Mapping land cover gradients through analysis of hyper-temporal NDVI imagery

The green cover of the earth exhibits various spatial gradients that represent gradual changes in space of vegetation density and/or in species composition. To date, land cover mapping methods differentiate at best, mapping units with different cover densities and/or species compositions, but typically fail to express such differences as gradients. Present interpretation techniques still make insufficient use of freely available spatial-temporal Earth Observation (EO) data that allow detection of existing land cover gradients. This study explores the use of hyper-temporal NDVI imagery to detect and delineate land cover gradients analyzing the temporal behavior of NDVI values. MODIS-Terra MVC-images (250 m, 16-day) of Crete, Greece, from February 2000 to July 2009 are used. The analysis approach uses an ISODATA unsupervised classification in combination with a Hierarchical Clustering Analysis (HCA). Clustering of class-specific temporal NDVI profiles through HCA resulted in the identification of gradients in landcover vegetation growth patterns. The detected gradients were arranged in a relational diagram, and mapped. Three groups of NDVI-classes were evaluated by correlating their class-specific annual average NDVI values with the field data (tree, shrub, grass, bare soil, stone, litter fraction covers). Multiple regression analysis showed that within each NDVI group, the fraction cover data were linearly related with the NDVI data, while NDVI groups were significantly different with respect to tree cover (adj. R² = 0.96), shrub cover (adj. R² = 0.83), grass cover (adj. R² = 0.71), bare soil (adj. R² = 0.88), stone cover (adj. R² = 0.83) and litter cover (adj. R² = 0.69) fractions. Similarly, the mean Sorenson dissimilarity values were found high and significant at confidence interval of 95% in all pairs of three NDVI groups. The study demonstrates that hyper-temporal NDVI imagery can successfully detect and map land cover gradients. The results may improve land cover assessment and aid in agricultural and ecological studies.     

Vegetation mapping with multitemporal NDVI in North Eastern China Transect (NECT)

Temporal changes in the normalized difference vegetation index (NDVI) have been widely used in vegetation mapping due to the usefulness of NDVI data in distinguishing characteristic seasonal differences in the phenology of greenness of vegetation cover. Research has also shown that NDVI provides potential to derive meaningful metrics that describe ecosystem functions. In this paper, we have applied both unsupervised “k-means” classification and supervised minimum distance classification as derived from temporal changes in NDVI measured in 1997 along the North Eastern China Transect (NECT), and we have also utilized the same two classification methods together with NDVI-derived metrics, namely maximum NDVI, mean NDVI, NDVI amplitude, NDVI threshold, total length of growing season, fraction of growing season during greenup, rate of greenup, rate of senescence, integrated NDVI during the growing season, and integrated NDVI during greenup/integrated NDVI during senescence to map vegetation. The main objectives of this study are: (1) to test the relative performance of NDVI temporal profile metrics and NDVI-derived metrics for vegetation cover discrimination in NECT; (2) to test the relative performance of unsupervised (k-means) and supervised (minimum distance) methods for vegetation mapping; (3) to test the accuracy of the IGBP-DIS released land cover map for NECT; (4) to provide an up-to-date vegetation map for NECT. The results suggest that the classifications based on NDVI temporal profile metrics have higher accuracies than those based on any other metrics, such as NDVI-derived metrics, or all (NDVI temporal profile metrics + NDVI-derived metrics), or 15 metrics (NDVI temporal profile + Rate of greenup, Rate of senescence, and Integrated NDVI in greenup/integrated NDVI in senescence) for both methods. And among them, unsupervised k-means classification had the highest overall accuracy of 52% and Kappa coefficient of 0.2057. Both unsupervised (k-means) and supervised (minimum distance) methods achieved similar accuracies for the same metrics. The accuracy of IGBP-DIS released land cover map had an overall accuracy of 37% and a Kappa coefficient is 0.1441, and can improve to 46% by decomposing the crop/natural vegetation mosaic to cropland and other natural vegetation types. The results support using unsupervised k-means classification based on NDVI temporal profile metrics to provide an up-to-date vegetation cover classification. However, new effort is necessary in the future in order to improve the overall performance on this issue.     

