环首都圈植被分布及环境效应的空间相关性研究
|
摘要
本文以环首都地区100km内为研究区域,根据矩形格网法对所研究区域进行不同边长尺度的格网划分,通过2013年的MODIS数据、TM数据等对所研究范围内的各项因子指标进行提取和整合。本研究采用MODIS遥感影像数据与实地调查相结合的手段对所研究数据进行获取,主要包括气象数据、土壤数据、植被数据(植被覆盖指数-N)、地形数据、环境数据等。利用北京市环保局提供的空气污染指标数据对2007-2012年6年间的空气质量进行对比分析,利用GS+软件的3种插值方法和莫兰指数——Moran's Ⅰ对所提取的变量数据进行空间自相关性的研究,计算在不同格网尺度下环首都地区的环境、土壤、水文、植被等各类因子的空间自相关效应的影响变程,并建立优化模型。本研究的创新点在于以北京二环为中心建立矩形格网,且拟定每个格网内部的各类要素均等,以中心格网为研究的中心样本点,按照3x3、5x5、7x7、9x9、11x11、13x13、15x15、17x17的8种宫格制对植被与环境间的空间相关性进行研究,按照空间距离Dr相关法推导出不同矩形格网边长不同宫格取值的情况下,植被对环境所能影响到的最远距离r。综合以上,本文得到的主要结论如下：
(1)插值算法后各因子的空间自相关性的辐射变程A
通过运用地统计学软件GS+对各类因子的空间效应的变程进行计算,得出：在不同的格网尺度下,以每个格网中心作为一个样本点,利用克里格插值、反距离加权插值和条件模拟插值3种方法对样本点进行空间插值,建立各类因子的空间效应的最佳模型。通过相关模型得出：年均降雨量、植被盖度、高程、土壤中磷含量、钾含量、有机质含量及空气污染指数等因子的空间自相关性的影响变程分别是：Range(A)降雨量max为113.45km,Range(A)降雨量min为103.23km,Range(A)植被盖度为74.40km,Range(A)DEM为132.15km,Range(a)P max为7.27km,Range(A)P min为232.20km,Range(A)K max为165.50km,Range(A)K min为93.30km,Range(A)Q为100.45km,Range(A)API为67.10km,得出的结论是随着样本点与点之间距离的增加各因子的空间相关性降低。
(2)莫兰指数法-Moran's ⅠⅠ推算不同格网尺度各因子空间自相关性的影响范围
本文利用莫兰指数-Moran's Ⅰ法对各类因子的点与点之间的空间自相关性进行研究,发现各因子的自相关影响区域半径普遍小于辐射变程,但与变异函数相同的是各类因子的空间自相关性也随着点与点间的距离增大而降低的结论,不同的是在合理的格网尺度下才会得到这种结论。经莫兰指数计算得知,年均降雨量、植被盖度、高程、土壤中磷含量、钾元素含量、有机质含量及空气污染指数等因子的空间自相关性的影响范围分别是：Ir植被盖度∈(6620m,7131m)、Ir降雨量max∈(6923m,9756m)、 Ir降雨量∈(7112m,8933m)、IrAPI∈(2998m,6864m)、IrK∈(6623m,6789m)、 IrP∈(6016m,8827m).Ir有机质∈(6621m,6721m)、IrDEM(8850m,8893m)。通过对环首都地区100km范围内能够影响环境状况的各类因子进行变异函数分析和莫兰指数分析,得出在一定的格网尺度范围内的各因子随空间距离变化的最优模型,模型的内符合精度都达到98%以上,同时对各类因子预留20%的样本点对所选模型进行F检验,得到的外符合精度也都在90%以上,模型拟合度较高。
(3)植被因子与环境因子间的空间相关性
研究了植被因子、气候因子、环境因子、土壤因子及地形因子的空间自相关性后,本文利用空间距离Dr相关法的公式推导植被因素(植被盖度C、生物量B)与环境因子(可吸入颗粒物PM、SO、NO2、平均降雨及平均温度)的空间相关性,并计算距离的幂指数r,得出在不同的矩形格网边长范围及不同的宫格取值的情况下,植被对环境的影响随距离的增大而减小的结论,得到植被因子对PM值、SO2、NO2、平均降雨量和平均温度的最大影响距离是14.74km、13.96km、13.59km、26.57km和18.0km。
综上所述可见,在环首都地区范围内,对环境影响的各类因子的区域间的交互效应随着点与点之间的距离增大而减小,即通过本文的研究,将环境污染区域限定在空间相关性的影响范围内,并针对该范围进行具体整治,如出台相应措施对汽车尾气进行合理控制,对周边排放超标的工厂进行适当迁移,增强市民保护环境的意识,根据“适地适树”原则进行植树造林,依照树种的吸附机理对树种进行合理配置等。环首都地区环境效应的研究是跟各种因素密切相关的,本研究只是在己知定量因子的基础上建立相关模型并求解了主要影响因子和影响半径,对各类定性因子如各类人为因素等的相关研究将有待进一步的分析。
This paper take the capital region100km loop as the study area, divided different areas of the grid scales based on the rectangular grid method, extraction and integration MODIS data, TM data, etc. of2013. The innovation of this study is to establish the rectangular grid take the Beijing Ring as the research center, and developed equalization of various elements inside the each grid, set radiation radius R. Using geo-statistics variogram to establish the best model of all kinds of factors changes followed with the radius. In this study, MODIS data and field survey are used to the data combination and acquisition, including meteorological data, soil data, vegetation data, terrain data and environmental data etc. Using air pollution index data provided by the Beijing Municipal Environmental Protection Bureau of air quality for6years from2007to2012were compared between three kinds of interpolation method of GS+software for variable data extracted spatial correlation studies. While taking advantage of Moran-Moran's I index to analysis spatial autocorrelation of these variables factors and to calculate the radius of influence spatial effects of environmental factors capital region, such as soil factors, hydrological factors, rings and other types of vegetation factors, which grid at different scales of radiation radius R, then established the optimization model. The innovation of this study is to establish the rectangular grid center in Beijing Ring, and develop all kinds of elements is equality inside of each grid. Take grid center as the center point of the study sample, researches on the correlation studies of vegetation and the environment factors in accordance with3x3、5x5、7x7、9x9、11x11、13x13、15x15and17x17, the eight kinds of grids. Calculate the maximum distance of the vegetation could effect on the environment, based on the idea of spatial distance correlation method derived under different side length of the rectangular grid values. Based on the above, the main conclusions of this paper are as follows:
(1)After interpolation algorithm relevance of each factor space radiation variable range A
Calculated radius of space radiation effects of various types of factors based on GS+ software of geo-statistics, take the conclusion as follows:under different grid scales to the center of each grid point as a sample using interpolate kriging, interpolate inverse distance the weighting, interpolate conditional simulation for spatial interpolation of sample points to establish the best model spatial effect of various types of factors. Through the relevant model drawn, correlation influence radius is: Range(A) rainfall-max is113.45km, Range(A) rainfall-min is103.23km,Range(A)vegetation cover is74.40km,Range(a)DEM is132.15km,Range(A)Pmax is7.27km,Range(A)Pmin is232.20km,Range(A)K max is165.50km, Range(A)K min is93.30km, Range(A)Q is100.45km, Range(A)API is67.10km, concluded that less space relevant of the point with the increasing of the distance.
(2) Moran index-Moran's I calculate different influence grid scale of spatial autocorrelation
By using three kinds of interpolation variogram analysis of the space under all kinds of different rectangular grid scale factor correlations, we use Moran index-Moran's I autocorrelation method to study space dots between the various types of factors, found that autocorrelation affected area is smaller than the radius of each factor generally change spatial correlation of radiation process, but with the same spatial variation function of various types of autocorrelation factor along with the distance between the dots increases reduced conclusions but the difference is in a reasonable scale grid will get this conclusion. Moran index was calculated the factors autocorrelation of influence is: Irvegetation cover∈(6620m,7131m),Irrainfall-max∈(6923m,9756m),Irrainfall-min∈(7112m,8933m), IrAPI∈(2998m,6864m),IrK∈(6623m,6789m), Irp∈(6016m,8827m), Irorgmic matter∈(6621m,6721m),IrDEM∈(8850m,8893m).Through Moran performed vario-gram and index analysis of all kinds of factors, which can affect the state of the environment in the Capital Region central100km range, it results in an optimal model at certain range of each factor grid-scale with spatial distance changes. The accuracy of the model are accord with above than98%, while all kinds of factors aside20%of the sample points to the selected model F-test, get outside accord accuracy above90%.
(3) Spatial correlation between vegetation and environmental factors
After studied the correlation of vegetation factors, climatic factors, environmental factors, spatial soil factors and landforms, we use the gravitational extended formula derivation spatial correlation of vegetation (vegetation cover, biomass) and environmental factors (re-spirable particulate matter P M,SO2,NO2, the average spatial correlation of rainfall and temperature)and calculate distance. Get the conclusion is the vegetation influence is decreased followed by the increase of the distance at different side length range and grid value. The maximum impact from PM, SO2, NO2, the average rainfall and average temperature is14.74km,13.96km,13.59km,26.57km and18.0km.
In summary, in the Capital Region, central range interactions between regions of the environmental impact of various types of factors as the distance between points increases and decreases, which through this study, the environmental pollution area defined in the space radiation radius correlation and remediation for that specific range, such as the introduction of appropriate measures to carry out the reasonable control of the car exhaust, factory emissions exceeding the surrounding proper migration, and enhance public awareness of environmental protection, according to " suitable tree " principle afforestation, in accordance with the adsorption mechanism of species such as the rational allocation of species. Environmental effect of central capital region is closely associated with a variety of factors, this study only known to establish the basis of quantitative factors related to model and solve a major factor and radius of influence of various types of qualitative factors, such as various types of human factors related research will be analysisied in the further.
