|
|
|
|
|
|
| Combining the Red Edge-Near Infrared Vegetation Indexes of DEM to
Extract Urban Vegetation Information |
| WANG Xiao-xuan1, LU Xiao-ping1*, LI Guo-qing2, WANG Jun2, YANG Zen-an1, ZHOU Yu-shi1, FENG Zhi-li1 |
1. Henan Polytechnic University, Key Laboratory of Spatio-Temporal Information and Ecological Restoration of Mines, Ministry of Natural Resources, Jiaozuo 454000, China
2. Henan Institute of Remote Sensing and Geomatics, Zhengzhou 450000, China |
|
|
|
|
Abstract With the continuous improvement of living standards, residents’ requirements for urban vegetation are also increasing. Urban vegetation has become one of the important criteria to measure the livability of cities and plays a very important role in assessing and protecting urban biodiversity. Therefore, rational planning of urban vegetation is an important means of solving environmental problems and improve the quality of life. To sum up, monitoring urban vegetation becomes the main task, and the extraction of urban vegetation becomes the top priority. At present, the problems of urban vegetation extraction mainly focus on two aspects. Vegetation extraction is affected by region and species. On the other hand, Vegetation extraction is affected by topography and the shadow of buildings. In order to solve the above problems, this paper proposes a red edge-near infrared vegetation index model based on DEM. In this experiment, worldView-3 remote sensing images with red-edge bands and high spectral and spatial resolution after radiation calibration and atmospheric correction were first selected. Then, according to the high sensitivity of the Red Edge band to vegetation and the good correlation between the spectral data within the red edge and the parameters reflecting vegetation growth, the DEM model and the spectral difference between the red edge were adopted to remove the shadow of terrain and buildings effectively. Finally, the red-border spectrum-near-infrared spectrum is constructed based on the feature space within the visible band, and the red-border near-infrared vegetation Index model is constructed. At the same time, the urban vegetation extraction is compared and analyzed with NDVI and EVI. The analysis methods are qualitative and quantitative. The former is to extract vegetation images for visual analysis by using a real vegetation image reference map and model. The latter is a quantitative analysis using user accuracy, producer accuracy, overall accuracy and Kappa coefficient. The result of the qualitative experiment shows that the DEM model can effectively remove the shadow of buildings and terrain by combining with the different information of the red edge band between shadow and vegetation. After removing the shadows, NDVI and EVI were used to extract urban vegetation from the images, which made the buildings and road pixels confused in the vegetation, resulting in the problem of misclassification and omission. However, RENVI can effectively eliminate the confusion between shadow pixels and vegetation pixels, accurately extract urban vegetation, reduce redundancy, and increase vegetation index information. The quantitative experimental results show that the RENVI model can accurately extract urban vegetation compared with NDVI and RVI. The overall accuracy of the 3 images is 89%, 81.4% and 91.8% respectively, and the Kappa coefficient is 0.852 8, 0.791 3 and 0.905 2 respectively. In summer, this method can effectively improve the extraction precision of urban vegetation and obtain a better visual effect of extraction.
|
|
Received: 2020-10-26
Accepted: 2022-04-04
|
|
|
|
Corresponding Authors:
LU Xiao-ping
E-mail: LXP@hpu.edu.cn
|
|
[1] Kutiel P, Cohen O, Shoshany M, et al. Landscape and Urban Planning, 2004, 67(1): 141.
[2] Arjasakusuma S, Mutaqin B W, Sekaranom A B, et al. International Journal of Remote Sensing, 2020, 41(22): 8631.
[3] Aswatha S M, Mukherjee J, Biswas P K, et al. International Journal of Remote Sensing, 2020, 41(14): 5277.
[4] QU Yong-hua, JIAO Si-hong, LIU Su-hong, et al(屈永华，焦思红，刘素红，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2015, 35(11): 3176.
[5] Patel N, Azemi T, Zaeem F, et al. Journal of Cardiac Surgery, 2013, 28(3): 321.
[6] Gim H J, Ho C H, Jeong S, et al. Agricultural and Forest Meteorology, 2020, 294(1): 108143.
[7] CHEN Xue-xiong, CHANG Qing-rui, BI Ru-tian, et al(陈学兄，常庆瑞，毕如田，等). Journal of Basic Science and Engineering(应用基础与工程科学学报), 2020, 28(2): 310.
[8] LI Yao-hui, WANG Jin-xin, LI Ying(李耀辉，王金鑫，李 颖). Journal of Remote Sensing(遥感学报), 2016, 20(3): 459.
[9] Jiang H, Wang S, Cao X J, et al. International Journal of Digital Earth, 2019, 12(9): 1013.
[10] LIU Xiao-nong, JIANG Hong, WANG Xiao-qin(柳晓农，江 洪，汪小钦). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2019, 35(20): 135.
[11] JIANG Hong, HE Guo-jin, HUANG Hai-ming, et al(江 洪，何国金，黄海明，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2017, 33(5): 156.
[12] Groetsch P M M, Gege P, Simis S G H, et al. Optics Letters, 2017, 42(17): 3359.
|
| [1] |
GUO Xian-yu1, 2, 4, LI Kun2, 3, 4*, SHAO Yun2, 3, 4, Juan M. Lopez-Sanchez5, WANG Zhi-yong1. Inversion of Rice Height Using Multitemporal TanDEM-X Polarimetric Interferometry SAR Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(03): 878-884. |
| [2] |
CHEN Dong-hua1,2, ZOU Chen3, WANG Su-ying1, LI Hu1*, ZHANG Xin-shi4，5 . Study on Spatial Interpolation of the Average Temperature in the Yili River Valley Based on DEM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2011, 31(07): 1925-1929. |
| [3] |
LIN Wen-peng1, LI Hou-zeng1, HUANG Jing-feng2, LIU Dong-yan1, ZONG Wei1, HU Xiao-meng1 . Analysis on Urban Vegetations Reflectance Characteristics in Shanghai [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2010, 30(11): 3111-3114. |
|
|
|
|
