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Dr. Xiaoyan Wang
lanzhou university

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Research Keywords & Expertise

0 Forest
0 Remote Sensing
0 Snow
0 Remote Sensing & Gis, Image Processing And Analysis
0 Remote Sensing & Gis

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Journal article
Published: 08 July 2021 in Remote Sensing
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China’s largest desert freshwater lake, Hongjian Nur (HN), which is the largest habitat of relict gull (Larus relictus), has rapidly changed in recent years. However, it is difficult to quantitatively monitor the dynamics of the lake and determine the causes of its changes due to the lack of in situ observation. In this study, a remote sensing-based approach was utilized to overcome these limitations. The monthly water areas during 1990–2017 were first extracted from Landsat multispectral images via an improved method based on the floating algae index (FAI). Then, lake surface elevations measured by real-time kinematics (RTK) were used to calculate the variations in the water storage of HN. Finally, the driving factors of the rapidly changed HN in different periods were investigated by correlation analysis. The result indicated that the drivers affecting the water storage of HN in different periods were not the same. Climate change was the main driving factor of lake level fluctuation during the HN relatively stable stage (1990–1998). Drought and the intensification of human activities were the main factors for the rapid shrinkage of the HN during 1999–2010. Human activities, especially coal-related industries and reservoir impoundment, likely was the primary factors driving the decrease in the water storage of HN from 2010 to 2015. After 2015, the policies that decreased the water consumed by human activities formulated by the government and humid climate were the main factor for the expansion of HN.

ACS Style

Zhiyong Jiang; Lian Feng; Sen Li; Jida Wang; Xiaobin Cai; Peirong Lin; Xiaoyan Wang; Hongmei Zhao. The Dynamics of Hongjian Nur, the Largest Desert Freshwater Lake in China, during 1990–2017. Remote Sensing 2021, 13, 2690 .

AMA Style

Zhiyong Jiang, Lian Feng, Sen Li, Jida Wang, Xiaobin Cai, Peirong Lin, Xiaoyan Wang, Hongmei Zhao. The Dynamics of Hongjian Nur, the Largest Desert Freshwater Lake in China, during 1990–2017. Remote Sensing. 2021; 13 (14):2690.

Chicago/Turabian Style

Zhiyong Jiang; Lian Feng; Sen Li; Jida Wang; Xiaobin Cai; Peirong Lin; Xiaoyan Wang; Hongmei Zhao. 2021. "The Dynamics of Hongjian Nur, the Largest Desert Freshwater Lake in China, during 1990–2017." Remote Sensing 13, no. 14: 2690.

Journal article
Published: 31 October 2020 in Remote Sensing
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Seasonal snow cover is closely related to regional climate and hydrological processes. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products from 2001 to 2018 were applied to analyze the snow cover variation in northern Xinjiang, China. As cloud obscuration causes significant spatiotemporal discontinuities in the binary snow cover extent (SCE), we propose a conditional probability interpolation method based on a space-time cube (STCPI) to remove clouds completely after combining Terra and Aqua data. First, the conditional probability that the central pixel and every neighboring pixel in a space-time cube of 5 × 5 × 5 with the same snow condition is counted. Then the snow probability of the cloud pixels reclassified as snow is calculated based on the space-time cube. Finally, the snow condition of the cloud pixels can be recovered by snow probability. The validation experiments with the cloud assumption indicate that STCPI can remove clouds completely and achieve an overall accuracy of 97.44% under different cloud fractions. The generated daily cloud-free MODIS SCE products have a high agreement with the Landsat–8 OLI image, for which the overall accuracy is 90.34%. The snow cover variation in northern Xinjiang, China, from 2001 to 2018 was investigated based on the snow cover area (SCA) and snow cover days (SCD). The results show that the interannual change of SCA gradually decreases as the elevation increases, and the SCD and elevation have a positive correlation. Furthermore, the interannual SCD variation shows that the area of increase is higher than that of decrease during the 18 years.

ACS Style

Siyong Chen; Xiaoyan Wang; Hui Guo; Peiyao Xie; Jian Wang; Xiaohua Hao. A Conditional Probability Interpolation Method Based on a Space-Time Cube for MODIS Snow Cover Products Gap Filling. Remote Sensing 2020, 12, 3577 .

