Spatiotemporal analysis of landscape patterns and its effect on soil loss in the Rmel river basin, Tunisia

Bouguerra, Sana; Jebari, Sihem; Tarhouni, Jamila

doi:10.17221/84/2019-swr

Cited by 8 publications

(3 citation statements)

References 31 publications

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“…In addition, several modeling tools are used in quantitative soil conservation studies: the soil and water assessment tool (SWAT) [24], the soil and water loss equation (RUSLE) [25], the universal soil loss equation (USLE) [26], and the sediment delivery ratio module of the Integrated Valuation of Ecosystem Services and Trade-offs model (InVEST-SDR). The InVEST-SDR model has the advantages of easy operation and strong spatial expression and is mostly used in studies to assess the dynamics of soil conservation at medium and large scales [27].…”

Section: Introductionmentioning

confidence: 99%

Influence of Landscape Pattern Evolution on Soil Conservation in a Red Soil Hilly Watershed of Southern China

Xie

Wang

et al. 2023

Sustainability

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The Tingjiang Watershed is a typical mountainous area with red soil in the south of China. Due to the high rainfall intensity, significant cultivated land expansion, and accelerated urbanization, ecological problems such as soil erosion are prominent in the study area. Based on the land use, precipitation, digital elevation model (DEM), normalized difference vegetation Index (NDVI), and soil types in 2000, 2010, and 2020, the landscape pattern and soil conservation in the Tingjiang Watershed were assessed at the sub-watershed scale. The spatial correlation between soil conservation and landscape pattern was analyzed using GeoDA software. The results show the following: (1) From 2000 to 2020, the total amount of soil conservation decreased by 4.15 × 108 t. In terms of spatial analysis, the amount of soil conservation in the Tingjiang Watershed showed an upward and then downward trend in the north and a downward trend in the south, with the most obvious downward trend in the southeast and the northeast. (2) Fragmentation of the overall landscape pattern in the Tingjiang Watershed has increased. The discrete degree and homogeneity of patches decreased in Changting County, while landscape heterogeneity and homogeneity increased in Shanghang, Liancheng, and Yongding Counties. (3) Soil conservation was significantly correlated with the landscape indices patch density (PD), landscape shape index (LSI), mean patch area (AREA_MN), patch cohesion index (COHESION), splitting index (SPLIT), and Shannon evenness index (SHEI). Sub-watersheds with low soil conservation had landscape splitting index, landscape dispersion, patch type richness, and boundary complexity. These areas were mainly distributed in the southern part of the watershed. Sub-watersheds with higher soil conservation were characterized by low patch fragmentation and strong connectivity of dominant patches, which were mainly located in the northern part of the watershed. (4) The spatial error model (SEM) fit better in 2000, 2010, and 2020 compared with the spatial lag model (SLM) and ordinary least squares regression (OLS). The diagnostic results of the SEM model show that among the six landscape indices, PD, SHEI, and AREA_MN are the main influencing factors affecting soil conservation in the watershed to different degrees. The purpose of this study was to investigate the response state of soil conservation capacity as landscape patterns evolve in the Tingjiang Watershed, with the goal of providing a reference for landscape planning and management as well as soil erosion management in the watershed.

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Section: Introductionmentioning

confidence: 99%

Influence of Landscape Pattern Evolution on Soil Conservation in a Red Soil Hilly Watershed of Southern China

Xie

Wang

et al. 2023

Sustainability

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“…The Beichuan River watershed is located throughout Datong County, Xining City, Qinghai Province, China, and this area is one of the important tributaries of the Yellow River. In ecologically fragile areas, the balance between ecological protection and economic development is becoming increasingly problematic (Bouguerra et al, 2021). In recent decades, this watershed has experienced large‐scale reforestation projects aimed at restoring and improving ecosystem functions and enhancing the quality of the ecological environment.…”

Section: Introductionmentioning

confidence: 99%

Interactions among ecosystem service key factors in vulnerable areas and their response to landscape patterns under the National Grain to Green Program

