Transforming Maasai Landscapes: Land Cover Changes and Their Implications for Pastoralism and Conservation

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Giliba This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5911943/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 Land cover change, particularly in landscapes inhabited by pastoralist communities like the Maasai, is a growing concern due to its environmental and socio-economic implications. The Maasai regions in Tanzania have experienced significant land cover shifts in recent years, which can affect biodiversity, ecosystem services, and traditional livelihoods. Despite the importance of these landscapes, there is limited understanding of how specific land cover types, such as rangelands, croplands, and tree cover, have changed over time, and what the drivers behind these changes are. To address this gap, this study examines the extent and nature of land cover changes in Maasai landscapes between 2017 and 2023. The study specifically focuses on the conversion of rangelands to other land uses, such as tree cover, croplands, and built areas, as well as the shifts from trees to cropland and built areas. By analyzing these trends, the study aims to provide insights into the factors driving land cover change and their implications for land management in the region. The findings reveal substantial transitions, including the conversion of 451,514 hectares of rangeland to tree cover, 152,064 hectares to cropland, and 10,181 hectares to built areas. These results highlight the urgent need for strategies that support sustainable land use while considering the ecological and socio-economic importance of Maasai landscapes. Land cover change Rangeland conversion Maasai landscapes Agricultural expansion Pastoralism Sustainable land use Figures Figure 1 Figure 2 Introduction Land cover change is a significant driver of ecological and socio-economic transformations across the globe, affecting natural resource management, biodiversity conservation, and human livelihoods(Ameneshewa et al., 2024; Chiaka et al., 2024). In East Africa, particularly in Maasai-inhabited landscapes, this issue has become increasingly pressing due to growing pressures from population expansion, agricultural intensification, and infrastructural development(Chebby et al., 2023). Historically, Maasai pastoral communities have relied on vast rangelands to sustain their cattle-based livelihoods(Hezron et al., 2024). However, the rapid conversion of these rangelands into croplands, afforestation, and built environments is disrupting traditional land-use practices and threatening both biodiversity and the pastoralist way of life (Homewood et al., 2009 ; Mwangi & Ostrom, 2009 ). A growing body of research highlights the impacts of land cover changes, but there is still a gap in understanding the specific nature and scale of these transformations within Maasai landscapes. The conversion of rangelands into croplands and other land uses can lead to land degradation, fragmentation of wildlife habitats, and diminished ecosystem services, such as carbon sequestration and water regulation (Reid et al., 2004). These changes, exacerbated by the increasing variability of climate conditions, also contribute to heightened competition for natural resources, leading to conflicts between wildlife conservation needs and local community livelihoods (Galvin, 2009 ). The problem becomes more acute in the face of limited data on the spatial and temporal dynamics of these land cover changes, particularly regarding their long-term impacts on Maasai pastoral systems. Although some studies have explored land use changes in the region (Kariuki et al., 2021), there is a clear gap in data-driven assessments that quantify the extent of land conversion over time, specifically focusing on the transformation from rangelands to croplands, tree cover, and built environments. Such data is critical for understanding the drivers of these changes and developing sustainable land-use strategies that can mitigate adverse effects. This study aims to address this gap by analyzing land cover changes in Maasai landscapes between 2017 and 2023. Specifically, it focuses on quantifying the transitions from rangeland to other land cover classes, including cropland, tree cover, and built-up areas. By providing a detailed analysis of these land cover transitions, the study seeks to offer insights into the scale of landscape transformation and its implications for pastoral livelihoods and biodiversity conservation. The findings will be crucial in informing policy interventions aimed at balancing socio-economic development with the conservation of natural ecosystems in Maasai regions. Methods 2.1 Study area The study area is located in northern Tanzania, approximately between latitudes 2.0°S and 5.0°S and longitudes 34.0°E and 38.0°E, encompassing six district councils (DC): Longido, Ngorongoro, Siha, Simanjiro, Monduli, and Arusha (Fig. 1 ). These DC are part of the Maasai pastoralist landscapes, known for their ecological diversity and cultural significance. The region is situated near key ecological zones such as the Ngorongoro Conservation Area, Serengeti National Park, and Mount Kilimanjaro. The altitude across the area varies significantly, from around 600 meters above sea level in the semi-arid lowlands of Longido and Simanjiro to as high as 3,000 meters in the highlands near Mount Kilimanjaro in Siha and Arusha districts. Rainfall in the study area ranges from 400 mm in the drier, semi-arid districts to 1,200 mm in the more fertile, highland regions. Temperatures vary accordingly, from cooler temperatures of around 15°C in the highlands to approximately 30°C in the lower, hotter regions. The soils in the area are predominantly volcanic near Kilimanjaro, supporting fertile agricultural lands, while sandy and loamy soils dominate the rangelands of Longido and Simanjiro. Alkaline soils are found in the highland areas of Ngorongoro. The vegetation is diverse, with savannah grasslands, acacia woodlands, and montane forests in the highlands. The rangelands, particularly in Longido and Simanjiro, are essential for supporting pastoralist livelihoods. Population density varies, with the highest concentrations in Arusha and Siha, while Longido and Simanjiro have more sparsely populated pastoralist communities. The main economic activities across the districts include livestock rearing (pastoralism), agriculture (particularly maize), tourism (centered around Ngorongoro and Kilimanjaro), and mining, notably tanzanite mining in Simanjiro. This area is a focal point for balancing traditional livelihoods, conservation, and modern development pressures. 