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Author: search.filters.author.Gong, Weishu

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    Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data
    (MDPI, 2018-06-14) Liu, Yanan; Gong, Weishu; Hu, Xiangyun; Gong, Jianya
    Carbon sink estimation and ecological assessment of forests require accurate forest type mapping. The traditional survey method is time consuming and labor intensive, and the remote sensing method with high-resolution, multi-spectral commercial satellite images has high cost and low availability. In this study, we explore and evaluate the potential of freely-available multi-source imagery to identify forest types with an object-based random forest algorithm. These datasets included Sentinel-2A (S2), Sentinel-1A (S1) in dual polarization, one-arc-second Shuttle Radar Topographic Mission Digital Elevation (DEM) and multi-temporal Landsat-8 images (L8). We tested seven different sets of explanatory variables for classifying eight forest types in Wuhan, China. The results indicate that single-sensor (S2) or single-day data (L8) cannot obtain satisfactory results; the overall accuracy was 54.31% and 50.00%, respectively. Compared with the classification using only Sentinel-2 data, the overall accuracy increased by approximately 15.23% and 22.51%, respectively, by adding DEM and multi-temporal Landsat-8 imagery. The highest accuracy (82.78%) was achieved with fused imagery, the terrain and multi-temporal data contributing the most to forest type identification. These encouraging results demonstrate that freely-accessible multi-source remotely-sensed data have tremendous potential in forest type identification, which can effectively support monitoring and management of forest ecological resources at regional or global scales.
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    Aerosol Retrieval over Land from the Directional Polarimetric Camera Aboard on GF-5
    (MDPI, 2022-11-11) Wang, Shupeng; Gong, Weishu; Fang, Li; Wang, Weihe; Zhang, Peng; Lu, Naimeng; Tang, Shihao; Zhang, Xingying; Hu, Xiuqing; Sun, Xiaobing
    The DPC (Directional Polarization Camera) onboard the Chinese GaoFen-5 (GF-5) satellite is the first operational aerosol monitoring instrument capable of performing multi-angle polarized measurements in China. Compared with POLDER (Polarization and Directionality of Earth’s Reflectance) which ended its mission in December 2013, DPC has similar band design, with a maximum of 12 imaging angles and a relatively higher spatial resolution of 3.3 km. The global aerosol optical depth (AOD) over land from October to December in 2018 was retrieved with multi-angle polarization measurements of DPC. Comparisons with MODIS (Moderate Resolution Imaging Spectroradiometer) AOD products show relatively good agreement over fine-aerosol-particle-dominated areas such as northern China and Huanghuai areas in eastern China, the southern foothills of the Himalayas and India. AERONET (Aerosol Robotic Network) measurements over Beijing, Xianghe and Kanpur were used to evaluate the accuracy of DPC AOD retrievals. The correlation coefficients are greater than 0.9 and the RMSE are lower than 0.08 for Beijing and Xianghe stations. For Kanpur, a relatively lower correlation of 0.772 and larger RMSE of 0.082 are found.
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    FINE RESOLUTION ASSESSMENT OF THE CARBON FLUXES FROM CONTEMPORARY FOREST DYNAMICS
    (2021) Gong, Weishu; Huang, Chengquan; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Current estimation of the Earth’s carbon budget contains large uncertainties, with the largest ones in its terrestrial components. With an overarching goal to improve the understanding of carbon budget at regional to global scales, this study aimed to: 1. Develop a grid-based carbon accounting (GCA) model for estimating carbon fluxes from forest disturbance, tested over North Carolina; 2. Develop a consistent timber product output (TPO) record for a globally important timber production region, including seven states in the southeast U.S.; and 3. Further improve the GCA model based on results from objectives 1 and 2, and use it to derive carbon source/sink estimates for all forest land in North Carolina.The results show that several inputs/parameters such as pre-disturbance carbon density, disturbance intensity, allocation of removed carbon among slash and different wood product pools, and forest growth rates could have large impact on carbon estimates. The total emission between 1986 and 2010 from logging over North Carolina was reduced by one third and two thirds, respectively, when remote sensing-based disturbance intensity and biomass data were used to replace parameter values inherited from the original bookkeeping carbon accounting (BCA) model, and was reduced by over 70% when both were used. Use of the TPO data derived in Chapter 3 to partition the removed carbon among slash and different wood product pools resulted in noticeably higher emission estimates than those derived using the partitioning ratios provided by the original BCA model. In addition, without considering legacy effect from wood products harvested before 1986, the emission value derived using the prompt release method was 50% higher than that derived using the delayed release method. This study addresses multiple sources of uncertainties related to the terrestrial carbon budget. The TPO study demonstrated an approach for deriving consistent TPO records for large timber production regions. The GCA model produced state level carbon estimates comparable to those reported by the U.S. Forest Service while providing critical spatial details needed to support carbon management and advance forest-driven climate change mitigation initiatives.