基于ETM+图像的植被覆盖度遥感估算模型

植被覆盖度(VFC)的定量遥感是多种地表过程研究的迫切需要.文章选用南京市一幅Landsat 7 ETM 图像,经大气校正后提取了归一化植被指数(NDVI),与地面实测的植被覆盖度进行回归分析,建立了1～4次多项式关系模型.结果表明,NDVI与VFC呈极显著的正相关关系(r = 0.874, P < 0.001).在NDVI-VFC的1～4次多项式关系模型中,模型幂次越高,拟合程度越好.综合考虑模型的精度和稳定性,3次多项式模型作为最优模型推荐使用:VFC = -1.3438 NDVI 3 0.9774 NDVI 2 0.9988 NDVI 0.1507 (R2 = 0.7961, RMSE = 0.1094),该模型精度在植被中等密集区域(VFC＝0.4～0.8)最高,植被稀疏区域(VFC < 0.4)最低,植被密集区域(VFC > 0.8)居中.模型可直接用于全图像的VFC计算,并可通过植被指数的校准,进行推广使用.     

基于Sentinel多源遥感数据的河北省景县农田土壤水分协同反演

植被覆盖层对微波遥感反演地表土壤水分产生重要影响。以河北省景县为研究区,基于Sentinel-1 SAR遥感数据和Sentinel-2光学遥感数据,采用改进水云模型和Oh模型的组合方法,对植被覆盖地表土壤水分进行定量反演研究。结果表明:在Sentinel-1 VV极化条件下,改进水云模型和Oh模型的组合方法具有较高的反演精度,决定系数(R~2)为0.653 0,均方根误差(RMSE)为0.040 1 cm~3·cm~(-3),平均绝对误差(MAE)为0.032 7 cm~3·cm~(-3),这3项反演精度评价指标均优于VH极化。该方法在获取高空间分辨率和高精度的植被覆盖区农田土壤水分信息方面具有较高的应用价值。     

黑河中游绿洲化地表特征参数研究

绿洲化影响地表水热传输和植被生长等能量物质的变化。以空间代替时间的方法,选择绿洲内部、荒漠绿洲过渡带及荒漠的不同演化阶段,采用地面调查和遥感反演的方法,定量分析了土壤水分、植被覆盖度、地表温度、地表反照率及地表净辐射等参数的变化及其之间的关系,为该地区地气系统能量与水分交换提供理论基础。结果表明,(1)绿洲化改变了地表的植被类型和土壤水分,依次为裸露沙丘(2.1%)-泡泡刺(Nitraria sphaerocarpa)+沙拐枣(Calligonum mongolicum)(2.4%)-梭梭(Haloxylon ammodendron)+柽柳(Tamarix chinensis)(2.2%)-沙枣(Elaeagnus angustifolia)+沙柳(Salix psammophila)(3.8%)-杨树(Populus tremula)(4.6%)-制种玉米(7.8%)。(2)绿洲化增加了地表的植被覆盖度和净辐射,植被覆盖度由8%增加到85%,净辐射由480 W·m~(-2)增加到756 W·m~(-2),而降低了地表温度和地表反照率,地表温度由325 K降低到302 K,地表反照率由0.235降低到0.125。(3)植被覆盖度决定了地表能量的分配,改变了地表温度、地表反照率、地表净辐射,即植被盖度每增加1%,地表净辐射增加3.64 W·m~(-2),地表温度降低0.31 K,地表反射率降低0.002。绿洲化地表能量、水分和植被生长等参数的变化研究有助于理解绿洲可持续发展及水土气生相互作用,对荒漠绿洲过渡带生态系统稳定健康发展具有重要的参考价值。     

