Impact assessment of land cover and land use changes on soil erosion changes (2005–2015) in Pakistan

Abstract

Abrupt changes in climatic conditions, exploitation of natural resources, and land degradation exacerbate soil erosion. This study provides a comprehensive assessment of soil erosion dynamics in Pakistan for 2005 and 2015 at 1 km 2 spatial resolution using six influencing factors: rainfall erosivity (R), soil erodibility (K), slope-length (L), slopesteepness (S), cover management (C), and conservation practice (P). The wellaccepted revised universal soil loss equation (RUSLE) was implemented to estimate soil erosion. The soil erosion maps were categorized into four classes: low (<1), medium (1-5), high (5-20), and very high (>20)and transitions among these classes were determined by applying the change matrix method to assess soil erosion (ton ha -1 yr -1 ) changes from 2005 to 2015. Furthermore, spatial patterns and soil erosion dynamics were evaluated for seven administrative units of Pakistan to examine conservation efforts and ecosystem services. Major findings of this study indicate, that at the national scale, an estimated soil erosion of 1.79 ± 11.52 ton ha -1 yr -1 in 2005, which increased to 2.47 ± 18.14 ton ha -1 yr -1 in 2015. This intensification of soil erosion is coupled with land cover and land use changes (LCLUC) due to population expansion, infrastructural development, and exploitation of natural resources. This study's analytical framework and outcomes could be used for developing effective conservation policies to control soil erosion at various spatial scales in Pakistan.

An integrated watershed modelling framework to explore the covariation between sediment connectivity and soil erosion

“…The RUSLE model was applied to calculate annual soil erosion and expressed in terms of soil erosion modulus. The RUSLE, which has been widely used since its promulgation (Ahmad et al, 2022; Renard et al, 1991; Shellberg, 2020): $$$$ A=R\times K\times L\times S\times C\times P, $$$$ where A is the erosion modulus (t ha −1 yr −1 ), R is the rainfall erosivity factor (MJ mm ha −1 hr −1 yr −1 ), K is the soil erodibility factor (t ha h ha −1 MJ −1 mm −1 ), L is the slope length factor (dimensionless), S is the slope factor (dimensionless), C is the cover management factor (dimensionless) and P is the soil conservation practice factor (dimensionless).…”

Temporal and spatial changes of soil erosion under land use and land cover change based on Chinese soil loss equation in the typical watershed on the Loess Plateau

“…The soil erosion modulus map and LULC map combined with the transition matrix help us understand the spatial and temporal evolution of land use patterns and soil erosion grades in the Jiuyuangou watershed. As per Gilani et al (2022), we stipulated soil erosion transformations from lower‐to‐higher erosion grades as ‘loss’ and higher‐to‐lower erosion grades as ‘gain’. For changes in the spatial pattern of land use, we specify the conversion of other land use types to cropland as ‘loss’ and cropland to other land use types as ‘gain.’ We conducted a binary variable spatial correlation analysis to determine how the two variables change simultaneously in the whole watershed space (Nandi & Shakoor, 2010).…”

Land use changes, degradation, and impact on ecosystem services in Asia and Southeast Asia

“…They used a supervised classification method for multi‐date land‐use/cover data and dynamic modeling with the Markov chain and cellular automata technique to predict forest cover loss, highlighting the role of various satellite data in assessing spatiotemporal changes. Gilani et al (2022) utilized MODIS 1 km data to determine soil erosion dynamics in Pakistan comprehensively. Employing a Revised Universal Soil Loss Equation (RUSLE) and MODIS data, they evaluated soil erosion patterns between 2005 and 2015, enabling the identification of soil erosion classes and transitions and underlining the contribution of remote sensing in understanding soil erosion dynamics.…”