内蒙古草原植被覆盖度遥感估算

doi:10.3724/SP.J.1258.2011.00615

摘要/Abstract

摘要：

以内蒙古锡林浩特市南部中国科学院草原生态系统定位研究站周围的草场为研究对象, 分析比较了统计模型和亚像元分解模型进行草地植被覆盖度(vegetation coverage, VC)遥感估算的适用性。结果表明, 根据Landsat-5 TM影像数据计算的比值植被指数(simple ratio vegetation index, SR)与观测的VC的相关性最高(R² = 0.761); 统计模型和亚像元分解模型生成的VC空间分布特征相似, 但亚像元分解模型得到的VC平均值比统计模型的结果高0.091; 在VC的低值和高值区, 两种方法得到的VC结果相似; 但在VC的中值区, 亚像元模型得到的结果较统计模型的结果偏高。

关键词: 内蒙古白音锡勒草原, 草地植被覆盖度, Landsat-5 TM影像, 统计模型, 亚像元分解模型

Abstract:

Aims Our objective was to estimate grassland vegetation coverage (VC) in Inner Mongolia prairie, China, using a statistical model and a sub-pixel model and determine which model was more applicable in this area.

Methods Field experiments were conducted around three experimental stations in the Inner Mongolia prairie using a digital camera and a LAI-2000 plant canopy analyzer. A spectrum information model was used to extract the measured VC value from the photos. Statistical models were built between those VC values and six vegetation indexes. Then the VC map was made by using the model with the highest R². The measured leaf area index (LAI) values were used in the sub-pixel model to make another VC map.

Important findings The simple ratio vegetation index extracted from the Landsat-5 TM (thematic mapper) image had the higher correlation with VC values calculated from the photos taken in the field (R²= 0.761). The VC spatial distribution maps generated by the two models were generally similar, but the VC average value gained from the statistical model was 0.09 lower than that of VC values retrieved by the sub-pixel model. These two methods had similar results in the areas with higher and lower VC values, but the results from the sub-pixel model were higher than the statistical model with mid VC values.

Key words: Baiyanxile grassland in Inner Mongolia, grassland vegetation coverage, Landsat-5 TM image, statistical model, sub-pixel model

朱敬芳, 邢白灵, 居为民, 朱高龙, 柳艺博. 内蒙古草原植被覆盖度遥感估算. 植物生态学报, 2011, 35(6): 615-622. DOI: 10.3724/SP.J.1258.2011.00615

ZHU Jing-Fang, XING Bai-Ling, JU Wei-Min, ZHU Gao-Long, LIU Yi-Bo. Remote-sensing estimation of grassland vegetation coverage in Inner Mongolia, China. Chinese Journal of Plant Ecology, 2011, 35(6): 615-622. DOI: 10.3724/SP.J.1258.2011.00615

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链接本文: https://www.plant-ecology.com/CN/10.3724/SP.J.1258.2011.00615

https://www.plant-ecology.com/CN/Y2011/V35/I6/615

图/表 8

图1 研究区和野外实测点的分布图。

Fig. 1 Distribution of study area and field sampling points.

表1 由不同植被指数建立的植被覆盖度统计模型

Table 1 Vegetation coverage statistical models developed by different vegetation indexes

植被指数 Vegetation index	线性回归模型 Linear regression model	R²	指数模型 Exponential model	R²
归一化植被指数 Normalized difference vegetation index	y = 1.702x - 0.502	0.691	y = 0.039e^3.918^x	0.514
修正的归一化植被指数 Modified normalized difference vegetation index	y = 1.329x - 0.075	0.671	y = 0.104e^3.069^x	0.502
比值植被指 Simple ratio vegetation index	y = 0.113x - 0.040	0.761	y = 0.121e^0.243^x	0.486
减小的比值植被指数 Reduced simple ratio vegetation index	y = 0.112x + 0.092	0.751	y = 0.160e^0.242^x	0.489
土壤调节指数 Soil adjusted vegetation index	y = 2.566x - 0.361	0.734	y = 0.061e^5.491^x	0.471
垂直植被指数 Perpendicular vegetation index	y = 5.787x - 0.059	0.705	y = 0.119e^12.11^x	0.433

图2 研究区30 m × 30 m分辨率的土地利用分类图。

Fig. 2 Landuse map of the study area in 30 m × 30 m resolution.

图3 利用统计模型生成的研究区植被覆盖度分布图。

Fig. 3 Vegetation coverage map of the study area generated by the statistical model.

图4 叶面积指数与修正的归一化植被指数的关系模型。

Fig. 4 Relation model between leaf area index and modified normalized difference vegetation index.

图5 亚像元分解模型生成的植被覆盖度分布图。

Fig. 5 Vegetation coverage map of the study area generated by sub-pixel model.

图6 统计模型与亚像元模型得到的样地植被覆盖度比较。

Fig. 6 Comparison of vegetation coverage (VC) in sampling sites computed by statistical model and sub-pixel model.

图7 统计模型和亚像元模型的植被覆盖度统计结果对比。

Fig. 7 Comparison of vegetation coverage statistical results of the statistical model and sub-pixel model.

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