2.2 Land cover data A 10-meter annual land cover data of Earth's land surface from 2017–2023 were downloaded from ESRI [ https://livingatlas.arcgis.com/landcover/ ]. Each time slice data is a composite of LULC predictions for 9 classes throughout the year, aiming to create a representative snapshot of each year (Table 1 ). Upon download, land cover data were uploaded to ArcGIS Pro version 2.6 (ESRI, 2020 ) to: 1) visualize the spatial distribution of land cover classes across each time slice within the Maasai landscapes. 2) Detect changes in land cover between 2017 and 2023 using the change detection wizard available in ArcGIS Pro version 2.6. I projected a map for each time slice from WGS 1984 to the Africa Albers Equal Area Conic projection using the Project tool in ArcGIS Pro version 2.6 to ensure accurate area calculations. I generated 315 accuracy assessment points per time step using stratified random sampling in ArcGIS Pro version 2.6 to assess the accuracy of the classified maps. I validated the land cover maps produced by ESRI (Giliba et al., 2023 ; Hu et al., 2013 ; Yu, 2013) using high-resolution images from Google Earth. The overall land cover classification accuracy for the 2017 and 2023 years was 94–95% with kappa coefficients of 93% and 94, respectively (Table S1 & S2). Table 1 Description of land cover classes adapted from (Karra et al., 2021 ) SN Class Class definition 1 Water Water Areas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains. 2 Trees Any significant clustering of tall (~ 15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath) 4 Flooded vegetation Areas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture. 5 Crops Human planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land. 7 Built Area Human made structures; major road and rail networks; large homogeneous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt. 8 Bare ground Areas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines. 9 Snow/Ice Large homogeneous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields. 10 Clouds No land cover information due to persistent cloud cover 11 Rangeland Open areas covered in homogeneous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants. Results 3.1 Time series land cover analysis Between 2017 and 2023, the Maasai landscapes experienced significant changes in land cover across various classes (Table 2 and Fig. 2 )). The water-covered area fluctuated significantly, beginning at 46,231.27 ha in 2017, reaching a peak of 125,258.96 ha in 2020, and then declining to 77,512.33 ha by 2023. This pattern suggests dynamic changes likely driven by seasonal rainfall and climate variability. Tree cover experienced a sharp increase from 432,343.19 ha in 2017 to 1,617,971.94 ha in 2020, followed by a decline to 834,394.90 ha by 2023. This increase may indicate successful reforestation efforts, but the subsequent decrease points to potential deforestation or land use conversion. Flooded vegetation also showed variability, starting at 2,610.80 ha in 2017, peaking at 11,773.92 ha in 2020, and then dropping to 1,359.90 ha by 2023. Hydrological shifts and seasonal flooding likely contribute to these changes. Crops steadily expanded from 210,640.27 ha in 2017 to 334,577.55 ha in 2023, indicating an increase in agricultural activities, potentially driven by population growth and the need for food production. Built-up areas also showed a gradual rise, growing from 28,981.71 ha in 2017 to 44,207.45 ha by 2023, indicating ongoing urbanization and infrastructure development. Bare ground experienced significant fluctuations, starting at 288,950.44 ha in 2017, dropping to 9,734.29 ha in 2020, and then rising again to 34,925.45 ha by 2023, likely reflecting cycles of land degradation and restoration efforts. Rangeland, the largest land cover class, steadily decreased from 4,216,853.47 ha in 2017 to 3,900,474.56 ha in 2023, indicating the conversion of rangelands to other uses, such as agriculture and settlements, which may have profound implications for pastoralist communities and wildlife relying on these areas for grazing. Overall, the landscape changes in Maasai regions are characterized by increasing agricultural expansion and urbanization, fluctuating tree and bare ground cover, and a steady reduction in rangelands, all of which highlight the impact of human activities and environmental factors on land use patterns. Table 2 Distribution of land cover within Maasai landscapes between 2017 and 2023 Land cover class 2017 2018 2019 2020 2021 2022 2023 Water 46231.27 100199.83 73344.39 125258.96 115884.40 87641.88 77512.33 Trees 432343.19 705616.13 690998.02 1617971.94 1037663.76 819808.10 834394.90 Flooded Vegetation 2610.80 5590.43 2266.35 11773.92 2929.84 1290.87 1359.90 Crops 210640.27 249677.70 299251.22 289555.29 332202.18 321847.26 334577.55 Built Area 28981.71 32211.80 36117.90 38862.12 38590.38 41449.55 44207.45 Bare Ground 288950.44 70317.00 83507.70 9734.29 10704.49 30711.63 34925.45 Clouds 1071.68 1674.00 282.10 3422.01 1103.02 169.12 224.79 Rangeland 4216853.47 4062395.94 4041915.15 3167617.17 3688604.76 3924764.42 3900474.56 3.2 Overall land cover change between 2017 and 2023 The land cover changes within Maasai landscapes between 2017 and 2023 reveal significant shifts in land use patterns (Table 2 ). The conversion of 451,514.10 ha of rangeland to tree cover indicates reforestation efforts or natural regeneration, potentially contributing to carbon sequestration, biodiversity, and water conservation. However, the transition of 152,064.70 ha from rangeland to cropland suggests increasing agricultural expansion, likely driven by population growth and food security demands. This shift could lead to reduced grazing areas for pastoral communities, potentially impacting livestock production and livelihoods. Additionally, 10,181.69 ha of rangeland was converted into built-up areas, indicating urbanization and infrastructure development. This change may reduce available land for traditional grazing and affect wildlife habitats, contributing to increased human-wildlife conflicts. The conversion of 2,943.54 ha of tree cover to cropland highlights land use pressures that may result in deforestation and loss of ecosystem services, such as water regulation and biodiversity support. Furthermore, 335.55 ha of tree cover transitioned to built-up areas, reflecting the ongoing urban sprawl at the expense of forested land. These land cover changes imply growing pressure on natural resources due to agricultural and infrastructural expansion. The reduction of rangeland and tree cover in favor of cropland and built-up areas poses challenges for environmental sustainability and pastoral livelihoods, which are dependent on intact ecosystems for grazing and natural resource use. Future land use planning in the region will need to balance development goals with conservation efforts to ensure long-term ecological and economic resilience. Discussion 4.1 Land cover changes The findings of land cover changes within the Maasai landscapes between 2017 and 2023 highlight key environmental and socio-economic shifts. The transition of 451,514.10 hectares from rangeland to tree cover suggests reforestation