用户名: 密码: 验证码:
露天矿区遥感监测及复垦区生态效应评价
详细信息    本馆镜像全文|  推荐本文 |  |   获取CNKI官网全文
摘要
露天矿区作为人类活动剧烈扰动区域,受煤矿开采的影响,土地原地表形态发生显著变化,土壤、植被遭到破坏,热环境效应显著增强。本文以平朔露天矿区为研究对象,以1987年、1996年、2001年、2005年、2010年和2013年的遥感影像为数据源,在遥感技术和GIS技术的支持下,系统分析了1987-2013年间的土地扰动类型、植被覆盖、地表温度以及土壤湿度在开采过程中的空间分布情况和动态变化过程,并基于获取的遥感信息,选取适当指标,对已复垦区域进行遥感生态效应评价。本文的主要研究结论如下:
     (1)开采活动的频度和强度决定了土地扰动类型的变化程度和变化方向。根据平朔露天矿区的开采特点,将研究区分为2个一级地类和5个二级地类,通过遥感影像解译得出:从1987年到2013年,剥离区面积增加6.32km2,露天采坑增加11.30km2,工业场地增加8.27km2,未复垦排土场增加17.69km2,已复垦排土场增加14.58km2。各扰动土地的变化方向与开采方向基本一致。
     (2)开采活动对植被以及植被的生长环境造成极大破坏,而复垦工作则通过植被种植改善矿山生态环境。基于遥感影像计算了研究区的植被指数和植被覆盖度,1987-2013年间,已复垦排土场的NDVI值最高,未扰动区域的NDVI值其次,露天采坑的NDVI值最低,而未复垦排土场、剥离区和工业场地的NDVI值相近,高覆盖度(80%-100%)的面积增加10.7km2,中覆盖度(40%-80%)的面积增加3.02km2,低覆盖度植被(0%-40%)的面积减少13.65km2。
     (3)研究区地表覆盖状况变化以及开采活动强度引起区域热环境变化。地表温度作为反映热环境的重要指标,可通过Landsat卫星的热红外波段反演得到。从1987到2010年,地表平均温度增加了13.14℃,地表温度最高值主要出现在露天采坑和工业场地区域,最低值出现在已复垦排土场和未扰动区域中。扰动强度和扰动范围影响了地表温度空间分布的均衡性,低温区域与高温区域存在激烈博弈。
     (4)在土地扰动类型、植被、降雨、地形以及人类活动等多重因素的共同作用下,土壤结构以及土壤水分都会受到影响。通过遥感影像反演得到研究区土壤湿度信息,1987-2010年间,潮湿、湿润和正常区域的面积呈“减少-增多-减少-增多”交替出现的特征;在已复垦排土场区域,土壤湿度呈正常偏湿润状态;在潮湿和湿润区域,已复垦排土场的保水能力好于未复垦排土场。
     (5)将研究区的地形高程、植被指数、地表温度和土壤湿度等遥感信息作为评价因子,探讨了复垦区的生态效应评价模型,通过评价因子叠置得到评价图,2001-2010年间,已复垦排土场的生态质量逐步提高。
Due to mining activities, open cast mine area has experienced a dramaticalterations in land surface. Meanwhile, soil and vegetation is destructed, and thermalenvironment effect is enhanced there. With application of remote sensing and GIS,this paper analyses the spatial distribution and change in land disturbed type,vegetation, land surface temperature and soil moisture from1987to2013. Moreover,it assessed the quality of reclamation based on above information. Landsat data isderived from1987,1996,2005,20012005,2010and2013. The main conclusions areas follows:
     (1) Frequency and intensity of mining activities determines the change degreeand change direction of land disturbance type. According to the characteristics ofmining activities in Pingshuo open cast area, the study area is divided into twoprimary types and five secondary types. Based on the remote sensing imageinterpretation, results are concluded that, from1987to2013, the excavated land areaincreased by6.32km2, the open-pit area increased by11.3km2, the industrial areaincreased by8.27km2, the un-reclaimed dump increased by17.69km2, the reclaimeddump increased by14.58km2. The change of each disturbed type happened with thesame direction of mining activities.
