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霞柳 彩
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

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Journal article
Published: 05 March 2021 in Remote Sensing
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Poyang Lake is the largest freshwater lake connecting the Yangtze River in China. It undergoes dramatic dynamics from the wet to the dry seasons. A comparison of the hydrological changes between the wet and dry seasons may be useful for understanding the water flows between Poyang Lake and Yangtze River or the river system in the watershed. Gauged measurements and remote sensing datasets were combined to reveal lake area, level and volume changes during 2000–2020, and water exchanges between Poyang Lake and Yangtze River were presented based on the water balance equation. The results showed that in the wet seasons, the lake was usually around 1301.85–3840.24 km2, with an average value of 2800.79 km2. In the dry seasons, the area was around 618.82–2498.70 km2, with an average value of 1242.03 km2. The inundations in the wet seasons were approximately quadruple those in the dry seasons. In summer months, the lake surface tended to be flat, while in winter months, it was inclined, with the angles at around 10′′–16′′. The mean water levels of the wet and dry seasons were separately 13.51 m and 9.06 m, with respective deviations of around 0–2.38 m and 0.38–2.15 m. Monthly lake volume changes were about 7.5–22.64 km3 and 1–5.80 km3 in the wet and dry seasons, respectively. In the wet seasons, the overall contributions of ground runoff, precipitation on the lake surface and lake evaporation were less than the volume flowing into Yangtze River. In the dry seasons, the three contributions decreased by 50%, 50% and 65.75%, respectively. Therefore, lake storages presented a decrease (−7.42 km3/yr) in the wet seasons and an increase (9.39 km3/yr) in the dry seasons. The monthly exchanges between Poyang Lake and Yangtze River were at around −14.22–32.86 km3. Water all flowed from the lake to the river in the wet seasons, and the chance of water flowing from Yangtze River in the dry seasons was only 5.26%.

ACS Style

Fangdi Sun; Ronghua Ma; Caixia Liu; Bin He. Comparison of the Hydrological Dynamics of Poyang Lake in the Wet and Dry Seasons. Remote Sensing 2021, 13, 985 .

AMA Style

Fangdi Sun, Ronghua Ma, Caixia Liu, Bin He. Comparison of the Hydrological Dynamics of Poyang Lake in the Wet and Dry Seasons. Remote Sensing. 2021; 13 (5):985.

Chicago/Turabian Style

Fangdi Sun; Ronghua Ma; Caixia Liu; Bin He. 2021. "Comparison of the Hydrological Dynamics of Poyang Lake in the Wet and Dry Seasons." Remote Sensing 13, no. 5: 985.

Journal article
Published: 24 July 2019 in Remote Sensing
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Monitoring forest height is crucial to determine the structure and biodiversity of forest ecosystems. However, detailed spatial patterns of forest height from 30 m resolution remotely sensed data are currently unavailable. In this study, we present a new method for mapping forest height by combining spaceborne Light Detection and Ranging (LiDAR) with imagery from multiple remote sensing sources, including the Landsat 5 Thematic Mapper (TM), the Phased Array L-band Synthetic Aperture Radars (PALSAR), and topographic data. The nationwide forest heights agree well with results obtained from 525 independent forest height field measurements, yielding correlation coefficient, root mean square error (RMSE), and mean absolute error (MAE) values of 0.92, 4.31 m, and 3.87 m, respectively. Forest heights derived from remotely sensed data range from 1.41 m to 38.94 m, with an average forest height of 16.08 ± 3.34 m. Mean forest heights differ only slightly among different forest types. In natural forests, conifer forests have the greatest mean forest heights, whereas in plantations, bamboo forests have the greatest mean forest heights. Important predictors for modeling forest height using the random forest regression tree method include slope, surface reflectance of red bands and HV backscatter. The uncertainty caused by the uneven distribution of Geoscience Laser Altimeter System (GLAS) footprints is estimated to be 0.64 m. After integrating PALSAR data into the model, the uncertainty associated with forest height estimation was reduced by 4.58%. Our finer-resolution forest height could serve as a benchmark to estimate forest carbon storage and would greatly contribute to better understanding the roles of ecological engineering projects in China.

ACS Style

Huabing Huang; Caixia Liu; Xiaoyi Wang. Constructing a Finer-Resolution Forest Height in China Using ICESat/GLAS, Landsat and ALOS PALSAR Data and Height Patterns of Natural Forests and Plantations. Remote Sensing 2019, 11, 1740 .

AMA Style

Huabing Huang, Caixia Liu, Xiaoyi Wang. Constructing a Finer-Resolution Forest Height in China Using ICESat/GLAS, Landsat and ALOS PALSAR Data and Height Patterns of Natural Forests and Plantations. Remote Sensing. 2019; 11 (15):1740.

