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In clustering analysis of remote sensing imagery, a commonly held assumption is that the feature space can be modelled as a mixture of Gaussians. However, the assumption is not true for many real data and therefore incorrect classification results are often obtained by parametric methods. Nonparametric methods in feature space analysis can avoid the use of the normality assumption. Arbitrarily structured feature spaces can be analysed only by means of nonparametric methods as these methods do not have embedded assumptions. The mean shift is a basic computational module of the nonparametric technique in pattern recognition. The mean shift procedure can be used to cluster multispectral remote sensing imagery. Earlier clustering techniques based on the mean shift used a single scale over the entire feature space and were not feasible for the analysis of complex multimodal feature spaces. In this paper, we present an adaptive mean shift method in which local scale information is involved. The proposed algorithm can find arbitrary density, size and shape clusters in remote sensing imagery. The method is a simple technique of unsupervised image classification. We demonstrate its advantages in classification accuracy over earlier methods described in this paper. [ABSTRACT FROM AUTHOR]

In order to extract quantitative water-leaving information from the Thematic Mapper (TM) image accurately in inland waters, atmospheric correction is a necessary step. Based on former researchers' results, the paper presents two atmospheric correction algorithms based on meteorological data (MD) and on Moderate Resolution Imaging Spectroradiometer (MODIS) Vicarious Calibration (MVC) for TM image in inland waters according to the theory of radiative transfer. Studying Taihu lake, China, in this paper we derived water remote sensing reflectance from a TM image of 26 July 2004 by these two atmospheric correction algorithms and we compare the results with that of dark object subtraction (DOS) and 6S code. The results show that the effect of atmospheric correction based on meteorological data and MODIS Vicarious Calibration is much better than that of DOS and 6S code. Although the MD is more accurate, MVC may be an ideal choice for TM images in inland water because TERRA MODIS images can be acquired easily than collecting meteorological data at the time of satellites passing over. [ABSTRACT FROM AUTHOR]

This paper proposes the work flow of multi-scale information extraction from high resolution remote sensing images based on features: rough classification - parcel unit extraction (subtle segmentation) - expression of features - intelligent illation - information extraction or target recognition. This paper then analyses its theoretical and practical significance for information extraction from enormous amounts of data on a large scale. Based on the spectrum and texture of images, this paper presents a region partition method for high resolution remote sensing images based on Gaussian Markov Random Field (GMRF)-Support Vector Machine (SVM), that is the image classification based on GMRF-SVM. This method integrates the advantages of GMRF-based texture classification and SVM-based pattern recognition with small samples and makes it convenient to utilize a priori knowledge. Finally, the paper reports tests on Ikonos images. The experimental results show that the method used here is superior to GMRF-based segmentation in terms of both the time expenditure and processing effect. In addition, it is actually meaningful for the stage of information extraction and target recognition. [ABSTRACT FROM AUTHOR]

Natural and anthropogenic processes at the Earth's surface operate at a range of spatial and temporal scales. Different scales of observation are required to match the spatial scales of the processes under observation. At the same time, the temporal sampling rate of the observing systems must be reconciled with the dynamics of the processes observed. Bringing together these issues requires insight, innovation and, inevitably, compromise. This paper reviews spatial and temporal considerations in remote sensing and introduces the papers in this Special Issue on ‘Scales and Dynamics in Observing the Environment’. The review comprises three main sections. The first section focuses on spatial variability in remote sensing, while the second section focuses on temporal variability in remote sensing. The third section links these two issues, focusing on the interplay of space and time in remote sensing. The review is primarily theoretical, explaining spatial and temporal properties of remote sensing and remotely sensed phenomena. Where appropriate, however, practical examples are included to demonstrate how remote sensing is used in environmental applications. Following the review, the papers included in the Special Issue are introduced, outlining their significance in the context of ‘Scales and Dynamics in Observing the Environment’. [ABSTRACT FROM AUTHOR]

Measurement of land surface temperature faces many problems. For example, each pixel over land surface is likely to be heterogeneous and non-isothermal with both vegetation canopy and background soil, and the three-dimensional structure of canopy often makes the canopy radiation angular dependent. It is difficult to define an overall land surface temperature and emissivity at pixel scale for heterogeneous and non-isothermal surfaces. After recalling several definitions of effective emissivities, component effective emissivity is defined in this paper under the conditions of local thermal equilibrium and a constant material emissivity for surface temperature variation in the normal Earth environment. Component effective emissivities make it possible to retrieve the component temperature based on multi-directional measurements. The sum of component effective emissivities is equal to the overall effective emissivity, which can be used to inverse pixel-averaged effective temperature. Taking the continuous plant/soil system as an example, the Monte Carlo method is used to simulate the effective emissivities, and an analytical expression equation (AEE) of the effective emissivities including direct-line emission and single scattering contribution is developed. Monte Carlo simulated results show that the sum of direct-line emission and single scattering effective emissivity is close to overall effective emissivity when soil and leaf are set to 0.94 and 0.98 respectively. Then component and overall effective emissivities calculated by Monte Carlo method and AEE are compared, and their differences are analysed for different soil and leaf emissivities. It is shown that when soil and leaf emissivities are set respectively to 0.94 and 0.98, the differences are less than 0.006 within a 50° view zenith angle. When soil or leaf emissivity is set to 0.9, the difference reaches 0.025 or 0.016, which is large enough to introduce a 1 K error in land surface temperature inversion when this effective emissivity is used. The paper finally proposes that the linear relationship of difference with soil and leaf emissivity can be used to compensate the errors. [ABSTRACT FROM AUTHOR]

Roads are important basic geographical phenomena and the automatic recognition and extraction of road features from remote sensing images has many applications. However, automated road extraction from high-resolution remote sensing imagery is problematic. In recent years, many approaches have been explored for automatic road extraction, particularly involving road edge detection. Traditional edge detection operators such as the Canny or the Sobel operator are used frequently but there are serious problems of over- or underdetection, and time-consuming and complicated post-processing work is often required. In this paper, a new revised parallel-beam Radon transform (RPRT) approach is proposed. The traditional PRT can have problems with step values, resulting in false edge detection. To overcome these problems we introduced the RPRT, using the harmonic average of the pixel value in every strip of the Radon slice. An algorithm suitable for straight edge detection of roads in high-resolution remote sensing imagery was designed based on the ridgelet transform with the RPRT. The experimental results show that our algorithm can detect straight road edges efficiently and accurately, and avoid cumbersome and complicated post-processing work. [ABSTRACT FROM AUTHOR]

