost perfect, although these divisions are obviously arbitrary and should only be used as general guidance for discussion (Palmieri et al. 2020). An analysis of land cover changes, evaluation of land cover transitions, and simulations of future land changes were conducted in this study using Land Change Modeler (LCM). It is a cutting-edge land planning and decision-making software tool used for conservation prioritisation and planning (Sahalu 2014 ). The software is included in IDRISI Selva Remote Sensing and GIS software. Therefore, to analyze and predict future changes in land cover, land cover maps classified from different dates are required in this study. To perform the analysis in this study, we follow three stages in LCM modeling of land use changes: The following steps were used to analyze the maps of land use and land cover of the study area obtained from image classifications for 1989, 1999, 2009, and 2019. i. Decadal changes (short-term changes) will be determined, i.e. (1989, 1999, 2009, and 2019). ii. Changes between 1989 and 2019 are determined, which refers to long-term changes. All the steps above were analyzed based on the principle of land change analysis, maps of gains and losses, contributions to net change, transitions of land cover classes between different categories, and spatial trend analysis both in the map and graphical form. To run the actual modeling, future land use modeling with LCM was performed using potential transition maps of acceptable accuracy. This study explores the potential power of explanatory variables by transitioning from a group model to a set of sub-models. The lists of all transitions between the two lands cover maps (1985) and (2019). The transitions specify which factors must be taken into account in order to generate the transition potential (Nuissl et al. 2009). In the case of this study, transitions from all land cover classes between 1989 and 2019 were considered. Logistic Regression and Multi-layer perceptron were used to model these selected transitions in LCM. Because of the advantage of the Multi-layer Perceptron neural network to run multiple transitions. A multi-Layer perceptron (MLP) is a feed-forward Artificial Neural Network (ANN) with one or more layers between input and output layers. The final step of transition potential modeling was used to run the transition sub-model to create the transition potential maps. Thus, the generated potential maps can be used to predict land use changes for future dates. The default procedure, Markov Chain analysis, was run for this study to determine the amount of change using two-land cover maps (1989 and 2019) along with the date specified. The procedure determines how much land would be expected to transition from the later date (2009) to the prediction date (2019) based on a projection of the transition potentials into the future and creates a transition probabilities file. We used soft prediction for future scenarios in the Change Allocation panel, which yielded a vulnerability change map (Sahalu 2014 ). Thus, a map for 2019 of the study area was simulated to compare with the ‘actual’ land cover map of 2019. This was done by running a 3-way cross-tabulation between the later land cover map (a map of 2019), the prediction map (simulated map of 2019), and a map of reality (actual map for 2019). See Fig. 2 for the methodology flow chart. The Human Influence Index (HII) is a measure of direct human influence on terrestrial ecosystems using the best available data sets on human settlement (population density, built-up areas), access (roads, railroads, etc.), landscape transformation (land use/landcover). Human Influence Index (HII) was determined using Land Use, buildings, urban polygon, road networks, population density, and protected areas variables. Variables were projected into the same projection, same processing extent, and same cell size for consistency of the analysis. Population density is scaled continuously from 0 to 10 within each cell. Land cover types in every cell are used to value buildings, urban polygons, and protected areas. Roads, rivers, and streams are scored according to direct and indirect effects; 500 m on either side of a road is given a score of 8, with a score of 4 exponentially decaying from 500 m away from the road out to 15 km. Rivers and streams are assigned a pressure score of 4, exponentially decaying to 15 km. HII values range from 0–50 for each cell. A raster calculator from Arcmap 10.8 was used to add all the variables and produce an HII map of the katsina state. Sample size According to the National Population Commission (2006) census, the study area's population is 1,081,703. People's views on the driving forces behind vegetation change were captured using a Key Informant Interview (KII) and semi-structured questionnaire. People's experience is necessary because the area in the extreme north is more prone to drought, land degradation, and desertification. The projection of the population is based on the population growth rate of 3% (Federal Republic of Nigeria 2009 ) using the formula: Where: Pt + n = future population (2019) Pt = base year population (2006); r = growth rate (3%); n = interval between future population and base year population (2019–2006) = 13years and e = exponential Based on the projected population of the study area 2019 (1,597,654) Yamane (Yamane 1967 ) formular for sample size determination was used to get the number of respondents for questionnaire administration: The formula was simplified and adjusted to be more accurate than Cochran’s sample size formula Where n = Number of samples N = number of populations under study e = error tolerance (level) or margin error at a proportion of population given as 0.05% 399.998, rounded up to 400 respondents, were selected for the questionnaire administration. The questionnaire was randomly administered to the selected household in the study area. However, the sample size for each ward varied with its population size through the use of: Where: N = total population of the study area; Q = total sample size and n = population of LGA. Sampling technique The cross-sectional study was carried out in six (6) LGAs selected from the entire state through purposive sampling. The selected LGAs are located at the extreme north in consideration of the three vegetation zones within the study area. The sample locations are Baure, Jibia, Kaita, Mai Adua, Mashi and Zango LGAs. Random and systematic sampling techniques were used to administer questionnaires on household heads per housing unit which comprises farmers and non-farmers (aged 40 and above) who lived in the area and or have cultivated land in the study area for at least 10 years or more, the first house was picked randomly from the selected areas to determine the starting point of questionnaire administration, and others are then picked at regular intervals predetermined by the research team. Four hundred (400) questionnaires were distributed to the sampled rural dwellers in the study area, and four hundred questionnaires (400) were dully completed and returned. Based on the objective, a scheduled interview was prepared to collect relevant information from the respondents. An interview schedule was used to conduct personal interviews with the respondents. Information about historical environmental changes was obtained through interviews with elderly people (heads of the village) in the study area. Table 2 presents the sampling size of the selected LGAs. Table 2 Sample size by the population of the selected LGAs in Katsina State S/N LGAs Population of the selected LGAs (2006) Projected population (2019) Sample size of selected LGAs 1 Jibia 167,435 247,298 62 2 Kaita 182,405 269,409 67 3 Mashi 171,070 252,667 63 4 Mai Adua 201,800 298,055 75 5 Zango 156,052 230,486 58 6 Baure 202,941 299,740 75 Total 1,081,703 1,597,655 400 Key Informant Interview (KII) Information about historical changes on the driving forces behind vegetation was obtained through interviews held with people that have first-hand knowledge about the community vegetation and climate change, elderly people (heads of village and community leaders) and agency representatives, community residents, and local business owners were chosen with the help of traditional rulers (Silva and McDill 2004 ). KII discussion was held with twelve key informants and two informants from each local government area using an open-ended interview schedule to respond to key informants of the study (see Appendix II). The key informants were identified during questionnaire administration. However, the researcher and research assistants were assisted by the villagers in translation and description during interviews with the key informants. The Key Informant Interviews was conducted at Baure, Jibia, Kaita, Mai Adua and Mashi LGAs. The responses of the interviewees were recorded, refined, and analyzed using descriptive statistics of frequency, percentages Principal Component Analysis (PCA) and regression analysis in the IBM SPSS Statistics 20 environment. Results Land Use Land Cover on Vegetation Dynamics Land use land cover datasets were used to assess the drivers of vegetation change over the study periods in the study area. The results of this assessment are presented in the form of maps and statistical table, respectively. The spatial analysis of land use land cover for Katsina state shows the extent of vegetation cover change over time. It is evident from the LULC maps that vegetation in the study area has undergone tremendous transformation due to human-induced conversion of the semi-natural vegetation to other land use types such as farmland. Results were verified during field research in October 2020 and March 2021. Land Use Land Cover of Katsina State Estimated from Landsat Data In this section, we broadly examine the changes in the LULC of Katsina state in four phases. The LULC changed from 1989, 1999 to 2009 and 2019. As a result of over-cultivation, deforestation, overgrazing, industrialization, and urbanization, vegetation, farmland, and other land use changed in Katsina state. One of the main focuses of carrying out this study is to prompt the investigation of land degradation leading to desertification. The supervised classification for 1989, 1999, 2009, and 2019 was carried out for the study area, using six land use classes: bare land, farmland, built-up area, vegetation, rock outcrop, and waterbody. Table 3 Area and Percentages of Land Use Land Cover Change of Katsina State (1989–1999) Land Use Land Cover Class Period 1989 1999 Area change (km 2 ) % Cover change Annual rate of change (km 2 /year) % Annual rate of change (%/year) Area (km 2 ) % Area (km 2 ) % Built-up area 16013.12 0.68 22564.95 0.95 6551.83 0.28 655.18 0.03 Vegetation 1873611.65 79.14 1690503.60 71.41 -183108.05 -7.73 -18310.81 -0.77 Water body 1497.42 0.06 1098.09 0.05 -399.33 -0.02 -39.93 0.00 Farmland 456650.62 19.29 638788.98 26.98 182138.36 7.69 18213.84 0.77 Bare land 11385.89 0.48 8576.83 0.36 -2809.06 -0.12 -280.91 -0.01 Rock outcrop 8173.25 0.35 5799.12 0.24 -2374.13 -0.10 -237.41 -0.01 Total 2367331.95 100.00 2367331.57 100.00 The Landsat data was used to evaluate variations in previous LULC (1989–2019) patterns using the Maximum Likelihood Supervised Classification algorithm (MLSC) (Figs. 3 , 4 , 5 , and 6 ). In the study area, 1989–1999 