Analysis of seasonal environmental fragility using the normalized difference vegetation index (NDVI) and soil loss estimation in the Urutu watershed, Brazil.

Analysis of seasonal environmental fragility using the normalized difference vegetation index (NDVI) and soil loss estimation in the Urutu watershed, Brazil. | 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 Analysis of seasonal environmental fragility using the normalized difference vegetation index (NDVI) and soil loss estimation in the Urutu watershed, Brazil. Víncler Fernandes Ribeiro de Oliveira Oliveira, Erivelton Pereira Vick Vick, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2557676/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 use intensification has contributed to the emergence of impacts on the environment such as soil loss, silting of watercourses, and biodiversity reduction, among others. Using models that can seasonally diagnose environmental damage is of fundamental importance in territorial planning and management. This work aimed to analyze the seasonal Environmental Fragility (EF) from the autumn of 2019 to the summer of 2020 using the soil loss estimate. To do this, data such as slope, erodibility, erosivity and the normalized difference vegetation index (NDVI) were used. Statistical tests were also applied to assess the significance level of the models in the seasonal evaluation, as well as in the validation based on ground truth points. The results showed that there is seasonal differentiation in the EF and in the soil loss estimation, in which NDVI and erosivity are two of the main responsible factors. Spring was the one that resulted in the largest area classified as high EF (27%) and with an estimated soil loss of 0.3733 t.ha-1month-3. The summer presented the highest soil loss estimation with an average value of 0.4393 t.ha-1month-3. Autumn (0.07683 t.ha-1month-3) and winter (0.0569 t.ha-1month-3) showed the lowest rates of soil loss and the largest areas classified in the low class of EF, as a result, mainly, of the erosivity of the rains. The results indicated by the seasonal models of EF and soil loss were validated through erosion points using spatial statistics tests. environmental fragility Revised Universal Soil Loss Equation (RUSLE) NDVI water quality environmental analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Land use intensification has contributed to the emergence of impacts on the environment (Anjinho et al. 2021), such as soil loss (Uddin et al. 2016 ), silting of watercourses (CRUZ et al. 2017 ), water quality problems (Su et al. 2016 ) and the loss of soil nutrients, among other factors (Garofalo and Ferreira 2015; Asciutti, 2019 ). Thus, in order to assess the environmental condition, a series of factors or variables together are needed to recognize their weaknesses, as the applications of environmental studies in watersheds are fundamental in understanding the processes that occur in these units (Cunha et al. 2013 ). In Brazil, there are widespread methodologies aiming to evaluate the environmental status (Costa et al., 2020 ) through models that combine natural and anthropic variables (ROSS, 1994 ; Crepani et al. 1996 , 2001 ; Ross 2012). There is a vast body of literature that has addressed the adaptation of the Environmental Fragility (EF) model to support territorial management and planning (Valle et al. 2016 ; Silva and Bacani 2017 ; Belato et al. 2019 ; Vieira and Vieira 2020; Campos et al. 2021 ). A difficulty in mapping the EF entails classifying land use and land cover as it is usually a slow process that involves several steps, where a degree of protection is assigned according to the type of vegetation cover at a given time. Vegetation indices can be used to demonstrate the performance of vegetation in terms of soil protection seasonally and not just classify it according to the type of cover. It is very important to determine land use and land cover (C factor), using vegetation indices seasonally (Benavidez et al. 2018 ) as a function of changes in vegetation cover throughout the year (Teramoto et al. 2018 ) and water availability (Becerra et al. 2009 ). Along these lines, some studies have used vegetation indices to model EF (Garófalo and Ferreira 2015 ; Santos 2018 ). In addition to land use and land cover, knowledge of other biophysical characteristics, such as slope and slope length (SL), rainfall erosivity (R) and soil erodibility (K) are important in environmental dynamics in relation to the potential soil loss, helping environmental planning. SL is related to soil water erosion on the influence of slope length and slope (Wischmeier and Smith 1978 ); R is related to the effect that precipitation has on the soil (Wischmeier and Smith 1978 ); and K is related to soil properties and its susceptibility to erosion (Renard et al. 1997 ). Soil loss has been supported by empirical models such as Universal Soil Loss Equation – USLE (Wischmeier and Smith 1978 ), the Revised Universal Soil Loss Equation – RUSLE (Renard et al. 1997 ) and Modified Universal Soil Loss Equation – MUSLE (Williams and Berndt 1972 ). Among these models, RUSLE (Renard et al. 1997 ) has proved to be easy to apply and widely used (Panagos and Katsoyiannis 2019 ). RUSLE considers only laminar erosion (Benavidez et al. 2018 ) and is the most used model to estimate soil loss (Kumar et al. 2014 ; Cunha et al. 2017 ). Benavidez et al. ( 2018 ) point out that in an attempt to improve the Revised Universal Soil Loss Equation (RUSLE), seasonality should also be considered. Thus, the soil loss estimation and distribution by RUSLE are important to identify areas with greater or less risk of erosion (Cunha et al. 2017 ). The RUSLE associated with the EF methodology, although not capable of quantitatively estimating soil loss, spatially translates the EF to erosive process risks, and is an essential tool in environmental analysis. Thus, this work aims to analyze the seasonal EF in the Urutu stream watershed, in the state of Mato Grosso do Sul, Brazil, in each season of the year, in the period between autumn 2019 to summer 2020, using the Normalized Difference Vegetation Index (NDVI) and the soil loss estimation through RUSLE. Material And Methods Study area The present study was carried out in the Urutu watershed (UW), located in the municipality of Aparecida do Taboado, east of the state of Mato Grosso do Sul, an important eucalyptus/cellulose producing region in Brazil. UW has an area of approximately 99.45 km², between the parallels of 20°02'26” S and 20°10'22” S and the meridians of 51° 36'86”W and 51°27'24” W (Fig. 1 ). The Urutu stream is a tributary on the left bank of the Pântano River, a tributary of the Paraná River. The UW is made up of lithologies from the Bauru Group, with the Santo Anastácio Formation, and the Caiuá Group, with the Vale do Rio do Peixe Formation, both deposited on basalts of the Serra Geral Formation (Fernandes and Coimbra, 2000 ). The soils present in the basin are: Red Oxisol, Red Ultisol and Alfisol Haplic. The climate for the region of Aparecida do Taboado is Aw according to the Köppen climate classification update, with characteristics of a tropical climate with a dry season in winter (Peel et al. 2007; Alvares et al. 2013). The region's rainfall regime is characterized by a historical average annual rainfall of 1332.6 mm between 1983 and 2016 (Ana 2019). The use and cover classes are planted pasture in savannah, wooded + grassy-woody savannah, wooded savannah without gallery forests, riparian vegetation, water (Silva et al. 2011 ) and forestry (Vick and Bacani, 2019 ). Methodology The present work was based on the EF proposals by Ross ( 1994 ), RUSLE (Renard et al. 1997 ), with an adaptation of factor C - NDVI (Durigon et al. 2014 ). The analyzed period corresponded to the four seasons of the year, as defined by INMET (2020). Starting with autumn, winter and spring 2019 and summer 2020. Figure 2 presents the technical-scientific procedures developed. From the digital elevation model of the ALOS satellite (Advanced Land Observing Satellite), the PALSAR sensor (Phased Array type L-band Synthetic Aperture Radar) (Jaxa/Meti, 2007), the L (ramp length) and S (slope) factors were made in ArcGIS 10.6 ® software (Esri, 2018 ). The drainage was extracted from the database from the GeoMS project (Silva et al. 2011 ); erodibility from Lima et al. ( 2021 ); precipitation data from nine rainfall stations, eight from the database of the National Water Agency (Agência Nacional Águas e Saneamento Básico – ANA) (Ana 2019), from 1983 to 2016, and one from the Ilha Solteira station (SP), from 1992 to 2017 (Canal Clima, 2019 ); NDVI (Rouse et al. 1974 ) were obtained from Sentinel 2B images, the Multispectral Instrument (MSI) sensor, bands 4 (Pred - red) and 8 (Pnir - near infrared). For each of the stations, the calculation of the median of the pixels of the images was applied, as the threshold of 2% of clouds, using the Google Earth Engine (Gorelick et al. 2017 ). In total, 25 images were used in the composition of the median of the NDVI: 6 for autumn, 10 for winter, 4 for spring and 5 for summer. For the soil loss estimation, the GISus-M extension (Oliveira 2015) in ArcGIS 10.6 ® (ESRI, 2018 ) was used. All input data for soil loss estimation were organized according to the references in Table 1 . Table 1 Component variables of the soil loss estimation. L Factor Desment and Govers (1996); McCool et al. ( 1989 ) S Factor Renard et al. (1998) R Factor Oliveira et al. ( 2012 ) K Factor Lima et al. ( 2021 ) C Factor Colman ( 2018 ) P Factor Bertoni and Lombardi Neto ( 2008 ) The EF component variables are standardized into five classes according to Ross ( 1994 ). Potential erosivity of the rains (R factor) The values of the erosive