Elsevier

CATENA

Volume 250, March 2025, 108770
CATENA

Evaluation of surface coal mines reclaimed to different vegetation types and their stability in semi-arid areas
对不同植被类型恢复的地表煤矿及其在半干旱地区的稳定性进行评估

简介IF(5) 5.9SCI升级版 农林科学1区SCI基础版 农林科学1区SCI Q1
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Highlights

  • •
    A method to identify the spatio-temporal growth dynamics of different vegetation.
    一种识别不同植被时空生长动态的方法。
  • •
    Spatio-temporal processes of vegetation changes were reconstructed at pixel level.
    植被变化的时空过程在像素级别上被重建。
  • •
    Evaluated vegetation restoration after reclamation combined with growth curves.
    评估了与生长曲线相结合的复垦后植被恢复情况。
  • •
    Predicted the temporal information and spatial pattern of stable reclamation effect.
    预测了稳定复垦效果的时间信息和空间模式。

Abstract

In mining areas, vegetation reclamation has been the focus of land reclamation research, and it is crucial to restoring the natural environment destroyed by humans. Unfortunately, land reclamation research is often limited by insufficient temporal and spatial data. Using satellite observation data and cloud computing, this research identifies the spatial and temporal dynamics of reclamation of various vegetation within the largest open-pit coal mine, the Pingshuo open-pit mine, in China. On the basis of vegetation growth curves, the study assesses the current impact of reclamation. There were two large and small reclamation areas formed by the mine in the southwest and northeast, with forest land, grassland, and cropland accounting for 15.79 %, 42.61 %, and 41.59 % of the total reclamation area, respectively. A significant portion of the reclamation activities took place between 2010 and 2015, peaking in the southwest part of the mine area before shifting to the northeastern part of the mine area. Historically, forest land reclamation shifted to grassland reclamation, but since 2010 it has shown an increase in arable land reclamation. As a result of our studies, we were able to determine that the average stabilization time of forest land, grassland, and cropland in the mine area had been 6.50 years, 5.42 years, and 4.64 years, respectively. In total, 50.54 % of the area had reached the stabilization stage of reclamation, with grassland accounting for 41.37 % of the stable vegetation and cropland representing 41.93 %, while forest land only accounted for 16.70 % of the stable vegetation. Using a simple and easy-to-use methodology, this study provides information suitable for a wide range of scenarios, thereby serving regional development and policy.
在采矿区，植被恢复一直是土地复垦研究的重点，对于恢复被人类破坏的自然环境至关重要。不幸的是，土地复垦研究常常受到时间和空间数据不足的限制。利用卫星观测数据和云计算，本研究识别了中国最大露天煤矿——平朔露天矿区内各种植被复垦的时空动态。基于植被生长曲线，研究评估了复垦的当前影响。矿区在西南和东北形成了两个大和小的复垦区域，森林、草地和农田分别占总复垦面积的 15.79%、42.61%和 41.59%。大量复垦活动发生在 2010 年至 2015 年之间，复垦高峰出现在矿区的西南部，随后转移到矿区的东北部。历史上，森林复垦转向草地复垦，但自 2010 年以来，耕地复垦有所增加。 通过我们的研究，我们能够确定矿区森林土地、草地和农田的平均稳定时间分别为 6.50 年、5.42 年和 4.64 年。总的来看，50.54%的面积已达到复垦的稳定阶段，其中草地占稳定植被的 41.37%，农田占 41.93%，而森林土地仅占稳定植被的 16.70%。本研究采用简单易用的方法，提供适用于多种场景的信息，从而为区域发展和政策服务。

