Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery

Typhoon Goni crossed several provinces in the Philippines where agriculture has high socioeconomic importance, including the top-3 provinces in terms of planted coconut trees. We have used a computational model to infer coconut tree density from satellite images before and after the typhoon’s passag...

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Autores principales:	Andrés C. Rodríguez, Rodrigo Caye Daudt, Stefano D’Aronco, Konrad Schindler, Jan D. Wegner
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	natural hazard deep learning Sentinel-2 tree density estimation change detection Science Q
Acceso en línea:	https://doaj.org/article/fdabab2cefcd4a1ebe5230cb356e3938
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spelling	oai:doaj.org-article:fdabab2cefcd4a1ebe5230cb356e39382021-11-11T18:53:27ZRobust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery10.3390/rs132143022072-4292https://doaj.org/article/fdabab2cefcd4a1ebe5230cb356e39382021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4302https://doaj.org/toc/2072-4292Typhoon Goni crossed several provinces in the Philippines where agriculture has high socioeconomic importance, including the top-3 provinces in terms of planted coconut trees. We have used a computational model to infer coconut tree density from satellite images before and after the typhoon’s passage, and in this way estimate the number of damaged trees. Our area of study around the typhoon’s path covers 15.7 Mha, and includes 47 of the 87 provinces in the Philippines. In validation areas our model predicts coconut tree density with a Mean Absolute Error of 5.9 Trees/ha. In Camarines Sur we estimated that 3.5 M of the 4.6 M existing coconut trees were damaged by the typhoon. Overall we estimated that 14.1 M coconut trees were affected by the typhoon inside our area of study. Our validation images confirm that trees are rarely uprooted and damages are largely due to reduced canopy cover of standing trees. On validation areas, our model was able to detect affected coconut trees with 88.6% accuracy, 75% precision and 90% recall. Our method delivers spatially fine-grained change maps for coconut plantations in the area of study, including unchanged, damaged and new trees. Beyond immediate damage assessment, gradual changes in coconut density may serve as a proxy for future changes in yield.Andrés C. RodríguezRodrigo Caye DaudtStefano D’AroncoKonrad SchindlerJan D. WegnerMDPI AGarticlenatural hazarddeep learningSentinel-2tree density estimationchange detectionScienceQENRemote Sensing, Vol 13, Iss 4302, p 4302 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	natural hazard deep learning Sentinel-2 tree density estimation change detection Science Q
spellingShingle	natural hazard deep learning Sentinel-2 tree density estimation change detection Science Q Andrés C. Rodríguez Rodrigo Caye Daudt Stefano D’Aronco Konrad Schindler Jan D. Wegner Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery
description	Typhoon Goni crossed several provinces in the Philippines where agriculture has high socioeconomic importance, including the top-3 provinces in terms of planted coconut trees. We have used a computational model to infer coconut tree density from satellite images before and after the typhoon’s passage, and in this way estimate the number of damaged trees. Our area of study around the typhoon’s path covers 15.7 Mha, and includes 47 of the 87 provinces in the Philippines. In validation areas our model predicts coconut tree density with a Mean Absolute Error of 5.9 Trees/ha. In Camarines Sur we estimated that 3.5 M of the 4.6 M existing coconut trees were damaged by the typhoon. Overall we estimated that 14.1 M coconut trees were affected by the typhoon inside our area of study. Our validation images confirm that trees are rarely uprooted and damages are largely due to reduced canopy cover of standing trees. On validation areas, our model was able to detect affected coconut trees with 88.6% accuracy, 75% precision and 90% recall. Our method delivers spatially fine-grained change maps for coconut plantations in the area of study, including unchanged, damaged and new trees. Beyond immediate damage assessment, gradual changes in coconut density may serve as a proxy for future changes in yield.
format	article
author	Andrés C. Rodríguez Rodrigo Caye Daudt Stefano D’Aronco Konrad Schindler Jan D. Wegner
author_facet	Andrés C. Rodríguez Rodrigo Caye Daudt Stefano D’Aronco Konrad Schindler Jan D. Wegner
author_sort	Andrés C. Rodríguez
title	Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery
title_short	Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery
title_full	Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery
title_fullStr	Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery
title_full_unstemmed	Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery
title_sort	robust damage estimation of typhoon goni on coconut crops with sentinel-2 imagery
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/fdabab2cefcd4a1ebe5230cb356e3938
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Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery

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