Super-resolution mapping is a relatively new field in remote sensing whereby classification is undertaken at a finer spatial resolution than that of the input remotely sensed multiple-waveband imagery. A variety of different methods for super-resolution mapping have been proposed, including spatial pixel-swapping, spatial simulated annealing, Hopfield neural networks, feed-forward back-propagation neural networks and geostatistical methods. The accuracy of all of these new approaches has been tested, but the tests have tended to focus on the new technique (i.e. with little benchmarking against other techniques) and have used different measures of accuracy. There is, therefore, a need for greater inter-comparison between the various methods available, and a super-resolution inter-comparison study would be a welcome step towards this goal. This paper describes some of the issues that should be considered in the design of such a study. [ABSTRACT FROM AUTHOR]

This paper presents an overview of uncertainty handling in remote sensing studies. It takes an image-mining perspective and identifies different ways of handling uncertainties. It starts with the pixel, and through object identification and modelling, proceeds towards monitoring and decision making. Methods presented originate both from probability- and fuzzy-logic-based approaches. The paper is illustrated with three examples, one from a geographic information system stored object, one from an object identified from a remotely sensed image directly and a practical case study from the Tibet plateau. An important remaining topic is to combine and integrate errors and uncertainties collected during the whole image-mining process. [ABSTRACT FROM AUTHOR]

Influence of topography must be corrected when fine-scale remote sensing data are used to estimate surface albedo in rugged terrain. In the case of coarse-scale satellite remote sensing data, the topographic effect on albedo estimation is generally ignored because the influence to albedo estimation of overall slope in coarse-scale pixels is usually considered negligible; however, because of the scale effect in albedo, neglecting within-pixel topology may cause errors in albedo estimation from coarse-scale data. This paper investigates the scale effect on land-surface albedo estimations, particularly in the case of converting fine-scale albedo to coarse-scale albedo in rugged terrain. Starting from the definition of difference-scale albedo in rugged terrain, a method is presented to convert fine-scale surface albedo to coarse-scale albedo, then deriving a factor to correct coarse-scale land-surface albedo. The performance and accuracy analysis of the method are investigated by using a simulated digital elevation model (DEM) with different mean slopes, as well as real DEM and Thematic Mapper images. Results show that the method is effective for scale-effect correction of land-surface albedo in rugged terrain. [ABSTRACT FROM AUTHOR]

Many factors influence the quality and value of a classification accuracy assessment and evaluation programme. This paper focuses on the size of the testing set(s) used with particular regard to the impacts on accuracy assessment and comparison. Testing set size is important as the use of an inappropriately large or small sample could lead to limited and sometimes erroneous assessments of accuracy and of differences in accuracy. Here, some of the basic statistical principles of sample size determination are outlined, including a discussion of Type II errors and their control. The paper provides a discussion on some of the basic issues of sample size determination for accuracy assessment and includes factors linked to accuracy comparison. With the latter, the researcher should specify the effect size (minimum meaningful difference in accuracy), significance level and power used in an analysis and ideally also fit confidence limits to derived estimates. This will help design a study and aid the use of appropriate sample sizes, as well as facilitate interpretation of results. In particular, it will help avoid problems, such as under-powered analyses, and provide a richer information base for classification evaluation. The paper includes equations that could be used to determine sample sizes for common applications in remote sensing, using both independent and related samples. [ABSTRACT FROM AUTHOR]

This paper presents the background to the special issue of the International Journal of Remote Sensing which is being published to celebrate the 20th anniversary of the coming into effect in January 1989 of the Montreal Protocol on substances that damage the amospheric ozone layer. Starting from the discovery of ozone and the ozone layer, we recall the proposition of Molina and Rowland that man-made CFCs pose a major threat to the ozone layer. This was followed by about 15 years of scientific research, scientific discussion, intense political discussions and international negotiations which led to the formulation of the Montreal Protocol that prohibits the manufacture and use of ozone-destroying substances. The papers in this special issue of the Journal are concerned with addressing the role of remote sensing in monitoring and assessing the success, or otherwise, of the Montreal Protocol. [ABSTRACT FROM AUTHOR]

In this paper we present the results of time series analysis for a coal mining region based on partial unmixing. We test the method also known as mixture tuned matched filtering on an eight image Landsat 5 TM and Landsat 7 ETM+ time series covering the period from 1987 to 2003. Common change detection methods often include the comparison of two interactively generated classification results, such as derived from Maximum Likelihood classification. These approaches often yield highly accurate results. However, disadvantages include a strong analyst bias and hardly repeatable results. For a quantitative monitoring of a single surface class' development over time they are often not recommendable. Our goal was to test an unbiased quantitative way to assess the development of coal surfaces, such as outcropping coal seams, coal storage piles, coal waste piles, and coal washery discard, within multiple date satellite imagery. Partial unmixing approaches were developed to detect one or few target materials surrounded by—or mixed with—an unknown background material. The main advantage is that only the spectral characteristics of the material of interest must be known, and the desired material can furthermore occur with subpixel coverage. Crisp pixel classificators like maximum likelihood on the other hand require knowledge of all classes. They can only map materials which dominate a pixel. Linear unmixing procedures such as partial unmixing require a thorough radiometric pre-processing of data. Furthermore, the accuracy and representativity of selected input spectra must be granted. In this paper we demonstrate that partial unmixing is a powerful method to detect and extract single landcover classes of interest relatively fast and unbiased. The subpixel fraction percentages should be interpreted in a relative way only. We furthermore show that partial unmixing represents a standardized method for time series analyses and allows for a quantitative assessment of the temporal development of an area. Challenges lie in the validation of partial unmixing results, which we realized through thresholding of unmixing results and accuracy assessment with ground truth polygons mapped in situ. Furthermore, we performed an indirect comparison with results of a multi-endmember unmixing. [ABSTRACT FROM AUTHOR]

Due to the lack of training samples, hyperspectral classification often adopts the minimum distance classification method based on spectral metrics. This paper proposes a novel multiresolution spectral-angle-based hyperspectral classification method, where band subsets will be selected to simultaneously minimize the average within-class spectral angle and maximize the average between-class spectral angle. The method adopts a pairwise classification framework (PCF), which decomposes the multiclass problem into two-class problems. Based on class separability criteria, the original set of bands is recursively decomposed into band subsets for each two-class problem. Each subset is composed of adjacent bands. Then, the subsets with high separability are selected to generate subangles, which will be combined to measure the similarity. Following the PCF, the outputs of all the two-class classifiers are combined to obtain the final output. Tested with an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data set for a six-class problem, the results demonstrate that our method outperforms the previous spectral metric-based classification methods. [ABSTRACT FROM AUTHOR]