potential energy of precipitation were calculated using the regionalized equation of Campo Grande, Mato Grosso do Sul (MS), (Oliveira et al. 2012 ). The values of potential rainfall erosivity were grouped according to the seasonal division (three months) for each of the rainfall stations. Afterward, the Inverse Distance Weighting (IDW) interpolator was applied to the data set of each of the stations and the monthly classification of erosivity was adapted from Carvalho ( 1994 ) (Table 2 ) to be used in the EF. Table 2 Classification of potential monthly rainfall erosivity. Hierarchical category Erosivity Classes (R) MJmm ha − 1 h − 1 (mês) −3 1 – Very Low Source: Adapted from Carvalho ( 1994 ). Erodibility (K factor) Erodibility data were extracted from Lima et al. ( 2021 ) and cut for the UW. For EF, erodibility was classified according to Mannigel et al. ( 2002 ) and in the literature (Sabóia de Aquino and Oliveira 2017 ; Demarchi et al. 2019 ; Giovanini Junior 2019 ), presented in Table 3 . Table 3 Classification of soil erodibility. Erodibility rating in hierarchical categories Erodibility values (T.ha.h/ha.MJ.mm) 1 – Very Low < 0.0090 2 – Low 0.0090–0.0150 3 - Mean 0.0150–0.0300 4 – High 0.0300–0.0450 5 – Very High 0.0450–0.0600 Source: Adapted from Mannigel et al. ( 2002 ) Ramp length (L factor) The GISus-M LS-TOOLS tool was used (Oliveira et al. 2015 ), proposed by Zhang et al. ( 2013 ), which uses the algorithm proposed by Desment and Govers (1996). Slope (S factor) In EF, the slope classification followed the proposal made by Ross ( 1994 ), which presents the classes by intervals (Table 4 ). Table 4 Slope Classes. Hierarchical Category Classes 1 – Very Low 0–6% 2 – Low 6–12% 3 - Mean 12–20% 4 - High 20–30% 5 – Very High > 30% Source: Ross ( 1994 ). In the soil loss estimation, the slope was performed by the GISus-M tool through the algorithm cited in Mccool et al. ( 1987 ). Land use and land cover (C factor) The NDVI was standardized in five classes, according to Ross's (1994) classes, classified using the Jenks natural break method, to be used in the EF analysis. The NDVI was also used to calculate the soil loss estimation, as described in the literature (Durigon et al. 2014 ; Aiello et al. 2015 ; Ostovari et al. 2017 ; Dissanayake et al. 2019 ; Decco 2021 ) based on the adaptation proposed by Colman ( 2018 ), and on the formula proposed by Durigon et al. ( 2014 ) according to Almagro et al. ( 2019 ), Sone et al. 2019 and Negese et al. ( 2021 ). Land use and land cover classes were also identified to understand the NDVI values, through object-oriented classification, in eCognition Developer 9.2 ® software (Trimble 2018 ), from bands 8, 4 and 3, MSI sensor (Multispectral Imager), from the Sentinel 2B satellite, dated May 25, 2019. Conservation Practices (P Factor) The P factor followed the values determined by Bertoni and Lombardi Neto ( 2008 ), with only contour planting in the area identified for pasture and forestry uses, thus adopting the value of P = 0.5 for the entire UW. Combination of variables in EF and RUSLE In EF, all variables were classified as slope (S), erosivity (R), erodibility (K) and land use and cover (C) and were subjected to weighted overlap by the ArcGis Weighted Overlay tool. In RUSLE, the unclassified variables were grouped in ArcGis in the GISus-M extension comprising the slope length (L factor), slope (S factor), erosivity (R factor), erodibility (K factor), land use and cover (C factor) and management practices (factor P) variables according to Eq. 1. An annual soil loss estimation was also generated through the sum of the results of the stations and classified according to Beskow et al. ( 2009 ). Equation 1: RUSLE = R.K.L.S.C.P Statistical validation Seasonal data from EF and RUSLE were resampled to 500 meters of spatial resolution as a function of the number of rows and columns. Afterward, the Kruskal Wallis test was applied to verify whether or not there were significant differences between the pairs formed by the stations, using the Jamovi 2.2 software (The Jamovi Project 2021 ). Afterward, the EF and RUSLE seasonal models were validated from ground truth points, which consisted of erosion points sampled at the UW. These points were collected from high spatial resolution images from Google Earth Pro (Sullivan 2009 ), with a total of 66 samples. The samples were submitted to the Kernel density calculation methodology proposed by Rizatti et al. ( 2020 ). Then, the mean values of the seasonal analysis models (EF and RUSLE) and the Kernel distribution were extracted through a regular grid with a resolution of 10 meters. The EF and RUSLE models were validated with erosion points, using the bivariate Moran Local Index (I), in the GeoDa 1.20 software (Anselin et al. 2006 ); indicating spatial autocorrelation (Lee 2001 ; Bone et al. 2013 ), based on similarities between neighbors. Results And Discussion Seasonal environmental fragility (EF) Seasonal EF revealed different temporal and spatial structures in UW as a function of seasonality, which made it possible to assess seasonal conditions previously unrevealed in studies that address this topic (Bacani et al. 2015 ; França 2018 ; Asciutti 2019 ; Souza et al. 2020 ). The EF classification revealed that there is proximity between autumn and winter, differently from what was observed in the other seasons, because, in general, the model showed statistically significant seasonal differences (p < 0.05). The low class is present in all seasons and its largest representation of area is in winter (64%) and autumn (55%) (Fig. 3 and Table 5 ), with distribution in practically the entire basin, especially in occupied areas by natural vegetation and forestry. In winter, there is a greater reduction in plant biomass due to the water availability in the system, and the association of autumn in the volume of precipitation corroborates the increase in the area of the low class. Table 5 Area occupied by UW seasonal EF class. Fragility Classes Autumn Winter Spring Summer km 2 % km 2 % km 2 % km 2 % 1 – Very Low 0 0% 0 0% 0 0% 0 0% 2 – Low 54.49 55% 63.54 64% 26.52 27% 37.61 38% 3 - Mean 34.44 35% 28.06 28% 45.88 46% 41.38 42% 4 – High 10.47 11% 7.85 8% 26.97 27% 20.37 20% 5 – Very High 0.05 0% 0 0 0.08 0% 0.09 0% Total 99.45 100% 99.45 100% 99.45 100% 99.45 100% The mean class, as in other studies that used weighted overlap in watersheds in Mato Grosso do Sul, also showed greater spatial distribution in pasture areas (Pires et al. 2015 ; Silva and Bacani 2017 ; Abrão and Bacani 2018 ; Silva et al. 2022 ). Other studies registered not only pasture areas but also forestry areas (Cunha and Bacani, 2016 ; Vick et al. 2021 ), which consequently showed an influence of the weights adopted for land use and land cover classes, and the attribution of fixed values can collaborate in an incongruous analysis of the landscape (Lira et al. 2022 ). The areas of high EF showed greater spatial extension in spring and summer (27% and 20%), grouped in the north, northeast and south, under pasture uses, exposed soil and harvested forestry area. Valle et al. ( 2016 ) pointed out that areas with considerable vegetation cover removal resulted in soil instability, which was classified in the high and very high EF classes. Vital et al. (1999) observed that the estimated soil loss doubled in value in the first year after the timber harvest but was lower than in agriculture. The very high class appears in three seasons with a small spatial distribution in summer (less than 1% of the area). In autumn and spring, it corresponds to approximately 1% of the total basin, varying spatially along the edges of the drainage network. In spring and summer, there is a gradual increase in the volume of precipitation, especially in the north of the area where the volumes are greater, contributing to the potential increase in erosivity, which coincides with the area with the highest erodibility rate, making it fragile to develop erosive processes. On the other hand, the vegetation cover, which has the functionality of minimizing the impact of erosivity according to the density of the vegetation (Valle et al. 2016 ; Belato et al. 2019 ), mitigated the erosive potential of the area. In the annual EF, established from the weighted average of the seasons, the very low class, as well as in the seasonal ones, was not identified. Very high class concentrations were observed on the banks of the water impoundment (fluvial plain); the high class in the north and northeast of the basin (pastures); the mean class in an area of harvested forestry and the low class in an area of forestry and natural vegetation (Fig. 4 and Table 6 ). Table 6 Area occupied by seasonal EF class in UW. Classes of Fragility Annual FE km 2 % 1 – Very Low 0.00 0% 2 – Low 35.01 35.21% 3 – Mean 48.92 49.19% 4 – High 15.43 15.52% 5 – Very High 0.08 0.08% Total 99.45 100% In the seasonal models (EF), the harvested forestry areas began to stand out in the spring and summer with the high class of fragility, while in the annual model these areas were associated with the mean class, which is a result of the composition of autumn and winter that still contained vegetation. Seasonal soil loss The soil loss estimation showed variations throughout the seasons (Ferreira and Panagopoulos, 2014 ). The soil loss estimation in autumn was between 0.0003 to 5.33 t.ha − 1 month − 3 , in winter between 0.0003 to 3.30 t.ha − 1 month − 3 , in spring between 0.0015 to 18.65 t.ha − 1 month − 3 and in the summer between 0.0020 to 28.79 t.ha − 1 month − 3 (Fig. 5 ). The seasons with the lowest erosivity index, such as autumn and winter, also presented the lowest average values ​​of soil loss 0.07683 t.ha − 1 month − 3 and 0.0569 t.ha − 1 month − 3 . In spring and summer, the highest soil loss