exhibits little changes in the LULC classes (See Table 3 ). This period (1989–1999) saw the expansion of farmlands at an annual rate of 0.77% changes per year and built-up area at 0.03% annual rate of change per year (Table 3 ). In 1989 vegetation was predominant across the study area (See Fig. 3 ), embedded with build-up areas and farmlands in the southern and central parts of the study area. Higher farmlands in the south of parts of the study area, such as Funtua, Dandume, Sabuwa, Bakori, and Danja LGAs, result from fertile land, and the inhabitants are predominantly farmers. Water bodies are seasonal and flow during the wet season. These seasonal rivers include Marigo, Damari, Maikategi, Magajin Dutse, and Kara. An analysis of the LULC shows that built-up areas are encroaching on other land uses (Fgure 3). Consequently, detecting and predicting LULC changes have become an essential consideration in a variety of fields, including vegetation modeling rural and urban plans (Paul 2021 ), identifying LULC change landscapes (Hussain et al. 2020 ) for advancing conservation efforts, studying dynamics of desertification and built-up expansion scenario in a particular watershed and region level. However, in 1999 built-up area began to expand due to an increase in population. However, the annual rate of changes in the built-up area was insignificant at 0.03% annual change per year. These changes in the LULC were at the vegetation's expense (Table 3 and Fig. 4 ). In the same period in 1999, the predominance of forest land began to decline because of farmland expansion. Other LULC classes, water bodies, bare land, and rock outcrops started to decline in spatial coverage (See Table 3 and Fig. 4 ). Table 4 Area and Percentages of Land Use Land Cover Change of Katsina State (1999–2009) Land Use Land Cover Class Period Area Change (km 2 ) % Cover Change Annual Rate of Change (km 2 /year) % Annual Rate of Change (%/year) 1999 2009 Area (km 2 ) % Area (km 2 ) % Built-Up Area 22564.95 0.95 39295.04 1.66 16730.09 0.71 1673.01 0.07 Vegetation 1690503.60 71.41 1538099.21 64.97 -152404.39 -6.44 -15240.44 -0.64 Water Body 1098.09 0.05 1973.51 0.08 875.42 0.04 87.54 0.00 Farmland 638788.98 26.98 768800.18 32.48 130011.20 5.49 13001.12 0.55 Bare Land 8576.83 0.36 10183.47 0.43 1606.64 0.07 160.66 0.01 Rock Outcrop 5799.12 0.24 8980.88 0.38 3181.76 0.13 318.18 0.01 Total 2367331.57 100.00 2367332.29 100.00 The percentage changes in the land use classes from 1999 to 2009 were computed (Table 4 ). Figure 5 and Table 4 show that the composition of LULC classes in the study area varied significantly at different dates, according to a comparative analysis of the total area for each LULC class in the area. Table 4 also suggests that the loss of vegetation cover between 1999 and 2009 in Katsina state was due to the expansion of both built-up areas and farmlands. From this, the built-up areas and farmlands show an increase in their distribution from 1999 to 2009 (see Table 4 and Fig. 5 ). Table 5 Area and Percentages of Land Use Land Cover Change of Katsina State (2009–2019) Land Use Land Cover Class Period Area Change (km 2 ) % Cover Change Annual Rate of Change (km 2 /year) % Annual Rate of Change (%/year) 2009 2019 Area (km 2 ) % Area (km 2 ) % Built-Up Area 39295.04 1.66 53098.77 2.24 13803.73 0.58 1380.37 0.06 Vegetation 1538099.21 64.97 1366899.37 57.74 -171199.84 -7.23 -17119.98 -0.72 Water Body 1973.51 0.08 1429.81 0.06 -543.70 -0.02 -54.37 0.00 Farmland 768800.18 32.48 928535.76 39.22 159735.58 6.75 15973.56 0.67 Bare Land 10183.47 0.43 9369.87 0.40 -813.60 -0.03 -81.36 0.00 Rock Outcrop 8980.88 0.38 7998.07 0.34 -982.81 -0.04 -98.28 0.00 Total 2367332.29 100.00 2367331.65 100.00 We explain some of the data and the direction of LULC in Table 4 . In 2019, built-up areas dominate, not only in Katsina, but also in smaller, scattered built-up areas that have expanded into adjacent local governments, like Daura, Funtua, Dutsin-Ma, and Malumfashi. Figure 5 and Table 5 show that between 2009 to 2019, forest areas declined significantly. Rapid growth in the absence of adequate plans and infrastructure expedites LULC changes associated with degrading ecosystem services and human well-being. Trends of change were manifested (see Fig. 5 ); a gradual increase in a built-up area and vegetation was decreased over the study period. The results showed that farmland and built-up areas had replaced vegetation. In addition, the built-up area and farmland are rising due to unplanned population growth and migration (Khan et al. 2014 ). A major finding of the study was that urban growth is influenced both by economic and geopolitical factors (Oyeleye 2013 ). Predicting future LULC change, the transition assessment among different LULC is a critical aspect (Leta et al. 2021 ). Urbanization triggered the LULC change in the study region in the last few decades. Table 6 Area and Percentage of Predicted LULCC of Katsina (2050) Land Use Land Cover Type Area (hectare) Percentage (%) Built-Up Area 1235808.70 5.17 Water Body 48214.30 0.20 Farmland 5063540.80 21.17 Rock Outcrop 100224.10 0.42 Bare Land 2049604.30 8.57 Vegetation 15420727.20 64.47 Total 23918119.40 100.00 Table 6 shows computed statistics of the predicted land use land cover changes of Katsina state (2050). The percentage changes in the LULC prediction indicate that build-up area, water bodies, farmland, and bare land will increase at the expense of vegetation (Table 6 and Fig. 7 ). By persistently reducing natural vegetation and its protective effect on the landscape, human activities are encroaching on natural vegetation, making the area vulnerable to desertification. The results were confirmed by the findings of Garba and Al-amin ( 2014 ) and Idris S et al. ( 2019 ) revealed that there is an increase in Farmland and Settlement and a decrease in vegetation because of deforestation and land degradation, which has resulted in desert encroachment. Changes are mostly driven by climate and socio-economic factors. Southern Katsina is greener and has a greater number of agricultural practices than northern Katsina due to differences in climate (Fig. 7 ). Managing natural resources to support human life and to maintain ecosystem stability will be possible if land use is properly monitored. Monitoring will help monitor the degradation of land, which leads to desertification. Land degradation, which leads to desertification, can be monitored through proper land use monitoring. Further, it will help plan, use, and manage natural resources that support humans and ecosystems. Based on the independent and dependent variables, we predicted land use land cover change. A total of twelve dependent variables, such as elevation, slope layer layer, aspect layer, distance from the protected areas, were considered (see Fig. 8 a, b, c, and d). The land use and land cover maps for 2009 and 2019 were considered independent variables. There is a range of elevation values in the study area, starting at 379m and ending at 755m from mean sea level (MSL) (See Fig. 8 a). There are flat surfaces in the study area based on slope and aspect maps (Fig. 8 b, c). Open Street Map vector layers were used to calculate distances to roads and streams (Fig. 9 a). The distance from the buildings indicates urban growth in the core area of the state and less growth in the northwest, southeast, and northeast parts of the state (Fig. 9 d). Most urban areas have high, modest, to minimal road densities (Fig. 9 a). The distance from streams is another important factor for the land use land cover dynamics (Fig. 9 c). LULCs are transformed into farms and residential areas in population dense areas (Fig. 10 a). Away from urban areas, LULC changes are less intense (Fig. 10 b). The temperature trend depends on the types of LULC and their conversions. The northern part of the study has high temperatures as a result of spare vegetation (Fig. 10 c). Rainfall influences vegetation growth, so the more rain, the more vegetation grows (Fig. 10 d). To generate the transition potential matrix, we used the independent and dependent variables above. Discussion Perceived Drivers of Vegetation Change Table 7 shows the result obtained from Principal Components Analysis (PCA) of the drivers behind vegetation changes in Katsina state. Five components were identified from the PCA results accounting for a cumulative variance of 74.851% in original variables using a cut-off point value (Eigenvalues) of 1. The first PCA contributed 28.516% of the total variance, while the second, third, fourth, and fifth contributed 18.545%, 11.485%, 9.247%, and 7.059%, respectively. The five PCA components are named based on their loadings in relation to the original variables. This result implies that the new model variables to explain the driving forces behind vegetation change in Katsina are the five resulting components of the PCA, named to identify the groups of states with which they are most closely associated (firewood collection, charcoal production, agricultural expansion, climate variability, and over-cultivation). Table 7 Perceived drivers of vegetation changes in Katsina using PCA Component Perceived Driver Rotation Sums of Squared Loadings Rotated Component Matrix Eigenvalues % of Variance Cumulative % 1 Firewood Collection 0.831 5.703 28.516 28.516 2 Charcoal Production 0.762 3.709 18.545 47.061 3 Agricultural expansion 0.795 2.297 11.485 58.545 4 Climate Variability 0.851 1.849 9.247 67.793 5 Overcultivation -0.845 1.412 7.059 74.851 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Figure 11 indicates the extent of the influence of humans on the vegetation dynamics in the study area. Maximum human impact on the vegetation is found around the northcentral part of the study area covering (Batagarawa, Rimi, Mani, and Bindawa LGAs), in the central part of the study area (Dan Musa, Sandamu, and Dutsin Ma), in the southern part (Bakori and Funtua LGAs). Overall, the maximum influence of humans on vegetation in the study area covers an area of (436835.59 ha), considerable human influence (697665.45 ha), medium influence (686878.09 ha), small human influence (383662.48 ha) and no influence (168036.57 ha) (See appendix V). Respondents identified five (5) factors as essential drivers contributing to vegetation changes in Katsina, especially during the period under study, 1989-2019. The study area's top five perceived drivers of vegetation changes were firewood collection, charcoal production, agricultural expansion, climate variability, and over-cultivation. Firewood collection and charcoal production ranked first and second, respectively (Table 7). During key informant interviews, the main causes of vegetation decline in the study area were identified as firewood collection, charcoal production, over-cultivation, population growth, poverty, and agricultural expansion. Based on the results from the questionnaires and key informant interviews, local communities perceived firewood collection, charcoal production, agricultural expansion, climate variability, and over-cultivation as the critical drivers of vegetation changes in the study area. High poverty levels, population growth, lack of law enforcement by the government, and high cost of agricultural input triggered these drivers. Influence of Socioeconomic Variables on the Perceived Drivers of Vegetation Change A