AMA Style

Siyong Chen, Xiaoyan Wang, Hui Guo, Peiyao Xie, Jian Wang, Xiaohua Hao. A Conditional Probability Interpolation Method Based on a Space-Time Cube for MODIS Snow Cover Products Gap Filling. Remote Sensing. 2020; 12 (21):3577.

Chicago/Turabian Style

Siyong Chen; Xiaoyan Wang; Hui Guo; Peiyao Xie; Jian Wang; Xiaohua Hao. 2020. "A Conditional Probability Interpolation Method Based on a Space-Time Cube for MODIS Snow Cover Products Gap Filling." Remote Sensing 12, no. 21: 3577.

Journal article
Published: 28 August 2020 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Northeast China is one of the primary snow-covered regions' and its forest coverage is over 40%. Forest snow identification is usually a challenge problem, and the SNOMAP algorithm tends to underestimate the amount of snow cover in forest regions for lower normalized difference snow index (NDSI). In this paper, an improved method of snow cover identification based on the Landsat Operational Land Imager (OLI) is proposed. One improvement includes using the normalized difference forest snow index (NDFSI) to discriminate between snow-covered and snow-free forests. The threshold value of the NDFSI in different forest types is set according to the normalized difference vegetation index (NDVI). On the other hand, the sun elevation is very low in winter in Northeast China with high latitude, as a result, the snow in shadow areas is usually classified as liquid water for its low near infrared (NIR) reflectance in the current SNOMAP algorithm. Then another improvement is introducing the land surface temperature (LST) which is retrieved from the thermal infrared band to distinguish liquid water from snow in shadow areas. We applied this improved method to evaluate forest areas in the Daxinganling, Xiaoxinganling and Changbai Mountain areas in different seasons. The total classification accuracy reached 97.5%, and the pixels that introduce omission error and commission error were mainly distributed in areas of dense forest shadows. This improved method retains the computational simplicity and effectiveness of the SNOMAP algorithm in non-forest areas and improves the underestimation of snow cover in forest regions and shadow areas.

ACS Style

Xiaoyan Wang; Siyong Chen; Jian Wang. An Adaptive Snow Identification Algorithm in the Forests of Northeast China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 5211 -5222.

AMA Style

Xiaoyan Wang, Siyong Chen, Jian Wang. An Adaptive Snow Identification Algorithm in the Forests of Northeast China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):5211-5222.

Chicago/Turabian Style

Xiaoyan Wang; Siyong Chen; Jian Wang. 2020. "An Adaptive Snow Identification Algorithm in the Forests of Northeast China." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 5211-5222.

Journal article
Published: 14 May 2020 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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The normalized difference snow index (NDSI) is the most popular snow detection index. Due to cloud cover, it is difficult to produce complete and gap-free NDSI datasets. In this study, a spatial and temporal adaptive gap-filling method (STAGFM) is developed, whereby a weighted cloud-free similar pixel function is established for NDSI prediction. Cloud-covered NDSI gaps are filled by combining daily MOD10A1 and MYD10A1, and adjacent temporal composite (ATC) is applied. STAGFM is implemented with long-time interval data to completely recover NDSI gaps. Moderate Resolution Imaging Spectroradiometer (MODIS) NDSI product data (from 1 November 2017 to 31 March 2018) of Northeast China are chosen as an example, and daily cloud-free NDSI time series over this period are produced. The method effectiveness was validated by cloud assumption and snow depth (SD) data, the results show that STAGFM completely removes clouds and achieves an average correlation coefficient (r), root-mean-square error (RMSE) and mean absolute error (MAE) of 0.95, 0.08 and 0.06, respectively.

ACS Style

Siyong Chen; Xiaoyan Wang; Hui Guo; Peiyao Xie; Abuobaida M. Sirelkhatim. Spatial and Temporal Adaptive Gap-Filling Method Producing Daily Cloud-Free NDSI Time Series. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 2251 -2263.

AMA Style

Siyong Chen, Xiaoyan Wang, Hui Guo, Peiyao Xie, Abuobaida M. Sirelkhatim. Spatial and Temporal Adaptive Gap-Filling Method Producing Daily Cloud-Free NDSI Time Series. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):2251-2263.

