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Leaf Area Index (LAI) remains one of the most important forest structural attributes, as accurate estimation of LAI is crucial for predicting the growth of different species. Using the Partial Least Squares Regression (PLS-R) algorithm, this study investigated three image processing techniques to determine the best technique for estimating LAI using WorldView-3 imagery in the Midlands, KwaZulu-Natal province of South Africa. The PLS-R texture ratios model achieved the highest accuracy of R2 = 0.70, RMSE = 1.21 (2.32% of the mean measured LAI) and R2 = 0.72, RMSE = 1.26 (2.41% of the mean measured LAI) for the wet and dry seasons, respectively. This was followed by the PLS-R single texture band model that produced an accuracy of R2 = 0.65, RMSE = 1.35 (2.58% of the mean measured LAI) and R2 = 0.67, RMSE = 1.32 (2.52% of the mean measured LAI) for the wet and dry seasons, respectively. The PLS-R model using a combination of vegetation indices had the lowest estimation accuracy of R2 = 0.59, RMSE = 1.38 (2.64% of the mean measured LAI) and R2 = 0.60, RMSE = 1.40 (2.67% of the mean measured LAI) for the wet and dry seasons, respectively. The results of this study provided evidence that image texture ratios can be used to estimate LAI effectively. [ABSTRACT FROM AUTHOR]

An accurate global carbon budget requires information on terrestrial carbon sink strength. Regenerating tropical forests are known to be important terrestrial carbon sinks but information on their location, extent and biomass (from which carbon content can be estimated) is incomplete. The use of remotely sensed data in optical wavelengths has been of limited use due to both the weak relationship between optical radiation and forest biomass and near‐constant cloud cover in the tropics. L‐band Synthetic Aperture Radar (SAR) backscatter, however, is related positively to biomass (but only up to an asymptote of around 40–90 T ha -1 ) and can be obtained independently of cloud cover. Both canopy structure and biomass change over time as pioneer species are replaced by early and late regenerating species. These structural changes are related to an increase in (i) tree height, (ii) tree species richness and (iii) canopy thickness and influence the roughness of the canopy surface and consequently SAR image texture. Therefore, we investigated the degree to which textural information could be used to increase the correlation between image tone (backscatter) and biomass. Field data were used to estimate the biomass of 37 regenerating forests plots in Brazilian Amazonia. Texture measures derived from local statistics, the grey level co‐occurrence matrix (GLCM) and the variogram were evaluated using simulated images on the basis of their ability to identify significant differences in image texture independently of image contrast. The selected texture measures were applied to L‐band JERS‐1 (Japanese Earth Resources Satellite) SAR images and the correlation between backscatter and biomass was determined for regenerating tropical forests. A strong correlation was found for the texture measures and biomass. The r a 2 (adjusted coefficient of determination), measuring the correlation between backscatter and biomass, increased from 0.74 to 0.82 with the addition of GLCM‐derived contrast. The addition of image texture (GLCM‐derived contrast) to image tone (backscatter) potentially increases the accuracy with which JERS‐1 SAR data can be used to estimate biomass in tropical forests. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new algorithm for remotely sensed image texture classification and segmentation. We observe that the traditional method least square error (LSE) is unstable in practical applications. This motivates us to develop a more stable method. We have proposed the regularization technique to suppress the instability of LSE in previous research. Our contribution in this paper is that we propose a new stable method, which is based on the total variation (TV) for reducing instability in texture analysis, and apply it to remotely sensed image texture classification and segmentation. Experimental results on remotely sensed images demonstrate that our new algorithm is superior to LSE and seems promising in applications. [ABSTRACT FROM AUTHOR]

