PDF(Pubmed)
Abstract：
Monitoring land use change dynamics is critical for tackling food security, climate change, and biodiversity loss on a global scale. This study is designed to classify land use and land cover in the upper Blue Nile River Basin (BNRB) using a random forest (RF) algorithm. The Landsat images for Landsat 45, Landsat 7, and Landsat 8 are used for classification purposes. The study area is classified into seven land use/land cover classes: cultivated lands, bare lands, built-ups, forests, grazing lands, shrublands, and waterbodies. The accuracy of classified images is 83%, 85%, and 91% using the Kappa index of agreements. From 1983 to 2022 periods, cultivated lands and built-up areas increased by 47541 and 1777 km2, respectively, at the expense of grazing lands, shrublands, and forests. Furthermore, the area of water bodies has increased by 662 km2 due to the construction of small and large-scale irrigation and hydroelectric power generation dams. The main factors that determine agricultural land expansion are related to population growth. Therefore, land use and land cover change detection using a random forest is an important technique for multispectral satellite data classification to understand the optimal use of natural resources, conservation practices, and decision-making for sustainable development.
摘要：
监测土地利用变化动态对于解决粮食安全至关重要，气候变化,以及全球范围内的生物多样性丧失。本研究旨在使用随机森林（RF）算法对上青尼罗河流域（BNRB）的土地利用和土地覆盖进行分类。Landsat45，Landsat7和Landsat8的Landsat图像用于分类目的。研究区域分为七个土地利用/土地覆盖类别：耕地，裸露的土地，构建,森林,放牧的土地，灌木丛,和水体。分类图像的准确率为83%，85%,91%使用Kappa协议指数。从1983年到2022年，耕地和建成区面积分别增加47541和1777平方公里，以牺牲牧场为代价,灌木丛,和森林。此外，由于修建了小型和大型灌溉和水力发电大坝，水体面积增加了662平方公里。决定农业用地扩张的主要因素与人口增长有关。因此,使用随机森林的土地利用和土地覆盖变化检测是多光谱卫星数据分类的重要技术，以了解自然资源的最佳利用，保护措施，和可持续发展决策。
