Response characteristics of vegetation ecology and land use changes tocoal mining in semi-arid regions

Authors

DU Zhen, School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaFollow
ZHANG Maosheng, School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaFollow
YUN Shaoqi, Shaanxi Spatial Planning Research Institute Co., Ltd., Xi’an 710075, China
SUN Pingping, School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, China
FENG Li, School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Abstract

Objective In semi-arid regions, intensive coal mining is prone to induce vegetation degradation and reshape the land use pattern. To identify the ecological impacts of coal mining and provide support for differential restoration, this study presents a quantitative comparison of changes in vegetation, soil moisture content, and land use types between mining-affected and non-mining areas.Methods Yulin City, Shaanxi Province was investigated in this study. Two parallel baseline frameworks were constructed: mining-affected and non-mining areas, which were compared under the sandy-fluvial terrace and loess hilly-gully landforms. The annual maximum normalized difference vegetation index (NDVI) derived from Landsat imagery was used to analyze pixel-level trends and significance (Z and P) using the Theil-Sen estimator and Mann-Kendall significance tests. Soil moisture content data on a spatiotemporally continuous 1-km scale (2000‒2020) were used to calculate average moisture content across various areas. Furthermore, primary land-use transition pathways were quantified by establishing the transfer matrix of land use types using multi-stage land-use data.Results The NDVI values of Yulin City increased overall, with averages rising from 0.273 5 to 0.497 4 with an average annual increase of 0.011 2. These values revealed that areas with significantly improved vegetation reached 17 224.17 km², those with slightly improved vegetation spanned an area of 23 686.34 km², and degraded areas covered an area of 493.23 km² (accounting for approximately 0.91%). Coal mining exerted significant effects. Specifically, the NDVI values of coal mining-affected areas increased by 92.17%, which was higher than that in non-mining-affected areas (81.31%). The mining-affected areas showed an annual growth rate of NDVI values reaching up to 4.85%, substantially surpassing that of non-mining-affected areas (4.33%). Additionally, the mining-affected areas generally exhibited high Z values and P<0.05, indicating a stable upward trend. In contrast, most non-mining-affected areas in the southern part showed P>0.08, indicating insignificant changes in NDVI values. For the sandy-fluvial terrace landform, the NDVI growth rate of mining-affected areas was 13.64% higher than that of non-mining-affected areas. In contrast, for the loess hilly-gully landform, the NDVI growth rate of mining-affected areas proved 2.11% higher than that of non-mining-affected areas. These findings enhanced the cross-landform comparability. The soil moisture content improved simultaneously in the mining-affected and non-mining-affected areas while remaining low overall. To be specific, the average soil moisture content increased by 25.1% from 0.202 5 m³/m³ to 0.253 5 m³/m³ in the whole study area, increasing by 27.0% from 0.180 0 m³/m³ to 0.228 6 m³/m³ in mining-affected areas and by +24.3% from 0.212 0 m³/m³ to 0.263 5 m³/m³ in the non-mining-affected areas. Furthermore, the soil moisture content in non-mining-affected areas remained higher than that in mining-affected areas, with a stable increased amplitude. Regarding the reshaping of the land use pattern and its correspondence to mining activity, the mining-affected areas showed grassland and forestland retention rates of 88.67% and 88.76%, respectively, lower than those (91.40% and 91.96%) of the non-mining-affected areas. In contrast, the construction land in the mining-affected areas expanded from 42.9 km² to 525.1 km² (increase: 12.2 times), while that in non-mining-affected areas grew from 119.6 km² to 381.0 km²(increase: 3.19 times).Conclusions From 2000 to 2020, Yulin City showed significantly improved fractional vegetation cover, with an increase of 82%, which, however, was accompanied by an increasingly significant spatial heterogeneity. Compared to the non-mining-affected areas, the mining-affected areas exhibited lower baseline NDVI levels, while their NDVI values fluctuated more significantly under enhanced soil moisture content. Additionally, the mining-affected areas demonstrated lower grassland and forestland retention rates compared to the non-mining-affected areas. Furthermore, these areas showed far higher degrees of construction land expansion, which was highly consistent with localized vegetation degradation spatially. This study quantitatively revealed the superimposed effects of mining disturbance and ecological restoration, highlighted the predominant controlling effect of the soil moisture content on vegetation response mechanisms, and proposed a zonal management and control framework integrating coal mining planning and ecological sensitivity zoning. The results of this study will provide new scientific support for differential restoration and policy formulation for semi-arid mining regions.