     (2) Mining activities disrupt both the vegetation and the vegetation growingcondition. While mine reclamation is carried out by planting vegetation with the aimof improving the natural environment. NDVI and vegetation fraction is calculatedfrom Landsat image. From1987to2013, the highest value of NDVI is found inreclaimed dump. The second highest value is found in un-disturbed area. The lowestvalue is found in open-pit. The value of NDVI in un-reclaimed dump, excavated areaand industry area is similar. The area shows a10.7km2increase in vegetation fractionfrom80%to100%, a3.02km2increase in vegetation fraction from40%to80%, and a13.65km2decrease in vegetation fraction from0%to40%.
     (3)Thermal environment is influenced by the change in land surface and theintensity of mining activities. Land surface temperature, as an important indicator ofthermal environment, can be retrieved from thermal band of Landsat image. From1987to2010, the mean land surface temperature increased by13.14℃. The highestvalue is found in the open-pit area and industrial area and the lowest value is found inthe reclaimed dump and un-disturbed land. The balance of the spatial distribution is influenced by the intensity and scope of disturbance. A game relationship existsbetween high-temperature area and low-temperature area.
     (4) Soil structure and soil moisture is affected under the combine effect of landuse types, vegetation, rainfall, topography and human activities. Soil moisture wasobtained from Landsat images. From1987to2010, the scope of damp area, moistarea and normal area shows a decreasing-increasing-decreasing-increasing trend. Inreclaimed dump, soil moisture is in a moist and normal state. In moist area and damparea, the water retention capacity in reclaimed dump is better than un-reclaimeddump.
     (5) Based on the factors, such as DEM, NDVI, land surface temperature and soilmoisture, an assessment model is established for ecological effect assessment. Allevaluation factors are used to generate an evaluation map with an overlay approach.From2001to2010, the effect of land reclamation is positive in reclaimed-dump.
引文
Anderson, J.R. A land use and land cover classification system for use with remote sensordata, US Government Printing Office,1976.
    Anding, D.,Kauth, R. Estimation of sea surface temperature from space. Remote Sensing ofEnvironment,1970,1(4),217-220
    Büttner, G.,Feranec, J.,Jaffrain, G.,et al. The CORINE land cover2000project. EARSeLeProceedings,2004,3(3),331-346
    Brown, M.E.,Pinzón, J.E.,Didan, K.,et al. Evaluation of the consistency of long-term NDVItime series derived from AVHRR, SPOT-Vegetation, SeaWiFS, MODIS, and Landsat ETM+sensors. Geoscience and Remote Sensing, IEEE Transactions on,2006,44(7),1787-1793
    Carlson, T.N.,Ripley, D.A. On the relation between NDVI, fractional vegetation cover, andleaf area index. Remote Sensing of Environment,1997,62(3),241-252
    Chander, G.,Markham, B.L.,Helder, D.L. Summary of current radiometric calibrationcoefficients for Landsat MSS, TM, ETM+, and EO-1ALI sensors. Remote Sensing ofEnvironment,2009,113(5),893-903
    Choudhury, B.J.,Ahmed, N.U.,Idso, S.B.,et al. Relations between evaporation coefficientsand vegetation indices studied by model simulations. Remote Sensing of Environment,1994,50(1),1-17
    Clavero, M.,Villero, D.,Brotons, L. Climate change or land use dynamics: do we know whatclimate change indicators indicate? PloS one,2011,6(4),e18581
    Deng, C.,Wu, C. Examining the impacts of urban biophysical compositions on surface urbanheat island: a spectral unmixing and thermal mixing approach. Remote Sensing of Environment,2013,131,262-274
    Di Gregorio, A.,Jansen, L.J.,2000, Land cover classification system: LCCS: classificationconcepts and user manual, Food and Agriculture Organization of the United Nations Rome.