双酚A类似物雌激素干扰效应的定量结构-活性关系模型

由于双酚A(BPA)具有内分泌干扰等毒性效应,有些国家已经开始限制其在部分工业品生产中使用。一些双酚A类似物因用于替代BPA而被大量使用。因其与双酚A具有相似的分子结构,双酚A类似物是否也具有内分泌干扰等毒性效应受到越来越多的关注。采用逐步多元线性回归(MLR)方法,构建了可预测双酚A类似物雌激素效应的定量结构-活性关系模型。模型的决定系数R~2=0.899,去一法交叉验证系数Q_(LOO)~2=0.868,Bootstrapping验证系数Q_(BOOT)~2=0.755,均方根误差RMSE=0.339,表明模型具有较好的拟合优度、稳健性;验证集决定系数Q_(EXT)~2=0.921,外部验证系数Q_(EXT)~2=0.810,均方根误差RMSE=0.638,表明模型具有较好的预测能力。采用欧几里德距离方法和Wil iams图表征了模型应用域,依据分子描述符对模型进行了机理解释,并用所建模型,填补了22种其他双酚A类似物缺失的雌激素干扰效应数据。     

西藏高原青稞三种植被指数对红外增温的初始响应

气候变暖影响着农作物生长及其植被指数。为了探讨西藏高原青稞(Hordeum vulgare Linn.var.nudum Hook.f.)归一化植被指数(normalized difference vegetation index,NDVI)、归一化绿波段差值植被指数(normalized green difference vegetation index,GNDVI)和土壤调节植被指数(soil adjusted vegetation index,SAVI)对气候变暖的初始响应,2014年5月在西藏达孜县布设了一个红外增温实验(3个水平,即对照,1 000和2 000 W红外增温)。通过对2014年6─9月利用农业多光谱相机获取的3种植被指数和利用HOBO微气候观测系统获取的两个深度(5和20 cm)的土壤温湿度的统计分析,探讨了西藏高原青稞植被指数对红外增温的响应及其与土壤温湿度的相互关系。结果表明,1 000和2 000 W的增温使5 cm的土壤温度(t5)分别升高了约1.62和1.77℃,使20 cm的土壤温度(t20)分别升高了约1.16和1.43℃;相反使5 cm的土壤湿度(SM5)分别下降了约1.8%和14.1%,使20 cm的土壤湿度(SM20)分别下降了21.6%和14.7%。1 000 W的增温使NDVI、GNDVI和SAVI分别增加了约2.4%、4.3%和0.5%;2 000 W的增温则使NDVI、GNDVI和SAVI分别增加了约5.5%、5.3%和4.8%,尽管增加幅度并不显著。单因子回归分析表明,t5与NDVI(r2=0.110,P=0.026)和GNDVI(r2=0.254,P=0.000 4)为负相关,而与SAVI无关(r2=0.069,P=0.082);t20与GNDVI为负相关(r2=0.218,P=0.001),而与NDVI(r2=0.040,P=0.190)和SAVI(r2=0.014,P=0.443)无关;SM5与NDVI(r2=0.277,P=0.000 2)、GNDVI(r2=0.394,P=0.000 0)和SAVI(r2=0.208,P=0.002)为正相关。SM20与GNDVI为正相关(r2=0.193,P=0.003),而与NDVI(r2=0.059,P=0.107)和SAVI(r2=0.037,P=0.209)无关。多重回归分析表明,SM5主导着NDVI、GNDVI和SAVI的变异。偏相关分析表明,NDVI、GNDVI和SAVI与SM5的相关系数分别为0.442(P=0.003)、0.412(P=0.007)和0.404(P=0.008);与SM20的相关系数分别为-0.042(P=0.792)、0.051(P=0.749)和-0.033(P=0.837);与t5的相关系数分别为-0.154(P=0.332)、-0.019(P=0.907)和-0.170(P=0.282);与t20的相关系数分别为0.228(P=0.147)、-0.041(P=0.795)和0.268(P=0.086)。因此,红外增温引起的干旱抑制了青稞的生长,进而影响了植被指数,即植被指数的不显著变化可能与红外增温引起的土壤干旱有关。     

