Estimation of Forest Leaf Area Index Based on GEE Data Fusion Method

IF 4.7 2区地球科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2025-01-13 DOI:10.1109/JSTARS.2025.3528429

Xinyi Liu;Li He;Zhengwei He;Yun Wei

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引用次数: 0

Abstract

Implementing forest protection measures, such as afforestation, can be an effective approach toward slowing down the increase of CO

$_{2}$

concentration and attaining carbon neutrality. The estimation of forest parameters is of great significance in understanding regional and global climate change patterns, and the Forest Leaf Area Index (LAI) is a crucial parameter. Current LAI products are mostly generated by moderate-resolution remote sensing data which does not meet the precision requirements for mountain forest ecosystems. To overcome this issue, there is an urgent need for higher resolution LAI data. This article proposes a data fusion method to map LAI in Wolong Nature Reserve that utilizes Sentinel-2 reflectance data, solar sensor geometry parameters, and vegetation indices extracted from the Google Earth Engine platform, along with canopy height data derived from canopy height estimation models in previous studies, combined with GLASS LAI V6 to estimate LAI using the random forest algorithm. The resulting LAI distribution map was plotted at a resolution of 20 m. The study demonstrated that incorporating canopy heights into the estimation model led to an

$R^{2}$

model accuracy of greater than 0.83. The 20-m resolution LAI map increased spatial details compared to the moderate-resolution LAI map, making it more suitable for mountain forest ecosystems that exhibit significant spatial heterogeneity.

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来源期刊

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 地学-成像科学与照相技术

CiteScore

9.30

自引率

10.90%

发文量

563

审稿时长

4.7 months

期刊介绍： The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.