    Dymond, J.,Stephens, P.,Newsome, P.,et al. Percentage vegetation cover of a degradingrangeland from SPOT. International Journal of Remote Sensing,1992,13(11),1999-2007
    Foley, J.A.,DeFries, R.,Asner, G.P.,et al. Global consequences of land use. science,2005,309(5734),570-574
    Gillies, R.R.,Carlson, T.N. Thermal remote sensing of surface soil water content with partialvegetation cover for incorporation into climate models. Journal of Applied Meteorology,1995,34(4),745-756
    Graetz, R.,Pech, R.P.,Davis, A. The assessment and monitoring of sparsely vegetatedrangelands using calibrated Landsat data. International Journal of Remote Sensing,1988,9(7),1201-1222
    Gu, Y.,Brown, J.F.,Verdin, J.P.,et al. A five‐year analysis of MODIS NDVI and NDWI forgrassland drought assessment over the central Great Plains of the United States. GeophysicalResearch Letters,2007,34(6)
    Gutman, G.,Ignatov, A. The derivation of the green vegetation fraction from NOAA/AVHRRdata for use in numerical weather prediction models. International Journal of Remote Sensing,1998,19(8),1533-1543
    Haregeweyn, N.,Fikadu, G.,Tsunekawa, A.,et al. The dynamics of urban expansion and itsimpacts on land use/land cover change and small-scale farmers living near the urban fringe: A casestudy of Bahir Dar, Ethiopia. Landscape and Urban Planning,2012,106(2),149-157
    Huang, G.,Zhou, W.,Cadenasso, M. Is everyone hot in the city? Spatial pattern of landsurface temperatures, land cover and neighborhood socioeconomic characteristics in Baltimore,MD. Journal of environmental management,2011,92(7),1753-1759
    Hwang, T.,Song, C.,Bolstad, P.V.,et al. Downscaling real-time vegetation dynamics byfusing multi-temporal MODIS and Landsat NDVI in topographically complex terrain. RemoteSensing of Environment,2011,115(10),2499-2512
    Islam, K.,Weil, R. Land use effects on soil quality in a tropical forest ecosystem ofBangladesh. Agriculture, Ecosystems and Environment,2000,79(1),9-16
    Jaramillo, J.,Setamou, M.,Muchugu, E.,et al. Climate Change or Urbanization? Impacts ona Traditional Coffee Production System in East Africa over the Last80Years. PloS one,2013,8(1),e51815
    Kahle, A.B. A simple thermal model of the Earth's surface for geologic mapping by remotesensing. Journal of Geophysical Research,1977,82(11),1673-1680
    Keramitsoglou, I.,Kiranoudis, C.T.,Ceriola, G.,et al. Identification and analysis of urbansurface temperature patterns in Greater Athens, Greece, using MODIS imagery. Remote Sensingof Environment,2011,115(12),3080-3090
    Kerr, Y.H.,Lagouarde, J.P.,Imbernon, J. Accurate land surface temperature retrieval fromAVHRR data with use of an improved split window algorithm. Remote Sensing of Environment,1992,41(2),197-209
    Kharol, S.K.,Kaskaoutis, D.,Badarinath, K.,et al. Influence of land use/land cover (LULC)changes on atmospheric dynamics over the arid region of Rajasthan state, India. Journal of AridEnvironments,2013,88,90-101
    Mannstein, H. Surface energy budget, surface temperature and thermal inertia, Remotesensing applications in meteorology and climatology, Springer,1987,391-410
    Mena, C.F.,Bilsborrow, R.E.,McClain, M.E. Socioeconomic drivers of deforestation in theNorthern Ecuadorian Amazon. Environmental Management,2006,37(6),802-815
    Moran, M.,Clarke, T.,Inoue, Y.,et al. Estimating crop water deficit using the relationbetween surface-air temperature and spectral vegetation index. Remote Sensing of Environment,1994a,49(3),246-263
    Moran, M.,Clarke, T.,Kustas, W.,et al. Evaluation of hydrologic parameters in a semiaridrangeland using remotely sensed spectral data. Water Resources Research,1994b,30(5),1287-1297
    Peng, S.,Piao, S.,Ciais, P.,et al. Surface urban heat island across419global big cities.Environmental science and technology,2011,46(2),696-703
    Price, J.C. On the analysis of thermal infrared imagery: the limited utility of apparent thermalinertia. Remote Sensing of Environment,1985,18(1),59-73
    Qin, Z.,Karnieli, A.,Berliner, P. A mono-window algorithm for retrieving land surfacetemperature from Landsat TM data and its application to the Israel-Egypt border region.International Journal of Remote Sensing,2001a,22(18),3719-3746
    Qin, Z.,Dall'Olmo, G.,Karnieli, A.,et al. Derivation of split window algorithm and itssensitivity analysis for retrieving land surface temperature from NOAA‐advanced very highresolution radiometer data. Journal of Geophysical Research: Atmospheres (1984–2012),2001b,106(D19),22655-22670
    Qin, Z.,Karnieli, A. Progress in the remote sensing of land surface temperature and groundemissivity using NOAA-AVHRR data. International Journal of Remote Sensing,1999,20(12),2367-2393
    Ratana, P.,Huete, A.R.,Yin, Y.,et al. Interrelationship among among MODIS vegetationproducts across an Amazon Eco-climatic gradient, Geoscience and Remote Sensing Symposium,2005. IGARSS'05. Proceedings.2005IEEE International, Volume4, IEEE,2005,3009-3012.
