西安科技大学 测绘科学与技术学院,陕西,西安,710054
纸质出版:2023
移动端阅览
文帆, 陈秋计, 黄兰, 等. 1986—2021年彬长矿区植被覆盖度时空变化及其影响因子[J]. 水土保持通报, 2023,43(6):304-310.
Wen Fan, Chen Qiuji, Huang Lan, et al. Spatial-temporal Variation of Vegetation Coverage and Its Influencing Factors in Binchang Mining Area from 1986 to 2021[J]. Bulletin of Soiland Water Conservation, 2023, 43(6): 304-310.
文帆, 陈秋计, 黄兰, 等. 1986—2021年彬长矿区植被覆盖度时空变化及其影响因子[J]. 水土保持通报, 2023,43(6):304-310. DOI: 10.13961/j.cnki.stbctb.2023.06.036.
Wen Fan, Chen Qiuji, Huang Lan, et al. Spatial-temporal Variation of Vegetation Coverage and Its Influencing Factors in Binchang Mining Area from 1986 to 2021[J]. Bulletin of Soiland Water Conservation, 2023, 43(6): 304-310. DOI: 10.13961/j.cnki.stbctb.2023.06.036.
[目的] 分析彬长矿区植被覆盖度变化特征及空间分布影响因素,判别矿区的生态状况断,为矿区复垦和生态恢复提供科学参考和理论依据。[方法] 基于Google Earth Engine云平台,获取1986—2021年30 m分辨率Landsat Surface Reflectance Tier 1 Data(地表反射率数据),基于像元二分模型,采用趋势分析法、F检验等方法对彬长矿区植被覆盖度多年时空变化作出定量分析;在此基础上运用地理探测器对植被覆盖度的空间分异性进行地理因子解析。[结果] ①1986—2021年彬长矿区植被覆盖度改善状况较好,总体呈现增长趋势,平均增长率为0.64%/a;研究区多年平均植被覆盖度水平较高,中覆盖度及以上面积占87.14%,空间分布上呈现“东南高,西北低”的特点。②植被覆盖度变化趋势上,研究区以显著改善区域为主,其面积所占比例为56.65%,但仍有些许地区植被显著退化,主要集中在靠近城市河流道路区域。③各因子对植被覆盖度的影响大小排序为:坡度>高程>年降水>GDP>人口密度>年均温>植被类型>土壤类型,坡度与年降水交互作用对植被覆盖度空间分异性影响最强。[结论] 1986—2021年彬长矿区植被覆盖状况良好,整体呈现显著增长趋势,植被改善情况明显,坡度为影响研究区植被覆盖度空间分异性的主导因子。
[Objective] The change characteristics and influencing factors of vegetation coverage and its spatial distribution in the Binchang mining area were analyzed
and the ecological status of the mining area was evaluated in order to provide a scientific reference and theoretical basis for reclamation and ecological restoration of the mining area. [Methods] Landsat Surface Reflectance Tier 1 data (30 m resolution) from 1986 to 2021 were obtained from the Google Earth Engine cloud platform. Based on a pixel binary model
the trend analysis method and F-test were used to quantitatively analyze the temporal and spatial variation of vegetation coverage in the Binchang mining area over many years. Geodetector was used to analyze the spatial heterogeneity of vegetation coverage. [Results] ① Vegetation coverage in the Binchang mining area increased from 1986 to 2021
and the overall growth trend was good (average growth rate of 0.64%/a). The average multi-year vegetation coverage in the study area was relatively high
with the area of medium coverage and greater accounting for 87.14% of the total area. The spatial distribution was characterized as "higher in the southeast and lower in the northwest". ② In terms of the change trend of vegetation coverage
the study area was dominated by significantly improved areas
accounting for 56.65% of the total area. However
there were still some areas with significant vegetation degradation
mainly concentrated in the area near urban river roads. ③ The factors influencing vegetation coverage followed the order of slope > elevation > annual precipitation > GDP> population density > annual average temperature > vegetation type > soil type. The interaction between slope and annual precipitation had the strongest influence on the spatial heterogeneity of vegetation coverage. [Conclusion] From 1986 to 2021
the vegetation coverage in the Binchang mining area was good
showing a significant growth trend as a whole
and the vegetation improvement was obvious. The dominant factor affecting the spatial heterogeneity of vegetation coverage in the study area was slope.
卞正富,雷少刚,刘辉,等.风积沙区超大工作面开采生态环境破坏过程与恢复对策[J].采矿与安全工程学报,2016,33(2):305-310.