efforts or natural regeneration. This shift is positive for biodiversity conservation and carbon sequestration but may reduce grazing areas critical for Maasai pastoralists, whose livelihoods depend on these landscapes for livestock rearing (Otsyina & Maghembe, 1998 ). The conflict between conservation and pastoral needs requires policies that integrate reforestation with community needs (Homewood et al., 2009 ). The conversion of 152,064.70 hectares of rangeland to cropland reflects the growing demand for agricultural land due to population pressure and economic activities. This transition improves food security and local economies but also raises concerns over rangeland degradation and sustainability (Fratkin & Mearns, 2003 ). Loss of grazing land for pastoralists could lead to overgrazing in remaining areas, further exacerbating land degradation (Said et al., 2017 ). Urbanization trends are evident in the shift of 10,181.69 hectares of rangeland to built-up areas, driven by population growth and infrastructural development (Olson et al., 2004 ). While urban expansion can boost local economies, it leads to habitat fragmentation, wildlife displacement, and increased human-wildlife conflicts (Homewood et al., 2009 ). Urbanization also puts pressure on natural resources like water and land, leading to unsustainable consumption. The conversion of 2,943.54 hectares of trees to cropland and 335.55 hectares to built-up areas signals deforestation trends in the region. The loss of forest cover leads to biodiversity loss, soil erosion, and reduced ecosystem services (Lambin et al., 2003 ). Although this land use change may provide short-term economic benefits, it could undermine long-term ecological sustainability and resilience to climate change (Lambin & Meyfroidt, 2011 ). To sum up, these changes indicate the need for integrated land-use planning to balance economic development with environmental sustainability. Policymakers must create strategies that promote sustainable agriculture, conserve rangelands, and protect biodiversity while supporting community livelihoods (Said et al., 2017 ). Community participation in land-use decisions and the implementation of sustainable practices are critical for mitigating the negative impacts of these land cover transitions. 4.2 Implications The findings of this study have several critical implications for environmental sustainability, livelihoods, biodiversity conservation, and land-use policy. Environmental sustainability is at risk as the shift from rangelands and forests to croplands and built-up areas imposes ongoing strain on ecosystems. This transformation leads to habitat loss, reduced biodiversity, and increased land degradation. Over time, unsustainable land-use practices may exacerbate the region’s vulnerability to climate change, soil erosion, and desertification. Livelihood challenges are evident for Maasai pastoralist communities, whose traditional practices rely on open rangelands for grazing. Agricultural expansion and urbanization are reducing these grazing areas, forcing pastoralists to either adopt alternative livelihoods or intensify grazing in limited spaces, resulting in overgrazing and environmental degradation. This trend threatens the traditional pastoral system and highlights the urgent need for integrated land-use planning that balances development with pastoral needs. Additionally, biodiversity conservation is severely affected by the conversion of forested land to agriculture and infrastructure development. Habitat fragmentation and the transformation of natural ecosystems into human-dominated landscapes contribute to declining wildlife populations and increasing human-wildlife conflicts. To address these challenges, development plans must incorporate conservation strategies that maintain ecological balance while meeting the socio-economic demands of the growing population. The study also underscores the importance of land-use planning and policy . There is a pressing need for comprehensive land management approaches that accommodate agricultural expansion, urbanization, and biodiversity conservation. Policies should encourage sustainable agricultural practices, protect critical habitats, and ensure urban development does not encroach on ecologically sensitive areas. Involving local communities in decision-making processes can help mitigate the adverse effects of land-use changes. To sum up, while the conversion of rangelands to tree cover offers potential ecological benefits, the large-scale shifts toward cropland and urban areas pose significant challenges to environmental sustainability, biodiversity conservation, and the livelihoods of pastoral communities. Policymakers must focus on sustainable development strategies that balance human needs with ecological integrity to ensure the long-term resilience of the Maasai landscapes. Conclusion The findings of this study highlight significant land cover changes within the Maasai landscapes between 2017 and 2023. The most notable transitions include the conversion of rangeland to tree cover, cropland, and built areas. The transformation of over 450,000 hectares of rangeland into tree cover, reflecting afforestation or natural reforestation efforts, was the largest observed change. However, the conversion of over 150,000 hectares of rangeland to cropland signals increasing agricultural expansion, which may pose challenges to the sustainability of traditional pastoralist livelihoods. Additionally, the conversion of rangeland to built-up areas, although smaller in scale, indicates ongoing urbanization and infrastructural development in the region. These land cover transitions suggest a dynamic shift in land use practices within the Maasai landscapes, driven by both environmental factors and socio-economic pressures. While afforestation efforts may offer environmental benefits, the conversion of rangeland into cropland and built areas could lead to long-term ecological consequences, including habitat fragmentation, loss of biodiversity, and diminished ecosystem services. The transition from trees to cropland and built areas, though less extensive, further underscores the competing demands for land resources in the region. Overall, the results underscore the need for integrated land use planning and sustainable management strategies that balance ecological conservation with the socio-economic needs of local communities. Further research is required to assess the broader impacts of these land cover changes on biodiversity, ecosystem services, and the livelihoods of indigenous communities. Policymakers and stakeholders must work together to develop solutions that promote sustainable land use practices while addressing the challenges posed by agricultural expansion and urban development. Declarations Ethical statement This study was conducted using publicly available remote sensing and GIS datasets in compliance with ethical guidelines for geospatial research. All