Land cover classification over large geographic areas using remotely sensed data is increasingly common as a result of the requirements of national inventory and monitoring programmes, scientific modelling and international environmental treaties. Although large-area land cover products are more prevalent, standard operational protocols for their validation do not exist. This paper provides a framework for the accuracy assessment of large-area land cover products and synthesizes some of the key decision points in the design and implementation of an accuracy assessment from the literature. The fundamental components of a validation plan are addressed and the framework is then applied to the land cover map of the forested area of Canada that is currently being produced by the Earth Observation for Sustainable Development programme. This example demonstrates the compromise between the theoretical aspects of accuracy assessment and the practical realities of implementation, over a specific jurisdiction. The framework presented in this paper provides an example for others embarking on the assessment of large-area land cover products and can serve as the foundation for planning a statistically robust validation. [ABSTRACT FROM AUTHOR]

The point of maximum slope on the reflectance spectrum of a plant leaf between red and near-infrared wavelengths is known as the red edge position (REP). The REP is strongly correlated with foliar chlorophyll content, and hence it provides a very sensitive indicator for a variety of environmental factors affecting leaves such its stress, drought and senescence. The REP is also present in spectra for vegetation recorded by remote sensing methods. Due to its importance for the application of inversion procedures, a number of techniques have been developed for determining the REP for foliar spectral reflectance. In this paper a new approach is proposed. It allows an unsupervised estimation of the REP. The accuracy of the new approach is evaluated by comparing REP estimates with values derived from measured spectral data for woody and herbaceous species available in the LOPEX (Leaf Optical Properties Experiment) database. [ABSTRACT FROM AUTHOR]

Timely and accurate change detection of Earth's surface features is extremely important for understanding relationships and interactions between human and natural phenomena in order to promote better decision making. Remote sensing data are primary sources extensively used for change detection in recent decades. Many change detection techniques have been developed. This paper summarizes and reviews these techniques. Previous literature has shown that image differencing, principal component analysis and post-classification comparison are the most common methods used for change detection. In recent years, spectral mixture analysis, artificial neural networks and integration of geographical information system and remote sensing data have become important techniques for change detection applications. Different change detection algorithms have their own merits and no single approach is optimal and applicable to all cases. In practice, different algorithms are often compared to find the best change detection results for a specific application. Research of change detection techniques is still an active topic and new techniques are needed to effectively use the increasingly diverse and complex remotely sensed data available or projected to be soon available from satellite and airborne sensors. This paper is a comprehensive exploration of all the major change detection approaches implemented as found in the literature. Abbreviations used in this paper 6S second simulation of the satellite signal in the solar spectrum ANN artificial neural networks ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer AVHRR Advanced Very High Resolution Radiometer AVIRIS Airborne Visible/Infrared Imaging Spectrometer CVA change vector analysis EM expectation-maximization algorithm ERS-1 Earth Resource Satellite-1 ETM+ Enhanced Thematic Mapper Plus, Landsat 7 satellite image GIS Geographical Information System GS Gramm-Schmidt transformation J-M distance Jeffries-Matusita distance KT Kauth-Thomas transformation or tasselled cap transformation LSMA linear spectral mixture analysis LULC land use and land cover MODIS Moderate Resolution Imaging Spectroradiometer MSAVI Modified Soil Adjusted Vegetation Index MSS Landsat Multi-Spectral Scanner image NDMI Normalized Difference Moisture Index NDVI Normalized Difference Vegetation Index NOAA National Oceanic and Atmospheric Administration PCA principal component analysis RGB red, green and blue colour composite RTB ratio of tree biomass to total aboveground biomass SAR synthetic aperture radar SAVI Soil Adjusted Vegetation Index SPOT HRV Satellite Probatoire d'Observation de la Terre (SPOT) high resolution visible image TM Thematic Mapper VI Vegetation Index [ABSTRACT FROM AUTHOR]

Actual evapotranspiration (AET) estimation using satellite sensors can improve water resources planningwater regulation in irrigated areas. This paper evaluates an AET estimation algorithm developed by integrating satellite remote sensingan environmental monitoring network in Oklahoma, USA for operational daily water management purpose. First, a surface energy balance evapotranspiration (ET) algorithm is implemented to estimate actual ET by integrating the twice-daily overpass of Moderate Resolution Imaging Spectroradiometer (MODIS) sensor dataOklahoma's world-class environmental monitoring network—Mesonet—with 5-minute data acquisition in real time. Second, accuracy of the estimated ET is evaluated at the site scale using Ameriflux tower latent heat fluxMesonet site crop ET on daily, 8-dayseasonal basis. The results showed that MODIS/Mesonet-AET (MM-AET) estimation showed agreement with ground observations, with daily ET bias less than 15%seasonal bias less than 8%. Additionally, actual ET modelled from a water balance budget analysis in a heavily instrumented basin compares favourably (bias < 3%) with the MM-AET at catchment scales with an order of several hundreds square kilometres. This study demonstrates that (1) the MM-AET estimation is acceptable for daily actual ET estimation(2) it is feasible to implement the proposed MM-AET algorithm in real time for irrigational water resources management at the scale of irrigation projects in Oklahoma. [ABSTRACT FROM AUTHOR]

Accurate and timely information describing wetland resources and their changes over time, especially in coastal urban areas, is becoming more important. In this study, we mapped and monitored land-cover change in an urban wetland using high spatial resolution IKONOS images acquired in June 2003 and January 2006. An optimal iterative unsupervised classification (OIUC) method was used to overcome the limitations of unsupervised classification. The images were categorized into six classes, and an accuracy assessment was conducted using error matrices and the Kappa coefficient. The overall accuracies were 83.2% and 86.3% for the 2003 and 2006 images, respectively. A post-classification comparison method was used to detect the wetland change by calculating a detailed land-cover type transformation matrix. The results indicated a decrease in the area of water bodies and an increase in the area of vegetation in the wetland. This paper shows that high spatial resolution remote sensing data is advanced in studying an urban wetland at a local scale. An OIUC method, combined with visual interpretation, could yield high classification accuracy. A post-classification comparison method is also efficient in wetland change detection. [ABSTRACT FROM AUTHOR]

