• 遥感科学与应用技术 •

### 不同植被红边指数在城市草地健康判别中的对比研究

1. 1. 福州大学环境与资源学院,福州 350116
2. 空间数据挖掘与信息共享教育部重点实验室,福州 350116
3. 福州大学遥感信息工程研究所,福州 350116
• 收稿日期:2017-05-24 修回日期:2017-08-04 出版日期:2017-10-20 发布日期:2017-11-13
• 通讯作者: 王琳 E-mail:1158985715@qq.com;wanglin@fzu.edu.cn
• 作者简介:

作者简介：方灿莹(1993-), 女, 福建漳州人, 硕士生, 主要从事城市化及其环境影响评价研究。E-mail: 1158985715@qq.com

• 基金资助:
国家自然科学基金项目(41501469);福建省测绘地理信息局项目(2017JX02)

### A Comparative Study of Different Red Edge Indices for Remote Sensing Detection of Urban Grassland Health Status

FANG Canying1,2(), WANG lin1,3,*(), XU Hanqiu1,2,3

1. 1. College of Environment and Resources, Fuzhou University, Fuzhou 350116, China
2. Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350116, China
3. Institute of Remote Sensing Information Engineering, Fuzhou University, Fuzhou 350116, China
• Received:2017-05-24 Revised:2017-08-04 Online:2017-10-20 Published:2017-11-13
• Contact: WANG lin E-mail:1158985715@qq.com;wanglin@fzu.edu.cn

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 (CIred-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.