satellite imagery and geospatial data were obtained from ESRI website ( https://www.esri.com/ ) Funding The author did not receive any funding to carry out this study. Author Contribution Conceptualization: RG; Data curation: RG; Formal analysis: RG; Methodology RG; Visualization: RG ; Writing original draft: RG; Review & editing: RG Acknowledgement I thank ESRI for allowing access to publicly available remote sensing and GIS datasets. Data Availability The ESRI land cover land use data used in this study is publicly available and can be accessed through the ESRI website (https://www.esri.com/). Additional information about the dataset and its usage guidelines can be obtained from ESRI's data repository. Any specific data processing or analysis steps applied to the ESRI land cover land use data in this study are detailed in the methods section. References Esri (2020). ArcGIS Pro (Version 2.6). Environmental Systems Research Institute, Redlands, CA. URL https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview Fratkin, E., & Mearns, R. (2003). Sustainability and pastoral livelihoods: Lessons from East African Maasai and Mongolia. Human Organization , 62 (2), 112–122. https://doi.org/10.17730/humo.62.2.48w1l9t476g65lp1 Galvin, K. A. (2009). Transitions: Pastoralists living with change. Annual Review of Anthropology , 38 , 185–198. https://doi.org/10.1146/annurev-anthro-091908-164442 Giliba, R. A., Fust, P., Kiffner, C., & Loos, J. (2023). Modelling elephant corridors over two decades reveals opportunities for conserving connectivity across a large protected area network. Plos One , e0292918. https://doi.org/10.1371/journal.pone.0292918 Homewood, K., Trench, P., & Brockington, D. (2009). Pastoralist livelihoods and wildlife revenues in East Africa: A case for coexistence? Pastoralism: Research Policy and Practice , 1 (1), 1–16. Hu, Q., Wu, W., Xia, T., Yu, Q., Yang, P., Li, Z., et al. (2013). Exploring the use of google earth imagery and object-based methods in land use/cover mapping. Remote Sensing , 5 , 6026–6042. https://doi.org/10.3390/rs5116026 Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J. C., Mathis, M., & Brumby, S. P. (2021). Global Land Use/Land Cover With Sentinel 2 and Deep Learning. International Geoscience and Remote Sensing Symposium (IGARSS), 2021-July, 4704–4707. https://doi.org/10.1109/IGARSS47720.2021.9553499 Lambin, E. F., Geist, H. J., & Lepers, E. (2003). Dynamics of land-use and land-cover change in tropical regions. Annual Review of Environment and Resources , 28 (1), 205–241. https://doi.org/10.1146/annurev.energy.28.050302.105459 Lambin, E. F., & Meyfroidt, P. (2011). Global land use change, economic globalization, and the looming land scarcity. Proceedings of the National Academy of Sciences, 108(9), pp.3465–3472. https://doi.org/10.1073/pnas.1100480108 Mwangi, E., & Ostrom, E. (2009). A century of institutions and ecology in East Africa’s rangelands: Linking institutional robustness with the ecological resilience of rangelands. Ecology and Society , 14 (2), 28. https://doi.org/10.5751/ES-02996-140228 Ntiati, P. (2002). Group ranches subdivision study in Loitokitok Division of Kajiado District, Kenya. Land Use Change Impacts and Dynamics (LUCID) Project Working Paper 7. International Livestock Research Institute (ILRI), Nairobi. Olson, J. M., Misana, S., Campbell, D. J., Mbonile, M., & Mugisha, S. (2004). The spatial patterns and root causes of land use change in East Africa. LUCID Project Working Paper, 47. Otsyina, R., & Maghembe, J. A. (1998). Agroforestry Extension Manual for Tanzania . International Centre for Research in Agroforestry (ICRAF). Said, M. Y., Leeuw, J., de Boer, W. F., & Du Toit, J. T. (2017). The impacts of climate change and land-use change on the abundance and distribution of large herbivores in the Mara-Serengeti Ecosystem. Global Change Biology , 23 (4), 1371–1382. https://doi.org/10.1111/gcb.13403 Yu, L., & Gong, P. (2012). Google Earth as a virtual globe tool for Earth science applications at the global scale: progress and perspectives. International Journal of Remote Sensing , 33 , 3966–3986. https://doi.org/10.1080/01431161.2011.636081 Additional Declarations No competing interests reported. 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The red inset indicates the broader location of the study area within Tanzania.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5911943/v1/ea0a9f2dfd5b0ab6ddeed9aa.png"},{"id":75310334,"identity":"1d11e931-4404-408a-8d4d-03fcd11bdcd8","added_by":"auto","created_at":"2025-02-03 09:01:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1452186,"visible":true,"origin":"","legend":"\u003cp\u003eLand cover changes in Maasai landscapes from 2017 (a) to 2023 (g), illustrating shifts in land cover classes such as water, trees, closed vegetation, open vegetation, crops, built areas, rangeland, and bare ground. Land cover changes between 2017 and 2023 (h) in Maasai landscapes, illustrating the conversion of rangeland to crops, trees, built areas, and vice versa across the districts.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5911943/v1/3ad50daf6ac6001a9f53d3a5.png"},{"id":78174291,"identity":"cf2b01e1-e76d-46a3-b604-0c99b33f5041","added_by":"auto","created_at":"2025-03-10 15:17:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2062294,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5911943/v1/9bce373b-38a2-493c-9650-73f482061879.pdf"},{"id":75310319,"identity":"cecdd919-32c0-466d-bebe-be3b1c04ce5e","added_by":"auto","created_at":"2025-02-03 09:01:07","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":17700,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-5911943/v1/58e38e1162aa8272f5c10528.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transforming Maasai Landscapes: Land Cover Changes and Their Implications for Pastoralism and Conservation ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLand cover change is a significant driver of ecological and socio-economic transformations across the globe, affecting natural resource management, biodiversity conservation, and human livelihoods(Ameneshewa et al., 2024; Chiaka et al., 2024). In East Africa, particularly in Maasai-inhabited landscapes, this issue has become increasingly pressing due to growing pressures from population expansion, agricultural intensification, and infrastructural development(Chebby et al., 2023). Historically, Maasai pastoral communities have relied on vast rangelands to sustain their cattle-based livelihoods(Hezron et al., 2024). However, the rapid conversion of these rangelands into croplands, afforestation, and built environments is disrupting traditional land-use practices and threatening both biodiversity and the pastoralist way of life (Homewood et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Mwangi \u0026amp; Ostrom, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA growing body of research highlights the impacts of land cover changes, but