Post-typhoon forest damage estimation using multiple vegetation indices and machine learning models-Reference-Cited by-同舟云学术

Post-typhoon forest damage estimation using multiple vegetation indices and machine learning models

Published:2022-12 Issue: Volume:38 Page:100494
ISSN:2212-0947
Container-title:Weather and Climate Extremes
language:en
Short-container-title:Weather and Climate Extremes

Author:

Chen Xinyu^ORCID,Avtar Ram^ORCID,Umarhadi Deha Agus^ORCID,Louw Albertus Stephanus^ORCID,Shrivastava Sourabh,Yunus Ali P.^ORCID,Khedher Khaled Mohamed^ORCID,Takemi Tetsuya^ORCID,Shibata Hideaki^ORCID

Funder

Ministry of Education, Culture, Sports, Science and Technology

Deanship of Scientific Research, King Faisal University

Kajima Foundation

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Atmospheric Science,Geography, Planning and Development

Reference74 articles.

1. Classification accuracy and trend assessments of land cover- land use changes from principal components of land satellite images;Abdu;Int. J. Rem. Sens.,2019

2. Accuracy and spatial pattern assessment of forest cover change datasets in central kalimantan;Arjasakusuma;Indones. J. Geogr.,2018

3. Automated mapping of tropical deforestation and forest degradation: CLASlite;Asner;J. Appl. Remote Sens.,2009

4. Land cover classification in an Ecuadorian mountain geosystem using a random forest classifier, spectral vegetation indices, and ancillary geographic data;Ayala-Izurieta;Geosciences,2017

5. Random forest in remote sensing: a review of applications and future directions;Belgiu;ISPRS J. Photogrammetry Remote Sens.,2016

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