Keywords

semi-arid region, coal mining, ecological environmental effect, land use change, vegetation index

DOI

10.12363/issn.1001-1986.25.05.0410

Recommended Citation

DU Zhen, ZHANG Maosheng, YUN Shaoqi, et al. (2025) "Response characteristics of vegetation ecology and land use changes tocoal mining in semi-arid regions," Coal Geology & Exploration: Vol. 53: Iss. 10, Article 8.
DOI: 10.12363/issn.1001-1986.25.05.0410
Available at: https://cge.researchcommons.org/journal/vol53/iss10/8

Reference

[1] 徐永，聂浩刚，冀显坤，等. 陕北榆林地区植被好转背景下面临的生态环境新挑战[J]. 西北地质，2024，57(5)：308−318.

XU Yong，NIE Haogang，JI Xiankun，et al. Ecological risk of Yulin area in northern Shaanxi under the condition of vegetation coverage improving[J]. Northwestern Geology，2024，57(5)：308−318.

[2] 佟彪，党安荣，周宏宇. 无定河流域城镇聚落的历史演变与人地耦合[J]. 自然资源学报，2021，36(1)：38−54.

TONG Biao，DANG Anrong，ZHOU Hongyu. The historical evolution of urban settlements and man–land coupling in Wuding River Basin[J]. Journal of Natural Resources，2021，36(1)：38−54.

[3] 汤伏全，杨倩. 西部黄土高原矿区采煤沉陷多源遥感监测技术进展与展望[J]. 煤炭科学技术，2023，51(12)：9−26.

TANG Fuquan，YANG Qian. Progress and prospects of multi–source remote sensing monitoring technology for coal mining subsidence in mining areas of the western Loess Plateau[J]. Coal Science and Technology，2023，51(12)：9−26.

[4] 王双明，申艳军，孙强，等. 西部生态脆弱区煤炭减损开采地质保障科学问题及技术展望[J]. 采矿与岩层控制工程学报，2020，2(4)：043531.

WANG Shuangming，SHEN Yanjun，SUN Qiang，et al. Scientific issues of coal detraction mining geological assurance and their technology expectations in ecologically fragile mining areas of Western China[J]. Journal of Mining and Strata Control Engineering，2020，2(4)：043531.

[5] 程洋，刘薇，张娜，等. 神东采煤沉陷区生态环境一体化监测技术与应用[J]. 煤田地质与勘探，2024，52(12)：143−154.

CHENG Yang，LIU Wei，ZHANG Na，et al. An integrated ecosystem monitoring technology for coal mining subsidence areas and its application in the Shendong mining area[J]. Coal Geology & Exploration，2024，52(12)：143−154.

[6] 张思锋，马策，张立. 榆林大柳塔矿区乌兰木伦河径流量衰减的影响因素分析[J]. 环境科学学报，2011，31(4)：889−896.

ZHANG Sifeng，MA Ce，ZHANG Li. The regulators of runoff of the Ulan Moron River in the Daliuta mine area：The effects of mine coal[J]. Acta Scientiae Circumstantiae，2011，31(4)：889−896.

[7] 李禹凝，王金满，张雅馥，等. 干旱半干旱煤矿区土壤水分研究进展[J]. 土壤，2023，55(3)：494−502.

LI Yuning，WANG Jinman，ZHANG Yafu，et al. Soil water in arid and semi–arid mining areas：A review[J]. Soils，2023，55(3)：494−502.

[8] 黄磊，程爱华，刘晓河，等. 基于PLUS–InVEST模型的榆林市土地利用变化与碳储量时空演变模拟及预测[J/OL]. 环境科学，2025：1–15 (2025-07-11) [2025-07-11]. https://link.cnki.net/doi/10.13227/j.hjkx.202501116

HUANG Lei，CHENG Aihua，LIU Xiaohe，et al. Simulation and prediction of land use change and carbon storage spatiotemporal evolution in Yulin City based on the PLUS–InVEST model[J/OL]. Environmental Science，2025：1–15 (2025-07-11) [2025-07-11]. https://link.cnki.net/doi/10.13227/j.hjkx.202501116.

[9] 何祖鑫，耿广坡，郑若琳. 退耕还林还草背景下黄河流域陕西段植被覆盖度变化特征及驱动力分析[J/OL]. 环境科学，2025：1–19 (2025-03-10) [2025-03-10]. https://link.cnki.net/doi/10.13227/j.hjkx.202409154

HE Zuxin，GENG Guangpo，ZHENG Ruolin. Variation characteristics and its driving forces of fractional vegetation cover in the Shaanxi section of the Yellow River Basin under the background of the grain for green program[J/OL]. Environmental Science，2025：1–19 (2025-03-10) [2025-03-10]. https://link.cnki.net/doi/10.13227/j.hjkx.202409154.

[10] 季翔林，阎跃观，郭伟，等. 耦合遥感生态指数模型的山西省及规划矿区生态环境评价[J]. 煤田地质与勘探，2023，51(3)：103−112.

