黄土高原植被覆盖度变化的地形分异及土地利用/覆被变化的影响

基于16 d合成MODIS NDVI数据提取的时间序列植被覆盖度数据,采用一元线性回归趋势分析,对黄土高原2000-2008年植被覆盖度的时空变化及其地形分异、土地利用/覆被变化的影响进行了定量分析。结果表明：（1）研究时段黄土高原植被覆盖度整体呈快速上升趋势,局部下降;（2）黄土高原植被覆盖度变化存在明显的地形分异,陡坡等植被恢复、重建和保育的主要区域植被覆盖度增速显著;（3）土地利用/覆被变化对植被覆盖度的增加影响突出,土地利用/覆被类型变更区植被覆盖度增速显著高于未变化区域,退耕还林还草区增速尤其突出;（4）土地利用/覆被类型未变化区域植被覆盖度总体上也呈增加趋势,但因植被覆盖度水平相对较高,增速明显低于土地利用/覆被类型变化区。上述结果表明,黄土高原植被保育、植被恢复和重建在植被覆盖度提升方面取得了明显成效。     

Analysis of vegetation response to rainfall with satellite images in Dongting Lake

We analyzed the Normalized Difference Vegetation Index (NDVI) from satellite images and precipitation data from meteorological stations from 1998 to 2007 in the Dongting Lake wetland watershed to better understand the eco-hydrological effect of atmospheric precipitation and its relationship with vegetation. First, we analyzed its general spatio-temporal distribution using its mean, standard deviation and linear trend. Then, we used the Empirical Orthogonal Functions (EOF) method to decompose the NDVI and precipitation data into spatial and temporal modes. We selected four leading modes based on North and Scree test rules and analyzed the synchronous seasonal and inter-annual variability between the vegetation index and precipitation, distinguishing time-lagged correlations between EOF modes with the correlative degree analysis method. According to our detailed analyses, the vegetation index and precipitation exhibit a prominent correlation in spatial distribution and seasonal variation. At the 90% confidence level, the time lag is around 110 to 140 days, which matches well with the seasonal variation.     

洞庭湖植被对降水的响应

We analyzed the Normalized Difference Vegetation Index (NDVI) from satellite images and precipitation data from meteorological stations from 1998 to 2007 in the Dongting Lake wetland watershed to better understand the eco-hydrological effect of atmospheric precipitation and its relationship with vegetation. First,we analyzed its general spatio-temporal distribution using its mean,standard deviation and linear trend. Then,we used the Empirical Orthogonal Functions (EOF) method to decompose the NDVI and precipitation data into spatial and temporal modes. We selected four leading modes based on North and Scree test rules and analyzed the synchronous seasonal and inter-annual variability between the vegetation index and precipitation,distinguishing time-lagged correlations between EOF modes with the correlative degree analysis method. According to our detailed analyses,the vegetation index and precipitation exhibit a prominent correlation in spatial distribution and seasonal variation. At the 90% confidence level,the time lag is around 110 to 140 days,which matches well with the seasonal variation.     

Land use/land cover (LULC) classification with MODIS time series data and validation in the Amur River Basin

There is a need for improved and up-to-date land use/land cover (LULC) data sets over an intensively changing area in the Amur River Basin (ARB) in support of science and policy applications focused on understanding of the role and response of the LULC to environmental change issues. The main goal of this study was to map LULC in the ARB using MODIS 250-m Normalized Difference Vegetation Index (NDVI), Land Surface Vegetation Index (LSWI), and reflectance time series data for 2001 and 2007. Another goal was to test the consistency of the classification results using relatively coarse resolution MODIS imagery data in order to develop a methodology for rapid production of an up-to-date LULC data set. The results on MODIS land cover were evaluated using existing land use/cover data as derived from Landsat TM data. It was found that the MODIS 250-m NDVI data sets featured sufficient spatial, spectral and temporal resolution to detect unique multi-temporal signatures for the region’s major land cover types. It turned out that MODIS 250 NDVI time series data have high potential for large-basin land use/land cover monitoring and information updating for purposes of environmental basin research and management.     