Chicago/Turabian Style

Siyong Chen; Xiaoyan Wang; Hui Guo; Peiyao Xie; Abuobaida M. Sirelkhatim. 2020. "Spatial and Temporal Adaptive Gap-Filling Method Producing Daily Cloud-Free NDSI Time Series." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 2251-2263.

Journal article
Published: 25 April 2018 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Seasonal snow cover is a critical component of the energy and water budgets of mountainous watersheds. Capturing the snow cover in complex environments is crucial for monitoring and understanding the temporal and spatial effects of climate change on alpine snow cover. The normalized difference snow index (NDSI) can be used to effectively and accurately estimate snow cover information from satellite images. However, the NDSI has limited utility for estimating the snow cover in heavily forested areas and relating this information to snowmelt-based runoff. In this study, a new algorithm based on a multi-index technique is proposed. The technique combines the NDSI, the normalized difference forest snow index, and the normalized difference vegetation index, and decision rules are established to increase the accuracy of snow mapping in forested areas. The new algorithm based on a multi-index technique is tested in the mountainous forested areas of North Xinjiang, China. In a winter image with full snow and a spring image with patchy snow, most of the forest snow, which is underestimated by the NDSI, is recognized by the multi-index technique. The accuracy of snow detection in forested areas is more than 90%. Additionally, in an experiment using a summer image without snow in forested areas no commission errors were detected. The snow detection algorithm based on a multi-index technique uses a simple set of decision rules for snow and can be run automatically without a priori knowledge of the surface characteristics.

ACS Style

Xiaoyan Wang; Jian Wang; Tao Che; Xiaodong Huang; Xiaohua Hao; Hongyi Li. Snow Cover Mapping for Complex Mountainous Forested Environments Based on a Multi-Index Technique. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2018, 11, 1433 -1441.

AMA Style

Xiaoyan Wang, Jian Wang, Tao Che, Xiaodong Huang, Xiaohua Hao, Hongyi Li. Snow Cover Mapping for Complex Mountainous Forested Environments Based on a Multi-Index Technique. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2018; 11 (5):1433-1441.

Chicago/Turabian Style

Xiaoyan Wang; Jian Wang; Tao Che; Xiaodong Huang; Xiaohua Hao; Hongyi Li. 2018. "Snow Cover Mapping for Complex Mountainous Forested Environments Based on a Multi-Index Technique." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, no. 5: 1433-1441.

Journal article
Published: 18 December 2015 in Remote Sensing
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The Normalized Difference Snow Index (NDSI) is an effective index for snow-cover mapping at large scales, but in forested regions the identification accuracy for snow using the NDSI is low because of forest cover effects. In this study, typical evergreen coniferous forest zones on Qilian Mountain in the Upper Heihe River Basin (UHRB) were chosen as example regions. By analyzing the spectral signature of snow-covered and snow-free evergreen coniferous forests with Landsat Operational Land Imager (OLI) data, a novel spectral band ratio using near-infrared (NIR) and shortwave infrared (SWIR) bands, defined as (ρnir − ρswir)/(ρnir + ρswir), is proposed. Our research shows that this band ratio, named the normalized difference forest snow index (NDFSI), can be used to effectively distinguish snow-covered evergreen coniferous forests from snow-free evergreen coniferous forests in UHRB.

ACS Style

Xiao-Yan Wang; Jian Wang; Zhi-Yong Jiang; Hong-Yi Li; Xiao-Hua Hao. An Effective Method for Snow-Cover Mapping of Dense Coniferous Forests in the Upper Heihe River Basin Using Landsat Operational Land Imager Data. Remote Sensing 2015, 7, 17246 -17257.

AMA Style

Xiao-Yan Wang, Jian Wang, Zhi-Yong Jiang, Hong-Yi Li, Xiao-Hua Hao. An Effective Method for Snow-Cover Mapping of Dense Coniferous Forests in the Upper Heihe River Basin Using Landsat Operational Land Imager Data. Remote Sensing. 2015; 7 (12):17246-17257.

Chicago/Turabian Style

Xiao-Yan Wang; Jian Wang; Zhi-Yong Jiang; Hong-Yi Li; Xiao-Hua Hao. 2015. "An Effective Method for Snow-Cover Mapping of Dense Coniferous Forests in the Upper Heihe River Basin Using Landsat Operational Land Imager Data." Remote Sensing 7, no. 12: 17246-17257.