A Compact Airborne Spectrographic Imager (CASI) multiresolution dataset, comprised of imagery with spatial resolutions of 60 cm, 1 m and 2 m, was used to asses the relationship between defoliation severity of aspen ( Populus tremuloides Michx.) stands infested with the Bruce spanworm ( Operophtera bruceata ), the Leaf Area Index (LAI) of these stands and the CASI image components comprising of Normalized Difference Vegetation Index (NDVI) and image texture. The study area was located in the foothills of the Alberta Canadian Rockies. Defoliation severity, LAI, and crown closure of the stands were measured on the ground. Multiple stepwise regression methods were used to develop relationships between the field and imagery data. Image texture derived from the grey-level co-occurrence matrix of the first principle component, or 'brightness' image, was incorporated into the discriminant analysis of defoliation severity classes. The highest spatial resolution imagery outperformed the coarser image resolutions. The characteristics of the defoliation changed the spectral response of the moderately and severely defoliated stands considerably when compared to healthy stands. The following paper demonstrates that aspen defoliation severity can be detected with CASI image data, specifically through the incorporation of imagery spatial component captured by image texture. [ABSTRACT FROM AUTHOR]

The suitability of optical IKONOS satellite data (multispectral and panchromatic) for the estimation of forest structural attributes – for example, stems per hectare (SPHA), diameter at breast height (DBH), mean tree height (MTH), basal area (BA) and volume in plantation forest environments – was assessed in this study. The relationships of these forest structural attributes to statistical image texture from IKONOS imagery were analysed. The coefficients of determination (R 2) of multilinear regression models developed for the estimation of SPHA, DBH, MTH, BA and volume using statistical texture features from multispectral data were 0.63, 0.68, 0.81, 0.86 and 0.86, respectively. When the statistical texture features from panchromatic data were applied, the R 2 for the respective forest structural attributes increased by 25%, 31%, 6%, 0.2% and 0.2%, respectively. Artificial neural network (ANN) models produced strong and significant relationships between estimated and actual measures of SPHA, DBH, MTH, BA and volume with an R 2 of 0.83, 0.83, 0.90, 0.90 and 0.92, respectively, based on multispectral IKONOS data. Based on panchromatic IKONOS imagery, the R 2 for the respective forest structural attributes increased by 18%, 12%, 5%, 3% and 6%, respectively. Results such as these bode well for the application of high spatial resolution imagery to forest structural assessment. [ABSTRACT FROM AUTHOR]

This study evaluated image texture within a multi-scale context. The multi-scale texture profile is a representation of texture as a function of scale. It is related to the variogram, but has the advantage of being a direct measure of local texture over a specified kernel. Directional texture is a method to capture the dependence of texture on orientation using 1-dimensional kernels, and is potentially useful for quantifying texture for classes with a linear shape, such as roads or rivers. Seven derived texture attributes were proposed from the multi-scale texture profile and directional texture: the minimum, median, mean, maximum, range, minimum directional, and minimum multi-scale and directional texture. The derived texture attributes were found to be useful in developing a locally adaptive texture measure, which uses different texture attributes for different locations in the image. In order to develop the rules for the adaptive method, seven characteristic multi-scale texture profiles were identified. Rules were established to identify the general classes, and each pixel was assigned to one class only, depending on that pixel's texture profile. Optimal texture measures were proposed for each class. In an analysis using selected texture classes from IKONOS test data from Jeju, Korea, it was found that compared to all other texture measures, the adaptive texture provided the highest average and minimum classification accuracy, as well as the second highest maximum accuracy (69%, 51% and 92%, respectively). In comparison, the best fixed size kernel, 11 × 11 pixels, had accuracies in the same categories of 68%, 38% and 87%, and the original panchromatic image band had accuracies of 41%, 5% and 92%. Future work is needed to enhance the texture rules, and apply the adaptive texture method to other scales of data. [ABSTRACT FROM PUBLISHER]

Land-use/cover change dynamics were investigated in a Mediterranean coastal wetland. Change Vector Analysis (CVA) without and with image texture derived from the co-occurrence matrix and variogram were evaluated for detecting land-use/cover change. Three Landsat Thematic Mapper (TM) scenes recorded on July 1985, 1993 and 2005 were used, minimizing change detection error caused by seasonal differences. Images were geometrically, atmospherically and radiometrically corrected. CVA without and with texture measures were implemented and assessed using reference images generated by object-based supervised classification. These outputs were used for cross-classification to determine the 'from-to' change used to compare between techniques. The Landsat TM image bands together with the variogram yielded the most accurate change detection results, with Kappa statistics of 0.7619 and 0.7637 for the 1985-1993 and 1993-2005 image pairs, respectively. [ABSTRACT FROM AUTHOR]