基于MODIS数据中国天山积雪面积时空变化特征分析

基于2011-2015年MOD10A2积雪产品和气象数据,通过几何校正、去云预处理,应用归一化差分积雪指数算法等获取中国境内天山山区积雪覆盖面积数据,分析了积雪面积的时空变化特征及与气温降水的关系。结果表明：（1）年内积雪面积呈单峰变化,9月开始积累,次年1月达峰值,3月气温回暖消融加速,至7月最小。春秋季波动较大但没有明显的增减趋势,夏季积雪面积最小,冬季最大且呈减小趋势。（2）2001-2015年积雪覆盖面积整体上呈减少趋势,积雪覆盖率最大值的波动比最小值的波动更加剧烈。（3）积雪覆盖率随着海拔升高而增大,海拔<1 500 m区域积雪覆盖率低于10%,海拔>4 500 m以上区域平均可达70%,为常年稳定积雪区。积雪覆盖率在西北坡最高,南坡最低。（4）年均气温升高是积雪覆盖面积减小的主因,年积雪覆盖面积变化与年降水量变化保持一致的下降趋势。     

Assessing vegetation cover and biomass in restored erosion areas in Iceland using SPOT satellite data

Due to highly erodible volcanic soils and a harsh climate, livestock grazing in Iceland has led to serious soil erosion on about 40% of the country's surface. Over the last 100 years, various revegetation and restoration measures were taken on large areas distributed all over Iceland in an attempt to counteract this problem. The present research aimed to develop models for estimating percent vegetation cover (VC) and aboveground biomass (AGB) based on satellite data, as this would make it possible to assess and monitor the effectiveness of restoration measures over large areas at a fairly low cost. Models were developed based on 203 vegetation cover samples and 114 aboveground biomass samples distributed over five SPOT satellite datasets. All satellite datasets were atmospherically corrected, and digital numbers were converted into ground reflectance. Then a selection of vegetation indices (VIs) was calculated, followed by simple and multiple linear regression analysis of the relations between the field data and the calculated VIs.Best results were achieved using multiple linear regression models for both %VC and AGB. The model calibration and validation results showed that R² and RMSE values for most VIs do not vary very much. For percent VC, R² values range between 0.789 and 0.822, leading to RMSEs ranging between 15.89% and 16.72%. For AGB, R² values for low-biomass areas (AGB < 800 g/m²) range between 0.607 and 0.650, leading to RMSEs ranging between 126.08 g/m² and 136.38 g/m². The AGB model developed for all areas, including those with high biomass coverage (AGB > 800 g/m²), achieved R² values between 0.487 and 0.510, resulting in RMSEs ranging from 234 g/m² to 259.20 g/m². The models predicting percent VC generally overestimate observed low percent VC and slightly underestimate observed high percent VC. The estimation models for AGB behave in a similar way, but over- and underestimation are much more pronounced.These results show that it is possible to estimate percent VC with high accuracy based on various VIs derived from SPOT satellite data. AGB of restoration areas with low-biomass values of up to 800 g/m² can likewise be estimated with high accuracy based on various VIs derived from SPOT satellite data, whereas in the case of high biomass coverage, estimation accuracy decreases with increasing biomass values. Accordingly, percent VC can be estimated with high accuracy anywhere in Iceland, whereas AGB is much more difficult to estimate, particularly for areas with high-AGB variability.     

基于遥感的民勤绿洲植被覆盖变化定量监测

基于混合像元条件下的TM影像植被覆盖度遥感反演,定量研究了甘肃省民勤绿洲1987年至2001年植被覆盖时空变化的规律与特点。结果表明,15年中,民勤绿洲的植被覆盖发生了很大的变化,农田植被(耕地)面积增加了53.11%,而中、高盖度植被的面积却减少了25.21%,这对民勤绿洲的长久生态安全构成了严重的威胁。民勤绿洲植被覆盖变化的空间转化过程复杂,但总体属于开垦—植被退化型。15年中因开垦和植被退化而损失的中、高盖度植被达42204.81hm2,占原面积的81.73%,而且损失的主要是绿洲边缘的防风固沙植被,其中因开垦损失的面积为18776.08hm2,占损失面积的44.5%。超采地下水引起的地下水位快速下降是导致绿洲边缘天然和人工灌丛植被退化的主要原因。控制开垦并培育相对稳定的灌丛型植被,应该是研究区生态环境建设的重点。     