(1)插值算法后各因子的空间自相关性的辐射变程A
通过运用地统计学软件GS+对各类因子的空间效应的变程进行计算,得出：在不同的格网尺度下,以每个格网中心作为一个样本点,利用克里格插值、反距离加权插值和条件模拟插值3种方法对样本点进行空间插值,建立各类因子的空间效应的最佳模型。通过相关模型得出：年均降雨量、植被盖度、高程、土壤中磷含量、钾含量、有机质含量及空气污染指数等因子的空间自相关性的影响变程分别是：Range(A)降雨量max为113.45km,Range(A)降雨量min为103.23km,Range(A)植被盖度为74.40km,Range(A)DEM为132.15km,Range(a)P max为7.27km,Range(A)P min为232.20km,Range(A)K max为165.50km,Range(A)K min为93.30km,Range(A)Q为100.45km,Range(A)API为67.10km,得出的结论是随着样本点与点之间距离的增加各因子的空间相关性降低。
(2)莫兰指数法-Moran's ⅠⅠ推算不同格网尺度各因子空间自相关性的影响范围
本文利用莫兰指数-Moran's Ⅰ法对各类因子的点与点之间的空间自相关性进行研究,发现各因子的自相关影响区域半径普遍小于辐射变程,但与变异函数相同的是各类因子的空间自相关性也随着点与点间的距离增大而降低的结论,不同的是在合理的格网尺度下才会得到这种结论。经莫兰指数计算得知,年均降雨量、植被盖度、高程、土壤中磷含量、钾元素含量、有机质含量及空气污染指数等因子的空间自相关性的影响范围分别是：Ir植被盖度∈(6620m,7131m)、Ir降雨量max∈(6923m,9756m)、 Ir降雨量∈(7112m,8933m)、IrAPI∈(2998m,6864m)、IrK∈(6623m,6789m)、 IrP∈(6016m,8827m).Ir有机质∈(6621m,6721m)、IrDEM(8850m,8893m)。通过对环首都地区100km范围内能够影响环境状况的各类因子进行变异函数分析和莫兰指数分析,得出在一定的格网尺度范围内的各因子随空间距离变化的最优模型,模型的内符合精度都达到98%以上,同时对各类因子预留20%的样本点对所选模型进行F检验,得到的外符合精度也都在90%以上,模型拟合度较高。
(3)植被因子与环境因子间的空间相关性
研究了植被因子、气候因子、环境因子、土壤因子及地形因子的空间自相关性后,本文利用空间距离Dr相关法的公式推导植被因素(植被盖度C、生物量B)与环境因子(可吸入颗粒物PM、SO、NO2、平均降雨及平均温度)的空间相关性,并计算距离的幂指数r,得出在不同的矩形格网边长范围及不同的宫格取值的情况下,植被对环境的影响随距离的增大而减小的结论,得到植被因子对PM值、SO2、NO2、平均降雨量和平均温度的最大影响距离是14.74km、13.96km、13.59km、26.57km和18.0km。
综上所述可见,在环首都地区范围内,对环境影响的各类因子的区域间的交互效应随着点与点之间的距离增大而减小,即通过本文的研究,将环境污染区域限定在空间相关性的影响范围内,并针对该范围进行具体整治,如出台相应措施对汽车尾气进行合理控制,对周边排放超标的工厂进行适当迁移,增强市民保护环境的意识,根据“适地适树”原则进行植树造林,依照树种的吸附机理对树种进行合理配置等。环首都地区环境效应的研究是跟各种因素密切相关的,本研究只是在己知定量因子的基础上建立相关模型并求解了主要影响因子和影响半径,对各类定性因子如各类人为因素等的相关研究将有待进一步的分析。
This paper take the capital region100km loop as the study area, divided different areas of the grid scales based on the rectangular grid method, extraction and integration MODIS data, TM data, etc. of2013. The innovation of this study is to establish the rectangular grid take the Beijing Ring as the research center, and developed equalization of various elements inside the each grid, set radiation radius R. Using geo-statistics variogram to establish the best model of all kinds of factors changes followed with the radius. In this study, MODIS data and field survey are used to the data combination and acquisition, including meteorological data, soil data, vegetation data, terrain data and environmental data etc. Using air pollution index data provided by the Beijing Municipal Environmental Protection Bureau of air quality for6years from2007to2012were compared between three kinds of interpolation method of GS+software for variable data extracted spatial correlation studies. While taking advantage of Moran-Moran's I index to analysis spatial autocorrelation of these variables factors and to calculate the radius of influence spatial effects of environmental factors capital region, such as soil factors, hydrological factors, rings and other types of vegetation factors, which grid at different scales of radiation radius R, then established the optimization model. The innovation of this study is to establish the rectangular grid center in Beijing Ring, and develop all kinds of elements is equality inside of each grid. Take grid center as the center point of the study sample, researches on the correlation studies of vegetation and the environment factors in accordance with3x3、5x5、7x7、9x9、11x11、13x13、15x15and17x17, the eight kinds of grids. Calculate the maximum distance of the vegetation could effect on the environment, based on the idea of spatial distance correlation method derived under different side length of the rectangular grid values. Based on the above, the main conclusions of this paper are as follows:
(1)After interpolation algorithm relevance of each factor space radiation variable range A
Calculated radius of space radiation effects of various types of factors based on GS+ software of geo-statistics, take the conclusion as follows:under different grid scales to the center of each grid point as a sample using interpolate kriging, interpolate inverse distance the weighting, interpolate conditional simulation for spatial interpolation of sample points to establish the best model spatial effect of various types of factors. Through the relevant model drawn, correlation influence radius is: Range(A) rainfall-max is113.45km, Range(A) rainfall-min is103.23km,Range(A)vegetation cover is74.40km,Range(a)DEM is132.15km,Range(A)Pmax is7.27km,Range(A)Pmin is232.20km,Range(A)K max is165.50km, Range(A)K min is93.30km, Range(A)Q is100.45km, Range(A)API is67.10km, concluded that less space relevant of the point with the increasing of the distance.
(2) Moran index-Moran's I calculate different influence grid scale of spatial autocorrelation
By using three kinds of interpolation variogram analysis of the space under all kinds of different rectangular grid scale factor correlations, we use Moran index-Moran's I autocorrelation method to study space dots between the various types of factors, found that autocorrelation affected area is smaller than the radius of each factor generally change spatial correlation of radiation process, but with the same spatial variation function of various types of autocorrelation factor along with the distance between the dots increases reduced conclusions but the difference is in a reasonable scale grid will get this conclusion. Moran index was calculated the factors autocorrelation of influence is: Irvegetation cover∈(6620m,7131m),Irrainfall-max∈(6923m,9756m),Irrainfall-min∈(7112m,8933m), IrAPI∈(2998m,6864m),IrK∈(6623m,6789m), Irp∈(6016m,8827m), Irorgmic matter∈(6621m,6721m),IrDEM∈(8850m,8893m).Through Moran performed vario-gram and index analysis of all kinds of factors, which can affect the state of the environment in the Capital Region central100km range, it results in an optimal model at certain range of each factor grid-scale with spatial distance changes. The accuracy of the model are accord with above than98%, while all kinds of factors aside20%of the sample points to the selected model F-test, get outside accord accuracy above90%.
(3) Spatial correlation between vegetation and environmental factors
After studied the correlation of vegetation factors, climatic factors, environmental factors, spatial soil factors and landforms, we use the gravitational extended formula derivation spatial correlation of vegetation (vegetation cover, biomass) and environmental factors (re-spirable particulate matter P M,SO2,NO2, the average spatial correlation of rainfall and temperature)and calculate distance. Get the conclusion is the vegetation influence is decreased followed by the increase of the distance at different side length range and grid value. The maximum impact from PM, SO2, NO2, the average rainfall and average temperature is14.74km,13.96km,13.59km,26.57km and18.0km.
In summary, in the Capital Region, central range interactions between regions of the environmental impact of various types of factors as the distance between points increases and decreases, which through this study, the environmental pollution area defined in the space radiation radius correlation and remediation for that specific range, such as the introduction of appropriate measures to carry out the reasonable control of the car exhaust, factory emissions exceeding the surrounding proper migration, and enhance public awareness of environmental protection, according to " suitable tree " principle afforestation, in accordance with the adsorption mechanism of species such as the rational allocation of species. Environmental effect of central capital region is closely associated with a variety of factors, this study only known to establish the basis of quantitative factors related to model and solve a major factor and radius of influence of various types of qualitative factors, such as various types of human factors related research will be analysisied in the further.
引文
1. GB/SL190-1996,土壤侵蚀分类分级标准[S]. [GB/SL190-1996, Standard for classification and gradation of soil erosion[S].1996,(5):79-82.
2.安俊岭,S王自发,黄美元等.区域空气质量数值预报模型[J].气候与环境研究,1999,4(3)：244-251.
3.曾伟生,周佑明.森林资源一类和二类调查存在的主要问题与对策[J].2003,22(4)：8-11.
4.陈昌鹏,吴保国,贾永刚等.数据挖掘技术在森林资源信息管理中的应用[J].河北林果研究,2004,19(2)：149-153.
5.陈刚.基于代理的分布式数据挖掘系统设计[J].计算机工程,2002,27(9)：65-671.
6.陈国阶.生态文明若干理论探讨[J].决策咨询,2013,(1)：31-35.
7.陈吉龙.重庆市三峡库区植被覆盖度的遥感估算及动态变化研究[D]：西南大学,2010.