Data on forest variables are required for environmental and forest management applications. Numerous authors have shown significant correlations between mean radar response intensity and forest variables (age, height or biomass) but few studies have explored the spatial characteristics of the radar response for varying forest states. In this Letter, variation in the most commonly used texture features is shown as a function of an indicator of forest growth (age) for a controlled homogeneous test site (monospecific, even-aged forest, with identical sylvicultural practices and a sampling that covers all forest stages from sowing to harvest). Significant linear relationships between some texture features and stand age are observed. Moreover, the quality of some fits indicates that texture could be used instead of the usual intensity-age relationships that saturate for mature stands. [ABSTRACT FROM AUTHOR]

Based on a differential box counting method and a gliding-box algorithm, a new method for estimating the lacunarity of grey scale digital image surfaces is introduced, and directionality of lacunarity defined. To test the performance of the new lacunarity measure, a Brodatz texture image mosaic is employed and several other texture analysis approaches are also applied to the texture mosaic. Quantitative comparison shows that the new lacunarity estimation method for grey-scale images can provide more accurate texture measurements than some existing lacunarity measures, the grey level co-occurrence matrix based texture measures, the Min-Max operator, and the fractal dimension. [ABSTRACT FROM AUTHOR]

In the past one difficulty of texture analysis was the lack of adequate tools to characterize different scales of texture effectively. Recent developments in multiresolution analysis such as the Gabor and wavelet transforms, help to overcome this difficulty. This paper introduces a new approach to characterize texture at multiple scales. The performances of the wavelet transform are measured in terms of sensitivity and selectivity for the classification of 25 types of remote sensing texture relief images under the condition of different wavelet decomposition models, different filter lengths, different resolutions and different mother bodies. The reliability exhibited by texture signatures of wavelet transforms are beneficial for accomplishing segmentation, classification and subtle discrimination of texture. [ABSTRACT FROM AUTHOR]

A Landsat TM image, part of a rainforest located in the state of Rondonia, Brazil, was analysed using Fourier transform techniques in order to extract textural features. The lattice-like texture pattern recorded was then compared with the geological structural trends of the area, and measures of the soil surface moisture treated by geostatistical analysis. The consistency between the different sets of analysis suggests that the spatial distribution of moisture at the soil surface is related to the structure of the bedrock. The near infrared channel of the Landsat TM may be used to record trends in water content distributions at the soil surface under homogeneous forest canopies. [ABSTRACT FROM AUTHOR]

The transition zone between the boreal forest and Arctic tundra, the forest-tundra ecotone (FTE), is an area of high ecological and climatological significance. Despite its importance, a globally consistent high spatial resolution mapping is lacking. Accurate mapping of the FTE requires the use of satellite remote sensing data. Here we use the Landsat Vegetation Continuous Fields (VCF) product and reference point data to derive the location and characteristics of the FTE. An image texture-based supervised classification scheme is developed based on a study area in Central Eurasia to statistically exploit the spatial patterns of the transition zone. Texture statistics for the VCF image are derived from the grey-level co-occurrence matrix (GLCM) based on which the study area is classified into forest, tundra, and FTEs. Adaptive parameterization is implemented to achieve optimal classification performance in the study area. This method is further applied to six additional study areas around the circumarctic region to test its adaptability. In all study areas, this method achieves better FTE delineation results than previously reported methods, showing better classification accuracies (average of 0.826) and more realistic and complete representation of the FTE as shown by visual examination. This shows the universal applicability of the method and it is potential to be used to achieve more detailed and accurate circumarctic mapping of the FTE, which could serve as the basis of time series analysis of FTE positions, eventually contributing to a better understanding of the inter-relations between climate change and shifts in sub-arctic vegetation. [ABSTRACT FROM AUTHOR]

