重庆市植被指数时空变化研究
|
摘要
植被的生长一般受到多方面因素的影响,即相对固定的地形因子,长时间累计变化的温度、降水等气候因子,人口分布、GDP增加及城市化过程等的人为因子。其中地形因子和气候因子可称为自然因子,它们奠定了植被空间分布的总体格局。人为因子则引起植被的局部变化。从长期来看,自然因子和人为因子都驱动着植被分布格局及其变化,但是在短期内,人类活动则是主要的驱动因素。本文主要研究地形因子、气候因子和人为因子与植被覆盖之间的关系,从而更好的解释植被变化情况,为重庆市生态环境的调节改善提供一定的依据。
本研究运用1998-2007年1 km空间分辨率的SPOT-VGTS10数据,提取重庆市归一化植被指数(normalized difference vegetation index, NDVI)影像,共计351景。采用Savitzky-Golay滤波平滑处理、最大值合成法、线性回归分析、时间序列分析等方法研究重庆市NDVI、降水量、温度、人口、GDP等的动态变化情况,构建重庆市的NDVI、气象因子、人为因子的时序数据集；分析重庆市NDVI及各因子的空间分布格局；探讨重庆市NDVI与气象因子,地形因子,人为因子之间的关系。主要研究成果包括：
(1)运用Savitzky-Golay滤波技术,可有效地去除NDVI数据中的云、气溶胶及异常值的影响,NDVI时序数据经过滤波平滑后,能更好地反映植被覆盖情况,为研究重庆市植被覆盖变化奠定了基础。
(2)重庆市NDVI的年平均值和各年NDVI值都表现出较强的稳定性。从1999年到2007年NDVI总体呈上升趋势,说明重庆市植被量逐年增加。空间分布特征表明,在四个种植业分区(渝东北、渝南、渝中和渝西)各年份NDVI值大小趋势基本保持一致。根据NDVI值排序表现为：渝东北>渝南>渝中>渝西。而各年NDVI则表现出明显的季节性,在1月份和8月份分别为谷值和峰值。另外NDVl年际变化率也表明重庆市近十年植被覆盖量增加,且西部除主城外最为明显。
(3)以区县为单位研究各区域旬NDVI与同期降水量、平均气温的相关性发现,NDVI与二者均有较好的相关性,但是NDVI与平均气温相关性远远大于与降水量的相关性。季节NDVI与同期降水量及温度相关性的空间分布结果表明,春季降水量与NDVI在空间上呈正相关；气温对NDVI的影响较为显著,正相关面积占总面积的比例达到72%。夏季NDVl与降水量仅在西北部及中北部个别地区为正相关,其余各地均呈负相关；NDVI与温度总体以正相关为主,在西部地区尤其是主城地区呈现较显著的负相关性；东南部地区及西南部相关性较小。秋季东北部大巴山区NDVI与降水量相关系数较大,其余地区呈负相关；NDVI与温度的相关性则明显呈现出东部地区为正相关,西部地区为负相关的现象。在冬季,东北及东南部各地NDVI受到降水量的影响较大,长江以北大部分地区与降水量呈负相关;与其他季节相比,东北部及东南部地区冬季NDVI受温度的影响相对有所减小,部分地区的植被情况受温度的影响并不大,而西部则呈现很强的正相关性,相关系数达到0.8左右。
(4)NDVI与高程有一定的相关性,其中渝东北地区相关性最为明显。而坡度、坡向与NDVI的相关性不明显。
(5)重庆市最大化NDVI与人口及GDP均呈负相关,负相关的面积分别达到51.59%和54.39%；通过研究1999年到2006年积分NDVI与城市化的关系表明,城市化对重庆市的植被也有一定的影响,与城市化率呈负相关关系。
The growth of vegetation was influenced by many factors such as terrain, temperature, precipitation, population distribution, GDP, and urbanization, etc. Among these factors, terrain and climate are classified as natural factors which formed the overall pattern of spatial distribution of vegetation. Human factor, such as population distribution, GDP, and urbanization, would influence on local changes of vegetation. In the long run, both natural factors and human factors would drive the changes of vegetation patterns. However, in the short term, human activities would be the main driving force in the change of vegetation. Thus, the objectives of this study were to investigate the relationships between the vegetation cover and terrain, between the vegetation cover and climate, and between the vegetation cover and human factors. The results of the study would provide a guideline for regulating the environment in Chongqing.
A total of 351 images of SPOT-VGTS10 with a spatial resolution of 1 km from 1998 to 2007 were used to extract normalized difference vegetation index (NDVI) for Chongqing. Several datasets, such as NDVI, climate, and human factors, were developed. Savitzky-Golay smoothing filter, maximum value composites, linear regression analysis, and time series analysis were applied to investigate the inter-annual variations of NDVI, precipitation, temperature, population, and GDP. and constructed the database of NDVI, meteorology, and human factors. The spatial distribution of NDVI and other factors were explored. The correlations between NDVI and meteorology, terrain attributes, and human factors were analyzed. The main results are as follows:
(1) Clouds, aerosols and outliers could be effectively removed from the raw NDVI dataset using Savitzky-Golay filtering technology. Smoothed and filtered NDVI time series data would better reflect the presence of the vegetation coverage of Chongqing and provide the data source for studying the vegetation cover change in Chongqing.
(2) The annual average NDVI and NDVI values for each year showed strong stability in Chongqing. The amount of vegetation increased yearly in Chongqing. The variations of NDVI were similar in four plantation areas, namely, northeastern, southern, central, and western Chongqing. The order of NDVI values was northeastern> southern> central> western Chongqing. The NDVI for each year showed a seasonal variation, the lowest value was found in January while the highest was in August. Moreover, the variations of inter-annual indicated that the vegetaion increase of Chongqing, especially in the western Chongqing.
(3) For each county, NDVI presented strong relationship with mean temperature and weak relationship with precipitation at 10-day intervals. The spatial relationships between NDVI and climate were also investigated at seasonal scale.
