Landsat TM spectral information to enhance the land cover of Scotland 1988 dataset

The Scottish Office's Land Cover of Scotland 1988 Survey (LCS88), was announced in May 1987 and was intended to provide the first-ever detailed census of land cover in Scotland. It came about as a result of increasing concern about the nature and rate of land use change in rural Scotland and the need to obtain objective baseline information on which to build and evaluate future countryside policy. One of the recommendations of a Scottish Office feasibility study carried out prior to the LCS88 survey, was that satellite remotely-sensed data should be considered for measuring landscape change in the future. This paper relates specifically to this recommendation and presents the results of an evaluation study to investigate the use of limited acquisition satellite imagery from Landsat Thematic Mapper, to derive a land cover classification and spectral segmentation information to enhance the existing LCS88 dataset. Although a successful land cover, primary as well as some individual cover features, was obtained from the satellite data, the overall accuracy comparison with the LCS88 cover features was limited. However, the opportunistic mapping of important agricultural crops and primary cover types, such as oilseed rape and forestry cover features, or the interpretation of some of the considerable confusion between semi-natural grassland and improved grassland cover features, provided for an enhanced LCS88 dataset. This was also true for the illustration of the considerable potential of a satellite classification and spectral data, for identifying the component parts of LCS88 Mosaic cover features and estimating vegetation quality.     

Analyses of moisture parameters and biomass of vegetation cover in southeast Moravia

The paper describes the analyses of moisture parameters and biomass of vegetation cover. These include a relative moisture classification, a relative biomass classification, and the actual aboveground biomass estimate. All analyses were carried out by applying spectral indices and fuzzy classification for assessment. The satellite imagery from the Thematic Mapper (TM) (Landsat 5) and the Enhanced Thematic Mapper Plus (ETM+) (Landsat 7) was used as input data. Biomass indices derived from the satellite imagery were correlated with table data of average crop yields in 2007. Since the imagery used was from three periods of time (May 2001, July 2007, August 2000), it could have been assessed in terms of both time dependence and phenological phases. The monitored variables during the year were the relative moisture and vegetation biomass. The territory of interest was the Trkmanka River basin in southeast Moravia. Time dependence was obtained with the results of classifications in terms of varying phenological phases as well as dependence between the two characteristics. The data from the comparison of May and June revealed primarily their increase (a decrease occurred only on arable land), whereas those from the comparison of July and August revealed mainly their decrease. The two characteristics show almost linear mutual dependence except for some forest land. We processed map outputs showing the spectral indices used, changes in relative moisture and relative biomass, the actual biomass estimate, and a deviation from a linear dependence of the two characteristics under examination. The selected indices were processed into Geoscientific Model Development (GMD) – models that can be repeatedly run in Model Maker in Earth Resources Data Analysis System (ERDAS) IMAGINE. Together with the models of spectral indices, these were optimized for the sensor TM and ETM+.     

A comparison of local variance,fractal dimension,and Moran's I as aids to multispectral image classification

The accuracy of traditional multispectral maximum‐likelihood image classification is limited by the multi‐modal statistical distributions of digital numbers from the complex, heterogenous mixture of land cover types in urban areas. This work examines the utility of local variance, fractal dimension and Moran's I index of spatial autocorrelation in segmenting multispectral satellite imagery with the goal of improving urban land cover classification accuracy. Tools available in the ERDAS Imagine^TM software package and the Image Characterization and Modeling System (ICAMS) were used to analyse Landsat ETM?+ imagery of Atlanta, Georgia. Images were created from the ETM?+ panchromatic band using the three texture indices. These texture images were added to the stack of multispectral bands and classified using a supervised, maximum likelihood technique. Although each texture band improved the classification accuracy over a multispectral only effort, the addition of fractal dimension measures is particularly effective at resolving land cover classes within urbanized areas, as compared to per‐pixel spectral classification techniques.     

