A phenology based geo-informatics approach to map land use and land cover (2003–2013) by spatial segregation of large heterogenic river basins

Accurate information on land use and land cover (LULC) is critical for policy decisions especially for management of land and water resources’ activities in large river basins around the world. Phenology based LULC classification is the most promising approach particularly in the areas with diversified cropping patterns. Sometimes in large river basins, local climate and topography provides two different phenological information sets for the same crops in the same season. Based on accurate phenological information of the main crops in spatially segregated units, the remote sensing based classification was used to map the LULC changes for a period of 2003–2013 in the Kabul River Basin (KRB) of Afghanistan. We used remotely sensed Normalized Difference Vegetation Index (NDVI) products of Moderate-resolution Imaging Spectroradiometer (MODIS) from Terra (MOD13Q1) and Aqua (MYD13Q1) with 250 m spatial resolution for this study. The overall accuracy (mean) of the LULC classification throughout the study period was around 68.15% ± 9.45while the producer and user accuracies (mean) were 75.9 ± 11.3% and 76.4 ± 11.2%, respectively. Results show that the cropping patterns vary significantly in the spatially disaggregated units. From 2003 till 2013, the ground coverage of wheat, barley and rice was increased by 31%, 7% and 32%, respectively. Overall, there has been only 2% increment in the agricultural area across the KRB between 2003 and 2013. This relatively increased trend of land cover change has taken place as a result of partial improvement in political stability as well as investment in irrigation infrastructure and agricultural development in the region. This study further provides insight to develop new agriculture strategies in order to maintain the ecosystem required to fulfil the rising food demands.     

基于MODIS数据的北京西北部地区土地覆盖分类研究

本文主要基于MODIS 16天合成的NDVI时间序列数据、8天合成 LST数据、1∶5万DEM数据以及其他辅助数据相结合,进行北京西北部地区土地覆盖分类的研究。首先选取适合于MODIS数据分类的土地覆盖分类系统,然后用PCA方法对NDVI时间序列数据进行信息增强与压缩处理,以排除各种干扰因素,提高分类精度。最后结合LST数据、DEM数据及降雨温度数据,利用?齂-均值非监督分类法,进行研究区的土地覆盖分类,经过分类后处理,得到北京西北部地区的土地覆盖分类图。分类结果表明,使用250m分辨率MODIS数据,结合本文所用方法,能够实现较大区域的土地覆盖分类,并且能达到较高的分类精度。     

基于MODIS 数据的长江三角洲地区土地覆盖分类

长江三角洲地区是我国经济最发达的地区之一, 人类活动对自然环境产生了很大影响。为了研究该地区人类活动与生态环境的相互作用, 利用250 m 分辨率MODIS 数据进行土地 覆盖制图研究, 采用的主要数据为增强型植被指数EVI 数据、反射率数据和DEM 数据。通过基于时间序列的滤波方法消除EVI 的噪声, 通过PCA 变换压缩数据量, 并计算均质度来表征空间维的纹理信息, 构造了一个综合性的分类数据矩阵, 依据高分辨率影像选取了训练区, 采用最大似然法进行分类, 并采用缓冲区分析技术进行分类修正, 得到长江三角洲地区的土地覆盖分类结果。利用高分辨率影像解译信息对分类结果进行了精度评价, 并将分类结果与 MODIS 土地覆盖产品进行了对比, 精度分析表明分类结果很好的反映了研究区的土地覆盖信息, 显示了本研究分类方法与技术处理在实践中的可行性及250 m 分辨率EVI 时间序列数据在区域尺度土地覆盖分类方面的优势与潜力。     