In spring, positive relationships were found between NDVI and precipitation and between NDVI and temperature. The area percentage of positive relations of NDVI and temperature was 72%. In summer, NDVI was positively related with precipitation in northwestern and northcentral Chongqing. Generally, NDVI showed positive relationship with temperature in the study area. However, significant negative relationships were found in western part and downtown and weak relationships existed in southeastern and southwestern regions. In autumn, NDVI was closely related with precipitation in Daba Mountain areas. NDVI showed positive relationship with temperature in eastern Chongqing and negative relationship in western Chongqing. In winter, NDVI was strongly influenced by precipitation in northeastern and southeastern regions and the correlation coefficient was negative in the north of the Yangtze River. However, the influence of temperature on NDVI in northeastern and southeastern regions was decreased compared with other seasons. NDVI of some areas was not affected by temperature, while NDVI was strongly positive related with temperature in western part.
(4) Elevation showed positive relation with NDVI, especially in eastern Chongiqng. However, no relationship was found between slope and NDVI and between aspect and NDVI.
(5) There was a negative correlation between maximum NDVI and population and between NDVI GDP in Chongqing, the area percentage was 51.59% and 54.39%, respectively. The integration of NDVI was nagetive related with urbanization ratio indicating the effects of urbanization on the vegetation coverage in the study area.
本研究运用1998-2007年1 km空间分辨率的SPOT-VGTS10数据,提取重庆市归一化植被指数(normalized difference vegetation index, NDVI)影像,共计351景。采用Savitzky-Golay滤波平滑处理、最大值合成法、线性回归分析、时间序列分析等方法研究重庆市NDVI、降水量、温度、人口、GDP等的动态变化情况,构建重庆市的NDVI、气象因子、人为因子的时序数据集；分析重庆市NDVI及各因子的空间分布格局；探讨重庆市NDVI与气象因子,地形因子,人为因子之间的关系。主要研究成果包括：
(1)运用Savitzky-Golay滤波技术,可有效地去除NDVI数据中的云、气溶胶及异常值的影响,NDVI时序数据经过滤波平滑后,能更好地反映植被覆盖情况,为研究重庆市植被覆盖变化奠定了基础。
(2)重庆市NDVI的年平均值和各年NDVI值都表现出较强的稳定性。从1999年到2007年NDVI总体呈上升趋势,说明重庆市植被量逐年增加。空间分布特征表明,在四个种植业分区(渝东北、渝南、渝中和渝西)各年份NDVI值大小趋势基本保持一致。根据NDVI值排序表现为：渝东北>渝南>渝中>渝西。而各年NDVI则表现出明显的季节性,在1月份和8月份分别为谷值和峰值。另外NDVl年际变化率也表明重庆市近十年植被覆盖量增加,且西部除主城外最为明显。
(3)以区县为单位研究各区域旬NDVI与同期降水量、平均气温的相关性发现,NDVI与二者均有较好的相关性,但是NDVI与平均气温相关性远远大于与降水量的相关性。季节NDVI与同期降水量及温度相关性的空间分布结果表明,春季降水量与NDVI在空间上呈正相关；气温对NDVI的影响较为显著,正相关面积占总面积的比例达到72%。夏季NDVl与降水量仅在西北部及中北部个别地区为正相关,其余各地均呈负相关；NDVI与温度总体以正相关为主,在西部地区尤其是主城地区呈现较显著的负相关性；东南部地区及西南部相关性较小。秋季东北部大巴山区NDVI与降水量相关系数较大,其余地区呈负相关；NDVI与温度的相关性则明显呈现出东部地区为正相关,西部地区为负相关的现象。在冬季,东北及东南部各地NDVI受到降水量的影响较大,长江以北大部分地区与降水量呈负相关;与其他季节相比,东北部及东南部地区冬季NDVI受温度的影响相对有所减小,部分地区的植被情况受温度的影响并不大,而西部则呈现很强的正相关性,相关系数达到0.8左右。
(4)NDVI与高程有一定的相关性,其中渝东北地区相关性最为明显。而坡度、坡向与NDVI的相关性不明显。
(5)重庆市最大化NDVI与人口及GDP均呈负相关,负相关的面积分别达到51.59%和54.39%；通过研究1999年到2006年积分NDVI与城市化的关系表明,城市化对重庆市的植被也有一定的影响,与城市化率呈负相关关系。
The growth of vegetation was influenced by many factors such as terrain, temperature, precipitation, population distribution, GDP, and urbanization, etc. Among these factors, terrain and climate are classified as natural factors which formed the overall pattern of spatial distribution of vegetation. Human factor, such as population distribution, GDP, and urbanization, would influence on local changes of vegetation. In the long run, both natural factors and human factors would drive the changes of vegetation patterns. However, in the short term, human activities would be the main driving force in the change of vegetation. Thus, the objectives of this study were to investigate the relationships between the vegetation cover and terrain, between the vegetation cover and climate, and between the vegetation cover and human factors. The results of the study would provide a guideline for regulating the environment in Chongqing.
A total of 351 images of SPOT-VGTS10 with a spatial resolution of 1 km from 1998 to 2007 were used to extract normalized difference vegetation index (NDVI) for Chongqing. Several datasets, such as NDVI, climate, and human factors, were developed. Savitzky-Golay smoothing filter, maximum value composites, linear regression analysis, and time series analysis were applied to investigate the inter-annual variations of NDVI, precipitation, temperature, population, and GDP. and constructed the database of NDVI, meteorology, and human factors. The spatial distribution of NDVI and other factors were explored. The correlations between NDVI and meteorology, terrain attributes, and human factors were analyzed. The main results are as follows:
(1) Clouds, aerosols and outliers could be effectively removed from the raw NDVI dataset using Savitzky-Golay filtering technology. Smoothed and filtered NDVI time series data would better reflect the presence of the vegetation coverage of Chongqing and provide the data source for studying the vegetation cover change in Chongqing.