Keywords

Surface coal mine
Reclamation
Cumulative NDVI
China
Google earth engine

1. Introduction

The vegetation is one of the most important components of terrestrial ecosystems. Not only does it cover nearly 70 % of the global terrestrial surface(Arellano et al., 2015), but it is also one of the most delicate elements in nature(Dufour et al., 2019). Human societies benefit in the long term from the performance of vegetation in ecological functions such as mitigating climate change and conserving biodiversity and water resources (Knoke et al., 2014, Onaindia et al., 2013, Strand et al., 2018). It has become clear, however, that land cover change caused by human activity is an important contributor to changes in vegetation composition and diversity(Bobbink et al., 2010, Walther et al., 2005). As a matter of extreme concern, the production of minerals, represented by surface coal mining, causes severe damage to mining ecosystems through the removal of vegetation and soil at large scales(Feng et al., 2019, Wang et al., 2022). Land exposed to the elements is more susceptible to impacts such as erosion and chemical releases, which worsen the environmental degradation(Xiao et al., 2023). It is therefore imperative that the mined lands be restored in some way so that they can maintain their healthy condition while still having a minimal impact on the surrounding environment as a result of the environmental impact of mining.
植被是陆地生态系统中最重要的组成部分之一。它不仅覆盖了全球近 70%的陆地表面（Arellano et al., 2015），而且也是自然界中最脆弱的元素之一（Dufour et al., 2019）。人类社会从植被在生态功能方面的表现中长期受益，例如缓解气候变化和保护生物多样性及水资源（Knoke et al., 2014，Onaindia et al., 2013，Strand et al., 2018）。然而，显而易见的是，由人类活动引起的土地覆盖变化是植被组成和多样性变化的重要因素（Bobbink et al., 2010，Walther et al., 2005）。极为令人担忧的是，以地表煤矿开采为代表的矿产生产通过大规模移除植被和土壤，对采矿生态系统造成了严重损害（Feng et al., 2019，Wang et al., 2022）。暴露在自然环境中的土地更容易受到侵蚀和化学物质释放等影响，这加剧了环境退化（Xiao et al., 2023）。 因此，恢复被开采的土地是必不可少的，以便它们能够维持健康状态，同时尽量减少因矿业活动对周围环境造成的影响。
As a matter of fact, the restoration of the damaged ground cover following a mine disaster is a long-term dynamic process. A recultivated ecosystem must undergo a period of maturation in order to stabilize and function(Guan et al., 2022), a process strongly correlated with factors such as the natural conditions of the region, reclamation measures, and the development time of different vegetation(Fleisher and Hufford, 2020). It takes more than just analyzing the spatial characteristics of the plant cover in mining regions to restore it to its closest pre-mining form. It also need knowledge of the dynamics of vegetation growth to spot possible issues with dynamic vegetation growth.(Yuan et al., 2018). When the vegetation is permitted to continue to develop, stabilize and mature, it is ensured that the reclaimed soil will provide nutrients, physical structure, and chemical conditions necessary for vegetation growth(H. Li et al., 2022). Aside from that, the timing of development and the type of vegetation are highly correlated(Jiao et al., 2021, Xu et al., 2018). Many mining sites are associated with different plantations of vegetation, and these complex vegetation types provide a greater range of ecosystem services and are more difficult to restore fully(Chen et al., 2018). Therefore, reclamation of mine vegetation is challenging, and crucially, it is difficult to observe the rapid change of vegetation in mine sites. Especially since large amounts of land are occupied by open-pit mines, destroying and disrupting vegetation for several years now.
事实上，矿灾后受损地表覆盖的恢复是一个长期的动态过程。一个再植被的生态系统必须经历一个成熟期，以便稳定并发挥作用（Guan et al., 2022），这个过程与区域的自然条件、复垦措施和不同植被的发展时间等因素密切相关（Fleisher and Hufford, 2020）。仅仅分析矿区植物覆盖的空间特征不足以将其恢复到最接近的采矿前状态。还需要对植物生长动态的知识，以便发现动态植被生长中可能出现的问题（Yuan et al., 2018）。当植物被允许继续发展、稳定和成熟时，可以确保恢复的土壤提供植物生长所需的养分、物理结构和化学条件（H. Li et al., 2022）。除此之外，发展时机和植被类型高度相关（Jiao et al., 2021，Xu et al., 2018）。 许多矿区与不同的植被种植区相关，这些复杂的植被类型提供了更广泛的生态系统服务，并且更难以完全恢复（Chen et al., 2018）。因此，矿区植被的复垦具有挑战性，关键是很难观察到矿区植被的快速变化。尤其是由于大面积土地被露天矿占用，已经破坏和干扰植被多年。
In order to monitor the status of vegetation following reclamation of mining areas, numerous technical tools have emerged(Zhou et al., 2022). Early studies typically employed methods such as field collection of soil samples, plant growth status surveys, characterization of microbial communities, and resident satisfaction surveys(Chasmer et al., 2018, Wang et al., 2021). Although the above methods can assist in removing the influence of subjective factors, they also usually take a great deal of time and effort. Due to their limitations, they are unable to account for the size of the study area, or quantify the dynamic processes of vegetation growth. By using remote sensing technology, it is possible to monitor the effects of vegetation reclamation on the environment(Ren et al., 2022). During the past decade, various sensor platforms, including MODIS, Landsat, and SPOT, have been operational in large numbers, collecting vast amounts of images at various spatial and temporal resolutions(Li et al., 2020, Pettorelli et al., 2014). It is widely used to detect mining disturbances and assess the environmental impact(Gastauer et al., 2018). It entails evaluating the effects of reclamation from multi-temporal remote sensing data, identifying the temporal and spatial areas of mine disturbance and reclamation, and tracking variations in reclamation across various vegetation types.(Ren et al., 2021), as well as identifying reclaimed vegetation recovery stages(Feng et al., 2022). Studies conducted under these conditions have demonstrated reliability and validity in monitoring and describing the effects of mine reclamation.
为了监测矿区复垦后植被的状态，出现了许多技术工具（Zhou et al., 2022）。早期研究通常采用现场采集土壤样本、植物生长状态调查、微生物群落特征分析和居民满意度调查等方法（Chasmer et al., 2018，Wang et al., 2021）。尽管上述方法可以帮助消除主观因素的影响，但通常需要大量的时间和精力。由于其局限性，它们无法考虑研究区域的大小，或量化植被生长的动态过程。通过使用遥感技术，可以监测植被复垦对环境的影响（Ren et al., 2022）。在过去十年中，包括 MODIS、Landsat 和 SPOT 在内的各种传感器平台已大量投入使用，收集了大量不同空间和时间分辨率的图像（Li et al., 2020，Pettorelli et al., 2014）。它被广泛用于检测采矿干扰和评估环境影响（Gastauer et al., 2018）。 这涉及评估来自多时相遥感数据的复垦效果，识别矿区干扰和复垦的时间和空间区域，并跟踪不同植被类型的复垦变化（Ren et al., 2021），以及识别复垦植被的恢复阶段（Feng et al., 2022）。在这些条件下进行的研究已证明在监测和描述矿山复垦效果方面的可靠性和有效性。
Using remote sensing to monitor vegetation reclamation is flourishing today, thanks to the advancement of time series analysis algorithms and the power of Google Earth Engine (GEE)(Wang et al., 2020). There have been several recent studies that support the inscription of the dynamics of vegetation after mine reclamation at the pixel scale(He et al., 2021, Wang et al., 2021). Through the analysis of NDVI time trajectories, these studies identify the difference between post-reclamation vegetation regeneration and pre-mining levels of vegetation(J. Li et al., 2022). There have also been studies that have identified the recovery cycles and critical stages of various reclaimed vegetation types utilizing time-series imagery and analyzing exclusively databases(Guan et al., 2022, Han et al., 2022). the current study relies on land use cover differences between single time-phase raster data, which makes it difficult to accurately characterize growth dynamics during vegetation restoration. Therefore, we used image-element-level time-series analysis techniques and fitted vegetation growth curve dynamics to improve the efficiency and effectiveness of monitoring vegetation reclamation after surface mining.
利用遥感监测植被恢复在今天蓬勃发展，这得益于时间序列分析算法的进步和 Google Earth Engine (GEE)的强大功能(Wang et al., 2020)。最近有几项研究支持在像素尺度上记录矿山恢复后植被的动态(He et al., 2021, Wang et al., 2021)。通过对 NDVI 时间轨迹的分析，这些研究识别了恢复后植被再生与采矿前植被水平之间的差异(J. Li et al., 2022)。还有一些研究利用时间序列影像和专门分析数据库识别了各种恢复植被类型的恢复周期和关键阶段(Guan et al., 2022, Han et al., 2022)。当前研究依赖于单一时间相位栅格数据之间的土地利用覆盖差异，这使得在植被恢复过程中准确表征生长动态变得困难。 因此，我们使用了图像元素级时间序列分析技术，并拟合了植被生长曲线动态，以提高表面采矿后植被恢复监测的效率和有效性。
We intend to accomplish the following objectives through some efforts in this study: (1) Identifying mining reclamation areas and post-reclamation vegetation types (forestland, grassland and cropland) separately using the LandTrendr algorithm, the CART classification in combination with the Normalized Difference Vegetation Index (NDVI); (2) By utilizing time-series data of cumulative NDVI (cumNDVI), different types of vegetation units can be identified pixel-wise with respect to their spatial distributions, restoration cycles, and critical time points; (3) the largest open-pit coal mining area in China will be used as a research object for monitoring the restoration effect of the current post-reclamation vegetation.
我们打算通过一些努力在本研究中实现以下目标：（1）使用 LandTrendr 算法、CART 分类结合归一化差异植被指数（NDVI）分别识别采矿复垦区域和复垦后的植被类型（森林、草地和农田）；（2）通过利用累积 NDVI（cumNDVI）的时间序列数据，可以逐像素识别不同类型的植被单元，考虑其空间分布、恢复周期和关键时间点；（3）中国最大的露天煤矿区域将作为研究对象，用于监测当前复垦后植被的恢复效果。