基于高光谱的邯郸市土壤重金属统计估算模型研究

土壤重金属富集严重制约城市发展,对居民健康造成潜在威胁。该研究旨在定量估算土壤重金属含量,提高土壤重金属监测时效性,从而为土壤保护管理工作提供决策参考和理论支持。该文依据化学检测所得土样重金属含量与地物光谱仪(ASD)获取的土壤反射光谱数据,通过对土壤重金属光谱特性的分析,确定光谱反演特征波段,研究并建立了邯郸市土壤重金属基于不同光谱变换指标的多元逐步回归(SMLR)和偏最小二乘回归(PLSR)统计估算模型,通过模型验证与对比,探索邯郸市土壤各重金属含量的最优反演模型。结果表明:(1)微分处理能普遍改善模型预测效果,二阶微分指标的PLSR与MLSR模型效果较佳;(2)PLSR与SMLR两种建模方法相比,总体上PLSR建模和预测的均方根误差RMSE较小、模型修正系数Adjust R~2较大,表明PLSR模型预测效果更好;(3)基于反射率倒数对数的二阶导数的PLSR模型反演效果较优,其中Cu、Ni、Zn、Hg,检验精度Adjust R2均超过0.8。     

羌塘高原高寒草地植物地上地下碳氮生态化学计量特征及其影响因素

研究高寒草地的植物生态化学计量特征对认识极端气候背景下的草地生态系统功能与服务具有重要的意义.选择羌塘高原高寒草地作为研究区,分析东西走向60个样点植物地上、地下部分的碳(C)、氮(N)含量与C:N的分布特征,及其各自的主要驱动因素.结果表明:高寒草地植物地上部分C、N含量(38.22%、1.82%)均高于地下部分(31.11%、1.15%),但C:N(22.08)却小于地下部分(28.88),且地上部分C含量、C:N与地下部分存在显著性差异(P0.05).干燥指数与植物地上部分C含量(R~2=0.072,P0.05)以及C:N(R~2=0.15,P0.005)呈负相关关系,却与植物地下部分C:N(R~2=0.53,P0.001)呈正相关关系;此外,年均降水量(R~2=0.13,P0.005)与地上部分C含量呈负相关关系,总生物量(R~2=0.13,P0.01)及植被总盖度(R~2=0.12,P0.01)与地下部分C含量呈正相关关系;海拔与地上部分C:N亦呈正相关关系(R 2=0.15,P0.005),而年均温却与地下部分C:N呈负相关关系(R~2=0.31,P0.001).可见,水热条件是影响羌塘高原植物地上、地下C含量以及C:N差异的主要因素,而干燥指数可以作为较好的度量指标.     

邻苯二甲酸酯的正辛醇/水分配系数定量结构-性质关系

有机污染物的环境归趋主要由其分配性质决定,如正辛醇/水分配系数(K_(OW)).本文采用密度泛函理论,在B3LYP/6-311G~(**)的水平上对PAEs的结构进行优化振动分析,Gaussian输出的15种量子化学参数被用来对该类物质正辛醇/水分配系数(K_(OW))进行定量结构-性质关系(QSPR)研究.采用一种顺序方法:相关分析、主成分分析、多元线性回归和统计验证,建立了QSPR模型.结果表明,单个描述符(平均极化率α)在确定K_(OW)中起重要作用,显式函数关系式为lg K_(OW)=-3.468+0.041α,lg K_(OW)的值随着α的增大而线性增大.模型具有良好的拟合能力(R~2=0.99,RMSE=0.33)、稳健性(Q~2_(LOO)=0.97,Q~2_(BOOT)=0.98)和预测能力(Q~2_(ext)=0.98),可利用该模型对其他PAEs分子的lg K_(OW)进行预测.     

