Onset of mining operations

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.研究动机 Maus等人利用近20年的陆地卫星（Landsat）数据，构建了首个矿区空间范围数据库。该数据集对于GlobES项目至关重要，因为世界自然保护联盟（IUCN）的生境分类体系中明确纳入了矿区类别。但该数据集存在时间维度静态化的缺陷。为弥补这一不足，我们通过挖掘Landsat档案数据，推断出矿区首次可被观测到的开采年份。 研究方法 针对每个矿区多边形，我们在其内部随机采集50个样本点，并沿其边界随机采集50个样本点，以此反映矿区开采启动后，矿区内部与外部区域之间光谱差异逐渐增大的特征。随后，针对每个样本点，我们利用谷歌地球引擎（Google Earth Engine）提取1990年至2020年间所有可用影像的光谱剖面。 完成影像提取后，我们为每个成像日期分别计算矿区内部与外部样本点的平均光谱剖面。在此过程中，我们利用Landsat的质量信息对受云及云阴影影响的像素进行掩膜处理。 基于各矿区的平均光谱剖面时间序列，针对每个唯一成像日期，我们对矿区内部与外部的多光谱平均值进行归一化处理，并计算二者之间的均方根误差（Root Mean Square Error, RMSE）。归一化步骤旨在突出光谱剖面的形状差异，而非受辐射校准误差或成像日期差异影响的具体数值，最终为每个矿区生成一条RMSE时间序列。 随后我们利用这些数据推断矿区首次开采年份。为此，我们首先对RMSE时间序列进行累积求和，以在保留突变方向变化的同时去除噪声。举例而言，若矿区设于森林区域，由于树木被砍伐，矿区内部的RMSE值会升高，而矿区周边仍为森林，此时累积RMSE值将呈现上升趋势。反之，若矿区开发伴随周边裸地常见区域的植被清除，则随着景观同质化程度提升，RMSE值更可能呈现下降趋势。 为检测RMSE值发生突变的日期，我们采用了Python库kneebow中实现的拐点/肘点检测算法，该算法通过曲线旋转来推断时间序列的拐点/偏转点。其中，下降趋势对应肘点，上升趋势对应膝点。为确定两种指标的适用性，我们引入曲线下面积（Area Under the Curve, AUC）进行判断：若时间序列呈凸形，则肘点为更合适的指标，此时AUC值大于50%；若曲线呈凹形，则膝点为更合适的指标。我们仅对包含至少100个时间步长的序列进行突变检测，若序列长度低于该阈值，则默认矿区（或维持开采的条件）自1990年起便已存在。 数据集内容 本仓库包含用于推断矿山开采起始时间的完整分析流程，其目录结构如下： 00_data：存放本次分析所需的基础数据，包括矿区轮廓的SHP（Shapefile）文件与验证样本集。 01_analysis：存放本次分析的各类输出结果： xy.tar.gz：各矿区的样本点坐标文件。 sr.tar.gz：各样本点的光谱剖面数据。 mine_start.csv：检测得到的各矿区首次开采年份。 02_code：包含本次分析使用的全部代码。 requirements.txt：可供pip直接安装的Python依赖模块列表，用于复现本研究。 config.yml：配置文件，包含所用Landsat产品的相关信息。