    Rouse Jr, J.,Haas, R.,Schell, J.,et al. Monitoring vegetation systems in the Great Plains withERTS. NASA special publication,1974,351,309
    Sandholt, I., Rasmussen, K., Andersen, J. A simple interpretation of the surfacetemperature/vegetation index space for assessment of surface moisture status. Remote Sensing ofEnvironment,2002,79(2),213-224
    Sellers, P.,Hall, F.,Asrar, G.,et al. The first ISLSCP field experiment (FIFE). Bulletin of theAmerican Meteorological Society,1988,69(1),22-27
    Townshend, J.R. Global data sets for land applications from the Advanced Very HighResolution Radiometer: an introduction. International Journal of Remote Sensing,1994,15(17),3319-3332
    Tucker, C.J. Red and photographic infrared linear combinations for monitoring vegetation.Remote Sensing of Environment,1979,8(2),127-150
    Turner, B.L.,Matson, P.A.,McCarthy, J.J.,et al. Illustrating the coupled human–environmentsystem for vulnerability analysis: three case studies. Proceedings of the National Academy ofSciences,2003,100(14),8080-8085
    Valor, E.,Caselles, V. Mapping land surface emissivity from NDVI: Application to European,African, and South American areas. Remote Sensing of Environment,1996,57(3),167-184
    Walsh, S.J.,Crawford, T.W.,Welsh, W.F.,et al. A multiscale analysis of LULC and NDVIvariation in Nang Rong district, northeast Thailand. Agriculture, Ecosystems and Environment,2001,85(1),47-64
    Weiss, E.,Marsh, S.,Pfirman, E. Application of NOAA-AVHRR NDVI time-series data toassess changes in Saudi Arabia's rangelands. International Journal of Remote Sensing,2001,22(6),1005-1027
    Weng, Q.,Yang, S. Managing the adverse thermal effects of urban development in a denselypopulated Chinese city. Journal of environmental management,2004,70(2),145-156
    Wittich, K.,Hansing, O. Area-averaged vegetative cover fraction estimated from satellitedata. International Journal of Biometeorology,1995,38(4),209-215
    Xie, M.,Wang, Y.,Fu, M.,et al. Pattern dynamics of thermal-environment effect duringurbanization: A case study in Shenzhen City, China. Chinese Geographical Science,2013,23(1),101-112