This paper reviews the current status of the use of remote sensing for the detection, extraction and monitoring of coastlines. The review takes the US system as an example. However, the issues at hand can be applied to any other part of the world. Visual interpretation of airborne remote sensing data is still widely and popularly used for coastal delineation. However, a variety of remote sensing data and techniques are available to detect, extract and monitor the coastline. The developed techniques have reached a level of maturity such that they are applied in operational settings. [ABSTRACT FROM AUTHOR]

A remote sensing approach is proposed in this paper for monitoring instantaneous underwater light climate in Dahuchi Lake, China. Water constituents that are optically significant to the lake water can be retrieved through this method based on an artificial neural network (ANN) algorithm with a satisfactory accuracy. In this study, the ANN was trained by spectral simulations which were generated by a water colour simulation model (WASI). An underwater light attenuation model has been built by fitting the field measurements and applied to calculate photosynthetically available radiation (PAR) at the bottom of the lake from the inputs of hyperspectral reflectance above the water surface and the water depth. Results show that this method can predict the concentration of suspended particle matter (SPM) with a high accuracy and explain more than half of the underwater PAR variation with a standard deviation of 94.8 μmol s-1 m-2. In order to test the portability of the algorithm, it has also been extended to three bands of Landsat TM image. Compared with hyperspectral inputs, ANN with broadband inputs has lower but still acceptable accuracy on SPM retrieval. The advantage of this method lies in its time-efficient and cost-efficient monitoring of underwater light climate, i.e. the simulations generated by a semi-physical model can be used for ANN training, instead of using a large number of field measurements. [ABSTRACT FROM AUTHOR]

This paper presents a method of validating the MODIS (Moderate-Resolution Imaging Spectroradiometer) surface reflectance (MOD09) based on ground spectral measurements and high-resolution remote sensing images. Given that the spatial resolution of the MODIS image is too low to directly compare ground measurements with the MODIS pixels, registration is first made between MODIS and a high-resolution image. Afterwards, the same sizable sample area to one MODIS pixel is chosen in a high resolution image (a pixel in MODIS corresponds to many pixels in a high-resolution image), after which image classification is conducted in each sample area. Finally, reflectance is measured for each class in each area. Taking the ratio of every class area to each sample area as the weight, the weighted mean reflectance is then calculated for each sample area (a pixel for MODIS), which is treated as the measured reflectance of the corresponding pixel in the MODIS image. A contradistinctive analysis is carried out between measured reflectance and MODIS retrieval reflectance from the space in order to determine the accuracy of the MODIS land surface reflectance product and correct its error. The experiment indicates that a linear positive correlativity between the MODIS pixel retrieval reflectance from space and the measured (calculated) surface reflectance exists. From this, we can build an error correction model of the MODIS surface reflectance product by linear regression. The accuracy of the MODIS surface reflectance products after error correction is remarkably improved. Likewise, the relative error can be controlled by 10%, thereby satisfying the requirements of various remote sensing applications. [ABSTRACT FROM AUTHOR]

This paper presents a new object-oriented land cover classification method that integrates raster analysis and vector analysis. The method adopts an improved colour structure code (CSC) for segmentation and support vector machine (SVM) for classification using high resolution (HR) QuickBird data. It combines the advantages of digital image processing (efficient improved CSC segmentation), geographical information systems (GIS) (vector-based feature selection), and data mining (intelligent SVM classification) to interpret images from pixels to objects and thematic information. The improved CSC segmentation not only achieves robust and accurate results but also combines boundary information that the traditional CSC algorithm does not consider. The SVM used for classification has the advantages of solving sparse sampling, nonlinear, high-dimensional and global optimum problems, compared with other classifiers. The results demonstrate that the new object-oriented classification method significantly outperforms some other objected-oriented classification methods such as the objected-oriented method based on traditional CSC and SVM, and perfect classification results are obtained from the classification processing, including not only the classification method, but also preprocessing, sample selection and post-processing. [ABSTRACT FROM AUTHOR]

The Three Gorges Area (TGA), along the Yangtze River, China has been experiencing drastic land use and land cover (LU/LC) changes since the commencement of the construction of the Three Gorges Dam in 1994. These changes may have environmental impacts. However, information about the changes is limited and difficult to obtain. In this paper, optical satellite imagery is used to detect LU/LC changes during the study period 1987-2006 for the complicated, high-relief environment. A case study was conducted in Zigui County of Hubei Province within the TGA. A site-specific procedure using a decision rule-based classification method and a post classification change detection technique is developed to combine spectral and spatial knowledge in the classification of multi-temporal images. Using the decision rule-based classification method, overall accuracies of LU/LC maps of seven classes between 73.4% and 89.5% were obtained, increased by 4-5% over that using the traditional method. The results show that the main trend in LU/LC change in the study area throughout the monitoring period was a steady reduction in natural vegetation areas. About 32% of the total area of natural vegetation, including forest, shrub and grass, was lost to built up areas, crop fields and orchards. [ABSTRACT FROM AUTHOR]

Estimation of noise contained within a remote sensing image is often a prerequisite to dealing with the deleterious effects of noise on the signal. Image based methods to estimate noise are attractive to researchers for a range of applications because they are in many cases automatic and do not depend on external data or laboratory measurement. In this paper, the geostatistical method for estimating image noise was applied to Compact Airborne Spectrographic Imager (CASI) imagery. Three CASI wavebands (0.46-0.49 μm (blue), 0.63-0.64 μm (red), 0.70-0.71 μm (near-infrared)) and four land covers (coniferous woodland, grassland, heathland and deciduous woodland) were selected for analysis. Five sub-images were identified per land cover resulting in 20 example cases per waveband. As in previous studies, the analysis showed that noise was related to land cover type. However, the noise estimates were not related to the mean of the signal in any waveband. Rather, the noise estimates were related to the square root of the semivariogram sill, which represents the variability in the underlying signal. These results suggest that the noise estimates produced using the geostatistical method may be inflated where the variance in the image is large. Regression of the noise estimates on the square root of the sill may lead to a stable noise estimate (i.e. the regression intercept), which is not affected by the variability in the image. This provides a refined geostatistical (GS) method that avoids the problems outlined above. [ABSTRACT FROM AUTHOR]

Object-oriented remote sensing software provides the user with flexibility in the way that remotely sensed data are classified through segmentation routines and user-specified fuzzy rules. This paper explores the classification and uncertainty issues associated with aggregating detailed 'sub-objects' to spatially coarser 'super-objects' in object-oriented classifications. We show possibility theory to be an appropriate formalism for managing the uncertainty commonly associated with moving from 'pixels to parcels' in remote sensing. A worked example with habitats demonstrates how possibility theory and its associated necessity function provide measures of certainty and uncertainty and support alternative realizations of the same remotely sensed data that are increasingly required to support different applications. [ABSTRACT FROM AUTHOR]