JI Xianglin，YAN Yueguan，GUO Wei，et al. Ecological environment assessment of Shanxi Province and planned mining area based on coupling Remote Sensing Ecological Index (RSEI) model[J]. Coal Geology & Exploration，2023，51(3)：103−112.

[11] 方良成，陈永春，安士凯，等. 基于Landsat时序数据的高潜水位煤矿区植被扰动分析[J]. 煤田地质与勘探，2021，49(2)：207−215.

FANG Liangcheng，CHEN Yongchun，AN Shikai，et al. Vegetation disturbance analysis in coal mining area with high ground water level based on Landsat time series NDVI data[J]. Coal Geology & Exploration，2021，49(2)：207−215.

[12] 王春博，高鹏远，左伟昆，等. 基于RS和GIS的河北省主要采煤区土壤侵蚀强度分析[J]. 环境生态学，2025，7(2)：9−14.

WANG Chunbo，GAO Pengyuan，ZUO Weikun，et al. Analysis of soil erosion intensity in major coal mining areas of Hebei Province based on RS and GIS[J]. Environmental Ecology，2025，7(2)：9−14.

[13] 刘健松，蔡音飞，王雅茹，等. 黄土高原典型矿区生态脆弱性时空特征与驱动分析[J]. 水土保持研究，2025，32(4)：217−226.

LIU Jiansong，CAI Yinfei，WANG Yaru，et al. Analysis of spatiotemporal characteristics and driving factors of ecological vulnerability in a typical coal mining area on the Loess Plateau[J]. Research of Soil and Water Conservation，2025，32(4)：217−226.

[14] 黄安东，赵明松，郜敏，等. 1980–2020年安徽省土地利用时空演化特征[J]. 科学技术与工程，2022，22(11)：4627−4635.

HUANG Andong，ZHAO Mingsong，GAO Min，et al. Characteristics of spatio–temporal evolution of land use in Anhui Province from 1980 to 2020[J]. Science Technology and Engineering，2022，22(11)：4627−4635.

[15] 刘婉如，陈春波，罗格平，等. 巴尔喀什湖流域土地利用/覆被变化过程与趋势[J]. 干旱区研究，2021，38(5)：1452−1463.

LIU Wanru，CHEN Chunbo，LUO Geping，et al. Change processes and trends of land use/cover in the Balkhash Lake Basin[J]. Arid Zone Research，2021，38(5)：1452−1463.

[16] 刘畅，张红，张霄羽，等. 半干旱地区矿区土地利用时空演变与预测[J]. 干旱区研究，2022，39(1)：292−300.

LIU Chang，ZHANG Hong，ZHANG Xiaoyu，et al. Spatio–temporal evolution and prediction of land use in semi–arid mining areas[J]. Arid Zone Research，2022，39(1)：292−300.

[17] 马增辉. 全域土地综合整治的可行性分析：以榆林市金鸡滩镇项目为例[J]. 粮食科技与经济，2020，45(8)：55–57

[18] 申涛，林平选，张晓团，等. 陕北能源化工基地资源特征与环境承载力[J]. 中国煤炭地质，2015，27(10)：37−40.

SHEN Tao，LIN Pingxuan，ZHANG Xiaotuan，et al. Northern Shaanxi energy chemical industry base resource characteristics and environmental bearing capacity[J]. Coal Geology of China，2015，27(10)：37−40.

[19] 张茂省，党学亚. 干旱半干旱地区水资源及其环境问题：陕北榆林能源化工基地例析[M]. 北京：科学出版社，2014.

[20] YANG Jilin，DONG Jinwei，XIAO Xiangming，et al. Divergent shifts in peak photosynthesis timing of temperate and alpine grasslands in China[J]. Remote Sensing of Environment，2019，233：111395.

[21] ZHENG C，JIA L，ZHAO T. (2022). Global daily surface soil moisture dataset at 1–km resolution (2000–2020). National Tibetan Plateau/Third Pole Environment Data Center. https://doi. org/10.11888/RemoteSen.tpdc.272760.

[22] 徐新良，刘纪远，张树文，等. 中国多时期土地利用遥感监测数据集(CNLUCC). 资源环境科学数据注册与出版系统(http://www.resdc.cn/DOI)，2018. DOI：10.12078/2018070201.

[23] BI Yinli，LIU Tao，PEI Yanru，et al. Landscape analysis and assessment of ecosystem stability based on land use and multitemporal remote sensing：A case study of the Zhungeer open–pit coal mining area[J]. Remote Sensing，2025，17(7)：1162.

[24] CHEN Shi，QIN Jiwei，DONG Shuning，et al. Assessing the impact of land use changes on ecosystem service values in coal mining regions using google earth engine classification[J]. Remote Sensing，2025，17(7)：1139.