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| DOI | 10.1109/JSTARS.2023.3332420 |
| Toward an Operational Monitoring of Oak Dieback With Multispectral Satellite Time Series: A Case Study in Centre-Val De Loire Region of France | |
| 发表日期 | 2024 |
| ISSN | 1939-1404 |
| EISSN | 2151-1535 |
| 起始页码 | 17 |
| 卷号 | 17 |
| 英文摘要 | This article studies the monitoring of oak dieback in forests of the Centre-Val de Loire region (France), where drought induced dieback has become a major concern due to climate change. The main objective of the study is to evaluate the applicability of multispectral satellite time series for operational monitoring of forest dieback. Using in situ data collected from 2017 to 2022 on approximately 2700 oak plots, a multiyear mapping of the analyzed region was performed using the random forest algorithm and Sentinel-2 images. Our results show that it is possible to detect oak dieback accurately (average overall accuracy = 80% and average balanced accuracy = 79%). A spatial cross-validation analysis also evaluates the performance of the model on regions that were never encountered during training, across all years, resulting in a slight decrease in accuracy (similar to 5%). The study also highlights the importance of measuring the stability and performance of the classification model over time, in addition to standard cross-validation metrics. A feature analysis shows that the shortwave infrared part of the spectrum is the most important for mapping forest dieback, while the red-edge portion of the spectrum can increase the stability of the model over time. Overall, both in situ data and model predictions showed evidence of forest decline in many areas of the study region. Our results suggest that large areas of forest can decline over short periods of time, highlighting the interest of satellite data to provide timely and accurate information on forest status. |
| 英文关键词 | Forestry; Monitoring; Remote sensing; Labeling; Classification algorithms; Random forests; Protocols; Machine learning; Droughts; Weather forecasting; Climate change; dieback detection; forest monitoring; machine learning (ML); random forest; remote sensing; sentinel-2 |
| 语种 | 英语 |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS类目 | Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:001123327600001 |
| 来源期刊 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
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| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/289532 |
| 作者单位 | Universite de Orleans; INRAE; Universite de Toulouse; Universite Toulouse III - Paul Sabatier; Centre National de la Recherche Scientifique (CNRS); Institut de Recherche pour le Developpement (IRD); INRAE; INRAE |
| 推荐引用方式 GB/T 7714 | . Toward an Operational Monitoring of Oak Dieback With Multispectral Satellite Time Series: A Case Study in Centre-Val De Loire Region of France[J],2024,17. |
| APA | (2024).Toward an Operational Monitoring of Oak Dieback With Multispectral Satellite Time Series: A Case Study in Centre-Val De Loire Region of France.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,17. |
| MLA | "Toward an Operational Monitoring of Oak Dieback With Multispectral Satellite Time Series: A Case Study in Centre-Val De Loire Region of France".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 17(2024). |
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