8.陈晋,陈云浩,何春阳,等.基于土地覆盖分类的植被覆盖率估算亚像元模型与应用[J].遥感学报,2001,(06)：416-422.
9.陈军明,赵平,郭晓寅.中国西部植被覆盖变化对北方夏季气候影响的数值模拟[J].气象学报,2010,(02)：173-181.
10.陈玉福,董鸣.生态学系统的空间异质性[J].生态学报,2003,23(2)：346-352.
11.陈云浩,李晓兵,史培军.非均匀陆面条件下区域蒸散量计算的遥感模型[J].气象学报,2002,60(4)：508-512.
12.程红芳,章文波,陈锋.植被覆盖度遥感估算方法研究进展[J].国土资源遥感,2008,(1)：13-18.
13.程家昌,黄鹏,熊昌盛.基于BP神经网络的土壤养分空间插值[J].广东农业科学,2013,7：64-71.
14.程勇,于林,姚安林.采用高斯模型分析输气管道泄漏后气体的扩散[J].内蒙古石油化工,2010,14：49-51.
15.崔一梅.北京市生态公益林补偿机制的理论与实践研究[D]：北京林业大学,2008.
16.丁宁,王倩,孙英君.高斯序贯模拟与克里格插值2种土壤重金属污染分析方法的比较[J].安徽农业科学,2012,40(7)：4162-4164.
17.冯克忠,万庆,励惠国.基于组件技术的GIS广义空间分析[J].地球信息科学,2003,5(1)：62-66.
18.冯土雍.抽样调查理论方法与实践[M].上海：上海科学技术出版社,1996：1-200.
19.高江波,蔡运龙.区域景观破碎化的多尺度空间变异研究一以贵州省乌江流域为例[J].地理科学,2010,30(5)：742-747.
20.高秀清.北京郊区生态环境建设指标体系探究[J].北京农业职业学院学报,2011,25(5)：24-29.
21.顾祝军,曾志远.遥感植被盖度研究[J].水土保持研究,2005,12(2)：18-21.
22.郭旭东,傅伯杰,马克明,等.基于GIS和地统计学的土壤养分空间变异特征研究--以河北省遵化市为例[J].应用生态学报,2000,11(4)：557-563.
23.韩佶兴.2000-2011年东北亚地区植被覆盖度变化研究[D]：中国科学院研究生院(东北地理与农业生态研究所),2012.
24.何建军,余晔,陈晋北,等.植被覆盖度对兰州地区气象场影响的模拟研究[J].高原气象,2012,(06)：1611-1621.
25.何宗贵,韩世民,崔道永等.空间自相关分析的统计量探讨[J].中国血吸虫病防治杂志,2008,20(4)：315-318.
26.侯景儒.中国地质统计学(空间信息统计学)发展的回顾与前景[J].地质与勘探,1997,33 (1)：53-58.
27.胡良军,邵明安.论水土流失研究中的植被覆盖度量指标[J].西北林学院学报,2001,16(1)：40-43.
28.胡琳,林杨,何晓嫒.基于API方法的西安城市大气环境质量评价[J].陕西气象,2007,(1)：18-20.
29.胡伟,邵明安,王全九.黄土高原退耕坡地土壤水分空间变异的尺度性研究[J].农业工程学报,2005,21(8)：11-16.
30.江厚龙,刘国顺,杨夏孟等.精准农业中不同取样间距下Kriging插值精度对比研究[J].土壤通报,2011,42(4)：799-886.
31.蒋岳新,闫厚EOS-MODIS数据在林业上的应用前景展望[D],2003.
32.焦一之,陈瑜,张锁成等.3S技术在森林资源调查中的应用[J].河北林果研究,2007,22(4)：360-362.
33.亢新刚.森林资源经营管理[M].北京：中国林业出版社,2001.
34.蓝斌,洪伟,吴承祯等.闽北森林资源管理的数学模型[J].福建林学院学报,1999,19(1)：66-701
35.李哈滨,王政权,王庆成.空间异质性定量研究理论与方法[J].应用生态学报,1998,(06)：93-99.
36.李琳.北京郊区植被覆盖度变化动态遥感监测—以怀柔区为例[D]：北京林业大学,2008.
37.李苗苗,吴炳方,颜长珍等.密云水库上游植被覆盖度的遥感估算[J].资源科学,2004,26(4)：153-159.
38.李苗苗.植被覆盖度的遥感估算方法研究[D]：中国科学院遥感应用研究所,2003.
39.李明阳,刘方,徐婷等.基于GIS的森林资源空间数据挖掘方法研究—以紫金山为例[J].西北林学院学报,2012,27(3)180-186.
40.李明阳,张向阳,吴文浩等.基于GIS的森林资源调查空间平衡抽样方法研究[J].林业资源管理,2008,(4)：137-150.
41.李鹏,韩洁,袁顺全等.北京山区生态屏障功能分区研究[J].现代农业科学,2009,16(11)：67-70.
42.李庆谋.多维分形克里格方法[J].地球科学进展,2005,20(2)：248-255.
43.李润林,姚艳敏,唐鹏钦等.县域耕地土壤锌含量的协同克里格插值及采样数量优化[J].土壤通报,2013,44(4)：830-838.
44.李天生,周国法.空间自相关与分布型指数研[J].生态学报,1994,14(3)：327-331.
45.李婷婷,伍世代,李永实等.福建省经济空间增长变异特征及驱动机制[J].地理科学,2010,30(6)：847-853.
46.李文龙,薛中正,郭述茂等.基于3S技术的玛曲县草地植被覆盖度变化及其驱动力[J].兰州大学学报(自然科学版),2010,46(1)：85-90,95.
47.李晓晖,袁峰,贾蔡等.基于地统计学插值方法的局部奇异性指数计算比较研究[J].地理科学,2012,32(2)：136-142.
48.李晓晖,袁峰,贾蔡等.基于反距离加权和克里格插值的S-A多重分形滤波对比研究[J].测绘科学,2012,37(3)：88-89.
49.李永才,张威.卫星遥感在森林资源调查中的应用初探[J].湖南林业科技,1997,24(4)：78-80.
50.李元寿,王根绪,丁永建等.青藏高原高寒草甸区土壤水分的空间异质性[J].水科学进展,2008,19(1)：61-67.
51.刘爱丽,王培法,丁园圆.地统计学概论.北京：科学出版社,2012.
52.刘安兴,蔡良良,佘光辉.森林资源二类调查新颁规定的应用分析[J].南京林业大学学报(自然科 学版),2006,30(2)127-130.
53.刘今朝,马杰.基于ArcGIS普通克里格插值在土地平整土方量计算中的应用[J].农业工程,2013,3(2)：50-55.
54.刘康,徐卫华,欧阳志云等.基于GIS的甘肃省土地沙漠化敏感性评价[J].水土保持通报,2002,22(5)：29-31,35.
55.刘庆,夏江宝,谢文军.半方差函数与Moran's I在土壤微量元素空间分布研究中的应用-以寿光市为例.武汉大学学报(信息科学版),2011,36(9)：1129-1133.
56.刘晓林,李文峰,杨林楠.基于ArcGIS地统计分析模块的土壤养分空间变异分析—以云南省建水县为例[J].土壤通报,2012,43(6)：1432-1436.
57.刘永生,李瑞敏.不同方法下土壤重金属元素空间最大变异距离研究——以浙江上虞为例[J].岩矿测试,2007,(04)：331-335.
58.刘永生.华北平原土壤重金属元素空间自相关研究[D]：中国地质大学(北京),2012.
59.柳锦宝,姚云军,张永福.基于分维的多尺度植被空间格局分析[J].水土保持研究,2007,14(1)：155-158.
60.陆娟.基于莫兰指数的盗窃犯罪率全局分布模式分析[J].电子警务,2014,(1)：45-46.
61.马超飞,马建文,布和敖斯尔USLE模型中植被覆盖因子的遥感数据定量估算[J].水土保持通报,2001,21(4)：6-9.
62.马志勇,沈涛,张军海等.基于植被覆盖度的植被变化分析[J].测绘通报,2007,(3)：45-48.
63.梅安新等.遥感导论[D].北京：高等教育出版社,2001.
64.莫菲,王彦辉,熊伟等.六盘山华北落叶松人工纯林枯落物储量的空间变异分析[J].林业科学,2009,45(9)：1-5.
65.牛宝茹,刘俊蓉,王政伟.干旱半干旱地区植被覆盖度遥感信息提取研究[J].武汉大学学报(信息科学版),2005,30(1)：27-30.
66.潘辉.森林资源动态预测方法的研究[J].福建林学院学报,1998,18(1)：73-781.
67.潘开文,吴宁,潘开忠.关于建设长江上游生态屏障的若干问题的讨论[J].生态学报,2004,24(3)：617-629.
68.彭木根.数据仓库技术与实现[M].北京：电子工业出版社,2002,1100-1101.
69.朴世龙,方精云.最近18年来中国植被覆盖的动态变化[J].第四纪研究,2001,(4)：294.
70.秦伟,朱清科,张学霞等.植被覆盖度及其测算方法研究进展[J].西北农林科技大学学报(自然科学版),2006,34(9)：163-170.
71.秦耀东.土壤空间变异研究中的半方差问题[J].Trans actions of the CSAE,1998,(4):42-47.
72.庆易微,杨林.应用人工神经网络法预报空气污染指数,青海大学学报2008,4(26)：25-31.
73.曲利娟.北京市森林旅游开发与评估研究[D]：首都师范大学,2008.
74.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.
75.沈思渊.土壤空间变异研究中地统计学的应用及其展望[J].土壤学年进展,1989,17(3)：11-24.
