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。     

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变化特征的南方涛动指数(SOI)之间的关系，进而，对ENSO驱动下的东亚地区土地覆盖年际变化的空间分布特征进行了总结。     

基于MODIS数据的环北京地区土地资源监测研究

本文基于MODIS 16天合成的NDVI时间序列数据及其他辅助数据,首先用PCA方法对NDVI时间序列数据进行信息增强与压缩处理,结合LST数据、DEM数据及降雨温度数据,利用模糊K-均值非监督分类法,进行环北京地区的土地覆盖分类,得到土地资源现状情况。然后利用变化矢量(CVA)分析方法对环北京地区的土地利用及植被覆盖的多年变化状况进行了分析。结果表明,MODIS数据能很好的应用于大范围的土地资源监测中,并能得到较好的结果。     

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     

Abrupt spatiotemporal land and water changes and their potential drivers in Poyang Lake, 2000–2012

Driven by various natural and anthropogenic factors, Poyang Lake, the largest freshwater lake in China, has experienced significant land use/cover changes in the past few decades. The aim of this study is to investigate the spatial–temporal patterns of abrupt changes and detect their potential drivers in Poyang Lake, using time-series Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day maximum value composite vegetation indices between 2000 and 2012. The breaks for additive seasonal and trend (BFAST) method was applied to the smoothed time-series normalized difference vegetation index (NDVI), to detect the timing and magnitude of abrupt changes in the trend component. Large part of Poyang Lake (98.9% for trend component) has experienced abrupt changes in the past 13 years, and the change patterns, including the distributions in timing and magnitudes of major abrupt trend changes between water bodies and land areas were clearly differentiated. Most water bodies had abrupt increasing NDVI changes between 2010 and 2011, caused by the sequential severe flooding and drought in the two years. In contrast, large parts of the surrounding land areas had abrupt decreasing NDVI changes. Large decreasing changes occurred around 2003 at the city of Nanchang, which were driven by urbanization. These results revealed spatial–temporal land cover changing patterns and potential drivers in the wetland ecosystem of Poyang Lake.     

用基于知识的决策树方法分层提取矿区土地类型——以湖北大冶为例

以湖北大冶为研究区,采用多时相陆地卫星遥感图像,通过不同波段组合,以及ironoxide指数和归一化差异植被指数(NDVI)等,详细分析了各地表地物光谱特征和空间特征,建立了研究区分类知识库表,采用决策二叉树法进行分类,得到了高精度分类结果图。基于不同时相分类结果的变化检测,通过对研究区水体污染、矿区复垦、耕地变化等分析,认为从1986～2002年,研究区水质虽有一定改善,但矿区植被退化严重,耕地大量减少,停产矿区复垦仅为20%,为合理保护矿区生态环境和科学管理采矿企业提供了有用资料。     

Assessing land cover change resulting from large surface mining development

A remote sensing based land cover change assessment methodology is presented and applied to a case study of the Oil Sands Mining Development in Athabasca, Alta., Canada. The primary impact was assessed using an information extraction method applied to two LANDSAT scenes. The analysis based on derived land cover maps shows a decrease of natural vegetation in the study area (715,094 ha) for 2001 approximately −8.64% relative to 1992. Secondary assessment based on a key resources indicator (KRI), calculated using normalized difference vegetation index (NDVI measurements acquired by NOAA–AVHRR satellites), air temperature and global radiation was performed for a time period from 1990 to 2002. KRI trend analysis indicates a slightly decreasing trend in vegetation greenness in close proximity to the mining development. A good agreement between the time series of inter-annual variations in NDVI and air temperature is observed increasing the confidence of NDVI as an indicator for assessing vegetation productivity and its sensitivity to changes in local conditions.     