    Yadav, V.,Malanson, G. Spatially explicit historical land use land cover and soil organiccarbon transformations in Southern Illinois. Agriculture, Ecosystems and Environment,2008,123(4),280-292
    Yang, W.,Yang, L.,Merchant, J. An assessment of AVHRR/NDVI-ecoclimatologicalrelations in Nebraska, USA. International Journal of Remote Sensing,1997,18(10),2161-2180
    Zhang, J.,Kuenzer, C. Thermal surface characteristics of coal fires1results of in-situmeasurements. Journal of Applied Geophysics,2007,63(3),117-134
    白中科,郭青霞.安家岭露天煤矿土地利用结构预测.煤炭学报,1999,24(2),207-211
    白中科,赵景逵.工矿区土地复垦,生态重建与可持续发展.科技导报,2001,,49-52
    卞正富.我国煤矿区土地复垦与生态重建研究.资源产业,2005,7(2),18-24
    常江,Koetter, T.从采矿迹地到景观公园.煤炭学报,2005,30(3),399-402
    常鲁群,卞正富,邓喀中. GIS支持下的矿区土壤含水量遥感反演及变化规律.金属矿山,2007,(2),55-57
    陈斌,张学霞,华开,等.温度植被干旱指数(TVDI)在草原干旱监测中的应用研究.干旱区地理,2013,36(5),930-937
    陈朝晖,朱江,徐兴奎.利用归一化植被指数研究植被分类,面积估算和不确定性分析的进展.气候与环境研究,2004,9(4),687-696
    陈锦如.煤炭能源建设中水资源的保护.合肥工业大学学报:自然科学版,2008,(10),1297-1300
    陈俊,王文,李子扬,等. Landsat—5卫星数据产品.遥感信息,2007,(3),85-88
    陈添宇,李照荣,陈乾,等.用GMS5卫星反演水汽场分析中国西北地区大气水汽分布的气候特征.大气科学,2005,29(6),864-871
    陈小瑜,林冰,郑伟民,等.基于ETM遥感影像的城市温度反演及结果分析——以福建省泉州市为例.重庆师范大学学报(自然科学版),2013,30(4),123-127
    陈有君.基于NDVI的石林风景区植被覆盖动态变化对比研究.林业调查规划,2009,34(4),30-33
    陈云浩.上海城市空间热环境的遥感图像分析与应用研究.测绘学报,1999,30(3),282-282
    陈云浩,郭达志.基于GIS对矿区环境的综合评价.煤矿环境保护,1995,12(2),47-49
    陈云浩,李晓兵,史培军,等.北京海淀区植被贾盖的遥感动态研究.,2001,
    戴声佩,张勃,王海军,等.基于SPOT NDVI的祁连山草地植被覆盖时空变化趋势分析.地理科学进展,2010,(9),1075-1080
    丁凤,徐涵秋.基于LandsatTM的3种地表温度反演算法比较分析[J].福建师范大学学报:自然科学版,2008,24(1),91-96
    杜培军.工矿区陆面演变与空间信息技术应用的研究[D].测绘学报,2001,32(1),94-94
    傅伯杰.陕北黄土高原土地评价研究.水土保持学报,1991,5(1),1-7
    耿殿明,姜福兴.我国煤炭矿区生态环境问题分析.中国煤炭,2002,28(7),21-24
    顾磊,黄丽,张继荣,等.基于Landsat5TM数据的地下煤火区地表温度反演.首都师范大学学报:自然科学版,2010,(4),63-67
    顾祝军,曾志远,史学正,等.基于ETM+图像的植被覆盖度遥感估算模型.生态环境,2008,17(2),771-776
    郭青霞,白中科.安家岭露天煤矿土地利用结构预测.山西农业大学学报:自然科学版,1998,18(4),340-344
    郭逍宇,张金屯,宫辉力,等.安太堡矿区复垦地植被种间关系及土壤因子分析.生物多样性,2007,15(1),46-52
    郭逍宇,张金屯,宫辉力,等.安太堡矿区复垦地植被恢复过程多样性变化.生态学报,2005,25(4),763-770
    国家林业局森林资源管理司调研组.全国矿区植被保护与生态恢复情况调研报告.林业经济,2008,(3),31-35
    何英彬,陈佑启.土地利用/覆盖变化研究综述.中国农业资源与区划,2004,25(2),58-62
    侯西勇,常斌,于信芳.基于CA-Markov的河西走廊土地利用变化研究农业工程学报,2004,20(5),286-291
    胡文亮,赵萍,董张玉.基于TM数据的煤矿区热环境效应及其生态意义.合肥工业大学学报:自然科学版,2010,33(5),741-744
    胡振琪,陈涛.基于ERDAS的矿区植被覆盖度遥感信息提取研究.西北林学院学报,2008,23(2),164-167