76.沈涛,刘良云,马金峰等.基于L- THIA模型的密云水库地区非点源污染空间分布特征[J].农业工程学报2007,23(5)：62-68.
77.施拥军,王珂.3S技术在林业中的应用[J].浙江林业科技,2002,22(6)：46-50.
78.税正全,陈慧蓉,李衍达.变异函数中指数、高斯模型的拟合[J].物探化探计算技 术,1997,19(3)：226-232.
79.宋金平.彭萍.北京山区生态环境问题与建设思路[J].北京规划建设,2004,1：57.
80.孙红雨,王长耀,布和敖斯尔等.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[J].遥感学报,1998,(03)：204-210.
81.孙英君,王劲峰,柏延臣等.地统计学的GIS空间分析功能扩展[J].2004,25(4)：436-439.
82.孙玉冰.舟山群岛的植被覆盖度与景观格局的变化研究[D]：华东师范大学,2010.
83.孙兆文.建设中国北方生态屏障的一些思考[J].中西部发展论坛,1999,(2)：4-8.
84.汤国安,杨昕等ArcGIS地理信息系统空间分析实验教程.北京：科学出版社,2012.
85.唐丽华,陆守一,吴达胜等WebGIS及其在森林资源信息管理中的应用与前景[J].浙江林学院学报,2004,21(1)：104-109.
86.田力,张晓盼,袁艳斌.规则格网系统全局Moran指数计算的改进方法[J].测绘科学,2014,39(2)：110-113.
87.佟乃兴,赵晶明,张鹏等.浅谈细颗粒物(PM2.5[J].化工管理,2013,8：143.
88.万丽.基于变异函数的空间异质性定量分析[J].统计与决策,2006,(4)：26-27.
89.王明明,谢永生等.水库集水区水土保持与流域产水量[J].中国水土保持科学,2007,5(1)：12-20.
90.王绍强,朱松丽,周成虎.中国土壤土层厚度的空间变异性特征[J].地理研究,2001,20(2)：161-169.
91.王阗,佘光辉.决策树C4.5算法在森林资源二类调查中的应用[J].南京林业大学学报(自然科学版),2007,31(3)：115-118.
92.王晓燕,郭芳,蔡新广等.密云水库潮白河流域非点源污染负荷[J].城市环境与城市生态,2003,16(1)：31-33.
93.王学军.空间分析技术与地理信息系统的结合[J].地理研究,1997,16(3)：70-74.
94.王艳妮,谢金梅,郭祥ArcGIS中的地统计克里格插值法及其应用[J].软件导刊,2008,12(7)：36-38.
95.王宇.北京市近10年空气环境质量变化特征及影响因素分析[J].中国环境科学学会学术年会论文集.2013,2572-2579.
96.王玉宽,孙雪峰,邓玉林.对生态屏障概念内涵与价值的认识[J].山地学报,2005,23(5)：431-435.
97.王政权,王庆成,李哈滨.红松老龄林主要树种的空间异质性特征与比较的定量研究[J].植物生态学报,2000,24(6)：718.
98.王政权.地统计学及其在生态学中的应用.北京：科学出版社,1999：65-132.
99.魏凤英,曹鸿兴,徐祥德.变异函数在降水场空间特征分析中的应用[J].南京气象学院学报,2002,25(6)：795-799.
100.邬建国.景观生态学格局、过程、尺度与等级[M].北京：高等教育出版社,2000：120-130.
101.吴达胜,范雪华,姜真杰.分布式数据挖掘在森林资源信息管理中的应用[J].福建林学院学报,2004,24(4)：340-343.
102.吴兑,廖碧婷,吴蒙等.环首都圈霾和雾的长期变化特征与典型个例的近地层输送条件[J].环境科学学报,2014,34(1)：1-11.
103.吴海龙,余新晓,师忱等.PM2.5特征及森林植被对其调控研究进展[J].中国水土保持科学,2012,(6)：116-122.
104.吴延熊等.区域森林资源预警技术系统的研建[J].浙江林学院学报,1999,16(1)：93-991.
105.吴云,曾源,吴炳方等.基于MODIS数据的三北防护林工程区植被覆盖度提取与分析[J].生态学杂志,2009,28(9)：1712-1718.
106.徐海兵,余金保,万志洲.南京中山陵园风景区森林资源消长变化情况调查与分析[J].江苏林业科技,2004,31(1)：9-11.
107.阳文锐,王如松,黄锦楼等.反距离加权插值法在污染场地评价中的应用[J].应用生态学报,2007,18(9)：2013-2018.
108.杨丹.北京市森林覆盖率遥感统计及三维表达[D]：北京林业大学,2005.
109.杨开忠.北京经济基础的基本特点与变化趋势,地理学报,1997,6(52)：481-490.
110.余松柏,林中大,叶金盛.GPS在森林资源清查中的应用[J].南京林业大学学报(自然科学版),2004,28(1)：84-86.
111.虞统.空气质量日报中的空气污染指数[J].城市管理与科技,2000,1(2)：23-26.
112.张海筱.黄土高原丘陵沟壑区植被覆盖度空间格局及其控制因子研究[D]：兰州大学,2013.
113.张红霞,谭术魁,周和义.土地市场中开发商行贿表征的量化分析—基于Logistic模型及Moran指数的应用[J].中国土地科学,2012,26(6)：26-34.
114.张锦明,郭丽萍,张小丹.反距离加权插值算法中插值参数对DEM插值误差的影响[J].测绘科学技术学报,2012,29(1)：52-56.
115.张璐璐,李凤全,叶玮等.基于条件高斯模拟的城市灰尘Pb元素的空间分布特征[J].环境科学与管理,2010,35(10)：34-38.
116.张清春,刘宝元,翟刚.植被与水土流失研究综述[J].水土保持研究,2002,9(4)：96-101.
117.张淑娟,何勇,方慧.基于GPS和GIS的田间土壤特性空间变异性的研究[J].农业工程学报,2003,19(2)：39-44.
118.张松林,张昆.空间自相关局部指标Moran指数和G系数研究[J].大地测量与地球动力学,2007,27(3)：31-34.
119.张松林,张昆.全局空间自相关Moran指数和G系数对比研究[J].中山大学学报(自然科学版),2007,46(4)：93-97.
120.张小飞,王仰麟,吴健生等.城市地域地表温度-植被覆盖定量关系分析--以深圳市为例[J].地理研究,2006,25(3)：369-377,561.
121.张雪莹.基于遥感的北京市植被时空分布规律及影响因素分析[D]：中国地质大学(北京),2010.
122.张云霞,李晓兵,陈云浩.草地植被盖度的多尺度遥感与实地测量方法综述[J].地球科学进展,2003,18(1)：85-93.
123.章文波,符素华,刘宝元.目估法测量植被覆盖度的精度分析[J].北京师范大学学报(自然科学版),2001,37(3)：402-408.
124.章文波,刘宝元,吴敬东.小区植被覆盖度动态快速测量方法研究[J].水土保持通报,2001,21(6)：60-63.
125.赵晓捷,穆冬,薛斌党.反距离加权插值自适应图像直方图均衡化算法[J].中国体视学与图像分析,2010,15(1)：8-12.
126.赵彦锋,孙志英,陈杰Kriging插值和序贯高斯条件模拟算法的对比分析[J].地球信息科学学报,2010,12(6)：767-776.
127.郑江华,郑志华,刘平.基于GIS的新疆山区森林资源信息系统的研制[J].福建林学院学报,2003, 23(4)：317-321.
128.郑晓丹,张东,张鹰等.空间插值与遥感反演的潮滩沉积物类型精度对比[J].海洋科学进展,2010,28(4)：515-522.
129.周鸿岐,樊振国.北京地区森林对降水量影响的研究[J].北京农学院学报,1987,2(2)：76-83.
130.周慧珍,龚子同.土壤空间变异性研究[J].土壤学报,1996,(03)：232-241.
131.周洁敏,寇文正.中国生态屏障格局分析与评价[J].南京林业大学学报(自然科学版),2009,33(5)：1-6.
132.祖元刚,马克明,张喜军.植被空间异质性的分形分析方法[J].生态学报,1997,(03)：333-337.
133. Anatoly A. Gitelson, Yoram J. Kaufman, Robert Stark, Don Rundquist. Novel algorithms for remote estimation of vegetation fraction[J]. Remote Sensing of Environment,2002,80(1):76-87.
134. A dams JE, A k in GE.A Light Interception Method for Measuring Row Crop Ground Cover[J].Soil Science Society of America Journal,1997,41(4):789-792.
135. B oyd D S, Foody G M, Ripp leW J. Evaluat ion of App roachesfor ForestC over E stim ation in th-e Pacific Northw est, USA, U singRem ote sens ing[J].A pp lied Geography,2002,22:375-392.
136. Bilici, E. (2009). A study on the integration of firebreaksand fireline with forest roads networks and it's planning and construction (A case study of GallipolyNational Park) Istanbul University. Faculty of ForestryJournal Series:A.,59(2),86-102.
137. Bonazountas, M., Kallidromitou, D., Kassomenos, P.,&Passas, N. (2007). A decision support system for managing forest fire casualties. Journal of Environmental Management,84(4),412-418.
138. Chen Z,Cheng Q,Chen J,et al.A novel iterative approach for mapping local singularities from geochemical data[J].Nonlinear Processes in Geophysics,2007,14(3):317-324.
139. Cole DavidN, Spildie David R.Vegetat ion and soil restoration on highly impacted campsites in the Eagle Cap Wilderness Oregon[R].Fort Collins, CO:U. S. Department of Agriculture, Forest Service, Rocky Mount ain Research Station,2007.