there is still a gap in understanding the specific nature and scale of these transformations within Maasai landscapes. The conversion of rangelands into croplands and other land uses can lead to land degradation, fragmentation of wildlife habitats, and diminished ecosystem services, such as carbon sequestration and water regulation (Reid et al., 2004). These changes, exacerbated by the increasing variability of climate conditions, also contribute to heightened competition for natural resources, leading to conflicts between wildlife conservation needs and local community livelihoods (Galvin, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe problem becomes more acute in the face of limited data on the spatial and temporal dynamics of these land cover changes, particularly regarding their long-term impacts on Maasai pastoral systems. Although some studies have explored land use changes in the region (Kariuki et al., 2021), there is a clear gap in data-driven assessments that quantify the extent of land conversion over time, specifically focusing on the transformation from rangelands to croplands, tree cover, and built environments. Such data is critical for understanding the drivers of these changes and developing sustainable land-use strategies that can mitigate adverse effects.\u003c/p\u003e \u003cp\u003eThis study aims to address this gap by analyzing land cover changes in Maasai landscapes between 2017 and 2023. Specifically, it focuses on quantifying the transitions from rangeland to other land cover classes, including cropland, tree cover, and built-up areas. By providing a detailed analysis of these land cover transitions, the study seeks to offer insights into the scale of landscape transformation and its implications for pastoral livelihoods and biodiversity conservation. The findings will be crucial in informing policy interventions aimed at balancing socio-economic development with the conservation of natural ecosystems in Maasai regions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eThe study area is located in northern Tanzania, approximately between latitudes 2.0\u0026deg;S and 5.0\u0026deg;S and longitudes 34.0\u0026deg;E and 38.0\u0026deg;E, encompassing six district councils (DC): Longido, Ngorongoro, Siha, Simanjiro, Monduli, and Arusha (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These DC are part of the Maasai pastoralist landscapes, known for their ecological diversity and cultural significance. The region is situated near key ecological zones such as the Ngorongoro Conservation Area, Serengeti National Park, and Mount Kilimanjaro. The altitude across the area varies significantly, from around 600 meters above sea level in the semi-arid lowlands of Longido and Simanjiro to as high as 3,000 meters in the highlands near Mount Kilimanjaro in Siha and Arusha districts.\u003c/p\u003e \u003cp\u003eRainfall in the study area ranges from 400 mm in the drier, semi-arid districts to 1,200 mm in the more fertile, highland regions. Temperatures vary accordingly, from cooler temperatures of around 15\u0026deg;C in the highlands to approximately 30\u0026deg;C in the lower, hotter regions. The soils in the area are predominantly volcanic near Kilimanjaro, supporting fertile agricultural lands, while sandy and loamy soils dominate the rangelands of Longido and Simanjiro. Alkaline soils are found in the highland areas of Ngorongoro.\u003c/p\u003e \u003cp\u003eThe vegetation is diverse, with savannah grasslands, acacia woodlands, and montane forests in the highlands. The rangelands, particularly in Longido and Simanjiro, are essential for supporting pastoralist livelihoods. Population density varies, with the highest concentrations in Arusha and Siha, while Longido and Simanjiro have more sparsely populated pastoralist communities. The main economic activities across the districts include livestock rearing (pastoralism), agriculture (particularly maize), tourism (centered around Ngorongoro and Kilimanjaro), and mining, notably tanzanite mining in Simanjiro. This area is a focal point for balancing traditional livelihoods, conservation, and modern development pressures.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.2 Land cover data\u003c/h3\u003e\n\u003cp\u003eA 10-meter annual land cover data of Earth's land surface from 2017\u0026ndash;2023 were downloaded from ESRI [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://livingatlas.arcgis.com/landcover/\u003c/span\u003e\u003cspan address=\"https://livingatlas.arcgis.com/landcover/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]. Each time slice data is a composite of LULC predictions for 9 classes throughout the year, aiming to create a representative snapshot of each year (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Upon download, land cover data were uploaded to ArcGIS Pro version 2.6 (ESRI, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) to: 1) visualize the spatial distribution of land cover classes across each time slice within the Maasai landscapes. 2) Detect changes in land cover between 2017 and 2023 using the change detection wizard available in ArcGIS Pro version 2.6. I projected a map for each time slice from WGS 1984 to the Africa Albers Equal Area Conic projection using the Project tool in ArcGIS Pro version 2.6 to ensure accurate area calculations. I generated 315 accuracy assessment points per time step using stratified random sampling in ArcGIS Pro version 2.6 to assess the accuracy of the classified maps. I validated the land cover maps produced by ESRI (Giliba et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hu et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Yu, 2013) using high-resolution images from Google Earth. The overall land cover classification accuracy for the 2017 and 2023 years was 94\u0026ndash;95% with kappa coefficients of 93% and 94, respectively (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e \u0026amp; S2).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of land cover classes adapted from (Karra et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClass definition\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWater Areas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAny significant clustering of tall (~\u0026thinsp;15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlooded vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuilt Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHuman made structures; major road and rail networks; large homogeneous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBare ground\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSnow/Ice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLarge homogeneous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClouds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo land cover information due to persistent cloud cover\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRangeland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOpen areas covered in homogeneous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Time series land cover analysis\u003c/h2\u003e \u003cp\u003eBetween 2017 and 2023, the Maasai landscapes experienced significant changes in land cover across various classes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)). The water-covered area fluctuated significantly, beginning at 46,231.27 ha in 2017, reaching a peak of 125,258.96 ha in 2020, and then declining to 77,512.33 ha by 2023. This pattern suggests dynamic changes likely driven by seasonal rainfall and climate variability. Tree cover experienced a sharp increase from 432,343.19 ha in 2017 to 1,617,971.94 ha in 2020, followed by a decline to 834,394.90 ha by 2023. This increase may indicate successful reforestation efforts, but the subsequent decrease points to potential deforestation or land use conversion.