[email protected] (H.S.); [email protected] ([email protected] (H.S.); [email protected] (D.L.); [email protected] (C.W.) Institute of Land and Urban-Rural Improvement,

[email protected] (H.S.); [email protected] (D.
[email protected] (H.S.); [email protected] (D.L.); [email protected] (C.W.) Institute of Land and Urban-Rural Improvement, Zhejiang University of Finance and Economics, Hangzhou 310058, China; [email protected] Correspondence: [email protected].cnCitation: Zhang, M.; He, T.; Li, G.; Xiao, W.; Song, H.; Lu, D.; Wu, C. Continuous Detection of Surface-Mining Footprint in Copper Mine Utilizing Google Earth Engine. Remote Sens. 2021, 13, 4273. https://doi.org/10.3390/rs13214273 Academic Editors: Jordi Crist al Rosselland Amin Beiranvand Pour Received: 19 August 2021 Cephalotin supplier Accepted: 21 October 2021 Published: 24 OctoberAbstract: Socioeconomic improvement is typically dependent on the production of mining sources, but both opencast and underground mining harm vegetation and also the eco-environment. Beneath the specifications on the construction for ecological civilization in China, more focus has been paid towards the reclamation of mines and mining management. Thus, it truly is the basement of formulating policies connected to mining management and implementing reclamation that detection of mining disturbance swiftly and accurately. This analysis carries on an empirical study in the Dexing copper mine, Jiangxi, China, aiming at exploring the method of distance and reclamation. According to the dense time-series stack derived from the Landsat archive on Google Earth Engine (GEE), the disturbance of surface mining within the 1986020 period has been detected applying the continuous change detection and classification (CCDC) algorithm. The results are that: (1) the all round 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 impact; (2) the surface-mining location was increasing from 1986020 inside the Dexing copper mine, along with the accumulation of mining damage is roughly 2865.96 ha with an annual location of 81.88 ha. We also discovered that the region was fluctuating with all the enhance. The detected natural restoration was appraised at a total of 544.95 ha within the 1988020 period with an average restoration of 16.03 ha. This means that it just restores much less normally; (three) it has often been the case that the Dexing mine is broken by mining and reclamation within the complete year (it’s most regularly damaged month is July). All imageries within the mine are detected by the CCDC algorithm, and they’re classified as 4 varieties by disturbing quantity in pixel scale (i.e., 0, 1, two, greater than two instances). Based on that, we discovered that the only when disturbed pixels account for 64.75 with the complete disturbed pixels, which can be the majority within the four classes; (four) this process gives an revolutionary perspective for obtaining the mining disturbed dynamic information timely and accurately and guarantees that the time and quantity of surface mining disturbed places are identified accurately. This approach can also be beneficial in other applications such as the detection of other equivalent regions. Keyword phrases: continuous modify detection; google earth engine; Landsat; disturbance; vegetation; NDVIPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.1. Introduction In China, mineral output and its proportion have elevated substantially. The nonferrous metals (copper, aluminum, lead, and zinc) improved from 950,000 tons in 1978, accounting for two.65 in the world’s output, to 56.86 million tons in 2019, accounting for 50.27 of your total output in the world [1]. M.