基于GEE多源遥感数据的干旱区植被地物类型提取

doi:10.13866/j.azr.2024.01.15

Abstract

Abstract:

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.

Key words: surface coverage, feature selection, support vector machine, classification optimization, Google Earth Engine

YAO Jinxi, XIAO Chengzhi, ZHANG Zhi, WANG Lang, ZHANG Kun. Vegetation feature type extraction in arid regions based on GEE multi-source remote sensing data[J].Arid Zone Research, 2024, 41(1): 157-168.

Figures/Tables 11

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References 31

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特征变量	简写	特征说明
光谱特征	B	B2~B8、B8A、B9、B11、B12
	NDVI	（B8A-B4）/（B8A+B4）
	EVI	2.5×（B8A-B4）/（B8A+6×B4-7.5×B2+1）
	GNDVI	（B8A-B3）/（B8A+B3）
	NDVIre1	（B8A-B5）/（B8A+B5）
	NDVIre2	（B8A-B6）/（B8A+B6）
	NDVIre3	（B8A-B7）/（B8A+B7）
	NDre1	（B6-B5）/（B6+B5）
	NDre2	（B7-B5）/（B7+B5）
	CIre	B7/B5-1
纹理特征	Gray_asm	角二阶矩
	Gray_contrast	对比度
	Gray_corr	相关性
	Gray_ent	熵
	Gray_var	方差
	Gray_idm	逆差矩
	Gray_savg	总和平均值
雷达特征	VV	VV极化后向散射系数
雷达特征	VH	VH极化后向散射系数

类别		草地	防护林	建筑物	裸地	梭梭林	枸杞种植园地	总体精度/%	Kappa系数
方案1	UA/%	90.41	93.12	94.71	87.23	83.84	90.75	91.42	0.8865
方案1	PA/%	74.16	85.49	97.39	85.60	93.96	89.53	91.42	0.8865
方案2	UA/%	75.79	97.05	93.72	85.60	86.96	96.12	93.21	0.9105
方案2	PA/%	80.90	94.18	95.86	87.20	95.76	90.47	93.21	0.9105
方案3	UA/%	93.67	97.86	96.34	88.60	90.48	97.04	95.51	0.9406
方案3	PA/%	83.15	94.87	97.44	91.20	96.08	95.14	95.51	0.9406

类别		草地	防护林	建筑物	裸地	梭梭林	枸杞种植园地	总体精度/%	Kappa系数
优化1	UA/%	96.00	96.58	96.27	87.47	90.42	97.17	95.23	0.9370
优化1	PA/%	80.90	94.67	97.01	91.20	95.43	95.20	95.23	0.9370
优化2	UA/%	95.06	97.28	96.46	89.03	91.21	98.14	95.89	0.9457
优化2	PA/%	84.62	95.74	97.72	90.69	95.72	95.82	95.89	0.9457
优化3	UA/%	93.33	99.05	98.27	91.25	90.65	95.12	96.06	0.9479
优化3	PA/%	78.65	92.40	99.07	91.73	94.94	97.43	96.06	0.9479

Vegetation feature type extraction in arid regions based on GEE multi-source remote sensing data

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