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Analyzing dynamic of changes in Land Use and Land Cover in Semi-arid of Eastern Sudan, Using Remote Sensing and GIS

Journal: International Journal of Environment, Agriculture and Biotechnology (Vol.7, No. 6)

Publication Date:

Authors : ;

Page : 174-186

Keywords : Landsat; change detection; remote sensing; semi-arid land; Sudan.;

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Mapping land use and land cover (LULC) changes at regional scales is essential for a wide range of applications, including landslide, erosion, land planning, global warming etc. LULC alterations (based especially on human activities), negatively affect the patterns of climate, the patterns of natural hazard and socio-economic dynamics in global and local scale. However, LULC change, especially those caused by human activities, is one of the most important component environmental changes (Jensen, 2005). LULC is an important component in understanding the interactions of the human activities with the environment and thus it is necessary to be able to simulate changes. The aim of this study is to identify, evaluate and examines the spatial and temporal change detection of LULC in the study area during the time periods of 1984 to 2018 with emphasis on accuracy assessment to judge the applicability of maximum likelihood classifier (MLC) method in this case of study, and to ensure the accurate change detection. To investigate the (LULCC) changes in the semi-arid of Eastern Sudan from 1984 to 2018, the study has been done through remote sensing and (GIS) approach incorporated with field verifications for extracting information. This was done by downloading free of cloud and processing multi-spectral Landsat satellite imageries covering the study area over successive periods (1984 and 2018). The maximum likelihood classifier (MLC) method applied for mapping of LULC based on pixel-by-pixel and image differencing, which are used to enhance the change assessment. Ground truth observations are also performing to check the accuracy assessment of the classification. The methods employed in this study were, data identification and acquisition, image pre-processing and processing, validation, post classification, matrix of change, interpretation and maps change presentation. The images were classified into five thematic LULC classes which were; Dense trees and shrubs, low dense vegetation, farmland bare/grassland, moving sand and stabilized sand by means of (MLC) based on supervised classification technique with acceptable accuracy assessment. Pre-classification and post-classification change detection (CD) methodologies were executed using image change detection (CD) and image differencing by matrix of change respectively. These methods gave different results in term of LULC areas, and it is generally concluding that supervised classification gave the most accurate results with the images of medium spatial resolution. The present study has brought to light that dense trees and shrubs that occupied an area about 27401.7ha (11.27%) of the study area in 1984 has increased to 46614.3ha (19.17%) in 2018. Whereas, the moving sand occupies an area about 38519.2ha (15.85%) in 1984 has increased to 43198ha (17.76%) in 2018 respectively, which are the most dominant classes in the study area. Low dense vegetation, farm, bare, grassland and stabilized sand also have experienced change. Low dense vegetation has decreases from 20.41% to 19.38%, while the farm, bare, grassland has decreases from 24.22% in 1984 to 19.75% in 2018, which represented the cultivated land, as well as decreases in stabilized sand from 26.65% in 1984 to 23.84% in 2018 respectively over the 34-year period. Maps of the LULC changes available in GIS platform can be used for enhancement of the available tools for further planning and environmental factor for future in the region.

Last modified: 2022-12-30 13:58:46