基于知识规则的土地利用/土地覆被分类方法——以黑河流域为例

本文提出了一种基于知识规则的土地利用/土地覆被分类的新方法。知识规则是基于专家经验建立起来的,反映研究区内不同分类系统下各类别的地理分布特性与地理分布交叉可能性。基于黑河流域90 m 分辨率DEM、2009 年逐月1 km 分辨率NDVI,参考美国地质调查局(USGS) 1 km分辨率土地利用/土地覆被数据在欧亚大陆上各类别的聚类中心,应用在上、中、下游分别建立的知识规则,以知识规则结合最近距离的USGS 类别聚类的方法,制作了一套与USGS全球土地覆被分类标准一致的、可以用于大气模式以及陆面过程模式的黑河流域土地覆被类型分布数据。本方法分类结果与以往研究采用的类别映射方法的分类结果及实际地物影像进行对比,表明知识规则下的分类结果更能准确表达流域地表覆盖特征,对冰雪、冻土类别和沙地荒漠类别的表现更优。     

Vegetation greenness modeling in response to climate change for Northeast Thailand

In Northeast Thailand, the climate change has resulted in erratic rainfall and tem- perature patterns. The region has experienced both periods of drought and seasonal floods with the increasing severity. This study investigated the seasonal variation of vegetation greenness based on the Normalized Difference Vegetation Index （NDVI） in major land cover types in the region. An assessment of the relationship between climate patterns and vegeta- tion conditions observed from NDVI was made. NDVI data were collected from year 2001 to 2009 using multi-temporal Terra MODIS Vegetation Indices Product （MOD13Q1）. NDVI pro- files were developed to measure vegetation dynamics and variation according to land cover types. Meteorological information, i.e. rainfall and temperature, for a 30 year time span from 1980 to 2009 was analyzed for their patterns. Furthermore, the data taken from the period of 2001-2009, were digitally encoded into GIS database and the spatial patterns of monthly rainfall and temperature maps were generated based on kriging technique. The results showed a decreasing trend in NDVI values for both deciduous and evergreen forests. The highest productivity and biomass were observed in dry evergreen forests and the lowest in paddy fields. Temperature was found to be increasing slightly from 1980 to 2009 while no significant trends in rainfall amounts were observed. In dry evergreen forest, NDVI was not correlated with rainfall but was significant negatively correlated with temperature. These re- sults indicated that the overall productivity in dry evergreen forest was affected by increasing temperatures. A vegetation greenness model was developed from correlations between NDVI and meteorological data using linear regression. The model could be used to observe the change in vegetation greenness and dynamics affected by temperature and rainfall.     

Explaining NDVI trends in northern Burkina Faso

Many studies have shown a ‘greening of the Sahel’ on the basis of analysis of time series of satellite images and this has shown to be, at least partly, explained by changes in rainfall. In northern Burkina Faso, an area stands out as anomalous in such analysis, since it is characterized by a distinct spatial pattern and strongly dominated by negative trends in Normalized Difference Vegetation Index (NDVI). The aim of the paper is to explain this distinct pattern. When studied over the period 2000–2012, using NDVI data from the MODIS sensor the spatial pattern of NDVI trends indicates that non-climatic factors are involved. By relating NDVI trends to landscape elements and land use change we demonstrate that NDVI trends in the north-western parts of the study area are mostly related to landscape elements, while this is not the case in the south-eastern parts, where rapidly changing land use, including. expansion of irrigation, plays a major role. It is inferred that a process of increased redistribution of fine soil material, water and vegetation from plateaus and slopes to valleys, possibly related to higher grazing pressure, may provide an explanation of the observed pattern of NDVI trends. Further work will focus on testing this hypothesis.     

基于MODIS NDVI的新疆土地覆盖类型划分与潜在荒漠化区域探测

采用2002年MODIS 1km的全年NDVI时序数据对新疆及周边地区进行了土地覆盖分类,在分类的过程中重点强调了稀疏植被覆盖区域,这些区域具有潜在荒漠化的趋势。介绍了一种针对不同土地覆盖类型并能重点突出稀疏植被的分类方法,这种方法较好地综合了季节性影响因素和多变的自然条件影响因素。从16天合成的优化过的时序NDVI图像上,通过分析物候变化,可以获得较好的分类结果。将具有潜在荒漠化趋势的区域模型化研究以后,研究结果表明新疆及周边地区40万km2的土地有潜在荒漠化的趋势。由于MODIS NDVI数据覆盖范围较大,并且对植被的生长变化有较高的敏感度,所以它可以被有效地应用于监测大尺度环境变化和荒漠化进程。     