Evaluating the Performance of Multi-Class and Single-Class Classification Approaches for Mountain Agriculture Extraction Using Time-Series NDVI

Supervised multi-class classification (MCC) approach is widely being used for regional-level land use–land cover (LULC) mapping and monitoring. However, it becomes inefficient if the end user wants to map only one particular class. Therefore, an improved single-class classification (SCC) approach is required for quick and reliable map production purpose. In this regard, the current study attempts to evaluate the performance of MCC and SCC approaches for extracting mountain agriculture area using time-series normalized differential vegetation index (NDVI). At first, samples of eight LULC classes were acquired using Google Earth image, and corresponding temporal signatures (TS) were extracted from time-series NDVI to perform classification using minimum distance to mean (MDM) and spectral angle mapper (i.e., multi-class SAM—MCSAM) under MCC approach. Secondly, under SCC approach, the TS of three agriculture classes (i.e., agriculture, mixed agriculture and plantation) were utilized as a reference to extract agriculture extent using Euclidean distance (ED) and SAM (i.e., single-class SAM—SCSAM) algorithms. The area of all four maps (i.e., MDM—19.77% of total geographical area (TGA), MCSAM—21.07% of TGA, ED—15.23% of TGA, SCSAM—13.85% of TGA) was compared with reference agriculture area (14.54% of TGA) of global land cover product, and SCC-based maps were found to have close agreement. Also, the class-wise detection accuracy was evaluated using random sample point-based error matrix which reveals the better performance of ED-based map than rest three maps in terms of overall accuracy and kappa coefficient.     

Mapping the Land Cover of Mexico Using AVHRR Time‐Series Data Sets

Abstract

An important methodological and analytical requirement for analyzing spatial relationships between regional habitats and species distributions in Mexico is the development of standard methods for mapping the country's land cover/land use formations. This necessarily involves the use of global data such as that produced by the Advanced Very High Resolution Radiometer (AVHRR). We created a nine‐band time‐series composite image from AVHRR Normalized Difference Vegetation Index (NDVI) bi‐weekly data. Each band represented the maximum NDVI for a particular month of either 1992 or 1993. We carried out a supervised classification approach, using the latest comprehensive land cover/vegetation map created by the Mexican National Institute of Geography (INEGI) as reference data. Training areas for 26 land cover/vegetation types were selected and digitized on the computer's screen by overlaying the INEGI vector coverage on the NDVI image. To obtain specific spectral responses for each vegetation type, as determined by its characteristic phenology and geographic location, the statistics of the spectral signatures were subjected to a cluster analysis. A total of 104 classes distributed among the 26 land cover types were used to perform the classification. Elevation data were used to direct classification output for pine‐oak and coastal vegetation types. The overall correspondence value of the classification proposed in this paper was 54%; however, for main vegetation formations correspondence values were higher (60‐80%). In order to obtain refinements in the proposed classification we recommend further analysis of the signature statistics and adding topographic data into the classification algorithm.     

Timing of erosion and satellite data: A multi-resolution approach to soil erosion risk mapping

Erosion reduces soil productivity and causes negative downstream impacts. Erosion processes occur on areas with erodible soils and sloping terrain when high-intensity rainfall coincides with limited vegetation cover. Timing of erosion events has implications on the selection of satellite imagery, used to describe spatial patterns of protective vegetation cover. This study proposes a method for erosion risk mapping with multi-temporal and multi-resolution satellite data. The specific objectives of the study are: (1) to determine when during the year erosion risk is highest using coarse-resolution data, and (2) to assess the optimal timing of available medium-resolution images to spatially represent vegetation cover during the high erosion risk period. Analyses were performed for a 100-km² pasture area in the Brazilian Cerrados. The first objective was studied by qualitatively comparing three-hourly TRMM rainfall estimates with MODIS NDVI time series for one full year (August 2002–August 2003). November and December were identified as the months with highest erosion risk. The second objective was examined with a time series of six available ASTER images acquired in the same year. Persistent cloud cover limited image acquisition during high erosion risk periods. For each ASTER image the NDVI was calculated and classified into five equally sized classes. Low NDVI was related to high erosion risk and vice versa. A DEM was used to set approximately flat zones to very low erosion risk. The six resulting risk maps were compared with erosion features, visually interpreted from a fine-resolution QuickBird image. Results from the October ASTER image gave highest accuracy (84%), showing that erosion risk mapping in the Brazilian Cerrados can best be performed with images acquired shortly before the first erosion events. The presented approach that uses coarse-resolution temporal data for determining erosion periods and medium-resolution data for effective erosion risk mapping is fast and straightforward. It shows good potential for successful application in other areas with high spatial and temporal variability of vegetation cover.     