Characterization of land cover types in Xilin River Basin using multi-temporal Landsat images

This study conducted computer-aided image analysis of land use and land cover in Xilin River Basin, Inner Mongolia, using 4 sets of Landsat TM/ETM+ images acquired on July 31, 1987, August 11, 1991, September 27, 1997 and May 23, 2000, respectively. Primarily, 17 sub-class land cover types were recognized, including nine grassland types at community level: F. sibiricum steppe, S. baicalensis steppe, A. chinensis + forbs steppe, A. chinensis + bunchgrass steppe, A. chinensis + Ar. frigida steppe, S. grandis + A. chinensis steppe, S. grandis + bunchgrass steppe, S. krylavii steppe, Ar. frigida steppe and eight non-grassland types: active cropland, harvested cropland, urban area, wetland, desertified land, saline and alkaline land, cloud, water body + cloud shadow. To eliminate the classification error existing among different sub-types of the same gross type, the 17 sub-class land cover types were grouped into five gross types: meadow grassland, temperate grassland, desert grassland, cropland and non-grassland. The overall classification accuracy of the five land cover types was 81.0% for 1987, 81.7% for 1991, 80.1% for 1997 and 78.2% for 2000.     

基于多时相陆地卫星图像的锡林河流域土地覆盖类型特征

1IntroductionThe growing concern over the impact of changes in land use and land cover on environmental conditions and the increasing human impact on the natural resources has captured worldwide attention of the political and scientific community (Ojima etal., 1991; Smith etal., 2001). It is expected that the changing land use/land cover pattern will be one of the driving forces of environmental changes superimposed on the natural changes at regional scale (Fu etal., 1993; Bonan, 1995). Arid…     

Implications of diurnal variations in land surface temperature to data assimilation using MODIS LST data

Based on the Beijing Climate Center's land surface model BCC_AVIM(Beijing Climate Center Atmosphere-Vegetation Interaction Model), the ensemble Kalman filter(En KF) algorithm has been used to perform an assimilation experiment on the Moderate Resolution Imaging Spectroradiometer(MODIS) land surface temperature(LST) product to study the influence of satellite LST data frequencies on surface temperature data assimilations. The assimilation results have been independently tested and evaluated by Global Land Data Assimilation System(GLDAS) LST products. The results show that the assimilation scheme can effectively reduce the BCC_AVIM model simulation bias and the assimilation results reflect more reasonable spatial and temporal distributions. Diurnal variation information in the observation data has a significant effect on the assimilation results. Assimilating LST data that contain diurnal variation information can further improve the accuracy of the assimilation analysis. Overall, when assimilation is performed using observation data at 6-hour intervals, a relatively good assimilation result can be obtained, indicated by smaller bias(2.2 K) and root-mean-square-error(RMSE)(3.7 K) and correlation coefficients larger than 0.60. Conversely, the assimilation using 24-hour data generally showed larger bias(2.2 K) and RMSE(4 K). Further analysis showed that the sensitivity of assimilation effect to diurnal variations in LST varies with time and space. The assimilation using observations with a time interval of 3 hours has the smallest bias in Oceania and Africa(both1 K); the use of 24-hour interval observation data for assimilation produces the smallest bias(2.2 K) in March, April and July.     

Linear regression relationships between NDVI, vegetation and rainfall in Etosha National Park, Namibia

Estimations of 10-day interval green vegetation cover and biomass, 10-day interval cumulative rainfall, as well as annual rainfall are compared with 10-day interval and rainy season NDVI and MVC using linear regression analysis. Raw data were smoothed by averaging and removing dry season outliers. Results indicate that the ability of NDVI and MVC to predict green vegetation cover, cumulative rainfall and annual rainfall is poorer for raw data than for averaged, outlier-removed data. It is recommended that the standard error of the raw data predictions are used to indicate the fundamental error in these relationships, and that the equations of the averaged, outlier-removed data are used to indicate the fundamental strength of NDVI or MVC in predicting vegetation or rainfall. The practical use of integrated rainy season MVC images are discussed.     

