Long-term continuous monitoring of the mining activities in open-pit coal mines is conducive to planning and management of the mining operations. Additionally, this faciliatates assessment on their environmental impact and supervises illegal mining behaviors. Interferometric Synthetic Aperture Radar (InSAR) technology can be effectively applied in the monitoring of open-pit mines where vegetation is sparse and land cover is dominated by bare rock. The main objective of this study is to monitor the mining activities of four open-pit coal mines in the Wucaiwan mining area in China from 2018 to 2020, namely No. 1, No. 2 (containing two mining areas), and No. 3. We use the normalized differential activity index (NDAI) based on the coherence coefficient as an indicator of the mine activity due to its robustness to temporal and spatial decorrelation. After analyzing and removing the decorrelation caused by rain and snow weather, 70 NDAI images in 12-day intervals are obtained from Sentinel-1A InSAR coherence images. Then, the annually-averaged NDAI images are applied to an RGB composite technique (red for 2018, green for 2019, blue for 2020) to express the interannual variation of the mining activities. Points of interest are then selected for NDAI time series analysis. The RGB composite results indicated that No. 1 and 3 open-pit coal mines were continuously mined during the three years; whereas, the two mining areas of No. 2 were mainly active in 2018. The 12-day NDAI time-series graphs of No. 2 open-pit coal mine also indicate that the coal piles located in the coal transferring area of the first mining area were not completely removed until April 2019. It is also seen that the second mining area was decommissioned in November 2018 and became rehabilitated in July 2019. Results were validated using the Sentinel-2A images and related background information confirming the efficiency of the proposed approach for monitoring the mining activity in open-pit mines.
During unexpected earthquake catastrophes, timely identification of damaged areas is critical for disaster management. On the 24 March 2021, Baicheng county was afflicted by a Mw 5.3 earthquake. The disaster resulted in three deaths and many human injuries. As an active remote sensing technology independent of light and weather, the increasingly accessible Synthetic Aperture Radar (SAR) is an attractive data for assessing building damage. This paper aims to use Sentinel-1A radar images to rapidly assess seismic damage in the early phases after the disaster. A simple and robust method is used to complete the task of surface displacement analysis and building disaster monitoring. In order to obtain the coseismic deformation field, differential interferometry, filtering and phase unwrapping are performed on images before and after the earthquake. In order to detect the damage area of buildings, the Interferometric Synthetic Aperture Radar (InSAR) and Polarimetric Synthetic Aperture Radar (PolSAR) techniques are used. A simple and fast method combining coherent change detection and polarimetric decomposition is proposed, and the complete workflow is introduced in detail. In our experiment, we compare the detection results with the ground survey data using an unmanned aerial vehicle (UAV) after the earthquake to verify the performance of the proposed method. The results indicate that the experiment can accurately obtain the coseismic deformation field and identify the damaged and undamaged areas of the buildings. The correct identification accuracy of collapsed and severely damaged areas is 86%, and that of slightly damaged and undamaged areas is 84%. Therefore, the proposed method is extremely effective in monitoring seismic-affected areas and immediately assessing post-earthquake building damage. It provides a considerable prospect for the application of SAR technology.
The sliding distribution of convergent orogenic belt is the key to understanding the tectonic deformation and evolution mechanism of collision orogens (
Drought is a meteorological phenomenon that threatens ecosystems, agricultural production, and living conditions. Central Asia is highly vulnerable to drought due to its special geographic location, water resource shortages, and extreme weather conditions, and poor management of water resources and reliance on irrigated agriculture exacerbate the effects of drought. In this study, the latest version of the Global Land Data Assimilation System was employed to calculate the Standardized Precipitation Evapotranspiration Index at different time scales during the period from 1981 to 2020. The varimax Rotated Empirical Orthogonal Function was applied for subregional delineation of drought patterns in Central Asia, and various methods were employed for a comparative analysis of the spatiotemporal characteristics of drought in these Central Asian subregions. The results show that drought patterns vary considerably in the Central Asian subregions. Over the past 40 years, alternating wet and dry conditions occurred in Central Asia. North Kazakhstan experienced more drought events with lower severity. East and west differences appear after 2001, the west becoming drier and the east becoming wetter. Some regions near lakes, such as Balkhash, Issyk-Kul, and the Aral Sea, suffer from droughts of long duration and high severity. In the Tianshan region, droughts in the northern slopes occur more frequently, with shorter durations and higher intensity and peaks. Northwestern China and western Mongolia have extensive agricultural land and grasslands with highly fragile ecosystems that have become progressively drier since 2001.
Under climate change, the sea surface temperature and salinity change greatly, which poses a considerable threat to sustainable food security. Sea surface temperature and salinity (SST/SSS) are selected to examine the annual output of swimming crab in 24 cities along the eastern China. The Copula-based function was used to construct the probability distribution model of the swimming crab yield with SST and SSS. The pure premium rate of the swimming crab production in these 24 cities are also examined. The results show that 1) There is significant positive correlations between the yield of swimming crab with temperature and salinity over the study area. The only exception is that the correlation between yield of swimming crab and salinity is not significant in the south of study area. 2) The span of the pure insurance premium rate of swimming crab in 24 cities increases rapidly with the increase of the protection level, the maximum span up to 2.04%, and the minimum span is only 1.6%. 3) The distribution of the swimming crab insurance premium rate is various in space. The insurance premium rate of 8 cities in the south of Taizhou is low with the highest premium rate at 5.6%. The insurance premium rate of 16 cities in north of Taizhou is relatively high with the rate between 6%-22%. The research can provide a theoretical basis for the pricing of insurance products for swimming crab in 24 cities in the typical aquaculture areas in eastern China.
Crop production security is an essential guarantee for the prosperity of country and the well-being of people. In the context of frequent occurrence of extreme weather events, how to comprehensively evaluate the risk of multiple hazards and quantitatively describe spatial differences will be one of the significant topics in agricultural disaster mitigation. In this study, based on the gridded meteorological data and other datasets (crop, socioeconomic, disaster loss, and actual planting location) from 2000 to 2020, multihazard risk model for different growth period was built to assess the comprehensive risk of winter wheat in the upper Huaihe River basin (UHRB), China. The conclusions were as follows: (a) The spatial distribution of continuous rain risk increased gradually from north to south; the area of the highest lodging risk was located in the east; the spatial trend of drought risk was the highest in the south and the lowest in the northeast. (b) The weights of continuous rain, lodging, and drought disasters were 41, 31, and 28%, respectively, which indicated that winter wheat in UHRB was most affected by continuous rain during the harvest period. (c) The spatial distribution of comprehensive disaster risk assessment showed that the higher risk areas were located in the central and southern parts (Huaibin, Linquan, Luoshan). After verification by historical disasters information, the results are credible, providing a concrete scientific basis for implementing regional agricultural disaster response measures.
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