基于MODIS NDVI的科尔沁沙地荒漠化动态监测

以MODIS植被指数产品为数据源,利用归一化植被指数(NDVI)与植被盖度的高相关性,以NDVI为荒漠化评价的定量依据进行荒漠化程度划分,得出科尔沁沙地2000年与2007年的NDVI分级分布图,并分别统计出相应的不同程度荒漠化土地面积所占比例; 最后,利用不同等级荒漠化土地面积所占比例和NDVI分级分布图所提供的空间分配状况来评价研究区荒漠化程度及动态变化状况.研究结果表明,2000年至2007年科尔沁沙地荒漠化程度总体上呈减少趋势.     

Frequency Histogram Legend in the Choropleth Map: A Substitute to Traditional Legends

This article presents the use of the frequency histogram legend (FHL) as a substitute to traditional legends in both classed and unclassed choropleth maps. Great variation in the size of mapping units can hinder readers' ability to comprehend statistical distributions from a choropleth map. Replacing conventional legends with FHL can aid readers in their understanding of spatial as well as statistical distributions of the mapped data simultaneously. A customized mapping application was designed in ArcInfo 9.0 to test the use of FHL in both classed and unclassed choropleth maps. Frequency histogram legends were tested on different types of statistical distributions. Although the comparison of the results shows that the FHL works best for a Gaussian or close to a Gaussian distribution for eight or fewer classes, the customized application permits users to generate choropleth maps with frequency histogram legends for any type of statistical distribution with any number of classes. The analysis reveals that readers' background in statistics helped them to effectively utilize and interpret frequency histogram legends in the choropleth maps.     

Fusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for central Great Basin rangelands,USA

Data fused from distinct but complementary satellite sensors mitigate tradeoffs that researchers make when selecting between spatial and temporal resolutions of remotely sensed data. We integrated data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra satellite and the Operational Land Imager sensor aboard the Landsat 8 satellite into four regression-tree models and applied those data to a mapping application. This application produced downscaled maps that utilize the 30-m spatial resolution of Landsat in conjunction with daily acquisitions of MODIS normalized difference vegetation index (NDVI) that are composited and temporally smoothed. We produced four weekly, atmospherically corrected, and nearly cloud-free, downscaled 30-m synthetic MODIS NDVI predictions (maps) built from these models. Model results were strong with R² values ranging from 0.74 to 0.85. The correlation coefficients (r ≥ 0.89) were strong for all predictions when compared to corresponding original MODIS NDVI data. Downscaled products incorporated into independently developed sagebrush ecosystem models yielded mixed results. The visual quality of the downscaled 30-m synthetic MODIS NDVI predictions were remarkable when compared to the original 250-m MODIS NDVI. These 30-m maps improve knowledge of dynamic rangeland seasonal processes in the central Great Basin, United States, and provide land managers improved resource maps.     

基于Aster数据的新疆渭——库绿洲及其周边地区土地沙化信息提取

以新疆渭干河——库车河绿洲及其周边地区为研究区,在野外调查的基础上,基于Aster数据,利用NDVI、植被盖度作为特征变量,结合偏最小二乘回归法模型反演得到的盐分含量(SSC)指标作为决策树分类的各节点的判别函数,通过决策树分类方法实现了沙化土地信息的提取与制图。结果表明结合植被覆盖信息与土壤特性能够在提取沙化信息的同时区分出盐渍化土壤,结果与野外调查较为一致。该研究为大区域土壤沙化信息提取与制图提供了较好的方法。     

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.     

