Optimising forest rehabilitation and restoration through remote sensing and machine learning: Mapping natural forests in the eThekwini Municipality-Reference-Cited by-同舟云学术

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Optimising forest rehabilitation and restoration through remote sensing and machine learning: Mapping natural forests in the eThekwini Municipality

Published:2024-11 Issue: Volume:36 Page:101335
ISSN:2352-9385
Container-title:Remote Sensing Applications: Society and Environment
language:en
Short-container-title:Remote Sensing Applications: Society and Environment

Author:

Buthelezi Mthokozisi Ndumiso Mzuzuwentokozo^ORCID,Lottering Romano^ORCID,Peerbhay Kabir,Mutanga Onisimo

Funder

National Research Foundation of Korea

National Research Foundation

Publisher

Elsevier BV

Reference119 articles.

1. Landslide susceptibility mapping: analysis of different feature selection techniques with artificial neural network tuned by bayesian and metaheuristic algorithms;Abbas;Rem. Sens.,2023

2. Using Landsat satellite data for assessing the land use and land cover change in Kashmir valley;Alam;Geojournal,2020

3. A comparative study of LightGBM, XGBoost, and GEP models in shear strength management of SFRC-SBWS;Alshboul;Structures,2024

4. Enhancing the prediction of student performance based on the machine learning XGBoost algorithm;Asselman;Interact. Learn. Environ.,2023

5. A study on gradient boosting algorithms for development of AI monitoring and prediction systems;Aziz,2020

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