Assessing Burned Area Detection in Indonesia Using the Stacking Ensemble Neural Network (SENN): A Comparative Analysis of C- and L-Band Performance

Burned area detection plays a critical role in assessing the impact of forest and land fires, particularly in Indonesia, where both peatland and non-peatland areas are increasingly affected. Optical remote sensing has been widely used for this task, but its effectiveness is limited by persistent clo...

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主要な著者:	Dodi Sudiana, Anugrah Indah Lestari, Mia Rizkinia, Indra Riyanto, Yenni Vetrita, Athar Abdurrahman Bayanuddin, Fanny Aditya Putri, Tatik Kartika, Argo Galih Suhadha, Atriyon Julzarika, Shinichi Sobue, Anton Satria Prabuwono, Josaphat Tetuko Sri Sumantyo
フォーマット:	Artigo
言語:	Inglês
出版事項:	MDPI AG 2025-08-01
シリーズ:	Computers
主題:	burned area detection synthetic aperture radar L-band SAR C-band SAR stacking ensemble neural network peatland and non-peatland
オンライン･アクセス:	https://www.mdpi.com/2073-431X/14/8/337
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