Disaggregating and mapping crop statistics using hypertemporal remote sensing

Governments compile their agricultural statistics in tabular form by administrative area, which gives no clue to the exact locations where specific crops are actually grown. Such data are poorly suited for early warning and assessment of crop production. 10-Daily satellite image time series of Andalucia, Spain, acquired since 1998 by the SPOT Vegetation Instrument in combination with reported crop area statistics were used to produce the required crop maps. Firstly, the 10-daily (1998–2006) 1-km resolution SPOT-Vegetation NDVI-images were used to stratify the study area in 45 map units through an iterative unsupervised classification process. Each unit represents an NDVI-profile showing changes in vegetation greenness over time which is assumed to relate to the types of land cover and land use present. Secondly, the areas of NDVI-units and the reported cropped areas by municipality were used to disaggregate the crop statistics. Adjusted R-squares were 98.8% for rainfed wheat, 97.5% for rainfed sunflower, and 76.5% for barley. Relating statistical data on areas cropped by municipality with the NDVI-based unit map showed that the selected crops were significantly related to specific NDVI-based map units. Other NDVI-profiles did not relate to the studied crops and represented other types of land use or land cover. The results were validated by using primary field data. These data were collected by the Spanish government from 2001 to 2005 through grid sampling within agricultural areas; each grid (block) contains three 700 m × 700 m segments. The validation showed 68%, 31% and 23% variability explained (adjusted R-squares) between the three produced maps and the thousands of segment data. Mainly variability within the delineated NDVI-units caused relatively low values; the units are internally heterogeneous. Variability between units is properly captured. The maps must accordingly be considered “small scale maps”. These maps can be used to monitor crop performance of specific cropped areas because of using hypertemporal images. Early warning thus becomes more location and crop specific because of using hypertemporal remote sensing.     

应用气象卫星NOAA-AVHRR图像进行植被研究的多时相途径

本文讨论了应用NOAA-AVHRR数字图像研究植被季相变化和进行植被分类的多时相方法。对生长季节的各时相植被指数VI图像进行图像分割,可揭示植被绿锋的进退和季相变化及其在空间上的表现。对多时相的植被指数VI图像进行主成分变换,据第一主成分的直方图各特征峰的形状和位置进行图像分割,可获得植被景观图,这一结果与现行的植被分布图相当一致。     

A methodology for semi detailed soil surveys using medium scale aerial photographs

A methodology for the preparation of semi detailed soil maps using medium scale aerial photographs for an area of about 3600 ha in Merida area, Spain is presented. The new concepts such as ‘Basic Land Units’, ‘Soil Consociation’ and ‘Soil Set’ developed by Elbersen (1976) were adopted for this study to see their utility for the preparation of semidetailed soil maps which can be used for land evaluation, land classification and also for making prodictions about the feasibility of a particular project for rural development plannning purposes. Basic land units and their subdivisions like major and minor compo-nents were used for the delineation of interpretation units. Mapping units, viz, Soil Consoication, Soil Complex and miscellaneous land type were used for mapping soils. Soils were classified upto family level and shown as subgroups in the 1:50,000 scale soil map. Soils were mapped as soil sets per basic land unit per subgroup. A model legend for use in the preparation of seimdetailed physiographic cum soil maps is given which is in terms of physiography and Soil Taxonomy qualified by soil sets.     

Mapping Large Spatial Flow Data with Hierarchical Clustering

It is challenging to map large spatial flow data due to the problem of occlusion and cluttered display, where hundreds of thousands of flows overlap and intersect each other. Existing flow mapping approaches often aggregate flows using predetermined high‐level geographic units (e.g. states) or bundling partial flow lines that are close in space, both of which cause a significant loss or distortion of information and may miss major patterns. In this research, we developed a flow clustering method that extracts clusters of similar flows to avoid the cluttering problem, reveal abstracted flow patterns, and meanwhile preserves data resolution as much as possible. Specifically, our method extends the traditional hierarchical clustering method to aggregate and map large flow data. The new method considers both origins and destinations in determining the similarity of two flows, which ensures that a flow cluster represents flows from similar origins to similar destinations and thus minimizes information loss during aggregation. With the spatial index and search algorithm, the new method is scalable to large flow data sets. As a hierarchical method, it generalizes flows to different hierarchical levels and has the potential to support multi‐resolution flow mapping. Different distance definitions can be incorporated to adapt to uneven spatial distribution of flows and detect flow clusters of different densities. To assess the quality and fidelity of flow clusters and flow maps, we carry out a case study to analyze a data set of 243,850 taxi trips within an urban area.     

Choosing Geographic Units for Choropleth Rate Maps,with an Emphasis on Public Health Applications

Choropleth maps are the most widely used map type for mapping rates, such as those involving disease, crime, and socioeconomic indicators. The essential step of choosing a geographic unit to map is often made in an ad hoc manner. Among the desirable characteristics of choropleth mapping units are high degree of resolution, homogeneity of population size, homogeneity of land area, observation of minimum population thresholds and land area thresholds, temporal stability and currency, compactness of shape, audience familiarity, data availability, and the functional relevance of the unit to the phenomena mapped. Because of the uneven distribution of human populations, no single geographic unit can meet all of these characteristics in practice, and a well designed choropleth map necessarily involves some compromise. We present guidelines for choosing geographic units that take into account the above criteria, considering 12 geographic units ranging from census blocks to states. Even allowing for differences in scale and purpose, some units confer clear advantages over others.     

