Coupling linear spectral unmixing and RUSLE2 to model soil erosion in the Boubo coastal watershed, Côte d'Ivoire

Coulibaly, Lenikpoho Karim; Guan, Qingfeng; Assoma, Tchimou Vincent; Fan, Xiaolong; Coulibaly, Naga

doi:10.1016/j.ecolind.2021.108092

Cited by 8 publications

(4 citation statements)

References 145 publications

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“…The RUSLE model is an internationally popular model [19,32] that compensates for suiting multiscale simulation investigations but it is also constrained by field observation in large-scale applications [33,34]. It can achieve better results in simulations at different scales, and considers precipitation erosion forces, soil erodibility, slope length, soil and water conservation steps, soil conservation measures, and other main factors affecting soil erosion [35,36]. The formula is as follows:…”

Section: Rusle Modelmentioning

confidence: 99%

Soil Erosion Characteristics and Scenario Analysis in the Yellow River Basin Based on PLUS and RUSLE Models

Zhang

Zhu

et al. 2023

IJERPH

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Soil erosion is an important global environmental issue that severely affects regional ecological environment and socio-economic development. The Yellow River (YR) is China’s second largest river and the fifth largest one worldwide. Its watershed is key to China’s economic growth and environmental security. In this study, six impact factors, including rainfall erosivity (R), soil erosivity (K), slope length (L), slope steepness (S), cover management (C), and protective measures (P), were used. Based on the revised universal soil loss equation (RUSLE) model, and combined with a geographic information system (GIS), the temporal and spatial distribution of soil erosion (SE) in the YR from 2000 to 2020 was estimated. The patch-generating land use simulation (PLUS) model was used to simulate the land-use and land-cover change (LUCC) under two scenarios (natural development and ecological protection) in 2040; the RUSLE factor P was found to be associated with LUCC in 2040, and soil erosion in the Yellow River Basin (YRB) in 2040 under the two scenarios were predicted and evaluated. This method has great advantages in land-use simulation, but soil erosion is greatly affected by rainfall and slope, and it only focuses on the link between land-usage alteration and SE. Therefore, this method has certain limitations in assessing soil erosion by simulating and predicting land-use change. We found that there is generally slight soil erosivity in the YRB, with the most serious soil erosion occurring in 2000. Areas with serious SE are predominantly situated in the upper reaches (URs), followed by the middle reaches (MRs), and soil erosion is less severe in the lower reaches. Soil erosion in the YRB decreased 11.92% from 2000 to 2020; thus, soil erosion has gradually reduced in this area over time. Based on the GIS statistics, land-use change strongly influences SE, while an increase in woodland area has an important positive effect in reducing soil erosion. By predicting land-use changes in 2040, compared to the natural development scenario, woodland and grassland under the ecological protection scenario can be increased by 1978 km2 and 2407 km2, respectively. Soil erosion can be decreased by 6.24%, indicating the implementation of woodland and grassland protection will help reduce soil erosion. Policies such as forest protection and grassland restoration should be further developed and implemented on the MRs and URs of the YR. Our research results possess important trend-setting significance for soil erosion control protocols and ecological environmental protection in other large river basins worldwide.

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Section: Rusle Modelmentioning

confidence: 99%

Soil Erosion Characteristics and Scenario Analysis in the Yellow River Basin Based on PLUS and RUSLE Models

Zhang

Zhu

et al. 2023

IJERPH

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“…"The integrated use of remote sensing and GIS could help to assess quantitative soil loss at various scales and also to identify areas that are at potential risk of soil erosion" (Nal et al, 202) [13]. "Several studies showed the potential utility of GIS technique for quantitatively assessing soil erosion hazard based on various models" [14][15][16][17]. "Considering the inaccessibility of the hilly terrain if it is extensive area, RS is essential to accommodate spatial variability and information.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Soil Fertility Status in Paroo and Saraiyan Blocks of Muzaffarpur District of Bihar Using GPS and GIS