140. Crookston Nicholas L, Stage Albert R. Percent canopy cover and stand structure statistics from the Forest Veget ation Simulator[R]. OgdenUT:U. S. Department of Agriculture, Forest Service, Rocky Mountain Research Station,1999.
141. Environmental technical terms for green food production area (NY/T391-2000).Takeda, A., Kimura, K., & Yamasaki, S. I. (2003). Analysis of 57 elements in Japanese soils, with special reference tosoil group and agricultural use. Geoderma,119,291-307. doi:10.1016/j.geoderma. 2003.08.006.
142. Ertugrul, M. (2005). The situations of forest fires in the world and in Turkey. ZKU Bartin Faculty of Forestry Journal,7(7),43-50.
143. Frieke M.,Lieven P.,Robert R.,et al. Feature selection by geneticalgorithms in object-based classification of IKONOS imagery forforest mapping in Flanders,Belgium[J].Remote Sensing of Environment,2007,110 (4):476-487.
144. G Gutman, A. Ignatov. The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models [J].International Journal of Remote Sensing,1998, 19(8):1533-1543.
145. H ansenM C, DeFries R S, Town shend JR, etal. Development of a MODIS Tree Cover Validation Data Set forW estern Provin ce, Zam bia[J]. Remote Sensing of Environment,2002,83:320-335.
146. H. Li, J. F. Reynolds. On definition and quantification of heterogeneity [J]. Oikos,1995,73: 280-284.
147. HanJiaw ei, MichelineKamber. DataMining:Concepts and Techniques [M].SanFrancisco:Morg anKaufmanPublishers,2001,10-120.
148. J. R. Dymond, P. R. Stephens, P.F. Newsome, R. H. Wilde. Percent vegetation cover of a degrading rangeland from SPOT [J]. International Journal of Remote Sensing,1992,13(11):1999-2007.
149. Jain Theresa B, Juil lerat Molly, Sandquist Jonathan, et al.Forest descriptions and photographs of forested areas along the breaks of the Missouri River in eastern Mont an USA[R]. Fort Collins, CO.U. S. Department of Agriculture, Forest Service, Rocky Mountain Research Station,2007.
150. Jaynes E T, Information theory and statistical mechanics[J]. Phys Rev,1957,106 (4):620-630.
151. Juang KW,Chen Y S,Lee D Y.Using sequential indicator simulation to assess the uncertainty of delineating heavy-metal contaminated[J].Environmental Pollution,2004,127(2):229-238.
152. Kim J Y, Kim L S, Hwang S H. An advanced contrastenhancement using partially overlapped sub2block histogram equalization [J]. IEEE Transact Circuits Systems Video Technol,2001,11 (4): 475-483.
153. Klade Richard J. Building a research legacy-The Intermountain Station1911-1997[R]. Fort Coll ins, CO:U. S. Department of Agriculture,Forest Service, Rocky Mountain Research Station,2006.
154. L. M. Montandon, E. E. Small. The impact of soil reflectance on the quantification of the green vegetation fraction from NDVI [J]. Remote Sensing of Environment,2002,112(4):1853-1845.
155. Lee, C. S., Li, X. D., Shi, W. Z., et al. (2006). Metal contamination in urban, suburban, and country park soils of Hong Kong:A study based on GIS and multivariate statistics.The Science of the Total Environment,356,45-61.doi:10.1016/j.scitotenv.2005.03.024.
156. Li, J., Xie, Z. M., Xu, J. M., Ye, L. J., & Liu, X. M. (2003).Evaluation on environmental quality of heavy metals in vegetable plantation soils in the suburb of Hangzhou.Ecology & Environment, 12(3),277-280 (in chinese).
157. Li, X., Poon, C. S., & Liu, P. S. (2001). Heavy metal contamination of urban soils and street dusts in Hong Kong. AppliedGeochemistry,16,1361-1368. doi:10.1016/S0883-2927(01)00045-2.
158. M ITRE Corporation. Image quality from the natural scene(1QM) [CP/OL]. [200928220]. http:/ /www.mitre,org/tech/mtf, Image Quality Evaluation, M ITRE Corporation.
159. M. Wulder, B.Boots. Local spatial autocorrelation characteristics of remotely sensed imagery assessed with the Getis Statistic [J]. International Journal of Remote Sensing,1998, 19(11):2223-2231.
160.Marie-Josee Fortin, Pierre Drapeau, Pierre Legendre, Spatial autocorrelation and sampling design in pant ecology [J], Vegetatio,1989,83(1-2):209-222.
161. Mark David M. Neighbor-based propert ies of some orderings of twodimensional space[J]. Geographical Analysis,1990,2:145-157.
162. Matheron B. The theory of Regionalized variables [M].Fontainebleau:Centre de Morphologie Mathematique,1971.1-200.
163. Matheron G.Principles of geostatistics[J].Economic Geology,1963,58(8):1246.
164. NillN B, Bouzas B. Objective image qualitymeasure derived from digital image power spectra [J]. Optical Engin,1992,31 (4):813-825.
165. Nitschke C, Innes J (2008) A tree and climate assessment tool for modelling ecosystem response to climate change. Ecol Model 210(3):263-277
166. Pierre Legendre, Marie Josee Fortin. Spatial pattern and ecological analysis [J]. Vegetatio,1989, 80(2):107-138.
167. Pizer SM, Amburn E P, Austin J D, et al. Adaptive histogram equalization and its variations [J]. Computer Vision, Graphics, and Image Processing,1987,39 (3):355-368.
168. Raghunath, R., Tripathi, R. M., Kumar, A. V., Sathe, A. P.,Khandekar, R. N., & Nambi, K. S. V. (1999). Assessmentof Pb, Cd, Cu, and Zn exposures of 6 to 10-year-old children in Mumbai. Environmental Research,80,215-221. doi:10.1006/enrs.1998.3919.
169. ROBERT A M. Evaluation forest models in a sustainable forest management context[J].FBMIS, 2003,1:35-47.
170. Sa'nchez-Camazano, M., Sa-nchez-Martin, M. J., & Lorenzo,L. F. (1994). Lead and cadmium in soils and vegetables from urban gardens of Salamanca (Spain). The Science of the Total Environment,146-147,163-168.State Development Center for Green-Food of China. (2000).
171. Shepard D. A two dimensional interpolation function for irregularly spaced data [C]//Proceedings of the 1968ACM National Conference, ACM:New York, NY, USA,1968:517-524.
172. St ehman St emphen V. Basic probabilit y sampling designs for the matic map accuracy assessm ents [J].Int ernat ional Journal of Remote Sensing,1999,20(12):2423-2441.
173. St evens Don L Jr, Olsen Anthony R. Spat ially balanced sampling of natural resources[J] Journal of the American Statistical Association.2004,99(465):262-278.
174. St evens Don L Jr. Variable density gridbased sampling designs for continuous spatial populations [J].Environmetrics,1997,8:164-195.
175. Stage A R. PrognosisModel for stand development [M]. Ogden UT:USDA Forest Service, Int ermountain Forest and Range Experiment St ation,1973.137-141.
176. Stark J A. Adap tive image contrast enhancement usinggeneralization of histogram equalization [J]. IEEETransac Image Processing,2000,9 (5):889-896.
177. Stevens Don L Jr, Olsen Anthony R. Variance est imation for spatially balanced samples of environment al resources [J].Environmet rics,2003,14:593-610.
178. Stratton Richard D. Guidance on spat ial wildland fire analysis:models,tools, and techniques [R]. Fort Collins, CO:U. S. Department of Agriculture, Forest Service, Rocky Mountain Research Stat ion,2006,24(3):151-160.
179. Susan Ringrose, Wilma Matheson, Piotr Wolski, Philippa huntsman-Mapila. Vegetation cover trends along the Botswana Kalahari transect [J] Journal of Arid Environments,2003, 54(2):297-317.
180. VAN DYCK M G. Keyword reference guide for the Forest Vegetation Simulator[M].Fort Collins, CO:Forest Management Service Center,2000.7-11.
181. Walter D. Koenig. Spatial autocorrelation of ecological phenomena [J].Trends in Ecology & Evolution,1999,14(7):278-279.
182. Wing, M. G., Eklund, A., & Sessions,J.(2010). Applying LiDAR technology for tree measurements in burned landscapes. International Journal of Wildland Fire,19,104-114.
183. ZHU Wenbin, JIA Shaofeng, LV Aifeng, YAN Tingting. Analyzing and modeling the coverage of vegetation in the Qaidam Basin of China:The role of spatial autocorrelation [J]. Journal of Geographical Sciences,2012,22 (02):346-358.
184. Zimmerman J B, Pizer SM, Staab E V, et al. An evalua2tion of the effectiveness of adap tive histogram equalization for contrast enhancement [J].IEEE Transac Med Imag,1988,7(4):304-312.
185. Zolbrod A, Peterson D (1999) Response of high-elevation forests in the Olympic Mountains to climatic change. Can J For Res 29:1966-1978
186. Zelazny V, Martin G, Toner M, Gorman M, Clayden S, Colpitts M, Veen H, Godin B, Mclnnis B, Steeves C,Wuest L, Roberts M (2007) Our landscape heritage:the story of ecological land classification in New Brunswick. Department of Natural Resources, NB, Fredericton, p:359-371.
2.安俊岭,S王自发,黄美元等.区域空气质量数值预报模型[J].气候与环境研究,1999,4(3)：244-251.
3.曾伟生,周佑明.森林资源一类和二类调查存在的主要问题与对策[J].2003,22(4)：8-11.
4.陈昌鹏,吴保国,贾永刚等.数据挖掘技术在森林资源信息管理中的应用[J].河北林果研究,2004,19(2)：149-153.
