不同植被红边指数在城市草地健康判别中的对比研究

doi:10.3724/SP.J.1047.2017.01382

摘要/Abstract

摘要：

遥感红边指数与表征绿色植物生长状况的重要生化参数有密切的关系,是植被长势监测的重要因子。为寻找出最适用于城市草地生长状况监测的红边指数,本文基于Sentinel-2A数据,对比分析了不同红边指数在城市草地健康状况估算方面的差异。本文以福州市和厦门市的城市草地为例,在全面分析各种健康水平草地光谱响应特征差异的基础上,选取了6种与草地生化参数相关的红边指数,即红边位置REP、地面叶绿素指数MTCI、归一化差值红边指数NDRE1、新型倒红边叶绿素指数IRECI、红边叶绿素指数CI_red-edge以及叶绿素吸收指数MCARI2,然后采用独立样本T检验及欧式距离对这6种红边指数在草地健康判别中的优劣进行了定量对比。结果表明：IRECI指数对草地健康状况最为敏感,该指数在不同健康等级草地的值域区间和均值都存在显著性差异,其判别总精度均大于85%;NDRE1和MCARI2指数次之,其他3个指数则难以判别草地的健康状况。因此,在基于Sentinel-2A影像的城市草地健康遥感判别中,推荐使用IRECI指数。

关键词: 遥感, Sentinel-2A影像, 红边波段, 红边指数, 草地健康

Abstract:

Being an important part of the green space system, urban grassland has played a significant role in landscaping environment, regulating microclimate and preventing soil from erosion. Therefore, it is of great importance to monitor the health status of urban grassland timely and efficiently. Remote sensing technique has been widely used for assessing vegetation growth status for decades. Numerous studies have found that red edge indices are closely related to the important biochemical parameters of green plants. Thus, they can be regarded as important indicators for monitoring health status of vegetation. However, there is no explicit conclusion about which index is more suitable for monitoring the health status of urban grasslands among the existing red edge indices. The European Sentinel-2A satellite was successfully launched in late June 2015, aiming to replace and improve the old generation of satellite sensors of high resolution (i.e. Landsat and SPOT), with improved spectral capabilities. The multispectral instrument (MSI) of Sentinel-2 has made available a set of 13 spectral bands ranging from visible (VIS) and near infrared (NIR) to shortwave infrared (SWIR), featuring four bands at 10 m, six bands at 20 m, and three bands at 60 m of spatial resolution. In comparison to the previous sensors, Sentinel-2 incorporates three new spectral bands in the red-edge region centered at 705, 740 and 783 nm, providing an opportunity for assessing red-edge spectral indices for monitoring the health status of urban grasslands. For this reason, the main objective of this paper is to find a red edge index that is more suitable for evaluating the growth status of urban grassland based on Sentinel-2A sensor data. Taking the urban grasslands in Fuzhou and Xiamen cities, Southeastern China, as examples, we firstly investigated the spectral responsive characteristics of grasslands in different health status using Sentinel-2A images dated on June 23, 2016 and August 22, 2016, respectively for Fuzhou and Xiamen. On this basis, six red edge indices related to grassland chlorophyll content were then selected to test their efficiency in detecting grassland health status. These are the red edge position (REP), the terrestrial chlorophyll index (MTCI), the normalized difference red edge index (NDRE1), the novel inverted red-edge chlorophyll index (IRECI), the red-edge chlorophyll index (CI_red-edge) and the modified chlorophyll absorption ratio index (MCARI2). Furthermore, independent sample T test and Euclidean distance methods were employed to evaluate the performance of the selected indices in the detection of grassland health status. Results showed that the six red edge indices had different performances. They have different degrees of sensitivity to the changes of grassland health status. In general, the IRECI was the most sensitive to the grassland health status among the six indices in the two study areas. The index can reveal significant differences in the numerical range and mean values between grasslands with different health status. The overall accuracy of the index is greater than 85% with a kappa coefficient exceeding 0.8 both in Fuzhou and Xiamen cases. The NDRE1 and MCARI2 indices ranked the second and third, while the other three indices were unable to effectively detect the health status of the grasslands. Accordingly, the IRECI is the optimal red edge index for evaluating the grassland health status using Sentinel-2A imagery.

