Yunjia Wang

Mapping and Evaluation of NDVI Trends from Synthetic Time Series Obtained by Blending Landsat and MODIS Data around a Coalfield on the Loess Plateau

The increasingly intensive and extensive coal mining activities on the Loess Plateau pose a threat to the fragile local ecosystems. Quantifying the effects of coal mining activities on environmental conditions is of great interest for restoring and managing the local ecosystems and resources. This paper generates dense NDVI (Normalized Difference Vegetation Index) time series between 2000 and 2011 at a spatial resolution of 30 m by blending Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) data using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and further evaluates its capability for mapping vegetation trends around a typical coalfield on the Loss Plateau. Synthetic NDVI images were generated using (1) STARFM-generated NIR (near infrared) and red band reflectance data (scheme 1) and (2) Landsat and MODIS NDVI images directly as inputs for STARFM (scheme 2). By comparing the synthetic NDVI images with the corresponding Landsat NDVI, we found that scheme 2 consistently generated better results (0.70 < R2 < 0.76) than scheme 1 (0.56 < R2 < 0.70) in this study area. Trend analysis was then performed with the synthetic dense NDVI time series and the annual maximum NDVI (NDVImax) time series. The accuracy of these trends was evaluated by comparing to those from the corresponding MODIS time series, and it was concluded that both the trends from synthetic/MODIS NDVI dense time series and synthetic/MODIS NDVImax time series (2000–2011) were highly consistent. Compared to trends from MODIS time series, trends from synthetic time series are better able to capture fine scale vegetation changes. STARFM-generated synthetic NDVI time series could be used to quantify the effects of mining activities on vegetation, but the test areas should be selected with caution, as the trends derived from synthetic and MODIS time series may be significantly different in some areas.

Accurate Determination of Glacier Surface Velocity Fields with a DEM-Assisted Pixel-Tracking Technique from SAR Imagery

We obtained accurate, detailed motion distribution of glaciers in Central Asia by applying digital elevation model (DEM) assisted pixel-tracking method to L-band synthetic aperture radar imagery. The paper firstly introduces and analyzes each component of the offset field briefly, and then describes the method used to efficiently and precisely compensate the topography-related offset caused by the large spatial baseline and rugged terrain with the help of DEM. The results indicate that the rugged topography not only forms the complex shapes of glaciers, but also affects the glacier velocity estimation, especially with large spatial baseline. The maximum velocity, 0.85 m∙d−1, was observed in the middle part on the Fedchenko Glacier, which is the world’s longest mountain glacier. The motion fluctuation on its main trunk is apparently influenced by mass flowing in from tributaries, as well as angles between tributaries and the main stream. The approach presented in this paper was proved to be highly appropriate for monitoring glacier motion and will provide valuable sensitive indicators of current and future climate change for environmental analysis.

Large deformation monitoring over a coal mining region using pixel-tracking method with high-resolution Radarsat-2 imagery

ABSTRACT Differential synthetic aperture radar interferometry (D-InSAR) is limited when exploited in high-intensity mining areas, because large deformation gradients lie beyond the maximum measurable value of the D-InSAR technique which breaks the prerequisite for successfully employing of the method. The SAR amplitude-based pixel-tracking method provides an alternative way to efficiently and robustly extract the large deformation distribution particularly when the D-InSAR technique is limited by loss of coherence. In addition, the deformation in the line-of-sight direction and the deformation along the azimuth direction are also presented in this paper with 24-day interval repeat-pass high-resolution Rardarsat-2 imagery. Combining both of these techniques can help to better understand the deformation mechanisms associated with underground mining activities. The accuracies of 0.12 m in slant-range direction and 0.19 m in the azimuth direction were achieved, respectively. Besides, the profiles across the maximum deformation region have verified that the deformation occurred during two acquisition periods is far beyond the theoretical maximum deformation gradient corresponding to high-resolution C-band SAR data. The obtained surface motion infers to the mining activities and assessed damage caused by the large deformation.

Reconstructing the Vertical Component of Ground Deformation from Ascending ALOS and Descending ENVISAT Datasets—A Case Study in the Cangzhou Area of China

Abstract. Due to the regular revisit periods of SAR platforms and the decorrelation of SAR images, the estimation of ground deformation retrieved from a particular SAR dataset may be of low resolution in both spatial and temporal dimensions. To enhance the spatial and temporal sampling rates in ground deformation estimation, a multiplatform SAR dataset combination strategy is proposed, based on the Multi-Temporal InSAR (MTInSAR) technique and the Same Name target point Pairs (SNPs) combination approach. The combination strategy has been tested and applied in the Cangzhou area, which has suffered from ground subsidence due mainly to groundwater extraction. The derived results show that the deformation monitoring sampling rates can be improved in the space and time dimensions when compared to the results obtained from a single-platform SAR dataset, which makes the multiplatform SAR dataset combination strategy useful for uneven (spatially variable) and nonlinear (temporally variable) deformation detection. Moreover, the results derived from the combination strategy tend to be more reliable and have fewer noise points.