A Comparative Analysis of Phenological Curves for Major Crops in Kansas

The goal of this research was to conduct an initial investigation into whether a time-series NDVI reference curve library for crops over a growing season for one year could be used to map crops for a different year. Time-series NDVI libraries of curves for 2001 and 2005 were investigated to ascertain whether or not the 2001 dataset could be used to map crops for 2005. The 2005 16-day composite MODIS 250 m NDVI data were used to extract NDVI values from 1,615 field sites representing alfalfa, corn, sorghum, soybeans, and winter wheat. A k-means cluster analysis of NDVI values from the field sites was performed to identify validation sites with time-series NDVI spectral profiles characteristic of the major crop types grown in Kansas. After completing the field site refinement process, there were 1,254 field sites retained for further analysis, referred to as "final" field sites. The methods employed to evaluate whether the MODIS-based NDVI profiles for major crops in Kansas are stable from year-to-year involved both graphical and statistical analyses. First, the time-series NDVI values for 2005 from the final field sites were aggregated by crop type and the crop NDVI profiles were then visually assessed and compared to the profiles of 2001 to ascertain if each crop's unique phenological pattern was consistent between the two years. Second, separability within each crop class in the time-series NDVI data between 2001 and 2005 was investigated numerically using the Jeffries-Matusita (JM) distance statistic. The results seem to suggest that time-series NDVI response curves for crops over a growing period for one year of valid ground reference data may be useful for mapping crops for a different year when minor temporal shifts in the NDVI values (resulting from inter-annual climate variations or changes in agricultural management practices) are taken into account.     

Automatic mapping and innovative on-demand mapping services at IGN France

Mapmaking has become widespread through the Internet, resulting in a wide range of cartographic quality. To achieve better quality, mapmaking needs tools and online services for intuitive and efficient on-demand mapping. A project team at IGN, the French National Mapping Agency, is working on producing a digital cartographic model (DCM) from various existing databases and maps on which such tools and services are based. This DCM ranges from detailed topographic maps to small general road maps. GeoServer Web Map Service capabilities were used extensively to produce quality maps with various legends. Special care was taken to make a default legend suitable for customer data overlays, both on-screen and on paper. Web-based interface prototypes were built to guide users in choosing colors and creating their own original map legends. Users can also rely on a growing catalog of harmonious color palettes and map samples as sources of inspiration.     

土壤调查地区级汇总的遥感制图试验研究

本研究的目的是探讨利用遥感影像数据,对县级土壤调查资料进行地区级汇总的制图工作方法。我们依据的制图综合原则是:(1)以现有的土壤图为基础,充分尊重原图上的界线,主要以土层界线重新综合;(2)研究并搞清各种土壤分布规律和它们之间的组合规律,然后进行归并和区分;(3)参照卫星影像特征和判读标志,对某些明显与实地不符之处,以影像和实地调查资料进行修正,有些土壤类型以组合形式表达。本文对利用遥感数据进行制图综合的工作方法进行了详细描述,其中包括:制图单元的综合和比例尺的改变,底图的制作和利用卫星影像对综合后的土壤图进行编制等。研究结果表明,此种方法与常规方法比较,特别是对有一定物质和技术条件的单位(如省级),不仅在土壤调查中,而且在资源调查中皆可采用此种编图技术,收到节省人力、物力和时间的效果。     

基于NDVI的植被覆盖度变化的研究与分析——以河北省张家口市为例

为分析河北省张家口市在经过三北防护林三期建设后林地覆盖度变化情况,通过利用张家口2006,2010年两景同期TM影像数据,使用ERDAS软件首先提取植被指数(NDVI),根据像元二分法利用ERDAS的建模工具Spatial Modeler计算出该地区植被覆盖度,利用非监督分类方法对植被覆盖度进行分类、赋色,最后得出张家口市2006—2010年的植被覆盖度分类图,结果表明四年间该市植被覆盖面积增加698.44 km2,与第二次国家林业调查数据基本相符,说明利用遥感反演的方法能够快速、准确地获取该地区的植被覆盖度信息,以及利用NDVI监测植被覆盖度变化方法的可行性。