(2) The annual average NDVI and NDVI values for each year showed strong stability in Chongqing. The amount of vegetation increased yearly in Chongqing. The variations of NDVI were similar in four plantation areas, namely, northeastern, southern, central, and western Chongqing. The order of NDVI values was northeastern> southern> central> western Chongqing. The NDVI for each year showed a seasonal variation, the lowest value was found in January while the highest was in August. Moreover, the variations of inter-annual indicated that the vegetaion increase of Chongqing, especially in the western Chongqing.
(3) For each county, NDVI presented strong relationship with mean temperature and weak relationship with precipitation at 10-day intervals. The spatial relationships between NDVI and climate were also investigated at seasonal scale.
In spring, positive relationships were found between NDVI and precipitation and between NDVI and temperature. The area percentage of positive relations of NDVI and temperature was 72%. In summer, NDVI was positively related with precipitation in northwestern and northcentral Chongqing. Generally, NDVI showed positive relationship with temperature in the study area. However, significant negative relationships were found in western part and downtown and weak relationships existed in southeastern and southwestern regions. In autumn, NDVI was closely related with precipitation in Daba Mountain areas. NDVI showed positive relationship with temperature in eastern Chongqing and negative relationship in western Chongqing. In winter, NDVI was strongly influenced by precipitation in northeastern and southeastern regions and the correlation coefficient was negative in the north of the Yangtze River. However, the influence of temperature on NDVI in northeastern and southeastern regions was decreased compared with other seasons. NDVI of some areas was not affected by temperature, while NDVI was strongly positive related with temperature in western part.
(4) Elevation showed positive relation with NDVI, especially in eastern Chongiqng. However, no relationship was found between slope and NDVI and between aspect and NDVI.
(5) There was a negative correlation between maximum NDVI and population and between NDVI GDP in Chongqing, the area percentage was 51.59% and 54.39%, respectively. The integration of NDVI was nagetive related with urbanization ratio indicating the effects of urbanization on the vegetation coverage in the study area.
引文
[1]马国明,王建,王雪梅.基于遥感的植被年际变化及其与气候关系研究进展[J].遥感学报,2006,10(3)：421～431.
[2]程麟生,宋振鑫,杨德保.青藏高原地表分类及其东北部辐射通量的二维数值模拟[J].高原气象,2000,19(2)：198～213.
[3]兰明娟,魏虹,熊春妮,等.基于TM影像的重庆市北碚区地表植被覆盖变化[J].西南大学学报,2009,31(4)：100-104.
[4]董永平,吴新宏等.草原遥感监测技术[M].北京：化学工业出版社,2005.
[5]郭铌.植被指数及其研究进展[J].干旱气象,2003,21(4)：71-75.
[6]Pearson R L, Miller D L.1972. Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie. In:Proceedings of the Eighth International Symposium on Remote Sensing of Environment [J]. Michigan:Ann Arbor,2:1357～1381.
[7]Rouse, J. W, Haas R H, Schell J A, et al. Monitoring Vegetation systems in the Great Plains with ERTS [A]. Proceedings of Third Earth Resources Technology Satellite-1 Symposium[C]. Greenbelt,1974(351):310～317.
[8]Kauth R J, Thomas G S. The tasseled cap-a graphic description of the spectra temporal development of agriculture crops as seen by Landsat. Pros Symposium on Machine Processing of Remotely Sensed Data. Purdue University, West Lafayette, Indiana,1976:41～51.
[9]Richardson A J and Wiegand C L. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing [A].1977,43:1541～1552.
[10]Jackson, R. D.1983. Spectral indices in n-space[J]. Remote Sensing Environ.13:409～421.
[11]Huete A R.1988. A soil-adjusted vegetation index (SAVI) [J].Remote Sense Environ,25:295～309.
[12]Baret F, Guyot G, Major D J. TSAVI:A vegetation index which minimizes soil brightness effects on LAI and APAR estimation. Proceedings of the 12th Canadian Symposium on Remote Sensing[J]. Vancouver, Canada, 1989.1355～1358.
[13]Demetriades-Shah T H, Steven M D, and Clark J A.1990. High resolution derivative spectra in remote sensing[J]. Remote Sense of Environments,33:55～64.
[14]Escadafal R. Remote sensing of arid soil surface color with Landsat Thematic Mapper[J]. Adv Space Res,1989, 9(1):1159～1163.
[15]Pinty B, and Verstraete M M.1992. GEMI:A Non-Linear Index to Monitor Global Vegetation from Satellites [J]. Vegetation,101:15～20.
[16]Kaufman Y J. and Tanre D.1992. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS [J]. IEEE Trans. on Geosciences and Remote Sensing,30(2):261～270.
[17]Bannari, A, Morin, D, Huette, A.R. and Bonn, F,1995, Areview of vegetation indices [J]. Remote Sensing Reviews, vol.13, p.95～120.
[18]Qi J A.1994. Modified soil adjusted vegetation index [J]. Remote Sense Environment,48:119～126.
[19]Plummer S E, North P R, Briggs S A. The angular vegetation index:an atmospherically resistance index for the second along track scanning radiometer (ATSR-2). Proceedings of the Sixth International Symposium of Physical Measurements and Signatures in Remote Sensing. Vald'Isere, France,1994.
[20]夏照华.基于时间序列的植被动态变化研究[D].北京林业大学,2007.
[21]高志海,魏怀东,丁峰,等.TM影像VI提取植被信息技术研究[J].干旱区资源与环境,1998,12(3)：98～104.
[22]池宏康.黄土高原地区提取植被信息方法的研究[J].植物学报,1998,38(1)：40～44.
[23]Vicente-Serrano S M, Lasanta T, Romo A. Analysis of Spatial and Temporal Evolution of Vegetation Cover in the Spanish Central Pyrenees:Role of Management[J].Environmental Management,2005,34(6):802～818.
[24]Rouse J W, Haas,R W, Schell J A, Deering D W, Harlan J C.Monitoring the vernal advancement and retrogradation (Greenvave efect) of natural vegetation. NASA/GSFCT Type Ⅲ Final Report, Greenbelt, MD, USA,1974.
