A new ensemble classification approach based on Rotation Forest and LightGBM

Gu, Qinghua; Sun, Wenjing; Li, Xuexian; Jiang, Song; Tian, Jingni

doi:10.1007/s00521-023-08297-3

Cited by 7 publications

(1 citation statement)

References 35 publications

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“…[ 45 , 46 ] A more recent approach for classifying land covers has been based on ensemble methods, such as random forests (RF). [ 47 ] RF is an ensemble learning algorithm based on the idea that a combination of bootstrap aggregated classifiers perform better than a single classifier. [ 48 ] The RF algorithm is based on tree classifiers.…”

Section: Introductionmentioning

confidence: 99%

Land Use and Land Cover Change Detection Using the Random Forest Approach: The Case of The Upper Blue Nile River Basin, Ethiopia

Tikuye,

Rusnak,

Manjunatha

et al. 2023

Global Challenges

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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.

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