Mapping and detection of land use change in a coal mining area using object-based image analysis

Pei, Wenming; Yao, Shuchun; Knight, Joseph F.; Dong, Shouhua; Pelletier, Keith C.; Rampi, Lian P.; Wang, Yan; Klassen, Jim

doi:10.1007/s12665-017-6444-9

Cited by 30 publications

(13 citation statements)

References 49 publications

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“…The results showed that the conversion of farmland into built-up land and water bodies caused losses from the SOC pool, which is in agreement with significantly decreased stored SOC caused by coal mining in land use with substantial reductions of farmland and woodland [23,24]. All of the farmland and woodland areas were converted into mining-industrial areas and the open pit at Pingshuo Opencast Mine from 1976 to 2009, which increased the loss of SOC to 250,966.72 tonnes [50].…”

Section: Reason For Soc Density Decrease In Mining-industrial Land Ansupporting

confidence: 61%

“…Therefore, A t0 served as the control area in this study. The most important factor that affects the SOC pool at a regional scale is land-use change [22][23][24]. In Jiuli Mining Area with a high groundwater level, the land use types were markedly different in the subsidence area before and after coal mining.…”

Section: Methods For Evaluating the Effect Of Coal Mining On The Soil mentioning

confidence: 99%

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Effect of Underground Coal Mining on the Regional Soil Organic Carbon Pool in Farmland in a Mining Subsidence Area

et al. 2019

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The soil organic carbon (SOC) pool in farmland is changing rapidly due to human activities, thereby greatly affecting the regional and global environment, as well as influencing soil fertility and crop yields. The present study investigated the effects of underground coal mining on the regional SOC pool in farmland in the Jiuli Mining Area of Xuzhou City in China as a typical coal mining region based on field sampling, chemical analysis, model construction, and spatial analysis using the software of ArcGIS. The results showed that in the mining subsidence area, spatial variations in the SOC content and soil bulk density were mainly caused by structural factors (mining subsidence, subsidence waterlogging, and other structural factors due to coal mining) at a regional scale. SOC storage in farmland soil decreased sharply in non-waterlogged subsidence farmland and seasonally waterlogged subsidence farmland in the areas with mining, whereas the SOC storage increased in waterlogged wetland after coal mining. The SOC was reduced by 102,882 tonnes (32.81%) compared with the original SOC stock as a consequence of coal mining, and thus the effect of underground coal mining on the regional SOC pool in farmland was characterized as a carbon loss process. Land-use changes, soil degradation and erosion contributed almost equally to the carbon loss process in the study area. The results of this study may facilitate evaluations of low-carbon land reclamation and ecological compensation in mining areas.

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Section: Reason For Soc Density Decrease In Mining-industrial Land Ansupporting

confidence: 61%

Section: Methods For Evaluating the Effect Of Coal Mining On The Soil mentioning

confidence: 99%

Effect of Underground Coal Mining on the Regional Soil Organic Carbon Pool in Farmland in a Mining Subsidence Area

et al. 2019

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“…These disadvantages limit application of hyperspectral images. Other studies concentrated on medium and high spatial resolution satellites [14][15][16]. The America National Land Cover Database (NLCD) based on Landsat TM was completed in the 1990s by the Multi-Resolution Land Characteristics (MRLC) Consortium, and revealed that the classification accuracy of LCCMA is lower than 60% with 48% omission [17].…”

Section: Development Of Land Cover Classification In Open-pit Mining mentioning

confidence: 99%

“…In this case, the product is of rather weaker value compared to a product with more detail [13,16,21]. This could be attributed to the low spatial resolution of the employed remote sensing data, lack of a remote sensing representation model, feature selection method, and classification algorithm used.…”