陈昀琳.基于Landsat和MODIS NDVI时序数据的青海湖流域植被覆盖度提取及其变化分析[D].北京:中国地质大学(北京),2019.
张远东,张笑鹤,刘世荣.西南地区不同植被类型归一化植被指数与气候因子的相关分析[J].应用生态学报,2011,22(2):323-330.
李卓,孙然好,张继超,等.京津冀城市群地区植被覆盖动态变化时空分析[J].生态学报,2017,37(22):7418-7426.
谭学玲,闫庆武,王瑾,等.榆神府矿区植被覆盖的动态变化及其影响因素[J].生态学杂志,2018,37(6):1645-1653.
杜华栋,宁本燕,拜梦童,等.1990-2019年榆神府矿区不同地貌植被覆盖度变化及驱动力探究[J].林业资源管理,2021(5):121-130.
钟琪,胡晋山,康建荣.基于像元二分法的大宁矿区植被覆盖度研究[J].金属矿山,2021(11):197-203.
刘英,雷少刚,陈孝杨,等.神东矿区植被覆盖度时序变化与驱动因素分析及引导恢复策略[J].煤炭学报,2021,46(10):3319-3331.
王国芳,毕如田,张吴平,等.典型矿区植被覆盖度时空分布特征及影响因素[J].生态学报,2020,40(17):6046-6056.
董欣,刘鹏程.基于GEE的土地利用变化对生态系统服务价值的影响研究:以京津冀地区为例[J].华中师范大学学报(自然科学版),2020,54(4):670-678.
黄珏,李正茂,张珂,等.基于GEE的中国湖泊浮游植物生物量时空动态分析[J].地理学报,2021,76(7):1693-1707.
曹娟,张朝,张亮亮,等.基于Google Earth Engine和作物模型快速评估低温冷害对大豆生产的影响[J].地理学报,2020,75(9):1879-1892.
郭永强,王乃江,褚晓升,等.基于Google Earth Engine分析黄土高原植被覆盖变化及原因[J].中国环境科学,2019,39(11):4804-4811.
霍高普,薛喜成,武超,等.彬长矿区直罗组砂岩含水层孔隙结构及分形特征研究[J].煤矿安全,2023,54(2):189-194.
肖乐乐,胡嵩岩,牛超,等.彬长矿区地下水化学特征及突(涌)水源判别[J].西安科技大学学报,2022,42(4):724-732.
汤万金,胡乃联,李祥仪,等.矿区可持续发展的评价[J].北京科技大学学报,1999,21(2):119-124.
何倩.彬长地区地质灾害特征及风险评价[D].陕西西安:西安科技大学,2018.
刘宪锋,杨勇,任志远,等.2000-2009年黄土高原地区植被覆盖度时空变化[J].中国沙漠,2013,33(4):1244-1249.
Fern R R, Foxley E A, Bruno A, et al.Suitability of NDVI and OSAVI as estimators of green biomass and coverage in a semi-arid rangeland [J].Ecological Indicators, 2018,94:16-21.
Holben B N.Characteristics of maximum-value composite images from temporal AVHRR data [J].International Journal of Remote Sensing, 1986,7(11):1417-1434.
李苗苗.植被覆盖度的遥感估算方法研究[D].北京:中国科学院遥感应用研究所,2003.
林妍敏,李文慧,南雄雄,等.基于地理探测器的宁夏贺兰山植被覆盖度时空分异及驱动因子[J].应用生态学报,2022,33(12):3321-3327.
Barsi J A, Schott J R, Hook S J, et al.Landsat-8 thermal infrared sensor(TIRS) vicarious radiometric calibration [J].Remote Sensing, 2014,6(11):11607-11626.
李登科,范建忠,王娟.陕西省植被覆盖度变化特征及其成因[J].应用生态学报,2010,21(11):2896-2903.
曹俊忠.一元线性回归显著性检验方法分析[J].西安工程科技学院学报,1988(增刊):78-82.
王劲峰,徐成东.地理探测器:原理与展望[J].地理学报,2017,72(1):116-134.
王逸男,孔祥兵,赵春敬,等.2000-2020年黄土高原植被覆盖度时空格局变化分析[J].水土保持学报,2022,36(3):130-137.
王一,郝利娜,许强,等.2001-2019年黄土高原植被覆盖度时空演化特征及地理因子解析[J].生态学报,2023,43(6):1-11.
0
浏览量
494
下载量
1
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621