Secondary forests cover large areas and are strong carbon sinks in tropical regions. They are important for ecosystem functioning, biodiversity conservation, watershed protection, and recovery of soil fertility. In this study, we used the Surface Reflectance Climate Data Record (CDR) product from 16 Thematic Mapper (TM)/Landsat-5 images (1984–2010) to continuously track the secondary succession (SS) of a forest following land abandonment in 1980. Changes in canopy structure and floristic composition were analysed using data from four field inventories (1995, 2002, 2007, and 2012). To characterize variations in brightness, greenness, spectral reflectance, and shadows with the natural regeneration of vegetation, we applied tasselled cap transformations, principal component analysis (PCA), and linear spectral mixture models to the TM datasets. Shade fractions were plotted over time and correlated with the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI). Because image texture may reflect the variability of the successional process, eight co-occurrence-based filter metrics were calculated for selected TM bands and plotted as a function of time since abandonment. The successional forest was compared to a nearby primary reference forest (PF) and had differences in the spectral and textural means evaluated using analysis of variance (ANOVA). The results showed increases of 35% and 10.4% over time in basal area and tree height, respectively. Species richness within the assemblage of sampling units increased from 14 to 71 between 1995 and 2012, and this trend was also confirmed using an individual-based rarefaction analysis. Species richness in 2012 was still lower than that observed in the PF site, which presented greater amounts of aboveground biomass (336.4 ± 17.0 ton ha−1for PFversus98.5 ± 21.4 ton ha−1for SS in 2012). Brightness and greenness tasselled cap differences between the SS and PF rapidly decreased from 1984 (SS at the age of 4 years) to 1991 (age of 11 years). Brightness also decreased from 1997 to 2003, as indicated by PC1 scores and surface reflectance of the TM bands 4 (near infrared) and 5 (shortwave infrared). Spectral mixture shade fraction increased from young to old successional stages with strata composition and canopy structure development, whereas NDVI and EVI decreased over time. Because EVI was strongly dependent on near infrared reflectance (r = + 0.96), it was also much more strongly correlated with the shade fraction (r = −0.93) than NDVI. Except for the image texture mean that decreased from young to old successional stages in TM bands 4 and 5, no clear trend was observed in the remaining texture metrics over the time period of vegetation regeneration. Overall, due to structural-floristic and spectral/textural differences with the PF, the SS site was still distinguishable using Landsat data 30 years after land abandonment. Most of the spectral metric means between PF and SS were significantly different over time at 0.01 significance level, as indicated by ANOVA. [ABSTRACT FROM AUTHOR]

Stereo dense image matching normally refers to per-pixel correspondence search between stereo pairs. However, their matching results actually depend on matching window sizes. Large windows usually obtain robust matching results in weak-textured regions, but serious mismatches in depth/disparity (parallax in the epipolar space) inconsistency regions. Small windows compute accurate matching results in depth/disparity inconsistency regions, but it may contain high matching uncertainties in weak-textured regions. To improve matching accuracies, this letter focuses on adaptively selecting appropriate matching windows for each pixel. In general, we propose a window size selection network (WSSN) with the basic assumption that appropriate window sizes are related to image textures and depth/disparity variations. WSSN firstly extracts both image texture features and disparity features by convolutional neural network and then utilizes the fully connected layers to conduct optimal window size selection. Experiments on an aerial image dataset show that our proposed method is capable of selecting appropriate matching windows for each pixel. It achieved the highest matching accuracy when compared with the matching results of a series of fixed matching windows and a state-of-the-art textured based window selection method. [ABSTRACT FROM AUTHOR]

This study presents a predictive modelling technique to map population distribution and abundance for rural areas in Africa. Prediction models were created using a generalized regression analysis and spatial prediction (GRASP) method that uses the generalized additive model (GAM) regression technique. Dwelling unit presence–absence was mapped from airborne images covering 98 km2 (30% of the study area) and used as a response variable. Remote-sensing-based (reflectance, texture and land cover) and geospatial (topography, climate and distance) data were used as predictors. For the rest of the study area (228 km2; 70%), GAM models were extrapolated, and prediction maps constructed. Model performance was measured as explanatory power (adj.D 2, adjusted deviance change), predictive power (area under the receiver operator curve, AUC) and kappa value (κ). GAM models explained 19–31% of the variation in dwelling-unit occurrence and 28–47% of the variation in human population abundance. The predictive power for population distribution GAM models was good (AUC of 0.80–0.86). This study shows that for the prediction of dwelling-unit distribution and for human population abundance, the best modelling performance was achieved using combined geospatial- and remote-sensing-based predictor variables. The best predictors for modelling the variability in human population distribution using combined predictors were angular second moment image-texture measurement, precipitation, mean elevation, surface reflectance for Satellite Pour l'Observation de la Terre (SPOT) red and near-infrared (NIR) bands, correlation image-texture measurement and distance to roads, respectively. The population-abundance modelling result was compared with two existing global population datasets: Gridded Population of the World version 3 (GPWv3) and LandScan 2005. The result showed that for regional and local-scale population-estimation probability, models created using remotely sensed and geospatial data were superior compared to GPWv3 or LandScan 2005 data products. Population models had high correlation with Kenyan population census data for 1999 in mountainous sub-locations and low correlation for sub-locations that also extended into the lowlands. [ABSTRACT FROM AUTHOR]