[25]Baret F, Guyot G. Potentials and limits of vegetation indices for LAI and APAR assessmentfJ]. Remote Sensing of Environment,1991,35 (2):161～173.
[26]Toby N, Carlson, David A Ripley. On the Relation between NDVI Fractional Vegetation Cover and Leaf Area Index[J]. Remote Sensing of Environment,1997,(62):241～252.
[27]胡良军,邵明安.论水土流失研究中的植被覆盖度量指标.西北林学院学报[J],2001,16(1)：40～43.
[28]Dajima T, Kajiwara K, Tateishi R. Global Land Cover Classification by NOAA/AVHRR Data[C]. Proceedings 11Acks. Guangzhou,1990,31～36.
[29]李晓兵,史培军.基于NOAAAVHRR数据的中国主要植被类型NDVI变化规律研究[J].植物学报,1999,41(3)：314～324.
[30]LuchtW, PrenticeC, MyneniRB, etal. Climatic Control of the High-Latitude Vegetation Greening Trend and Pinatubo Effect[J]. Science,2002,296 (31):1687～1689.
[31]盛永伟,陈维英,肖乾广,等.利用气象卫星植被植树进行我国植被的宏观分类[J].科学通报.1995,40(1)：68～71.
[32]郭志华,彭少麟,王伯荪.GIS和RS支持下广东省植被吸收PAR的估算及其时空分布[J].生态学报,1999,19(4)：1444～1449.
[33孙睿,朱其疆.中国陆地植被净第一性生产力及季节变化研究[J].地理学报,2000,55(1)36-45.
[34]朴世龙,方精云,郭庆华.1982-1999年我国植被净第一性生产力及其时空变化[J].北京大学学报,2001,37(4)：536～569.
[35]池宏康.冬小麦单产的光谱数据估测模型研究[J].植物生态学报,1995,19(4)：337-344.
[36]李建龙,蒋平.利用3S技术动态监测天山草地农业产量及其成因分析[J].安全与环境学报,2003,3(2)：8-12.
[37]陈述彭,赵英石.遥感地学分析[M].测绘出版社,1990.
[38]毕晓丽,覃德华,葛剑平.起伏型时间序列模型初探[J].应用与环境生物学报,2004,10(4)：399～401.
[39]Jin Chen, Per. Jonson, Masayuki Tamura, etal. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter[J]. Remote Sensing of Environment,91 (2004) 332～344.
[40]任建强,陈仲新,唐华俊.基于MODIS-NDVI的区域冬小麦遥感估产—以山东省济宁市为例[J].应用生态学报,2006,17(12)：2371-2375.
[41]辜智慧.中国农作物复种指数的遥感估算方法研究—基于SPOT／VGT多时相NDVI遥感数据[D].北京 师范大学,2003.
[42]Rikie Suzuki, Tomoyuki Nomaki and Tetsuzo Yasunart, Spatial distribution and its seasonality of satellite-drived vegetation index (NDVI) and climate in Siberia[J]. International journal of climathology,2001,21:1321～1335.
[43]Jeremy L. Weissa, David S. Gutzlera, Julia E. Allred Coonrodc & Clifford N. Dahmd.2004. Long-term vegetation monitoring with NDVI in a diverse semi-arid setting, central New Mexico, USA[J]. Journal of Arid Environments, 58:249～272.
[44]Pieter S.A. Beck, Clement Atzberger, Kjell Arild Hgda, Bernt Johansen & Andrew K. Skidmore.2006. Improved monitoring of vegetation dynamics at very high latitudes:A new method using MODIS NDVI[J]. Remote Sensing of Environment,100:321～334.
[45]齐腊,刘良云,赵春江.基于遥感影像时间序列的冬小麦种植监测最佳时相选择研究[J].遥感技术与应用,2008,23(2)：154～160.
[46]索玉霞,王正兴,基于决策树和MODIS植被指数时间序列的中亚土地覆盖分类[J].世界地理研究,2008,17(3)：126～130.
[47]Schmidt H, Gitelson A. Temporal and spatial vegetation cover changes in Israeli transition zone:AVHRR-based assessment of rainfall impact[J]. International journal of remote sensing,2000,21:997～1010.
[48]L. Milich, E. Weiss. GAC NDVI images:relationship to rainfall and potential evaporation in the grazing lands of the Gourma (northern Sahel) and in the croplands of the Niger-Nigeria border (southern Sahel) [J].International journal of remote sensing,2000,21(2):261～280.
[49]J.Wang, P. M. Rich and K.P. Price. Temporal responses of NDVI to precipitation and temperature in the Central Great Plains, U.S.A[J]. International journal of remote sensing,2003,24(11):2345～2364.
[50]高志强,刘纪远.基于遥感和GIS的中国植被指数变化的驱动因子分析及模型研究[J].气候与环境研究,2000,5(2)：156～164.
[51]张井勇,董文杰,叶笃正,等.中国植被覆盖对夏季气候影响的新证据[J]..科学通报,2003,48(1)：91～95.
[52]杨建平,丁永建,陈仁升.长江黄河源区高寒植被变化的记录[J].地理学报,2005,60(3)：467～478.
[53]Nelson A, Oberthur T, Cook S.2007. Multi-scale correlations between topography and vegetation in a hillside catchment of Honduras[J]. International Journal of Geographical Information Science,21:145～174.
[54]Walsh SJ, Crawford TW, Welsh W F, et al.2001. A multiscale analysis of LULC and NDVI variation in Nang Rong district, northeast Thailand[J]. Agriculture, Ecosystems & Environment,85:47～64.
[55]焦超伟,赵牡丹,汤国安.基于GIS的植被空间格局特征与地形因子的相关关系—以陕西省耀县为例[J].水土保持通报,2005,25(6)：19～23.
[56]陈瑶,胥晓,张德然,等.四川龙门山西北部植被分布与地形因子的相关性[J].生态学杂志,2006,25(9)：1052～1055.
[57]李双成,高伟明,周巧富.基于小波变换的NDVI与地形因子多尺度空间相关分析[J].生态学报,2006,20(12)：4198～4203.