天山山区草地变化与气候要素的时滞效应分析

通过选取新疆天山山区作为研究区,分析该地区气候参数(降水、温度、光照)对草地季节变化影响的滞后性特征.利用研究区内各气象站点的气温、降水和日照时数的逐句数据、SPOTVGT时序数据和土地利用覆盖数据,运用时滞相关分析和GIS空间分析方法.根据13个滞后期(0～12旬)和13个时间尺度(1～13旬)分析了植被NDVI与同...     

气候和土地利用变化对塔里木河干流区植被覆盖变化的影响

基于遥感和地理信息系统技术,利用1998—2008年SPOT-VEGETATION归一化植被指数（NDVI）数据对塔里木河干流区1998—2007年植被覆盖的时空变化进行了监测,并从气候变化和土地利用变化双重角度分析了植被覆盖变化的原因。研究表明,塔里木河干流区植被覆盖变化经历了两个阶段:1998—2001年植被覆盖严重退化时期;2002—2007年植被覆盖度由急剧上升到缓慢下降再到持续升高时期,NDVI明显高于20世纪末期水平。塔里木河干流区植被覆盖变化存在显著的空间差异,绿洲农业灌溉区和退耕还林还草生态恢复区的植被覆盖度显著提高,天然草地植被区的植被退化严重。塔里木河干流区植被覆盖变化是气候和土地利用变化共同作用的结果。温度对植被覆盖变化的影响表现为对植被生长年内韵律的控制和秋季植被生长期的延长,年降水量的波动式上升是导致塔里木河干流区植被覆盖变化两个阶段呈现差异的主导因素。     

Mesoscale Satellite Bioclimatology

This paper examines the strength of relationships between the Normalized Difference Vegetation Index (NDVI) and climatic data, when examined at the mesoscale. Mean monthly AVHRR NDVI data for 1988-1996 for the months of April through October for State of Kansas, its nine climatic divisions (CDs), and dominant land cover types within each CD were used. Corresponding climatic and water budget data were obtained or derived from National Climatic Data Center data. Temperature, precipitation, and NDVI deviations from normal were determined. Statistical analysis revealed significant relationships between NDVI and climatic variables, although strengths of the associations were modest. The highest correlation coefficient (r) for the state as a whole was 0.53, between NDVI and estimated actual evapotranspiration. When examined by climatic division or major land cover type, relationships between NDVI and a drought index were statistically significant in most cases and ranged from 0.30 to 0.56.     

Mesoscale Satellite Bioclimatology

This paper examines the strength of relationships between the Normalized Difference Vegetation Index (NDVI) and climatic data, when examined at the mesoscale. Mean monthly AVHRR NDVI data for 1988‐1996 for the months of April through October for State of Kansas, its nine climatic divisions (CDs), and dominant land cover types within each CD were used. Corresponding climatic and water budget data were obtained or derived from National Climatic Data Center data. Temperature, precipitation, and NDVI deviations from normal were determined. Statistical analysis revealed significant relationships between NDVI and climatic variables, although strengths of the associations were modest. The highest correlation coefficient (r) for the state as a whole was 0.53, between NDVI and estimated actual evapotranspiration. When examined by climatic division or major land cover type, relationships between NDVI and a drought index were statistically significant in most cases and ranged from 0.30 to 0.56.     