基于地理加权回归模型的能源“金三角”地区植被时空演变及主导因素分析

宁陕蒙接壤地区的能源"金三角"为我国提供了丰富的能源,其地表植被状况与我国的生态恢复与重建密切相关。应用RS和GIS技术,以归一化植被指数产品(MODIS NDVI)为数据源,借助逐像元趋势分析法研究了2005—2015年间的植被动态变化;基于规则网格构建地理加权回归模型(GWR),探索了高程、坡度、土壤黏粒含量、多年平均气温、多年平均降水、距煤矿区距离及距道路距离7个因子对植被变化的影响及其空间非平稳性。结果表明:(1)时间上,2005—2015年研究区平均NDVI整体呈现波动上升趋势,增长率为0.083·(10 a)~(-1)(P0.05);空间上,NDVI呈现由东南向西北递减的分布格局;趋势上,NDVI变化呈增加趋势的区域(27.11%)远大于减少区域(0.64%),显著增加区域主要分布在榆林市东部。(2)与2005—2012年相比,2013—2015年归一化植被指数(NDVI)在全区显著减少,且具有更强的空间聚集性(Moran's I值为0.851),但变化程度具有空间异质性。(3)2个时段全区植被变化受气候等自然因素的影响较大,影响植被变化的主要因素在时间与空间上具有差异性,人类活动对植被变化具有双重作用。     

藏北高原不同海拔高度高寒草甸植被指数与环境温湿度的关系

藏北高寒草甸是全球高寒草地的重要组成部分,是对气候变化最敏感的植被类型之一。关于高寒草地植被指数与环境温湿度因子的关系还存在着诸多不确定性,这限制了准确预测高寒草地植被生长对将来气候变化的响应。定量化高寒草地植被指数与气候因子的关系利于预测将来气候变化对高寒草地植被生长的影响。该研究基于相关分析和多重逐步回归分析探讨了藏北高原不同海拔高度(4300、4500和4700 m)的高寒草甸2011─2014年每年6─9月的归一化植被指数(normalized difference vegetation index,NDVI)、增强型植被指数(Enhanced Vegetation Index,EVI)与土壤温度、土壤湿度、空气温度、相对湿度、饱和水汽压差的相互关系。相关分析表明,3种海拔的NDVI(4 300 m:r=0.79,P=0.000;4 500 m:r=0.80,P=0.000;4 700 m:r=0.52,P=0.005)和EVI(4 300 m:r=0.61,P=0.001;4 500 m:r=0.66,P=0.000;4 700 m:r=0.53,P=0.004)都随着土壤湿度的增加显著增加;3种海拔的NDVI(4 300 m:r=-0.68,P=0.000;4 500 m:r=-0.56,P=0.002;4 700 m:r=-0.40,P=0.037)和EVI(4 300 m:r=-0.56,P=0.002;4 500 m:r=-0.49,P=0.008;4 700 m:r=-0.46,P=0.014)都随着饱和水汽压差的增加显著降低;植被指数与环境温湿度因子的相关系数随着海拔的变化而变化;NDVI和EVI与环境温湿度因子的相关系数存在差异。多重逐步回归分析表明,土壤湿度一个因子解释了3种海拔的归一化植被指数、海拔4 300和4 500 m的增强型植被指数的变异,而海拔4 700 m的土壤湿度和土壤温度共同了解释了增强型植被指数的变异,其中土壤湿度的贡献较大。因此,在藏北高寒草甸,植被指数对气候变化的敏感性可能随着海拔的变化而变化,NDVI和EVI对气候变化的敏感性可能不同,土壤湿度主导着NDVI和EVI的季节变化。     

Estimation and validation of biomass of a mountainous agroecosystem by means of sampling,spectral data and QuickBird satellite image

Biomass estimation in agroecosystems (AESs) is important to understand their role in carbon exchange for a sustainable environment. We used field spectra and sampled biomass of an AES including cultivated and abandoned croplands to develop a simple biomass estimation model. The digital number (DN) of a QuickBird (QB) satellite image was converted to a reflectance factor using the dark object subtraction method and the spectral reflectance of asphalt. The relationship between the reflectance factor of field-based spectra and the QB image obtained in early July 2007 was insignificant in the blue (R ² = 0.15) and green (R ² = 0.18) bands but was significant (p < 0.05) in the red (R ² = 0.57) and near-infrared (NIR, R ² = 0.45) bands in the AES. Better correlations were obtained between field-based and QB-based vegetation indices (VIs). The best correlations were obtained with the normalized difference vegetation index (NDVI) (R ² = 0.97, p < 0.001) and the ratio vegetation index (RVI) (R ² = 0.99, p < 0.001). Biomass was significantly correlated with both field-based NDVI and RVI (R ² = 0.79 and 0.72, respectively, p < 0.001). Although RVI saturated at higher biomass densities (>600 g m^?2), NDVI showed a linear relationship. Other field-based VIs showed poorer correlations with biomass. The model was evaluated by incorporating it into high-resolution QB images to obtain the observed biomass. The relationship between field-estimated and QB-observed biomass appeared to be a one-to-one linear relationship (R ² = 0.79). Thus, models using field spectra and sampled biomass can be applied to QB images for remote estimation of biomass in an AES.     

