Paper Info
Title
Vegetation Monitoring for Mountainous Regions Using a New Integrated Topographic Correction (ITC) of the SCS plus C Correction and the Shadow-Eliminated Vegetation Index
Abstract
The mountainous vegetation is important to regional sustainable development. However, the topographic effect is the main obstacle to the monitoring of mountainous vegetation using remote sensing. Aiming to retrieve the reflectance of frequently-used red-green-blue and near-infrared (NIR) wavebands of rugged mountains for vegetation mapping, we developed a new integrated topographic correction (ITC) using the SCS + C correction and the shadow-eliminated vegetation index. The ITC procedure consists of image processing, data training, and shadow correction and uses a random forest machine learning algorithm. Our study using the Landsat 8 Operational Land Imager (OLI) multi-spectral images in Fujian province, China, showed that the ITC achieved high performance in topographic correction of regional mountains and in transferability from the sunny area of a scene to the shadow area of three scenes. The ITC-corrected multi-spectral image with an NIR-red-green composite exhibited flat features with impressions of relief and topographic shadow removed. The linear regression of corrected waveband reflectance vs. the cosine of the solar incidence angle showed an inclination that nearly reached the horizontal, and the coefficient of determination decreased to 0.00 similar to 0.01. The absolute relative errors of the cast shadow and the self-shadow all dramatically decreased to the range of 0.30-6.37%. In addition, the achieved detection rate of regional vegetation coverage for the three cities of Fuzhou, Putian, and Xiamen using the ITC-corrected images was 0.92-6.14% higher than that using the surface reflectance images and showed a positive relationship with the regional topographic factors, e.g., the elevation and slope. The ITC-corrected multi-spectral images are beneficial for monitoring regional mountainous vegetation. Future improvements can focus on the use of the ITC in higher-resolution imaging.
Year
DOI
Venue
2022
10.3390/rs14133073
REMOTE SENSING
Keywords
DocType
Volume
integrated topographic correction, mountainous vegetation, random forest, cast shadow, regional vegetation coverage, transferability
Journal
14
Issue
ISSN
Citations 
13
2072-4292
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Hong Jiang189.37
Ailin Chen200.34
Yongfeng Wu300.34
Chunying Zhang400.34
Zhaohui Chi502.70
Mengmeng Li66512.85
Xiaoqin Wang700.34