基于NDVI序列影像精化结果的植被覆盖变化研究

植被归一化指数(NDVI)是地表植被覆盖特征的重要指标之一。本文以三峡库区2001-2003年MODIS遥感数据反演的NDVI时间序列影像为例,研究NDVI影像序列的精化问题,包括降云及去噪处理的有效方法。在改进的BISE技术降云处理的基础上,采用小波软阈值降噪方法提取有效变化趋势。然后进行库区2001-2003年植被变化的变化矢量分析,采用阈值分割的方法将库区变化强度影像分为未变化、小变化、中等变化与剧烈变化四个类型。研究成果可为三峡库区宏观生态环境变化的掌握提供一定的依据。     

基于遥感的长沙市城市热岛与土地利用/覆盖变化研究

基于多时相Landsat TM/ETM+影像,首先计算长沙市地表亮度温度,然后利用NDVI(归一化植被指数)、MNDWI(改进 的归一化水体指数)、NDBI(归一化建筑指数)和NDBaI(归一化裸土指数)4个指数,采用决策树分类方法对长沙市影像进行 土地利用/覆盖分类。在此基础上,对长沙市城市热岛的空间分布特征、时空演变特征以及城市热岛与土地利用/覆盖变化和各种影 响因子之间的关系进行研究。结果表明,随着长沙市城区范围的不断扩张,城市热岛范围也不断增大; 土地利用/覆盖类型的变化 会改变地表温度的空间分布,城市用地和裸地是城市热岛强度的主要贡献因素,水体和林地具有较好的降温作用。地表温度与4种 归一化指数的回归分析表明,它们之间存在明显的相关性,不同土地利用/覆盖类型的地表温度存在较大差异。     

河南省植被指数变化特征研究

为了研究河南省植被指数变化特征,采用最大值合成法(MVC)对MODIS-NDVI和MODIS-EVI两种指数产品进行处理,然后进行时空变化分析,得到归一化植被指数(NDVI)与增强型植被指数(EVI)两种指数产品的特点,实验结果表明:1)在时间分布特征上,两种植被指数均随季节呈现规律性变化,并且最大值均出现在7月或8月,但EVI相比NDVI更具季节性规律,能够更好地反映高植被覆盖区的植被季节性变化特征;2)在空间分布特征上,两种植被指数的区域性都非常明显,但在高植被覆盖区,NDVI出现饱和现象,而EVI未出现饱和现象。     

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.     

Vegetation Dynamics from Denoised NDVI Using Empirical Mode Decomposition

A novel approach to study vegetation dynamics is introduced, using the Empirical Mode Decomposition (EMD) to analyze NDVI time series. The NDVI time series which is nonlinear and nonstationary can be decomposed by EMD into components called intrinsic mode functions (IMFs), based on inherent temporal scales. The highest frequency component which has been found to represent noise is subtracted from the original NDVI series; thus smoothing the noisy signal. The different key features describing vegetation phenology have been extracted by analyzing the noise free signal. The lowest frequency component (last IMF) is the trend in the NDVI series. The trend in the series has been identified finding the Sen’s slope of last IMF, and the non-parametric seasonal Mann–Kendall test has been used to confirm the significance of the observed trend. The method has been applied on per–pixel basis to the SPOT Vegetation NDVI product covering Northeast India and surrounding regions for the time span of 1998–2009. Results show that the method has performed well in identifying the pixel clusters with significant trends. Hotspot regions with severe vegetation degeneration have been identified, and the relationship of the observed trends with the expected causative variables such as land use and land cover, topographic relief, and anthropogenic causes has been explored. The spatial locations of these critical regions closely matches with the findings of the previous studies carried out locally in the region, mainly indicating the shifting cultivation practice to be the main cause for land cover change.     

基于傅立叶变换的混合分类模型用于NDVI时序影像分析

应用2004年MODIS的时序NDVI数据，在分析湖北省不同地物类型的NDVI曲线季节性变化特征的基础上，设置对应的阈值，先后将水体、居民地与其他地物类型分离开。将去除了水体和居民地影响的剩余的NDVI序列影像傅立叶变换的1／12频率分量引入到地表覆盖分类的特征空间中，与其最大值影像和平均值影像组合，经过归一量化处理后合成一个类似具有三波段的卫星影像。在合成后的影像上利用最大似然法对其他地类进行分类。研究表明，引入傅立叶变换的特殊频率分量是分析多时相MODIS数据及提取地表植被覆盖信息的有效工具。     

Remote sensing monitoring of land restoration interventions in semi-arid environments with a before–after control-impact statistical design

