DOI: 10.5281/zenodo.8214548
title: Onset of mining operations

description: Motivation


Maus et al created the first database of the spatial extent of mining areas by mobilizing nearly 20 years of Landsat data. This dataset is imperative for GlobES, as mining areas are specified in the IUCN habitat class scheme. Yet, this dataset is temporally static. To tackle this flaw, we mined the Landsat archive to infer the first observable year of mining.


Approach


For each mining area polygon, we collected 50 random samples within it and 50 random samples along its borders. This was meant to reflect increasing spectral differences between areas within and outside a mining exploration after its onset. Then, for each sample, we used Google Earth Engine to extract spectral profiles for every available acquisition between 1990 and 2020.


After completing the extraction, we estimate mean spectral profiles for each acquisition date, once for the samples “inside” the mining area, and another for those “outside” of it. In this process, we masked pixels afflicted by clouds and cloud shadows using Landsat's quality information.


Using the time-series of mean profiles, at each mining site and for each unique date, we normalized the “inside” and “outside” multi-spectral averages and estimated the Root Mean Square Error (RMSE) between them. The normalization step aimed at emphasizing differences in the shape of the spectral profiles rather than on specific values, which can be related to radiometric innacuracies, or simply to differences in acquisition dates. This resulted in an RMSE time-series for each mining site.


We then used these data to infer the first mining year. To achieve this, we first derived a cumulative sum of the RMSE time-series with the intent of removing noise while preserving abrupt directional changes. For example, if a mine was introduced in a forest, it would drive an increase in the RMSE due to the removal of trees, whereas the outskirts of the mine would remain forested. In this example, the accumulated values would tilt upwards. However, if a mining exploration was accompanied by the removal of vegetation along its outskirts where bare land was common, a downwards shift is RMSE values is more likely as the landscape becomes more homogenization.


To detect the date marking a shift in RMSE values, we used a knee/elbow detection algorithm implemented in the python package kneebow, which uses curve rotation to infer the inflection/deflection point of a time series. Here, downward trends correspond to the elbow and upward trends to the knee. To determine which of these metrics was the most adequate, we use the Area Under the Curve (AUC). An elbow is characterized by a convex shape of a time-series which makes the AUC greater than 50%. However, if the shape of the curve is concave, the knee is the most adequate metric. We limited the detection of shifts to time-series with at least 100 time steps. When below this threshold, we assumed the mine (or the the conditions to sustain it) were present since 1990.


Content


This repository contains the infrastructure used to infer the start of a mining operation, which is organized as following:



	
00_data - Contains the base data required for the operation, including a SHP file with the mining area outlines, and validation samples.
	
01_analysis - Contains several outputs of our analysis:
	

		
xy.tar.gz - Sample locations for each mining site.
		
sr.tar.gz - Spectral profiles for each sample location.
		
mine_start.csv - First year when we detected the start of mining.
	
	
	
02_code - Includes all code used in our analysis.
	
requirements.txt - Python module requirements that can be fed to pip to replicate our study.
	
config.yml - Configuration file, including information on the Landsat products used.