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

IF 3.4 2区 农林科学 Q1 FORESTRY Journal of Forestry Research Pub Date : 2024-03-20 DOI:10.1007/s11676-024-01700-2
Xin Chen, Yujun Sun
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引用次数: 0

摘要

自 2010 年推出谷歌地球引擎(GEE)云平台以来,该平台已得到广泛应用,产生了大量有价值的信息。然而,GEE 在森林资源管理方面的潜力尚未得到充分挖掘。为了提取优势木本植物物种,GEE 将哨兵一号(S1)和哨兵二号(S2)数据与国家森林资源清查(NFRI)和地形数据相结合,形成了中国东南部亚热带森林的 10 米分辨率多模态地理空间数据集。计算了 S1 和 S2 数据的光谱和纹理特征、红边带以及植被指数。通过分层模型获得了森林分布和面积以及主要木本植物物种的信息。结果表明,与单独使用其中一种数据源相比,将 S1 冬季和 S2 全年范围的数据源结合使用可提高森林分布和面积提取的准确性。同样,对于优势木本物种的识别,使用 S1 冬季和 S2 四季数据也很准确。加入地形因素和去除 NFRI 样本点的空间相关性进一步提高了识别准确率。最佳森林提取的总体准确率(OA)为 97.4%,地图级图像分类效率(MICE)为 96.7%。优势物种提取的 OA 和 MICE 分别为 83.6% 和 80.7%。高准确度和高效力值表明,基于多模态遥感数据的分层识别模型在提取木本植物优势物种信息方面表现非常出色。利用 GEE 应用程序对结果进行可视化处理,可以直观地显示森林和物种的分布情况,为森林资源监测提供了极大的便利。
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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.

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来源期刊
CiteScore
7.30
自引率
3.30%
发文量
2538
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.
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