基于MODIS和TRMM数据的黄土高原农业干旱监测

农业干旱对农业生产影响最为严重,基于站点观测数据的干旱指数不能准确监测区域尺度的农业干旱特征。因此,利用2003—2015年MODIS地表温度(LST)、植被指数(NDVI)和TRMM降水(3B43)数据以及1960—2015年黄土高原地区及周边92个气象站点的月均温和月降水量数据,构建了综合遥感干旱监测模型规模干旱条件指数(Scale Drought Condition Index,SDCI),对黄土高原地区农用地生长季(4～10月)旱情的时空分布特征进行研究,结果表明:黄土高原地区农用地生长季多年平均干旱状态为中度干旱,干旱程度在空间上表现为西北部较严重,东南部较轻。2003—2015年黄土高原地区旱情年际变化总体呈波动减轻趋势,2003—2007年旱情越来越严重,2007—2014年旱情波动减轻,2014—2015年旱情有所加重。黄土高原地区旱情年内变化表现4～8月持续减轻,8～10月持续加重,干旱程度具体表现为4月、5月、6月和10月呈中度干旱,7月、8月和9月呈轻度干旱。研究表明利用多源遥感数据构建的具有适当权重的SDCI可以有效监测黄土高原地区作物生长季的干旱状况。     

基于MODIS的珠江三角洲地区区域热岛的分布特征

通过MODIS地温数据,研究了珠三角地区由于快速的城市化过程造成的区域性大范围温度升高现象,即"区域"热岛现象。分析结果表明,MODIS数据能够较好地反映出区域城市化进程中区域地表热环境的变化。不同下垫面的温度差异是形成区域热岛的基础。在大规模连片的城市化过程中,城镇用地的周边区域受到温度升高的影响,地表温度也相应升高,从而造成了区域大面积的温度升高,形成了区域热岛。从空间形态看,区域热岛的空间格局与城镇用地的空间布局具有较高的相关性,大城市或城市连绵区往往是区域热岛的中心。城市连绵区及其周边区域的热岛现象十分明显,而位于研究区的西南和东北方位的城镇分布比较分散,对应的区域热岛现象并不显著。     

基于SPOT VEGETATION数据的中国西北植被覆盖变化分析

 基于遥感和地理信息系统的技术,利用SPOT-VEGETATION NDVI(Normalized Difference Vegetation Index)数据对我国西部地区植被覆盖的情况进行了动态监测。采用MVC（Maximum Value Composites）、一元线性回归趋势分析和变化幅度百分比等方法分析西部地区植被变化特征,并结合西北五省土地利用类型图,分析不同植被类型的年最大化NDVI(MNDVI)变化趋势及特点。其结果是:近7 a来植被覆盖存在普遍退化的趋势,且2000-2001与2001-2002年度的变化幅度较大。在局部区域植被有改善的趋势, 但总的改善幅度小于退化幅度。分析结果表明, 植被改善的区域主要分布在陕西和宁夏的大部分地区以及新疆西北部和西南部地区。大部分地区植被退化。而且不同植被的MNDVI在相同的年份表现出相似的变化特点和趋势。     

Albedo-NDVI特征空间及沙漠化遥感监测指数研究

利用遥感数据和野外调查数据分析了沙漠化与地表定量参数之间的关系,提出了Albedo-NDVI特征空间的概念以及基于Albedo-NDVI特征空间的沙漠化遥感监测模型,即沙漠化遥感监测差值指数模型(DDI)。这个模型充分利用了多维遥感信息,指标反映了沙漠化土地地表覆盖、水热组合及其变化,具有明确的生物物理意义。而且指标简单、易于获取,有利于沙漠化的定量分析与监测。     

基于阈值分割的黑龙江省森林类型遥感识别

全球变化背景下,准确获取森林覆盖是监测森林资源动态、实现林业可持续发展的重要基础。为将省级尺度森林资源清查面积资料空间化,以黑龙江省为例,利用1999-2003年该省森林资源清查面积数据,结合2000年500 m分辨率的MODIS数据,构建了基于阈值分割的森林类型遥感识别方法。该方法利用不同地表覆被类型归一化植被指数时间序列的季节分异特征,以森林资源清查面积为标准,设定森林类型的划分阈值,识别了黑龙江省森林类型的空间分布。最后,基于分层随机抽样和精度评价方法,表明森林类型识别结果与地面参考数据具有较高的一致性,总体分类精度为78.1%;特别是季节特征明显的落叶林,精度可达80%以上。本文所构建的方法可将森林清查统计数据进行准确的空间定位,同时结合多期森林资源连续清查资料和遥感信息,可为识别并量化区域生态系统生物量和碳库变化等提供科技支撑。     