[58]刘传胜,张万昌,雍斌,等.和田绿洲景观异质性及其特征尺度的遥感研究[J].遥感信息,2007,(3)：39-44.
[59]黄桂芳,刘茂松,徐驰,等.南京市城区景观特征尺度动态[J].应用生态学报,2008,19(3)：606～612.
[60]邱炳文,苏簪铀,陈崇成.基于小波变换的武夷山自然保护区NDVI与地形因子多尺度空间相关分析[J].生 态学杂志,2009,28(9)：1915～1920.
[61]White,M A, Nemani R R, Thomton P E, and Runni S W. Satellite evidence of phonological differences between urbanized and rural areas of the eastern United States deciduous broadleaf forest[J]. Ecosystems,2002(5): 260～273.
[62]de Beurs K M, Henebry G M. Land surface phenology, climatic variation, and institutional change:Analyzing agricultural land cover change in Kazakhstan[J],Remote Sensing of Environment,2004(89):497～509.
[63]Kinzig A P, Warren P, Martin C, Hope D, et al. The effects of human socioeconomic status and cultural characteristics on urban patterns of biodiversity [J/OL].Ecology and Society,2005,10(1):23.
[64]White M A, Hoffman F, Hargrove W W, and Nemani R R, A globa monitoring phonological respomses to climate change[J], Geophysical. R200,32,L04705, doi:10,1029/2004 GL021961.
[65]韩贵锋,徐建华.人口与经济发展对植被的影响研究—以重庆市为例[J].长江流域资源与环境,2008,17(5),785～792.
[66]韩贵锋,徐建华.长江三角洲地区城市化与植被活动之间的时空相关性[J].生态科学,2008,27(1),1-5.
[67]韩贵锋.中国东部地区植被覆盖的时空变化及其人为因素的影响研究[D].华东师范大学,2007.
[68]孙红雨,王长耀.中国地表植被覆盖变化及其气候因子关系-基于NOAA时间序列数据分析[J].遥感学报,1998,2(3)：204～210.
[69]易佳,田永中,高阳华,等.基于RS的山地城市热岛效应及其与土地覆被变化关系研究—以重庆市主城区为例[J].云南师范大学学报,2008,28(6)：62～69.
[70]http://www.cqates.com/fjlist.asp
[71]Chen J, Jonsson P, TamuraM, et al.2004. A simp lemethod for reconstructing a high-quality NDV I time-series data set based on the Savitzky-Golay filter. Rem ote Sens Environ,91:332～344.
[72]Savitzky A, GolayM J E.1964. Smoothing and differentiation of data by simplified least squares procedures. Anal Chem,36:1627～1639.
[73]冯锦明,赵天保,张英娟.基于台站降水资料对不同空间内插方法的比较[J].气候与环境研究,2004,9(2)：261～277.
[74]朱会义,贾绍凤.降雨信息空间插值的不确定性分析[J].地理科学进展,2004,23(2)：34-42.
[75]方书敏,钱正堂,李远平.甘肃省降水的空间内插方法比较[J].干旱区资源与环境,2005,19(3)：47～50.
[76]Ardiansyah P O D, Yokoyama R. DEM generation method from contour lines based on the steepest slope segment chain and a monotone interpolation function. ISPRS J. Photogramm.2002,57:86～101.
[77]Wu W, Fan Y, Wang Z Y, Liu H B. Assessing effects of digital elevation model resolutions on soil-landscape correlations in a hilly area. Agric. Ecosyst. Environ.2008,126:209～216.
[78]King D, Bourrennane H, Isambert M, Macaire J J. Relationship of the presence of a non-calcareous clay-loam horizon to DEM attributes in a gently sloping area. Geoderma,1999,89:95～111.
[79]O'Callaghan J F, Mark D M. The extraction of drainage networks from digital elevation data. Comput. Vis. Graph. Image Process.1984,28(3):323～344.
[80]Lee J, Stucky D. On applying viewshed analysis fro determining least-cost paths on digital elevation model. Int. J. Geogr. Inf. Sci.1998,12(8):891～905.
[81]Gruber U, Haefner H. Avalanche hazard mapping with satellite data and a digital elevation model. Appl. Geogr. 1995,15(2):99～113.
[82]Taud H, Parrot J F, Alvarez R. DEM generation by contour line dilation. Comput. Geosci.1999,25:775～783.
[83]Kornus W, Alamus R, Ruiz A, Talaya J. DEM generation from SPOT-5 3-fold along track stereoscopic imagery using autocalibration. ISPRS J. Photogramm.2006,60:147～159.
[84]Hutchinson M F. Documentation for ANUDEM Version 4.4.1995, Centre for Resource and Enviromental Studies, Australian National University, Canberra.
[85]Fu C B, Wen G. Variation of ecosystems over East Asia in association with seasonal, interannual and decadal monsoon climate variability [J]. Climatic Change,1999,43:477～494.
[86]Kawabata A, Ichii K, Yamaguehi Y. Global monitoring of interannual changes in vegetation activities using NDVI and its relationships to temperature and precipitation[J]. International Journal of Remote Sensing,2001, 22(7):1377～1382.
[87]龚道溢,史培军,何学兆..北半球春季植被NDVI对温度变化响应的区域差异[J].地理学报,2002,57(5)：505～514.
[88]孔宁宁,曾辉,李书娟.四川卧龙自然保护区植被的地形分异格局研究[J].北京大学学报(自然科学版),2002,38(4)：543～549.
[89]沈泽昊,张新时,金义兴.地形对亚热带山地景观尺度植被格局影响的梯度分析[J].植物生态学报,2000,24(4)：430～435.
[90]张金屯.植被数量生态学方法[M].北京：中国科学技术出版社,1995,105～257.
[91]Stow D, Hope A, McGuire D, et al. Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems[J]. Remote Sensing of Environment,2004,89:281～308.
[92]Hope A, Boynton W, Stow D, et al. Inter-annual growth dynamics of vegetation in the Kuparuk River watershed based on the normalized difference vegetation index [J]. International Journal of Remote Sensing, 2003,24(17):3413～3425.
