基于NDVI-Ts特征空间的中国土地覆盖分类研究

doi:10.17521/cjpe.2005.0115

摘要/Abstract

摘要：

归一化植被指数(NDVI)与地表温度(T_s)是描述地表覆盖特征的两个重要参数,其构成的NDVI-T_s特征空间具有丰富的地学和生态学内涵。该文在NOAA/AVHRR连续时间序列数据反演T_s的基础上,通过主成分分析、非监督分类和基于DEM的分类后处理等方法,以T_s/NDVI为指标对中国土地覆盖进行分类。结果表明,T_s/NDVI对中国较大尺度上不同土地覆盖类型的差异具有较强的敏感性,其对中国土地覆盖分类结果的野外抽样检验精度比传统的单独利用NDVI时间序列进行非监督分类提高了3.3%,Kappa系数提高了0.020 2;在综合其它反映植被特征及其环境的指标(如气候、地形等)的基础上,利用T_s/NDVI将有可能较为准确地提取中国植被或土地覆盖的信息,有利于对其进行分类和变化监测,具有深远的研究潜力和应用价值。

关键词: T_s, NDVI, T_s/NDVI, 土地覆盖分类

Abstract:

Mapping and quantifying land use and land cover changes are important for evaluating recent changes in the regional and global environment and for simulating future changes under different climate change and human land use scenarios. The normalized difference vegetation index (NDVI) and surface temperature (T_s) are two important parameters used to describe the characteristics of land cover. The NDVI-T_s feature space combines these two parameters into one variable. By and large, T_s/NDVI is synchronized to the growing season of vegetation so it can approximate the different phases and status of vegetation growth. Compared to NDVI and T_s, NDVI-T_s contains more land cover information and should be more suitable for characterizing the distribution of vegetation or land cover.
The aim of this paper was to discuss the feasibility of using the NDVI-T_s feature space to better characterize the current distribution and changes in vegetation and land cover in China. We used several classification methods, including Principal Component Analysis (PCA), unsupervised classification and post-classification sustained based on digital elevation models (DEM). The results indicated that T_s/NDVI was highly sensitive and could discriminate different vegetation cover categories in China at large-scales. The accuracy of vegetation classification based on T_s/NDVI was 72.0%, a 3.3% improvement in accuracy as compared to NDVI images, using unsupervised classification, and the Kappa value increased 0.020 2. Moreover, because of the simplexity of remote sensing information, the classification based on seasonal T_s/NDVI data could not avoid completely the mixed classification phenomena. It was necessary to add other information, reflecting vegetation characteristic and its environment to implement post-classification, such as DEM.
When the inversing accuracy of T_s improved, T_s/NDVI data can precisely describe the status of vegetation or land cover in China and improve monitoring of land use changes. This technique has great research potential and practical value.

Key words: T_s, NDVI, T_s/NDVI, Land cover classification

喻锋, 李晓兵, 王宏, 余弘婧, 陈云浩. 基于NDVI-T_s特征空间的中国土地覆盖分类研究. 植物生态学报, 2005, 29(6): 934-944. DOI: 10.17521/cjpe.2005.0115

YU Feng, LI Xiao-Bing, WANG Hong, YU Hong-Jing, CHEN Yun-Hao. LAND COVER CLASSIFICATION IN CHINA BASED ON THE NDVI-T<sub>S</sub> FEATURE SPACE. Chinese Journal of Plant Ecology, 2005, 29(6): 934-944. DOI: 10.17521/cjpe.2005.0115