The multipurpose ultraviolet-visible (UV-Vis) remote sensing equipment SPATRAM (SPectrometer for Atmospheric TRAcers Monitoring) is a scanning spectrometer for measurement of electromagnetic radiation in the 250-950 nm spectral range. In this paper SPATRAM will be presented and new solutions will be discussed. The monochromator is based on the one installed in GASCOD (Gas Analyzer Spectrometer Correlating Optical Differences) developed during the 1990s. The most important improvements of SPATRAM relative to GASCOD are: (i) the wider spectral range scanned, allowing for the detection of more atmospheric compounds than with GASCOD; (ii) an increased number of inputs, resulting in the possibility of quasi-simultaneous measurements from different optical devices; (iii) the focusing optic system, which permits a simple optical alignment procedure and low cost; (iv) electronic self-thermoregulation, allowing for reliable spectral measurements unaffected by mechanical deformation caused by variation of temperature; (v) adoption of a CCD sensor, resulting in an increase of equipment sensitivity and therefore an enhancement in time resolution of the measurements; (vi) the use of an advanced CPU and a standard OS, guaranteeing full stability of the equipment; and (vii) the development of a new software tool for complete control of the whole instrument and for pre-processing of the measured data. [ABSTRACT FROM AUTHOR]

Unsupervised classification is an essential step in the automatic analysis of SAR remote sensing data. Classification results make SAR data easier to interpret and can serve as a starting point for automated analysis techniques that apply to homogeneous regions of the observed scene. Polarimetric SAR data are particularly interesting for unsupervised classification purposes, since they contain a great amount of information, allowing robust statistical clustering of the image content on the one hand and a direct physical interpretation of the result on the other. This paper proposes a new unsupervised classification approach for polarimetric SAR data. Assuming Wishart-distributed polarimetric covariance matrices, it combines spectral clustering based on the covariance matrices themselves with spatial clustering by statistical analysis of local neighbourhoods. Instead of working with binary assignments of samples to class centres, a soft decision rule is used in which each pixel is assigned to all class centres in the spectral and spatial domains. The local neighbourhood is taken into account by altering the probabilities of class membership by a neighbourhood function, obtained from normalized compatibility coefficients, describing cluster sizes and mutual tolerance. In this way, robust and homogenous classification results can be obtained even in the presence of strong speckle noise. [ABSTRACT FROM AUTHOR]

The agricultural management practice of leaving land fallow during winter is a key pressure on ground water quality in Canterbury, New Zealand. This is because any nitrate present is likely to be leached down through the soil profile since there is no plant uptake. Remote sensing imagery has been successfully used to identify land with low potential for nitrate uptake due to having bare soil or dead vegetation for significant periods in winter and early spring. This was achieved by use of classification rules on a time series of three 20-30-m resolution satellite images. Our rules are based on percentage live vegetation cover, so a regression relationship between percentage live vegetation cover (from field data) and a satellite-image-derived vegetation index was derived. This paper describes the rules and analyses the sources of error in this low-nitrate-uptake land classification process. The objective was to provide a low-nitrate-uptake map that included uncertainty, and to determine the most significant error source, a task made more difficult due to a lack of reference data. Sources of error include suitability of the logical model, timing of image acquisition, incorrect reference data, geometric errors between images, and radiometric variation both within and between images (month to month and year to year). A pragmatic approach was adopted where each potential error source was systematically examined in turn, quantified where possible, and combined to produce a map indicating where predictions were less reliable or certain. This approach is suitable for situations where there is very limited reference data and more quantitative approaches cannot be applied. It has the advantage of using subsidiary information about errors, and produces spatial information on uncertainty for end users, albeit qualitative and very dependent on expert opinion. The most significant source of uncertainty in the rule-based maps of low nitrate uptake was missing images or too large an interval between images. [ABSTRACT FROM AUTHOR]

Quantitative mapping by means of hyperspectral remote sensing (HRS) can be hampered by reflectance anisotropy emerging in large field of view (FOV) optics, and may contain spectral radiometric distortions. This paper presents an algorithm for the rectification of reflectance anisotropy for rough terrain. A new method is offered for correction of radiometric bias caused by topography and sensing geometry. The correction of HRS data of lawn grass is demonstrated, and the method is tested on a large park area. To record elevation we used airborne laser scanning data to obtain a digital surface model (DSM). The Compact Airborne Spectral Imager (CASI) recorded reflectance of the same area. Anisotropy of reflectance was recorded by a laboratory spectro-goniometer. An analysis of the effect of correction on the normalized difference vegetation index (NDVI) shows that even moderate slopes, medium sensor FOV and high illumination conditions will result in reflectance anisotropy. Further analysis shows a clear inverse relationship between sensitivity of interpretation and spatial or spectral resolutions. We conclude with an outlook on the utilization of this method among other pre-processing tasks. [ABSTRACT FROM AUTHOR]

The uncertainty in the top-of-the-atmosphere (TOA) radiance is a result of uncertainties in aerosol components, water-leaving radiance (due to seawater constitutions) and whitecap radiance. This paper investigates the variability of these individual terms over the Arabian Sea and particularly in Lakshadweep region, to establish a site for vicarious calibration of the Ocean Colour Monitor (OCM). We found that fractional coverage of whitecap radiance is less than 0.5% for winds lower than 8 m s-1 and its radiance contribution can be assigned to a constant value. For higher winds, the contribution from whitecap radiance to TOA radiance has to be considered along with the atmospheric stability factor. The Lakshadweep Sea, for most of the time, is characterized by a low concentration of chlorophyll-a, an oligotrophic water body and maritime aerosol. [ABSTRACT FROM AUTHOR]