summary of the socio-economic factors and the nature of their influence on the vegetation change are presented in appendix 1. Several socio-economic factors influence vegetation change in this study. The socio-economic factors considered and tested using logistic regression model include age, sex, education level, marital status, monthly income, source of livelihood, number of dependents, and farming experience. Results revealed that education level negatively and significantly affected (p < 0.05) high perceptions of local communities on firewood collection, climate variability, and over-cultivation as vegetation change drivers in Katsina (See appendix 1). Charcoal production and expansion in agriculture were not significantly influenced by age, sex, education level, marital status, monthly income, source of livelihood, number of dependents, or farming experience. The results show a positive regression coefficient of the respondent’s age, farming experience, and marital status, which implied a relationship. This suggests that an increase in the variables of the respondents increases the chance of climate variability being the driving force for vegetation change in the study area. An increase in the factors of the respondents of the community increases the possibility of increasing environmental degradation. However, a negative regression coefficient was noticed in the level of education, source of livelihood, and sex, which presents an indirect relationship. A person is exposed to knowledge on wise use of resources, including agricultural practices, because an educated person will tend to practice more environmentally friendly agricultural land use. Conclusion The expansion of farmland and urban areas and excessive deforestation have caused land degradation and soil erosion, degrading vegetation cover. This study assesses the influence of local perception and land use land cover dynamics on vegetation change in Katsina State, Nigeria. Using GIS and remote sensing helps to integrate the spatial and attribute data from respondent discussions and to analyze vegetation and LULC, as well as respondents' perceptions of land use. In Katsina state, humans are regarded as the main drivers of vegetation change through different LULC practices. The study area's top five factors driving vegetation change were firewood collection, charcoal production, agricultural expansion, climate variability, and over-cultivation. By 2050, according to this study, there will be an alarming increase in the rate of vegetation and LULC in the study area. However, this study recommends that further research adopt other geospatial simulation models, such as TOPSIS, to predict the condition of vegetation to its driving forces across the study area. Moreover, because of positive link between climate variability and vegetation in the state, the tree-planting exercise by NGOs and any other body/individuals should target the wet season for the survival and development of such plantations. Educating people on the impact of deforestation and climate change on the environment will help to ensure that LULC practices are sustainable in the long run by reducing deforestation. Declarations Ethics approval and consent to participate This study does not involve human or animal participation. It has no experiments and does not involve human data and/or tissue. Consent for publication The study does not involve children or individual details and/or 100% data usage Availability of data and materials All data generated or analyzed during the study are included in the published article(s) cited within the text and acknowledged in the reference section. Code Availability Not applicable Competing interest All authors declare no competing interest Funding The study is funded by the Tertiary Education Trust Fund (TETFUND) of Nigeria (TETF/ES/UNIV/JIGAWA STATE/TSAS/2019) Authors’ contribution Conceptualization, M.H.A. and A.A., methodology, A.A., software, M.H.A.., formal analysis, M.H.A.; investigation, S.S.D., writing—original draft preparation, A.A.and M.H.A. writing—review and editing, M.H.A.; supervision, M.Y.I., project administration, J.M.A.; funding acquisition, Z.J References Abbas II, Muazu KM, Ukoje JA (2010) Mapping Land Use - land Cover and Change Detection in Kafur Local Government , Katsina , Nigeria ( 1995 - 2008 ) Using Remote Sensing and Gis. Res J Environ Earth Sci 2:6–12 AbdulHakim I. K., Kabiru I. A. MND (2017) ASSESSMENT OF WOODY VEGETATION DIVERSITY IN BABURA AREA, NORTHWESTERN NIGERIA. 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Environ Sci Pollut Res 14:182–189 Yamane T (1967) Statistics, An IntroductoryAnalysis Yaro A, Abdulrashid L (2017) Land use and Land Cover Changes in a Semi-Arid Region of Katsina state, Nigeria. Dutse J Pure Appl Sci 3:430–442 Additional Declarations No competing interests reported. Supplementary Files SupplementarymaterialsEMAS.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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19:44:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2402739/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2402739/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":31129463,"identity":"6c0b90f2-c08a-4158-87cd-22ba69ff29fa","added_by":"auto","created_at":"2023-01-04 22:44:56","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":337016,"visible":true,"origin":"","legend":"\u003cp\u003e(a) map of the study area, (b) ecological condition map, (c) soil texture, and (d) crop grown in the study area\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-2402739/v1/c6186eab4259a829d70b91be.jpeg"},{"id":31129943,"identity":"8bb561be-4aed-490a-a8db-4f2a548613ca","added_by":"auto","created_at":"2023-01-04 22:52:56","extension":"png","order_by":2,"title":"Figure 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6","display":"","copyAsset":false,"role":"figure","size":309997,"visible":true,"origin":"","legend":"\u003cp\u003eLand Use Land Cover Map of Katsina for 2019\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-2402739/v1/5f90e35e2f3ca1d9e4db0db4.jpeg"},{"id":31129947,"identity":"0fcc251d-990d-49cf-9107-03206d2dd9fa","added_by":"auto","created_at":"2023-01-04 22:52:57","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1064447,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted LULC Map of Katsina State 2050\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-2402739/v1/87d21105e00b602f7f738a68.png"},{"id":31129468,"identity":"2afca526-9aac-408a-ac1d-bf6ab0a1eb9f","added_by":"auto","created_at":"2023-01-04 22:44:56","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":561821,"visible":true,"origin":"","legend":"\u003cp\u003eContributing factors to land use and land cover change a) elevation, b) slope layer, c) aspect layer, d) distance from protected\u003c/p\u003e","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-2402739/v1/9c635b4416d8915a7d04e371.jpeg"},{"id":31129470,"identity":"79491962-6204-4894-9991-cd178584523d","added_by":"auto","created_at":"2023-01-04 22:44:57","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":526758,"visible":true,"origin":"","legend":"\u003cp\u003eContributing factors to land use and land cover change and HII a) distance from road, b) distance from river, c) distance from stream and d) distance urban areas\u003c/p\u003e","description":"","filename":"floatimage11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-2402739/v1/20dc9ffa6c8f453f4ea3b886.jpeg"},{"id":31129472,"identity":"9cb50f26-a740-48b1-80e5-ffd5053527e7","added_by":"auto","created_at":"2023-01-04 22:44:57","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":401369,"visible":true,"origin":"","legend":"\u003cp\u003eContributing factors to land use and land cover change and HII a) population density, b) distance from urban area, c) mean annual temperature, and d) mean annual rainfall\u003c/p\u003e","description":"","filename":"floatimage12.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-2402739/v1/617d97644990dea916aeb009.jpeg"},{"id":31129469,"identity":"45ea6df4-4409-4f87-8cbe-cf53d059dab2","added_by":"auto","created_at":"2023-01-04 22:44:56","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":875436,"visible":true,"origin":"","legend":"\u003cp\u003eHuman influence index on vegetation\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-2402739/v1/99eb05004fb5ec86ae6b175b.png"},{"id":31130828,"identity":"6460fb42-0189-451a-867b-a53ea0318944","added_by":"auto","created_at":"2023-01-04 23:09:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2486727,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2402739/v1/ad0a6ad0-047d-425d-bccf-0b109db2e4de.pdf"},{"id":31129461,"identity":"6d994562-2083-4cd1-809d-aff20e528967","added_by":"auto","created_at":"2023-01-04 22:44:56","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":42321,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialsEMAS.docx","url":"https://assets-eu.researchsquare.com/files/rs-2402739/v1/639cdaf043919f96d16b7420.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Vegetation Change in Katsina State, Nigeria: Influence of Local Perceptions and Land Use Land Cover Dynamics","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs a result of climate change, land use, and habitat loss, global vegetation has changed dramatically over the past 18,000 years (Mottl et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the magnitude and patterns of vegetation change are poorly understood at the global scale (Chapin \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Mottl et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The term LULC change refers to the transformation of the natural environment or wilderness into a built environment, such as fields, pastures, industrialization, settlement, agricultural practices, etc (Abbas et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). It has been demonstrated that LULC changes have significant impacts on the environment at the local, regional, and global levels (Kilic et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Yaro and Abdulrashid \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; da Silva et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). LULC change is a cause and consequence of global environmental change (Song et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). There is no doubt that anthropogenic activities have the potential to cause long-term effects on the loss of habitat in different regions of the world based on the structure and composition of vegetation across different land uses. The Sahel, for example, may experience seasonal changes in vegetation distribution due to the variability of annual precipitation (Jibrin and Jaiyeoba \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Changes in vegetation cover influences climate through biogeochemical, anthropogenic, and biophysical processes (Duveiller et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). As a result of LULC, global vegetation cover has changed over the last decade. There have been shifts in physiognomic vegetation characteristics, attributed to natural and anthropogenic factors, especially in arid and semi-arid Africa (Vanacker et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Wittig et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Deforestation is one of the biggest impacts of