\u003c/p\u003e \u003cp\u003eFlooded vegetation also showed variability, starting at 2,610.80 ha in 2017, peaking at 11,773.92 ha in 2020, and then dropping to 1,359.90 ha by 2023. Hydrological shifts and seasonal flooding likely contribute to these changes. Crops steadily expanded from 210,640.27 ha in 2017 to 334,577.55 ha in 2023, indicating an increase in agricultural activities, potentially driven by population growth and the need for food production. Built-up areas also showed a gradual rise, growing from 28,981.71 ha in 2017 to 44,207.45 ha by 2023, indicating ongoing urbanization and infrastructure development. Bare ground experienced significant fluctuations, starting at 288,950.44 ha in 2017, dropping to 9,734.29 ha in 2020, and then rising again to 34,925.45 ha by 2023, likely reflecting cycles of land degradation and restoration efforts.\u003c/p\u003e \u003cp\u003eRangeland, the largest land cover class, steadily decreased from 4,216,853.47 ha in 2017 to 3,900,474.56 ha in 2023, indicating the conversion of rangelands to other uses, such as agriculture and settlements, which may have profound implications for pastoralist communities and wildlife relying on these areas for grazing. Overall, the landscape changes in Maasai regions are characterized by increasing agricultural expansion and urbanization, fluctuating tree and bare ground cover, and a steady reduction in rangelands, all of which highlight the impact of human activities and environmental factors on land use patterns.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of land cover within Maasai landscapes between 2017 and 2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand cover class\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46231.27\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100199.83\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73344.39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e125258.96\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e115884.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e87641.88\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e77512.33\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrees\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e432343.19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e705616.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e690998.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1617971.94\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1037663.76\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e819808.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e834394.90\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlooded Vegetation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2610.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5590.43\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2266.35\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11773.92\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2929.84\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1290.87\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1359.90\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrops\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e210640.27\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e249677.70\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e299251.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e289555.29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e332202.18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e321847.26\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e334577.55\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt Area\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28981.71\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32211.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36117.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38862.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38590.38\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e41449.55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e44207.45\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBare Ground\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e288950.44\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70317.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83507.70\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9734.29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10704.49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30711.63\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e34925.45\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClouds\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1071.68\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1674.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e282.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3422.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1103.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e169.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e224.79\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRangeland\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4216853.47\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4062395.94\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4041915.15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3167617.17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3688604.76\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3924764.42\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3900474.56\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e3.2 Overall land cover change between 2017 and 2023\u003c/h3\u003e\n\u003cp\u003eThe land cover changes within Maasai landscapes between 2017 and 2023 reveal significant shifts in land use patterns (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The conversion of 451,514.10 ha of rangeland to tree cover indicates reforestation efforts or natural regeneration, potentially contributing to carbon sequestration, biodiversity, and water conservation. However, the transition of 152,064.70 ha from rangeland to cropland suggests increasing agricultural expansion, likely driven by population growth and food security demands. This shift could lead to reduced grazing areas for pastoral communities, potentially impacting livestock production and livelihoods.