东亚土地覆盖对ENSO事件的响应特征

对1982—1993年气候年际变化的强信号——ENSO进行了确认及再分类。以美国地质调查局EROS中心提供的AVHRR 8km NDVI为数据源，应用地理信息系统技术，计算了1982—1993年每年夏季(5—9月)NDVI平均影像。在此基础上用数据断面分析法对ENSO年东亚地区土地覆盖的空间分布进行了分析，再用主成分分析法对同一时间序列NDVI平均影像进行了运算，发现其第7主成分影像所反映的土地覆盖分布与数据断面分析法所反映的结果是一致的。对此，进一步分析了第7主成分的特征向量与代表ENSO变化特征的南方     

老挝万象土地利用变化分析

采用灰度比值法、归一化植被覆盖法(NDVI)及主成分分析法(PCA)和分类后比较法对老挝万象1999年和2006年的地表覆盖变化/土地利用变化进行了分析比较实验。实验表明,灰度比值法、归一化植被覆盖法(NDVI)及主成分分析法变化信息提取的组合结果明显优于分类后比较法,因此作者选取组合方法的变化信息提取结果,分析了老挝万象1999年和2006年土地利用变化情况,得到变化分布图和统计表。研究表明,老挝万象1999年和2006年城市得到大规模扩张(城市面积增长71%),而农业、林地、水体面积都明显减少。     

NDVI-Ts空间全国土地覆盖分类方法研究

利用NDVI-Ts空间进行全国土地覆盖分类的方法。该方法利用1995年NOAA 10天合成的ch4、ch5通道亮温，先计算出陆地表面温度(Ts)，然后用最大值合成法计算每月的最大Ts和NDVI，以每月最大Ts和NDVI建立NDVI-Ts空间。根据像素点(NDVI，Ts)在空间中的位置矢量，求出矢量在空间中的方向角度，并作归一化处理，得到温度植被角度(NTVA)。对12个月NTVA做主成分变换提取前三个主分量，辅以全年总NDVI和大于O℃ Ts积温，用模糊K-均值法进行全国土地覆盖分类。研究结果表明，基于NDVI-Ts空间的NTVA与NDVI、Ts一起作为分类特征在土地覆盖分类中具有较高的分类精度，能够取得较好的分类效果。     

陕西省1998～2008年植被覆盖度的时空变化研究

以1998-04～2008-07的372景逐旬SPOT4VEGETATION数据(S10)为主要数据源,利用MVC法、一元线性回归趋势分析法和差值法,分析1998～2007年陕西省年最大化NDVI的变化趋势,并对年最大化NDVI和月最大化NDVI的年际变化规律和陕西省植被覆盖度动态变化及其空间分布规律进行分析。结果表明,1998～2008年间,年最大化NDVI整体呈变好的趋势,但是月最大化NDVI的年际变化趋势在不同月份存在很大的差异;年最大化NDVI和月最大化NDVI在每相邻2a间的变化均存在很大差异,植被退化与改善波动出现;1998～2008年陕西各地区植被覆盖度变化是很明显的,一般8、9月份植被覆盖度最高。从空间分布上看,陕北北部地区(榆林市的东南部和延安市北部地区)植被覆盖度显著增加,宝鸡市中南部、西安市、商洛和安康部分地区植被改善也较明显。     

ASTER和TM/ETM+遥感数据融合监测土地覆盖变化

在人们纷纷选择IKONOS、QUICKBIRD、SPOT-5等高分辨率影像监测土地利用/覆盖变化之际,以北京海淀区为例,尝试采用Brovey变换和主成分分析(PCA)法融合ASTER、TM/ETM+中等分辨率影像,充分利用ASTER、TM/ETM+数据的多光谱和较高空间分辨率特性,挖掘其在土地覆盖变化监测中的潜力,为大规模监测土地利用/覆盖变化提供科学参考。研究将2003年ASTER多光谱3N、2、1波段与1999年ETM+PAN波段进行Brovey变换;1992年TM543与1999年ETM+PAN波段进行PCA融合,快速发现土地覆盖变化信息。经验证,变化发现精度达92.50%,符合项目精度要求。试验表明:在缺乏高分辨率影像的地区,选择价格相对便宜的AS-TER和TM/ETM+数据,采用Brovey变换和主成分分析(PCA)法进行融合,可有效监测土地覆盖变化,节约动态监测成本,二者具有很大的应用价值,值得推广。     