ENVISAT multisensor data for fire monitoring and impact assessment

The European ENVISAT satellite provides both optical and radar measurements of the Earth's surface. In this Letter, three ENVISAT instruments were used to investigate the extent and impact of the forest and peatland fires that devastated large areas in Central Kalimantan, Indonesia in 2002. Reduced spatial resolution MERIS imagery was used to identify simple land cover features and smoke plumes. Fire hotspots were detected by band 3.7?µm of Advanced Along Track Scanning Radiometer (AATSR) night-time acquisitions, and burnt areas were detected by Advanced Synthetic Aperture Radar (ASAR) wide swath radar imagery acquired before and after the fire event. The capability of ENVISAT to acquire data from different sensors simultaneously or within a short period of time greatly enhances the possibilities to monitor fire occurrence and assess fire impact.     

Maximum likelihood classification combined with spectral angle mapper algorithm for high resolution satellite imagery

The generation of precise land cover classification maps is an important application of high resolution satellite multispectral imagery. In this study, Spectral Angle Mapper algorithm (SAM) was used to extract the spectral characteristics from multispectral imagery. The spectral angle between neighbouring pixels was calculated. The distribution of spectral characteristics was derived from the average and variance of the calculated spectral angle in a 3×3 window of the image. The extracted spectral characteristics were combined with original multispectral imagery, and the data were classified by the maximum likelihood method. This approach was applied to Quickbird multispectral imagery. The extracted spectral characteristics highlighted boundaries between different types of land cover. The method proposed in this study exhibits an increase in overall classification accuracy relative to the original maximum likelihood method.     

Comparison and integration of spaceborne optical and radar data for mapping in Sudan

The purpose of this study was to determine how different procedures and data, such as multiple wavelengths of radar imagery and radar texture measures, independently and in combination with optical imagery influence land-cover/use classification accuracies for a study site in Sudan. Radarsat-2 C-band and phased array L-band synthetic aperture radar (PALSAR) L-band quad-polarized radar were registered with ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) optical data. Spectral signatures were obtained for multiple landscape features, classified using a maximum-likelihood decision rule, and thematic accuracies were obtained using separate validation data. There were surprising differences between the thematic accuracies of the two radar data sets, with Radarsat-2 only having a 51% accuracy and PALSAR 73%. In contrast, the optical ASTER overall accuracy was 81%. Combining the original radar and a variance texture measure increased the Radarsat-2 to 78% and PALSAR to 80%, whereas the two original radar bands together had an accuracy of 87%. Sensor fusion of optical and radar obtained an accuracy of 93%. Based on these results, the use of multiwavelength quad-polarized radar imagery combined or integrated with optical imagery has great potential in improving the accuracy of land-cover/use classifications. In tropical and high-latitude regions of the world, where persistent cloud cover hinders the use of optical satellite systems, land management programmes may find this research promising.     

Mapping agricultural land use using retrospective ground referenced data,satellite sensor imagery and GIS

A postal survey, using questionnaires, has been used to collect retrospective land cover information for comparison with Landsat TM imagery. The questionnaires targeted selected farms in Warwickshire, UK based on spectral data from an image produced by an unsupervised classification of TM bands 2, 4 and 5. The information from the questionnaires was used as 'training data' in a supervised classification of the imagery and as 'testing data' for the assessment of classification accuracy. The analysis was performed using IDRISI, a raster based Geographical Information System (GIS). The overall accuracy of the classified image was 87%. Individual class accuracy ranged from 80% for oilseed rape to 94% for water. The Kappa coefficient for the classified image was 86.5%. The total area and percentage occupied by each class on the classified image was calculated. Comparisons with independent ground survey data indicated that difference in terms of area percentage coverage ranged from 0.45% for wheat to 1.49% for grassland. The methodology is workable for obtaining and using compatible ground referenced data with imagery taken in the recent past.     