Mapping rubber tree growth in mainland Southeast Asia using time-series MODIS 250 m NDVI and statistical data

Expanding global and regional markets are driving the conversion of traditional subsistence agricultural and occupied non-agricultural lands to commercial-agricultural purposes. In many parts of mainland Southeast Asia rubber plantations are expanding rapidly into areas where the crop was not historically found. Over the last several decades more than one million hectares of land have been converted to rubber trees in areas of China, Laos, Thailand, Vietnam, Cambodia and Myanmar, where rubber trees were not traditionally grown. This expansion of rubber plantations has replaced ecologically important secondary forests and traditionally managed swidden fields and influenced local energy, water and carbon fluxes. Accurate and up-to-date monitoring and mapping of rubber tree growth is critical to understanding the implications of this changing ecosystem. Discriminating rubber trees from second-growth forests and fallow land has proven challenging. Previous experiments using machine-learning approaches with hard classifications on remotely sensed data, when faced with the realities of a heterogeneous plant-life mixture and high intra-class variance, have tended to overestimate the areas of rubber tree growth. Our current research sought to: 1) to investigate the potential of using a Mahalanobis typicality model to deal with mixed pixels; and 2) to explore the potential for combining MOderate Resolution Imaging Spectroradiometer (MODIS) imagery with sub-national statistical data on rubber tree areas to map the distribution of rubber tree growth across this mainland Southeast Asia landscape. Our study used time-series MODIS Terra 16-day composite 250 m Normalized Difference Vegetation Index (NDVI) products (MOD13Q1) acquired between March 2009 and May 2010. We used the Mahalanobis typicality method to identify pixels where rubber tree growth had the highest probability of occurring and sub-national statistical data on rubber tree growth to quantify the number of pixels of rubber tree growth mapped per administrative unit. We used Relative Operating Characteristic (ROC) and error matrix analysis, respectively, to assess the viability of Mahalanobis typicalities and to validate classification accuracy. High ROC values, over 0.8, were achieved with the Mahalanobis typicality images of both mature and young rubber trees. The proposed method greatly reduced the commission errors for the two types of rubber tree growth to 1.9% and 2.8%, respectively (corresponding to user’s accuracies of 98.1% and 97.2%, respectively). Results indicate that integrating Mahalanobis typicalities with MODIS time-series NDVI data and sub-national statistics can successfully overcome the earlier overestimation problem.     

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.     

黄河流域植被覆盖度动态变化与降水的关系

利用8km分辨率Pathfinder NOAA-NDVI数据，对黄河流域1982－1999年地表植被覆盖的空间分布及时间序列变化进行了分析，并通过计算不同时段降水量与年最大NDVI之间的相关系数分析了降水对流域植被覆盖的影响。结果发现近20年来黄河流域平均植被覆盖度有增加趋势，但青藏高原上有所减小；汛期降水量的多少对地表植被覆盖度的年际变化起主要作用，其中草原地区影响最显著，而在森林植被区及部分灌溉农作区，降水的年际变化对地表覆盖的影响比较小。     

冀北地区植被指数变化特征及影响因素分析

利用8km分辨率的NOVV/AVHRR NDV I数据、土地利用/覆被变化TM影像解译数据、气候数据、DEM数据和经济统计数据,对冀北地区1987~2000年植被覆被变化特征及影响因素进行了分析,得出主要结论如下:冀北地区土地覆被变化下NDVI平均增加值为0.35,变化特征为集中连片。土地利用变化导致NDVI平均减小值为0.17,变化特征呈斑块状离散分布。土地利用类型变化、错季蔬菜生产等人为因素对土地覆被变化下NDVI变化贡献率较低,降水、特别是生长季降水分布特征是影响NDVI变化的重要因素。