2. Study area and data sources

2.1. Study area

Since it is difficult to monitor the recovery of vegetation after disturbance in mining areas in semi-arid zones, if the problem of monitoring this type of area can be solved, it can be applied to the vast majority of mining areas, and for this reason we chose a typical mining area, the Pingshuo mining area. Here the monitoring of vegetation recovery will be based on the identification of mining disturbances. Disturbance will be defined as the complete loss of vegetation due to mining-related activities. As shown in Fig. 1, the Pingshuo mining district is located in the northern part of the Shanxi Province, China, and was established under the jurisdiction of Shuozhou City, Shanxi Province. It is one of the largest coal concentration development areas in China(Xu et al., 2021). The geographical coordinates are 112°16′20″- 112°30′32 “E and 39°26′10″- 39°35′30 ”N. From east to west, the area measures 67.9 km by 69.5 km, with a total area of 517.48 km2. This study area encompasses three districts in Shuozhou, namely Pinglu, Shuocheng and Shanyin, and is situated in the border area between the Loess Plateau and the northern Tushan Mountains. A climate with average annual precipitation of 428.2–449.0 mm and evaporation of 1786.6–2598.0 mm, of which 75 % of the precipitation occurs during July, August, and September(K. Yang et al., 2022). This type of natural environment makes the ecosystem in the study area weak in terms of resilience, severe in terms of soil erosion, and possesses only a limited amount of vegetation cover. Furthermore, long-term mining operations and town construction have significantly changed the land's cover and use, leading to severe ecological deterioration on the already delicate Loess Plateau. Land use management in the area has long been concerned about environmental degradation in mining sites as well as the ongoing reclamation of mining zones..
由于在半干旱地区的采矿区监测植被恢复困难，如果能够解决监测此类区域的问题，则可以应用于绝大多数采矿区，因此我们选择了一个典型的采矿区——平朔矿区。在这里，植被恢复的监测将基于对采矿干扰的识别。干扰将被定义为由于与采矿相关的活动导致的植被完全丧失。如图 1 所示，平朔矿区位于中国山西省北部，隶属于山西省朔州市。它是中国最大的煤炭集中开发区之一（Xu et al., 2021）。地理坐标为 112°16′20″- 112°30′32 “E 和 39°26′10″- 39°35′30 ”N。东西长 67.9 公里，南北宽 69.5 公里，总面积为 517.48 平方公里。该研究区域包括朔州市的三个区，即平鲁、朔城和山阴，位于黄土高原与北部土山之间的边界地区。 年平均降水量为 428.2–449.0 毫米，蒸发量为 1786.6–2598.0 毫米，其中 75%的降水发生在七月、八月和九月（K. Yang et al., 2022）。这种自然环境使得研究区的生态系统在恢复力方面较弱，土壤侵蚀严重，植被覆盖仅限。此外，长期的采矿作业和城镇建设显著改变了土地的覆盖和使用，导致已经脆弱的黄土高原生态严重恶化。该地区的土地利用管理长期以来关注采矿地点的环境退化以及采矿区的持续复垦。
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Fig. 1. Schematic diagram of the geographical location. The study area is located in Shuozhou City, Shanxi Province, China.

