Abstract
Mining activities and associated actions cause land-use/land-cover (LULC) changes across the world. The objective of this study were to evaluate the historical impacts of mining activities on surface biophysical characteristics, and for the first time, to predict the future changes in pattern of vegetation cover and land surface temperature (LST). In terms of the utilized data, satellite images of Landsat, and meteorological data of Sungun mine in Iran, Athabasca oil sands in Canada, Singrauli coalfield in India and Hambach mine in Germany, were used over the period of 1989–2019. In the first step, the spectral bands of Landsat images were employed to extract historical LULC changes in the study areas based on the homogeneity distance classification algorithm (HDCA). Thereafter, a CA-Markov model was used to predict the future of LULC changes based on the historical changes. In addition, LST and vegetation cover maps were calculated using the single channel algorithm, and the normalized difference vegetation index (NDVI), respectively. In the second step, the trends of LST and NDVI variations in different LULC change types and over different time periods were investigated. Finally, a CA-Markov model was used to predict the LST and NDVI maps and the trend of their variations in future. The results indicated that the forest and green space cover was reduced from 9.95 in 1989 to 5.9 Km2 in 2019 for Sungun mine, from 42.14 in 1999 to 33.09 Km2 in 2019 for Athabasca oil sands, from 231.46 in 1996 to 263.95 Km2 in 2016 for Singrauli coalfield, and from 180.38 in 1989 to 133.99 Km2 in 2017 for Hambach mine, as a result of expansion and development of of mineral activities. Our findings about Sungun revealed that the areal coverage of forest and green space will decrease to 15% of the total study area by 2039, resulting in reduction of the mean NDVI by almost 0.06 and increase of mean standardized LST from 0.52 in 2019 to 0.61 in 2039. our results further indicate that for Athabasca oil sands (Singrauli coalfield, Hambach mine), the mean values of standardized LST and NDVI will change from 0.5 (0.44 and 0.4) and 0.38 (0.38, 0.35) in 2019 (2016, 2017) to 0.57 (0.5, 0.47) and 0.33 (0.32, 0.28), in 2039 (2036, 2035), respectively. This can be mainly attributed to the increasing mining activities in the past as well as future years. The discussion and conclusions presented in this study can be of interest to local planners, policy makers, and environmentalists in order to observe the damages brought to the environment and the society in a larger picture.
Highlights
Over the last three centuries, the Earth’s surface has changed significantly due to the human activities (Hurtt et al, 2011; Crutzen, 2016)
In order to achieve the designated objectives of this study, a set of tasks, as presented in Fig. 3, were followed: a) the optical bands of Landsat images were employed to extract LULC changes from 1989 to 2019 based on the homogeneity distance classification algorithm (HDCA); b) the LULC changes by future were predicted using the CAMarkov model; c) the land surface temperature (LST) and vegetation cover maps were calculated based on single channel algorithm and normalized difference vegetation index (NDVI), respectively for different dates; d) the LST and NDVI variations trend caused by each LULC changes type in different time periods were investigated; e) the CAMarkov model was employed to predict the future maps of LST and NDVI
This study represents an example of a mining activity in a precious national biosphere where its impacts on the surrounding sur face biophysical characteristics, in terms of changes in vegetation cover and LST, were investigated quantitatively
Summary
Over the last three centuries, the Earth’s surface has changed significantly due to the human activities (Hurtt et al, 2011; Crutzen, 2016). LULC changes might have positive or negative effects on natural resources at local and global scales (Mialhe et al, 2015; Symeonakis, 2016; Mousi vand and Arsanjani, 2019). Deforestation has caused a series of envi ronmental effects, including CO2 emissions, climate change, environ mental quality, biodiversity decline, surface ecological status, and surface biophysical characteristics (Harris et al, 2012; Peralta-Rivero et al, 2014; Mahmood et al, 2016). All these suggest that LULC change studies are highly important to understand their negative im pacts (e.g., on past and future deforestations) and their key driving factors. Several investigations have been conducted under both local and global scales (Hansen et al, 2008, 2013; Ernst et al, 2013)
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