Vegetation feature type extraction in arid regions based on GEE multi-source remote sensing data
Received date: 2023-02-14
Revised date: 2023-08-17
Online published: 2024-01-24
Nuomuhong region is an important Wolfberry cultivation base in Qinghai Province, China. Accurate and rapid extraction of the primary vegetation types is of critical significance for the sustainable development of agriculture in this region. However, the arid nature of the Nuomuhong area, characterized by sparse vegetation cover and significant soil background effects, presents challenges for vegetation extraction using only a limited number of remote sensing sources or partial features. Therefore, integrating multiple remote sensing data sources, exploring significant features for vegetation classification, and experimenting with different classification and optimization methods are paramount for enhancing the accuracy and reliability of vegetation classification in arid regions. Based on the Google Earth Engine (GEE) platform, this study used Sentinel-1 Synthetic Aperture Radar and Sentinel-2 optical data to explore the importance of red edge, texture, and radar features in extracting vegetation types in arid regions. Additionally, it verifies the feasibility of using the GINI index (GINI) to determine the optimal feature combination. The main geospatial types in Nomu Hong, Qinghai, China, in 2021 were extracted by combining them with the support vector machine algorithm. The classification results were processed using decision fusion methods. The results showed that: (1) Sentinel-2 red edge index, texture data, and Sentinel-1 radar band were beneficial for the extraction of vegetation-related information, with an overall classification accuracy and Kappa coefficient of 95.51% and 0.9406, respectively. (2) Based on the importance obtained by the GINI index, the features involved in the classification were reduced from 29 to 17, and the significance was radar polarization features > spectral features > texture features. (3) Using a simple noniterative clustering algorithm and neighborhood filtering voting decision fusion method not only achieved the optimal overall accuracy and Kappa coefficient but also had an excellent suppression effect on isolated noise. Using the GEE remote sensing cloud platform, multisource remote sensing data, and machine learning algorithms, this study can accurately, quickly, and efficiently extract large-scale arid region geospatial information, which can have great application potential.
Jinxi YAO , Chengzhi XIAO , Zhi ZHANG , Lang WANG , Kun ZHANG . Vegetation feature type extraction in arid regions based on GEE multi-source remote sensing data[J]. Arid Zone Research, 2024 , 41(1) : 157 -168 . DOI: 10.13866/j.azr.2024.01.15
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