Post-fire vegetation regrowth detection in the Deiva Marina region (Liguria-Italy) using Landsat TM and ETM+ data

Obtaining quantitative information about the recovery of fire-affected ecosystems is of utmost importance from the management and decision-making point of view. Nowadays the concern about natural environment protection and recovery is much greater than in the past. However, the resources and tools available for its management are still not sufficient. Thus, attention and precision is needed when decisions must be taken. Quantitative estimates on how the vegetation is recovering after a fire can be of help for evaluating the necessity of human intervention on the fire-affected ecosystem, and their importance will grow as the problem of forest fires, climate change and desertification increases.This article performs a comparison of methods to extract quantitative estimates of vegetation cover regrowth with Landsat TM and ETM+ data in an area that burned during the summer of 1998 in the Liguria region (Italy). In order to eliminate possible sources of error, a thorough pre-processing was carried out, including a careful geometric correction (reaching RMSE lower than 0.3 pixels), a topographic correction by means of a constrained Minnaert model and a combination of absolute and relative atmospheric correction methods. Pseudo Invariant Features (PIF) were identified by implementing an automated selection method based in temporal Principal Component Analysis (PCA), which has been called multi-Temporal n-Dimensional Principal Component Analysis (mT-nD-PCA).Spectral Mixture Analysis (SMA) was compared against quantitative vegetation indices which are based on well known traditional vegetation indices like Normalized Difference Vegetation Index (NDVI) and Modified Soil Adjusted Vegetation Index (MSAVI). Accuracy assessment was performed by regressing vegetation cover results obtained with each method against field data gathered during the fieldwork campaign carried out in the study area. Results obtained showed how vegetation cover fractions extracted from the NDVI based quantitative index were the most accurate, being superior to the rest of the techniques applied, including SMA.     

Spatial error propagation when computing linear combinations of spectral bands: The case of vegetation indices

The collection of accurate and timely information on land use, crops, forest and vegetation are increasingly based on remote sensing spectral measurements produced by satellites. The most recent spacecrafts like the Earth Observing 1 (EO-1) produce a rich source of information being endowed with hyperspectral sensors that can provide up to 200 or more channels. In many instances such a multivariate signal has to be reduced to just one single value per pixel representing a particular characteristic of land. Linear combinations of bands are the general form of many indices. Since each individual image used to construct indices contains errors, when combined they produce a propagation of errors, a process that can distort a final output map. In this paper we measure the extent of error propagation when building linear vegetation indices. We consider three types of indices: the difference vegetation index (DVI), selected Kauth-Thomas indices (SBI, GVI and WET), and principal components, using benchmarking examples taken from the remote sensing literature. The main implication emerging from these examples is that the SBI and the first principal component are the indices more prone to error propagation. The formalization presented here allows a user to derive measures of error propagation in cases where technical characteristics of a sensor and physical characteristics of a landscape are known. These results can help a user to choose between alternative vegetation indices, and to associate a measure of reliability with such indices.     

