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Open AccessArticle

Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery

by Nisha Bao 1,2,*, Wenwen Li 3, Xiaowei Gu 1,2 and Yanhui Liu 1
1
School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China
2
Science and Technology Innovation Center of Smart Water and Resource Environment, Northeastern University, Shenyang 110819, China
3
School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85281, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2855; https://doi.org/10.3390/rs11232855
Received: 28 October 2019 / Revised: 26 November 2019 / Accepted: 28 November 2019 / Published: 1 December 2019
The surface mining activities in grassland and rangeland zones directly affect the livestock production, forage quality, and regional grassland resources. Mine rehabilitation is necessary for accelerating the recovery of the grassland ecosystem. In this work, we investigate the integration of data obtained via a synthetic aperture radar (Sentinel-1 SAR) with data obtained by optical remote sensing (Worldview-3, WV-3) in order to monitor the conditions of a vegetation area rehabilitated after coal mining in North China. The above-ground biomass (AGB) is used as an indicator of the rehabilitated vegetation conditions and the success of mine rehabilitation. The wavelet principal component analysis is used for the fusion of the WV-3 and Sentinel-1 SAR images. Furthermore, a multiple linear regression model is applied based on the relationship between the remote sensing features and the AGB field measurements. Our results show that WV-3 enhanced vegetation indices (EVI), mean texture from band8 (near infrared band2, NIR2), the SAR vertical and horizon (VH) polarization, and band 8 (NIR2) from the fused image have higher correlation coefficient value with the field-measured AGB. The proposed AGB estimation model combining WV-3 and Sentinel 1A SAR imagery yields higher model accuracy (R2 = 0.79 and RMSE = 22.82 g/m2) compared to that obtained with any of the two datasets only. Besides improving AGB estimation, the proposed model can also reduce the uncertainty range by 7 g m−2 on average. These results demonstrate the potential of new multispectral high-resolution datasets, such as Sentinel-1 SAR and Worldview-3, in providing timely and accurate AGB estimation for mine rehabilitation planning and management. View Full-Text
Keywords: mine rehabilitation; biomass; remote sensing; Worldview-3; Sentinel-1 SAR mine rehabilitation; biomass; remote sensing; Worldview-3; Sentinel-1 SAR
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MDPI and ACS Style

Bao, N.; Li, W.; Gu, X.; Liu, Y. Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery. Remote Sens. 2019, 11, 2855.

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