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

Abstract

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 (R 2 = 0.79 and RMSE = 22.82 g/m 2 ) 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.

Use of Remotely Sensed Data to Enhance Estimation of Aboveground Biomass for the Dry Afromontane Forest in South-Central Ethiopia

“…The observed moderate relationships of independent variables of the RS data with AGB demonstrated the potential of optical RS data for developing models to enhance AGB estimation. The observed negative correlation coefficients between the mean of SB reflectance and AGB agree with results of similar studies conducted in various forest types [19,75,76]. The negative correlation coefficients indicate the inverse relationship between reflectance values from the SBs and AGB.…”

Deep Learning-Based Estimation of Crop Biophysical Parameters Using Multi-Source and Multi-Temporal Remote Sensing Observations

“…Considerable researches have been conducted to estimate various crop parameters using satellite Earth observations, including RADARSAT-2 [1,17,[19][20][21], RapidEye [5,19,20,22,23], Sentinel-1 [7,17,[24][25][26][27][28], Sentinel-2 [7,25,[29][30][31][32], Landsat-5 Thematic Mapper (TM) [33,34], Landsat-7 Enhanced Thematic Mapper Plus (ETM+) [34][35][36], Landsat-8 Operational Land Imager (OLI) [7,17,31,32,[35][36][37], Worldview-2/3 [17,27,28,38,39], and MODIS [40,41].…”

Remote Sensing of Mine Site Rehabilitation for Ecological Outcomes: A Global Systematic Review

“…Beyond the use of passive optical sensors, active sensors demonstrate the capacity for ecological monitoring of rehabilitation but are still relatively under-utilized. Bao et al (2019) found that fusing WorldView-3 optical bands with Sentinel-1 SAR bands increased the correlation with on-ground measurements of above ground biomass and resulted in a model with a higher prediction accuracy (r 2 = 0.79, RMSE = 22.82gm −2 ) compared to WorldView-3 and Sentinel SAR, separately in a rehabilitated semi-arid grassland in North China [24]. Other studies showed improvement in accuracy by including auxiliary non-spectral light detection and ranging (LiDAR) data.…”

“…However, remote sensing assessments of mine site rehabilitation require considerable knowledge and skill to interpret outputs of analysis and derive meaningful results. This is highlighted by a number of studies emphasizing the heterogeneous nature of mine site rehabilitation and the unique spatial and temporal challenges for remote sensing assessments [21][22][23][24][25][26][27][28][29][30]. Alternatively, the distinctive spectral, temporal and spatial patterns of rehabilitated landscapes provides opportunities for remote sensing to derive valuable ecological information.…”

“…As a constructed landscape, rehabilitation is typically made up of discrete patches of vegetation in different stages of development, with underlying soil characteristics and landform slopes and topography that can vary significantly over short distances; particularly when compared to neighboring unmined land [12]. Additionally, rehabilitation managers often vary inputs, such as seed mixes, topsoil depth, and site preparation techniques, resulting in high levels of spectral heterogeneity within and between patches, across post-mining landscapes [21][22][23][24][25][26][27][28][29][30]. Nonetheless, the advantage of a remote sensing approach that uses discrete classification at fine spatial scales, is the potential to characterize the heterogeneity and the patterns associated with fine scale rehabilitation history, such as topsoil distribution, landform contour banks, and seeding patterns, using innovative classification techniques [31].…”