Detection of Tailings Dams Using High-Resolution Satellite Imagery and a Single Shot Multibox Detector in the Jing–Jin–Ji Region, China

A remote sensing analysis method for soil heavy metal pollution sources at site scale considering source-sink relationships

Conventional methods for identifying soil heavy metal (HM) pollution sources are limited to area scale, failing to accurately pinpoint sources at specific sites due to the spatial heterogeneity of HMs in mining areas. Furthermore, these methods primarily focus on existing solid waste polluted dumps, defined as "direct pollution sources", while neglecting existing HM pollution hotspots generated by historical anthropogenic activities (e.g., mineral development, industrial discharges), defined as "potential pollution sources". Addressing this gap, a novel remote sensing analysis method is proposed to identify both direct and potential pollution sources at site scale, considering source-sink relationships. Direct pollution sources are extracted using a supervised classification algorithm on high-resolution multispectral imagery. Potential pollution sources depend on the spatial distribution of HM pollution, mapped using a machine learning model with hyperspectral imagery. Additionally, a source identification algorithm is developed for gridded pollution source analysis. Validated through a case study in a cadmium (Cd)-polluted mine area, the proposed method successfully extracted 21 solid waste polluted dumps with an overall accuracy approaching 90 % and a Kappa coefficient of 0.80. Simultaneously, 4167 HM pollution hotspots were identified, achieving optimal inversion accuracy for Cd (Rv2 = 0.91, RMSEv = 4.27, and RPDv = 3.02). Notably, the spatial distribution patterns of these identified sources exhibited a high degree of similarity. Further analysis employing the identification algorithm indicated that 3 polluted dumps and 258 pollution hotspots were pollution sources for a selected high-value point of Cd content. This innovative method provides a valuable methodological reference for precise prevention and control of soil HM pollution.

Impacts of metal mining on river systems: a global assessment

An estimated 23 million people live on floodplains affected by potentially dangerous concentrations of toxic waste derived from past and present metal mining activity. We analyzed the global dimensions of this hazard, particularly in regard to lead, zinc, copper, and arsenic, using a georeferenced global database detailing all known metal mining sites and intact and failed tailings storage facilities. We then used process-based and empirically tested modeling to produce a global assessment of metal mining contamination in river systems and the numbers of human populations and livestock exposed. Worldwide, metal mines affect 479,200 kilometers of river channels and 164,000 square kilometers of floodplains. The number of people exposed to contamination sourced from long-term discharge of mining waste into rivers is almost 50 times greater than the number directly affected by tailings dam failures.

Development of a geospatial database of tailing storage facilities in Australia using satellite images

Tailings storage facilities (TSFs) are the main source of pollution from mining operations. However, TSFs are increasingly being considered as the potential secondary sources of some critical minerals. Recovering the critical minerals from TSFs is important due to both environmental and economic implications. Yet, identification of the potential TSFs is the major challenge in this venture due to the lack of publicly available database of TSFs. The objective of this study was to identify the TSFs and document their status in the form of a database for Australia. Visual inspection and interpretation of satellite images in Google Earth were used to identify the TSFs in 6 states and the publicly available database of TSFs for Western Australia (WA) was validated in this study to incorporate into a national-level database. This study has identified 331 active and 759 inactive TSFs in Australia. Among the sites, 42 active and 56 inactive mine sites with TSFs were found within 2 km of urban centres in the studied states. Coal and gold were the major commodities of 27% of active mine sites with the TSFs and 38% of inactive mine sites with TSFs, respectively. Approximately 16% of active mine sites with TSFs and 28% of inactive mine sites with TSFs were found to process copper as a major commodity. Considering the companionability matrix, many of these TSFs could be explored for the possible recovery of critical minerals (e.g. rare earth elements, cobalt). This study has developed a national-level database of TSFs for Australia for the first time, and it could be used for a number of applications.

A Full-Spectrum Registration Method for Zhuhai-1 Satellite Hyperspectral Imagery

Accurate registration is an essential prerequisite for analysis and applications involving remote sensing imagery. It is usually difficult to extract enough matching points for inter-band registration in hyperspectral imagery due to the different spectral responses for land features in different image bands. This is especially true for non-adjacent bands. The inconsistency in geometric distortion caused by topographic relief also makes it inappropriate to use a single affine transformation relationship for the geometric transformation of the entire image. Currently, accurate registration between spectral bands of Zhuhai-1 satellite hyperspectral imagery remains challenging. In this paper, a full-spectrum registration method was proposed to address this problem. The method combines the transfer strategy based on the affine transformation relationship between adjacent spectrums with the differential correction from dense Delaunay triangulation. Firstly, the scale-invariant feature transform (SIFT) extraction method was used to extract and match feature points of adjacent bands. The RANdom SAmple Consensus (RANSAC) algorithm and the least square method is then used to eliminate mismatching point pairs to obtain fine matching point pairs. Secondly, a dense Delaunay triangulation was constructed based on fine matching point pairs. The affine transformation relation for non-adjacent bands was established for each triangle using the affine transformation relation transfer strategy. Finally, the affine transformation relation was used to perform differential correction for each triangle. Three Zhuhai-1 satellite hyperspectral images covering different terrains were used as experiment data. The evaluation results showed that the adjacent band registration accuracy ranged from 0.2 to 0.6 pixels. The structural similarity measure and cosine similarity measure between non-adjacent bands were both greater than 0.80. Moreover, the full-spectrum registration accuracy was less than 1 pixel. These registration results can meet the needs of Zhuhai-1 hyperspectral imagery applications in various fields.