rates were observed, whose averages were, respectively, 0.3733 t.ha − 1 month − 3 and 0.4393 t.ha − 1 month − 3 . The seasonal evaluation showed a significant difference, i.e. p < 0.05, and there was also proximity in the soil loss values, forming two different groups: the first, Autumn-Winter, and the second, Spring-Summer. In the paired comparison between soil losses by season, it was observed that there are significant differences (p < 0.01) between all seasons (Winter-Spring; Winter-Summer; Autumn-Spring; Autumn-Summer), except in the Autumn-Winter (p < 0.868), and Spring and Summer (p < 0.999) pairs. In autumn, the vegetation cover presents maximum levels of plant biomass with less erosivity compared to spring and summer, providing the soil loss minimization; the contrary was observed in spring; the winter presents low levels of soil loss due to the low erosivity detected between the seasons, although it already shows a lower density of vegetation cover. The hypothesis of higher soil loss rates in the summer was confirmed, because in the Cerrado, between the months of May and October (autumn and winter), there is a decrease in water in the system, causing water stress. As a result, there is a formation of a litter layer (Inkotte et al. 2022 ), aiming to minimize water loss by plants and contributing to soil moisture retention (Oliveira et al. 2015 b), however in summer, decomposition processes tend to occur faster due to the increase in temperature and humidity, reducing soil protection. In areas of vegetation such as cerrado and riparian vegetation, soil loss values, in general, also present low values, as in other studies (Bruijnzeel 2004 ; Colman et al. 2018; Oliveira et al. 2015 b), as forest native vegetation perform better in soil protection (Kouli et al. 2009 ; Oliveira et al. 2013 ). The same is also observed in forestry (Cândido et al. 2014 ). Vegetation cover also plays a key role in soil protection, as precipitation intercepted by vegetation leaves minimizes the potential energy of water (Cândido et al. 2014 ; Valle et al. 2016 ). The association of topographic factors (Prasannakumar et al. 2011 ) with high levels of vegetation cover density (Mancino et al. 2016 ) resulted in less development of the soil erosion potential. The combination of geomorphological characteristics, the high density of the vegetation cover and the accumulation of litter (due to the advanced development of silviculture) are characteristics of UW, which contributed to the dissipation of the potential of the kinetic force of water (Martins et al. 2010 ). Annual soil loss mapping, generated from the sum of the seasons, showed an average of 0.9471 t.ha − 1 year − 1 (Fig. 6 ), a value above that seasonally detected, with a higher concentration of soil loss on the pasture and harvested forestry areas, settled on the region of high erodibility index and Ultisol, indicating that the rate of soil loss demonstrated dependence on the local characteristics of the UW. In the UW, there is a predominance of Slightly Light and Mild to Moderate classes with 85.05% and 11.36%, respectively, of the total area of soil loss, as shown in Table 7 . Table 7 Classification of soil loss according to Beskow, 2009. Soil loss class (BESKOW, 2009) Area (km 2 ) Area (%) Mean t.ha − 1 ano − 1 SD Slightly Light (< 2.5) 84.58 85.05 0.28 0.65 Mild to Moderate (2.5–5) 11.30 11.36 3.77 0.74 Moderate (5–10) 3 3.02 6.69 1.32 Moderate to High 0.43 0.43 11.71 1.38 High (15–20) 0.8 0.08 16.97 1.42 High to Very High (20–50) 0.5 0.06 25.37 5.83 Very High (50–100) - - - - Extremely High (> 100) - - - - The lowest average soil loss estimations were detected over land use and land cover classes such as riparian vegetation (1.05 t.ha − 1 year − 1 ) and Cerrado (1.03 t.ha − 1 year − 1 ) and presented in other studies (Oliveira et al. 2015 ; Cunha et al. 2017 ; Cunha et al. 2022 ). Forestry, among all use classes, presented the lowest estimate of soil loss (0.66 t.ha − 1 year − 1 ). This reduction may be related to the age of the monoculture (Martins et al. 2010 ; Oliveira et al. 2013 ). Soil loss estimates were also below the soil loss tolerance, as shown in Table 8 . Table 8 Soil types and soil loss tolerance. Soil Type Soil Loss (t.ha -1 ano -1 ) Tolerance (t.ha -1 ano -1 ) Red Ultisol medium texture 1.39 9.04 Red Oxisol medium texture 0.59 12.26 Alfisol Haplic medium/sandy texture 0.48 5.74 Source: loss tolerance by soil type: Red Ultisol (PVe) and Red Oxisol (LVd) according to Lima et al. ( 2019 ); Alfisol Haplic (SX) of Mannigel et al. ( 2002 ). Validation of EF and RUSLE seasonal models with erosion points The Local Moran bivariate (I) values in the EF models showed positive bivariate spatial autocorrelation values (between erosion points and EF) in the pasture (high-high) and forestry (low-low) areas in the UW, which resulted in Moran values of 0.322 in autumn, 0.345 in winter, 0.241 in spring, 0.266 in summer and 0.298 in the year, from the weighted superposition of the seasons (Fig. 7 ). The positive autocorrelation between the EF models and the erosion points is marked by the similarity of the average values between the neighbors as in the results of the forestry areas (blue color) and mainly in the pasture areas (red color) to the north of the UW; the other Moran classes indicated that the variables have values that are different from each other and/or in transition between spatial patterns (Ramos 2014 ; Luzardo et al. 2017 ). In the RUSLE model, the Moran index showed positive bivariate autocorrelation values (between erosion points and estimated erosion) in autumn of 0.215, in winter of 0.214, in spring of 0.190, in summer of 0.181 and in the year of 0.214, from the sum of the stations. In RUSLE, the Moran values were lower than the EF due to the spatial heterogeneity of the distribution of the estimated soil loss, reflecting greater sensitivity in certain areas of the UW to soil loss (Fig. 8 ). The positive autocorrelation observed between the two models (RUSLE and EF) and the erosion points revealed concentrated and significant spatial relationships between pasture and forestry areas, revealing the sensitivity of both models in the seasonal detection of different levels of environmental degradation. However, the EF model was more sensitive to seasonal changes reflected in the NDVI, as well as higher values of positive spatial correlation of Moran I with erosion points, both in the annual comparison and by seasons in relation to the RUSLE model. Conclusion The composition of this EF model using the erodibility, erosivity and NDVI variables were essential in the EF composition and, together with RUSLE, reflected in the improvement of environmental monitoring on a seasonal basis. The seasonal analysis also observed that the EF has variations, as well as the soil loss estimation (RUSLE), which collaborated in measuring the values over the entire basin, making the seasonal analysis an important condition in planning. Additionally, the EF and RUSLE seasonal models showed spatial autocorrelation with terrestrial truth points (erosion points), validating the models. For both seasonal and annual models, there was correspondence between the areas of greater fragility and the soil loss estimation. Among the seasons, spring was the season that presented the largest area classified with the high class of environmental fragility due to the increase in precipitation and the low density of vegetation cover, mainly over pasture areas in the northern region. This is because this area stands out in the annual EF models and soil loss estimation. The main advantages of incorporating the seasonality factor in the EF analysis are: a) Seasonality makes it possible to assess the integrated behavior of different environmental aspects between seasons. b) It favors the development of strategies and the application of preventive actions to control problems related to soil loss in certain periods of the year. c) It clarifies the balance between all environmental components to minimize possible effects of specific variables on soil loss, especially land use policies and, consequently, limit negative impacts on soil. Declarations Acknowledgments This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001 and in part by the Fundação Universidade Federal de Mato Grosso do Sul – UFMS/MEC – Brazil. We would like to thank the Conselho Nacional de Desenvolvimento Científico and Tecnológico (CNPq) for funding a research project (process nº 403993/2021-0) and research productivity scholarship awarded to the last author (process nº 306448/2020-3); and the Graduate Program in Geography at the Três Lagoas Campus of UFMS. Funding This work was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES) - Financing Code 001 and doctoral scholarship granted to the first and second author; the Federal University of Mato Grosso do Sul Foundation – UFMS/MEC – Brazil; the Conselho Nacional de Desenvolvimento Científico and Tecnológico (CNPq) for funding the research project (process nº 403993/2021-0) and research productivity scholarship granted to the last author (process nº 306448/2020-3); and the Graduate Program in Geography at the Três Lagoas Campus of UFMS. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Víncler Fernandes Ribeiro de Oliveira]. The first draft of the manuscript was written by [Víncler Fernandes Ribeiro de Oliveira] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. References Abrão CMR, Bacani VM (2018) Diagnóstico da fragilidade ambiental na bacia hidrográfica do rio Santo Antônio, MS: subsídio ao zoneamento ambiental. Boletim Goiano de Geografia. (Online) 38:619-645. https://doi.org/10.5216/bgg.v38i3.56362 Aiello A, Adamo M, Canora F (2015) Remote sensing and GIS to assess soil erosion with RUSLE3D and USPED at river basin scale in southern Italy. 