Section: Development Of Land Cover Classification In Open-pit Mining mentioning

confidence: 99%

“…References opencast stope, mineral processing land, dumping site 3 [35] reclaimed herbaceous vegetation, reclaimed woody vegetation, barren (including haul roads, active quarries, land disturbed by mining) 3 [22][23][24] mine-reclaimed grassland (including reclaimed land within mine sites and valley fills dominated by herbaceous vegetation) 1 [25] open stope, stripping area, waste-dump area, mine industrial area 4 [26] active, disturbed vegetation and pasture, rehabilitation, remnant, spoil/waste, and water management 6 [27] coal deposit, over burden dump, mine dump 3 [28] tree cover, dense grass, sparse grass, bare ground 4 [29] coal mining excavation cities, coal dump areas 2 [30] active mine, reclaimed mine (including grass, woodland, forest) 2 [16] opencast mining (coal), overburden dump 2 [12] bare tailings, exposed lime, water/wet tailings 3 [31] tailings zone, dry vegetation zone, transition zone, vegetated islands 4 [32] mine, dump, coal stockpile 3 [33,34] …”

Section: Sub-classes Of Mining Areasmentioning

confidence: 99%

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A Review of Fine-Scale Land Use and Land Cover Classification in Open-Pit Mining Areas by Remote Sensing Techniques

Chen

et al. 2017

Remote Sensing

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Abstract:Over recent decades, fine-scale land use and land cover classification in open-pit mine areas (LCCMA) has become very important for understanding the influence of mining activities on the regional geo-environment, and for environmental impact assessment procedure. This research reviews advances in fine-scale LCCMA from the following aspects. Firstly, it analyzes and proposes classification thematic resolution for LCCMA. Secondly, remote sensing data sources, features, feature selection methods, and classification algorithms for LCCMA are summarized. Thirdly, three major factors that affect LCCMA are discussed: significant three-dimensional terrain features, strong LCCMA feature variability, and homogeneity of spectral-spatial features. Correspondingly, three key scientific issues that limit the accuracy of LCCMA are presented. Finally, several future research directions are discussed: (1) unitization of new sensors, particularly those with stereo survey ability; (2) procurement of sensitive features by new sensors and combinations of sensitive features using novel feature selection methods; (3) development of robust and self-adjusted classification algorithms, such as ensemble learning and deep learning for LCCMA; and (4) application of fine-scale mining information for regularity and management of mines.

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The long‐term effects of underground mining on the growth of tree, shrub, and herb communities in arid and semiarid areas in China

Yang

Hou

et al. 2020

Land Degrad Dev

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Underground mining (as opposed to open‐cast) often causes large‐scale subsidence, leading to various types of disturbances to surface vegetation. Adequate quantitative assessment of the long‐term effects of underground mining on the growth of different plant communities is important and still lacking. To address these issues, a vegetation growth contract model (VGCM) was proposed, and six indicators including the growth trend (GT), annual growth (AG), normalized spectrum entropy (Hsn), as well as the average value of annual‐average normalized difference vegetation index (NDVI; ANDVIave), annual‐maximum NDVI (ANDVImax), and annual‐minimum NDVI (ANDVImin) were selected. The long‐term effects of underground mining (EM) on the herb, shrub, and tree communities in the Nanjiao mining area, China, from 1987 to 2017 were evaluated. The results show that the plant communities, which maintained the same type in the areas influenced and not influenced by mining, accounting for 48.07% and 46% of the total area, respectively. As for these plant communities, underground mining had a significant negative effect on the AG, ANDVIave, and ANDVImax of both the herb and tree communities, while it had a positive effect on the GT and Hsn of the shrub community. Overall, underground mining had a negative effect on these three types of plant communities, and the EMs of the herb, tree, and shrub communities were −15.10, −6.79, and −4.03%, respectively. This research could provide a reference for evaluating the long‐term effects of mining activities on vegetation, and also give more insights into the effects of underground mining on different plant communities.

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Mapping and detection of land use change in a coal mining area using object-based image analysis

Cited by 30 publications

References 49 publications

Effect of Underground Coal Mining on the Regional Soil Organic Carbon Pool in Farmland in a Mining Subsidence Area

Effect of Underground Coal Mining on the Regional Soil Organic Carbon Pool in Farmland in a Mining Subsidence Area

A Review of Fine-Scale Land Use and Land Cover Classification in Open-Pit Mining Areas by Remote Sensing Techniques

The long‐term effects of underground mining on the growth of tree, shrub, and herb communities in arid and semiarid areas in China

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