Effects of postsocialist reforms on land cover and land use in South-Eastern Albania

This paper examines effects of postsocialist reforms on land cover and land use through a case study from South-eastern Albania. The paper uses satellite data to measure changes in land cover between 1988 and 2003, draws on a village survey to assess changes in local land-use practices, and examines shifts in the determinants of land cover through seemingly unrelated regressions at the village level. The results show a high incidence of cropland abandonment especially in lower-lying areas closer to markets. Socio-economic factors have emerged as new determinants of spatial variation, suggesting a growing influence of market principles on land use.     

人工绿洲生态系统中土壤和植被对土地利用变化的响应——以北屯绿洲为例

在遥感(RS)和地理信息系统(GIS)技术的支持下,对干旱区典型人工绿洲-北屯绿洲1989和2005年两期遥感影像进行了处理,分析了绿洲16年来的土地利用/覆盖变化.通过野外调查、实验室分析及"时-空替代法"等方法,对人工绿洲生态系统中的土壤和植被对LUCC的响应机制进行了分析.结果表明:(1)北屯绿洲耕地和盐碱沼泽地面积不断增加,垦荒和撂荒成为土地利用变化的两个主要过程.(2)绿洲土地利用变化对土壤养分和pH值的影响显著.(3)耕地的土壤盐分含量低于弃耕地和荒地.(4)随着土地利用类型的变化,群落组成发生较大变化,优势种更替明显.     

Spatial-temporal patterns of vegetation dynamics and their relationships to climate variations in Qinghai Lake Basin using MODIS time-series data

Global warming has led to significant vegetation changes in recent years. It is necessary to investigate the effects of climatic variations（temperature and precipitation） on vegetation changes for a better understanding of acclimation to climatic change. In this paper, we focused on the integration and application of multi-methods and spatial analysis techniques in GIS to study the spatio-temporal variation of vegetation dynamics and to explore the vegetation change mechanism. The correlations between EVI and climate factors at different time scales were calculated for each pixel including monthly, seasonal and annual scales respectively in Qinghai Lake Basin from the year of 2001 to 2012. The primary objectives of this study are to reveal when, where and why the vegetation change so as to support better understanding of terrestrial response to global change as well as the useful information and techniques for wise regional ecosystem management practices. The main conclusions are as follows：（1） Overall vegetation EVI in the region increased 6% during recent 12 years. The EVI value in growing seasons（i.e. spring and summer） exhibited very significant improving trend, accounted for 12.8% and 9.3% respectively. The spatial pattern of EVI showed obvious spatial heterogeneity which was consistent with hydrothermal condition. In general, the vegetation coverage improved in most parts of the area since nearly 78% pixel of the whole basin showed increasing trend, while degraded slightly in a small part of the area only.（2） The EVI change was positively correlated with average temperature and precipitation. Generally speaking, in Qinghai Lake Basin, precipitation was the dominant driving factor for vegetation growth; however, at different time scale its weight to vegetation has differences.（3） Based on geo-statistical analysis, the autumn precipitation has a strong correlation with the next spring EVI values in the whole region. This findings explore the autumn precipitation is an important indicator     

基于SPOT NDVI的华北北部地表植被覆盖变化趋势

为分析华北北部地表植被覆盖变化趋势,探寻合理土地利用方式,基于1999年1月至2006年12月的SPOT-VEGETATION逐旬NDVI数据,采用国际通用的MVC(最大合成法)获得月NDVI值,用均值法求出年均NDVI数值。在此基础上,用一元线性回归斜率定量描述地表覆盖动态变化,以Hurst指数表示其时间依存性,利用GIS工具表征其空间格局并进行空间统计分析。研究结果表明:近8年来华北北部地表植被覆盖整体得到改善的区域比植被覆盖退化的区域面积大,得到改善的区域约占总面积的66.04%,基本不变的区域约占14.39%,退化区域约占19.57%。各种土地利用类型Hurst指数平均值均为0.5     