Image texture is a complex visual perception. With the ever-increasing spatial resolution of remotely sensed data, the role of image texture in image classification has increased. Current approaches to image texture analysis rely on a single band of spatial information to characterize texture. This paper presents a multiscale approach to image texture where first and second-order statistical measures were derived from different sizes of processing windows and were used as additional information in a supervised classification. By using several bands of textural information processed with different window sizes (from 5×5 to 15×15) the main forest stands in the image were improved up to a maximum of 40%. A geostatistical analysis indicated that there was no single window size that would adequately characterize the range of textural conditions present in this image. A number of different statistical texture measures were compared for this image. While all of the different texture measures provided a degree of improvement (from 4 to 13% overall), the multiscale approach achieved a higher degree of classification accuracy regardless of which statistical procedure was used. When compared with single band texture measures, the level of overall improvement varied between 4 and 8%. The results indicate that this multiscale approach is an improvement over the current single band approach to analysing image texture. [ABSTRACT FROM AUTHOR]

Image texture can be an important source of data in the image classi-fication process. Although not as easily measurable as image spectral attributes, image texture has proved in a number of cases to be a valuable source of data capable of increasing the accuracy of the classification process. In remote sensing there are cases in which classes are spectrally very similar, but present distinct spatial distribution, i.e. different textural characteristics. Image texture becomes then an important source of information in the classification process. The aim of this study is (1) to develop and test a supervised image classification method based on the image spatial texture as extracted by the Gabor filtering concept and (2) to investigate experimentally the performance of the classification process as a function of the Gabor filter's parameters. A set of Gabor filters is initially generated for the given image data. The filter parameters related to the relevant spatial frequencies present in the image are estimated from the available samples via the Fourier transform. Each filter generates one filtered image which characterizes the particular spatial frequency implemented by the filter parameters. As a result, a number of filtered images, sometimes referred to as 'textural bands', are generated and the originally univariate problem is transformed into a multivariate one, every pixel being defined by a vector with dimension identical to the number of filters used. The multidimensional image data can then be classified by implementing an appropriate supervised classification method. In this study the Euclidean Minimum Distance and the Gaussian Maximum Likelihood classifiers are used. The adequacy of the selected Gabor filter parameters (namely, the spatial frequency and the filter's spatial extent) are then examined as a function of the resulting classification accuracy. The proposed supervised methodology is tested using both synthetic and real image data. Results... [ABSTRACT FROM AUTHOR]

In order to improve the utilization rate of spectroscopic data and texture information, this study proposes a method for optimal selection of spectrum and texture features based on automatic subspace division and rough set theory. This method takes advantage of rough set reduct ideology in order to realize the reduction of different types of ground object spectral features on the basis of the conventional subspace division method. In using this method, the primary spectral band based on spectral information can be determined. Then, the grey-level co-occurrence matrix method can be used to calculate the texture information of the primary spectral band and determine the reduction and optimization in order to obtain the final band based on the spectrum and texture information. Verification of this method is made by using CASI data of Heihe Region, China, and AVIRIS data of the Indiana Region, USA, and also using Support Vector Machine (SVM) classification of the original spectral, primary spectral, and final bands. The results indicate the following. (1) The method for optimal selection of the critical spectral band and texture band, based on the rough set theory, can efficiently improve the classification accuracy of high-spatial resolution remote-sensing images. However, the effects for the low-spatial resolution images are minimal. (2) For high-spatial-resolution remote-sensing images, such as roads, trenches, buildings, and other types of object with obvious textural features, the addition of image texture information can increase the degree of distinction of these different types and thereby improve the classification accuracy. However, the addition of the textural information for some objects with similar texture features will cause misclassification and reduce the classification accuracy for these types of images. (3) This method can realize the optimal selection of spectrum and texture bands of a hyperspectral image and has a certain universality. Also, the texture information will be richer and this method will be more practical through increasing the spatial resolution of images. [ABSTRACT FROM AUTHOR]