3. Methodology

3.1. Data acquisition and preprocessing

This study included all surface reflectance images (30 m x 30 m) covering the study area. The surface reflectance data consists of 34 time-series Landsat TM/OLI images spanning the period 1987 to 2021, including Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI satellites. Data were collected from the United States Geological Survey (USGS; https://glovis.usgs.gov/) and processed on GEE platform. In view of the vegetation characteristics as well as the interference of clouds and other atmospheric conditions, all imageries were taken from April to September with less than 10 % cloud cover(Liao et al., 2019). For the calibration of the sensor radiance of Landsat 5, 7 and 8 images for surface reflectance, the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) (Park et al., 2020) and Landsat Surface Reflectance Code (LaSRC) (Myroniuk et al., 2020) were used. The C implementation of Function of Mask (CFMASK) determines important pixel data quality flag information suggestive of clouds, shadows, water, and snow(Stillinger et al., 2019). Section 2.3 corresponds to step 2 of the flow diagram of the whole study (Fig. 2), and sections 2.3, 2.4, and 2.5 correspond to steps 2, 3, and 4, respectively.
这项研究包括覆盖研究区域的所有表面反射率图像（30米x 30米）。表面反射率数据由1987年至2021年期间的34幅时间序列Landsat TM/OLI图像组成，包括Landsat 5 TM、Landsat 7 ETM+和Landsat 8 OLI卫星。数据收集自美国地质调查局（USGS；https://glovis.usgs.gov/）并在GEE平台上处理。鉴于植被特征以及云和其他大气条件的干扰，所有图像都是在4月至9月拍摄的，云量低于10%（廖等人，2019）。为了校准Landsat 5、7和8图像的表面反射率的传感器辐射，使用了Landsat生态系统干扰自适应处理系统（LEDAPS）（Park等人，2020年）和Landsat表面反射率代码（LaSRC）（Myroniuk等人，2020年）。掩码函数（CFMASK）的C实现确定了暗示云、阴影、水和雪的重要像素数据质量标志信息（Stillinger等人，2019）。第2.3节对应于整个研究流程图的步骤2（图2），第2.3、2.4和2.5节分别对应于步骤2、3和4。
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Fig. 2. Flow diagram of research.

3.2. LandTrendr for automatic extraction of variation areas

It is visible that open pit coal mining and reclamation events primarily result in the stripping of surface vegetation and soil, evidence of which is contained in various remote sensing datasets monitored by satellite sensors. Apart from being able to obtain spatial and temporal information on the occurrence of mining and reclamation events through visual interpretation, it is now technically feasible to directly interpret disturbance information for any pixel within a mining area. The LandTrendr algorithm, as one such technique, is widely used for disturbance monitoring in mining areas, and by using temporal segmentation, we can determine when and where disturbances occur(Cohen et al., 2018). LandTrendr is a brevity algorithm that listens to the annual, verbose, noisy detail of a pixel’s story and writes an abridged version. In practice, LandTrendr takes a single point-of-view from a pixel’s spectral history, like a band or an index, and goes through a process to identify breakpoints separating periods of durable change or stability in spectral trajectory, and records the year that changes occurred. Using Landsat images (path/row 092/076) and LandTrendr, we examined the vegetation growth in the Pingshuo mine region from 1986 to 2021. It is intended to minimize the effects of varying physical and climatic conditions, flooding, or changes in the solar geometry. The results from the previous studies on mining disturbances and reclamations indicate that, NDVIs in normal reference areas ranged from 0.242 to 0.505, with a vegetation index below 0.116, while the restored vegetation index increased between 0.145 and 0.494(Yang et al., 2018). In both the Pingshuo open-pit coal mine and the study area created by this discovery, semi-arid zones are prevalent. Thus, we gathered the disturbance characteristics of the Pingshuo mine area by aggregating image elements with the same disturbance characteristics using time series data of NDVI. We divided the total disturbance area into three categories in order to conduct this study, namely undisturbed area, disturbed and reclamation area and disturbed non reclamation area. A particular focus was placed on the vegetation growth in the disturbed and reclaimed area.
显然，露天煤矿开采和复垦事件主要导致表面植被和土壤的剥离，相关证据包含在各种遥感数据集中，这些数据集由卫星传感器监测。除了能够通过视觉解释获取关于采矿和复垦事件发生的空间和时间信息外，现在在技术上可行的是直接解释采矿区内任何像素的干扰信息。LandTrendr 算法作为一种技术，在矿区的干扰监测中被广泛使用，通过时间分段，我们可以确定何时何地发生干扰（Cohen et al., 2018）。LandTrendr 是一个简化算法，它倾听一个像素故事的年度、冗长、嘈杂细节，并写出一个摘要版本。在实践中，LandTrendr 从像素的光谱历史中获取单一视角，如一个波段或一个指数，并经历一个过程，以识别分隔持久变化或光谱轨迹稳定期的断点，并记录变化发生的年份。 使用 Landsat 图像（路径/行 092/076）和 LandTrendr，我们研究了 1986 年至 2021 年间平朔矿区的植被生长。目的是尽量减少物理和气候条件的变化、洪水或太阳几何变化的影响。先前关于采矿扰动和恢复的研究结果表明，正常参考区域的 NDVI 范围为 0.242 到 0.505，植被指数低于 0.116，而恢复的植被指数则在 0.145 到 0.494 之间（Yang et al., 2018）。在平朔露天煤矿和这一发现所创建的研究区域，半干旱区占主导地位。因此，我们通过聚合具有相同扰动特征的图像元素，使用 NDVI 的时间序列数据收集了平朔矿区的扰动特征。为了进行这项研究，我们将总扰动区域分为三类，即未扰动区域、扰动和恢复区域以及扰动未恢复区域。特别关注的是扰动和恢复区域的植被生长。

