Mapping hydrothermal alteration is an important means of mineral exploration, therefore, effective improvement of the accuracy of identification of hydrothermal alteration minerals is a hot topic. In this study, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Sentinel-2A data were selected for the Pulang copper deposit, to improve the accuracy of hydrothermal alteration mineral mapping. Three data fusion methods of Principal Component Analysis (PCA), Gram–Schmidt (GS) and High-Pass Filtering (HPF) were employed, and method of spectral matching was introduced to obtain high spatial resolution and high spectral fidelity fused data. The comparison showed that the HPF fusion method had the best effect. The fused data by the HPF fusion method were then implemented to map hydrothermal alteration minerals by using a multifractal-based method of PCA + Spectrum–Area. Through the verification of a field investigation, the results exhibited a similar hydrothermal alteration distribution, compared with the results obtained from the original ASTER data, but with higher identification accuracy. Therefore, the case study strongly suggests that the image fusion method with spectral matching is an effective tool to increase spatial resolution, while maintaining high spectral fidelity. Thus, it is a valuable and economic method for improving the identification accuracy of hydrothermal alteration minerals. HIGHLIGHTSThe spectral matching method was introduced to unify the surface reflectance of ASTER and Sentinel-2A data.Principal Component Analysis (PCA), Gram-Schmidt (GS) and High-Pass Filtering (HPF) methods were employed to fuse AST ER and Sentinel-2A data.The results show that the extraction accuracy of hydrothermal alteration minerals based on AST ER - Sentinel-2A fused data has 6.98% higher than that of original ASTER data.
A coastline is the boundary zone between land and sea, an active zone of human social production activities and an area where the ecology is fragile and easy to change. The traditional method to analyze temporal and spatial changes in the coastline is to extract the coastline through remote sensing, LiDAR, and field sampling and analyze the temporal and spatial changes with statistical data. The coastline extracted by these methods has high spatial and temporal resolution, but it requires remote sensing images and data obtained by other sensors, so it is impossible to extract coastlines from before the emergence of remote sensing technology. This paper improves the coastline generation algorithm. Firstly, a triangulated irregular network is used to generate the preliminary rough coastline, and then, each line segment is optimized with Python language according to the influence range of the place names to further approach the real coastline. The accuracy of the coastline extracted by this method can reach 80% within 500 m, which is of great significance in the mapping and analysis of small- and medium-scale coastlines. This paper analyzes the changes in the coastline of Hainan Island before the founding of China (pre-founding) and in modern times and analyzes the impact of coastal development on coastline change. Through the analysis, it is found that, from before the founding of the People’s Republic of China to the present, the natural coastline of Hainan Island has become shorter, the artificial coastline has become longer, and the coastline generally presents a trend of advancing toward the ocean. This method realizes coastline construction under the condition of missing remote sensing images and puts forward a new way to study historical coastline changes.
Hydrothermal alteration minerals are an effective prospecting indicator. Advanced spaceborne thermal emission and reflection radiometer (ASTER) satellite data are some of the most commonly adopted multispectral data for the mapping of hydrothermal alteration minerals. Compared to multispectral data, hyperspectral data have stronger ground object recognition ability. Chinese Gaofen-5 (GF-5) is the first hyperspectral satellite independently developed by China that has the advantages of both wide-width and high-spectral-resolution technology. However, the mapping ability of GF5 data for hydrothermal alteration minerals requires further study. In this study, ASTER and GF-5 satellite data were implemented to map hydrothermal alteration minerals in the Longtoushan Pb-Zn deposit, SW China. Selective principal component analysis (SPCA) technology was employed to map iron oxide/hydroxides, argillic, quartz, and carbonate minerals at the pixel level using ASTER data, and the mixture tuned matched filtering (MTMF) method was implemented for the extracted hematite, kaolinite, calcite, and dolomite at the sub-pixel level using GF-5 data. When mapping the hydrothermal alteration minerals, the distribution features of the hydrothermal alteration minerals from the Longtoushan Pb-Zn deposit were systematically revealed. A comprehensive field investigation and petrographic study were conducted to verify the extraction accuracy of the hydrothermal alteration minerals. The results showed that the overall accuracies for the ASTER and GF-5 data were 82.6 and 92.9 and that the kappa coefficients were 0.78 and 0.90, respectively. This indicates that the GF-5 data are able to map hydrothermal alteration minerals well and that they can be promoted as a hyperspectral data source for mapping systematic hydrothermal alteration minerals in the future.
