期刊
REMOTE SENSING
卷 12, 期 8, 页码 -出版社
MDPI
DOI: 10.3390/rs12081288
关键词
tree crown delineation; tropical forests; optical satellite images; deep learning
类别
资金
- project BIO-RED Biomes of Brazil-Resilence, Recovery, and Diversity - Sao Paulo Research Foundation (FAPESP) [2015/50484-0]
- U.K. Natural Environment Research Council (NERC) [NE/N012542/1]
- FAPESP [2018/06072-7, 2016/17652-9, 2015/22987-7, 2018/15001-6]
- National Council for Scientific and Technological Development (CNPq) [305054/2016-3]
- CNPq [312924/2017-8]
Tropical forests concentrate the largest diversity of species on the planet and play a key role in maintaining environmental processes. Due to the importance of those forests, there is growing interest in mapping their components and getting information at an individual tree level to conduct reliable satellite-based forest inventory for biomass and species distribution qualification. Individual tree crown information could be manually gathered from high resolution satellite images; however, to achieve this task at large-scale, an algorithm to identify and delineate each tree crown individually, with high accuracy, is a prerequisite. In this study, we propose the application of a convolutional neural network-Mask R-CNN algorithm-to perform the tree crown detection and delineation. The algorithm uses very high-resolution satellite images from tropical forests. The results obtained are promising-the Recall, Precision, and F1 score values obtained were were 0.81, 0.91, and 0.86, respectively. In the study site, the total of tree crowns delineated was 59,062. These results suggest that this algorithm can be used to assist the planning and conduction of forest inventories. As the algorithm is based on a Deep Learning approach, it can be systematically trained and used for other regions.
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