3.3. Vegetation classification using phenological index

Restoration effects are highly correlated with the type of vegetation used for reclamation. The vegetation types where reclamation occurred following disturbance were identified based on Google Earth observations of historical images and field surveys. A classification of the vegetation was then made based on the differences in ecosystem service supply capacity between forest, grassland, and cropland vegetation. CART classification in NDVI and machine learning has proven to be an effective tool for distinguishing different types of vegetation(Liu et al., 2020). Once the areas where mining disturbances occurred and reclaimed were identified, we defined the physical differences between different vegetation types, labeled the remote sensing image elements, and processed the data using GEE (Fig. 3). It should be noted that the whole classification process only collected NDVI time series data for the last year of the study interval, and only determined the final characteristics of spatial distributions of different vegetation types. It should be further noted that as part of this study, physical characteristics were extracted to improve the accuracy of land cover classifications, including the beginning and ending dates of the growing season, the length of the growing season, the seasonal magnitude and the maximum fitted NDVI values. A growing season begins and ends when the fitted NDVI curve reaches 50 % of its seasonal amplitude measured from left and right minima, respectively(Jia et al., 2014). As an additional measure of validation of the classification results obtained using the phenological features, four statistical temporal characteristics of the fused NDVI data series, including maximum, minimum, mean, and standard deviation values, were extracted to fully demonstrate the reliability of the study.
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Fig. 3. NDVI dynamic curves for monitoring different vegetation by phenology information.

3.4. Identification of reclamation stages based on CumNDVI

Vegetation growth in mining areas is a dynamic process that changes over time, and it exhibits different states at different times after planting, but it is not possible to visually identify what growth cycle it is in. A general trend of growth of the restored vegetation indicates a S-shaped exponential growth from slow to rapid and then from rapid to slow, reaching a steady state(Zeng et al., 2020). This basic law served as the basis for the design of experiments for data mining using the S-logistic fitting function for cumulative NDVI at the pixel level, as shown in Fig. 4, which restored the core logic of the entire process using S-shaped curves. In accordance with the growth characteristics of vegetation, the cumNDVI will gradually converge to a stable value, but not reach it. We therefore utilize this trend information in conjunction with the concept of limit in calculus to uncover the period of vegetation reclamation using the maximum value of the second-order derivative. While the maximum value of the first-order derivative of the curve represents the most rapid stage in the reclamation of vegetation in the mining area, its vicinity represents the most rapid stage in vegetation growth during this period. In this stage, the vegetation will undergo a process of slow recovery followed by rapid recovery, followed by gradual stabilization.
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Fig. 4. Dynamic growth curves of vegetation and determination of thresholds. f(x) is the growth curve function of vegetation; f’(x) is the first-order derivative of the vegetation growth function to identify the growth turning point; f’’(x) is the second-order derivative of the vegetation growth function to identify the reclamation stage.

It is widely used for the monitoring of vegetation dynamics and crop yield prediction based on NDVI values(Chu et al., 2019, Lai et al., 2018). By combining NDVI with net primary productivity (NPP), cumNDVI measures crop productivity while avoiding the uncertainties introduced when converting NDVI to net primary productivity (NPP)(Hill and Donald, 2003, Yang et al., 2022). A cumNDVI was calculated using Landsat NDVI data along with growing season data for each of the three vegetation ranges in the mine area. equations are as follows:(1)Nr=∑i=SDT10EDT10NDVI
As is shown in equation (1), Nr is the cumNDVI value per area of each vegetation type, SDT10 is the start date and EDT10 is the end date of the 5-day moving average temperature T ≥ 10 °C. The cumNDVI was extracted based on NDVI smoothed values from Landsat's 16-day reentry cycle in order to maximize the response to the vegetation's phenology and to fit the growth curve. Compared to previous studies that utilized NDVI or VFC as indicators for monitoring reclamation phases(Song et al., 2022, Zhang et al., 2019), cumNDVI takes into account the differences among different vegetation types in terms of productivity characteristics, thus avoiding the perturbation of the monitoring process by anomalous values. By aggregating patches with the same cumNDVI at the pixel level, more accurate reclamation stage delineation results can be achieved.

4. Results

4.1. Extraction of the reclaimed areas and vegetation

For the purpose of continuously monitoring the growth dynamics of the vegetation, the area and the type of vegetation that will be utilized for reclamation were initially determined (Fig. 5). All data used for this process was open source, and no dedicated database was required from the mine site. Reclamation areas were determined by using LandTrendr's algorithm to monitor 95 % of the NDVI for the full period of 2021, which is non-complex and easy to implement, and the final areas were determined by pixel aggregation, thus reducing errors(Yang et al., 2018). The majority of reclamation activities have been concentrated in northeastern and southwest corners of the mine area, resulting in a large and small vegetation distribution pattern. In terms of the final reclamation area structure, forest land, grassland, and cropland contributed 15.79 %, 42.61 %, and 41.59 % respectively. In comparison with the northeast corner, more vegetation area was reclaimed in the southwest, where forest, grasslands, and crops were spatially and massively regularly distributed. Forests are primarily concentrated near mine boundaries, forming three large patches and a scattered distribution. A belt of cultivated land surrounds the forest land and is distributed to the northeast of the gathering center. From a quantitative perspective, arable land occupies the majority of the mine area. Any reclaimed area will contain grasslands in between forest and crops as a supplement to the reclaimed vegetation. A reclaimed area of grassland and cropland was found in the northeastern gathering center, and the ratio of both was primarily dominated by cropland.
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Fig. 5. Distribution of the reclaimed areas and vegetation and their amount structure.

