Continuous Detection of Surface-Mining Footprint in Copper Mine Using Google Earth Engine

Socioeconomic development is often dependent on the production of mining resources, but both opencast and underground mining harm vegetation and the eco-environment. Under the requirements of the construction for ecological civilization in China, more attention has been paid to the reclamation of mines and mining management. Thus, it is the basement of formulating policies related to mining management and implementing reclamation that detection of mining disturbance rapidly and accurately. This research carries on an empirical study in the Dexing copper mine, Jiangxi, China, aiming at exploring the process of distance and reclamation. Based on the dense time-series stack derived from the Landsat archive on Google Earth Engine (GEE), the disturbance of surface mining in the 1986–2020 period has been detected using the continuous change detection and classification (CCDC) algorithm. The results are that: (1) the overall accuracy of damage and recovery is 92% and 88%, respectively, and the Kappa coefficient is 85% and 84% respectively. This means that we obtained an ideal detection effect; (2) the surface-mining area was increasing from 1986–2020 in the Dexing copper mine, and the accumulation of mining damage is approximately 2865.96 ha with an annual area of 81.88 ha. We also found that the area was fluctuating with the increase. The detected natural restoration was appraised at a total of 544.95 ha in the 1988–2020 period with an average restoration of 16.03 ha. This means that it just restores less in general; (3) it has always been the case that the Dexing mine is damaged by mining and reclamation in the whole year (it is most frequently damaged month is July). All imageries in the mine are detected by the CCDC algorithm, and they are classified as four types by disturbing number in pixel scale (i.e., 0, 1, 2, more than 2 times). Based on that, we found that the only once disturbed pixels account for 64.75% of the whole disturbed pixels, which is the majority in the four classes; (4) this method provides an innovative perspective for obtaining the mining disturbed dynamic information timely and accurately and ensures that the time and number of surface mining disturbed areas are identified accurately. This method is also valuable in other applications including the detection of other similar regions.

[1]  Na Li,et al.  Remote sensing monitoring recent rapid increase of coal mining activity of an important energy base in northern China, a case study of Mu Us Sandy Land , 2015 .

[2]  D. Lagomasino,et al.  The large footprint of small-scale artisanal gold mining in Ghana. , 2021, The Science of the total environment.

[3]  Xiaohua Tong,et al.  Optimized Sample Selection in SVM Classification by Combining with DMSP-OLS, Landsat NDVI and GlobeLand30 Products for Extracting Urban Built-Up Areas , 2017, Remote. Sens..

[4]  Thomas Hilker,et al.  Linking ground-based to satellite-derived phenological metrics in support of habitat assessment , 2012 .

[5]  Chengquan Huang,et al.  Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine , 2020 .

[6]  Saro Lee,et al.  Novel Credal Decision Tree-Based Ensemble Approaches for Predicting the Landslide Susceptibility , 2020, Remote. Sens..

[7]  Thomas R. Loveland,et al.  A review of large area monitoring of land cover change using Landsat data , 2012 .

[8]  Jun Chen,et al.  Analysis and Applications of GlobeLand30: A Review , 2017, ISPRS Int. J. Geo Inf..

[9]  Wenqi Chen,et al.  Mapping Annual Land Disturbance and Reclamation in a Surface Coal Mining Region Using Google Earth Engine and the LandTrendr Algorithm: A Case Study of the Shengli Coalfield in Inner Mongolia, China , 2020, Remote. Sens..

[10]  Tingting He,et al.  Mapping Paddy Rice with Sentinel-1/2 and Phenology-, Object-Based Algorithm - A Implementation in Hangjiahu Plain in China Using GEE Platform , 2021, Remote. Sens..

[11]  Zhe Zhu,et al.  Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications , 2017 .

[12]  Hui Luo,et al.  An efficient approach to capture continuous impervious surface dynamics using spatial-temporal rules and dense Landsat time series stacks , 2019, Remote Sensing of Environment.

[13]  S. Goward,et al.  An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks , 2010 .

[14]  Christopher E. Holden,et al.  Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000–2014) , 2016 .

[15]  Qikai Lu,et al.  Crops Fine Classification in Airborne Hyperspectral Imagery Based on Multi-Feature Fusion and Deep Learning , 2021, Remote. Sens..