A pilot study on the effect of Cu,Zn, and Cd on the spectral curves and chlorophyll of wheat canopy at tiller stage

The aim of this study was to investigate canopy spectral reflectance responses to different levels of heavy metals copper (Cu), zinc (Zn), and cadmium (Cd) induced stress. Random blocks design experiment was conducted to simulate Cu, Zn, and Cd at five concentration levels. Chlorophyll and visible and near infrared canopy reflectance were measured for each treatment 56 days after seeding using chlorophyll meter SPAD-502 and spectroradiometer, respectively. New vegetative indices termed ratio of inclination angles (RIA), MERIS (the medium resolution imaging spectrometer) terrestrial chlorophyll index (MTCI), and normalized difference vegetation index (NDVI) were used to assess the response of canopy spectral reflectance to different heavy metal levels. Significant spectral variability especially in blue, red, and near infrared reflectance was observed for different heavy metal treatments. One-sample test showed significant difference for NDVI, MTCI, and RIA among five-level treatments at the 0.01 level. Correlation analysis demonstrated that the two vegetative indices (RIA and MTCI) were significantly correlated with chlorophyll meter values for Cd, Cu, and Zn treatments. However, NDVI was only significantly correlated with chlorophyll meter values for Cu and Zn treatments. Linear regression analysis also illustrated that RIA and MTCI were potential indices for predicting chlorophyll concentration with significant F test under Cu-, Zn-, and Cd-mediated stress.     

基于MODIS时序数据北回归线(云南段)地区植被物候时空变化及其对气候响应分析

植被物候作为自然界规律性、周期性事件,对开展全球气候变化、植被长势观测等研究具有重要价值。以北回归线(云南段)穿过的县域为研究区,基于长时间序列MODIS EVI(Enhanced Vegetation Index,EVI)、土地利用类型和气候因子数据,采用S-G滤波、动态阈值、相关分析等方法分析19 a(2001—2019年)间植被物候的时空分布特征及其对水热因子的响应。结果表明,(1)海拔和地势起伏在物候地域分异中作用显著,植被物候存在明显的垂直地带性分布特征。山地与河谷、坝子、低海拔区的物候值差异较大,山地地区的植被生长季开始期(Start of Season,SOS)在192—240 d,生长季结束期(End of Season,EOS)在次年144—192 d,生长季长度(Length of Season,LOS)为272—304 d;河谷、坝子、东部低海拔地区的植被SOS在80—112 d,EOS在337至次年17 d,LOS在224—256 d。(2)19 a间植被物候年际变化总体特征为SOS显著提前(R²=0.51,P=0.001<0.05),平均提前1.14 d·(10 a)^?1;EOS推迟(R²=0.01,P=0.756>0.05),平均推迟0.07 d·(10 a)^?1;LOS显著延长(R²=0.47,P=0.001<0.05),平均延长1.07 d·(10 a)^?1。(3)不同植被类型的物候期及其变化趋势不同,研究区森林植被生长期最长,草地次之,耕地最短;19 a间SOS、EOS、LOS变化最大的分别是常绿阔叶林(?1.68 d·(10 a)^?1)、耕地(1.25 d·(10 a)^?1)、木本热带稀树草原(1.28 d·(10 a)^?1)。(4)水热组合对植被生长影响显著,河谷、坝子、东部低海拔地区的植被SOS、EOS分别主要受2月降水(负相关)和4月气温(正相关)、9月降水和气温(正相关)影响,山地地区植被SOS、EOS分别主要受6月降水(正相关)和5月气温(正相关)、5月降水(正相关)和4月气温(正相关)影响。     

旅游干扰下五台山不同植被景观区物种多样性特征

利用双向指示种分析方法(Two-way indicator species analysis,TWINSPAN)和6个物种多样性指数,研究了旅游干扰下五台山不同植被景观区物种多样性的特征.结果表明:1)TWINSPAN将所有样地划分为9类植被景观区,从Ⅰ区到Ⅸ区,随着旅游干扰程度的增加,植被景观大致由乔灌草区向灌草区、草本区和居民区方向变化.2)乔木层物种的丰富度和综合多样性随着旅游干扰的减小而趋于增加,但是其均匀度没有表现出明显的规律性.3)灌木层物种的丰富度和综合多样性也随着旅游干扰的减小而趋于增加,至于其均匀度,则呈现出在中度干扰下值最大,干扰很小的地方次之,在重度干扰下则最小.4)草本层物种的丰富度、均匀度和综合多样性指数均在旅游干扰适度的地方达到了最大值,在旅游干扰很小的地方,各种值则次之,在干扰严重的地方为最小.5)从整个植被层物种多样性的角度看,随着旅游干扰程度的减少,物种丰富度指数、均匀度指数和综合多样性指数均呈现趋于增加的趋势.表4参15     