4.2. Spatial and temporal patterns of vegetation change in reclaimed areas

Data derived from NDVI interannual time series were used to estimate the year-by-year growth dynamics of different vegetation types in the reclaimed area by pixel aggregation using the LandTrendr algorithm. Accordingly, both temporal and spatial characteristics of forest, grassland, and cropland were identified for the period 1986–2021. It is likely that long-term temporal dynamics will interfere with our observation of vegetation growth, thereby demonstrating the growth process of the three types of vegetation reclaimed in stage form with a five-year interval to enable further analysis.
In accordance with Fig. 6, reclamation activities in the mine area vary greatly before and after 2004, with the majority of the activities occurring between 2010 and 2015. Reclamation activities began in the southwest corner of the mine area, and the reclamation area in the southwest peaked after 2010 and gradually shifted to the northeast within the next five years. As reclamation efforts continued in the southwest after 2016, the northeast has become the focus of these efforts. Different vegetation types represent important reclamation options, and all three types of vegetation have been identified in terms of spatial and temporal processes. It has been found that, forested lands were reclaimed very early, and from 1992 to 2003, forest lands dominated the vegetation reclamation process. From 2004 to 2009, however, the mine began to utilize cropland and grassland for reclamation, and both reclamation areas were comparable to forest lands, indicating that the reclamation policy had changed. The reclamation rate of cropland was much faster between 2010 and 2015 than the reclamation rate of forest and grassland, as well as the area of cropland reclamation being greater than the sum of the two, despite the fact that forest and grassland also increased during this period of time. Between 2016 and 2021, virtually no new forest was established, while cropland and grassland dominated the reclaimed area.
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Fig. 6. Spatial and temporal patterns of vegetation change in reclaimed areas for forest, grassland and cropland.

The reclamation areas for each vegetation type were counted year after year in order to demonstrate clearly the changes in area over time (Fig. 7). From 1986 to 2021, forest land, pasture land, and cropland were reclaimed in quantities of 9.10 km2, 24.54 km2, and 23.95 km2, respectively, although reclamation rates varied from one vegetation type to another. Reclamation of grassland and cropland initiated later than that of forest land, but the extremely rapid pace soon exceeded the area of forest land reclamation, especially the rapid growth of cropland in 2010. Also, vegetation growth in the total reclaimed area did not follow the rhythm of mining activities, but after 2001, widespread planting of vegetation began. In the period 1998–2012, 2001–2017, and 2004–2017, respectively, forest, grasslands, and cropland increased significantly; however, their peak rates varied significantly. Forest lands reclaimed during 2009 reached 1.91 km2, while grassland reclaimed during 2009 reached 1.96 km2 and cropland reclaimed during 2013. The new areas of the three vegetation types have not changed in recent years and are basically zero, indicating that reclamation activities at the Pingshuo mine have been discontinued. However, the effect of different vegetation varies widely in practice, and it is unclear whether the reclaimed vegetation will remain stable until 2021.
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Fig. 7. Spatial and temporal change of vegetation amount in reclaimed areas for forest, grassland and cropland, The area is in square kilometers.

4.3. Monitoring the restoration of vegetation in the reclaimed areas

For the purpose of analyzing a forest, grassland, and cropland's growth dynamics and monitoring their contribution to the final reclamation effect, CumNDVI was used to identify the stabilization process of vegetation. According to the method described in Section 2 for categorizing vegetation reclamation stages, vegetation belonging to reclamation stages in the reclamation area was extracted, which basically covered all vegetation identified in the reclamation area. According to our research, forest land, grassland, and cropland in the mine area stabilized on average after 6.50 years, 5.42 years, and 4.64 years, respectively (Fig. 8 (b)). Moreover, most of the reclaimed vegetation is still in the rapid development stage of reclamation and is primarily cultivated, with small areas of forests and grasslands reclaimed. There were two types of vegetation identified in the continuous reclamation site, vegetation aged 1–4 years and vegetation aged 4–7 years. These vegetation types were primarily cropland and grassland. It has been determined that the stability time in the forest area typically falls within the range of 7–10 years, occupying about 15.0 % of the total area (Fig. 8 (a)). Reclamation stability was determined by examining the time required by each vegetation type. Forest are generally stable vegetation; however, grasslands and croplands still require a greater period of time in order to become stable, especially those located in the northeast corner of the mine area.
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Fig. 8. Stabilization Phases in the vegetation growing season and years of stability for different vegetation. (a) shows the distribution of time spent in the stabilization phase, the legend represents the year; (b) shows the statistics of time spent at the pixel level for different vegetation types.

According to the vegetation growth curves developed for the study, as well as the stability time results, the stable state and structural proportions of the vegetation were identified (Fig. 8 (b)). There is no doubt that the types of vegetation exhibit a wide range of fluctuating and distinct stable time distribution structures, which is the result of our statistics in whole numbers of years. In view of the fact that the statistics of this study cover a period of 36 years, it is reliable to determine the number of years of stability between different kinds of vegetation, which is sufficient for us to understand the differences in growth and development status among different vegetations. In accordance with our statistics, 50.54 % of the total area reclaimed for mining has reached the stable stage of reclamation, with 41.37 % grassland and 41.93 % cropland representing the stable vegetation, while forest represents only 16.70 %. A similar proportion of structural vegetation remains among those remnants of vegetation that have not yet reached the stable stage of reclamation. In this regard, it is evident that mine reclamation is a relatively stable and continuous process, rather than concentrated on reclamation after mining is complete.