5.陈刚.基于代理的分布式数据挖掘系统设计[J].计算机工程,2002,27(9)：65-671.
6.陈国阶.生态文明若干理论探讨[J].决策咨询,2013,(1)：31-35.
7.陈吉龙.重庆市三峡库区植被覆盖度的遥感估算及动态变化研究[D]：西南大学,2010.
8.陈晋,陈云浩,何春阳,等.基于土地覆盖分类的植被覆盖率估算亚像元模型与应用[J].遥感学报,2001,(06)：416-422.
9.陈军明,赵平,郭晓寅.中国西部植被覆盖变化对北方夏季气候影响的数值模拟[J].气象学报,2010,(02)：173-181.
10.陈玉福,董鸣.生态学系统的空间异质性[J].生态学报,2003,23(2)：346-352.
11.陈云浩,李晓兵,史培军.非均匀陆面条件下区域蒸散量计算的遥感模型[J].气象学报,2002,60(4)：508-512.
12.程红芳,章文波,陈锋.植被覆盖度遥感估算方法研究进展[J].国土资源遥感,2008,(1)：13-18.
13.程家昌,黄鹏,熊昌盛.基于BP神经网络的土壤养分空间插值[J].广东农业科学,2013,7：64-71.
14.程勇,于林,姚安林.采用高斯模型分析输气管道泄漏后气体的扩散[J].内蒙古石油化工,2010,14：49-51.
15.崔一梅.北京市生态公益林补偿机制的理论与实践研究[D]：北京林业大学,2008.
16.丁宁,王倩,孙英君.高斯序贯模拟与克里格插值2种土壤重金属污染分析方法的比较[J].安徽农业科学,2012,40(7)：4162-4164.
17.冯克忠,万庆,励惠国.基于组件技术的GIS广义空间分析[J].地球信息科学,2003,5(1)：62-66.
18.冯土雍.抽样调查理论方法与实践[M].上海：上海科学技术出版社,1996：1-200.
19.高江波,蔡运龙.区域景观破碎化的多尺度空间变异研究一以贵州省乌江流域为例[J].地理科学,2010,30(5)：742-747.
20.高秀清.北京郊区生态环境建设指标体系探究[J].北京农业职业学院学报,2011,25(5)：24-29.
21.顾祝军,曾志远.遥感植被盖度研究[J].水土保持研究,2005,12(2)：18-21.
22.郭旭东,傅伯杰,马克明,等.基于GIS和地统计学的土壤养分空间变异特征研究--以河北省遵化市为例[J].应用生态学报,2000,11(4)：557-563.
23.韩佶兴.2000-2011年东北亚地区植被覆盖度变化研究[D]：中国科学院研究生院(东北地理与农业生态研究所),2012.
24.何建军,余晔,陈晋北,等.植被覆盖度对兰州地区气象场影响的模拟研究[J].高原气象,2012,(06)：1611-1621.
25.何宗贵,韩世民,崔道永等.空间自相关分析的统计量探讨[J].中国血吸虫病防治杂志,2008,20(4)：315-318.
26.侯景儒.中国地质统计学(空间信息统计学)发展的回顾与前景[J].地质与勘探,1997,33 (1)：53-58.
27.胡良军,邵明安.论水土流失研究中的植被覆盖度量指标[J].西北林学院学报,2001,16(1)：40-43.
28.胡琳,林杨,何晓嫒.基于API方法的西安城市大气环境质量评价[J].陕西气象,2007,(1)：18-20.
29.胡伟,邵明安,王全九.黄土高原退耕坡地土壤水分空间变异的尺度性研究[J].农业工程学报,2005,21(8)：11-16.
30.江厚龙,刘国顺,杨夏孟等.精准农业中不同取样间距下Kriging插值精度对比研究[J].土壤通报,2011,42(4)：799-886.
31.蒋岳新,闫厚EOS-MODIS数据在林业上的应用前景展望[D],2003.
32.焦一之,陈瑜,张锁成等.3S技术在森林资源调查中的应用[J].河北林果研究,2007,22(4)：360-362.
33.亢新刚.森林资源经营管理[M].北京：中国林业出版社,2001.
34.蓝斌,洪伟,吴承祯等.闽北森林资源管理的数学模型[J].福建林学院学报,1999,19(1)：66-701
35.李哈滨,王政权,王庆成.空间异质性定量研究理论与方法[J].应用生态学报,1998,(06)：93-99.
36.李琳.北京郊区植被覆盖度变化动态遥感监测—以怀柔区为例[D]：北京林业大学,2008.
37.李苗苗,吴炳方,颜长珍等.密云水库上游植被覆盖度的遥感估算[J].资源科学,2004,26(4)：153-159.
38.李苗苗.植被覆盖度的遥感估算方法研究[D]：中国科学院遥感应用研究所,2003.
39.李明阳,刘方,徐婷等.基于GIS的森林资源空间数据挖掘方法研究—以紫金山为例[J].西北林学院学报,2012,27(3)180-186.
40.李明阳,张向阳,吴文浩等.基于GIS的森林资源调查空间平衡抽样方法研究[J].林业资源管理,2008,(4)：137-150.
41.李鹏,韩洁,袁顺全等.北京山区生态屏障功能分区研究[J].现代农业科学,2009,16(11)：67-70.
42.李庆谋.多维分形克里格方法[J].地球科学进展,2005,20(2)：248-255.
43.李润林,姚艳敏,唐鹏钦等.县域耕地土壤锌含量的协同克里格插值及采样数量优化[J].土壤通报,2013,44(4)：830-838.
44.李天生,周国法.空间自相关与分布型指数研[J].生态学报,1994,14(3)：327-331.
45.李婷婷,伍世代,李永实等.福建省经济空间增长变异特征及驱动机制[J].地理科学,2010,30(6)：847-853.
46.李文龙,薛中正,郭述茂等.基于3S技术的玛曲县草地植被覆盖度变化及其驱动力[J].兰州大学学报(自然科学版),2010,46(1)：85-90,95.
47.李晓晖,袁峰,贾蔡等.基于地统计学插值方法的局部奇异性指数计算比较研究[J].地理科学,2012,32(2)：136-142.
48.李晓晖,袁峰,贾蔡等.基于反距离加权和克里格插值的S-A多重分形滤波对比研究[J].测绘科学,2012,37(3)：88-89.
49.李永才,张威.卫星遥感在森林资源调查中的应用初探[J].湖南林业科技,1997,24(4)：78-80.
50.李元寿,王根绪,丁永建等.青藏高原高寒草甸区土壤水分的空间异质性[J].水科学进展,2008,19(1)：61-67.
51.刘爱丽,王培法,丁园圆.地统计学概论.北京：科学出版社,2012.
52.刘安兴,蔡良良,佘光辉.森林资源二类调查新颁规定的应用分析[J].南京林业大学学报(自然科 学版),2006,30(2)127-130.
53.刘今朝,马杰.基于ArcGIS普通克里格插值在土地平整土方量计算中的应用[J].农业工程,2013,3(2)：50-55.
54.刘康,徐卫华,欧阳志云等.基于GIS的甘肃省土地沙漠化敏感性评价[J].水土保持通报,2002,22(5)：29-31,35.
55.刘庆,夏江宝,谢文军.半方差函数与Moran's I在土壤微量元素空间分布研究中的应用-以寿光市为例.武汉大学学报(信息科学版),2011,36(9)：1129-1133.
56.刘晓林,李文峰,杨林楠.基于ArcGIS地统计分析模块的土壤养分空间变异分析—以云南省建水县为例[J].土壤通报,2012,43(6)：1432-1436.
57.刘永生,李瑞敏.不同方法下土壤重金属元素空间最大变异距离研究——以浙江上虞为例[J].岩矿测试,2007,(04)：331-335.
58.刘永生.华北平原土壤重金属元素空间自相关研究[D]：中国地质大学(北京),2012.
59.柳锦宝,姚云军,张永福.基于分维的多尺度植被空间格局分析[J].水土保持研究,2007,14(1)：155-158.
60.陆娟.基于莫兰指数的盗窃犯罪率全局分布模式分析[J].电子警务,2014,(1)：45-46.
61.马超飞,马建文,布和敖斯尔USLE模型中植被覆盖因子的遥感数据定量估算[J].水土保持通报,2001,21(4)：6-9.
62.马志勇,沈涛,张军海等.基于植被覆盖度的植被变化分析[J].测绘通报,2007,(3)：45-48.
63.梅安新等.遥感导论[D].北京：高等教育出版社,2001.
64.莫菲,王彦辉,熊伟等.六盘山华北落叶松人工纯林枯落物储量的空间变异分析[J].林业科学,2009,45(9)：1-5.
65.牛宝茹,刘俊蓉,王政伟.干旱半干旱地区植被覆盖度遥感信息提取研究[J].武汉大学学报(信息科学版),2005,30(1)：27-30.
66.潘辉.森林资源动态预测方法的研究[J].福建林学院学报,1998,18(1)：73-781.
67.潘开文,吴宁,潘开忠.关于建设长江上游生态屏障的若干问题的讨论[J].生态学报,2004,24(3)：617-629.
68.彭木根.数据仓库技术与实现[M].北京：电子工业出版社,2002,1100-1101.
69.朴世龙,方精云.最近18年来中国植被覆盖的动态变化[J].第四纪研究,2001,(4)：294.
70.秦伟,朱清科,张学霞等.植被覆盖度及其测算方法研究进展[J].西北农林科技大学学报(自然科学版),2006,34(9)：163-170.
71.秦耀东.土壤空间变异研究中的半方差问题[J].Trans actions of the CSAE,1998,(4):42-47.
