ffa8afa394054c4f74f9031cc3825ea5 land-13-01301-v3.pdf 438077274ee248a2c3a302fe3c957482069b4f62 land-13-01301-v3.pdf 78a059cad8fe560f5360619131ac8bd30d91b95f19142c8cc5dbbc5f227b4973 land-13-01301-v3.pdf Title: Monitoring of Open-Pit Mining Areas Using Landsat Series Imagery (1984–2023) and Cloud Processing Subject: While open-pit mining activities represent one of the human-derived most impactful land cover changes, these changes and the linked restoration processes can be challenging to assess. This article presents a reproducible methodology carried out with cloud processing of satellite images (Google Earth Engine (GEE)) to evaluate the evolution of open-pit mining activities and their restoration in a Mediterranean landscape. For this purpose, the calculation of the normalized differential vegetation index (NDVI) was used to obtain a quantitative parameter to monitor vegetation presence in each extractive area. To validate these results, confusion matrices were performed between the classification obtained in the study and the official land cover mapping, using randomly selected mining areas as test points, with an average accuracy of 88%. According to the methodology used, the surface of areas denuded by mining in the period 1984–2023 has fluctuated over time, with a maximum in 2005 coinciding with the peak of the Spanish construction boom, and a subsequent decrease towards the present. From these results, it can be concluded that Landsat-type data processed using GEE provide a quick and useful tool for monitoring the evolution of mining activity, including restoration trends, becoming particularly valuable for public bodies’ inspections or decision making. Keywords: GEE; open-pit mining; mineral extraction evolution; integrated restoration; NDVI Author: Pau Montero, Edgardo Bustos, Joan-Cristian Padró and Vicenç Carabassa Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25 CreationDate: Mon Aug 19 07:03:08 2024 CEST ModDate: Tue Aug 27 06:07:13 2024 CEST Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 11 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 4618892 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- SOZKOC+VnURWPalladioL-Bold Type 1 Custom yes yes yes 10 0 NOECDO+URWPalladioL-Bold Type 1 Custom yes yes yes 16 0 PJEWEO+URWPalladioL-Roma Type 1 Custom yes yes yes 22 0 LTGHYX+URWPalladioL-Ital Type 1 Custom yes yes yes 27 0 SJBBGU+VnURWPalladioL Type 1 Custom yes yes yes 32 0 VQJIRC+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 48 0 NJAOJC+CMSY10 Type 1 Builtin yes yes yes 68 0 EGEJAP+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 76 0 EGEJBA+PalatinoLinotype CID TrueType Identity-H yes yes yes 79 0 EGEJBB+PalatinoLinotype TrueType WinAnsi yes yes no 85 0 EGEJBD+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 88 0 EGEJGC+PalatinoLinotype TrueType MacRoman yes yes no 91 0 EGEJAP+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 101 0 EGEJBA+PalatinoLinotype CID TrueType Identity-H yes yes yes 104 0 EGEJBB+PalatinoLinotype TrueType WinAnsi yes yes no 110 0 EGEJBD+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 113 0 EGEJGC+PalatinoLinotype TrueType MacRoman yes yes no 116 0 EGEJAP+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 129 0 EGEJBB+PalatinoLinotype TrueType WinAnsi yes yes no 132 0 EGEJBD+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 135 0 EGEJAP+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 142 0 EGEJBB+PalatinoLinotype TrueType WinAnsi yes yes no 145 0 EGEJBD+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 148 0 EGEJAP+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 159 0 EGEJBB+PalatinoLinotype TrueType WinAnsi yes yes no 162 0 EGEJBD+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 165 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2024-10-24 02:46:25 CEST RepresentationInformation: land-13-01301-v3.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2024-09-25 10:34:08 CEST Size: 4618892 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 333 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Materials and Methods Destination: section.2 Children: Item: Title: Study Area Destination: subsection.2.1 Item: Title: Materials Destination: subsection.2.2 Children: Item: Title: Landsat Series Satellite Images Destination: subsubsection.2.2.1 Item: Title: Cloud-Based Platform Destination: subsubsection.2.2.2 Item: Title: Land Cover Maps Destination: subsubsection.2.2.3 Item: Title: Methods Destination: subsection.2.3 Children: Item: Title: Data Processing with GEE Destination: subsubsection.2.3.1 Item: Title: Data Management Destination: subsubsection.2.3.2 Item: Title: Results Destination: section.3 Item: Title: Discussion Destination: section.4 Item: Title: Conclusions Destination: section.5 Item: Title: References Destination: appendix.A. Info: Title: Monitoring of Open-Pit Mining Areas Using Landsat Series Imagery (1984 2023) and Cloud Processing Author: Pau Montero, Edgardo Bustos, Joan-Cristian Padró and Vicenç Carabassa Subject: While open-pit mining activities represent one of the human-derived most impactful land cover changes, these changes and the linked restoration processes can be challenging to assess. This article presents a reproducible methodology carried out with cloud processing of satellite images (Google Earth Engine (GEE)) to evaluate the evolution of open-pit mining activities and their restoration in a Mediterranean landscape. For this purpose, the calculation of the normalized differential vegetation index (NDVI) was used to obtain a quantitative parameter to monitor vegetation presence in each extractive area. To validate these results, confusion matrices were performed between the classification obtained in the study and the official land cover mapping, using randomly selected mining areas as test points, with an average accuracy of 88%. According to the methodology used, the surface of areas denuded by mining in the period 1984 2023 has fluctuated over time, with a maximum in 2005 coinciding with the peak of the Spanish construction boom, and a subsequent decrease towards the present. From these results, it can be concluded that Landsat-type data processed using GEE provide a quick and useful tool for monitoring the evolution of mining activity, including restoration trends, becoming particularly valuable for public bodies’ inspections or decision making. 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