The growing interest in the development of small satellites and the demand for high-resolution imaging has made the pointing and drift rate requirements of a satellite more stringent. To achieve high pointing accuracy, star sensors can be used, but their size and weight are too large for small satellites. The need for keeping the overall cost of the spacecraft down and still achieve adequate pointing accuracies has provoked the development of relatively inexpensive and high-performance attitude systems that can provide competitive pointing accuracies during imaging operations. In order to realize such a system, this research describes a novel approach for finding the attitude of a satellite at any arbitrary rotation by using inter-band offsets from a single multi-spectral imager (MSI). For Earth observational imagery, UK Disaster Monitoring Constellation Earth-pointing MSI is used. This research focuses on the potential use of a narrow angle between the bands of a pushbroom sensor for determining the attitude of a spacecraft. The technique investigated does not require ground control points or knowledge of any ground features, but rather estimates the orientation of the platform through analysis of perspective and timing-based distortions between images. These distortions are assumed translational and affine in nature, with two-dimensional shifts being extracted from imagery using a Singular Value Decomposition-based registration scheme. In order to better understand the effect of attitude on imagery, a model was developed for predicting inter-band shifts given an attitude. This was then used to estimate the shifts between imagery at nominal attitude and given a series of simulated manoeuvres. Several simulations have shown that the row and column offsets represented a straight line. Hence, we expressed the row and column shifts in terms of straight line parameters. These geometric attributes are then represented in terms of Euler axis/angle to find the mapping for the elements of the general rotation matrix. Once the mapping is computed for the elements of the rotation matrix, we used the standard equations to determine the angle of rotation and Euler axis. The accuracy of attitude estimates depends on the magnitude of angular separation between the cameras, orientation of spacecraft, sensor resolution, image texture, and image registration method. The technique proposed in this study may however be applied to any satellite with pushbroom sensors that have a discernable along track separation and sufficient overlap. [ABSTRACT FROM AUTHOR]

This article proposes a new multispectral image texture segmentation algorithm using a multi-resolution fuzzy Markov random field model for a variable scale in the wavelet domain. The algorithm considers multi-scalar information in both vertical and lateral directions. The feature field of the scalable wavelet coefficients is modelled, combining with the fuzzy label field describing the spatially constrained correlations between neighbourhood features to achieve a more accurate parameter estimation. The extended scalable label field models the label data from different scales to obtain more homogeneous areas; image segmentation results are finally obtained according to the Bayesian rule from a coarser to a finer scale. Multispectral texture images and remote-sensing images are used to test the effectiveness of the the proposed method. Segmentation results show that the new method simultaneously presents a better performance in achieving the homogeneity of the region and accuracy of detected boundaries compared with existing image segmentation algorithms. [ABSTRACT FROM PUBLISHER]

Many organisms rely on reedbed habitats for their existence, yet, over the past century there has been a drastic reduction in the area and quality of reedbeds in the UK due to intensified human activities. In order to develop management plans for conserving and expanding this threatened habitat, accurate up-to-date information is needed concerning its current distribution and status. This information is difficult to collect using field surveys because reedbeds exist as small patches that are sparsely distributed across landscapes. Hence, this study was undertaken to develop a methodology for accurately mapping reedbeds using very high resolution QuickBird satellite imagery. The objectives were to determine the optimum combination of textural and spectral measures for mapping reedbeds; to investigate the effect of the spatial resolution of the input data upon classification accuracy; to determine whether the maximum likelihood classifier (MLC) or artificial neural network (ANN) analysis produced the most accurate classification; and to investigate the potential of refining the reedbed classification using slope suitability filters produced from digital terrain data. The results indicate an increase in the accuracy of reedbed delineations when grey-level co-occurrence textural measures were combined with the spectral bands. The most effective combination of texture measures were entropy and angular second moment. Optimal reedbed and overall classification accuracies were achieved using a combination of pansharpened multispectral and texture images that had been spatially degraded from 0.6 to 4.8 m. Using the 4.8 m data set, the MLC produced higher classification accuracy for reedbeds than the ANN analysis. The application of slope suitability filters increased the classification accuracy of reedbeds from 71% to 79%. Hence, this study has demonstrated that it is possible to use high resolution multispectral satellite imagery to derive accurate maps of reedbeds through appropriate analysis of image texture, judicious selection of input bands, spatial resolution and classification algorithm and post-classification refinement using terrain data. [ABSTRACT FROM PUBLISHER]