Zuo,

Gao,

2023

Land Degrad Dev

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A deeper understanding of the interactions between ecosystem services (ESs) and the drivers of the landscape patterns behind them is critical to how ecological restoration projects in ecologically sensitive and fragile areas are planned and managed. However, few studies have explored the response patterns of ESs to changes in landscape patterns from a geospatial perspective. This study aimed to reveal the relationship between key ESs and landscape pattern responses at spatial scales and to propose the optimization of ecological restoration measures in response to ES trade‐offs problem. The InVEST and CASA models were used to assess the spatial and temporal characteristics of five key ESs in the Beichuan River Basin since the completion of the National Grain to Green Program (2000–2020), selected five key factors are as below: habitat quality (HQ); net primary productivity (NPP); water supply (WS); soil conservation (SC); and water conservation (WC). Geographically weighted regression (GWR) models were used to reveal the trade‐offs and synergies between ESs and the driving mechanisms of landscape patterns from a geospatial perspective. The results showed that landscape connectivity in the study area improved and landscape fragmentation decreased during 2000–2020, accompanied by the increase in HQ, SC, and WC, while NPP and WS decreased. The trade‐offs between ESs appeared mainly between NPP and other ESs, with the NPP–WS pair trade‐off being the strongest. Spearman correlation analysis, geographic detectors, and GWR revealed that the dominant landscape pattern indices affecting ESs showed good agreement. The interaction of the two landscape pattern indices had stronger explanatory power for the variation in ESs than any single landscape pattern index. The patch density index, as the most important landscape pattern index affecting NPP and WS, should be the primary consideration in future landscape planning to reconcile the contradiction between WS and NPP. Because of the inherent spatial heterogeneity of ESs and landscape patterns, analyzing their response relationships from a geospatial perspective can provide scientific guidance to decision‐makers.

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“…This allows decision-makers to assess quantified tradeoffs associated with alternative management choices for environmentally friendly energy projects. Moreover, the model incorporates the process of sedimentation of watersheds to give information on the destination of eroded materials [8,9].…”

Section: Introductionmentioning

confidence: 99%

Considering the Environmental Impacts of Bioenergy Technologies to Support German Energy Transition

et al. 2021

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Clean energy for all, as listed in the United Nation’s SDG7, is a key component for sustainable environmental development. Therefore, it is imperative to uncover the environmental implications of alternative energy technologies. SustainableGAS project simulates different process chains for the substitution of natural gas with renewable energies in the German gas market. The project follows an interdisciplinary approach, taking into account techno-social and environmental variabilities. However, this research highlights the project results from the environmental perspective. So far, a detailed assessment of the environmental costs of alternative gas technologies with a focus on the process of energy transition has remained rare. Although such data constitute key inputs for decision-making, this study helps to bridge a substantial knowledge gap. Competing land-use systems are examined to secure central ecosystem services. To fulfill this obligation, an Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) serves as the modelling tool. InVEST assesses ecosystem services (ES) that are or may be affected by alternative bioenergy technologies. Spatially explicit model results include the water provisioning from the Water Yield Model (WYM), soil erosion and sedimentation described by the Sediment Delivery Ratio (SDR), and nutrient fluxes (N) in response to changing land use are obtained through the Nutrient Delivery Ratio (NDR). The detailed model results are finally extrapolated, which provides a comprehensive image of the environmental impacts associated with bioenergy expansion in Germany from our combination of unique Renewable Gas Plants (RGPs). The final result shows that nutrient load will reduce in southern Germany by the year 2050 compared to the reference state, and biomass use reduced by 46% crops.

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Spatiotemporal analysis of landscape patterns and its effect on soil loss in the Rmel river basin, Tunisia

Cited by 8 publications

References 31 publications

Influence of Landscape Pattern Evolution on Soil Conservation in a Red Soil Hilly Watershed of Southern China

Influence of Landscape Pattern Evolution on Soil Conservation in a Red Soil Hilly Watershed of Southern China

Interactions among ecosystem service key factors in vulnerable areas and their response to landscape patterns under the National Grain to Green Program

Considering the Environmental Impacts of Bioenergy Technologies to Support German Energy Transition

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