[93]郭仁忠.空间分析(第二版)[M].北京：高等教育出版社,2001：246-248.
[94]Tucker C J,Sellers P. Satellite remote Sensing of primary production[J].International Journal of Remote Sensing. 1986,7:1395～1416.
[95]Prince S D.Satellite remote sensing of primary production:comparison of results for sahelian grasslands 1981-1988[J].International Journal of Remote Sensing.1991.12:1301～1311.
[96]Quan Wang, Jian Ni, John Tenhunen.Application of a geographically-weighted regression analysis to estimate net primary production of Chinese forest ecosystems[J].Global Ecology and Biogeography,2005(14):379～393.
[97]Reed BC,Brown JF,VanderZee D,et al. Measuring phonological variability from satellite imagery. Journal of Vegetation Science,1994,5:703～714.
[98]李荣平,周广胜,张慧玲.植物物候研究进展[J].应用生态学报,2006,17(3)：541～544.
[2]程麟生,宋振鑫,杨德保.青藏高原地表分类及其东北部辐射通量的二维数值模拟[J].高原气象,2000,19(2)：198～213.
[3]兰明娟,魏虹,熊春妮,等.基于TM影像的重庆市北碚区地表植被覆盖变化[J].西南大学学报,2009,31(4)：100-104.
[4]董永平,吴新宏等.草原遥感监测技术[M].北京：化学工业出版社,2005.
[5]郭铌.植被指数及其研究进展[J].干旱气象,2003,21(4)：71-75.
[6]Pearson R L, Miller D L.1972. Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie. In:Proceedings of the Eighth International Symposium on Remote Sensing of Environment [J]. Michigan:Ann Arbor,2:1357～1381.
[7]Rouse, J. W, Haas R H, Schell J A, et al. Monitoring Vegetation systems in the Great Plains with ERTS [A]. Proceedings of Third Earth Resources Technology Satellite-1 Symposium[C]. Greenbelt,1974(351):310～317.
[8]Kauth R J, Thomas G S. The tasseled cap-a graphic description of the spectra temporal development of agriculture crops as seen by Landsat. Pros Symposium on Machine Processing of Remotely Sensed Data. Purdue University, West Lafayette, Indiana,1976:41～51.
[9]Richardson A J and Wiegand C L. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing [A].1977,43:1541～1552.
[10]Jackson, R. D.1983. Spectral indices in n-space[J]. Remote Sensing Environ.13:409～421.
[11]Huete A R.1988. A soil-adjusted vegetation index (SAVI) [J].Remote Sense Environ,25:295～309.
[12]Baret F, Guyot G, Major D J. TSAVI:A vegetation index which minimizes soil brightness effects on LAI and APAR estimation. Proceedings of the 12th Canadian Symposium on Remote Sensing[J]. Vancouver, Canada, 1989.1355～1358.
[13]Demetriades-Shah T H, Steven M D, and Clark J A.1990. High resolution derivative spectra in remote sensing[J]. Remote Sense of Environments,33:55～64.
[14]Escadafal R. Remote sensing of arid soil surface color with Landsat Thematic Mapper[J]. Adv Space Res,1989, 9(1):1159～1163.
[15]Pinty B, and Verstraete M M.1992. GEMI:A Non-Linear Index to Monitor Global Vegetation from Satellites [J]. Vegetation,101:15～20.
[16]Kaufman Y J. and Tanre D.1992. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS [J]. IEEE Trans. on Geosciences and Remote Sensing,30(2):261～270.
[17]Bannari, A, Morin, D, Huette, A.R. and Bonn, F,1995, Areview of vegetation indices [J]. Remote Sensing Reviews, vol.13, p.95～120.
[18]Qi J A.1994. Modified soil adjusted vegetation index [J]. Remote Sense Environment,48:119～126.
[19]Plummer S E, North P R, Briggs S A. The angular vegetation index:an atmospherically resistance index for the second along track scanning radiometer (ATSR-2). Proceedings of the Sixth International Symposium of Physical Measurements and Signatures in Remote Sensing. Vald'Isere, France,1994.
[20]夏照华.基于时间序列的植被动态变化研究[D].北京林业大学,2007.
[21]高志海,魏怀东,丁峰,等.TM影像VI提取植被信息技术研究[J].干旱区资源与环境,1998,12(3)：98～104.
[22]池宏康.黄土高原地区提取植被信息方法的研究[J].植物学报,1998,38(1)：40～44.
[23]Vicente-Serrano S M, Lasanta T, Romo A. Analysis of Spatial and Temporal Evolution of Vegetation Cover in the Spanish Central Pyrenees:Role of Management[J].Environmental Management,2005,34(6):802～818.
[24]Rouse J W, Haas,R W, Schell J A, Deering D W, Harlan J C.Monitoring the vernal advancement and retrogradation (Greenvave efect) of natural vegetation. NASA/GSFCT Type Ⅲ Final Report, Greenbelt, MD, USA,1974.
[25]Baret F, Guyot G. Potentials and limits of vegetation indices for LAI and APAR assessmentfJ]. Remote Sensing of Environment,1991,35 (2):161～173.
[26]Toby N, Carlson, David A Ripley. On the Relation between NDVI Fractional Vegetation Cover and Leaf Area Index[J]. Remote Sensing of Environment,1997,(62):241～252.
[27]胡良军,邵明安.论水土流失研究中的植被覆盖度量指标.西北林学院学报[J],2001,16(1)：40～43.
[28]Dajima T, Kajiwara K, Tateishi R. Global Land Cover Classification by NOAA/AVHRR Data[C]. Proceedings 11Acks. Guangzhou,1990,31～36.
[29]李晓兵,史培军.基于NOAAAVHRR数据的中国主要植被类型NDVI变化规律研究[J].植物学报,1999,41(3)：314～324.
[30]LuchtW, PrenticeC, MyneniRB, etal. Climatic Control of the High-Latitude Vegetation Greening Trend and Pinatubo Effect[J]. Science,2002,296 (31):1687～1689.
