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Improved Forest Canopy Closure Estimation Using Multispectral Satellite Imagery within Google Earth Engine

Remote sensing(2022)SCI 2区SCI 3区

Chinese Acad Sci

Cited 4|Views18
Abstract
The large area estimation of forest canopy closure (FCC) using remotely sensed data is of high interest in monitoring forest changes and forest health, as well as in assessing forest ecological services. The accurate estimation of FCC over the regional or global scale is challenging due to the difficulty of sample acquisition and the slow processing efficiency of large amounts of remote sensing data. To address this issue, we developed a novel bounding envelope methodology based on vegetation indices (BEVIs) for determining vegetation and bare soil endmembers using the normalized differences vegetation index (NDVI), modified bare soil index (MBSI), and bare soil index (BSI) derived from Landsat 8 OLI and Sentinel-2 image within the Google Earth Engine (GEE) platform, then combined the NDVI with the dimidiate pixel model (DPM), one of the most commonly used spectral-based unmixing methods, to map the FCC distribution over an area of more than 90,000 km(2). The key processing was the determination of the threshold parameter in BEVIs that characterizes the spectral boundary of vegetation and soil endmembers. The results demonstrated that when the threshold equals 0.1, the extraction accuracy of vegetation and bare soil endmembers is the highest with the threshold range given as (0, 0.3), and the estimated spatial distribution of FCC using both Landsat 8 and Sentinel-2 images were consistent, that is, the area with high canopy density was mainly distributed in the western mountainous region of Chifeng city. The verification was carried out using independent field plots. The proposed approach yielded reliable results when the Landsat 8 data were used (R-2 = 0.6, RMSE = 0.13, and 1-rRMSE = 80%), and the accuracy was further improved using Sentinel-2 images with higher spatial resolution (R-2 = 0.81, RMSE = 0.09, and 1-rRMSE = 86%). The findings demonstrate that the proposed method is portable among sensors with similar spectral wavebands, and can assist in mapping FCC at a regional scale while using multispectral satellite imagery.
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Key words
forest canopy closure,endmembers determination,dimidiate pixel model,spectral vegetation indices,regional scale
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要点】:本文提出了一种基于多光谱卫星影像和Google Earth Engine平台的新型边界包络方法(BEVIs),结合半像素模型(DPM)准确估算区域森林冠层封闭度(FCC),提高了数据处理效率和估算精度。

方法】:通过使用归一化植被指数(NDVI)、改进的裸土指数(MBSI)和裸土指数(BSI)确定植被和裸土端元,结合DPM模型映射FCC分布。

实验】:在超过90,000平方公里的区域,利用Landsat 8 OLI和Sentinel-2影像,确定了BEVIs的阈值参数,并使用独立地面样方进行验证,得到Landsat 8数据的使用结果为R-2 = 0.6,均方根误差(RMSE)= 0.13,1-rRMSE = 80%,而Sentinel-2影像的精度更高,R-2 = 0.81,RMSE = 0.09,1-rRMSE = 86%。