72.庆易微,杨林.应用人工神经网络法预报空气污染指数,青海大学学报2008,4(26)：25-31.
73.曲利娟.北京市森林旅游开发与评估研究[D]：首都师范大学,2008.
74.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.
75.沈思渊.土壤空间变异研究中地统计学的应用及其展望[J].土壤学年进展,1989,17(3)：11-24.
76.沈涛,刘良云,马金峰等.基于L- THIA模型的密云水库地区非点源污染空间分布特征[J].农业工程学报2007,23(5)：62-68.
77.施拥军,王珂.3S技术在林业中的应用[J].浙江林业科技,2002,22(6)：46-50.
78.税正全,陈慧蓉,李衍达.变异函数中指数、高斯模型的拟合[J].物探化探计算技 术,1997,19(3)：226-232.
79.宋金平.彭萍.北京山区生态环境问题与建设思路[J].北京规划建设,2004,1：57.
80.孙红雨,王长耀,布和敖斯尔等.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[J].遥感学报,1998,(03)：204-210.
81.孙英君,王劲峰,柏延臣等.地统计学的GIS空间分析功能扩展[J].2004,25(4)：436-439.
82.孙玉冰.舟山群岛的植被覆盖度与景观格局的变化研究[D]：华东师范大学,2010.
83.孙兆文.建设中国北方生态屏障的一些思考[J].中西部发展论坛,1999,(2)：4-8.
84.汤国安,杨昕等ArcGIS地理信息系统空间分析实验教程.北京：科学出版社,2012.
85.唐丽华,陆守一,吴达胜等WebGIS及其在森林资源信息管理中的应用与前景[J].浙江林学院学报,2004,21(1)：104-109.
86.田力,张晓盼,袁艳斌.规则格网系统全局Moran指数计算的改进方法[J].测绘科学,2014,39(2)：110-113.
87.佟乃兴,赵晶明,张鹏等.浅谈细颗粒物(PM2.5[J].化工管理,2013,8：143.
88.万丽.基于变异函数的空间异质性定量分析[J].统计与决策,2006,(4)：26-27.
89.王明明,谢永生等.水库集水区水土保持与流域产水量[J].中国水土保持科学,2007,5(1)：12-20.
90.王绍强,朱松丽,周成虎.中国土壤土层厚度的空间变异性特征[J].地理研究,2001,20(2)：161-169.
91.王阗,佘光辉.决策树C4.5算法在森林资源二类调查中的应用[J].南京林业大学学报(自然科学版),2007,31(3)：115-118.
92.王晓燕,郭芳,蔡新广等.密云水库潮白河流域非点源污染负荷[J].城市环境与城市生态,2003,16(1)：31-33.
93.王学军.空间分析技术与地理信息系统的结合[J].地理研究,1997,16(3)：70-74.
94.王艳妮,谢金梅,郭祥ArcGIS中的地统计克里格插值法及其应用[J].软件导刊,2008,12(7)：36-38.
95.王宇.北京市近10年空气环境质量变化特征及影响因素分析[J].中国环境科学学会学术年会论文集.2013,2572-2579.
96.王玉宽,孙雪峰,邓玉林.对生态屏障概念内涵与价值的认识[J].山地学报,2005,23(5)：431-435.
97.王政权,王庆成,李哈滨.红松老龄林主要树种的空间异质性特征与比较的定量研究[J].植物生态学报,2000,24(6)：718.
98.王政权.地统计学及其在生态学中的应用.北京：科学出版社,1999：65-132.
99.魏凤英,曹鸿兴,徐祥德.变异函数在降水场空间特征分析中的应用[J].南京气象学院学报,2002,25(6)：795-799.
100.邬建国.景观生态学格局、过程、尺度与等级[M].北京：高等教育出版社,2000：120-130.
101.吴达胜,范雪华,姜真杰.分布式数据挖掘在森林资源信息管理中的应用[J].福建林学院学报,2004,24(4)：340-343.
102.吴兑,廖碧婷,吴蒙等.环首都圈霾和雾的长期变化特征与典型个例的近地层输送条件[J].环境科学学报,2014,34(1)：1-11.
103.吴海龙,余新晓,师忱等.PM2.5特征及森林植被对其调控研究进展[J].中国水土保持科学,2012,(6)：116-122.
104.吴延熊等.区域森林资源预警技术系统的研建[J].浙江林学院学报,1999,16(1)：93-991.
105.吴云,曾源,吴炳方等.基于MODIS数据的三北防护林工程区植被覆盖度提取与分析[J].生态学杂志,2009,28(9)：1712-1718.
106.徐海兵,余金保,万志洲.南京中山陵园风景区森林资源消长变化情况调查与分析[J].江苏林业科技,2004,31(1)：9-11.
107.阳文锐,王如松,黄锦楼等.反距离加权插值法在污染场地评价中的应用[J].应用生态学报,2007,18(9)：2013-2018.
108.杨丹.北京市森林覆盖率遥感统计及三维表达[D]：北京林业大学,2005.
109.杨开忠.北京经济基础的基本特点与变化趋势,地理学报,1997,6(52)：481-490.
110.余松柏,林中大,叶金盛.GPS在森林资源清查中的应用[J].南京林业大学学报(自然科学版),2004,28(1)：84-86.
111.虞统.空气质量日报中的空气污染指数[J].城市管理与科技,2000,1(2)：23-26.
112.张海筱.黄土高原丘陵沟壑区植被覆盖度空间格局及其控制因子研究[D]：兰州大学,2013.
113.张红霞,谭术魁,周和义.土地市场中开发商行贿表征的量化分析—基于Logistic模型及Moran指数的应用[J].中国土地科学,2012,26(6)：26-34.
114.张锦明,郭丽萍,张小丹.反距离加权插值算法中插值参数对DEM插值误差的影响[J].测绘科学技术学报,2012,29(1)：52-56.
115.张璐璐,李凤全,叶玮等.基于条件高斯模拟的城市灰尘Pb元素的空间分布特征[J].环境科学与管理,2010,35(10)：34-38.
116.张清春,刘宝元,翟刚.植被与水土流失研究综述[J].水土保持研究,2002,9(4)：96-101.
117.张淑娟,何勇,方慧.基于GPS和GIS的田间土壤特性空间变异性的研究[J].农业工程学报,2003,19(2)：39-44.
118.张松林,张昆.空间自相关局部指标Moran指数和G系数研究[J].大地测量与地球动力学,2007,27(3)：31-34.
119.张松林,张昆.全局空间自相关Moran指数和G系数对比研究[J].中山大学学报(自然科学版),2007,46(4)：93-97.
120.张小飞,王仰麟,吴健生等.城市地域地表温度-植被覆盖定量关系分析--以深圳市为例[J].地理研究,2006,25(3)：369-377,561.
121.张雪莹.基于遥感的北京市植被时空分布规律及影响因素分析[D]：中国地质大学(北京),2010.
122.张云霞,李晓兵,陈云浩.草地植被盖度的多尺度遥感与实地测量方法综述[J].地球科学进展,2003,18(1)：85-93.
123.章文波,符素华,刘宝元.目估法测量植被覆盖度的精度分析[J].北京师范大学学报(自然科学版),2001,37(3)：402-408.
124.章文波,刘宝元,吴敬东.小区植被覆盖度动态快速测量方法研究[J].水土保持通报,2001,21(6)：60-63.
125.赵晓捷,穆冬,薛斌党.反距离加权插值自适应图像直方图均衡化算法[J].中国体视学与图像分析,2010,15(1)：8-12.
126.赵彦锋,孙志英,陈杰Kriging插值和序贯高斯条件模拟算法的对比分析[J].地球信息科学学报,2010,12(6)：767-776.
127.郑江华,郑志华,刘平.基于GIS的新疆山区森林资源信息系统的研制[J].福建林学院学报,2003, 23(4)：317-321.
128.郑晓丹,张东,张鹰等.空间插值与遥感反演的潮滩沉积物类型精度对比[J].海洋科学进展,2010,28(4)：515-522.
129.周鸿岐,樊振国.北京地区森林对降水量影响的研究[J].北京农学院学报,1987,2(2)：76-83.
130.周慧珍,龚子同.土壤空间变异性研究[J].土壤学报,1996,(03)：232-241.
131.周洁敏,寇文正.中国生态屏障格局分析与评价[J].南京林业大学学报(自然科学版),2009,33(5)：1-6.
132.祖元刚,马克明,张喜军.植被空间异质性的分形分析方法[J].生态学报,1997,(03)：333-337.
133. Anatoly A. Gitelson, Yoram J. Kaufman, Robert Stark, Don Rundquist. Novel algorithms for remote estimation of vegetation fraction[J]. Remote Sensing of Environment,2002,80(1):76-87.
134. A dams JE, A k in GE.A Light Interception Method for Measuring Row Crop Ground Cover[J].Soil Science Society of America Journal,1997,41(4):789-792.
135. B oyd D S, Foody G M, Ripp leW J. Evaluat ion of App roachesfor ForestC over E stim ation in th-e Pacific Northw est, USA, U singRem ote sens ing[J].A pp lied Geography,2002,22:375-392.
136. Bilici, E. (2009). A study on the integration of firebreaksand fireline with forest roads networks and it's planning and construction (A case study of GallipolyNational Park) Istanbul University. Faculty of ForestryJournal Series:A.,59(2),86-102.
137. Bonazountas, M., Kallidromitou, D., Kassomenos, P.,&Passas, N. (2007). A decision support system for managing forest fire casualties. Journal of Environmental Management,84(4),412-418.
