Using Erosion Mapping for Assessment of Biophysical and Socioeconomic Factors in O. Rmel Watershed (Tunisia)

被引:0
|
作者
Attia, R. [1 ]
Agrebaoui, S. [1 ]
Hamrouni, H. [1 ]
机构
[1] D Ressources Sols DG ACTA, Ariana 2080, Tunisia
关键词
D O I
暂无
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The Mediterranean coastal areas are undergoing both natural and human activities which tend to affect its resources and the community's quality of living. In addition, the agriculture activities of population have led to noticeable environmental degradation including soil erosion. This document has been prepared with the technical and management assistance of the Priority Actions Programme Regional Activity Centre (PAP/RAC) of the Mediterranean Action Plan. Two pilot areas were chosen in O. Rmel watershed according natural and physical diversity: Ethological, morphological, and hydrological behaviour. Obviously, the two areas have lots of differences in terms of their problems which make the assessment of their degradation processes more meaningful and educational for the purpose of the project. The PAP/CAR approach integrates biophysical and socio-economic components of land degradation at different scales, recognizing that socio-economic issues are also driving forces of pressures that impact on land conditions. This study starts with the predictive soil map (result of diagnostic analysis) according to physical parameters: slope, soil cover, land use and agro practice, which allows later to produce maps showing erosion risk and land degradation processes in the two regions. The second component is to analysis of the human impact and economic activities that cause land degradation on stabilized and unstable areas, in order to identify prioritize areas for intervention. Finally the third component was to consolidate the outline of management planning activities based on remedial measures desired. These activities were supported by Geographic Information System (GIS) which includes all produced data.
引用
收藏
页码:69 / 77
页数:9
相关论文
共 50 条
  • [41] Badland erosion susceptibility mapping using machine learning data mining techniques, Firozkuh watershed, Iran
    Mohammady, Majid
    [J]. NATURAL HAZARDS, 2023, 117 (01) : 703 - 721
  • [42] Using the remote sensing and GIS technology for erosion risk mapping of Kartalkaya Dam Watershed in Kahramanmaras, Turkey
    Yuksel, Alaaddin
    Gundogan, Recep
    Akay, Abdullah E.
    [J]. SENSORS, 2008, 8 (08): : 4851 - 4865
  • [43] Badland erosion susceptibility mapping using machine learning data mining techniques, Firozkuh watershed, Iran
    Majid Mohammady
    [J]. Natural Hazards, 2023, 117 : 703 - 721
  • [44] Spatial assessment of soil erosion rate using remote sensing and GIS techniques in Mediterranean Watershed
    El Harche, Sanae
    Chikhaoui, Mohamd
    Mustapha, Naimi
    [J]. 2023 INTERNATIONAL CONFERENCE ON MACHINE INTELLIGENCE FOR GEOANALYTICS AND REMOTE SENSING, MIGARS, 2023, : 190 - 193
  • [45] Assessment of soil erosion in cultivated fields using a survey methodology for rills in the Chemoga watershed, Ethiopia
    Bewket, W
    Sterk, G
    [J]. AGRICULTURE ECOSYSTEMS & ENVIRONMENT, 2003, 97 (1-3) : 81 - 93
  • [46] Soil Erosion Assessment Using the RUSLE Model, Remote Sensing, and GIS in the Woybo Watershed, Ethiopia
    Nesru, Mudesir
    [J]. AGRICULTURAL RESEARCH, 2024,
  • [47] Erosion hotspot mapping using integrated morphometric parameters and Land use/land cover of Jigjiga Watershed, Ethiopia
    Tesema, Tesfu Abebe
    [J]. HELIYON, 2022, 8 (06)
  • [48] Soil Erosion Spatial Prediction using Digital Soil Mapping and RUSLE methods for Big Sioux River Watershed
    Taghizadeh-Mehrjardi, Ruhollah
    Bawa, Arun
    Kumar, Sandeep
    Zeraatpisheh, Mojtaba
    Amirian-Chakan, Alireza
    Akbarzadeh, Ali
    [J]. SOIL SYSTEMS, 2019, 3 (03) : 1 - 15
  • [49] Investigating soil erosion using cesium-137 tracer under two different cultivated lands in El Kbir watershed, Tunisia
    Gdiri, Amira
    Ben Cheikha, Lilia
    Oueslati, Mansour
    Saiidi, Salwa
    Reguigui, Nafaa
    [J]. EURO-MEDITERRANEAN JOURNAL FOR ENVIRONMENTAL INTEGRATION, 2024, 9 (02) : 783 - 796
  • [50] Assessment of the importance of gully erosion effective factors using Boruta algorithm and its spatial modeling and mapping using three machine learning algorithms
    Amiri, Mandis
    Pourghasemi, Hamid Reza
    Ghanbarian, Gholam Abbas
    Afzali, Sayed Fakhreddin
    [J]. GEODERMA, 2019, 340 : 55 - 69