[31]盛永伟,陈维英,肖乾广,等.利用气象卫星植被植树进行我国植被的宏观分类[J].科学通报.1995,40(1)：68～71.
[32]郭志华,彭少麟,王伯荪.GIS和RS支持下广东省植被吸收PAR的估算及其时空分布[J].生态学报,1999,19(4)：1444～1449.
[33孙睿,朱其疆.中国陆地植被净第一性生产力及季节变化研究[J].地理学报,2000,55(1)36-45.
[34]朴世龙,方精云,郭庆华.1982-1999年我国植被净第一性生产力及其时空变化[J].北京大学学报,2001,37(4)：536～569.
[35]池宏康.冬小麦单产的光谱数据估测模型研究[J].植物生态学报,1995,19(4)：337-344.
[36]李建龙,蒋平.利用3S技术动态监测天山草地农业产量及其成因分析[J].安全与环境学报,2003,3(2)：8-12.
[37]陈述彭,赵英石.遥感地学分析[M].测绘出版社,1990.
[38]毕晓丽,覃德华,葛剑平.起伏型时间序列模型初探[J].应用与环境生物学报,2004,10(4)：399～401.
[39]Jin Chen, Per. Jonson, Masayuki Tamura, etal. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter[J]. Remote Sensing of Environment,91 (2004) 332～344.
[40]任建强,陈仲新,唐华俊.基于MODIS-NDVI的区域冬小麦遥感估产—以山东省济宁市为例[J].应用生态学报,2006,17(12)：2371-2375.
[41]辜智慧.中国农作物复种指数的遥感估算方法研究—基于SPOT／VGT多时相NDVI遥感数据[D].北京 师范大学,2003.
[42]Rikie Suzuki, Tomoyuki Nomaki and Tetsuzo Yasunart, Spatial distribution and its seasonality of satellite-drived vegetation index (NDVI) and climate in Siberia[J]. International journal of climathology,2001,21:1321～1335.
[43]Jeremy L. Weissa, David S. Gutzlera, Julia E. Allred Coonrodc & Clifford N. Dahmd.2004. Long-term vegetation monitoring with NDVI in a diverse semi-arid setting, central New Mexico, USA[J]. Journal of Arid Environments, 58:249～272.
[44]Pieter S.A. Beck, Clement Atzberger, Kjell Arild Hgda, Bernt Johansen & Andrew K. Skidmore.2006. Improved monitoring of vegetation dynamics at very high latitudes:A new method using MODIS NDVI[J]. Remote Sensing of Environment,100:321～334.
[45]齐腊,刘良云,赵春江.基于遥感影像时间序列的冬小麦种植监测最佳时相选择研究[J].遥感技术与应用,2008,23(2)：154～160.
[46]索玉霞,王正兴,基于决策树和MODIS植被指数时间序列的中亚土地覆盖分类[J].世界地理研究,2008,17(3)：126～130.
[47]Schmidt H, Gitelson A. Temporal and spatial vegetation cover changes in Israeli transition zone:AVHRR-based assessment of rainfall impact[J]. International journal of remote sensing,2000,21:997～1010.
[48]L. Milich, E. Weiss. GAC NDVI images:relationship to rainfall and potential evaporation in the grazing lands of the Gourma (northern Sahel) and in the croplands of the Niger-Nigeria border (southern Sahel) [J].International journal of remote sensing,2000,21(2):261～280.
[49]J.Wang, P. M. Rich and K.P. Price. Temporal responses of NDVI to precipitation and temperature in the Central Great Plains, U.S.A[J]. International journal of remote sensing,2003,24(11):2345～2364.
[50]高志强,刘纪远.基于遥感和GIS的中国植被指数变化的驱动因子分析及模型研究[J].气候与环境研究,2000,5(2)：156～164.
[51]张井勇,董文杰,叶笃正,等.中国植被覆盖对夏季气候影响的新证据[J]..科学通报,2003,48(1)：91～95.
[52]杨建平,丁永建,陈仁升.长江黄河源区高寒植被变化的记录[J].地理学报,2005,60(3)：467～478.
[53]Nelson A, Oberthur T, Cook S.2007. Multi-scale correlations between topography and vegetation in a hillside catchment of Honduras[J]. International Journal of Geographical Information Science,21:145～174.
[54]Walsh SJ, Crawford TW, Welsh W F, et al.2001. A multiscale analysis of LULC and NDVI variation in Nang Rong district, northeast Thailand[J]. Agriculture, Ecosystems & Environment,85:47～64.
[55]焦超伟,赵牡丹,汤国安.基于GIS的植被空间格局特征与地形因子的相关关系—以陕西省耀县为例[J].水土保持通报,2005,25(6)：19～23.
[56]陈瑶,胥晓,张德然,等.四川龙门山西北部植被分布与地形因子的相关性[J].生态学杂志,2006,25(9)：1052～1055.
[57]李双成,高伟明,周巧富.基于小波变换的NDVI与地形因子多尺度空间相关分析[J].生态学报,2006,20(12)：4198～4203.
[58]刘传胜,张万昌,雍斌,等.和田绿洲景观异质性及其特征尺度的遥感研究[J].遥感信息,2007,(3)：39-44.
[59]黄桂芳,刘茂松,徐驰,等.南京市城区景观特征尺度动态[J].应用生态学报,2008,19(3)：606～612.
[60]邱炳文,苏簪铀,陈崇成.基于小波变换的武夷山自然保护区NDVI与地形因子多尺度空间相关分析[J].生 态学杂志,2009,28(9)：1915～1920.
[61]White,M A, Nemani R R, Thomton P E, and Runni S W. Satellite evidence of phonological differences between urbanized and rural areas of the eastern United States deciduous broadleaf forest[J]. Ecosystems,2002(5): 260～273.
[62]de Beurs K M, Henebry G M. Land surface phenology, climatic variation, and institutional change:Analyzing agricultural land cover change in Kazakhstan[J],Remote Sensing of Environment,2004(89):497～509.