Effect of inter-image spectral variation on land cover separability in heterogeneous areas

Supervised classification is a popular approach for deriving land cover data from satellite imagery, but the collection of suitable training data of large areas is expensive. Signature extension has been proposed as a method of limiting the number of training areas. Signature extension is particularly difficult in large, heterogeneous areas where the spectral characteristics of land cover classes are highly variable.

The quantification of spectral separability can be used to determine to what extent a set of training areas collected in a small area can be extended to classify a larger area. This article investigates the changes in spectral separability of land cover classes in an increasing geographical area. A highly heterogeneous study area, containing nine different vegetation biomes, was chosen. Separability analyses were carried out on four Landsat-8 scenes that were sequentially mosaicked. The effect of multi-seasonal imagery on separability was also investigated. The results show that the mean spectral separability did not change when the geographical area was increased. We conclude that supervised classification with a small subset of training data should be possible in the chosen study area, since there is high separability between the classes. Some classes, however, require multi-temporal imagery as input.     

A comparison of TerraSAR-X Quadpol backscattering with RapidEye multispectral vegetation indices over rice fields in the Mekong Delta,Vietnam

Satellite-based multispectral imagery and/or synthetic aperture radar (SAR) data have been widely used for vegetation characterization, plant physiological parameter estimation, crop monitoring or even yield prediction. However, the potential use of satellite-based X-band SAR data for these purposes is not fully understood. A new generation of X-band radar satellite sensors offers high spatial resolution images with different polarizations and, therefore, constitutes a valuable information source. In this study, we utilized a TerraSAR-X satellite scene recorded during a short experimental phase when the sensor was running in full polarimetric ‘Quadpol’ mode. The radar backscatter signals were compared with a RapidEye reference data set to investigate the potential relationship of TerraSAR-X backscatter signals to multispectral vegetation indices and to quantify the benefits of TerraSAR-X Quadpol data over standard dual- or single-polarization modes. The satellite scenes used cover parts of the Mekong Delta, the rice bowl of Vietnam, one of the major rice exporters in the world and one of the regions most vulnerable to climate change. The use of radar imagery is especially advantageous over optical data in tropical regions because the availability of cloudless optical data sets may be limited to only a few days per year. We found no significant correlations between radar backscatter and optical vegetation indices in pixel-based comparisons. VV and cross-polarized images showed significant correlations with combined spectral indices, the modified chlorophyll absorption ratio index/second modified triangular vegetation index (MCARI/MTVI2) and transformed chlorophyll absorption in reflectance index/optimized soil-adjusted vegetation index (TCARI/OSAVI), when compared on an object basis. No correlations between radar backscattering at any polarization and the normalized difference vegetation index (NDVI) were observed.     

基于RS与GIS的野鸭湖湿地土地利用/土地覆盖动态变化研究

通过对野鸭湖湿地的Landsat—TM影像和印度的IRS影像进行融合处理，得到卫星影像分类图。结合实地调查，标定土地利用类型，运用ArcView的解译及数据统计功能，分析研究野鸭湖湿地6年来土地利用／土地覆盖的变化。研究结果表明：耕地、居民点及工矿用地面积增加，水域面积减少，湿地生态环境受到严重破坏。其主要是由自然条件、人口和经济增长所致。水域面积、植被覆盖率的减少，使栖息和越冬鸟类丧失了大量的栖息地。为保护湿地环境，应逐步退耕还草、还林；恢复芦苇、沼泽，确保区内生态平衡和系统生态质量不断优化。     

Land use qualities identified in remotely‐sensed images

Land use can be defined as the intentional use of a specific piece of land resulting in patterns of ecological responses that are visible in land cover and landscape. The responses to land use often result in a heterogeneous combination of classes of land cover. Existing methods used in the classification of satellite imagery are limited in their capacity to handle categories consisting of heterogeneous or multiple land cover classes. Accordingly, a spatial relational post‐classification (SRPC) method has been developed which uses a spatial relational post‐classification of land cover classes based on the incorporation of information about identified land use qualities. This paper explains how this method works, and presents the results from a case study of the surroundings of Sötåsa village located in southern Sweden. Different land cover classes were aggregated semantically into two land use quality classes. In conclusion, it is argued that it is possible to make the semantic shift from reflectance to land use qualities using the developed method on satellite data, and that this provides considerable scope for the future analysis of land use.     

