Your browser doesn't support javascript.
loading
Extraction and spatiotemporal changes of open-pit mines during 1985-2020 using Google Earth Engine: A case study of Qingzhou City, Shandong Province, China.
Ruifeng, Liu; Kai, Yuan; Xing, Li; Xiaoli, Liu; Xitao, Zhao; Xiaocheng, Guo; Juan, Fu; Shixin, Cao.
Afiliación
  • Ruifeng L; Shandong Provincial Territorial Ecological Restoration Center, Jinan, 250014, China.
  • Kai Y; School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou, 221116, China.
  • Xing L; School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou, 221116, China. lixing@jsnu.edu.cn.
  • Xiaoli L; Shandong Provincial Territorial Ecological Restoration Center, Jinan, 250014, China.
  • Xitao Z; Shandong Provincial Territorial Ecological Restoration Center, Jinan, 250014, China.
  • Xiaocheng G; Qingzhou Natural Resources and Planning Bureau, Qingzhou, 262500, China.
  • Juan F; Shandong Provincial Territorial Ecological Restoration Center, Jinan, 250014, China.
  • Shixin C; Shandong Provincial Territorial Ecological Restoration Center, Jinan, 250014, China.
Environ Monit Assess ; 195(1): 209, 2022 Dec 19.
Article en En | MEDLINE | ID: mdl-36534206
ABSTRACT
The global use of mineral resources has increased exponentially for decades and will continue to grow for the foreseeable future, resulting in increasingly negative impacts on the surrounding environment. However, to date, there are a lack of historical and current spatial extent datasets with high accuracy for mining areas in many parts of the world, which has hindered a more comprehensive understanding of the environmental impacts of mining. Using the Google Earth Engine cloud platform and the Landsat normalized difference vegetation index (NDVI) datasets, the spatial extent data of open-pit mining areas for eight years (1985, 1990, 1995, 2000, 2005, 2010, 2015, and 2020) was extracted by the Otsu algorithm. The limestone mining areas in Qingzhou, Shandong Province, China, was selected as a case study. The annual maximum NDVI was first derived from the Landsat NDVI datasets, and then the Otsu algorithm was used to segment the annual maximum NDVI images to obtain the extent of the mining areas. Finally, the spatiotemporal characteristics of the mining areas in the study region were analyzed in reference to previous survey data. The results showed that the mining areas were primarily located in Shaozhuang Town, Wangfu Street and the northern part of Miaozi Town, and the proportion of mining areas within these three administrative areas has increased annually from 88% in 1985 to more than 98% in 2010. Moreover, the open-pit mining areas in ​​Qingzhou gradually expanded from a scattered, point-like distribution to a large, contiguous distribution. From 1985 to 2020, the open-pit mining area expanded to more than 10 times its original size at a rate of 0.5 km2/year. In 2015, this area reached its maximum size of 19.7 km2 and slightly decreased in 2020. Furthermore, the expansion of the mining areas in Qingzhou went through three stages a slow growth period before 1995, a rapid expansion period from 1995 to 2005, and a shutdown and remediation period after 2005. A quantitative accuracy assessment was performed by calculating the Intersection over Union (IoU) of the extraction results and the visual interpretation results from Gaofen-2 images with 1-m spatial resolution. The IoU reached 72%. The results showed that it was feasible to threshold the Landsat annual maximum NDVI data by the Otsu algorithm to extract the annual spatial extent of the open-pit mining areas. Our method will be easily transferable to other regions worldwide, enabling the monitoring of mine environments.
Asunto(s)
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Monitoreo del Ambiente / Motor de Búsqueda País/Región como asunto: Asia Idioma: En Revista: Environ Monit Assess Asunto de la revista: SAUDE AMBIENTAL Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Monitoreo del Ambiente / Motor de Búsqueda País/Región como asunto: Asia Idioma: En Revista: Environ Monit Assess Asunto de la revista: SAUDE AMBIENTAL Año: 2022 Tipo del documento: Article País de afiliación: China