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1","display":"","copyAsset":false,"role":"figure","size":20494576,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of\u003cstrong\u003e \u003c/strong\u003eUW, Aparecida do Taboado.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-2557676/v1/524ff3cfee8a3983d186aac5.png"},{"id":32926387,"identity":"1078acd8-801b-4dca-8eba-76ee2bf88c71","added_by":"auto","created_at":"2023-02-14 16:11:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":112511,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological flowchart.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-2557676/v1/039f8fbf049c81ea1bab90b2.png"},{"id":32927169,"identity":"6a4017f1-001a-4f17-9eab-a15b7df1d7fe","added_by":"auto","created_at":"2023-02-14 16:27:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5647922,"visible":true,"origin":"","legend":"\u003cp\u003eMap of seasonal EF of the Urutu watershed –UW, Aparecida do Taboado/MS.\u003c/p\u003e","description":"","filename":"Figura3.png","url":"https://assets-eu.researchsquare.com/files/rs-2557676/v1/dcc86711060c1631c8a78715.png"},{"id":32926392,"identity":"2b76045f-8482-4739-8211-29e442fa292d","added_by":"auto","created_at":"2023-02-14 16:11:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":9492272,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual EF from the weighted overlap of seasonal fragility.\u003c/p\u003e","description":"","filename":"Figura4.png","url":"https://assets-eu.researchsquare.com/files/rs-2557676/v1/f7a76dac1a04fc4372e6b473.png"},{"id":32926881,"identity":"73b89813-6160-4e39-b43b-f987f705961a","added_by":"auto","created_at":"2023-02-14 16:19:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":8625485,"visible":true,"origin":"","legend":"\u003cp\u003eUW soil loss degrees seasonally estimated by RUSLE.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-2557676/v1/76f940ed4b8a2ee058bf63ae.png"},{"id":32926393,"identity":"9d16d248-a93f-4ef5-88ff-690c3cf9cd2f","added_by":"auto","created_at":"2023-02-14 16:11:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":8448111,"visible":true,"origin":"","legend":"\u003cp\u003eMapping of the estimated annual soil loss of the UW, Aparecida do Taboado - MS.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-2557676/v1/0171f708e3680e19989b56ea.png"},{"id":32926390,"identity":"fdb482c3-61b7-42a3-8d44-7c454222a1ae","added_by":"auto","created_at":"2023-02-14 16:11:20","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":612203,"visible":true,"origin":"","legend":"\u003cp\u003eLocal Moran bivariate spatial autocorrelation between EF and erosion points.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-2557676/v1/aa6e9e3e9d6fa0cd82c0613f.png"},{"id":32926058,"identity":"f7436a6f-587a-4a71-a4a7-e46eae935529","added_by":"auto","created_at":"2023-02-14 16:03:20","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":467357,"visible":true,"origin":"","legend":"\u003cp\u003eLocal Moran bivariate spatial autocorrelation between RUSLE and erosion points.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-2557676/v1/25b1ee9137d0059a73c8cf4d.png"},{"id":36673336,"identity":"5870dd92-50b9-4893-8966-f666b3c10c78","added_by":"auto","created_at":"2023-05-06 15:29:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6891132,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2557676/v1/fb9a6cd8-46b2-4678-a65e-7a23b325580c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of seasonal environmental fragility using the normalized difference vegetation index (NDVI) and soil loss estimation in the Urutu watershed, Brazil.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLand use intensification has contributed to the emergence of impacts on the environment (Anjinho et al. 2021), such as soil loss (Uddin et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), silting of watercourses (CRUZ et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), water quality problems (Su et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and the loss of soil nutrients, among other factors (Garofalo and Ferreira 2015; Asciutti, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThus, in order to assess the environmental condition, a series of factors or variables together are needed to recognize their weaknesses, as the applications of environmental studies in watersheds are fundamental in understanding the processes that occur in these units (Cunha et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Brazil, there are widespread methodologies aiming to evaluate the environmental status (Costa et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) through models that combine natural and anthropic variables (ROSS, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Crepani et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1996\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Ross 2012). There is a vast body of literature that has addressed the adaptation of the Environmental Fragility (EF) model to support territorial management and planning (Valle et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Silva and Bacani \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Belato et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Vieira and Vieira 2020; Campos et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA difficulty in mapping the EF entails classifying land use and land cover as it is usually a slow process that involves several steps, where a degree of protection is assigned according to the type of vegetation cover at a given time. Vegetation indices can be used to demonstrate the performance of vegetation in terms of soil protection seasonally and not just classify it according to the type of cover. It is very important to determine land use and land cover (C factor), using vegetation indices seasonally (Benavidez et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) as a function of changes in vegetation cover throughout the year (Teramoto et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and water availability (Becerra et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Along these lines, some studies have used vegetation indices to model EF (Gar\u0026oacute;falo and Ferreira \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Santos \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to land use and land cover, knowledge of other biophysical characteristics, such as slope and slope length (SL), rainfall erosivity (R) and soil erodibility (K) are important in environmental dynamics in relation to the potential soil loss, helping environmental planning. SL is related to soil water erosion on the influence of slope length and slope (Wischmeier and Smith \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e1978\u003c/span\u003e); R is related to the effect that precipitation has on the soil (Wischmeier and Smith \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e1978\u003c/span\u003e); and K is related to soil properties and its susceptibility to erosion (Renard et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSoil loss has been supported by empirical models such as Universal Soil Loss Equation \u0026ndash; USLE (Wischmeier and Smith \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e1978\u003c/span\u003e), the Revised Universal Soil Loss Equation \u0026ndash; RUSLE (Renard et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) and Modified Universal Soil Loss Equation \u0026ndash; MUSLE (Williams and Berndt \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e1972\u003c/span\u003e). Among these models, RUSLE (Renard et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) has proved to be easy to apply and widely used (Panagos and Katsoyiannis \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRUSLE considers only laminar erosion (Benavidez et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and is the most used model to estimate soil loss (Kumar et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Cunha et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Benavidez et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) point out that in an attempt to improve the Revised Universal Soil Loss Equation (RUSLE), seasonality should also be considered.\u003c/p\u003e \u003cp\u003eThus, the soil loss estimation and distribution by RUSLE are important to identify areas with greater or less risk of erosion (Cunha et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The RUSLE associated with the EF methodology, although not capable of quantitatively estimating soil loss, spatially translates the EF to erosive process risks, and is an essential tool in environmental analysis.\u003c/p\u003e \u003cp\u003eThus, this work aims to analyze the seasonal EF in the Urutu stream watershed, in the state of Mato Grosso do Sul, Brazil, in each season of the year, in the period between autumn 2019 to summer 2020, using the Normalized Difference Vegetation Index (NDVI) and the soil loss estimation through RUSLE.\u003c/p\u003e"},{"header":"Material And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThe present study was carried out in the Urutu watershed (UW), located in the municipality of Aparecida do Taboado, east of the state of Mato Grosso do Sul, an important eucalyptus/cellulose producing region in Brazil. UW has an area of approximately 99.45 km\u0026sup2;, between the parallels of 20\u0026deg;02'26\u0026rdquo; S and 20\u0026deg;10'22\u0026rdquo; S and the meridians of 51\u0026deg; 36'86\u0026rdquo;W and 51\u0026deg;27'24\u0026rdquo; W (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The Urutu stream is a tributary on the left bank of the P\u0026acirc;ntano River, a tributary of the Paran\u0026aacute; River.