Restoration interventions to combat land degradation are carried out in arid and semi-arid areas to improve vegetation cover and land productivity. Evaluating the success of an intervention over time is challenging due to various constraints (e.g. difficult-to-access areas, lack of long-term records) and the lack of standardised and affordable methodologies. We propose a semi-automatic methodology that uses remote sensing data to provide a rapid, standardised and objective assessment of the biophysical impact, in terms of vegetation cover, of restoration interventions. The Normalised Difference Vegetation Index (NDVI) is used as a proxy for vegetation cover. Recognising that changes in vegetation cover are naturally due to environmental factors such as seasonality and inter-annual climate variability, conclusions about the success of the intervention cannot be drawn by focussing on the intervention area only. We therefore use a comparative method that analyses the temporal variations (before and after the intervention) of the NDVI of the intervention area with respect to multiple control sites that are automatically and randomly selected from a set of candidates that are similar to the intervention area. Similarity is defined in terms of class composition as derived from an ISODATA classification of the imagery before the intervention. The method provides an estimate of the magnitude and significance of the difference in greenness change between the intervention area and control areas. As a case study, the methodology is applied to 15 restoration interventions carried out in Senegal. The impact of the interventions is analysed using 250-m MODIS and 30-m Landsat data. Results show that a significant improvement in vegetation cover was detectable only in one third of the analysed interventions, which is consistent with independent qualitative assessments based on field observations and visual analysis of high resolution imagery. Rural development agencies may potentially use the proposed method for a first screening of restoration interventions.     

遥感高时空融合方法的研究进展及应用现状

针对遥感图像的"时空矛盾",评述了当前解决这一问题最主要的方法即遥感时空信息融合的方法,包括基于变化模型的融合、基于重建模型的融合以及基于学习模型的融合。通过分析各个模型的研究现状,指出了每种模型方法的优劣,特别重点介绍了影响较大的自适应时空融合方法的理论以及对其的改进算法。同时本文总结了当前时空融合模型在长时间序列模拟以及大区域数据集生成等方面的实际应用的效果,以及分析了影响时空融合结果的主要因素。最后基于这些问题和影响因素提出了今后时空融合模型发展的目标和方向。     

多尺度城市地表温度降尺度方法

针对目前星载热红外传感器的空间分辨率低,无法满足城市尺度的生态环境研究需求的现状,该文选择地表覆盖类型复杂的区域,根据研究区土地覆盖类型,选取归一化植被指数(NDVI)、城市不透水面指数(ISA)、改进的归一化差异水体指数(MNDWI)等因子加入DisTrad模型,采用移动窗口逐步回归统计地表温度和因子的线性关系,利用半方差曲线函数和均方根误差综合确定最优移动窗口的大小,以提高地表温度降尺度精度。研究结果表明:改进的DisTrad模型在地表覆盖类型复杂区域,具有良好的降尺度目视效果,且具有较高的降尺度精度,尤其在低植被覆盖的建筑区、水体区域具有更高的精度。     

Forest cover classification using Landsat ETM+ data and time series MODIS NDVI data

Forest cover plays a key role in climate change by influencing the carbon stocks, the hydrological cycle and the energy balance. Forest cover information can be determined from fine-resolution data, such as Landsat Enhanced Thematic Mapper Plus (ETM+). However, forest cover classification with fine-resolution data usually uses only one temporal data because successive data acquirement is difficult. It may achieve mis-classification result without involving vegetation growth information, because different vegetation types may have the similar spectral features in the fine-resolution data. To overcome these issues, a forest cover classification method using Landsat ETM+ data appending with time series Moderate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data was proposed. The objective was to investigate the potential of temporal features extracted from coarse-resolution time series vegetation index data on improving the forest cover classification accuracy using fine-resolution remote sensing data. This method firstly fused Landsat ETM+ NDVI and MODIS NDVI data to obtain time series fine-resolution NDVI data, and then the temporal features were extracted from the fused NDVI data. Finally, temporal features combined with Landsat ETM+ spectral data was used to improve forest cover classification accuracy using supervised classifier. The study in North China region confirmed that time series NDVI features had significant effects on improving forest cover classification accuracy of fine resolution remote sensing data. The NDVI features extracted from time series fused NDVI data could improve the overall classification accuracy approximately 5% from 88.99% to 93.88% compared to only using single Landsat ETM+ data.     