1983—1992年中国陆地植被NDVI演变特征的变化矢量分析

以NDVI时序资料为基本数据源，综合应用变化矢量分析和主成分分析方法对1983年至1992年中国陆地植被NDVI的变化强度、变化类型及空间结构变化特征进行了分析。研究结果表明在此期间中国陆地植被NDVI变化有以下特点：（1）十年间NDVI变化东西分异明显，东部变化幅度远大于西部。NDVI变化整体表现为稳中略增，增加区主要分布在台湾、福建、四川、河南等地；减少区主要分布在云南省和新疆北部等地。（2）空间结构信息表现了景观异质性，其变化主要发生在南方，反映了植被的生长和衰老过程及地形（山脉走向）变化。     

近30年来渭河流域植被与气候变化互影响模式的探寻分析

基于1982-2006年间的GIMMS NDVI和2001-2013年的MODIS NDVI数据对渭河流域30年来植被NDVI的年际变化和空间分布特征进行了分析,并结合研究区内的气象数据探讨了植被NDVI与气候因子的相互影响关系。研究显示,近32年间渭河流域植被NDVI整体呈上升趋势,且空间差异显著,主要表现为流域西北地区的黄土丘陵沟壑区及北部的黄土高原区NDVI较低,植被覆盖较差;流域南部的秦岭山区、关中平原区等地植被生长状况较好。流域气温和降水呈现缓慢增长趋势。植被NDVI与年均气温整体上表现为负相关,与年降水量间呈正相关。总体上,降水是渭河流域植被生长的主要影响因子。     

中国近海TOPEX/Poseidon卫星测高海平面变化的CPCA分析

用复主成分分析方法，对中国近海TOPEX／Poseidon卫星6年的测高的海平面变化资料进行了分析，给出了中国南海，黄海和东海的海平面变化的空间和时间分布特重征，利用小波方法分析了主要主成分时间变化序列的时－频特征，分析结果表明，主要主成分的空间分布特征与海洋环充相对应，南海，黄海和东海的海平面变化存在显著的年际和2个月的非稳态振荡信号，南海具有较明显的半年周期信号，而黄海和东海的半年周期信号不明显。     

赣州市土地利用动态变化及其驱动因子分析

将赣州市作为研究对象,选取2000、2004、2010、2014和2017年5期Landsat遥感影像,利用RS和GIS技术,采用人机交互解译辅助实地考察方式提取赣州市2000—2017年土地利用类型图并分析土地利用变化特征;结合2000—2017年赣州市社会经济影响指标,运用主成分分析法分析得到赣州市土地利用变化的驱...     

基于PCA/NDVI的森林覆盖遥感信息提取方法研究

摘要： 以大兴安岭为试验区，提出将主成分分析（PCA）得到的第1分量、归一化植被指数（NDVI）和Landsat TM 1~TM 7某一波段进行合成，增强森林覆盖区和背景区信息的反差，并利用最大似然法对影像进行监督分类，分类精度超过92%。通过对不同云雾量和森林覆盖的2个时相影像试验表明，本方法提高了遥感影像森林覆盖信息提取的自动化程度和精度。     

Spatiotemporal analysis of vegetation index after typhoons in the mountainous watershed

