Monitoring of Vegetation Disturbance and Restoration at the Dumping Sites of the Baorixile Open-Pit Mine Based on the LandTrendr Algorithm

Abstract

Overstocked dumping sites associated with open-pit coal mining occupy original vegetation areas and cause damage to the environment. The monitoring of vegetation disturbance and restoration at dumping sites is important for the accurate planning of ecological restoration in mining areas. This paper aimed to monitor and assess vegetation disturbance and restoration in the dumping sites of the Baorixile open-pit mine using the LandTrendr algorithm and remote sensing images. Firstly, based on the temporal datasets of Landsat from 1990 to 2021, the boundaries of the dumping sites in the Baorixile open-pit mine in Hulunbuir city were extracted. Secondly, the LandTrendr algorithm was used to identify the initial time and duration of vegetation disturbance and restoration, while the Normalized Difference Vegetation Index (NDVI) was used as the input parameter for the LandTrendr algorithm. Thirdly, the vegetation restoration effect at the dumping sites was monitored and analyzed from both temporal and spatial perspectives. The results showed that the dumping sites of the Baorixile open-pit mine were disturbed sharply by the mining activities. The North dumping site, the South dumping site, and the East dumping site (hereinafter referred to as the North site, the South site, and the East site) were established in 1999, 2006, and 2010, respectively. The restored areas were mainly concentrated in the South site, the East site, and the northwest of the North site. The average restoration intensity in the North site, South site, and East site was 0.515, 0.489, and 0.451, respectively, and the average disturbance intensity was 0.371, 0.398, and 0.320, respectively. The average restoration intensity in the three dumping sites was greater than the average disturbance intensity. This study demonstrates that the combination of temporal remote sensing images and the LandTrendr algorithm can follow the vegetation restoration process of an open-pit mine clearly and can be used to monitor the progress and quality of ecological restoration projects such as vegetation restoration in mining areas. It provides important data and support for accurate ecological restoration in mining areas.

Mapping Forest Abrupt Disturbance Events in Southeastern China—Comparisons and Tradeoffs of Landsat Time Series Analysis Algorithms

“…In view of such advantages, more and more long time series forest monitoring missions have taken LandTrendr algorithm as the first choice, whether it is applied in integration with other methods such as VCT, EWMACD, MIICA [44], and CCDC [45] or adding deep learning algorithms to further refine the monitoring results [16,46], and these efforts have made LandTrendr a reliable and efficient algorithm suitable for further developments. From a broader perspective, the perfect presentation of GEE version LandTrendr can be expanded not only in the field of forest monitoring but also in other domains related to forest change, such as impervious surface expansion [19], fire-induced forest recovery and renewal [47], and ecological monitoring of open pit mines [48].…”

Interannual Monitoring of Cropland in South China from 1991 to 2020 Based on the Combination of Deep Learning and the LandTrendr Algorithm

“…Originally, the LandTrendr algorithm was employed to study disturbances and recovery trends in forests [41]. Later, it became common for tracking changes and recovery in vegetation [42,43], water [44], and built-up land [45]. We developed the LandTrendr algorithm by leveraging its ability to detect abrupt and gradual changes.…”

A LandTrendr Algorithm-Based Study of Forest Disturbance from 2000 to 2020 in Jilin Province, China

“…The Normalised Burn Ratio (NBR) index [19] can be used to monitor forest fires and assess their severity [20]. The Normalised Difference Vegetation index (NDVI) [21] can be used to measure vegetation canopy leaf density and greenness and to characterise vegetation growth [22][23][24]. NDVI index is a useful indicator for monitoring vegetation growth in an ecosystem and is widely used to analyse vegetation change [25,26].…”