Assessment of Spatial Expansion of Rift Valley Lakes Using Satellite Data  

作　　者：Rose Yang Mulama Jephter Ongige Ondieki Rose Yang Mulama;Jephter Ongige Ondieki(Department of Physics, University of Nairobi, Nairobi, Kenya;School of Aerospace Engineering, Sapienza University of Rome, Roma, Italy)

机构地区：[1]Department of Physics, University of Nairobi, Nairobi, Kenya [2]School of Aerospace Engineering, Sapienza University of Rome, Roma, Italy

出　　处：《Advances in Remote Sensing》2023年第3期88-98,共11页遥感技术进展（英文）

摘　　要：The present work assessed the expansion and fluctuation of Lake Nakuru in Kenya by using satellite data and information. Surface water magnitude was measured from optical sensors, such as Landsat. ENVI software was used to process and analyze data from the satellite images. The data was then used to create shapefile to get the area of the lake only. The shapefiles were classified using both Supervised and Unsupervised classification, and the area of the lake was obtained in hectares. The obtained area in hectares was recorded in a table and graphs were plotted to show the trend of the lake in the years 1972-2019. Furthermore, correlation was done by assuming the area of the shapefile before any classification is more accurate, therefore it was compared with the other results obtained by using different methods. Maximum likelihood gave the best correlation values. For R² it gave 0.8627 and R was 0.9312.The present work assessed the expansion and fluctuation of Lake Nakuru in Kenya by using satellite data and information. Surface water magnitude was measured from optical sensors, such as Landsat. ENVI software was used to process and analyze data from the satellite images. The data was then used to create shapefile to get the area of the lake only. The shapefiles were classified using both Supervised and Unsupervised classification, and the area of the lake was obtained in hectares. The obtained area in hectares was recorded in a table and graphs were plotted to show the trend of the lake in the years 1972-2019. Furthermore, correlation was done by assuming the area of the shapefile before any classification is more accurate, therefore it was compared with the other results obtained by using different methods. Maximum likelihood gave the best correlation values. For R² it gave 0.8627 and R was 0.9312.

关 键 词：Remote Sensing LANDSAT Soil Erosion Supervised Classification 

分 类 号：P20[天文地球—测绘科学与技术]