石羊河流域海拔、植被覆盖与景观类型空间关系研究

 以Landsat/TM为数据源,在桌面GIS支持下解译出研究区各景观类型,根据植被的生长周期,结合植被指数[WTBX]NDVI[WTBZ]作为研究因子,采用目前较为流行的主成分变换法（PCA）,结合景观类型图和数字高程模型(DEM),参考相关地理基础图件,对石羊河流域海拔和植被覆盖进行分级分类。运用叠置分析法研究了不同海拔高度和不同植被覆盖下[WTBX]NDVI[WTBZ]变化情况;同时分析了各景观类型与海拔和植被覆盖三者之间的空间关系。结果表明：研究区各景观类型大体依海拔呈垂直性分布,各自然要素组成垂直分异性特征亦较为明显。植被覆盖、景观类型与海拔之间呈垂直型分布规律,低海拔区景观类型单一,主要为荒漠系统,且与植被覆盖关系密切,相关度也最高;高海拔区景观类型较为复杂并交错分布,与植被覆盖关系不大,拟合效果不理想。此次研究目的在于对石羊河流域景观格局特征和格局优化研究提供参考思路,对流域生态规划和治理提供依据,对干旱内陆河流域植被及生态环境变化研究具有深远意义。     

陕西大理河流域土地利用/覆被变化的水文效应

近年来,土地利用/覆被变化的水文效应研究已成为国际的前沿和重点。在大理河流域,LUCC(包括水土保持措施)对水循环和水量平衡产生了深远影响,该流域LUCC水文效应的研究势在必行。根据研究区1990年代三期土地利用数据分析了LUCC的时空变化特征,采用特征变量时间序列法及降水-径流模型对LUCC水文效应进行了研究。结果表明:流域土地利用类型以耕地和草地为主,近10年来,耕地和草地面积有所减少,而林地和建设用地面积持续增加;流域年径流和月径流演化过程均表现出明显的下降趋势;LUCC及水土保持具有减少流域年径流、汛期流量以及增加枯季流量的作用,相对于降水因素,人类活动对流域水文的作用占主导地位;在1990~2000年期间,主要由LUCC引起的年均径流减少量达2616.6×10⁴ m³,占该期间实测减水总量的62.19%。     

Driving forces of land use and land cover change (LUCC) in the Zoige Wetland, Qinghai-Tibetan Plateau

The Zoige Wetland is located in the northeastern Qinghai-Tibetan Plateau, which is highly sensitive to global environment change and human disturbance because of its high elevation and cold environment, thus, it’s a hotspot for land use and land cover change (LUCC) research. We used Landsat MSS images from 1975, Landsat ETM images from 2000, and Landsat TM images from 1990 and 2005 to assess the LUCC in the study area, using GIS techniques, as well as topographic, vegetation, and soil maps combined with field investigations. The monitoring result shows that the study area’s environment degraded rapidly between 1975 and 2005, including wetland shrinkage from 5,308 km² to 4,980 km², sandy land expansion from 112 km² to 137 km², forest land decreasing from 5,686 km² to 5,443 km², and grassland degradation from 12,309 km² to 10,672 km². According to the analysis of meteorological data and social-economic statistical data, we concluded that the LUCC in the Zoige Wetland was caused by both natural and anthropogenic factors, but human activities were primarily responsible for the observed LUCC, thereby, we suggest human behaviors must be adjusted to control environmental degradation.     

Identification of land cover/land use changes in the greater area of the Preveza peninsula in Greece using Landsat satellite data

This study investigates the Land Use & Land Cover (LULC) changes in a coastal area of the southwest part of Epirus region, called Preveza, situated in North-western Greece. Remote sensing imagery coming from the Enhanced Thematic Mapper (ETM+) sensor on board at the Landsat 7 satellite platform is used for this purpose. More specifically, we identified LULC changes in this environmentally sensitive coastal area, using Landsat image scenes for the dates of June 19th, 2000 and July 22nd, 2009. During this period, there was an increasing tourist activity and a high growth in the construction sector of the study area. The land-use changes were identified, examining several vegetation indices and band combinations, along with the implementation of different well-known classification techniques. The Normalized Difference Vegetation Index (NDVI) and the Brightness Index (BI) have proved to be the most suitable indices to successfully identify discrete land surface classes for this study area. Regarding the classifiers, a series of traditional and modern algorithms were tested. The Artificial Neural Networks (ANNs) and the Support Vector Machines (SVMs) gave improved results in comparison to other more traditional classification techniques. The best overall accuracy for the study area was achieved with the SVM classifier and reached 96.25% and 97.15% on the dates of June 19th, 2000 and July 22nd, 2009 respectively. The classification results depicted notable urbanization, small deforestation and important LULC changes in the agriculture sector, indicating a rapid coastal environment change in the region of interest.