Abstract
Long-term surface mining and subsequent vegetation recovery greatly alter land cover types, reshape landscape patterns and impose several impacts on local ecosystem services. However, studies on the history of forest changes in mining areas from the 1960s to the present have not been reported. This study developed a new idea to investigate the spatial and temporal dynamics of forest cover in a mining area of Mufu Mountain (Mt. Mufu) from 1967 to 2019 by integrating Landsat and Corona data, and to explore the relationships among the forest changes, landscape structures and ecosystem functions. Firstly, we applied the vegetation change tracker (VCT) algorithm and visual interpretation to create annual forest change datasets. Subsequently, the forest loss process was divided into subdivision, shrinkage, perforation and attrition components. An improved forest restoration model in this study extended the recovery process to bridge, branch, infilling and increment components. Finally, remote sensing variables and crown density were coupled to assess the forest aboveground biomass (AGB) to reflect the ecosystem function in the restoration area. Results showed that the combined use of Corona and the dense time series of Landsat can provide more detailed information on forest changes. Forest cover sharply decreased from 343.89 in 1967 to 298.44 ha in 1990, and after 2003, the forest area substantially increased and finally reached a maximum of 434.16 ha in 2019. Subdivision and bridge not only occupied the larger areas in the process of forest loss and restoration, but also they had strong correlations with forest changes and the Pearson correlation coefficients (r) were respectively 0.96 and 0.91. These all revealed that forest changes mainly affected landscape structure connectivity. The total forest AGB of Mt. Mufu increased from 20,173.35 in 2006 to 31,035.77 t in 2017, but the increases in AGB were only 30-40 t/ha in most recovery areas with high structure connectivity (bridge regions), indicating there is room for improving restoration projects in the future. The obtained findings can provide mining site restoration managers with clear, long-term forest change information and mine restoration assessment methods.
Highlights
Deforestation, the subsequent increase in degraded landscapes and the resulting loss of forest ecological functions are some of the most severe environmental challenges [1,2,3]
Traditional field measurement methods to dynamically monitor mining sites are usually restricted to small areas, and the time frequency of monitoring is low, which leads to an incomplete understanding of forest loss and recovery processes [11]
Accuracy Assessment for Ortho-Rectification and Forest Cover where y is the observed aboveground biomass (AGB), y is the predicted AGB based on the model, y is the mean of all obseWrveedgeAnGerBataendd1n1 iGsCthPesnounmthbeeroortfhsoa-mrepclteifis.ed Corona image and Landsat to assess the errors of ortho-rectification
Summary
Deforestation, the subsequent increase in degraded landscapes and the resulting loss of forest ecological functions are some of the most severe environmental challenges [1,2,3]. Quantifying and analyzing the spatio-temporal dynamics of forest cover, clarifying the landscape spatial process and assessing the ecosystem functions of restored mining areas can help decision makers evaluate the restoration progress of the mining area, draw lessons from the past and propose reasonable management strategies. Certain studies have applied the technique to forest cover monitoring in opencast mining areas, which can be summarized into three categories: (1) Spatio-temporal analysis [6,10,15]. These studies aimed to analyze the location and time of forest changes in mining areas based on automated or semi-automated algorithms. The resampled forest covers from Corona data were overlaid with those from Landsat to quantify forest cover changes from 1967 to 2019
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