\u003c/p\u003e \u003cp\u003eAdditionally, 10,181.69 ha of rangeland was converted into built-up areas, indicating urbanization and infrastructure development. This change may reduce available land for traditional grazing and affect wildlife habitats, contributing to increased human-wildlife conflicts. The conversion of 2,943.54 ha of tree cover to cropland highlights land use pressures that may result in deforestation and loss of ecosystem services, such as water regulation and biodiversity support. Furthermore, 335.55 ha of tree cover transitioned to built-up areas, reflecting the ongoing urban sprawl at the expense of forested land. These land cover changes imply growing pressure on natural resources due to agricultural and infrastructural expansion. The reduction of rangeland and tree cover in favor of cropland and built-up areas poses challenges for environmental sustainability and pastoral livelihoods, which are dependent on intact ecosystems for grazing and natural resource use. Future land use planning in the region will need to balance development goals with conservation efforts to ensure long-term ecological and economic resilience.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e\n\n "},{"header":"Discussion","content":"\u003ch2\u003e4.1 Land cover changes\u003c/h2\u003e\u003cp\u003eThe findings of land cover changes within the Maasai landscapes between 2017 and 2023 highlight key environmental and socio-economic shifts. The transition of 451,514.10 hectares from rangeland to tree cover suggests reforestation efforts or natural regeneration. This shift is positive for biodiversity conservation and carbon sequestration but may reduce grazing areas critical for Maasai pastoralists, whose livelihoods depend on these landscapes for livestock rearing (Otsyina \u0026amp; Maghembe, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The conflict between conservation and pastoral needs requires policies that integrate reforestation with community needs (Homewood et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe conversion of 152,064.70 hectares of rangeland to cropland reflects the growing demand for agricultural land due to population pressure and economic activities. This transition improves food security and local economies but also raises concerns over rangeland degradation and sustainability (Fratkin \u0026amp; Mearns, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Loss of grazing land for pastoralists could lead to overgrazing in remaining areas, further exacerbating land degradation (Said et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUrbanization trends are evident in the shift of 10,181.69 hectares of rangeland to built-up areas, driven by population growth and infrastructural development (Olson et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). While urban expansion can boost local economies, it leads to habitat fragmentation, wildlife displacement, and increased human-wildlife conflicts (Homewood et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Urbanization also puts pressure on natural resources like water and land, leading to unsustainable consumption. The conversion of 2,943.54 hectares of trees to cropland and 335.55 hectares to built-up areas signals deforestation trends in the region. The loss of forest cover leads to biodiversity loss, soil erosion, and reduced ecosystem services (Lambin et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Although this land use change may provide short-term economic benefits, it could undermine long-term ecological sustainability and resilience to climate change (Lambin \u0026amp; Meyfroidt, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo sum up, these changes indicate the need for integrated land-use planning to balance economic development with environmental sustainability. Policymakers must create strategies that promote sustainable agriculture, conserve rangelands, and protect biodiversity while supporting community livelihoods (Said et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Community participation in land-use decisions and the implementation of sustainable practices are critical for mitigating the negative impacts of these land cover transitions.\u003c/p\u003e\u003ch3\u003e4.2 Implications\u003c/h3\u003e\u003cp\u003eThe findings of this study have several critical implications for environmental sustainability, livelihoods, biodiversity conservation, and land-use policy. \u003cem\u003eEnvironmental sustainability\u003c/em\u003e is at risk as the shift from rangelands and forests to croplands and built-up areas imposes ongoing strain on ecosystems. This transformation leads to habitat loss, reduced biodiversity, and increased land degradation. Over time, unsustainable land-use practices may exacerbate the region’s vulnerability to climate change, soil erosion, and desertification. \u003cem\u003eLivelihood challenges\u003c/em\u003e are evident for Maasai pastoralist communities, whose traditional practices rely on open rangelands for grazing. Agricultural expansion and urbanization are reducing these grazing areas, forcing pastoralists to either adopt alternative livelihoods or intensify grazing in limited spaces, resulting in overgrazing and environmental degradation. This trend threatens the traditional pastoral system and highlights the urgent need for integrated land-use planning that balances development with pastoral needs.\u003c/p\u003e\u003cp\u003eAdditionally, \u003cem\u003ebiodiversity conservation\u003c/em\u003e is severely affected by the conversion of forested land to agriculture and infrastructure development. Habitat fragmentation and the transformation of natural ecosystems into human-dominated landscapes contribute to declining wildlife populations and increasing human-wildlife conflicts. To address these challenges, development plans must incorporate conservation strategies that maintain ecological balance while meeting the socio-economic demands of the growing population. The study also underscores the importance of \u003cem\u003eland-use planning and policy\u003c/em\u003e. There is a pressing need for comprehensive land management approaches that accommodate agricultural expansion, urbanization, and biodiversity conservation. Policies should encourage sustainable agricultural practices, protect critical habitats, and ensure urban development does not encroach on ecologically sensitive areas. Involving local communities in decision-making processes can help mitigate the adverse effects of land-use changes. To sum up, while the conversion of rangelands to tree cover offers potential ecological benefits, the large-scale shifts toward