This paper presents the development of a framework for classifying and inventorying Eastern US forestland based on the level of anthropogenic disturbance and fragmentation using high spatial resolution remote sensing data and a multiscale object-based classification system. We implemented the framework using a suburban area in Baltimore County, Maryland, USA as a case study. We developed a three-level hierarchical scheme of image objects. The object-based, multiscale classification and inventory framework provides an effective and flexible way of showing different mixes of human development and forest cover in a hierarchical fashion for human-dominated forest ecosystems. At the finest scale (level 1), the classification nomenclature describes basic land cover feature types, which are divided up into trees and individual features that fragment forests. The overall accuracy of the classification was 91.25%. At level 2, forest patches were delineated and classified into different categories based on the degree of human disturbance. At level 3, major roads were used to segment the study area into larger objects, which were classified on the basis of relative composition and spatial arrangement of forests and fragmenting features. This study provides decision makers, planners and the public with a new methodological framework that can be used to more precisely classify and inventory forest cover. The comparisons of the estimates of forest cover from our analyses with those from the 2001 National Land Cover Dataset (NLCD) show that aggregated figures of forest cover are misleading and that much of what is mapped as forest is highly degraded and is more suburban than natural in its land use. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to disaster monitoring using an automatic change detection system onboard small satellites that features image tiling and fuzzy inference. Unlike other onboard change detection systems for satellites, the proposed system performs change detection on an image tile level rather than on a pixel-by-pixel basis. This image tiling approach allows for more robust change detection performance in the presence of misregistration errors. An important block in the automatic change detection system is the fuzzy inference engine, which generates control signals that trigger different onboard tasks such as image compression, issuing of warning alerts, transmission and rescheduling. The proposed scheme uses not only spectral information as the input data but also cloud cover information to improve the change detection results. Experimental results on accuracy of change detection and flood detection using satellite images are presented. [ABSTRACT FROM AUTHOR]

SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY) is a passive remote sensing spectrometer observing backscattered radiation from the atmosphere and the Earth's surface, in the wavelength range between 240 and 2380 nm. The instrument is onboard ENVironmental SATellite (ENVISAT) which was launched on 1 March 2002. The Medium Resolution Imaging Spectrometer (MERIS) is also one of the 10 instruments onboard the ENVISAT satellite. MERIS is a 68.5° field-of-view nadir-pointing imaging spectrometer which measures the solar radiation reflected by the Earth in 15 spectral bands (visible and near-infrared). It obtains a global coverage of the Earth in three days. Its main objective is to measure sea colour and quantify ocean chlorophyll content and sediment, thus providing information on the ocean carbon cycle and thermal regime. It is also used to derive the cloud top height, aerosol and cloud optical thickness, and water vapour column. The ground spatial resolution of the instrument is 260 m × 290 m. This paper is aimed at determining the cloud fraction in SCIAMACHY pixels (typically, 30 km × 60 km ground scenes) using MERIS observations and number of thresholds for MERIS top-of-atmosphere reflectances and their ratios. Thresholds utilize the fact that clouds are bright white objects having similar reflectances in the blue and red. The MERIS cloud fraction has been derived for a number of SCIAMACHY states with area of 916 km × 400 km. The results are compared with correspondent cloud fractions obtained using SCIAMACHY polarization measurement devices (PMDs). Large differences are found between cloud fractions derived using SCIAMACHY and MERIS measurements. It is recommended to use highly spatially resolved MERIS observations instead of SCIAMACHY PMD measurements to retrieve cloud fractions in SCIAMACHY pixels. The improvements advised will enhance SCIAMACHY trace gas and cloud retrievals in the presence of broken cloud fields. [ABSTRACT FROM AUTHOR]

Currently, there are two types of measure in image classification accuracy assessment: pixel-level measures and category-level/map-level measures. These have their own limitations for representing the uncertainty at pixel and category/map levels. In addition, some of these measures derived from the error matrix are obtained by collecting reference data and then they may be affected by factors related to the sampling. This paper uses rough set theory to obtain the rough degree, rough entropy, quality of approximation and accuracy of approximation. Incorporating traditional measures, they compose one kind of three-level architecture for the classified image, which contains pixel-level measures, object/category-level measures and map-level measures. Unlike some conventional measures, these new measures can be derived directly from the supervised classification result without collecting reference data. A case study on the Landsat TM image is used to substantiate the conceptual arguments. The results demonstrate that the proposed measures are valid for measuring the accuracy of classified remotely sensed imagery and can provide additional information to conventional measures. [ABSTRACT FROM AUTHOR]

Climate and land-atmosphere models rely on accurate land-surface parameters, such as Leaf Area Index (LAI). It is crucial that the estimation of LAI represents actual ground truth. Yet it is known that the LAI values retrieved from remote sensing images suffer from scaling effects. The values retrieved from coarse resolution images are generally smaller. Scale transformations aim to derive accurate leaf area index values at a specific scale from values at other scales. In this paper, we study the scaling effect and the scale transformation algorithm of LAI in regions with different vegetation distribution characteristics, and analyse the factors that can affect the scale transformation algorithm, so that the LAI values derived from a low resolution dataset match the average LAI values of higher resolution images. Using our hybrid reflectance model and the scale transformation algorithm for continuous vegetation, we have successfully calculated the LAI values at different scales, from reflectance images of 2.5 m and 10 m spatial resolution SPOT-5 data as well as 250 m and 500 m spatial resolution MODIS data. The scaling algorithm was validated in two geographic regions and the results agreed well with the actual values. This scale transformation algorithm will allow researchers to extend the size of their study regions and eliminate the impact of remote sensing image resolution. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate if the incorporation of the uncertainty associated with the classification of surface elements into the classification of landscape units (LUs) increases the results accuracy. To this end, a hybrid classification method is developed, including uncertainty information in the classification of very high spatial resolution multi-spectral satellite images, to obtain a map of LUs. The developed classification methodology includes the following steps: (1) a pixel-based hard classification with a probabilistic Bayesian classifier; (2) computation of the posterior probabilities and quantification of the classification uncertainty using an uncertainty measure; (3) image segmentation and (4) object classification based on decision rules. The classification of the resulting objects into LUs is performed considering a set of decision rules that incorporate the pixel-based classification uncertainty. The proposed methodology was tested on the classification of an IKONOS satellite image. The accuracy of the classification was computed using an error matrix. The comparison between the results obtained with the proposed approach and those obtained without considering the classification uncertainty revealed a 12% increase in the overall accuracy. This shows that the information about uncertainty can be valuable when making decisions and can actually increase the accuracy of the classification results. [ABSTRACT FROM AUTHOR]

In this paper we propose a method for characterizing UHIs by using a non-parametric model. Landsat 5 and 7 images obtained over the city of Indianapolis were used for analysis of UHI over space and time. The images were subjected to kernel convolution implementing Gaussian bi-variate function for non-parametric characterization of the UHI. The convoluted images were then analyzed, for pattern over space and over time. It was found that the spread of the UHI in Indianapolis increased from 13.90 km to 21.63 km in the west-east direction and from 10.98 km to 15.90 km in the north-south direction from 1985 to 2000. The changes in the UHI from 1995 to 2000 were evident in all cardinal directions. The model aided in successfully characterizing UHI in terms of its location, spread, and the rate of increase, facilitating a clearer image of UHI through space and time. [ABSTRACT FROM AUTHOR]