Chicago/Turabian Style

Huabing Huang; Caixia Liu; Xiaoyi Wang. 2019. "Constructing a Finer-Resolution Forest Height in China Using ICESat/GLAS, Landsat and ALOS PALSAR Data and Height Patterns of Natural Forests and Plantations." Remote Sensing 11, no. 15: 1740.

Journal article
Published: 29 April 2019 in Remote Sensing
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Grassland ecosystems in China have experienced degradation caused by natural processes and human activities. Time series segmentation and residual trend analysis (TSS-RESTREND) was applied to grasslands in eastern China. TSS-RESTREND is an extended version of the residual trend (RESTREND) methodology. It considers breakpoint detection to identify pixels with abrupt ecosystem changes which violate the assumptions of RESTREND. With TSS-RESTREND, in Xilingol (111°59′–120°00′E and 42°32′–46°41′E) and Hulunbuir (115°30′–122°E and 47°10′–51°23′N) grassland, 5.5% and 3.3% of the area experienced a decrease in greenness between 1984 and 2009, 80.2% and 73.2% had no significant change, 4.9% and 2.6% increased in greenness, and 9.4% and 20.9% were undetermined, respectively. RESTREND may underestimate the greening trend in Xilingol, but both TSS-RESTREND and RESTREND revealed no significant differences in Hulunbuir. The proposed TSS-RESTREND methodology captured both the time and magnitude of vegetation changes.

ACS Style

Caixia Liu; John Melack; Ye Tian; Huabing Huang; Jinxiong Jiang; Xiao Fu; Zhouai Zhang. Detecting Land Degradation in Eastern China Grasslands with Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) and GIMMS NDVI3g Data. Remote Sensing 2019, 11, 1014 .

AMA Style

Caixia Liu, John Melack, Ye Tian, Huabing Huang, Jinxiong Jiang, Xiao Fu, Zhouai Zhang. Detecting Land Degradation in Eastern China Grasslands with Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) and GIMMS NDVI3g Data. Remote Sensing. 2019; 11 (9):1014.

Chicago/Turabian Style

Caixia Liu; John Melack; Ye Tian; Huabing Huang; Jinxiong Jiang; Xiao Fu; Zhouai Zhang. 2019. "Detecting Land Degradation in Eastern China Grasslands with Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) and GIMMS NDVI3g Data." Remote Sensing 11, no. 9: 1014.

Journal article
Published: 14 January 2016 in Remote Sensing
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Continuous monitoring of forest cover condition is key to understanding the carbon dynamics of forest ecosystems. This paper addresses how to integrate single-year airborne LiDAR and time-series Landsat imagery to derive forest cover change information. LiDAR data were used to extract forest cover at the sub-pixel level of Landsat for a single year, and the Landtrendr algorithm was applied to Landsat spectral data to explore the temporal information of forest cover change. Four different approaches were employed to model the relationship between forest cover and Landsat spectral data. The result shows incorporating the historic information using the temporal trajectory fitting process could infuse the model with better prediction power. Random forest modeling performs the best for quantitative forest cover estimation. Temporal trajectory fitting with random forest model shows the best agreement with validation data (R2 = 0.82 and RMSE = 5.19%). We applied our approach to Youyu county in Shanxi province of China, as part of the Three North Shelter Forest Program, to map multi-decadal forest cover dynamics. With the availability of global time-series Landsat imagery and affordable airborne LiDAR data, the approach we developed has the potential to derive large-scale forest cover dynamics.

ACS Style

Xiaoyi Wang; Huabing Huang; Peng Gong; Gregory S. Biging; Qinchuan Xin; Yanlei Chen; Jun Yang; Caixia Liu. Quantifying Multi-Decadal Change of Planted Forest Cover Using Airborne LiDAR and Landsat Imagery. Remote Sensing 2016, 8, 62 .

AMA Style

Xiaoyi Wang, Huabing Huang, Peng Gong, Gregory S. Biging, Qinchuan Xin, Yanlei Chen, Jun Yang, Caixia Liu. Quantifying Multi-Decadal Change of Planted Forest Cover Using Airborne LiDAR and Landsat Imagery. Remote Sensing. 2016; 8 (1):62.

Chicago/Turabian Style

Xiaoyi Wang; Huabing Huang; Peng Gong; Gregory S. Biging; Qinchuan Xin; Yanlei Chen; Jun Yang; Caixia Liu. 2016. "Quantifying Multi-Decadal Change of Planted Forest Cover Using Airborne LiDAR and Landsat Imagery." Remote Sensing 8, no. 1: 62.