land use on land cover, especially in Africa (Kouassi et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNigeria loses about 350,000\u0026ndash;400,000 hectares of vegetation a year due to LULC, human activities, and climate change (Akpu et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). As a result of LULC, Nigeria lost 21% of its forests between 1990 and 2005 (Aju et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Cropland is increasing at an average of 554,657 hectares per year, while the forested area is diminishing at a rate of 105,865 hectares per year in Nigeria (Ogar et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Much vegetation has been deliberately removed for infrastructural development, fuel wood, mineral exploration, expansion of settlements, and other LULC (Akpu et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Akpu et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For example, 65 of 560 tree species in Nigeria face extinction, while many others are at different stages of risk (Soule et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Yaro and Abdulrashid (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) emphasized that built-up areas and farmland dominate the LULC in Katsina state, Nigeria, at the expense of vegetation\u003c/p\u003e \u003cp\u003eIn most developing countries, like Nigeria, vegetation cover is regarded as an open-access resource (Osemeobo \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), which can therefore be overexploited in the present with no regard for the needs of future generations (Klee et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Hula \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Kankara \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). There is a decline in plant diversity in the dry forests of Nigeria, which are important sources of fruit, food, and medicinal resources and are under threat from climate change (AbdulHakim et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Additionally, natural and anthropogenic threats and direct and indirect consequences of socio-economic development have contributed to the destruction of vegetation in all the ecological zones in Nigeria (Ishaya et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). It is well known that tropical deforestation is one of the major causes of environmental change in Nigeria, and many of the completed LULC studies have focused on tropical deforestation (e.g., (Mengistu Bahir et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Olokeogun et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Elijah et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Olorunfemi et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Njoku and Tenenbaum \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). There are still a lot of places outside Nigeria's tropical forests where documentation of changes in LULC is a challenge (Yaro and Abdulrashid \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Much of the available research on LULC change in Nigeria focused on forest and sub-humid zones located in the southern parts of the country (Yaro and Abdulrashid \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe objective of this study is to assess the influence of local perception and land use land cover dynamics on vegetation change in Katsina State, Nigeria. In addition to planning, geographers, environmentalists, and policymakers, remote sensing and GIS are crucial for mapping and detecting changes in LULC (Abbas et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Accordingly, monitoring vegetation change and attribution analyses have become an essential part of environmental research globally (Buitenwerf et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The factors that influence land use and vegetation cover change in Katsina are not well understood. Indeed, few gray studies have focused attention on LULC change at a small scale at local government or district levels in Katsina state (e.g., (Abbas et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Yaro and Abdulrashid \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Idris et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The study area in the Sahelian part of Nigeria faces rainfall variability and population growth (Abbas et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), which increase pressure on natural vegetation (Musa and Kabuga \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This study provides hands-on information for policymakers in planning LULC for present and future generations and preserving the vegetation and the environment.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003ch2\u003eThe study area\u003c/h2\u003e\n\u003cp\u003eKatsina state is located in the Northwestern part of Nigeria (Tukur and Akobundu 2014), geographically lies between latitude 110 07\u0026rsquo; 49\u0026rdquo; and 130 22\u0026rsquo; 57\u0026rdquo; North of the equator and longitude 60 52\u0026rsquo; 03\u0026rdquo; and 90 09\u0026rsquo; 02\u0026rdquo; East of the Greenwich meridian (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea). Katsina state has an estimated population of 5,801,587 (Federal Republic of Nigeria \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) and covers an area of about 23,938 square kilometers (Ahmadu et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The state covers three agroecological zones; the Sahel, Sudan, and the Northern Guinea Savanna with Savanna and wet soils (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb). The climate is tropical wet and dry, with semi-arid steppe types coded AW and BS (Umar et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Annual rainfall in Katsina state ranges between 350 to 1000 mm, and temperature between 290C to 310C (Idris et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). The rainfall pattern in the zone is characterized by high inter-annual variability in spatial and temporal dimensions that frequently cause severe and widespread droughts (Oladipo 1993). The rainy season is between April and October when the prevailing wind is from the Southwest (Ohunakin et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). The dry season lasts from November to March when the prevailing wind is from the Sahara, known as the Harmattan (Umar \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). The study area has four distinct seasons: hot and dry weather between February and May. From June to October, the rainy or Damina season begins, with over 90% of the annual rainfall; the Cool Dry Season (Kaka) is the harvest season between October and November, with less than 8% of the annual rainfall (Ahmad and Daura \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Between November and February, the dry air from the north brings no rainfall, but the transported harmattan dust is deposited and replenishes soil nutrients. A high level of dust circulates during the day and a low level of chilly air at night during this period (Ahmad and Daura \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Soil texture and major crops in the study area is presented in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ec and \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ed.\u003c/p\u003e\n\u003ch2\u003eReconnaissance survey\u003c/h2\u003e\n\u003cp\u003eThis was the first activity carried out in the area for this research. It made the researcher acquainted with the study area. During this activity, observation was made on the nature and distribution of vegetation in the area to assess vegetation change driven by climatic indices and land use land cover in the study area. The field visit is necessary to identify areas going through serious vegetation changes and to identify key informants who know the study locations. These include people using vegetation resources for their livelihood, such as charcoal sellers, farmers, livestock production, etc.\u003c/p\u003e\n\u003ch2\u003eType and sources of data\u003c/h2\u003e\n\u003cp\u003eThe data types include Landsat Multispectral scanner (MSS), Thematic Mapper \u0026trade; 1999, Enhanced Thematic Mapper (ETM), and Operational Land Imager (OLI) as well as Sentinel 2A (2019) (see Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). These data were sourced from Global land cover facility of the University of Maryland. Population data were obtained from the National Bureau of Statistics, Nigeria, website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.nigerianstat.gov.ng/download\u003c/span\u003e\u003c/span\u003e) to postulate the relationship between population, human activities such as wood extraction, infrastructure extension, and farming intensity.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSatellite images with their acquisition dates, resolution, and cloud cover\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSatellite ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSensor ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePath/Row\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eImage Acquisition Date\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpatial Resolution\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCloud Cover\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandsat 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e188/51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1989-12-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandsat 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189/51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1989-03-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandsat 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189/52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1989-03-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandsat 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e188/51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1999-02-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandsat 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189/51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1999-03-27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandsat 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189/52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1999-02-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandsat 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eETM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e188/51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2009-11-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandsat 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eETM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189/51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2009-11-23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandsat 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eETM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189/52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2009-11-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandsat 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOLI/TIRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e188/51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2019-11-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n 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\" width=\"605\" height=\"226\"\u003e\u003c/p\u003e\n\u003cp\u003eKappa statistics between 0.61\u0026ndash;0.80 are often considered substantial, and values\u0026thinsp;\u0026gt;\u0026thinsp;0.81 are almost perfect, although these divisions are obviously arbitrary and should only be used as general guidance for discussion (Palmieri et al. 2020).