[63]Kinzig A P, Warren P, Martin C, Hope D, et al. The effects of human socioeconomic status and cultural characteristics on urban patterns of biodiversity [J/OL].Ecology and Society,2005,10(1):23.
[64]White M A, Hoffman F, Hargrove W W, and Nemani R R, A globa monitoring phonological respomses to climate change[J], Geophysical. R200,32,L04705, doi:10,1029/2004 GL021961.
[65]韩贵锋,徐建华.人口与经济发展对植被的影响研究—以重庆市为例[J].长江流域资源与环境,2008,17(5),785～792.
[66]韩贵锋,徐建华.长江三角洲地区城市化与植被活动之间的时空相关性[J].生态科学,2008,27(1),1-5.
[67]韩贵锋.中国东部地区植被覆盖的时空变化及其人为因素的影响研究[D].华东师范大学,2007.
[68]孙红雨,王长耀.中国地表植被覆盖变化及其气候因子关系-基于NOAA时间序列数据分析[J].遥感学报,1998,2(3)：204～210.
[69]易佳,田永中,高阳华,等.基于RS的山地城市热岛效应及其与土地覆被变化关系研究—以重庆市主城区为例[J].云南师范大学学报,2008,28(6)：62～69.
[70]http://www.cqates.com/fjlist.asp
[71]Chen J, Jonsson P, TamuraM, et al.2004. A simp lemethod for reconstructing a high-quality NDV I time-series data set based on the Savitzky-Golay filter. Rem ote Sens Environ,91:332～344.
[72]Savitzky A, GolayM J E.1964. Smoothing and differentiation of data by simplified least squares procedures. Anal Chem,36:1627～1639.
[73]冯锦明,赵天保,张英娟.基于台站降水资料对不同空间内插方法的比较[J].气候与环境研究,2004,9(2)：261～277.
[74]朱会义,贾绍凤.降雨信息空间插值的不确定性分析[J].地理科学进展,2004,23(2)：34-42.
[75]方书敏,钱正堂,李远平.甘肃省降水的空间内插方法比较[J].干旱区资源与环境,2005,19(3)：47～50.
[76]Ardiansyah P O D, Yokoyama R. DEM generation method from contour lines based on the steepest slope segment chain and a monotone interpolation function. ISPRS J. Photogramm.2002,57:86～101.
[77]Wu W, Fan Y, Wang Z Y, Liu H B. Assessing effects of digital elevation model resolutions on soil-landscape correlations in a hilly area. Agric. Ecosyst. Environ.2008,126:209～216.
[78]King D, Bourrennane H, Isambert M, Macaire J J. Relationship of the presence of a non-calcareous clay-loam horizon to DEM attributes in a gently sloping area. Geoderma,1999,89:95～111.
[79]O'Callaghan J F, Mark D M. The extraction of drainage networks from digital elevation data. Comput. Vis. Graph. Image Process.1984,28(3):323～344.
[80]Lee J, Stucky D. On applying viewshed analysis fro determining least-cost paths on digital elevation model. Int. J. Geogr. Inf. Sci.1998,12(8):891～905.
[81]Gruber U, Haefner H. Avalanche hazard mapping with satellite data and a digital elevation model. Appl. Geogr. 1995,15(2):99～113.
[82]Taud H, Parrot J F, Alvarez R. DEM generation by contour line dilation. Comput. Geosci.1999,25:775～783.
[83]Kornus W, Alamus R, Ruiz A, Talaya J. DEM generation from SPOT-5 3-fold along track stereoscopic imagery using autocalibration. ISPRS J. Photogramm.2006,60:147～159.
[84]Hutchinson M F. Documentation for ANUDEM Version 4.4.1995, Centre for Resource and Enviromental Studies, Australian National University, Canberra.
[85]Fu C B, Wen G. Variation of ecosystems over East Asia in association with seasonal, interannual and decadal monsoon climate variability [J]. Climatic Change,1999,43:477～494.
[86]Kawabata A, Ichii K, Yamaguehi Y. Global monitoring of interannual changes in vegetation activities using NDVI and its relationships to temperature and precipitation[J]. International Journal of Remote Sensing,2001, 22(7):1377～1382.
[87]龚道溢,史培军,何学兆..北半球春季植被NDVI对温度变化响应的区域差异[J].地理学报,2002,57(5)：505～514.
[88]孔宁宁,曾辉,李书娟.四川卧龙自然保护区植被的地形分异格局研究[J].北京大学学报(自然科学版),2002,38(4)：543～549.
[89]沈泽昊,张新时,金义兴.地形对亚热带山地景观尺度植被格局影响的梯度分析[J].植物生态学报,2000,24(4)：430～435.
[90]张金屯.植被数量生态学方法[M].北京：中国科学技术出版社,1995,105～257.
[91]Stow D, Hope A, McGuire D, et al. Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems[J]. Remote Sensing of Environment,2004,89:281～308.
[92]Hope A, Boynton W, Stow D, et al. Inter-annual growth dynamics of vegetation in the Kuparuk River watershed based on the normalized difference vegetation index [J]. International Journal of Remote Sensing, 2003,24(17):3413～3425.
[93]郭仁忠.空间分析(第二版)[M].北京：高等教育出版社,2001：246-248.
[94]Tucker C J,Sellers P. Satellite remote Sensing of primary production[J].International Journal of Remote Sensing. 1986,7:1395～1416.
[95]Prince S D.Satellite remote sensing of primary production:comparison of results for sahelian grasslands 1981-1988[J].International Journal of Remote Sensing.1991.12:1301～1311.
[96]Quan Wang, Jian Ni, John Tenhunen.Application of a geographically-weighted regression analysis to estimate net primary production of Chinese forest ecosystems[J].Global Ecology and Biogeography,2005(14):379～393.
[97]Reed BC,Brown JF,VanderZee D,et al. Measuring phonological variability from satellite imagery. Journal of Vegetation Science,1994,5:703～714.
[98]李荣平,周广胜,张慧玲.植物物候研究进展[J].应用生态学报,2006,17(3)：541～544.