Drivers of land cover and land use changes in St. Louis metropolitan area over the past 40 years characterized by remote sensing and census population data

In this study, we explored the spatial and temporal patterns of land cover and land use (LCLU) and population change dynamics in the St. Louis Metropolitan Statistical Area. The goal of this paper was to quantify the drivers of LCLU using long-term Landsat data from 1972 to 2010. First, we produced LCLU maps by using Landsat images from 1972, 1982, 1990, 2000, and 2010. Next, tract level population data of 1970, 1980, 1990, 2000, and 2010 were converted to 1-km square grid cells. Then, the LCLU maps were integrated with basic grid cell data to represent the proportion of each land cover category within a grid cell area. Finally, the proportional land cover maps and population census data were combined to investigate the relationship between land cover and population change based on grid cells using Pearson's correlation coefficient, ordinary least square (OLS), and local level geographically weighted regression (GWR). Land cover changes in terms of the percentage of area affected and rates of change were compared with population census data with a focus on the analysis of the spatial-temporal dynamics of urban growth patterns. The correlation coefficients of land cover categories and population changes were calculated for two decadal intervals between 1970 and 2010. Our results showed a causal relationship between LCLU changes and population dynamics over the last 40 years. Urban sprawl was positively correlated with population change. However, the relationship was not linear over space and time. Spatial heterogeneity and variations in the relationship demonstrate that urban sprawl was positively correlated with population changes in suburban area and negatively correlated in urban core and inner suburban area of the St. Louis Metropolitan Statistical Area. These results suggest that the imagery reflects processes of urban growth, inner-city decline, population migration, and social spatial inequality. The implications provide guidance for sustainable urban planning and development. We also demonstrate that grid cells allow robust synthesis of remote sensing and socioeconomic data to advance our knowledge of urban growth dynamics from both spatial and temporal scales and its association with population change.     

Spatial Analysis of Urban Growth Impacts on Vegetative Greenness with Landsat TM Data

Abstract

Landsat Thematic Mapper (TM) data have been used to monitor land cover types and to estimate biophysical parameters. However, studies examining the spatial relationships between land cover change and biophysical parameters are generally lacking. With the integration of remote sensing and Geographic Information Systems (GIS), these relationships can be better explored. The research reported in this paper applies this integrated approach for detecting urban growth and assessing its impact on vegetative greenness in the Zhujiang Delta, China. Multi‐temporal Landsat TM data were utilized to map urban growth and to extract and identify changes in vegetative greenness. GIS analyses were conducted to examine the changing spatial patterns of urban growth and greenness change. Statistical analyses were then used to examine the impact of urban growth on vegetative greenness. The results revealed that there was a notably uneven urban growth pattern in the delta, and urban development had reduced the scaled Normalized Difference Vegetation Index (NDVI) value by 30% in the urbanized area.     

Measures of spatio-temporal accuracy for time series land cover data

Remote sensing is a useful tool for monitoring changes in land cover over time. The accuracy of such time-series analyses has hitherto only been assessed using confusion matrices. The matrix allows global measures of user, producer and overall accuracies to be generated, but lacks consideration of any spatial aspects of accuracy. It is well known that land cover errors are typically spatially auto-correlated and can have a distinct spatial distribution. As yet little work has considered the temporal dimension and investigated the persistence or errors in both geographic and temporal dimensions. Spatio-temporal errors can have a profound impact on both change detection and on environmental monitoring and modelling activities using land cover data. This study investigated methods for describing the spatio-temporal characteristics of classification accuracy. Annual thematic maps were created using a random forest classification of MODIS data over the Jakarta metropolitan areas for the period of 2001–2013. A logistic geographically weighted model was used to estimate annual spatial measures of user, producer and overall accuracies. A principal component analysis was then used to extract summaries of the multi-temporal accuracy. The results showed how the spatial distribution of user and producer accuracy varied over space and time, and overall spatial variance was confirmed by the principal component analysis. The results indicated that areas of homogeneous land cover were mapped with relatively high accuracy and low variability, and areas of mixed land cover with the opposite characteristics. A multi-temporal spatial approach to accuracy is shown to provide more informative measures of accuracy, allowing map producers and users to evaluate time series thematic maps more comprehensively than a standard confusion matrix approach. The need to identify suitable properties for a temporal kernel are discussed.