\u003c/p\u003e\n\u003cp\u003eAn analysis of land cover changes, evaluation of land cover transitions, and simulations of future land changes were conducted in this study using Land Change Modeler (LCM). It is a cutting-edge land planning and decision-making software tool used for conservation prioritisation and planning (Sahalu \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). The software is included in IDRISI Selva Remote Sensing and GIS software. Therefore, to analyze and predict future changes in land cover, land cover maps classified from different dates are required in this study. To perform the analysis in this study, we follow three stages in LCM modeling of land use changes:\u003c/p\u003e\n\u003cp\u003eThe following steps were used to analyze the maps of land use and land cover of the study area obtained from image classifications for 1989, 1999, 2009, and 2019.\u003c/p\u003e\n\u003cp\u003ei. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Decadal changes (short-term changes) will be determined, i.e. (1989, 1999, 2009, and 2019).\u003c/p\u003e\n\u003cp\u003eii. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Changes between 1989 and 2019 are determined, which refers to long-term changes.\u003c/p\u003e\n\u003cp\u003eAll the steps above were analyzed based on the principle of land change analysis, maps of gains and losses, contributions to net change, transitions of land cover classes between different categories, and spatial trend analysis both in the map and graphical form.\u003c/p\u003e\n\u003cp\u003eTo run the actual modeling, future land use modeling with LCM was performed using potential transition maps of acceptable accuracy. This study explores the potential power of explanatory variables by transitioning from a group model to a set of sub-models. The lists of all transitions between the two lands cover maps (1985) and (2019). The transitions specify which factors must be taken into account in order to generate the transition potential (Nuissl et al. 2009). In the case of this study, transitions from all land cover classes between 1989 and 2019 were considered. Logistic Regression and Multi-layer perceptron were used to model these selected transitions in LCM. Because of the advantage of the Multi-layer Perceptron neural network to run multiple transitions. A multi-Layer perceptron (MLP) is a feed-forward Artificial Neural Network (ANN) with one or more layers between input and output layers. The final step of transition potential modeling was used to run the transition sub-model to create the transition potential maps. Thus, the generated potential maps can be used to predict land use changes for future dates.\u003c/p\u003e\n\u003cp\u003eThe default procedure, Markov Chain analysis, was run for this study to determine the amount of change using two-land cover maps (1989 and 2019) along with the date specified. The procedure determines how much land would be expected to transition from the later date (2009) to the prediction date (2019) based on a projection of the transition potentials into the future and creates a transition probabilities file. We used soft prediction for future scenarios in the Change Allocation panel, which yielded a vulnerability change map (Sahalu \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). Thus, a map for 2019 of the study area was simulated to compare with the \u0026lsquo;actual\u0026rsquo; land cover map of 2019. This was done by running a 3-way cross-tabulation between the later land cover map (a map of 2019), the prediction map (simulated map of 2019), and a map of reality (actual map for 2019). See Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e for the methodology flow chart.\u003c/p\u003e\n\u003cp\u003eThe Human Influence Index (HII) is a measure of direct human influence on terrestrial ecosystems using the best available data sets on human settlement (population density, built-up areas), access (roads, railroads, etc.), landscape transformation (land use/landcover).\u003c/p\u003e\n\u003cp\u003eHuman Influence Index (HII) was determined using Land Use, buildings, urban polygon, road networks, population density, and protected areas variables. Variables were projected into the same projection, same processing extent, and same cell size for consistency of the analysis. Population density is scaled continuously from 0 to 10 within each cell. Land cover types in every cell are used to value buildings, urban polygons, and protected areas. Roads, rivers, and streams are scored according to direct and indirect effects; 500 m on either side of a road is given a score of 8, with a score of 4 exponentially decaying from 500 m away from the road out to 15 km. Rivers and streams are assigned a pressure score of 4, exponentially decaying to 15 km. HII values range from 0\u0026ndash;50 for each cell. A raster calculator from Arcmap 10.8 was used to add all the variables and produce an HII map of the katsina state.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample size\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the National Population Commission (2006) census, the study area\u0026apos;s population is 1,081,703. People\u0026apos;s views on the driving forces behind vegetation change were captured using a Key Informant Interview (KII) and semi-structured questionnaire. People\u0026apos;s experience is necessary because the area in the extreme north is more prone to drought, land degradation, and desertification. The projection of the population is based on the population growth rate of 3% (Federal Republic of Nigeria \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) using the formula:\u003c/p\u003e\n\u003cdiv class=\"Equation\" id=\"Equa\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"131\" height=\"42\"\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eWhere:\u003c/p\u003e\n\u003cp\u003ePt\u0026thinsp;+\u0026thinsp;n\u0026thinsp;=\u0026thinsp;future population (2019)\u003c/p\u003e\n\u003cp\u003ePt\u0026thinsp;=\u0026thinsp;base year population (2006); r\u0026thinsp;=\u0026thinsp;growth rate (3%); n\u0026thinsp;=\u0026thinsp;interval between future population and base year population (2019\u0026ndash;2006)\u0026thinsp;=\u0026thinsp;13years and e\u0026thinsp;=\u0026thinsp;exponential\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv class=\"Equation\" id=\"Equb\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"261\" height=\"176\"\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eBased on the projected population of the study area 2019 (1,597,654) Yamane (Yamane \u003cspan class=\"CitationRef\"\u003e1967\u003c/span\u003e) formular for sample size determination was used to get the number of respondents for questionnaire administration:\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv class=\"Equation\" id=\"Equf\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"151\" height=\"69\"\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThe formula was simplified and adjusted to be more accurate than Cochran\u0026rsquo;s sample size formula\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv class=\"Equation\" id=\"Equg\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"195\" height=\"115\"\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eWhere n\u0026thinsp;=\u0026thinsp;Number of samples\u003c/p\u003e\n\u003cp\u003eN\u0026thinsp;=\u0026thinsp;number of populations under study\u003c/p\u003e\n\u003cp\u003ee\u0026thinsp;=\u0026thinsp;error tolerance (level) or margin error at a proportion of population given as 0.05%\u003c/p\u003e\n\u003cp\u003e399.998, rounded up to 400 respondents, were selected for the questionnaire administration. The questionnaire was randomly administered to the selected household in the study area. However, the sample size for each ward varied with its population size through the use of:\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv class=\"Equation\" id=\"Equh\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equh\" name=\"EquationSource\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"56\" height=\"62\"\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eWhere: N\u0026thinsp;=\u0026thinsp;total population of the study area; Q\u0026thinsp;=\u0026thinsp;total sample size and n\u0026thinsp;=\u0026thinsp;population of LGA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSampling technique\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cross-sectional study was carried out in six (6) LGAs selected from the entire state through purposive sampling. The selected LGAs are located at the extreme north in consideration of the three vegetation zones within the study area. The sample locations are Baure, Jibia, Kaita, Mai Adua, Mashi and Zango LGAs. Random and systematic sampling techniques were used to administer questionnaires on household heads per housing unit which comprises farmers and non-farmers (aged 40 and above) who lived in the area and or have cultivated land in the study area for at least 10 years or more, the first house was picked randomly from the selected areas to determine the starting point of questionnaire administration, and others are then picked at regular intervals predetermined by the research team. Four hundred (400) questionnaires were distributed to the sampled rural dwellers in the study area, and four hundred questionnaires (400) were dully completed and returned. Based on the objective, a scheduled interview was prepared to collect relevant information from the respondents. An interview schedule was used to conduct personal interviews with the respondents. Information about historical environmental changes was obtained through interviews with elderly people (heads of the village) in the study area. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the sampling size of the selected LGAs.