An extensive land cover change was triggered by a series of typhoons, especially Typhoon Morakot in 2009 in Taiwan. The normalized difference vegetation index (NDVI) series from multiple satellite images were applied to monitor the change processes of land cover. This study applied spatiotemporal analysis tools, including empirical orthogonal functions (EOF), and multiple variograms in analyzing space–time NDVI data, and detected the effects of large chronological disturbances in the characteristics of land cover changes. Spatiotemporal analysis delineated the temporal patterns and spatial variability of NDVI caused by these large typhoons. Results showed that mean of NDVI decreased but spatial variablity of NDVI increased after typhoons in the study area. The EOF can clarify the major component of NDVI variations and identify the core area of the NDVI changes. Various approaches showed consistent results that Typhoon Morakot significantly lowered the NDVI in land cover change process. Furthermore, the spatiotemporal analysis is an effective monitoring tool, which advocates the use of the index for the quantification of land cover change and resilience.     

中国陆地生态系统脆弱带遥感模型

本研究通过对我国陆地生态系统8个典型样地的植被指数取样实验和图像计算结果发现，这8个样地植被指数随着水、热因子的季节变化，在时间和空间上具有一定的“绿波推移”和“景观更替”规律。在中国东部湿润的季风区（样地1－3），随着纬度的增高，其月平均植被指数与月平均气温有较大的相关。发现降水相对丰沛的地带，热量和光照条件的变化成为植被生长和变化的自然限制因子；而在中国北方森林－森林草原－典型昌原－荒漠草原－荒漠地带上，随着从东部（湿润地区）到西部（干旱地区）干湿条件的更替，月平均植被指数与降水多寡有较大的正相关关系。在8个样地上都呈现出共同的规律，即定向风的分布与植被指数的分布在时间和空间上具有逆相分布的“套合关系”。尤其在时间上有相逆套合关系，这正是中国北方沙尘暴和沙漠化加剧的自然原因。本研究定量地给出了我国陆地不同经纬度带生态系统脆弱季节和累积时间的分布。     

Spatial and Temporal Relationships among NDVI,Climate Factors,and Land Cover Changes in Northeast Asia from 1982 to 2009

This study uses a multiple linear regression method to composite standard Normalized Difference Vegetation Index (NDVI) time series (1982-2009) consisting of three kinds of satellite NDVI data (AVHRR, SPOT, and MODIS). This dataset was combined with climate data and land cover maps to analyze growing season (June to September) NDVI trends in northeast Asia. In combination with climate zones, NDVI changes that are influenced by climate factors and land cover changes were also evaluated. This study revealed that the vegetation cover in the arid, western regions of northeast Asia is strongly influenced by precipitation, and with increasing precipitation, NDVI values become less influenced by precipitation. Spatial changes in the NDVI as influenced by temperature in this region are less obvious. Land cover dynamics also influence NDVI changes in different climate zones, especially for bare ground, cropland, and grassland. Future research should also incorporate higher-spatial-resolution data as well as other data types (such as greenhouse gas data) to further evaluate the mechanisms through which these factors interact.     

Comparing spatiotemporal patterns in Eurasian FPAR derived from two NDVI-based methods

Abstract

A long-term, consistent Fraction of Absorbed Photosynthetically Active Radiation (FPAR) product is necessary to study the spatial and temporal patterns of vegetation dynamics associated with climatic changes and human activities. In this study, Eurasia was selected as the study area. The relationship between FPAR and simple infrared/red ratio relationship (SR FPAR), and that between Moderate Resolution Imaging Spectroradiometer (MODIS) FPAR and a Normalised Difference Vegetation Index (NDVI) look-up table (LUT FPAR) were employed to estimate FPAR from 1982 to 2006 by different land cover types, focusing on the comparisons of spatiotemporal FPAR patterns between the two FPAR datasets. The results showed high agreement between MODIS standard FPAR and estimated FPAR in seasonal dynamics with peak values in July. The LUT FPAR was close to MODIS standard FPAR and larger than SR FPAR. The SR and LUT FPAR showed the same spatial distribution and inter-annual variation patterns and were primarily determined by land cover types. An overall increasing trend in FPAR was observed from 1982 to 2006, with reductions from 1991 to 1994 and 2000 to 2002. The inter-annual dynamics in evergreen broadleaf forests showed a decreasing trend over 25 years, while non-forest vegetation FPAR values had slow, stable growth in inter-annual variation.     

