Appraisal of forest changes using change detection analysis in Alsunt Forest Khartoum State of Sudan

• Main Author: Ahmed Attia, Nusiba Eissa
• Format: Thesis
• Language: English
• Published: 2021
• Subjects: G70.212-70.215 Geographic information system
• Online Access: http://eprints.utm.my/id/eprint/96855/1/NusibaEissaAhmedMFAB2021.pdf.pdf; http://eprints.utm.my/id/eprint/96855/; http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:142086
• Institution: Universiti Teknologi Malaysia

Effective geospatial monitoring of land use land cover changes particularly forest cover changes is necessary to overcome the degradation of the terrestrial ecosystem to further mitigate the effects of global climate change. Therefore, this research aims at mapping, estimating, and monitoring the forest cover changes in Alsunt forest of Sudan using geographical information system (GIS) and satellite-based approaches. This research was achieved by the following objectives to a) map and estimate the spatial extents of land use land cover in the study area; b) monitor the forest cover changes within the study site, and c) analyzed the forest cover changes using GIS tool and descriptive statistical techniques. The ?Maximum Likelihood Classification (MLC)? technique was used to map the spatial extents of land use land cover (forest, water body, build-up areas, bear land, and agriculture) within the study sites and further estimate the distribution of the extents. The historical monitoring of the forest cover changes for 3 epochs (2000, 2010, and 2019) was accomplished using Landsat 8 Operational Land Imager (OLI) and Landsat 5 Thematic Mapper (TM). Whereas, the analyses of the forest cover change were achieved using descriptive statistical techniques. The results realised results are a) spatial distribution map and estimation of land use land cover changes; b) forest cover maps showing changes (hectares (ha))in the year 2000 (599153.3ha), 2010 (57778.38ha), and 2019 (34920.99ha); and c) analyses of the forest cover changes for the three epochs revealed R2 0.780, 0.857, and 0.891, p-value (0.005, 0.001, 0.001) and Root Mean Square Error (RMSE) ± 0.910, ± 0.510, and ± 0.620 for the year 2000, 2010, and 2019 respectively. Hence, this research shall serve as a guide for planning and management of forest conservation and restoration for sustainability and climate change mitigation.