Multiscale Models for Target Detection and Background Discrimination in Synthetic Aperture Radar Imagery

Multiscale models can be used to capture the scale-dependent behavior of the statistics in radar imagery. This behavior is expected to be different for natural background compared to objects of interest such as vehicles. We demonstrate that multiscale autoregressive models can discriminate between samples of these two major classes extracted from 1.5-m-resolution radar imagery. We also show that it is possible to discriminate between two types of natural background in SAR imagery, “grassland” and “woodland,” using multiscale models. This latter result could be exploited in adaptive algorithms for automated target detection.     

Detecting war‐induced abandoned agricultural land in northeast Bosnia using multispectral,multitemporal Landsat TM imagery

The use of satellite technology by military planners has a relatively long history as a tool of warfare, but little research has used satellite technology to study the effects of war. This research addresses this gap by applying satellite remote sensing imagery to study the effects of war on land‐use/land‐cover change in northeast Bosnia. Although the most severe war impacts are visible at local scales (e.g. destroyed buildings), this study focuses on impacts to agricultural land. Four change detection methods were evaluated for their effectiveness in detecting abandoned agricultural land using Landsat Thematic Mapper (TM) data from before, during and after the 1992–95 war. Ground reference data were collected in May 2006 at survey sites selected using a stratified random sampling approach based on the derived map of abandoned agricultural land. Fine‐resolution Quickbird imagery was also used to verify the accuracy of the classification. Results from these analyses show that a supervised classification of the Landsat TM data identified abandoned agricultural land with an overall accuracy of 82.5%. The careful use of freely available Quickbird imagery, both as training data for the supervised classifier and as supplementary ground reference data, suggests that these methods are applicable to other civil wars too dangerous for researchers' fieldwork.     

NDVI-derived land cover classifications at a global scale

Phenological differences among vegetation types, reflected in temporal variations in the Normalized Difference Vegetation Index (NDVI) derived from satellite data, have been used to classify land cover at continental scales. Extending this technique to global scales raises several issues: identifying land cover types that are spectrally distinct and applicable at the global scale; accounting for phasing of seasons in different parts of the world; validating results in the absence of reliable information on global land cover; and acquiring high quality global data sets of satellite sensor data for input to land cover classifications. For this study, a coarse spatial resolution (one by one degree) data set of monthly NDVI values for 1987 was used to explore these methodological issues. A result of a supervised, maximum likelihood classification of eleven cover types is presented to illustrate the feasibility of using satellite sensor data to increase the accuracy of global land cover information, although the result has not been validated systematically. Satellite sensor data at finer spatial resolutions that include other bands in addition to NDVI, as well as methodologies to better identify and describe gradients between cover types, could increase the accuracy of results of global land cover data sets derived from satellite sensor data.     

Evaluation of MK-4 multispectral satellite photography in land cover classification of eastern Ontario

Russian MK-4 multispectral satellite photography has been investigated for potential in land cover classification. Thematic maps were generated using maximum likelihood, neural network and context classifiers. Classifications of the raw spectral data, of spectral transforms, and of combined spectral/textural data were evaluated. Low point-based class accuracies resulted for land cover types exhibiting high spatial variability at the given pixel spacing of 7.5m, while more spatially homogeneous cover types were well classified. Several issues arose which need to be addressed for effective future use of high-resolution satellite sensors in regional land cover mapping. They include the need for further research in techniques for classification and accuracy assessment which are sensitive to the spatial variance of such high resolution imagery, and optimization of class attribute definitions.     

