APPLYING RANDOM FOREST CLASSIFICATION TO MAP LAND USE/LAND COVER USING LANDSAT 8 OLI

20 citations as recorded by crossref.

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2. “Human elephant conflict in changing land-use land-cover scenario in and adjoining region of Buxa tiger reserve, India” C. Nad et al. 10.1016/j.envc.2021.100384
3. Multi-Temporal Passive and Active Remote Sensing for Agricultural Mapping and Acreage Estimation in Context of Small Farm Holds in Ethiopia T. Mengesha et al. 10.3390/land13030335
4. Detailed and automated classification of land use/land cover using machine learning algorithms in Google Earth Engine X. Pan et al. 10.1080/10106049.2021.1917005
5. Land Use and Land Cover Scenarios for Optimum Water Yield and Sediment Retention Ecosystem Services in Klong U-Tapao Watershed, Songkhla, Thailand J. Srichaichana et al. 10.3390/su11102895
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7. Urban Ecosystem Services Quantification through Remote Sensing Approach: A Systematic Review P. Tavares et al. 10.3390/environments6050051
8. Estimating Land-Use Change Using Machine Learning: A Case Study on Five Central Coastal Provinces of Vietnam N. Giang et al. 10.3390/su14095194
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10. Comparing global and local maps of the Caribbean pine forests of Andros, home of the critically endangered Bahama Oriole J. Antalffy et al. 10.1007/s10661-021-09560-7
11. LULC changes to riverine flooding: A case study on the Jamuna River, Bangladesh using the multilayer perceptron model M. Hasan et al. 10.1016/j.rineng.2023.101079
12. Adoption of Machine Learning Technique in Nile River Islands Classification N. Kamal et al. 10.2166/hydro.2022.154
13. Relative performance evaluation of machine learning algorithms for land use classification using multispectral moderate resolution data T. Ramachandra et al. 10.1007/s42452-023-05496-4
14. Land Use/Land Cover Mapping Using Multitemporal Sentinel-2 Imagery and Four Classification Methods—A Case Study from Dak Nong, Vietnam H. Nguyen et al. 10.3390/rs12091367
15. The use of Landsat-8 and Sentinel-2 imageries in detecting and mapping rubber trees N. Yusof et al. 10.1007/s42464-020-00078-0
16. Landscape context importance for predicting forest transition success in central Panama G. Bardino et al. 10.1007/s10980-023-01694-y
17. Limiting the Collection of Ground Truth Data for Land Use and Land Cover Maps with Machine Learning Algorithms U. Ali et al. 10.3390/ijgi11060333
18. Extent of Lake Tana’s Papyrus Swamps (1985–2020), North Ethiopia A. Kahsay et al. 10.1007/s13157-022-01651-6
19. Classification of land use/land cover using artificial intelligence (ANN-RF) E. Alshari et al. 10.3389/frai.2022.964279
20. Multilayer perceptron and Markov Chain analysis based hybrid-approach for predicting land use land cover change dynamics with Sentinel-2 imagery H. Abbas et al. 10.1080/10106049.2023.2256297