Monitoring and Evaluating Restoration Vegetation Status in Mine Region Using Remote Sensing Data: Case Study in Inner Mongolia, China

Wang, Wei; Liu, Rongyuan; Gan, Fuxi; Ping, Zhou; Zhang, Xiangwen; Ding, Ling

doi:10.3390/rs13071350

Cited by 28 publications

(16 citation statements)

References 48 publications

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“…As an important ecological barrier in the northern region of China, the ecological condition in Inner Mongolia has an important impact in North China, and exploring the driving mechanism of vegetation change is an important prerequisite for solving ecological problems. Climate change and human activities are the main mechanisms studied in vegetation research [38,39]. The Fifth Assessment Report of the Intergovernmental Panel on Climate Change states that many aspects of vegetation phenology, composition, and productivity have been affected by global warming over the past 30 years [40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019

et al. 2021

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Inner Mongolia in China is a typically arid and semi-arid region with vegetation prominently affected by global warming and human activities. Therefore, investigating the past and future vegetation change and its impact mechanism is important for assessing the stability of the ecosystem and the ecological policy formulation. Vegetation changes, sustainability characteristics, and the mechanism of natural and anthropogenic effects in Inner Mongolia during 2000–2019 were examined using moderate resolution imaging spectroradiometer normalized difference vegetation index (NDVI) data. Theil–Sen trend analysis, Mann–Kendall method, and the coefficient of variation method were used to analyze the spatiotemporal variability characteristics and sustained stability of the NDVI. Furthermore, a trend estimation method based on a Seasonal Trend Model (STM), and the Hurst index was used to analyze breakpoints and change trends, and predict the likely future direction of vegetation, respectively. Additionally, the mechanisms of the compound influence of natural and anthropogenic activities on the vegetation dynamics in Inner Mongolia were explored using a Geodetector Model. The results show that the NDVI of Inner Mongolia shows an upward trend with a rate of 0.0028/year (p < 0.05) from 2000 to 2019. Spatially, the NDVI values showed a decreasing trend from the northeast to the southwest, and the interannual variation fluctuated widely, with coefficients of variation greater than 0.15, for which the high-value areas were in the territory of the Alxa League. The areas with increased, decreased, and stable vegetation patterns were approximately equal in size, in which the improved areas were mainly distributed in the northeastern part of Inner Mongolia, the stable and unchanged areas were mostly in the desert, and the degraded areas were mainly in the central-eastern part of Inner Mongolia, it shows a trend of progressive degradation from east to west. Breakpoints in the vegetation dynamics occurred mainly in the northwestern part of Inner Mongolia and the northeastern part of Hulunbuir, most of which occurred during 2011–2014. The future NDVI trend in Inner Mongolia shows an increasing trend in most areas, with only approximately 10% of the areas showing a decreasing trend. Considering the drivers of the NDVI, we observed annual precipitation, soil type, mean annual temperature, and land use type to be the main driving factors in Inner Mongolia. Annual precipitation was the first dominant factor, and when these four dominant factors interacted to influence vegetation change, they all showed interactive enhancement relationships. The results of this study will assist in understanding the influence of natural elements and human activities on vegetation changes and their driving mechanisms, while providing a scientific basis for the rational and effective protection of the ecological environment in Inner Mongolia.

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Section: Introductionmentioning

confidence: 99%

Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019

et al. 2021

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“…However, due to the various sizes, complex quantities, and scattered distribution of the types of ground features in the mining area, the traditional manual sampling method is inadequate for accurately classifying the types of ground features in the mining area 9 . Exploring high-performance feature classification methods based on remote sensing data is gradually becoming an essential tool for ecological monitoring in mining areas 10 – 14 …”

Section: Introductionmentioning

confidence: 99%

Identification of open-pit mines and surrounding vegetation on high-resolution satellite images based on improved bilateral segmentation network semantic segmentation model

Chen,

Yang,

Wang

et al. 2023

J. Appl. Rem. Sens.

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“…Relying only on traditional ground sampling experiments to monitor vegetation condition requires tremendous manpower and financial resources, which is not realistic. Compared with traditional methods, remote sensing monitoring can accomplish large-scale vegetation monitoring and provide effective and timely vegetation information [6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Vegetation Index of Reclamation Mining Areas Calculated by Multi-Source Remote Sensing Data

Bai

et al. 2022

Land

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Following vegetation reclamation in mining areas, secondary damage may occur at any time, especially in locations that have been mined for decades or even hundreds of years. Effective monitoring strategies are required to accurately assess plant growth and to detect the ecological effects of reclamation. Single satellite monitoring is often difficult to ensure vegetation monitoring needs, therefore multi-source remote sensing is preferred. Different sensor parameters and variation in spectral bands can lead to differences in the type of data obtained, and subsequently, methods for evaluating these differences are required for simultaneous sensor/band use. In this study, NDVI was selected to characterize the vegetation growth of the Antaibao Open-pit Coal Mine Dump by analyzing the correlation between different types of sensors (Landsat 8, HJ, Sentinel-2) and vegetation greenness in order to facilitate satellites’ replacement and supplement. Results show that: (1) Landsat 8 and Sentinel-2 satellite have a high relevance for monitoring the vegetation, but the correlation between these two sensors and HJ is relatively low, (2) the correlation between NDVI values varied by vegetation type, tree (R = 0.8698) > combined grass, shrub and tree (R = 0.7788) > grass (R = 0.7619) > shrub (R = 0.7282), and (3) the phenomenon of “Low value is high, high value is low” in the NDVI value with HJ satellite monitoring may have been caused by a weak signal strength and low sensitivity of the HJ sensor. Comparing the correlation of multi-source sensors to monitor the vegetation in the mining areas can be helpful to determine the alternative supplement of sensors through conversion formulas, which are helpful in realizing the long-term monitoring of dumps and detecting reclamation response in mining areas.

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Monitoring and Evaluating Restoration Vegetation Status in Mine Region Using Remote Sensing Data: Case Study in Inner Mongolia, China

Cited by 28 publications

References 48 publications

Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019

Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019

Identification of open-pit mines and surrounding vegetation on high-resolution satellite images based on improved bilateral segmentation network semantic segmentation model

Comparison of the Vegetation Index of Reclamation Mining Areas Calculated by Multi-Source Remote Sensing Data

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