{"title":"利用基于多模态地理空间数据的分层模型识别亚热带森林的优势木本植物物种","authors":"Xin Chen, Yujun Sun","doi":"10.1007/s11676-024-01700-2","DOIUrl":null,"url":null,"abstract":"<p>Since the launch of the Google Earth Engine (GEE) cloud platform in 2010, it has been widely used, leading to a wealth of valuable information. However, the potential of GEE for forest resource management has not been fully exploited. To extract dominant woody plant species, GEE combined Sentinel-1 (S1) and Sentinel-2 (S2) data with the addition of the National Forest Resources Inventory (NFRI) and topographic data, resulting in a 10 m resolution multimodal geospatial dataset for subtropical forests in southeast China. Spectral and texture features, red-edge bands, and vegetation indices of S1 and S2 data were computed. A hierarchical model obtained information on forest distribution and area and the dominant woody plant species. The results suggest that combining data sources from the S1 winter and S2 yearly ranges enhances accuracy in forest distribution and area extraction compared to using either data source independently. Similarly, for dominant woody species recognition, using S1 winter and S2 data across all four seasons was accurate. Including terrain factors and removing spatial correlation from NFRI sample points further improved the recognition accuracy. The optimal forest extraction achieved an overall accuracy (OA) of 97.4% and a map-level image classification efficacy (MICE) of 96.7%. OA and MICE were 83.6% and 80.7% for dominant species extraction, respectively. The high accuracy and efficacy values indicate that the hierarchical recognition model based on multimodal remote sensing data performed extremely well for extracting information about dominant woody plant species. Visualizing the results using the GEE application allows for an intuitive display of forest and species distribution, offering significant convenience for forest resource monitoring.</p>","PeriodicalId":15830,"journal":{"name":"Journal of Forestry Research","volume":"47 1","pages":""},"PeriodicalIF":3.4000,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dominant woody plant species recognition with a hierarchical model based on multimodal geospatial data for subtropical forests\",\"authors\":\"Xin Chen, Yujun Sun\",\"doi\":\"10.1007/s11676-024-01700-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Since the launch of the Google Earth Engine (GEE) cloud platform in 2010, it has been widely used, leading to a wealth of valuable information. However, the potential of GEE for forest resource management has not been fully exploited. To extract dominant woody plant species, GEE combined Sentinel-1 (S1) and Sentinel-2 (S2) data with the addition of the National Forest Resources Inventory (NFRI) and topographic data, resulting in a 10 m resolution multimodal geospatial dataset for subtropical forests in southeast China. Spectral and texture features, red-edge bands, and vegetation indices of S1 and S2 data were computed. A hierarchical model obtained information on forest distribution and area and the dominant woody plant species. The results suggest that combining data sources from the S1 winter and S2 yearly ranges enhances accuracy in forest distribution and area extraction compared to using either data source independently. Similarly, for dominant woody species recognition, using S1 winter and S2 data across all four seasons was accurate. Including terrain factors and removing spatial correlation from NFRI sample points further improved the recognition accuracy. The optimal forest extraction achieved an overall accuracy (OA) of 97.4% and a map-level image classification efficacy (MICE) of 96.7%. OA and MICE were 83.6% and 80.7% for dominant species extraction, respectively. The high accuracy and efficacy values indicate that the hierarchical recognition model based on multimodal remote sensing data performed extremely well for extracting information about dominant woody plant species. Visualizing the results using the GEE application allows for an intuitive display of forest and species distribution, offering significant convenience for forest resource monitoring.</p>\",\"PeriodicalId\":15830,\"journal\":{\"name\":\"Journal of Forestry Research\",\"volume\":\"47 1\",\"pages\":\"\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-03-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Forestry Research\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.1007/s11676-024-01700-2\",\"RegionNum\":2,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"FORESTRY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Forestry Research","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1007/s11676-024-01700-2","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"FORESTRY","Score":null,"Total":0}
Dominant woody plant species recognition with a hierarchical model based on multimodal geospatial data for subtropical forests
Since the launch of the Google Earth Engine (GEE) cloud platform in 2010, it has been widely used, leading to a wealth of valuable information. However, the potential of GEE for forest resource management has not been fully exploited. To extract dominant woody plant species, GEE combined Sentinel-1 (S1) and Sentinel-2 (S2) data with the addition of the National Forest Resources Inventory (NFRI) and topographic data, resulting in a 10 m resolution multimodal geospatial dataset for subtropical forests in southeast China. Spectral and texture features, red-edge bands, and vegetation indices of S1 and S2 data were computed. A hierarchical model obtained information on forest distribution and area and the dominant woody plant species. The results suggest that combining data sources from the S1 winter and S2 yearly ranges enhances accuracy in forest distribution and area extraction compared to using either data source independently. Similarly, for dominant woody species recognition, using S1 winter and S2 data across all four seasons was accurate. Including terrain factors and removing spatial correlation from NFRI sample points further improved the recognition accuracy. The optimal forest extraction achieved an overall accuracy (OA) of 97.4% and a map-level image classification efficacy (MICE) of 96.7%. OA and MICE were 83.6% and 80.7% for dominant species extraction, respectively. The high accuracy and efficacy values indicate that the hierarchical recognition model based on multimodal remote sensing data performed extremely well for extracting information about dominant woody plant species. Visualizing the results using the GEE application allows for an intuitive display of forest and species distribution, offering significant convenience for forest resource monitoring.
期刊介绍:
The Journal of Forestry Research (JFR), founded in 1990, is a peer-reviewed quarterly journal in English. JFR has rapidly emerged as an international journal published by Northeast Forestry University and Ecological Society of China in collaboration with Springer Verlag. The journal publishes scientific articles related to forestry for a broad range of international scientists, forest managers and practitioners.The scope of the journal covers the following five thematic categories and 20 subjects:
Basic Science of Forestry,
Forest biometrics,
Forest soils,
Forest hydrology,
Tree physiology,
Forest biomass, carbon, and bioenergy,
Forest biotechnology and molecular biology,
Forest Ecology,
Forest ecology,
Forest ecological services,
Restoration ecology,
Forest adaptation to climate change,
Wildlife ecology and management,
Silviculture and Forest Management,
Forest genetics and tree breeding,
Silviculture,
Forest RS, GIS, and modeling,
Forest management,
Forest Protection,
Forest entomology and pathology,
Forest fire,
Forest resources conservation,
Forest health monitoring and assessment,
Wood Science and Technology,
Wood Science and Technology.