Modeling the effect of photosynthetic vegetation properties on the NDVI--LAI relationship

Developing a relationship between the normalized difference vegetation index (NDVI) and the leaf area index (LAI) is essential to describe the pattern of spatial or temporal variation in LAI that controls carbon, water, and energy exchange in many ecosystem process models. Photosynthetic vegetation (PV) properties can affect the estimation of LAI, but no models integrate the effects of multiple species. We developed four alternative NDVI-LAI models, three of which integrate PV effects: no PV effects, leaf-level effects, canopy-level effects, and effects at both levels. The models were fit to data across the natural range of variation in NDVI for a widespread High Arctic ecosystem. The weight of evidence supported the canopy-level model (Akaike weight, wr = 0.98), which includes species-specific canopy coefficients that primarily scale fractional PV cover to LAI by accounting for the area of unexposed PV. Modeling the canopy-level effects improved prediction of LAI (R2 = 0.82) over the model with no PV effect (R2 = 0.71) across the natural range of variation in NDVI but did not affect the site-level estimate of LAI. Satellite-based methods to estimate species composition, a variable in the model, will need to be developed. We expect that including the effects of PV properties in NDVI-LAI models will improve prediction of LAI where species composition varies across space or changes over time.     

扬州城市绿地景观格局分析

对2002年扬州市城区绿地斑块分布及其景观结构特征进行了研究,结果表明:小斑块(≤1 hm2)在研究区分布广,数量最多,占绿地斑块总数的59.86%;其次是面积在1~5 hm2的斑块,占总量的33.4%,大于10 hm2的斑块仅占4.2%。面积在1~5 hm2的斑块和大于10 hm2的绿地斑块,分别占绿地总面积的37.6%和39.6%。绿地率在20%~30%的绿地斑块最丰富,占总数的41.7%,占绿地总面积的24.8%;绿地率大于60%的斑块占总面积的32.8%;绿地率≤20%的绿地以小斑块为主,占总面积的8.51%。对绿地景观指数分析表明:城市绿地多样性指数高,总体分布均衡。从老城区到新城区,绿地景观特征表现为总面积增加,平均绿地斑块面积增大,景观破碎度降低,城市绿化的理念不断提升。加强广陵区内绿地斑块的改造,提高斑块间的连接度,增加维扬区和邗江区绿化廊道和绿地斑块的营造,应成为今后城市绿化的重点。     

京津冀地区近20年NDVI时空变化特征

健康稳定的自然生态系统是保障城市发展的重要基础.了解京津冀城镇快速发展过程中自然生态系统的变化,有助于该区域城镇绿色协调可持续发展.植被指数NDVI时空变化特征可反映地区自然生态系统状况及其演变规律.基于MOD13Q1和Landsat遥感影像数据,利用一元线性回归趋势分析法分析近20年(2000—2019年)京津冀地区...     

库布齐沙漠自然植被与人工植被土壤水盐的空间异质性比较研究

干旱半干旱地区土壤水盐的空间分布对土地利用和生态恢复具有重要作用。运用传统统计学和地统计学对库布齐沙漠5种自然植被和5种人工植被0～10和10～20cm深度土壤水分和盐分的空间异质性进行了小尺度比较分析。结果表明：9种群落的土壤水盐平均值下层大于表层,且盐分的空间相关性较水分更高;人工植被土壤水分（CV=5.3%～22.7%）和盐分（CV=15.7%～51.2%）具有空间分布均匀、层间差异不明显等特征,而自然植被水分（CV=9.9%～32.6%）和盐分的（CV=26.9%～180.0%）却与之相反;小尺度上,人工植被土壤不同层间的水分关系、盐分关系以及水盐关系可能随建植时间的增加会越来越不明显,格局强度将不断减弱,这也极有可能改变大尺度上的水盐运移状况,进而影响研究区生态系统的稳定性和安全性。