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2005.0115

https://www.plant-ecology.com/CN/Y2005/V29/I6/934

图/表 6

参考文献 29

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主成分 Principal component		NDVI、T_s和T_s/NDVI主成分变换特征向量 Eigenvectors of principal components for NDVI,T_s and T_s/NDVI												方差(%) Expressed variance
主成分 Principal component		1	2	3	4	5	6	7	8	9	10	11	12	方差(%) Expressed variance
NDVI	PC₁	0.26	0.26	0.27	0.27	0.29	0.31	0.32	0.32	0.31	0.29	0.28	0.27	81
	PC₂	0.25	0.32	0.36	0.13	-0.12	-0.33	-0.40	-0.37	-0.26	0.05	0.25	0.37	14.7
	PC₃	-0.44	-0.15	0.07	-0.54	-0.08	0.31	-0.05	-0.17	0.01	0.36	0.36	0.29	1.5
	PC₄	-0.05	-0.14	-0.16	0.29	0.67	0.35	-0.22	-0.41	-0.25	0.04	0.03	-0.13	1.4
T_s	PC₁	0.13	0.19	0.25	0.32	0.36	0.37	0.36	0.36	0.33	0.28	0.20	0.15	96.5
	PC₂	-0.52	-0.41	-0.21	0.01	0.17	0.23	0.23	0.20	0.11	-0.04	-0.29	-0.49	3.0
T_s/NDVI	PC₁	0.13	0.19	0.27	0.33	0.37	0.37	0.36	0.35	0.32	0.28	0.20	0.15	96.3
	PC₂	-0.54	-0.43	-0.20	-0.01	0.15	0.21	0.24	0.22	0.13	-0.01	-0.27	-0.47	2.8

主成分 Principal component		NDVI、T_s和T_s/NDVI主成分变换特征向量 Eigenvectors of principal components for NDVI,T_s and T_s/NDVI												方差(%) Expressed variance
主成分 Principal component		1	2	3	4	5	6	7	8	9	10	11	12	方差(%) Expressed variance
NDVI	PC₁	0.26	0.26	0.27	0.27	0.29	0.31	0.32	0.32	0.31	0.29	0.28	0.27	81
	PC₂	0.25	0.32	0.36	0.13	-0.12	-0.33	-0.40	-0.37	-0.26	0.05	0.25	0.37	14.7
	PC₃	-0.44	-0.15	0.07	-0.54	-0.08	0.31	-0.05	-0.17	0.01	0.36	0.36	0.29	1.5
	PC₄	-0.05	-0.14	-0.16	0.29	0.67	0.35	-0.22	-0.41	-0.25	0.04	0.03	-0.13	1.4
T_s	PC₁	0.13	0.19	0.25	0.32	0.36	0.37	0.36	0.36	0.33	0.28	0.20	0.15	96.5
	PC₂	-0.52	-0.41	-0.21	0.01	0.17	0.23	0.23	0.20	0.11	-0.04	-0.29	-0.49	3.0
T_s/NDVI	PC₁	0.13	0.19	0.27	0.33	0.37	0.37	0.36	0.35	0.32	0.28	0.20	0.15	96.3
	PC₂	-0.54	-0.43	-0.20	-0.01	0.15	0.21	0.24	0.22	0.13	-0.01	-0.27	-0.47	2.8

分类指标 Index for classification	GPS取样区 Sampling area for GPS spots	总点数 Number of total spots	正确点 Number of correct	错(混)分点 Number of incongruous (mixed)	精度 Accuracy(%)
T_s/NDVI	海南地区 Hainan region	79	50	29	63.3
	内蒙古地区 Inner Mongolia region	219	163	56	74.4
	新疆地区 Xinjiang region	282	201	81	71.3
	东北地区 Northeast region	87	66	21	75.9
	整个研究区 The whole study region	667	480	187	72.0
NDVI	海南地区 Hainan region	79	54	25	68.4
	内蒙古地区 Inner Mongolia region	219	152	67	69.4
	新疆地区 Xinjiang region	282	192	90	68.1
	东北地区 Northeast region	87	60	27	69.0
	整个研究区 The whole study region	667	458	209	68.7

分类指标 Index for classification	GPS取样区 Sampling area for GPS spots	总点数 Number of total spots	正确点 Number of correct	错(混)分点 Number of incongruous (mixed)	精度 Accuracy(%)
T_s/NDVI	海南地区 Hainan region	79	50	29	63.3
	内蒙古地区 Inner Mongolia region	219	163	56	74.4
	新疆地区 Xinjiang region	282	201	81	71.3
	东北地区 Northeast region	87	66	21	75.9
	整个研究区 The whole study region	667	480	187	72.0
NDVI	海南地区 Hainan region	79	54	25	68.4
	内蒙古地区 Inner Mongolia region	219	152	67	69.4
	新疆地区 Xinjiang region	282	192	90	68.1
	东北地区 Northeast region	87	60	27	69.0
	整个研究区 The whole study region	667	458	209	68.7