Remote sensing technologies are one of the major research topics in fusing multisource data and generating high-quality satellite images. In this paper, a new two-dimensional empirical mode decomposition (EMD) method is presented and used to fuse high-resolution panchromatic and multispectral images. First, the new two-dimensional EMD technique, which includes determining the ending criterion of the sifting procedure, the method of boundary effect resolving and seeking extrema, is presented. Then, based on the proposed two-dimensional EMD, an image fusion method for high-resolution panchromatic and multispectral images is proposed. Finally, two experiments were conducted to verify the performance of the proposed method. The experimental results demonstrate that the new method is effective and promising in terms of both visual effect and quantitative analysis. [ABSTRACT FROM AUTHOR]

The fraction of vegetation cover (FVC) and the leaf area index (LAI) are important parameters for many agronomic, ecological and meteorological applications. Several in-situ and remote sensing techniques for estimating FVC and LAI have been developed in recent years. In this paper, the uncertainty of in-situ FVC and LAI measurements was evaluated by comparing estimates from LAI-2000 and digital hemispherical photography (DHP). The accuracy achieved with a spectral mixture analysis algorithm and two vegetation indices-based methods was assessed using atmospherically corrected Landsat Thematic Mapper (TM) data over the Barrax cropland area where the European Space Agency (ESA) SENtinel-2 and FLuorescence EXperiment (SEN2FLEX) field campaign was carried out in July 2005. The results indicate that LAI-2000 and DHP performances are comparable, with uncertainties of 5% for FVC and 15% for effective LAI. The selected remote sensing methods are shown to be consistent, with a notable overall accuracy (root mean square error, RMSE) of 0.07 (10% in relative terms) for FVC and 0.8 (30%) for LAI. Similar bounds were found on upscaling in-situ measurements with empirical transfer functions (TFs). These results suggest that the pragmatic methods considered applied at high resolution with minimum calibration data could be useful for mapping FVC and LAI in the study area, reducing in-situ labour-intensive characterization necessities for validation studies. [ABSTRACT FROM AUTHOR]

Influencing factor analysis is required for assessing the performance of vegetation fractional coverage (VFC) models with remote sensing imagery. This paper analyses influences of the radiometric correction level (RL), vegetation index (VI) and polynomial exponential power (EP) choice on VI-VFC relationship models. A SPOT 5 HRG image of Nanjing, China was chosen, and three RLs (digital number, top of atmosphere reflectance and post atmospheric correction reflectance) were used to derive six VIs, such as normalized difference vegetation index (NDVI) and ratio vegetation index (RVI). Fifty-four models describing the VI-VFC relationship were established, and the influences of the RL, VI and EP choice on the VI-VFC models were analysed based on a statistical analysis of determination coefficients (R 2) of these models. The results showed that model robustness was jointly influenced by the three factors, so these three factors should be synthetically taken into account in VI-VFC modelling. It is recommended to establish different models between the VFC and various VIs derived from different RLs, and then to select the better ones. [ABSTRACT FROM AUTHOR]

Information on biomass distribution is needed to estimate GHG emissions and removals from land use changes in Canada's north for UNFCCC reporting. This paper reports aboveground biomass measurements along the Dempster Highway transect in 2004, and around Yellowknife and the Lupin Gold Mine in 2005. The measured aboveground biomass ranges are 10-100 t ha-1 for woodlands, 1-100 t ha-1 for shrub sites, and 0.5-10 t ha-1 for grass/herbs sites. The root mean squared error (RMSE) of measurements is 21%, and the median absolute percentage error (MedAPE) is 14%. The combination of JERS backscatter and Landsat TM4/TM5 gives the best biomass equation for the Dempster Highway transect, with r 2 = 0.72 when using a one-step approach (i.e. using all points) and 0.78 when using a two-step approach (i.e. stratifying data into three classes: grass, shrub, and woodlands). The two-step approach reduces the MedAPE from 53% to 33%. The validation against Yellowknife & Lupin data indicates that the equations have good transferability. The improvement of two-step approach over the one-step approach, however, is not significant for the validation dataset, suggesting that the one-step approach is as good as the two-step approach when applied over areas outside where the equations are developed. The relationships and error analysis of this study, as well as the final estimate of GHG emission/removal over Canada's north have been incorporated into Canada's 2006 UNFCCC report. [ABSTRACT FROM AUTHOR]

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 Sotåsa 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. [ABSTRACT FROM AUTHOR]

A new target detection algorithm named the spectra sort encoding (SSE) algorithm is presented in this paper. It has two steps. Firstly, relative error (cErr) between the reference and image spectra is calculated. When cErr is greater than the relative error defined by the user (rErr), the current pixel is regarded as none-target and encoded as zero. Otherwise, the second step is executed to confirm whether or not the current pixel is target. In this second step, the similarity between reference and image spectra is calculated by sorting them respectively. When the similarity is greater than the identification error (the least error limits between reference and image spectra) defined by the user, the current pixel is encoded as one; otherwise, it is encoded as zero. A detailed description is provided of the backgrounds and principles of SSE, and its accuracy is evaluated using multiple categories. Experiments indicated that when the identification error is 15%, the mean accuracy of SSE is 95%, which is 41.9% higher than that of constrained energy minimization (CEM) and 46.9% higher than that of spectrally second-order derivative (SSD). Results of target detection experiments using Enhanced Thematic Mapper Plus (ETM+) and Hyperion images revealed that it could be used in both multispectral and hyperspectral remote sensing. [ABSTRACT FROM AUTHOR]