\u003c/p\u003e \u003cp\u003eThe UW is made up of lithologies from the Bauru Group, with the Santo Anast\u0026aacute;cio Formation, and the Caiu\u0026aacute; Group, with the Vale do Rio do Peixe Formation, both deposited on basalts of the Serra Geral Formation (Fernandes and Coimbra, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe soils present in the basin are: Red Oxisol, Red Ultisol and Alfisol Haplic.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe climate for the region of Aparecida do Taboado is Aw according to the K\u0026ouml;ppen climate classification update, with characteristics of a tropical climate with a dry season in winter (Peel et al. 2007; Alvares et al. 2013). The region's rainfall regime is characterized by a historical average annual rainfall of 1332.6 mm between 1983 and 2016 (Ana 2019).\u003c/p\u003e \u003cp\u003eThe use and cover classes are planted pasture in savannah, wooded\u0026thinsp;+\u0026thinsp;grassy-woody savannah, wooded savannah without gallery forests, riparian vegetation, water (Silva et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and forestry (Vick and Bacani, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMethodology\u003c/h2\u003e \u003cp\u003eThe present work was based on the EF proposals by Ross (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), RUSLE (Renard et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), with an adaptation of factor C - NDVI (Durigon et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The analyzed period corresponded to the four seasons of the year, as defined by INMET (2020). Starting with autumn, winter and spring 2019 and summer 2020.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the technical-scientific procedures developed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFrom the digital elevation model of the ALOS satellite (Advanced Land Observing Satellite), the PALSAR sensor (Phased Array type L-band Synthetic Aperture Radar) (Jaxa/Meti, 2007), the L (ramp length) and S (slope) factors were made in ArcGIS 10.6 \u0026reg; software (Esri, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe drainage was extracted from the database from the GeoMS project (Silva et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2011\u003c/span\u003e); erodibility from Lima et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); precipitation data from nine rainfall stations, eight from the database of the National Water Agency (Ag\u0026ecirc;ncia Nacional \u0026Aacute;guas e Saneamento B\u0026aacute;sico \u0026ndash; ANA) (Ana 2019), from 1983 to 2016, and one from the Ilha Solteira station (SP), from 1992 to 2017 (Canal Clima, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); NDVI (Rouse et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1974\u003c/span\u003e) were obtained from Sentinel 2B images, the Multispectral Instrument (MSI) sensor, bands 4 (Pred - red) and 8 (Pnir - near infrared). For each of the stations, the calculation of the median of the pixels of the images was applied, as the threshold of 2% of clouds, using the Google Earth Engine (Gorelick et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In total, 25 images were used in the composition of the median of the NDVI: 6 for autumn, 10 for winter, 4 for spring and 5 for summer.\u003c/p\u003e \u003cp\u003eFor the soil loss estimation, the GISus-M extension (Oliveira 2015) in ArcGIS 10.6 \u0026reg; (ESRI, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) was used. All input data for soil loss estimation were organized according to the references in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eComponent variables of the soil loss estimation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL Factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDesment and Govers (1996); McCool et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1989\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS Factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRenard et al. (1998)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR Factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOliveira et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK Factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLima et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC Factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColman (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP Factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBertoni and Lombardi Neto (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe EF component variables are standardized into five classes according to Ross (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePotential erosivity of the rains (R factor)\u003c/h2\u003e \u003cp\u003eThe values of the erosive potential energy of precipitation were calculated using the regionalized equation of Campo Grande, Mato Grosso do Sul (MS), (Oliveira et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe values of potential rainfall erosivity were grouped according to the seasonal division (three months) for each of the rainfall stations. Afterward, the Inverse Distance Weighting (IDW) interpolator was applied to the data set of each of the stations and the monthly classification of erosivity was adapted from Carvalho (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) to be used in the EF.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eClassification of potential monthly rainfall erosivity.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHierarchical category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eErosivity Classes (R) MJmm ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eh\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e(m\u0026ecirc;s)\u003csup\u003e\u0026minus;3\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 \u0026ndash; Very Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 \u0026ndash; Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e250\u0026ndash;500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 - Mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500\u0026ndash;750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 \u0026ndash; High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e750\u0026ndash;1000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 \u0026ndash; Very High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1000 \u0026gt;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Adapted from Carvalho (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eErodibility (K factor)\u003c/h2\u003e \u003cp\u003eErodibility data were extracted from Lima et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and cut for the UW. For EF, erodibility was classified according to Mannigel et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) and in the literature (Sab\u0026oacute;ia de Aquino and Oliveira \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Demarchi et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Giovanini Junior \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification of soil erodibility.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eErodibility rating\u003c/p\u003e \u003cp\u003ein hierarchical categories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eErodibility values\u003c/p\u003e \u003cp\u003e(T.ha.h/ha.MJ.mm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 \u0026ndash; Very Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 \u0026ndash; Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0090\u0026ndash;0.0150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 - Mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0150\u0026ndash;0.0300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 \u0026ndash; High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0300\u0026ndash;0.0450\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 \u0026ndash; Very High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0450\u0026ndash;0.0600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eSource: Adapted from Mannigel et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2002\u003c/span\u003e)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eRamp length (L factor)\u003c/h2\u003e \u003cp\u003eThe GISus-M LS-TOOLS tool was used (Oliveira et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), proposed by Zhang et al. (\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), which uses the algorithm proposed by Desment and Govers (1996).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSlope (S factor)\u003c/h2\u003e \u003cp\u003eIn EF, the slope classification followed the proposal made by Ross (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), which presents the classes by intervals (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSlope Classes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHierarchical Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClasses\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 \u0026ndash; Very Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 \u0026ndash; Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u0026ndash;12%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 - Mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u0026ndash;20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 - High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 \u0026ndash; Very High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Ross (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the soil loss estimation, the slope was performed by the GISus-M tool through the algorithm cited in Mccool et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1987\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eLand use and land cover (C factor)\u003c/h2\u003e \u003cp\u003eThe NDVI was standardized in five classes, according to Ross's (1994) classes, classified using the Jenks natural break method, to be used in the EF analysis.