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Quantitative studies on how mining activities shape the evolution of regional landscape patterns can contribute to the scientific understanding of how mining cities develop. Based on the theories of life cycle and landscape ecology, this paper takes Jixi, a typical Chinese mining city, as a case study to analyze the landscape pattern features at di...
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... a total area of 2.250 million km 2 , Jixi is located the southeast of Heilongjiang province, and has six districts: Jiguan, Hengshan, Didao, Lishu, and Mashan ( Figure 2). Jixi City has a cold temperate continental monsoon climate, and its terrain is dominated by mountains, hills, and plains. ...
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... The landscape pattern index has been widely used in landscape-scale spatial analysis because it is highly condensed with information related to the landscape pattern and can better describe the structural composition and spatial configuration of different landscape elements [23]. In the analysis of the spatial granularity effect of landscape pattern indices, scholars usually adopt the resampling method [25] to analyze the scale effect of the study area and use the area loss evaluation [26] and the inflection point identification methods of the landscape pattern response curve [27] to determine the suitable granularity of the study area landscape. Although current studies have explored the effects of suitable spatial grain size on landscape pattern changes from a landscape pattern perspective, the impact of scale effects on landscape ecological risk has been ignored. ...
Analysis and prediction of urban ecological risk are crucial means for resolving the dichotomy between ecological preservation and economic development, thereby enhancing regional ecological security and fostering sustainable development. This study uses Nanning, a Chinese landscape garden city, as an example. Based on spatial granularity and extent perspectives, using 30 m land use data, the optimal scale for an ecological risk assessment (ERA) and prediction is confirmed. This study also explores the patterns of spatial and temporal changes in ecological risk in Nanning on the optimal scale. At the same time, the Patch-generating Land Use Simulation model is used to predict Nanning’s ecological risk in 2036 under two scenarios and to propose ecological conservation recommendations in light of the study results. The study results show that: a spatial granularity of 120 m and a spatial extent of 7 km are the best scales for ERA and prediction in Nanning. Although the spatial distribution of ecological risk levels is obviously different, the overall ecological risk is relatively low, and under the scenario of ecological protection in 2036, the area of high ecological risk in Nanning is small. The results can provide theoretical support for ERA and the prediction of landscape cities and ecological civilization construction.
... The development of mining cities can be classified into three stages: lead-in period, growth period, mature period, and transition-decay period (Krzemińska et al., 2022;Shang et al., 2022;Takam Tiamgne et al., 2022). Most Chinese mining cities, including Panzhihua city, are classified as in mature period, with much less ones in growth period and decay period. ...
Mining cities are clusters of communities that specialized in mining and extractive industries. The extensive mining activities in these cities have stimulated widespread and substantial ecological stresses to the surrounding environment, that significantly jeopardize the health condition of vegetation and human. Given the recent recognition of remote sensing in monitoring large-scale environmental change, we incorporated Ziyuan #1-02D, a recently released hyperspectral remote sensing data, into the ecological vulnerability assessment framework, using Panzhihua city as a case study, which is recognized as one of the most representative mining cities in China. The multi-spectral imaging data was widely applied in previous research. However, with the wide bands, multi-spectral imaging data cannot depict detailed characteristics of spectral targeted. As a result, we introduce indexes from the hyperspectral imaging to ecological vulnerability assessment proposed in this study, which can depict and monitor the growth and restoration of vegetation more accurately. We used the optimum index factor method to select bands from the satellite-based Ziyuan #1-02D data for quantifying vegetation indexes and red edge. Besides, we obtained inventory data, land-use, soil type, and typography of Panzhihua city to reconstruct its ecological vulnerability index (EVI) for 2020 and 2021. Comparing to the multi-spectral data, the ecological vulnerability results from hyperspectral imaging performed better in precision and concentration in EVI values, reaching the conclusions more directly. Specifically, the mining area and the relevant hazard types and impact areas were delineated through intensive fieldwork. Results suggested that the east and west districts, and north of Renhe district suffer great ecological stress, in which we observed intensive coal and metal-related mining industry. The central region, which occupies vanadium titanomagnetite mines, also shows substantial ecological issues, while the other mining industries, such as granite ore did not significantly influence the local environment. Although the newly released satellite-based data only have two-year periods, we still observed improving ecological conditions, with the southeast and west regions showing much lower ecological vulnerability values. The spatial autocorrelation analysis suggested that the high-high clustering region of EVI is located in the east, west, and Renhe districts, primarily due to the mining industries of variations scales. We also found that the clustering of low ecological vulnerable regions, mostly surrounds the vanadium titanomagnetite excavation industry, thanks to the local restoration projects.