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSample size by the population of the selected LGAs in Katsina State\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS/N\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLGAs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePopulation of the selected LGAs (2006)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProjected population (2019)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample size of selected LGAs\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJibia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e167,435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e247,298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKaita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e182,405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e269,409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMashi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e171,070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e252,667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMai Adua\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e201,800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e298,055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZango\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e156,052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230,486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e202,941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e299,740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,081,703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,597,655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKey Informant Interview (KII)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformation about historical changes on the driving forces behind vegetation was obtained through interviews held with people that have first-hand knowledge about the community vegetation and climate change, elderly people (heads of village and community leaders) and agency representatives, community residents, and local business owners were chosen with the help of traditional rulers (Silva and McDill \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eKII discussion was held with twelve key informants and two informants from each local government area using an open-ended interview schedule to respond to key informants of the study (see Appendix II). The key informants were identified during questionnaire administration. However, the researcher and research assistants were assisted by the villagers in translation and description during interviews with the key informants. The Key Informant Interviews was conducted at Baure, Jibia, Kaita, Mai Adua and Mashi LGAs. The responses of the interviewees were recorded, refined, and analyzed using descriptive statistics of frequency, percentages Principal Component Analysis (PCA) and regression analysis in the IBM SPSS Statistics 20 environment.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eLand Use Land Cover on Vegetation Dynamics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLand use land cover datasets were used to assess the drivers of vegetation change over the study periods in the study area. The results of this assessment are presented in the form of maps and statistical table, respectively. The spatial analysis of land use land cover for Katsina state shows the extent of vegetation cover change over time. It is evident from the LULC maps that vegetation in the study area has undergone tremendous transformation due to human-induced conversion of the semi-natural vegetation to other land use types such as farmland. Results were verified during field research in October 2020 and March 2021.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLand Use Land Cover of Katsina State Estimated from Landsat Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this section, we broadly examine the changes in the LULC of Katsina state in four phases. The LULC changed from 1989, 1999 to 2009 and 2019. As a result of over-cultivation, deforestation, overgrazing, industrialization, and urbanization, vegetation, farmland, and other land use changed in Katsina state. One of the main focuses of carrying out this study is to prompt the investigation of land degradation leading to desertification. The supervised classification for 1989, 1999, 2009, and 2019 was carried out for the study area, using six land use classes: bare land, farmland, built-up area, vegetation, rock outcrop, and waterbody.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eArea and Percentages of Land Use Land Cover Change of Katsina State (1989\u0026ndash;1999)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eLand Use\u003c/p\u003e\n \u003cp\u003eLand Cover Class\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003ePeriod\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1989\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1999\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eArea change (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e% Cover change\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAnnual rate of change (km\u003csup\u003e2\u003c/sup\u003e/year)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e% Annual rate of change (%/year)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuilt-up area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16013.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22564.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6551.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e655.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1873611.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1690503.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-183108.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-7.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-18310.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater body\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1497.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1098.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-399.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-39.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFarmland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e456650.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e638788.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e182138.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18213.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBare land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11385.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8576.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2809.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-280.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRock outcrop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8173.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5799.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2374.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-237.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2367331.95\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2367331.57\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThe Landsat data was used to evaluate variations in previous LULC (1989\u0026ndash;2019) patterns using the Maximum Likelihood Supervised Classification algorithm (MLSC) (Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e,\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e,\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, and \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). In the study area, 1989\u0026ndash;1999 exhibits little changes in the LULC classes (See Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). This period (1989\u0026ndash;1999) saw the expansion of farmlands at an annual rate of 0.77% changes per year and built-up area at 0.03% annual rate of change per year (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). In 1989 vegetation was predominant across the study area (See Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), embedded with build-up areas and farmlands in the southern and central parts of the study area. Higher farmlands in the south of parts of the study area, such as Funtua, Dandume, Sabuwa, Bakori, and Danja LGAs, result from fertile land, and the inhabitants are predominantly farmers. Water bodies are seasonal and flow during the wet season. These seasonal rivers include Marigo, Damari, Maikategi, Magajin Dutse, and Kara. An analysis of the LULC shows that built-up areas are encroaching on other land uses (Fgure 3). Consequently, detecting and predicting LULC changes have become an essential consideration in a variety of fields, including vegetation modeling rural and urban plans (Paul \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), identifying LULC change landscapes (Hussain et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) for advancing conservation efforts, studying dynamics of desertification and built-up expansion scenario in a particular watershed and region level.\u003c/p\u003e\n\u003cp\u003eHowever, in 1999 built-up area began to expand due to an increase in population. However, the annual rate of changes in the built-up area was insignificant at 0.03% annual change per year. These changes in the LULC were at the vegetation\u0026apos;s expense (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). In the same period in 1999, the predominance of forest land began to decline because of farmland expansion. Other LULC classes, water bodies, bare land, and rock outcrops started to decline in spatial coverage (See Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eArea and Percentages of Land Use Land Cover Change of Katsina State (1999\u0026ndash;2009)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eLand Use Land Cover Class\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003ePeriod\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eArea Change (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e% Cover Change\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eAnnual Rate of Change (km\u003csup\u003e2\u003c/sup\u003e/year)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e% Annual Rate of Change (%/year)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1999\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuilt-Up Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22564.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39295.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16730.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1673.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1690503.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1538099.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-152404.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-15240.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater Body\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1098.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1973.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e875.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFarmland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e638788.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e768800.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e130011.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13001.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBare Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8576.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10183.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1606.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e160.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRock Outcrop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5799.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8980.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3181.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e318.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2367331.57\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2367332.29\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThe percentage changes in the land use classes from 1999 to 2009 were computed (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e show that the composition of LULC classes in the study area varied significantly at different dates, according to a comparative analysis of the total area for each LULC class in the area. Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e also suggests that the loss of vegetation cover between 1999 and 2009 in Katsina state was due to the expansion of both built-up areas and farmlands. From this, the built-up areas and farmlands show an increase in their distribution from 1999 to 2009 (see Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab5\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eArea and Percentages of Land Use Land Cover Change of Katsina State (2009\u0026ndash;2019)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLand Use Land Cover Class\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePeriod\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eArea Change (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e% Cover Change\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eAnnual Rate of Change (km\u003csup\u003e2\u003c/sup\u003e/year)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e% Annual Rate of Change (%/year)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuilt-Up Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39295.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53098.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13803.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1380.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1538099.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1366899.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-171199.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-7.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-17119.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater Body\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1973.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1429.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-543.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-54.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFarmland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e768800.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e928535.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e159735.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15973.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBare Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10183.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9369.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-813.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-81.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRock Outcrop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8980.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7998.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-982.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-98.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2367332.29\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2367331.65\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eWe explain some of the data and the direction of LULC in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. In 2019, built-up areas dominate, not only in Katsina, but also in smaller, scattered built-up areas that have expanded into adjacent local governments, like Daura, Funtua, Dutsin-Ma, and Malumfashi. Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e show that between 2009 to 2019, forest areas declined significantly. Rapid growth in the absence of adequate plans and infrastructure expedites LULC changes associated with degrading ecosystem services and human well-being. Trends of change were manifested (see Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e); a gradual increase in a built-up area and vegetation was decreased over the study period. The results showed that farmland and built-up areas had replaced vegetation. In addition, the built-up area and farmland are rising due to unplanned population growth and migration (Khan et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). A major finding of the study was that urban growth is influenced both by economic and geopolitical factors (Oyeleye \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003ePredicting future LULC change, the transition assessment among different LULC is a critical aspect (Leta et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Urbanization triggered the LULC change in the study region in the last few decades.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab6\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eArea and Percentage of Predicted LULCC of Katsina (2050)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLand Use Land Cover Type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (hectare)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuilt-Up Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1235808.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater Body\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48214.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFarmland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5063540.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRock Outcrop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100224.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBare Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2049604.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15420727.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e23918119.40\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e shows computed statistics of the predicted land use land cover changes of Katsina state (2050). The percentage changes in the LULC prediction indicate that build-up area, water bodies, farmland, and bare land will increase at the expense of vegetation (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). By persistently reducing natural vegetation and its protective effect on the landscape, human activities are encroaching on natural vegetation, making the area vulnerable to desertification. The results were confirmed by the findings of Garba and Al-amin (\u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Idris S et al. (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) revealed that there is an increase in Farmland and Settlement and a decrease in vegetation because of deforestation and land degradation, which has resulted in desert encroachment. Changes are mostly driven by climate and socio-economic factors. Southern Katsina is greener and has a greater number of agricultural practices than northern Katsina due to differences in climate (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). Managing natural resources to support human life and to maintain ecosystem stability will be possible if land use is properly monitored. Monitoring will help monitor the degradation of land, which leads to desertification. Land degradation, which leads to desertification, can be monitored through proper land use monitoring. Further, it will help plan, use, and manage natural resources that support humans and ecosystems.\u003c/p\u003e\n\u003cp\u003eBased on the independent and dependent variables, we predicted land use land cover change. A total of twelve dependent variables, such as elevation, slope layer layer, aspect layer, distance from the protected areas, were considered (see Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ea, b, c, and d). The land use and land cover maps for 2009 and 2019 were considered independent variables. There is a range of elevation values in the study area, starting at 379m and ending at 755m from mean sea level (MSL) (See Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003ea). There are flat surfaces in the study area based on slope and aspect maps (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eb, c). Open Street Map vector layers were used to calculate distances to roads and streams (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ea).\u003c/p\u003e\n\u003cp\u003eThe distance from the buildings indicates urban growth in the core area of the state and less growth in the northwest, southeast, and northeast parts of the state (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ed). Most urban areas have high, modest, to minimal road densities (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ea). The distance from streams is another important factor for the land use land cover dynamics (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ec). LULCs are transformed into farms and residential areas in population dense areas (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003ea). Away from urban areas, LULC changes are less intense (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eb). The temperature trend depends on the types of LULC and their conversions. The northern part of the study has high temperatures as a result of spare vegetation (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003ec). Rainfall influences vegetation growth, so the more rain, the more vegetation grows (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003ed). To generate the transition potential matrix, we used the independent and dependent variables above.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003ePerceived Drivers of Vegetation Change\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 7 shows the result obtained from Principal Components Analysis (PCA) of the drivers behind vegetation changes in Katsina state. Five components were identified from the PCA results accounting for a cumulative variance of 74.851% in original variables using a cut-off point value (Eigenvalues) of 1. The first PCA contributed 28.516% of the total variance, while the second, third, fourth, and fifth contributed 18.545%, 11.485%, 9.247%, and 7.059%, respectively. The five PCA components are named based on their loadings in relation to the original variables. This result implies that the new model variables to explain the driving forces behind vegetation change in Katsina are the five resulting components of the PCA, named to identify the groups of states with which they are most closely associated (firewood collection, charcoal production, agricultural expansion, climate variability, and over-cultivation).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7\u0026nbsp;\u003c/strong\u003ePerceived drivers of vegetation changes in Katsina using PCA\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"13.26530612244898%\"\u003e\n \u003cp\u003e\u003cstrong\u003eComponent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"22.448979591836736%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerceived Driver\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" width=\"64.28571428571429%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRotation Sums of Squared Loadings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"29.032258064516128%\"\u003e\n \u003cp\u003eRotated Component Matrix\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.967741935483872%\"\u003e\n \u003cp\u003eEigenvalues\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.806451612903224%\"\u003e\n \u003cp\u003e% of Variance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.193548387096776%\"\u003e\n \u003cp\u003eCumulative %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.402061855670103%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.68041237113402%\"\u003e\n \u003cp\u003eFirewood Collection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.556701030927837%\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.402061855670103%\"\u003e\n \u003cp\u003e5.703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e28.