Trajectory analysis of land cover change in arid environment of China

Remotely sensed data have been utilized for environmental change study over the past 30 years. Large collections of remote sensing imagery have made it possible for spatio‐temporal analyses of the environment and the impact of human activities. This research attempts to develop both conceptual framework and methodological implementation for land cover change detection based on medium and high spatial resolution imagery and temporal trajectory analysis. Multi‐temporal and multi‐scale remotely sensed data have been integrated from various sources with a monitoring time frame of 30 years, including historical and state‐of‐the‐art high‐resolution satellite imagery. Based on this, spatio‐temporal patterns of environmental change, which is largely represented by changes in land cover (e.g., vegetation and water), were analysed for the given timeframe. Multi‐scale and multi‐temporal remotely sensed data, including Landsat MSS, TM, ETM and SPOT HRV, were used to detect changes in land cover in the past 30 years in Tarim River, Xinjiang, China. The study shows that by using the auto‐classification approach an overall accuracy of 85–90% with a Kappa coefficient of 0.66–0.78 was achieved for the classification of individual images. The temporal trajectory of land‐use change was established and its spatial pattern was analysed to gain a better understanding of the human impact on the fragile ecosystem of China's arid environment.     

Using remote sensing and GIS to analyse land cover change and its impacts on regional sustainable development

Policy and decision making in the context of sustainable development requires rapid, effective and efficient access to and integration of appropriate current information from a wide range of sources and disciplines, including land cover dynamic information derived from remotely sensed data. The analysis of data from high spatial resolution satellite sensors has potential in land cover monitoring. In this paper, a post-classification method is used to detect land cover change from multi-temporal satellite data, and particular attention is given to the selection of an appropriate method for land cover classification. The use of a Geographic Information System (GIS) allows further spatial analysis of the data derived from remotely sensed images and analysis of the impact of land cover change on regional sustainable development. The satellite-derived data used in this study are Thematic Mapper (TM) data acquired by Landsat-5 on 14 May 1985, 20 May 1987, 26 April 1990 and 20 May 1993 in Ansan City, Korea. The results obtained show that land cover change in the west coastal zone of Korea has occurred in the past decade as a result of both natural forces and human activities, which has in turn impacted on the regional sustainable development. The results thus provide very useful information to local government for decision making and policy planning.     

A linearised pixel-swapping method for mapping rural linear land cover features from fine spatial resolution remotely sensed imagery

Accurate maps of rural linear land cover features, such as paths and hedgerows, would be useful to ecologists, conservation managers and land planning agencies. Such information might be used in a variety of applications (e.g., ecological, conservation and land management applications). Based on the phenomenon of spatial dependence, sub-pixel mapping techniques can be used to increase the spatial resolution of land cover maps produced from satellite sensor imagery and map such features with increased accuracy. Aerial photography with a spatial resolution of 0.25 m was acquired of the Christchurch area of Dorset, UK. The imagery was hard classified using a simple Mahalanobis distance classifier and the classification degraded to simulate land cover proportion images with spatial resolutions of 2.5 and 5 m. A simple pixel-swapping algorithm was then applied to each of the proportion images. Sub-pixels within pixels were swapped iteratively until the spatial correlation between neighbouring sub-pixels for the entire image was maximised. Visual inspection of the super-resolved output showed that prediction of the position and dimensions of hedgerows was comparable with the original imagery. The maps displayed an accuracy of 87%. To enhance the prediction of linear features within the super-resolved output, an anisotropic modelling component was added. The direction of the largest sums of proportions was calculated within a moving window at the pixel level. The orthogonal sum of proportions was used in estimating the anisotropy ratio. The direction and anisotropy ratio were then used to modify the pixel-swapping algorithm so as to increase the likelihood of creating linear features in the output map. The new linear pixel-swapping method led to an increase in the accuracy of mapping fine linear features of approximately 5% compared with the conventional pixel-swapping method.