    胡振琪,谢宏全.基于遥感图像的煤矿区土地利用/覆盖变化.煤炭学报,2005,30(1),44-48
    贾宝全.基于TM卫星影像数据的北京市植被变化及其原因分析.生态学报,2013,33(5),1654-1666
    江东,王乃斌. NDVI曲线与农作物长势的时序互动规律.生态学报,2002,22(2),247-253
    李璐璐,黄贤金,钟太洋.区域土地利用变化态势及其对土地可持续利用影响分析——以江苏省为例.水土保持研究,2006,13(2),202-205
    李宇,董锁成.基于GIS的定西地区黄土高原土地利用变化研究.农业工程学报,2004,20(3),248-252
    李正国,王仰麟,吴健生,等.基于TVDI的黄土高原地表干燥度与土地利用的关系研究.地理研究,2006,25(5),913-920
    连达军,汪云甲,张华.矿区生态环境要素的采动损害定量评价方法研究.有色金属:矿山部分,2009,(5),10-14
    梁宏,刘晶淼,章建成,等.青藏高原大气总水汽量的反演研究.高原气象,2007,25(6),1055-1063
    刘宝勇,刘珊依,范军富.露天矿排土场植被恢复的小气候变化效应.辽宁工程技术大学学报:自然科学版,2009,28(A02),226-228
    刘春国,卢晓峰,高松峰. Lansat-7ETM+热红外波段高低增益状态数据反演亮度温度比较研究.河南理工大学学报:自然科学版,2011,30(5),561-566
    刘放,吕弋培,江利明,等. MODIS亮温与气温及地温的相关性分析.地震地质,2010,32(1),127-137
    刘敏,赵翠薇,施明辉.贵州山区土地利用变化多尺度空间自相关分析.农业工程学报,2012,28(20),239-246
    刘蕊,杨青,王敏仲.再分析资料与经验关系计算的新疆地区大气水汽含量比较分析.干旱区资源与环境,2010,24(4),77-85
    刘霞,沙晋明.基于ETM+影像的福州市部分城区的地表温度反演与分析[J].海洋技术,2010,29(3),87-91
    刘艳中,李江风,张祚,等.生态足迹模型在我国土地可持续利用评价中的应用及启示.地理与地理信息科学,2008,24(1),80-84
    刘志明,张柏,晏明,等.土壤水分与干旱遥感研究的进展与趋势.地球科学进展,2003,18(4),576-583
    吕春娟,白中科,秦俊梅,等.黄土区大型排土场岩土侵蚀特征研究——以平朔矿区排土场为例.水土保持研究,2006,13(4),233-236
    明庆忠.人地关系和谐:中国可持续发展的根本保证——一种地理学的视角.清华大学学报:哲学社会科学版,2008,22(6),114-121
    潘德成,邓春晖,吴祥云,等.矿山复垦区土壤水分时空分布对植被恢复的影响.干旱区资源与环境,2014,28(3),96-100
    彭德福,王凤俊,田占.还自然一片绿色——平朔露天煤矿土地复垦与生态重建的调查.国土资源,2003,4,28-29
    彭建,蒋一军,吴健生,等.我国矿山开采的生态环境效应及土地复垦典型技术.地理科学进展,2005,24(2),38-48
    彭文甫,周介铭,杨存建,等.基于RS与GIS的县级土地利用变化分析——以四川省成都市双流县为例.遥感技术与应用,2008,23(1),24-30
    秦伟,朱清科,张学霞,等.植被覆盖度及其测算方法研究进展.西北农林科技大学学报(自然科学版),2006,34(9),163-170
    邱文玮,侯湖平.基于RS的矿区生态扰动地表温度变化研究.矿业研究与开发,2013,(002),68-71
    邱扬,傅伯杰,王军,等.土壤水分时空变异及其与环境因子的关系.生态学杂志,2007,26(1),100-107
    闰峰,覃志豪,李茂松,等.基于MODIS数据的上海市热岛效应研究.武汉大学学报·信息科学版,2007,32(7),576-580
    邵璞,曾晓东.土地利用和土地覆盖变化对气候系统影响的研究进展.气候与环境研究,2012,17(1),103-111
    师学义,杨玉敏,孟繁华.五阳矿区采煤塌陷地混推和剥离复垦比较研究.煤炭学报,2003,28(4),385-388
    隋洪智,田国良.农田蒸散双层模型及其在干旱遥感监测中的应用.遥感学报,1997,1(3),220-224
    孙红雨,王长耀,牛铮,等.中国地表植被覆盖变化及其与气候因子关系──基于NOAA时间序列数据分析.遥感学报,1998,2(3),204-210
    孙艳玲,郭鹏,高晓燕.基于Landsat TM/ETM+的天津地区地表温度时空分布特征研究.天津师范大学学报(自然科学版),2012,1,011
    汤竞煌,聂智龙.遥感图像的几何校正.测绘与空间地理信息,2007,30(2),100-102
    陶健,徐跃通,丁娟,等.南屯矿区土地利用时空演化分析.测绘科学,2010,35(1),182-185
    田国良,杨希华,郑柯.冬小麦旱情遥感监测模型研究.环境遥感,1992,7(2),83-89
