Jianbang Wang

Dr. Jianbang Wang


Research Focus & Metrics

Academic Career Length

6Years (2021-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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29 December 2021 in Remote Sensing
Verified
Journal Article
Open access
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CGChao Gao
Chao Gao
Chang HuangChang Huang
Chang Huang
JWJianbang Wang
Jianbang Wang
Zhi LiZhi Li
Zhi Li
The sustainability of wetlands is threatened by the past and present land use practices. Hydrological connectivity is one of the most important aspects to consider for wetland rehabilitation planning purposes. Circuit theory and connectivity indices can be used to model and assess hydrological connectivity. The aim of this study was to assess spatiotemporal variation in the hydrological connectivity of the Zoigê area from 2000–2019 using both methods. The study area contains a Ramsar wetland of international importance, namely the Sichuan Ruoergai Wetland National Nature Reserve. We used a global surface water observation product as the major input for both methods, and then analyzed the temporal and spatial characteristics, in terms of important components and patches. We found that the overall connectivity has increased slightly in the last 20 years, while the probability of connection between patches of surface water has increased significantly. Important components and patches represent steppingstone habitat for the dispersal of organisms in the landscape. The main determinants of hydrological connectivity are mostly human oriented, predominantly a decrease in large livestock population size and population increase.

29 July 2022 in Remote Sensing
Verified
Journal Article
Open access
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CWchunling wang
chunling wang
JWJianbang Wang
Jianbang Wang
HZHe Zhuoyu
He Zhuoyu
MFMin Feng
Min Feng
Forest dynamics is critical to forested ecosystems, and considerable efforts have been devoted to monitoring long-term forest dynamics with the goals of sustainable management and conservation of forests. However, little attention has been given to mountain forests, which are more challenging to monitor due to complex topography, weather, and their distribution. We developed a 30-m resolution tree-canopy cover (TCC) and forest change dataset for the Eastern Himalayas from 1986 to 2021. The tree-canopy cover estimation was validated against estimates from the space-borne Global Ecosystem Dynamics Investigation (GEDI), demonstrating strong consistency (R-square greater than 0.81). A comprehensive assessment for the forest change dataset was performed using 448 visually interpreted points and reported high accuracy of the dataset, i.e., 97.7% and 95.9% for forest loss and gain, respectively. Higher producer and user accuracies were reported for forest loss (PA = 78.0%, UA = 60.9%) than these for forest gain (PA = 61.7%, UA = 56.7%). The results indicated that (1) the mean tree-canopy cover in the region increased by 2.76% over the past three decades, from 40.67% in 1990 to 43.43% in 2020, suggesting the forests have improved during the period; (2) forest loss was identified for a total area of 6990 km2 across the study area, which is less than the 10,700 km2 identified as forest gain; (3) stronger forest gains were found at elevations greater than 3000 m asl, indicating faster forest growth in high elevations likely influenced by the warming temperatures in the Eastern Himalayas.

22 June 2022 in Remote Sensing
Verified
Journal Article
Open access
PDF file
JWJianbang Wang
Jianbang Wang
HZHe Zhuoyu
He Zhuoyu
CWchunling wang
chunling wang
MFMin Feng
Min Feng
YPYong Pang
Yong Pang
XLXin Li
Xin Li
Forest dynamics, including forest loss and gain, are long-term complex ecological processes affected by nature and human activities. It is particularly important to understand the long-term forest dynamics of protected areas to evaluate their conservation efforts. This study adopted the Landsat tree-canopy cover (TCC) method to derive annual TCC data for the period 1984–2020 for the protected areas of northeast China, where protection policies have been carried out since the end of the 20th century, e.g., the Natural Forest Conversion Program (NFCP). A strong correlation was found between the TCC estimates derived from Landsat and LiDAR observations, suggesting the high accuracy of TCC. Forest loss and gain events were also identified from the time series of TCC estimates. High correlations were reported for both forest loss (Producer’s accuracy = 85.21%; User’s accuracy = 84.26%) and gain (Producer’s accuracy = 87.74%; User’s accuracy = 88.31%), suggesting that the approach can be used for monitoring and evaluating the effectiveness of the NFCP and other forest conservation efforts. The results revealed a fluctuating upward trend of the TCC of the protected area from 1986 to 2018. The increased area of TCC was much larger than the decreased area, accounting for 59.68% and 40.34%, respectively, suggesting the effectiveness of protection policies. Since the NFCP was officially implemented in 1998, deforestation was effectively curbed, the area of forest loss was significantly reduced (slope: −14.24%/year), and the area of forest gain significantly increased (slope: 4.13%/year). We found that regional forest changes were mainly manifested in “forest gain after loss (forest recovery)” and “forest gain with no loss (forest newborn)”, accounting for 40.29% and 37.28% of the total area of forest change, respectively. Moreover, the forest gain area far exceeds the forest loss area, reaching 11.24 million hectares, suggesting a successful forest recovery due to forest protection.