\u003c/p\u003e \u003cp\u003eThe NDVI was also used to calculate the soil loss estimation, as described in the literature (Durigon et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Aiello et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ostovari et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dissanayake et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Decco \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) based on the adaptation proposed by Colman (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and on the formula proposed by Durigon et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) according to Almagro et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Sone et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2019\u003c/span\u003e and Negese et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLand use and land cover classes were also identified to understand the NDVI values, through object-oriented classification, in eCognition Developer 9.2 \u0026reg; software (Trimble \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), from bands 8, 4 and 3, MSI sensor (Multispectral Imager), from the Sentinel 2B satellite, dated May 25, 2019.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eConservation Practices (P Factor)\u003c/h3\u003e\n\u003cp\u003eThe P factor followed the values determined by Bertoni and Lombardi Neto (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), with only contour planting in the area identified for pasture and forestry uses, thus adopting the value of P\u0026thinsp;=\u0026thinsp;0.5 for the entire UW.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCombination of variables in EF and RUSLE\u003c/h2\u003e \u003cp\u003eIn EF, all variables were classified as slope (S), erosivity (R), erodibility (K) and land use and cover (C) and were subjected to weighted overlap by the ArcGis Weighted Overlay tool.\u003c/p\u003e \u003cp\u003eIn RUSLE, the unclassified variables were grouped in ArcGis in the GISus-M extension comprising the slope length (L factor), slope (S factor), erosivity (R factor), erodibility (K factor), land use and cover (C factor) and management practices (factor P) variables according to Eq.\u0026nbsp;1. An annual soil loss estimation was also generated through the sum of the results of the stations and classified according to Beskow et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEquation 1:\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eRUSLE\u0026thinsp;=\u0026thinsp;R.K.L.S.C.P\u003c/h2\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical validation\u003c/h2\u003e \u003cp\u003eSeasonal data from EF and RUSLE were resampled to 500 meters of spatial resolution as a function of the number of rows and columns. Afterward, the Kruskal Wallis test was applied to verify whether or not there were significant differences between the pairs formed by the stations, using the Jamovi 2.2 software (The Jamovi Project \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAfterward, the EF and RUSLE seasonal models were validated from ground truth points, which consisted of erosion points sampled at the UW.\u003c/p\u003e \u003cp\u003eThese points were collected from high spatial resolution images from Google Earth Pro (Sullivan \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), with a total of 66 samples. The samples were submitted to the Kernel density calculation methodology proposed by Rizatti et al. (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Then, the mean values of the seasonal analysis models (EF and RUSLE) and the Kernel distribution were extracted through a regular grid with a resolution of 10 meters.\u003c/p\u003e \u003cp\u003eThe EF and RUSLE models were validated with erosion points, using the bivariate Moran Local Index (I), in the GeoDa 1.20 software (Anselin et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2006\u003c/span\u003e); indicating spatial autocorrelation (Lee \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Bone et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), based on similarities between neighbors.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results And Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSeasonal environmental fragility (EF)\u003c/h2\u003e \u003cp\u003eSeasonal EF revealed different temporal and spatial structures in UW as a function of seasonality, which made it possible to assess seasonal conditions previously unrevealed in studies that address this topic (Bacani et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Fran\u0026ccedil;a \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Asciutti \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Souza et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe EF classification revealed that there is proximity between autumn and winter, differently from what was observed in the other seasons, because, in general, the model showed statistically significant seasonal differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThe low class is present in all seasons and its largest representation of area is in winter (64%) and autumn (55%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), with distribution in practically the entire basin, especially in occupied areas by natural vegetation and forestry. In winter, there is a greater reduction in plant biomass due to the water availability in the system, and the association of autumn in the volume of precipitation corroborates the increase in the area of the low class.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eArea occupied by UW seasonal EF class.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFragility Classes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAutumn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ekm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ekm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ekm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ekm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1 \u0026ndash; Very Low\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2 \u0026ndash; Low\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3 - Mean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e41.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4 \u0026ndash; High\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5 \u0026ndash; Very High\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e99.45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e99.45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e100%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e99.45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e100%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e99.45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e100%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe mean class, as in other studies that used weighted overlap in watersheds in Mato Grosso do Sul, also showed greater spatial distribution in pasture areas (Pires et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Silva and Bacani \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Abr\u0026atilde;o and Bacani \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Silva et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Other studies registered not only pasture areas but also forestry areas (Cunha and Bacani, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Vick et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which consequently showed an influence of the weights adopted for land use and land cover classes, and the attribution of fixed values can collaborate in an incongruous analysis of the landscape (Lira et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe areas of high EF showed greater spatial extension in spring and summer (27% and 20%), grouped in the north, northeast and south, under pasture uses, exposed soil and harvested forestry area. Valle et al. (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) pointed out that areas with considerable vegetation cover removal resulted in soil instability, which was classified in the high and very high EF classes. Vital et al. (1999) observed that the estimated soil loss doubled in value in the first year after the timber harvest but was lower than in agriculture.\u003c/p\u003e \u003cp\u003eThe very high class appears in three seasons with a small spatial distribution in summer (less than 1% of the area). In autumn and spring, it corresponds to approximately 1% of the total basin, varying spatially along the edges of the drainage network.\u003c/p\u003e \u003cp\u003eIn spring and summer, there is a gradual increase in the volume of precipitation, especially in the north of the area where the volumes are greater, contributing to the potential increase in erosivity, which coincides with the area with the highest erodibility rate, making it fragile to develop erosive processes. On the other hand, the vegetation cover, which has the functionality of minimizing the impact of erosivity according to the density of the vegetation (Valle et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Belato et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), mitigated the erosive potential of the area.\u003c/p\u003e \u003cp\u003eIn the annual EF, established from the weighted average of the seasons, the very low class, as well as in the seasonal ones, was not identified. Very high class concentrations were observed on the banks of the water impoundment (fluvial plain); the high class in the north and northeast of the basin (pastures); the mean class in an area of harvested forestry and the low class in an area of forestry and natural vegetation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eArea occupied by seasonal EF class in UW.