cropland and urban areas pose significant challenges to environmental sustainability, biodiversity conservation, and the livelihoods of pastoral communities. Policymakers must focus on sustainable development strategies that balance human needs with ecological integrity to ensure the long-term resilience of the Maasai landscapes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe findings of this study highlight significant land cover changes within the Maasai landscapes between 2017 and 2023. The most notable transitions include the conversion of rangeland to tree cover, cropland, and built areas. The transformation of over 450,000 hectares of rangeland into tree cover, reflecting afforestation or natural reforestation efforts, was the largest observed change. However, the conversion of over 150,000 hectares of rangeland to cropland signals increasing agricultural expansion, which may pose challenges to the sustainability of traditional pastoralist livelihoods. Additionally, the conversion of rangeland to built-up areas, although smaller in scale, indicates ongoing urbanization and infrastructural development in the region. These land cover transitions suggest a dynamic shift in land use practices within the Maasai landscapes, driven by both environmental factors and socio-economic pressures. While afforestation efforts may offer environmental benefits, the conversion of rangeland into cropland and built areas could lead to long-term ecological consequences, including habitat fragmentation, loss of biodiversity, and diminished ecosystem services. The transition from trees to cropland and built areas, though less extensive, further underscores the competing demands for land resources in the region.\u003c/p\u003e\u003cp\u003eOverall, the results underscore the need for integrated land use planning and sustainable management strategies that balance ecological conservation with the socio-economic needs of local communities. Further research is required to assess the broader impacts of these land cover changes on biodiversity, ecosystem services, and the livelihoods of indigenous communities. Policymakers and stakeholders must work together to develop solutions that promote sustainable land use practices while addressing the challenges posed by agricultural expansion and urban development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical statement\u003c/h2\u003e \u003cp\u003eThis study was conducted using publicly available remote sensing and GIS datasets in compliance with ethical guidelines for geospatial research. All satellite imagery and geospatial data were obtained from ESRI website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.esri.com/\u003c/span\u003e\u003cspan address=\"https://www.esri.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe author did not receive any funding to carry out this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: RG; Data curation: RG; Formal analysis: RG; Methodology RG; Visualization: RG ; Writing original draft: RG; Review \u0026amp; editing: RG\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eI thank ESRI for allowing access to publicly available remote sensing and GIS datasets.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe ESRI land cover land use data used in this study is publicly available and can be accessed through the ESRI website (https://www.esri.com/). Additional information about the dataset and its usage guidelines can be obtained from ESRI's data repository. Any specific data processing or analysis steps applied to the ESRI land cover land use data in this study are detailed in the methods section.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEsri (2020). ArcGIS Pro (Version 2.6). Environmental Systems Research Institute, Redlands, CA. URL \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.esri.com/en-us/arcgis/products/arcgis-pro/overview\u003c/span\u003e\u003cspan address=\"https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFratkin, E., \u0026amp; Mearns, R. (2003). 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Google Earth as a virtual globe tool for Earth science applications at the global scale: progress and perspectives. \u003cem\u003eInternational Journal of Remote Sensing\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e, 3966\u0026ndash;3986. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/01431161.2011.636081\u003c/span\u003e\u003cspan address=\"10.1080/01431161.2011.636081\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\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":"Land cover change, Rangeland conversion, Maasai landscapes, Agricultural expansion, Pastoralism, Sustainable land use","lastPublishedDoi":"10.21203/rs.3.rs-5911943/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5911943/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLand cover change, particularly in landscapes inhabited by pastoralist communities like the Maasai, is a growing concern due to its environmental and socio-economic implications. The Maasai regions in Tanzania have experienced significant land cover shifts in recent years, which can affect biodiversity, ecosystem services, and traditional livelihoods. Despite the importance of these landscapes, there is limited understanding of how specific land cover types, such as rangelands, croplands, and tree cover, have changed over time, and what the drivers behind these changes are. To address this gap, this study examines the extent and nature of land cover changes in Maasai landscapes between 2017 and 2023. The study specifically focuses on the conversion of rangelands to other land uses, such as tree cover, croplands, and built areas, as well as the shifts from trees to cropland and built areas. By analyzing these trends, the study aims to provide insights into the factors driving land cover change and their implications for land management in the region. The findings reveal substantial transitions, including the conversion of 451,514 hectares of rangeland to tree cover, 152,064 hectares to cropland, and 10,181 hectares to built areas. These results highlight the urgent need for strategies that support sustainable land use while considering the ecological and socio-economic importance of Maasai landscapes.\u003c/p\u003e","manuscriptTitle":"Transforming Maasai Landscapes: Land Cover Changes and Their Implications for Pastoralism and Conservation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-03 09:01:00","doi":"10.21203/rs.3.rs-5911943/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":"9602f230-4b21-4867-9377-0cb1ce12ce53","owner":[],"postedDate":"February 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-10T15:09:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-03 09:01:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5911943","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5911943","identity":"rs-5911943","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}