[16]  K. Seto,et al.  Conceptualizing and characterizing micro-urbanization: A new perspective applied to Africa , 2019, Landscape and Urban Planning.

[17]  V. Radeloff,et al.  Mapping agricultural land abandonment from spatial and temporal segmentation of Landsat time series , 2018, Remote Sensing of Environment.

[18]  Rob J Hyndman,et al.  Detecting trend and seasonal changes in satellite image time series , 2010 .

[19]  Hankui K. Zhang,et al.  Using the 500 m MODIS Land Cover Product to Derive a Consistent Continental Scale 30 m Landsat Land Cover Classification , 2017 .

[20]  Jing Li,et al.  Identification of cultivated land change trajectory and analysis of its process characteristics using time-series Landsat images: A study in the overlapping areas of crop and mineral production in Yanzhou City, China. , 2022, The Science of the total environment.

[21]  Gemma Bell,et al.  Using Continuous Change Detection and Classification of Landsat Data to Investigate Long-Term Mangrove Dynamics in the Sundarbans Region , 2019, Remote. Sens..

[22]  Qiang Zhou,et al.  Lessons learned implementing an operational continuous United States national land change monitoring capability: The Land Change Monitoring, Assessment, and Projection (LCMAP) approach , 2020, Remote Sensing of Environment.

[23]  Qiang Zhou,et al.  Monitoring Landscape Dynamics in Central U.S. Grasslands with Harmonized Landsat-8 and Sentinel-2 Time Series Data , 2019, Remote. Sens..

[24]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[25]  Subodh Kumar Maiti,et al.  Assessment of the capability of remote sensing and GIS techniques for monitoring reclamation success in coal mine degraded lands. , 2016, Journal of environmental management.

[26]  Zhenqi Hu,et al.  Identification of waterlogging in Eastern China induced by mining subsidence: A case study of Google Earth Engine time-series analysis applied to the Huainan coal field , 2020 .

[27]  John L. Dwyer,et al.  Landsat: building a strong future , 2012 .

[28]  C. Woodcock,et al.  Monitoring temperate forest degradation on Google Earth Engine using Landsat time series analysis , 2021 .

[29]  C. Woodcock,et al.  Sources of bias and variability in long-term Landsat time series over Canadian boreal forests , 2016 .

[30]  Wu Xiao,et al.  Continues monitoring of subsidence water in mining area from the eastern plain in China from 1986 to 2018 using Landsat imagery and Google Earth Engine , 2021 .

[31]  D. Mulligan,et al.  Detecting the dynamics of vegetation disturbance and recovery in surface mining area via Landsat imagery and LandTrendr algorithm. , 2018 .

[32]  Wei Li,et al.  Satellite Image Time Series Decomposition Based on EEMD , 2015, Remote. Sens..

[33]  G. Sarp Determination of Vegetation Change Using Thematic Mapper Imagery in Afşin-Elbistan Lignite Basin; SE Turkey , 2012 .

[34]  Miaomiao Xie,et al.  Monitoring ecosystem restoration of multiple surface coal mine sites in China via LANDSAT images using the Google Earth Engine , 2021, Land Degradation & Development.

[35]  Jin Yu-jie RS and GIS Based Ecological Landscape Restoration in Xuzhou Northern Coal Mining Area , 2010 .

[36]  Clayton C. Kingdon,et al.  Changes in the extent of surface mining and reclamation in the Central Appalachians detected using a 1976-2006 Landsat time series , 2009 .

[37]  Xin Huang,et al.  The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019 , 2021, Earth System Science Data.

[38]  S. Kaishan,et al.  Agricultural Development and Implication for Wetlands Sustainability: A Case from Baoqing County, Northeast China , 2019 .

[39]  Zhiqiang Yang,et al.  A LandTrendr multispectral ensemble for forest disturbance detection , 2018 .

[40]  Zhiqiang Yang,et al.  Implementation of the LandTrendr Algorithm on Google Earth Engine , 2018, Remote. Sens..

[41]  Russell G. Congalton,et al.  Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat, Random Forest, and Google Earth Engine , 2020 .