Satellite Remote Sensing Images of Crown Segmentation and Forest Inventory Based on BlendMask

This study proposes a low-cost method for crown segmentation and forest inventory based on satellite remote sensing images and the deep learning model BlendMask. Taking Beijing Jingyue ecoforestry as the experimental area, we combined the field survey data and satellite images, and constructed the d...

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Bibliographic Details
Published in:Forests 2024-08, Vol.15 (8), p.1320
Main Authors: Ji, Zicheng, Xu, Jie, Yan, Lingxiao, Ma, Jiayi, Chen, Baozhe, Zhang, Yanfeng, Zhang, Li, Wang, Pei
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Artificial satellites in remote sensing
biomass
carbon
China
Classification
Climate change
data collection
Deep learning
Forest ecology
forest inventory
Forest management
Forest resources
Forests
Image processing
Image segmentation
Koelreuteria paniculata
Machine learning
Management
Neural networks
oils
Pinus tabuliformis
Plant species
Remote sensing
Resource management
Satellite imagery
Satellites
Segmentation
species
Styphnolobium japonicum
trees
Unmanned aerial vehicles
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Summary:This study proposes a low-cost method for crown segmentation and forest inventory based on satellite remote sensing images and the deep learning model BlendMask. Taking Beijing Jingyue ecoforestry as the experimental area, we combined the field survey data and satellite images, and constructed the dataset independently, for model training. The experimental results show that the F1-score of Sophora japonica, Pinus tabulaeformis, and Koelreuteria paniculata reached 87.4%, 85.7%, and 86.3%, respectively. Meanwhile, we tested for the study area with a total area of 146 ha, and 27,403 tree species were identified in nine categories, with a total crown projection area of 318,725 m2. We also fitted a biomass calculation model for oil pine (Pinus tabulaeformis) based on field measurements and assessed 205,199.69 kg of carbon for this species across the study area. Additionally, we compared the model to U-net, and the results showed that BlendMask has strong crown-segmentation capabilities. This study demonstrates that BlendMask can effectively perform crown segmentation and forest inventory in large-scale complex forest areas, showing its great potential for forest resource management.
ISSN:1999-4907
1999-4907
DOI:10.3390/f15081320