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

doi:10.21203/rs.3.rs-2557676/v1

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

https://doi.org/10.21203/rs.3.rs-2557676/v1

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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

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.

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^− 1h^− 1(mês)⁻³
1 – Very Low	< 250
2 – Low	250–500
3 - Mean	500–750
4 – High	750–1000
5 – Very High	1000 >

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.

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
	km²	%	km²	%	km²	%	km²	%
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
	km²	%
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^− 1month^− 3, in winter between 0.0003 to 3.30 t.ha^− 1month^− 3, in spring between 0.0015 to 18.65 t.ha^− 1month^− 3 and in the summer between 0.0020 to 28.79 t.ha^− 1month^− 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^− 1month^− 3 and 0.0569 t.ha^− 1month^− 3. In spring and summer, the highest soil loss rates were observed, whose averages were, respectively, 0.3733 t.ha^− 1month^− 3 and 0.4393 t.ha^− 1month^− 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. 2015b), 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. 2015b), 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^− 1year^− 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 (km²)	Area (%)	Mean t.ha^− 1ano^− 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^− 1year^− 1) and Cerrado (1.03 t.ha^− 1year^− 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^− 1year^− 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^-1ano^-1)	Tolerance (t.ha^-1ano^-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.

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.

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.

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

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Abstract

Figures

Introduction

Material And Methods

Study area

Methodology

Potential erosivity of the rains (R factor)

Erodibility (K factor)

Ramp length (L factor)

Slope (S factor)

Land use and land cover (C factor)

Conservation Practices (P Factor)

Combination of variables in EF and RUSLE

RUSLE = R.K.L.S.C.P

Statistical validation

Results And Discussion

Seasonal environmental fragility (EF)

Seasonal soil loss

Validation of EF and RUSLE seasonal models with erosion points

Conclusion

Declarations

Acknowledgments

Funding

Competing Interests

Author Contributions

References

Additional Declarations

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