5. Discussion

5.1. Reliability of the reclamation phase division

A better understanding of the growth characteristics of reclaimed vegetation after mining is crucial for the development of a sustainable mining industry. One of the most critical factors in the reclamation process is whether the vegetation can be stabilized after years of growth. By doing so, it is possible to discern the contribution of planted vegetation to the ecological development of the mining area, which can prove very useful in subsequent replanting and replanting selection. In this study, we analyzed cumNDVI curves over time to determine reclamation stages, identified key nodes, and reconstructed the whole growth dynamics of vegetation. As feasible and highly efficient as current studies aimed at identifying vegetation recovery in mining areas based on time series data(Yang et al., 2018). Indicators of vegetation obtained from remote sensing can be obtained in a short time period, at high resolution, and with a long-term availability, which is related to the capability of Landsat satellites to mine and analyze mesoscale long-term time series remote sensing indicators(Myroniuk et al., 2020). With the advancement of remote sensing big data, these studies are now capable of providing a wide range of information regarding vegetation growth. Moreover, when compared with traditional NDVI metrics, cumNDVI incorporates the different distribution characteristics of a large number of pixels, thus eliminating the possibility of outliers. In this manner, some progress has been made towards restoring the dynamics of vegetation growth(W. Yang et al., 2022). Also, by utilizing the LandTrend algorithm on the GEE remote sensing cloud computing platform, it is possible to extract boundary and temporal information spatially by using pixel aggregation(Liu et al., 2020). By utilizing these new techniques, it has been possible to ensure accuracy of data results and the reliability of identification information when monitoring vegetation details at the site level. As an additional demonstration of the reliability of the reclamation stages delineated, the mean CumNDVI values for the three vegetation types within the study area were counted and plotted as line graphs in different years (Fig. 9). Our findings in Section 3.3 indicate that the mean stabilization time for forest land, grassland and cropland within the mine area is 6.50, 5.42 and 4.64 years, respectively. This statistic also indicates that it took eight, six, and five years for forest, grassland, and cropland to reach 70 % of maximum CumNDVI, respectively. As a result of the use of vegetation growth curves to simulate the growth of vegetation processes and evaluate their performance, the results of the modeling are slightly conservative, but they are reliable.
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Fig. 9. Statistics on the timing of stabilization cycles in different vegetation growing seasons.

5.2. Assessment of the time required for future vegetation stabilization

A monitoring of the vegetation maturation cycle in mining areas is crucial to determining the effectiveness of reclamation following mining, particularly when it comes to determining the growth cycle of different vegetation types. The results of our study indicate that 49.46 % of the vegetation within the entire mine area has not reached its peak of development, and this estimate is relatively conservative. Fig. 10 illustrates the spatial and temporal distribution of growth and development of the three types of vegetation from 1986 in order to underscore the significance of the study. The results of Fig. 10 (a-c) reveal that both woodland and cropland are still primarily at an early stage of development, with most development occurring between 1 and 7 years, whereas more than half of the grasslands are within the 7–10 year range. Therefore, Fig. 10 (d) was generated using thresholds for the growth of different vegetation to a stable stage. According to our findings, the vegetation in the mine area will gradually stabilize in the near future. In specific terms, the forest and grassland in the southwest part of the mine will gradually stabilize within two years, while the cropland in this part is on the verge of stabilizing within two years, and the northeast part will mature in 2–4 years. Grassland and cropland cover the northeast portion of the mine, where vegetation will primarily stabilize and grow over the next 2–4 years. Clearly, this is relevant to the sequence and timeframe involved in mining reclamation. Further demonstrating the accuracy of the reclamation results identification, the Pingshuo mine site has very accurate reclamation results and guidance regarding the rehabilitation of the site. Our method can also be used to provide reasonable assistance in mining or other areas where similar natural conditions and vegetation types are occurring, which allows reclamation activities to be evaluated accurately based on spatial and temporal information, rather than limited to the initial planting period.
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Fig. 10. Growth time of the three vegetation types as of 2021 and the time needed to reach steady state in the future. (a) (b) and (c) are the current growth times of forest, grassland and cropland in order, and the legend unit is year; (d) indicates the years it will take to reach a stable state of vegetation in the future.