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e28.516\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.402061855670103%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.68041237113402%\"\u003e\n \u003cp\u003eCharcoal Production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.556701030927837%\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.402061855670103%\"\u003e\n \u003cp\u003e3.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e18.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e47.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.402061855670103%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.68041237113402%\"\u003e\n \u003cp\u003eAgricultural expansion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.556701030927837%\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.402061855670103%\"\u003e\n \u003cp\u003e2.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e11.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e58.545\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.402061855670103%\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.68041237113402%\"\u003e\n \u003cp\u003eClimate Variability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.556701030927837%\"\u003e\n \u003cp\u003e0.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.402061855670103%\"\u003e\n \u003cp\u003e1.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e9.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e67.793\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.402061855670103%\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.68041237113402%\"\u003e\n \u003cp\u003eOvercultivation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.556701030927837%\"\u003e\n \u003cp\u003e-0.845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.402061855670103%\"\u003e\n \u003cp\u003e1.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e7.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e74.851\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eExtraction Method: Principal Component Analysis. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Rotation Method: Varimax with Kaiser Normalization.\u003c/p\u003e\n\u003cp\u003eFigure 11 indicates the extent of the influence of humans on the vegetation dynamics in the study area. Maximum human impact on the vegetation is found around the northcentral part of the study area covering (Batagarawa, Rimi, Mani, and Bindawa LGAs), in the central part of the study area (Dan Musa, Sandamu, and Dutsin Ma), in the southern part (Bakori and Funtua LGAs). Overall, the maximum influence of humans on vegetation in the study area covers an area of (436835.59 ha), considerable human influence (697665.45 ha), medium influence (686878.09 ha), small human influence (383662.48 ha) and no influence (168036.57 ha) (See appendix V).\u003c/p\u003e\n\u003cp\u003eRespondents identified five (5) factors as essential drivers contributing to vegetation changes in Katsina, especially during the period under study, 1989-2019. The study area\u0026apos;s top five perceived drivers of vegetation changes were firewood collection, charcoal production, agricultural expansion, climate variability, and over-cultivation. Firewood collection and charcoal production ranked first and second, respectively (Table 7). During key informant interviews, the main causes of vegetation decline in the study area were identified as firewood collection, charcoal production, over-cultivation, population growth, poverty, and agricultural expansion. Based on the results from the questionnaires and key informant interviews, local communities perceived firewood collection, charcoal production, agricultural expansion, climate variability, and over-cultivation as the critical drivers of vegetation changes in the study area. High poverty levels, population growth, lack of law enforcement by the government, and high cost of agricultural input triggered these drivers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInfluence of Socioeconomic Variables on the Perceived Drivers of Vegetation Change\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;A summary of the socio-economic factors and the nature of their influence on the vegetation change are presented in appendix 1. Several socio-economic factors influence vegetation change in this study. The socio-economic factors considered and tested using logistic regression model include age, sex, education level, marital status, monthly income, source of livelihood, number of dependents, and farming experience. Results revealed that education level negatively and significantly affected (p \u0026lt; 0.05) high perceptions of local communities on firewood collection, climate variability, and over-cultivation as vegetation change drivers in Katsina (See appendix 1).\u003c/p\u003e\n\u003cp\u003eCharcoal production and expansion in agriculture were not significantly influenced by age, sex, education level, marital status, monthly income, source of livelihood, number of dependents, or farming experience. The results show a positive regression coefficient of the respondent\u0026rsquo;s age, farming experience, and marital status, which implied a relationship. This suggests that an increase in the variables of the respondents increases the chance of climate variability being the driving force for vegetation change in the study area. An increase in the factors of the respondents of the community increases the possibility of increasing environmental degradation. However, a negative regression coefficient was noticed in the level of education, source of livelihood, and sex, which presents an indirect relationship. A person is exposed to knowledge on wise use of resources, including agricultural practices, because an educated person will tend to practice more environmentally friendly agricultural land use.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe expansion of farmland and urban areas and excessive deforestation have caused land degradation and soil erosion, degrading vegetation cover. This study assesses the influence of local perception and land use land cover dynamics on vegetation change in Katsina State, Nigeria. Using GIS and remote sensing helps to integrate the spatial and attribute data from respondent discussions and to analyze vegetation and LULC, as well as respondents' perceptions of land use. In Katsina state, humans are regarded as the main drivers of vegetation change through different LULC practices. The study area's top five factors driving vegetation change were firewood collection, charcoal production, agricultural expansion, climate variability, and over-cultivation. By 2050, according to this study, there will be an alarming increase in the rate of vegetation and LULC in the study area. However, this study recommends that further research adopt other geospatial simulation models, such as TOPSIS, to predict the condition of vegetation to its driving forces across the study area. Moreover, because of positive link between climate variability and vegetation in the state, the tree-planting exercise by NGOs and any other body/individuals should target the wet season for the survival and development of such plantations. Educating people on the impact of deforestation and climate change on the environment will help to ensure that LULC practices are sustainable in the long run by reducing deforestation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study does not involve human or animal participation. It has no experiments and does not involve human data and/or tissue.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study does not involve children or individual details and/or 100% data usage\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during the study are included in the published article(s) cited within the text and acknowledged in the reference section.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no competing interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study is funded by the Tertiary Education Trust Fund (TETFUND) of Nigeria (TETF/ES/UNIV/JIGAWA STATE/TSAS/2019)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, M.H.A. and A.A., methodology, A.A., software, M.H.A.., formal analysis, M.H.A.; investigation, S.S.D., writing\u0026mdash;original draft preparation, A.A.and M.H.A. writing\u0026mdash;review and editing, M.H.A.; supervision, M.Y.I., project administration, J.M.A.; funding acquisition, Z.J\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbbas II, Muazu KM, Ukoje JA (2010) Mapping Land Use - land Cover and Change Detection in Kafur Local Government , Katsina , Nigeria ( 1995 - 2008 ) Using Remote Sensing and Gis. 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Dutse J Pure Appl Sci 3:430\u0026ndash;442\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"vegetation change, local perception, land use land cover, Nigeria","lastPublishedDoi":"10.21203/rs.3.rs-2402739/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2402739/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChanging vegetation affects microclimates, groundwater tables, desertification, and biodiversity at the landscape level. The objective of this study is to assess the land cover dynamics and local perception of the influence of land use on vegetation change in Katsina State, Nigeria. Remote sensing and Geographic Information System (GIS)-based analysis, key informant interviews, and a semi-structured questionnaire covering 400 households were used to examine the driving forces behind vegetation change across Katsina State. As a result of the household survey, 86.5% (n = 400) of respondents reported a decline in vegetation in the study area, aligning with the Land Use Land Cover analysis phase of the study. The key drivers behind the observed vegetation depletion in the study area include firewood collection, charcoal production, and population growth. There has been an increasing awareness that education has emerged as one of the most significant socioeconomic factors influencing respondents' perceptions of these drivers. In spite of this, the unsustainable vegetation changes observed in this study have a negative impact on rural livelihoods and the management of natural resources in rural areas. This study recommends the implementation of sustainable land use policies that promote land-use practises that support economic growth and development.\u003c/p\u003e","manuscriptTitle":"Vegetation Change in Katsina State, Nigeria: Influence of Local Perceptions and Land Use Land Cover Dynamics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-01-04 22:44:51","doi":"10.21203/rs.3.rs-2402739/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c71cbb1e-87a5-4ab8-970d-740a32ffed71","owner":[],"postedDate":"January 4th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-01-04T22:52:54+00:00","versionOfRecord":[],"versionCreatedAt":"2023-01-04 22:44:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2402739","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2402739","identity":"rs-2402739","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}