Image fusion is one of the most commonly used image enhancement techniques for improving the spatial quality of the source image with minimal spectral distortion in remote sensing. Until now, data fusion algorithms were developed and applied to improve the spatial resolution of the multispectral images and also their performances were evaluated depending on the source images such as Landsat Enhanced Thematic Mapper Plus (ETM+), Landsat Multispectral (MS)/Panchromatic (PAN), Satellite pour l'Observation de la Terre (SPOT) XS/PAN and IKONOS MS/PAN datasets. This paper assesses whether hyperspectral images, having very narrow bands compared to multispectral images, can be fused with high spatial resolution panchromatic images using common and current new algorithms including Intensity-Hue Saturation (IHS), Principal Component Substitution (PCS), Gram Schmidt Transformation (GST), Smoothing Filter-based Intensity Modulation (SFIM), Discrete Wavelet Transform (DWT), wavelet-based IHS (DWT-IMS) and PCS (DWT-PCS) and Fast Fourier Transform (FFT)-enhanced IHS. We also examine the performance of the fused hyperspectral images with respect to the fused multispectral images. For this purpose, two different source datasets (EO1 Hyperion/ALI PAN and EO1 ALI MS/PAN) were used. Some qualitative and quantitative analyses were implemented to assess the spatial and spectral quality of the fused images. The results show that it was possible to carry out the fusion of a narrow-band hyperspectral image and a high spatial resolution panchromatic image. The fusion of EO1 Hyperion/ALI PAN and EO1 ALI MS/PAN datasets using the SFIM, DWT-PCS, DWT-IHS and FFT-IHS algorithms produces better results than other techniques. Also, the results show that the fusion methods behaved for both datasets in the same performances except the DWT algorithm. The DWT method has a lower performance for the hyperspectral image compared to the multispectral image. Therefore the DWT algorithm should be further studied to improve the spectral qualities of a fused hyperspectral image based on wavelet transformation. [ABSTRACT FROM AUTHOR]

Though atmospheric correction model version 2 (ATCOR2) and version 3 (ATCOR3), developed by Richter (International Journal of Remote Sensing, 18, 1099-1111, 2007), have been successfully applied to various applications, these models simplify the azimuthal dependence of atmospheric functions in the atmospheric correction (AC) algorithm by using lookup tables (LUT) only at relative azimuthal angles of 30° and 150° for SPOT HRV data. Thus, they may generate errors as indicated by Liu (Terrestrial, Atmospheric and Oceanic Sciences, 15, 89-96, 2004). Since the error of retrieved surface reflectance was not considered as the one propagated from the error of the retrieved aerosol optical depth, it may not truly account for the error in AC. In this paper, errors in the AC processing chain are considered. The results show that the errors are more significant than those in the study by Liu. The errors can be significant along not only the principal plane but also the cross-principal plane, especially for urban aerosol with higher absorption than continental aerosol. Hence, the accuracy of AC functions considered by ATCOR2 and ATCOR3 for SPOT sensors needs to be improved. A simple and effective method to reduce the errors is also discussed. [ABSTRACT FROM AUTHOR]

Standard methodologies for estimating the thematic accuracy of hard classifications, such as those using the confusion matrix, do not provide indications of where thematic errors occur. However, spatial variation in thematic error can be a key variable affecting output errors when operations such as change detection are applied. One method of assessing thematic error on a per-pixel basis is to use the outputs of a classifier to estimate thematic uncertainty. Previous studies that have used this approach have generally used a single classifier and so comparisons of the relative accuracy of classifiers for predicting per-pixel thematic uncertainty have not been made. This paper compared three classification methods for predicting thematic uncertainty: the maximum likelihood, the multi-layer perceptron and the probabilistic neural network. The results of the study are discussed in terms of selecting the most suitable classifier for mapping land cover or predicting thematic uncertainty. [ABSTRACT FROM AUTHOR]

This paper systematically derives and analyses the generic phenomenon of space-invariant predictability of spatio-temporal observation fields using past multitemporal observations. We focus on thermal infrared remote sensing as a non-trivial example illustrating the predictability concept. The phenomenon and the systematic analysis thereof are experimentally demonstrated to be productive for developing effective automated anomaly detection and classification methods operating under the assumption of dynamic environment and sensor response. Using a simple preliminary experiment involving uncalibrated tower-based high-resolution thermal infrared surveillance, we test the conceptual validity of the space-invariant multitemporal prediction and exemplify its potential applications. In addition, we use a MODIS thermal image sequence and the task of hot anomaly detection to demonstrate the applicability of the approach for monitoring the status of large territories from space-borne platforms. [ABSTRACT FROM AUTHOR]

In this paper, a general formula has been modified, proving that the scaling difference of a surface parameter depends not only on the variance of the surface parameter itself but also on the function structure of the surface parameter. Through quantitatively describing the relationship between scaling differences and measuring scale, in terms of the concept of information fractal dimension and topological dimension, a definition of information fractal dimension used in remote sensing was proposed. By computing the information fractal dimension of Leaf Area Index and surface temperature, we found that the method describes not only the information on spatial texture and spatial structure of remotely sensed data as the traditional methods did, but also illustrates the connection between the scaling difference and measuring scale. Where the information fractal dimension of a surface parameter in some areas is known, the scaling difference can be obtained according to the measuring scale, then it can be eliminated and more accurate results could be achieved after scaling transform. At last, the problems about the relativity of true values of surface parameters were discussed. [ABSTRACT FROM AUTHOR]

Land remote-sensing images are the primary means of assessing land change. There have been major land changes in the planet in the last decades, especially in tropical forest areas. Identifying the agents of deforestation is important for establishing public policies that can help preserve the environment. This paper proposes a method for detecting the agents of land change in remote-sensing image databases. We associate each land-change pattern, detected in a remote-sensing image, to one of the agents of change. The proposed method uses a decision-tree classifier to describe shapes found in land-use maps extracted from remote-sensing images and then associates these shape descriptions to the different types of social agents involved in land-use change. We support our proposal with two case studies for detecting land-change agents in Amazonia, using the remote-sensing image database of the Brazilian National Institute for Space Research (INPE). [ABSTRACT FROM AUTHOR]

Remote-sensing change detection based on multitemporal, multispectral, and multisensor imagery has been developed over several decades and provided timely and comprehensive information for planning and decision-making. In practice, however, it is still difficult to select a suitable change-detection method, especially in urban areas, because of the impacts of complex factors. This paper presents a new method using multitemporal and multisensor data (SPOT-5 and Landsat data) to detect land-use changes in an urban environment based on principal-component analysis (PCA) and hybrid classification methods. After geometric correction and radiometric normalization, PCA was used to enhance the change information from stacked multisensor data. Then, a hybrid classifier combining unsupervised and supervised classification was performed to identify and quantify land-use changes. Finally, stratified random and user-defined plots sampling methods were synthetically used to obtain total 966 reference points for accuracy assessment. Although errors and confusion exist, this method shows satisfying results with an overall accuracy to be 89.54% and 0.88 for the kappa coefficient. When compared with the post-classification method, PCA-based change detection also showed a better accuracy in terms of overall, producer's, and user's accuracy and kappa index. The results suggested that significant land-use changes have occurred in Hangzhou City from 2000 to 2003, which may be related to rapid economy development and urban expansion. It is further indicated that most changes occurred in cropland areas due to urban encroachment. [ABSTRACT FROM AUTHOR]