\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=\"char\" char=\".\" 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\u003eClasses of\u003c/p\u003e \u003cp\u003eFragility\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAnnual FE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ekm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1 \u0026ndash; Very Low\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2 \u0026ndash; Low\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.21%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3 \u0026ndash; Mean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.19%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4 \u0026ndash; High\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.52%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5 \u0026ndash; Very High\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e99.45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the seasonal models (EF), the harvested forestry areas began to stand out in the spring and summer with the high class of fragility, while in the annual model these areas were associated with the mean class, which is a result of the composition of autumn and winter that still contained vegetation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSeasonal soil loss\u003c/h2\u003e \u003cp\u003eThe soil loss estimation showed variations throughout the seasons (Ferreira and Panagopoulos, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The soil loss estimation in autumn was between 0.0003 to 5.33 t.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003emonth\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, in winter between 0.0003 to 3.30 t.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003emonth\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, in spring between 0.0015 to 18.65 t.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003emonth\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and in the summer between 0.0020 to 28.79 t.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003emonth\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe seasons with the lowest erosivity index, such as autumn and winter, also presented the lowest average values ​​of soil loss 0.07683 t.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003emonth\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 0.0569 t.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003emonth\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e. In spring and summer, the highest soil loss rates were observed, whose averages were, respectively, 0.3733 t.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003emonth\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 0.4393 t.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003emonth\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe seasonal evaluation showed a significant difference, i.e. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and there was also proximity in the soil loss values, forming two different groups: the first, Autumn-Winter, and the second, Spring-Summer. In the paired comparison between soil losses by season, it was observed that there are significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) between all seasons (Winter-Spring; Winter-Summer; Autumn-Spring; Autumn-Summer), except in the Autumn-Winter (p\u0026thinsp;\u0026lt;\u0026thinsp;0.868), and Spring and Summer (p\u0026thinsp;\u0026lt;\u0026thinsp;0.999) pairs.\u003c/p\u003e \u003cp\u003eIn autumn, the vegetation cover presents maximum levels of plant biomass with less erosivity compared to spring and summer, providing the soil loss minimization; the contrary was observed in spring; the winter presents low levels of soil loss due to the low erosivity detected between the seasons, although it already shows a lower density of vegetation cover.\u003c/p\u003e \u003cp\u003eThe hypothesis of higher soil loss rates in the summer was confirmed, because in the Cerrado, between the months of May and October (autumn and winter), there is a decrease in water in the system, causing water stress. As a result, there is a formation of a litter layer (Inkotte et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), aiming to minimize water loss by plants and contributing to soil moisture retention (Oliveira et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2015\u003c/span\u003eb), however in summer, decomposition processes tend to occur faster due to the increase in temperature and humidity, reducing soil protection.\u003c/p\u003e \u003cp\u003eIn areas of vegetation such as cerrado and riparian vegetation, soil loss values, in general, also present low values, as in other studies (Bruijnzeel \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Colman et al. 2018; Oliveira et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2015\u003c/span\u003eb), as forest native vegetation perform better in soil protection (Kouli et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Oliveira et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The same is also observed in forestry (C\u0026acirc;ndido et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVegetation cover also plays a key role in soil protection, as precipitation intercepted by vegetation leaves minimizes the potential energy of water (C\u0026acirc;ndido et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Valle et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe association of topographic factors (Prasannakumar et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) with high levels of vegetation cover density (Mancino et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) resulted in less development of the soil erosion potential. The combination of geomorphological characteristics, the high density of the vegetation cover and the accumulation of litter (due to the advanced development of silviculture) are characteristics of UW, which contributed to the dissipation of the potential of the kinetic force of water (Martins et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnnual soil loss mapping, generated from the sum of the seasons, showed an average of 0.9471 t.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), a value above that seasonally detected, with a higher concentration of soil loss on the pasture and harvested forestry areas, settled on the region of high erodibility index and Ultisol, indicating that the rate of soil loss demonstrated dependence on the local characteristics of the UW.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the UW, there is a predominance of Slightly Light and Mild to Moderate classes with 85.05% and 11.36%, respectively, of the total area of soil loss, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification of soil loss according to Beskow, 2009.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil loss class\u003c/p\u003e \u003cp\u003e(BESKOW, 2009)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003cp\u003e(km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003cp\u003et.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eano\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlightly Light (\u0026lt;\u0026thinsp;2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild to Moderate (2.5\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate (5\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate to High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh (15\u0026ndash;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh to Very High (20\u0026ndash;50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery High (50\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtremely High (\u0026gt;\u0026thinsp;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe lowest average soil loss estimations were detected over land use and land cover classes such as riparian vegetation (1.05 t.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and Cerrado (1.03 t.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and presented in other studies (Oliveira et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Cunha et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Cunha et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Forestry, among all use classes, presented the lowest estimate of soil loss (0.66 t.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyear\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). This reduction may be related to the age of the monoculture (Martins et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Oliveira et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSoil loss estimates were also below the soil loss tolerance, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSoil types and soil loss tolerance.