Monitoring of recent ground surface subsidence in the Cangzhou region by the use of the InSAR time-series technique with multi-orbit Sentinel-1 TOPS imagery

ABSTRACT By collecting 39 scenes of descending images and 26 scenes of ascending images of Sentinel-1 synthetic aperture radar (SAR), we monitored the ground subsidence situation of Cangzhou in China during the period from March 2015 to February 2017 with the advanced synthetic aperture radar interferometry (InSAR) time-series technique and obtained the time-series subsidence rates of Cangzhou. We then selected the two sets of results of the monitoring obtained during the same period (from July 2015 to December 2016) to verify the results’ accuracy, considering three overlapping areas (Xinhua District, Botou County, and Dongguang County). This analysis clearly indicated that both types of results have good consistency, and the maximum subsidence occurred in Dongguang County. By further study of the central area of Dongguang and the related urban development, we found that the subsidence reached about 80.0 mm over the study period and there was a close relationship between the subsidence trend and the main direction of the city development. Moreover, by combining the two sets of results, we confirmed that there has been subsidence of the high-speed railway line in the whole of the Cangzhou area, among which the most obvious subsidence has occurred in Dongguang and Qing Counties. Finally, it was proved that the Sentinel-1 data can be used to monitor ground surface subsidence, and the data are especially effective in identifying persistent scatterer points along a linear feature. Therefore, this article could provide reliable data to assist with important decisions about urban development projects in the Cangzhou area in the next few years.

Integration of D-InSAR and GIS technology for identifying illegal underground mining in Yangquan District, Shanxi Province, China

The illegal mining events could be found in coal-rich regions around the world, which could not only seriously damage mineral resources and ecological environment, but also cause mine disasters and great economic loss, as well as threatening safety production and social stability. Due to wide distribution of mines and strong concealment of underground illegal mining activities, it is hard to find out these behaviors promptly and accurately depending only on mine law-enforcing departments whose investigations will be time–energy–finance-consuming. Therefore, it is an urgent problem to quickly and accurately identify illegal mining events. To solve the problem, this paper uses the new mining subsidence monitoring by D-InSAR to accurately get the surface deformation and establishes a space–time relationship model of surface deformation and underground mining characterized by subsidence. On the basis of this, the integration of D-InSAR and GIS technology is used to develop a quick, efficient, and accurate way to identify illegal underground mining areas. Then, a case study is conducted in the district of Yangquan, Shanxi Province, China. The identification results have been compared with the data about illegal mining by local law-enforcing departments during the same period. The research results indicate that the identification results are basically the same as the actual illegal mining events. Therefore, the proposed method based on integration of D-InSAR and GIS technology could be utilized for real-time and dynamic monitoring of illegal mining events. The results could also provide important technical support in guiding mine law-enforcing departments to timely crack down and remove illegal underground mining events, maintain mining orders, and protect the ecological environment.

Effects of underground mining on vegetation and environmental patterns in a semi-arid watershed with implications for resilience management

A rapid increase in underground mining in a semi-arid area of China has led to serious concerns about the health of vegetation overlying these coal seams. However, there have been no empirical studies to illustrate the response and persistence of surface vegetation in these underground mining areas. A combination of field assessments with remote sensing was used to examine vegetation patterns and responses to underground mining, while laying a foundation for environmental protection. The study area lies in a vulnerable watershed exposed to hazards caused by underground coal mining, located on the southern edge of Inner Mongolia in China. The results demonstrate that hydrological factors and soil attributes, including groundwater levels, soil organic matter, and soil moisture, control the structure of the local vegetation community. After mining begins, the vegetation community index based on plant density, coverage, and biomass in areas affected by subsidence fractures decreases by 0–21.5%. Nevertheless, the average Normalized Differential Vegetation Index at the entire watershed scale increased by 15% from 2001 to 2016, although this change appeared to be primarily related to rainfall. This study confirmed that underground coal mining in the watershed has not caused extensive vegetation degradation as feared. Positive climatic trends, the maintenance of important mudstone strata below a phreatic aquifer and the adaptation of vegetation to drought, contributed to the persistence of surface vegetation in underground mining areas. Considering that mining activities usually last for several years, resilience management, including approaches such as protection of important variables, long-term monitoring, and adaptive management, should be adopted in support of conservation and sustainable mining in this watershed and at similar mine sites.