Key words: remote sensing, Sentinel-2A, red-edge bands, red edge index, grassland health

方灿莹, 王琳, 徐涵秋. 不同植被红边指数在城市草地健康判别中的对比研究[J]. 地球信息科学学报, 2017, 19(10): 1382-1392.DOI:10.3724/SP.J.1047.2017.01382

FANG Canying,WANG lin,XU Hanqiu. A Comparative Study of Different Red Edge Indices for Remote Sensing Detection of Urban Grassland Health Status[J]. Journal of Geo-information Science, 2017, 19(10): 1382-1392.DOI:10.3724/SP.J.1047.2017.01382

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指数	计算公式	对应的Sentinel-2A波段	描述	参考文献
REP	REP=705+35×(0.5×(ρ₆₆₅+ ρ₇₈₃)-ρ₇₀₅)/(ρ₇₄₀-ρ₇₀₅)	B4、B7、B5、B6	红边范围内植被反射光谱曲线斜率最大的位置。当植物叶片的叶绿素含量增加时,REP向长波方向移动,反之则向短波方向移动^[25]	Guyot等^[25]
MTCI	MTCI = (ρ_753.75-ρ_708.75)/(ρ_708.75-ρ_681.25)	B6、B5、B4	对植物叶片叶绿素含量较为敏感,其值越大代表叶绿素含量越高^[12]	Dash等^[12]
NDRE1	NDRE1= (ρ₇₅₀-ρ₇₀₅)/(ρ₇₅₀+ρ₇₀₅)	B6、B5	NDRE1是用红边的峰和谷来代替传统NDVI中的红光和近红外波段,可用于估算植物叶面积指数和叶绿素含量^[13]	Gitelson等^[27]
IRECI	IRECI = (ρ₇₈₃-ρ₆₆₅)/(ρ₇₀₅/ρ₇₄₀)	B7、B4、B5、B6	该指数与植物冠层叶绿素含量和叶面积指数具有很好的相关关系,可定量表征植物的叶绿素含量^[26]	Frampton等^[26]
CI_red-edge	CI _red-edge= (ρ_750-800/ρ_690-725)-1	B7、B5	该指数与植物叶绿素,氮素含量具有显著的线性关系^[27-28]	Gitelson等^[27-28]
MCARI2	MCARI 2= ((ρ₇₅₀-ρ₇₀₅)-0.2× (ρ₇₅₀-ρ₅₅₀)) ×(ρ₇₅₀/ρ₇₀₅)	B6、B5、B3	该指数对植物中的叶绿素含量较为敏感,其值越大表示叶绿素含量越高^[14]	Wu等^[14]

	p值	REP	MTCI	CI_red-edge
福州市	好-中	0.000**	0.000**	0.000**
	好-差	0.000**	0.000**	0.000**
	中-差	0.174	0.011*	0.013*
厦门市	好-中	0.001**	0.000**	0.000**
	好-差	0.000**	0.000**	0.000**
	中-差	0.012*	0.016*	0.000**

	红边指数	好-中	排序	中-差	排序	好-差	排序	总欧式距离	综合排序
福州市	IRECI	2.97	1	2.11	1	4.49	1	9.57	1
	NDRE1	2.43	3	2.01	2	4.37	2	8.81	2
	MCARI2	2.46	2	1.91	3	3.67	3	8.04	3
	CI_red-edge	1.13	5	0.33	4	1.47	4	2.93	4
	MTCI	1.23	4	0.29	5	1.26	5	2.78	5
	REP	1.12	6	0.18	6	1.07	6	2.37	6
厦门市	IRECI	1.49	1	1.93	1	2.71	2	6.13	1
	MCARI2	1.35	2	1.45	2	2.64	3	5.44	2
	NDRE1	1.32	3	1.29	3	2.81	1	5.42	3
	CI_red-edge	1.29	4	1.29	4	2.32	4	5.00	4
	REP	0.88	5	0.27	5	1.06	5	2.21	5
	MTCI	0.82	6	0.26	6	1.01	6	2.09	6

指数类型	福州市		厦门市
指数类型	总精度/%	Kappa系数	总精度/%	Kappa系数
REP	62.12	0.4311	67.83	0.5195
MTCI	69.70	0.5480	63.16	0.4478
NDRE1	87.37	0.8103	83.62	0.7647
IRECI	91.41	0.8909	89.47	0.8419
CI_red-edge	74.24	0.6167	83.04	0.7457
MCARI2	85.86	0.7876	84.79	0.7716