Singh,

Tagung,

Kashiwar

et al. 2023

IJPSS

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The experiment was conducted during 2019‒21 at Paroo and Saraiya blocks of Muzaffarpur district of Bihar, India to identify the nutrients status for crop management. Soil fertility inventory research was carried out in these two identified blocks (Paroo and Saraiyan) for generation of thematic soil fertility map. For nutrient assessment altogether 40 (forty) geo-referenced composite soil samples were collected from the various locations of the studied area using a hand-held GARMIN GPS device. The processed soil samples were used for analysis of various soil fertility parameters by using prescribed standard methods. Soil nutrient status and fertility maps were created by using ArcGIS 10.4.1 software employing Inverse Distance Weighted (IDW) interpolation techniques. The results clearly indicated that the soils pH was alkaline in nature ranges from 7.4‒9.5. The mean value of Soil organic matter (0.55%) and potassium (150 to 250 kg ha-1) was found to be low to medium whereas, available nitrogen (119.9 to 319.1 kg ha-1) found 90% deficient, however, phosphorus (4.34 to 18.4 kg ha-1), and sulphur were recorded low in the blocks. However, most of the sample of micronutrients like copper (0.23 to 1.07 mg kg-1) and Iron (1.64 to 13.2 ppm) showed above critical limits while manganese, zinc deficiency were occurred in wide area. The intensive cropping system and imbalanced use of chemical fertilizer is degrading the soil quality in study area.

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“…Semantic information obtained from satellite images is widely used in thematic mapping for analysis of the diverse phenomena and processes on Earth. Examples include mapping land cover fractions [1] and vegetation types [2][3][4][5][6], hydrologic and geomorphic modelling [7], urban sprawl and geological exploration [8,9]. Aside from satellite images, another source of RS data is provided by airborne images [10,11] and light detection and ranging (LiDAR) data for three-dimensional (3D) terrain analysis [12][13][14][15][16][17][18].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Satellite Image Processing by Python and R Using Landsat 9 OLI/TIRS and SRTM DEM Data on Côte d’Ivoire, West Africa

Lemenkova

Debeir

2022

J. Imaging

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In this paper, we propose an advanced scripting approach using Python and R for satellite image processing and modelling terrain in Côte d’Ivoire, West Africa. Data include Landsat 9 OLI/TIRS C2 L1 and the SRTM digital elevation model (DEM). The EarthPy library of Python and `raster’ and `terra’ packages of R are used as tools for data processing. The methodology includes computing vegetation indices to derive information on vegetation coverage and terrain modelling. Four vegetation indices were computed and visualised using R: the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index 2 (EVI2), Soil-Adjusted Vegetation Index (SAVI) and Atmospherically Resistant Vegetation Index 2 (ARVI2). The SAVI index is demonstrated to be more suitable and better adjusted to the vegetation analysis, which is beneficial for agricultural monitoring in Côte d’Ivoire. The terrain analysis is performed using Python and includes slope, aspect, hillshade and relief modelling with changed parameters for the sun azimuth and angle. The vegetation pattern in Côte d’Ivoire is heterogeneous, which reflects the complexity of the terrain structure. Therefore, the terrain and vegetation data modelling is aimed at the analysis of the relationship between the regional topography and environmental setting in the study area. The upscaled mapping is performed as regional environmental analysis of the Yamoussoukro surroundings and local topographic modelling of the Kossou Lake. The algorithms of the data processing include image resampling, band composition, statistical analysis and map algebra used for calculation of the vegetation indices in Côte d’Ivoire. This study demonstrates the effective application of the advanced programming algorithms in Python and R for satellite image processing.

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Coupling linear spectral unmixing and RUSLE2 to model soil erosion in the Boubo coastal watershed, Côte d'Ivoire

Cited by 8 publications

References 145 publications

Soil Erosion Characteristics and Scenario Analysis in the Yellow River Basin Based on PLUS and RUSLE Models

Soil Erosion Characteristics and Scenario Analysis in the Yellow River Basin Based on PLUS and RUSLE Models

Assessment of Soil Fertility Status in Paroo and Saraiyan Blocks of Muzaffarpur District of Bihar Using GPS and GIS

Satellite Image Processing by Python and R Using Landsat 9 OLI/TIRS and SRTM DEM Data on Côte d’Ivoire, West Africa

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