Airborne sensor image texture derived following a geostatistical analysis can increase the accuracy of forest classification because the resulting texture is insensitive to random variations in spectral response but related to the structural features of interest at the scale of a forest inventory (e.g. tree species). The combination of spectral and textural data derived from a kriging surface provided 86% classification accuracy in 36 pure and mixed-wood stands in seven forest classes in Alberta. This is an increase over the classification accuracy obtained when texture was derived from the original image data, and when the spectral response patterns were used alone. [ABSTRACT FROM AUTHOR]

Human colour vision has the ability to recover a measure of contextdependent surface reflectance from observed areas. This can be seen as an analogue to radiometric corrections employed in remote sensing. Procedures based on human colour vision applied in the context of remote sensing could simplify image preprocessing and classification. This study evaluates an algorithm based on Land's colour vision or 'retinex' theory. When tested on remotely sensed images, the algorithm had difficulty coping with the presence of image texture. A new framework is presented that adapts Land's procedure to processing of remotely sensed images using adaptive thresholding techniques. [ABSTRACT FROM AUTHOR]

Although research with digital airborne remote sensing data has been undertaken in different ecoregions to classify forested areas, the potential role of such imagery in deriving information to assist forest management has not yet been fully defined. The objective of this study was to determine the extent that the addition of texture could improve spectral classification of high spatial resolution images (pixel size 1m). These images represented pure and mixed wood forest stands from ecoregions in Alberta and New Brunswick, Canada. This study employed a judicious, selective application of texture to stands within a hierarchical classification framework. In Alberta, the addition of texture made a modest improvement in classification accuracy from 60% to 65%. In New Brunswick, the application of texture to selected land cover types resulted in an overall 12% improvement in classification accuracy. The addition of image texture increased classification accuracy for high spatial detail imagery relative to low spatial detail imagery. Incorporating texture into classification also improved classification accuracies for hardwood stands more so than for softwood stands, but greater attention to stand structure and composition will be needed in future work. Classification accuracies on the order of 60-65% were achieved with simple texture derivatives, maximum likelihood decision rules and conventional classification methods. [ABSTRACT FROM AUTHOR]

This study used four near-real-time multispectral Systeme pour l'Observation de la Terre (SPOT) high-resolution visible (HRV ) images to establish land cover and forest classes of relevance to slash-and-burn agriculture. The study was conducted in 1.43 million ha of the Alternative to Slash-and-Burn (ASB) global benchmark research area for Africa selected as representative of the entire Congolese basin of Central Africa and located in Southern Cameroon. The land cover and forest classes mapped have different impacts on global phenomena as well as their own management challenges and include slash-and-burn dominant farmlands, C hromolenea orodata dominant short-fallows, Imperata cylindrica dominant weeds, long-fallows or regenerated forests, raphia palm-dominant lowlands, permanently flooded swamp forests, and primary and secondary forests. In order to map these distinct and complex classes the study proposes and implements a piecemeal approach to classification involving stratification of the image into several distinct discrete subsets of forest corridors, lowlands, and uplands. The approach involves the use of image texture indicators in conjunction with groundtruth data to divide the image into discrete subsets, performing unsupervised classification on these discrete subsets, masking problem classes and reclassifying them, editing certain spectrally inseparable areas, adopting post-classification strategies, and finally mosaicking the discrete subsets into one seamless image. This approach led to an overall mapping accuracy of 82.4% with individual classes mapped at accuracies above 72.9% (user's), and above 66.7% (producer's). [ABSTRACT FROM AUTHOR]