138. Chen Z,Cheng Q,Chen J,et al.A novel iterative approach for mapping local singularities from geochemical data[J].Nonlinear Processes in Geophysics,2007,14(3):317-324.
139. Cole DavidN, Spildie David R.Vegetat ion and soil restoration on highly impacted campsites in the Eagle Cap Wilderness Oregon[R].Fort Collins, CO:U. S. Department of Agriculture, Forest Service, Rocky Mount ain Research Station,2007.
140. Crookston Nicholas L, Stage Albert R. Percent canopy cover and stand structure statistics from the Forest Veget ation Simulator[R]. OgdenUT:U. S. Department of Agriculture, Forest Service, Rocky Mountain Research Station,1999.
141. Environmental technical terms for green food production area (NY/T391-2000).Takeda, A., Kimura, K., & Yamasaki, S. I. (2003). Analysis of 57 elements in Japanese soils, with special reference tosoil group and agricultural use. Geoderma,119,291-307. doi:10.1016/j.geoderma. 2003.08.006.
142. Ertugrul, M. (2005). The situations of forest fires in the world and in Turkey. ZKU Bartin Faculty of Forestry Journal,7(7),43-50.
143. Frieke M.,Lieven P.,Robert R.,et al. Feature selection by geneticalgorithms in object-based classification of IKONOS imagery forforest mapping in Flanders,Belgium[J].Remote Sensing of Environment,2007,110 (4):476-487.
144. G Gutman, A. Ignatov. The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models [J].International Journal of Remote Sensing,1998, 19(8):1533-1543.
145. H ansenM C, DeFries R S, Town shend JR, etal. Development of a MODIS Tree Cover Validation Data Set forW estern Provin ce, Zam bia[J]. Remote Sensing of Environment,2002,83:320-335.
146. H. Li, J. F. Reynolds. On definition and quantification of heterogeneity [J]. Oikos,1995,73: 280-284.
147. HanJiaw ei, MichelineKamber. DataMining:Concepts and Techniques [M].SanFrancisco:Morg anKaufmanPublishers,2001,10-120.
148. J. R. Dymond, P. R. Stephens, P.F. Newsome, R. H. Wilde. Percent vegetation cover of a degrading rangeland from SPOT [J]. International Journal of Remote Sensing,1992,13(11):1999-2007.
149. Jain Theresa B, Juil lerat Molly, Sandquist Jonathan, et al.Forest descriptions and photographs of forested areas along the breaks of the Missouri River in eastern Mont an USA[R]. Fort Collins, CO.U. S. Department of Agriculture, Forest Service, Rocky Mountain Research Station,2007.
150. Jaynes E T, Information theory and statistical mechanics[J]. Phys Rev,1957,106 (4):620-630.
151. Juang KW,Chen Y S,Lee D Y.Using sequential indicator simulation to assess the uncertainty of delineating heavy-metal contaminated[J].Environmental Pollution,2004,127(2):229-238.
152. Kim J Y, Kim L S, Hwang S H. An advanced contrastenhancement using partially overlapped sub2block histogram equalization [J]. IEEE Transact Circuits Systems Video Technol,2001,11 (4): 475-483.
153. Klade Richard J. Building a research legacy-The Intermountain Station1911-1997[R]. Fort Coll ins, CO:U. S. Department of Agriculture,Forest Service, Rocky Mountain Research Station,2006.
154. L. M. Montandon, E. E. Small. The impact of soil reflectance on the quantification of the green vegetation fraction from NDVI [J]. Remote Sensing of Environment,2002,112(4):1853-1845.
155. Lee, C. S., Li, X. D., Shi, W. Z., et al. (2006). Metal contamination in urban, suburban, and country park soils of Hong Kong:A study based on GIS and multivariate statistics.The Science of the Total Environment,356,45-61.doi:10.1016/j.scitotenv.2005.03.024.
156. Li, J., Xie, Z. M., Xu, J. M., Ye, L. J., & Liu, X. M. (2003).Evaluation on environmental quality of heavy metals in vegetable plantation soils in the suburb of Hangzhou.Ecology & Environment, 12(3),277-280 (in chinese).
157. Li, X., Poon, C. S., & Liu, P. S. (2001). Heavy metal contamination of urban soils and street dusts in Hong Kong. AppliedGeochemistry,16,1361-1368. doi:10.1016/S0883-2927(01)00045-2.
158. M ITRE Corporation. Image quality from the natural scene(1QM) [CP/OL]. [200928220]. http:/ /www.mitre,org/tech/mtf, Image Quality Evaluation, M ITRE Corporation.
159. M. Wulder, B.Boots. Local spatial autocorrelation characteristics of remotely sensed imagery assessed with the Getis Statistic [J]. International Journal of Remote Sensing,1998, 19(11):2223-2231.
160.Marie-Josee Fortin, Pierre Drapeau, Pierre Legendre, Spatial autocorrelation and sampling design in pant ecology [J], Vegetatio,1989,83(1-2):209-222.
161. Mark David M. Neighbor-based propert ies of some orderings of twodimensional space[J]. Geographical Analysis,1990,2:145-157.
162. Matheron B. The theory of Regionalized variables [M].Fontainebleau:Centre de Morphologie Mathematique,1971.1-200.
163. Matheron G.Principles of geostatistics[J].Economic Geology,1963,58(8):1246.
164. NillN B, Bouzas B. Objective image qualitymeasure derived from digital image power spectra [J]. Optical Engin,1992,31 (4):813-825.
165. Nitschke C, Innes J (2008) A tree and climate assessment tool for modelling ecosystem response to climate change. Ecol Model 210(3):263-277
166. Pierre Legendre, Marie Josee Fortin. Spatial pattern and ecological analysis [J]. Vegetatio,1989, 80(2):107-138.
167. Pizer SM, Amburn E P, Austin J D, et al. Adaptive histogram equalization and its variations [J]. Computer Vision, Graphics, and Image Processing,1987,39 (3):355-368.
168. Raghunath, R., Tripathi, R. M., Kumar, A. V., Sathe, A. P.,Khandekar, R. N., & Nambi, K. S. V. (1999). Assessmentof Pb, Cd, Cu, and Zn exposures of 6 to 10-year-old children in Mumbai. Environmental Research,80,215-221. doi:10.1006/enrs.1998.3919.
169. ROBERT A M. Evaluation forest models in a sustainable forest management context[J].FBMIS, 2003,1:35-47.
170. Sa'nchez-Camazano, M., Sa-nchez-Martin, M. J., & Lorenzo,L. F. (1994). Lead and cadmium in soils and vegetables from urban gardens of Salamanca (Spain). The Science of the Total Environment,146-147,163-168.State Development Center for Green-Food of China. (2000).
171. Shepard D. A two dimensional interpolation function for irregularly spaced data [C]//Proceedings of the 1968ACM National Conference, ACM:New York, NY, USA,1968:517-524.
172. St ehman St emphen V. Basic probabilit y sampling designs for the matic map accuracy assessm ents [J].Int ernat ional Journal of Remote Sensing,1999,20(12):2423-2441.
173. St evens Don L Jr, Olsen Anthony R. Spat ially balanced sampling of natural resources[J] Journal of the American Statistical Association.2004,99(465):262-278.
174. St evens Don L Jr. Variable density gridbased sampling designs for continuous spatial populations [J].Environmetrics,1997,8:164-195.
175. Stage A R. PrognosisModel for stand development [M]. Ogden UT:USDA Forest Service, Int ermountain Forest and Range Experiment St ation,1973.137-141.
176. Stark J A. Adap tive image contrast enhancement usinggeneralization of histogram equalization [J]. IEEETransac Image Processing,2000,9 (5):889-896.
177. Stevens Don L Jr, Olsen Anthony R. Variance est imation for spatially balanced samples of environment al resources [J].Environmet rics,2003,14:593-610.
178. Stratton Richard D. Guidance on spat ial wildland fire analysis:models,tools, and techniques [R]. Fort Collins, CO:U. S. Department of Agriculture, Forest Service, Rocky Mountain Research Stat ion,2006,24(3):151-160.
179. Susan Ringrose, Wilma Matheson, Piotr Wolski, Philippa huntsman-Mapila. Vegetation cover trends along the Botswana Kalahari transect [J] Journal of Arid Environments,2003, 54(2):297-317.
180. VAN DYCK M G. Keyword reference guide for the Forest Vegetation Simulator[M].Fort Collins, CO:Forest Management Service Center,2000.7-11.
181. Walter D. Koenig. Spatial autocorrelation of ecological phenomena [J].Trends in Ecology & Evolution,1999,14(7):278-279.
182. Wing, M. G., Eklund, A., & Sessions,J.(2010). Applying LiDAR technology for tree measurements in burned landscapes. International Journal of Wildland Fire,19,104-114.
183. ZHU Wenbin, JIA Shaofeng, LV Aifeng, YAN Tingting. Analyzing and modeling the coverage of vegetation in the Qaidam Basin of China:The role of spatial autocorrelation [J]. Journal of Geographical Sciences,2012,22 (02):346-358.
184. Zimmerman J B, Pizer SM, Staab E V, et al. An evalua2tion of the effectiveness of adap tive histogram equalization for contrast enhancement [J].IEEE Transac Med Imag,1988,7(4):304-312.
185. Zolbrod A, Peterson D (1999) Response of high-elevation forests in the Olympic Mountains to climatic change. Can J For Res 29:1966-1978
186. Zelazny V, Martin G, Toner M, Gorman M, Clayden S, Colpitts M, Veen H, Godin B, Mclnnis B, Steeves C,Wuest L, Roberts M (2007) Our landscape heritage:the story of ecological land classification in New Brunswick. Department of Natural Resources, NB, Fredericton, p:359-371.
© 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 |