\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil Loss\u003c/p\u003e \u003cp\u003e(t.ha\u003csup\u003e-1\u003c/sup\u003eano\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTolerance\u003c/p\u003e \u003cp\u003e(t.ha\u003csup\u003e-1\u003c/sup\u003eano\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed Ultisol medium texture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed Oxisol medium texture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlfisol Haplic medium/sandy texture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: loss tolerance by soil type: Red Ultisol (PVe) and Red Oxisol (LVd)\u003c/p\u003e \u003cp\u003eaccording to Lima et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); Alfisol Haplic (SX) of Mannigel et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eValidation of EF and RUSLE seasonal models with erosion points\u003c/h2\u003e \u003cp\u003eThe Local Moran bivariate (I) values in the EF models showed positive bivariate spatial autocorrelation values (between erosion points and EF) in the pasture (high-high) and forestry (low-low) areas in the UW, which resulted in Moran values of 0.322 in autumn, 0.345 in winter, 0.241 in spring, 0.266 in summer and 0.298 in the year, from the weighted superposition of the seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe positive autocorrelation between the EF models and the erosion points is marked by the similarity of the average values between the neighbors as in the results of the forestry areas (blue color) and mainly in the pasture areas (red color) to the north of the UW; the other Moran classes indicated that the variables have values that are different from each other and/or in transition between spatial patterns (Ramos \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Luzardo et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the RUSLE model, the Moran index showed positive bivariate autocorrelation values (between erosion points and estimated erosion) in autumn of 0.215, in winter of 0.214, in spring of 0.190, in summer of 0.181 and in the year of 0.214, from the sum of the stations. In RUSLE, the Moran values were lower than the EF due to the spatial heterogeneity of the distribution of the estimated soil loss, reflecting greater sensitivity in certain areas of the UW to soil loss (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe positive autocorrelation observed between the two models (RUSLE and EF) and the erosion points revealed concentrated and significant spatial relationships between pasture and forestry areas, revealing the sensitivity of both models in the seasonal detection of different levels of environmental degradation. However, the EF model was more sensitive to seasonal changes reflected in the NDVI, as well as higher values of positive spatial correlation of Moran I with erosion points, both in the annual comparison and by seasons in relation to the RUSLE model.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe composition of this EF model using the erodibility, erosivity and NDVI variables were essential in the EF composition and, together with RUSLE, reflected in the improvement of environmental monitoring on a seasonal basis.\u003c/p\u003e\n\u003cp\u003eThe seasonal analysis also observed that the EF has variations, as well as the soil loss estimation (RUSLE), which collaborated in measuring the values over the entire basin, making the seasonal analysis an important condition in planning. Additionally, the EF and RUSLE seasonal models showed spatial autocorrelation with terrestrial truth points (erosion points), validating the models. For both seasonal and annual models, there was correspondence between the areas of greater fragility and the soil loss estimation.\u003c/p\u003e\n\u003cp\u003eAmong the seasons, spring was the season that presented the largest area classified with the high class of environmental fragility due to the increase in precipitation and the low density of vegetation cover, mainly over pasture areas in the northern region. This is because this area stands out in the annual EF models and soil loss estimation.\u003c/p\u003e\n\u003cp\u003eThe main advantages of incorporating the seasonality factor in the EF analysis are:\u003c/p\u003e\n\u003cp\u003ea) Seasonality makes it possible to assess the integrated behavior of different environmental aspects between seasons.\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eb) It favors the development of strategies and the application of preventive actions to control problems related to soil loss in certain periods of the year.\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003ec) It clarifies the balance between all environmental components to minimize possible effects of specific variables on soil loss, especially land use policies and, consequently, limit negative impacts on soil.\u003c/p\u003e\n\u003c/span\u003e\n"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis study was financed in part by the Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior \u0026ndash; Brasil (CAPES) \u0026ndash; Finance Code 001 and in part by the Funda\u0026ccedil;\u0026atilde;o Universidade Federal de Mato Grosso do Sul \u0026ndash; UFMS/MEC \u0026ndash; Brazil. We would like to thank the Conselho Nacional de Desenvolvimento Cient\u0026iacute;fico and Tecnol\u0026oacute;gico (CNPq) for funding a research project (process n\u0026ordm; 403993/2021-0) and research productivity scholarship awarded to the last author (process n\u0026ordm; 306448/2020-3); and the Graduate Program in Geography at the Tr\u0026ecirc;s Lagoas Campus of UFMS.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThis work was supported by the Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior - Brazil (CAPES) - Financing Code 001 and doctoral scholarship granted to the first and second author; the Federal University of Mato Grosso do Sul Foundation \u0026ndash; UFMS/MEC \u0026ndash; Brazil; the Conselho Nacional de Desenvolvimento Cient\u0026iacute;fico and Tecnol\u0026oacute;gico (CNPq) for funding the research project (process n\u0026ordm; 403993/2021-0) and research productivity scholarship granted to the last author (process n\u0026ordm; 306448/2020-3); and the Graduate Program in Geography at the Tr\u0026ecirc;s Lagoas Campus of UFMS.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [V\u0026iacute;ncler Fernandes Ribeiro de Oliveira]. The first draft of the manuscript was written by [V\u0026iacute;ncler Fernandes Ribeiro de Oliveira] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbr\u0026atilde;o CMR, Bacani VM (2018) Diagn\u0026oacute;stico da fragilidade ambiental na bacia hidrogr\u0026aacute;fica do rio Santo Ant\u0026ocirc;nio, MS: subs\u0026iacute;dio ao zoneamento ambiental. Boletim Goiano de Geografia. (Online) 38:619-645. https://doi.org/10.5216/bgg.v38i3.56362 \u003c/li\u003e\n\u003cli\u003eAiello A, Adamo M, Canora F (2015) Remote sensing and GIS to assess soil erosion with RUSLE3D and USPED at river basin scale in southern Italy. 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Revista do Departamento de Geografia \u0026ndash; USP 29:212-245. https://doi.org/10.11606/rdg.v29i0.102118 \u003c/li\u003e\n\u003cli\u003eGiovanini JunioR N (2019) An\u0026aacute;lise e Aplica\u0026ccedil;\u0026atilde;o de Metodologias de Predi\u0026ccedil;\u0026atilde;o de Processos Erosivos Utilizando SIG na Bacia Hidrogr\u0026aacute;fica do C\u0026oacute;rrego do Engano, Nova Cana\u0026atilde; Paulista \u0026ndash; SP. Disserta\u0026ccedil;\u0026atilde;o, University Estadual J\u0026uacute;lio de Mesquita, Ilha Solteira - SP.\u003c/li\u003e\n\u003cli\u003eGorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18\u0026ndash;27. https://doi.org/10.1016/j.rse.2017.06.031 \u003c/li\u003e\n\u003cli\u003eIBGE - Instituto Brasileiro de Geografia e Estat\u0026iacute;stica (2019) Base cartogr\u0026aacute;fica continua do Brasil. Escala 1:250.000 -BC250. 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Computers \u0026amp; Geosciences 52:77-188.\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":"environmental fragility, Revised Universal Soil Loss Equation (RUSLE), NDVI, water quality, environmental analysis","lastPublishedDoi":"10.21203/rs.3.rs-2557676/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2557676/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLand use intensification has contributed to the emergence of impacts on the environment such as soil loss, silting of watercourses, and biodiversity reduction, among others. Using models that can seasonally diagnose environmental damage is of fundamental importance in territorial planning and management. This work aimed to analyze the seasonal Environmental Fragility (EF) from the autumn of 2019 to the summer of 2020 using the soil loss estimate. To do this, data such as slope, erodibility, erosivity and the normalized difference vegetation index (NDVI) were used. Statistical tests were also applied to assess the significance level of the models in the seasonal evaluation, as well as in the validation based on ground truth points. The results showed that there is seasonal differentiation in the EF and in the soil loss estimation, in which NDVI and erosivity are two of the main responsible factors. Spring was the one that resulted in the largest area classified as high EF (27%) and with an estimated soil loss of 0.3733 t.ha-1month-3. The summer presented the highest soil loss estimation with an average value of 0.4393 t.ha-1month-3. Autumn (0.07683 t.ha-1month-3) and winter (0.0569 t.ha-1month-3) showed the lowest rates of soil loss and the largest areas classified in the low class of EF, as a result, mainly, of the erosivity of the rains. The results indicated by the seasonal models of EF and soil loss were validated through erosion points using spatial statistics tests.\u003c/p\u003e","manuscriptTitle":"Analysis of seasonal environmental fragility using the normalized difference vegetation index (NDVI) and soil loss estimation in the Urutu watershed, Brazil.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-02-14 16:03:13","doi":"10.21203/rs.3.rs-2557676/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":"04b81e2d-b968-48ff-9a0a-48810e010b9d","owner":[],"postedDate":"February 14th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-05-06T15:29:21+00:00","versionOfRecord":[],"versionCreatedAt":"2023-02-14 16:03:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2557676","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2557676","identity":"rs-2557676","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}