Development of Semantic Maps of Vegetation Cover from UAV Images to Support Planning and Management in Fine-Grained Fire-Prone Landscapes

编号 030033202

推送时间 20220228

研究领域 森林经理 

年份 2022 

类型 期刊 

语种 英语

标题 Development of Semantic Maps of Vegetation Cover from UAV Images to Support Planning and Management in Fine-Grained Fire-Prone Landscapes

来源期刊 REMOTE SENSING

期 第332期

发表时间 20220304

关键词 U-Net;  convolutional neural network (CNN);  shrub detection;  heterogeneous land cover mapping;  UAV imagery;  Mediterranean forest; 

摘要 In Mediterranean landscapes, the encroachment of pyrophytic shrubs is a driver of more frequent and larger wildfires. The high-resolution mapping of vegetation cover is essential for sustainable land planning and the management for wildfire prevention. Here, we propose methods to simplify and automate the segmentation of shrub cover in high-resolution RGB images acquired by UAVs. The main contribution is a systematic exploration of the best practices to train a convolutional neural network (CNN) with a segmentation network architecture (U-Net) to detect shrubs in heterogeneous landscapes. Several semantic segmentation models were trained and tested in partitions of the provided data with alternative methods of data augmentation, patch cropping, rescaling and hyperparameter tuning (the number of filters, dropout rate and batch size). The most effective practices were data augmentation, patch cropping and rescaling. The developed classification model achieved an average F1 score of 0.72 on three separate test datasets even though it was trained on a relatively small training dataset. This study demonstrates the ability of state-of-the-art CNNs to map fine-grained land cover patterns from RGB remote sensing data. Because model performance is affected by the quality of data and labeling, an optimal selection of pre-processing practices is a requisite to improve the results.

服务人员 付贺龙

服务院士 唐守正

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