5.3. Policy implications for land reclamation management

Using cumNDVI data over a long period of time, we are able to identify characteristics of vegetation succession at the pixel level of land reclamation areas, eliminating the impact of object-oriented classification, vegetation growth cycle, and stand characteristics on land reclamation evaluation(Kattenborn et al., 2021). In addition to being simple and efficient, such a method does not involve human subjective factors at the technical level and yields results that can be regarded as reliable and credible. There are substantial implications for future improvements in the management of land reclamation from the results obtained, which are reliable and credible. It is possible to collect many useful information about the Pinshuo mine in the future by evaluating its reclamation effect.
To begin with, the Pingshuo mine was largely an open pit mine, and a large amount of vegetation was reclaimed for the drainage field unit; however, the type of reclaimed vegetation changed as time progressed. Forest-based reclamation strategies were originally selected for the mine. However, woodlands are more resource-intensive and take longer to develop, so cropland and grassland reclamation methods have been selected. Therefore, it is important to investigate whether the change in reclamation strategy achieved both ecological and economic benefits by monitoring the effects of different vegetation types on the ecology of the reclaimed mine site. Second, due to the current reclamation pattern, the mature vegetation does not occupy the entire reclaimed area, and future attention must be paid to the unstabilized vegetation. In many studies, degradation of vegetation has been found in reclaimed mining areas, a phenomenon that undermines the initial reclamation goals and efforts, and reclaimed vegetation should not be neglected once reclamation activities are completed. There is still a need to monitor mine site vegetation reclamation for up to eight years. Finally, the planting of vegetation in mining areas is concentrated and lags behind, and in fact, “reclamation while mining” is not achieved, which will have a detrimental impact on a damaged ecosystem. Ecosystems possess an elasticity of their own, and sudden changes are likely to adversely affect their original stability. As important as it is to preserve the mining ecosystem, it is more important to maintain the pattern of reclaimed vegetation. Further research is needed to determine the impact of both disturbances on the ecology of mine sites.
As a matter of fact, we believe that our approach would be very useful for other similar mines and even other areas where vegetation reclamation is taking place. Moreover, the methodology can be replicated and scalable, and the data used can also be accessed and adapted freely. For mining sites generally, the identification of the key stages of land reclamation and the assessment of the effectiveness of that reclamation are essential parts of reclamation management(Zhang et al., 2019). It is economically viable to assess plant growth dynamics based on remote sensing data, rather than making direct investments in human and material resources without understanding their dynamics. The identification of the reclamation stage allows for finely adjusting management resources after it has been recognized. This is achieved by monitoring the dynamics of vegetation succession. The development of vegetation can also be withdrawn from all inputs and controls when it reaches a mature and stable stage. This allows regeneration to control itself without any intervention. As mining development globally changes, and environmental concerns become increasingly important, it is likely that many coal mines will require reclamation, and difficulties in obtaining mining information, budget constraints, and personnel limitations will eventually make guiding the whole process extremely challenging. With the advancement of remote sensing data, computing power, and algorithms, land reclamation monitoring has demonstrated great potential, and it is imperative that methods such as this study are properly disseminated in order to ensure a sustainable mining industry.

5.4. Limitations and prospects

Through this study, the difficulty of obtaining mining information is avoided, and the impact of open pit mine reclamation is monitored from the perspective of the dynamic changes of different types of common vegetation after reclamation. It is expected that the results of this study will be used to enhance the reclamation of mining areas and to alleviate deficiencies in existing reclamation efforts. There are, however, some problems with this study that need to be addressed. First of all, we utilized Landsat data with a spatial resolution of 30 m, which is sufficient for determining the location of the largest open-pit coal mine in China. While monitoring reclamation activities in other small and medium-sized mines requires the use of remote sensing data products with higher accuracy, taking into account the local conditions, it is also necessary to use data products from remote sensing with higher accuracy(Purwadi et al., 2020). As a result, our study is still limited in this regard. The Sentinel satellite, along with other data products, are now available as open source data for global users(Forkuor et al., 2020). Moreover, a significant number of mining reclamation activities are conducted on behalf of the state, which still enables official authorities to use high resolution remote sensing products. Moreover, for the determination of more accurate vegetation types, further research by peers in the field of remote sensing algorithm identification is needed. Third, although obtaining accurate information on the timing of mining reclamation and associated vegetation types would be valuable in validating our methodology, such information is difficult to obtain globally, especially in China. However, many studies have used visual interpretation and statistics to demonstrate the reliability of the results, so this is acceptable (Townsend et al., 2009, Wang et al., 2024). Future development of remote sensing techniques will enable more accurate identification of finer vegetation species, thereby assisting in the reclamation of land. Even though it is difficult to do this using existing capabilities, it is possible to monitor the Pingshuo mine site based on the ease of using Landsat data. As ecosystem restoration theory and technology advance rapidly, remote sensing technology has become a very promising tool for guiding land reclamation management.

6. Conclusion

An analysis of Landsat 30 m NDVI long time-series data from 1986 to 2021 was conducted in this study to develop a methodology, by which spatial and temporal dynamic information about the growth of different vegetation types is identified and the effect of vegetation restoration following reclamation of mining areas assessed. After mining disturbance, we determined the reclamation area and vegetation types of the largest open pit mine in China, and the evolution of vegetation growth curves and the key nodes of reclamation effect were monitored. A mining area was found to contain two large and small reclamation areas in the southwest and northeast of the mine. Forest land, grassland and cropland accounted for 15.79 %, 42.61 % and 41.59 % of the overall reclamation area, respectively. As well as this, reclamation activities were concentrated between 2010 and 2015, and the whole reclamation process commenced in the southwest part of the mine area before shifting to the northeast. During the previous period, forest land reclamation gradually changed to grassland reclamation, however since 2010 an increase in arable land has been observed. As a result of our analysis of the forests, grasslands, and crops in the mine area, we found that the stabilization time was on average 6.50 years, 5.42 years, and 4.64 years, respectively. A total of 50.54 % of the area had reached the stabilization stage of reclamation. The percentage of stable vegetation was divided between grassland and cropland, with trees accounting for only 16.70 % of the vegetation. There are several advantages to this study, including the potential to use it to monitor vegetation reclamation in any mining area, or similar areas. As the method is simple and inexpensive, it can be applied widely.

CRediT authorship contribution statement

Jiwang Guo: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Methodology, Formal analysis, Data curation, Conceptualization. Tingting He: Software, Methodology. Wenkai Zhang: Validation. Wu Xiao: Writing – review & editing, Supervision, Resources, Methodology. Kaige Lei: Formal analysis, Data curation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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