Wild edible mushrooms are a characteristic product in Yunnan, but no quantitative evaluation system yet exists for them. This study puts forward a sustainable development potential index of characteristic agricultural products (SDPI) based on various methods. It also performs a correlation analysis of multi-source points of interest (POI) and online shopping data related to wild edible mushrooms in Yunnan from a quantitative point of view, to understand the economics of wild edible mushrooms and to explore the sustainable development potential of such mushrooms in Yunnan from the perspectives of the tourism and sales markets. The results show that Dêqên Tibetan and the central region dominated by Kunming dominate both the tourism and sales markets and have a high SDPI. In contrast, the current situation and development prospects of the wild edible mushroom market in cities such as Lincang and Nujiang Lisu are poor. Yunnan Province has a large wild edible mushroom market and a promising development prospect. This paper provides comprehensive reference information for the development of Yunnan wild edible mushroom production economics.
China’s rapid economic development has resulted in a series of serious environmental pollution problems, such as atmospheric particulate pollution. However, the socioeconomic factors affecting energy-related PM2.5 emissions are indistinct. Therefore, this study first explored the change in PM2.5 emissions over time in China from 1995 to 2012. Then the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model was adopted for quantitatively revealing the mechanisms of various factors on energy-related PM2.5 emissions. Finally, the Environmental Kuznets Curve (EKC) hypothesis was adopted to examine whether an EKC relationship between affluence and energy-related PM2.5 emissions is present from a multiscale perspective. The results showed that energy-related PM2.5 emissions in most regions showed an increasing trend over the study period. The influences of the increase in population, energy intensity, and energy use mix on energy-related PM2.5 emissions were positive and heterogeneous, and population scale was the major driving force of energy-related PM2.5 emissions. The effects of the increase in the urbanization level and the proportion of tertiary industry increased value to GDP on energy-related PM2.5 emissions varied from area to area. An inverse U-shape EKC relationship for energy-related PM2.5 emissions was not verified except for eastern China. The conclusions are valuable for reducing PM2.5 emissions without affecting China’s economic development.
The Huize Pb–Zn deposit is one of the largest and high–grade Pb–Zn deposits in the world, containing more than 7 Mt Pb and Zn reserves at an ore grade of ∼30–35 % Pb + Zn. Therefore, brownfield exploration of additional Pb and Zn orebodies at Huize Pb–Zn deposit is of high priority. Hydrothermal alteration minerals and structural features are typically associated with Pb–Zn mineralization, which can be identified quickly and economically using a multi-source remote sensing satellite dataset. In this study, Sentinel-2, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Gaofen-5 (GF-5) satellite remote sensing imagery were employed for brownfield exploration of additional Pb–Zn orebodies in the Huize Pb–Zn deposit. The Sentinel-2 satellite remote sensing imagery was used to interpret the structure and map oxides/hydroxides by implementing the band ratio (BR) and principal components analysis (PCA) method; the ASTER satellite data were applied to map argillic, quartz, and carbonate hydrothermal alteration minerals using the PCA method; the GF-5 satellite data were utilized to map hematite, kaolinite, calcite, and dolomite minerals by the mixture tuned matched filtering (MTMF) method; and the mapped hydrothermal alteration information was verified by field reconnaissance and laboratory analysis. Finally, remote sensing geological exploration characteristics of the Huize Pb–Zn deposit were summarized by comprehensively analyzing the hydrothermal alteration minerals and structural characteristics, and three new brownfield exploration target zones of Pb–Zn mineralization were delineated. The study can provide a reference for the brownfields exploration in the vicinity of an existing Pb–Zn deposit around the world.
Efficient and accurate identification of canopy gaps is the basis of forest ecosystem research, which is of great significance to further forest monitoring and management. Among the existing studies that incorporate remote sensing to map canopy gaps, the object-oriented classification has proved successful due to its merits in overcoming the problem that the same object may have different spectra while different objects may have the same spectra. However, mountainous land cover is unusually fragmented, and the terrain is undulating. One major limitation of the traditional methods is that they cannot finely extract the complex edges of canopy gaps in mountainous areas. To address this problem, we proposed an object-oriented classification method that integrates multi-source information. Firstly, we used the Roberts operator to obtain image edge information for segmentation. Secondly, a variety of features extracted from the image objects, including spectral information, texture, and the vegetation index, were used as input for three classifiers, namely, random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). To evaluate the performance of this method, we used confusion matrices to assess the classification accuracy of different geo-objects. Then, the classification results were screened and verified according to the area and height information. Finally, canopy gap maps of two mountainous forest areas in Yunnan Province, China, were generated. The results show that the proposed method can effectively improve the segmentation quality and classification accuracy. After adding edge information, the overall accuracy (OA) of the three classifiers in the two study areas improved to more than 90%, and the classification accuracy of canopy gaps reached a high level. The random forest classifier obtained the highest OA and Kappa coefficient, which could be used for extracting canopy gap information effectively. The research shows that the combination of the object-oriented method integrating multi-source information and the RF classifier provides an efficient and powerful method for extracting forest gaps from UAV images in mountainous areas.
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