    田振坤,黄妙芬,刘良云,等.使用单窗算法研究北京城区热岛效应.遥感信息,2006,(1),21-24
    王辉,韩宝平,卞正富.充填复垦区土壤水分空间变异性研究.河南农业科学,2007,(7),67-70
    王建平,邓军,孙忠实.我国东西部矿产资源开发问题探讨.中国矿业,2003,12(9),19-22
    王力,卫三平,王全九.黄土丘陵区燕沟流域农林草地土壤水库充失水过程模拟.林业科学,2011,47(1),29-35
    王倩倩,覃志豪,王斐.基于多源遥感数据反演地表温度的单窗算法.地理与地理信息科学,2012,28(003),24-26
    王秀荣,徐祥德,苗秋菊.西北地区夏季降水与大气水汽含量状况区域性特征.气候与环境研究,2003,8(1),35-42
    王瑜,孟令奎.基于MODIS的区域动态干旱监测方法.测绘信息与工程,2010,35(4),20-22
    吴立新,梁跃.中国煤矿环境挑战及战略对策.中国煤炭,1996,(10),15-17
    吴立新,马保东,刘善军.基于SPOT卫星NDVI数据的神东矿区植被覆盖动态变化分析.煤炭学报,2009,34(9),1217-1222
    夏既胜,刘晓芳,谈树成,等.露天矿区生态问题及生态重建方法探讨.金属矿山,2009,(6),163-166
    肖庆文,倪晋仁,李天宏.基于土壤水分分布的土地利用空间优化方法.自然资源学报,2005,20(3),317-325
    肖思思,吴春笃,储金宇.1980--2005年太湖地区土地利用变化及驱动因素分析.农业工程学报,2012,28(23),1-11
    谢宏全,胡振琪.论基于遥感的矿区土地利用/覆盖分类体系.辽宁工程技术大学学报,2004,23(6),751-753
    谢苗苗,白中科,付梅臣,等.大型露天煤矿地表扰动的温度分异效应.煤炭学报,2011,4,643-647
    徐希孺,庄家礼.热红外多角度遥感和反演混合像元组分温度.北京大学学报:自然科学版,2000,36(4),555-560
    杨景梅,邱金植.我国可降水量同地面水汽压关系的经验表达式.大气科学,1996,20(5),620-626
    杨静,庄家尧,张金池.基于RS和GIS的徐州市20年间土地利用变化研究.南京林业大学学报(自然科学版),2013,37(2),85-91
    杨磊,卫伟,莫保儒,等.半干旱黄土丘陵区不同人工植被恢复土壤水分的相对亏缺.,2011,31(11),3060-3068
    杨青,刘晓阳,崔彩霞,等.塔里木盆地水汽含量的计算与特征分析.地理学报,2010,65(7),853-862
    杨青,魏文寿,李军.塔克拉玛干沙漠及周边地区大气水汽量的时空变化.科学通报,2009,(S2)
    杨沈斌,赵小艳,申双和,等.基于Landsat TM/ETM+数据的北京城市热岛季节特征研究.大气科学学报,2010,(004),427-435
    姚春生,张增祥,汪潇.使用温度植被干旱指数法(TVDI)反演新疆土壤湿度.遥感技术与应用,2004,19(6),473-478
    叶宝莹,白中科,孔登魁,等.安太堡露天煤矿土地破坏与土地复垦动态变化的遥感调查.北京科技大学学报,2008,30(9),972-976
    叶公强.地籍管理.北京:中国农业出版社,2002.
    尹超,王艳芳,张爱国.基于NDVI的临汾市植被覆盖动态变化遥感监测研究.山西师范大学学报:自然科学版,2011,25(03),125-128
    余涛,田国良.热惯量法在监测土壤表层水分变化中的研究.遥感学报,1997,1(1),24-31
    占瑞芬,李建平.青藏高原地区大气红外探测器(AIRS)资料质量检验及揭示的上对流层水汽特征.大气科学,2008,32(2),242-260
    张国良,水利环保,1997,矿区环境与土地复垦,中国矿业大学出版社,31-35p.
    张佳华,2010,城市热环境遥感,北京:气象出版社.
    张仁华.改进的热惯量模式及遥感土壤水分.地理研究,1990,9(2),101-112
    张学文.可降水量与地面水汽压力的关系.气象,2004,30(2),9-11
    张玉斌,郑粉莉.近地表土壤水分条件对坡面土壤侵蚀过程的影响.中国水土保持科学,2007,5(2),5-10
    张月丛,赵志强,李双成,等.基于SPOT NDVI的华北北部地表植被覆盖变化趋势.,2008,27(4),745-755
    赵强,宫辉力,邓伟,等.基于Landsat TM数据的潮白河流域植被覆盖变化研究.遥感信息,2005,(3),21-23
    赵学胜,吴立新,王金庄.矿区土地环境与资源信息系统初探.矿山测量,1996,(3),22-48
    赵振家,杨晓梅,李永华.土地利用/土地覆盖变化与全球环境变化.地理译报,1996,15(3),2-6
    周亚萍,安树青.生态质量与生态系统服务功能.生态科学,2009,(2),85-90

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700