增强型空间像元分解时空遥感影像融合算法

高空间、高时间分辨率的遥感影像对地表与大气环境的实时精细监测具有重要作用,但单一卫星传感器获取的遥感影像存在空间与时间分辨率相互制约的问题,时空融合技术发展成为了低成本、高效生成满足不同应用需求的高时空分辨率遥感影像的有效手段.近年来,国内外学者提出了大量的时空融合算法,但对于复杂的地物类型变化的空间细节修复仍存在挑战...     

Land Cover Change and Associated Trends in Surface Reflectivity and Vegetation Index in Southwest Kansas: 1972-1992

Abstract

Land cover is an important component of the earth system. Human induced surface alteration can affect earth systems directly, through loss or degradation of ecosystems, or indirectly through impact on the climate and biogeochemical cycles necessary to sustain life on earth. The significance of the earth's surface has made land use/land cover change an important issue in global change research. Alteration of land cover occurs at a variety of spatial scales, but as with many environmental change issues, the impacts of surface changes are often conceptualized at the global scale. In this study, we investigate the effects of land cover change on total reflected radiation and the Normalized Difference Vegetation Index (NDVI) in a 10,000 km² local area in the High Plains of southwestern Kansas. Landsat MSS data from five years of record within the twenty‐year period 1973 to 1992 were classified into cool season crop, warm season crop, and pasture/prairie. Mean values of summer reflectance and NDVI from each cover type and for the study area as a whole were then analyzed for systematic change over the study period. Both reflectivity and vegetation index increased during the study period, although causes for the increase appear to be different. Results suggest that changes in mean surface reflectance in the study site are strongly influenced by land cover change, whereas changes in NDVI are more closely linked to 50‐day antecedent precipitation.     

Spatial-temporal changes of NDVI and their relations with precipitation and temperature in Yangtze River basin from 1981 to 2001

Multitemporal NOAA/AVHRR NDVI images and monthly temperature and precipitation data were obtained across Yangtze River basin covering the period 1981–2001. The spatial and temporal patterns of NDVI are the same, while spatial analysis shows that the NDVI is influenced by the vegetation types growing in the study regions, and NDVI presents an increasing trend during the study period in the whole basin. The climate indicators play an important role in the changes of vegetation cover in the river basin. In the two Indicators, temperature has a significant effect on the NDVI values than precipitation in the whole basin. However, in the 11 subbasins, the different rules are shown in different subbasins.     

A data mining approach for evaluation of optimal time-series of MODIS data for land cover mapping at a regional level

Optical Earth Observation data with moderate spatial resolutions, typically MODIS (Moderate Resolution Imaging Spectroradiometer), are of particular value to environmental applications due to their high temporal and spectral resolutions. Time-series of MODIS data capture dynamic phenomena of vegetation and its environment, and are considered as one of the most effective data sources for land cover mapping at a regional and national level. However, the time-series, multiple bands and their derivations such as NDVI constitute a large volume of data that poses a significant challenge for automated mapping of land cover while optimally utilizing the information it contains. In this study, time-series of 10-day cloud-free MODIS composites and its derivatives – NDVI and vegetation phenology information, are fully assessed to determine the optimal data sets for deriving land cover. Three groups of variable combinations of MODIS spectral information and its derived metrics are thoroughly explored to identify the optimal combinations for land cover identification using a data mining tool.The results, based on the assessment using time-series of MODIS data, show that in general using a longer time period of the time-series data and more spectral bands could lead to more accurate land cover identification than that of a shorter period of the time-series and fewer bands. However, we reveal that, with some optimal variable combinations of few bands and a shorter period of time-series data, the highest possible accuracy of land cover classification can be achieved.