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• The addition of texture features could improve the accuracy of soil interpretations with remote sensing. • The optimal single-scale window size of the feature parameters was extracted. • The optimal multiscale combination of window sizes for each parameter were determined. • The multiscale fusion of texture feature windows was better than the single-scale window in soil mapping. Studying the spatial distribution of soil types is an important academic and practical issue in agriculture. With the rapid development of remote sensing technology, the role of image texture as an auxiliary variable in remote sensing identification of objects has increased. It is of great importance to ascertain the optimal window size for extracting texture features and the multiscale fusion of texture feature parameters under the optimal window for different soil types. To reach this goal, soil types in a typical area of the Jiaodong Peninsula were selected as the subject investigated, homogeneity and entropy were selected as the two texture feature parameters, and the ability to identify the different soil types based on the textural features was systematically analyzed by using Landsat 8 remote sensing images. Moreover, the optimal window sizes for extracting texture features were determined, and the role of multiscale textural features in the classification of the soil types was also evaluated. The results show that the accuracy of classification significantly increased with the addition of textural features. The optimal single-scale window sizes for the homogeneity and entropy feature parameters were 19 × 19 and 21 × 21, respectively. The fusion of multiscale textural features further improved the classification accuracy. The optimal multiscale window sizes for the homogeneity were 7 × 7, 13 × 13, 19 × 19 and 21 × 21 and those for entropy were 5 × 5, 15 × 15, 21 × 21 and 23 × 23. Therefore, the method of using texture information in remote sensing images as auxiliary variables in digital soil mapping was feasible. The method of multiscale fusion of texture features, which resulted in greater classification accuracy, was better than that of single-scale window. These conclusions could play an important guiding role in soil digital mapping with remote sensing. [ABSTRACT FROM AUTHOR]

Abstract: Quantifying the above ground biomass of tropical forests is critical for understanding the dynamics of carbon fluxes between terrestrial ecosystems and the atmosphere, as well as monitoring ecosystem responses to environmental change. Remote sensing remains an attractive tool for estimating tropical forest biomass but relationships and methods used at one site have not always proved applicable to other locations. This lack of a widely applicable general relationship limits the operational use of remote sensing as a method for biomass estimation, particularly in high biomass ecosystems. Here, multispectral Landsat TM and JERS-1 SAR data were used together to estimate tropical forest biomass at three separate geographical locations: Brazil, Malaysia and Thailand. Texture measures were derived from the JERS-1 SAR data using both wavelet analysis and Grey Level Co-occurrence Matrix methods, and coupled with multispectral data to provide inputs to artificial neural networks that were trained under four different training scenarios and validated using biomass measured from 144 field plots. When trained and tested with data collected from the same location, the addition of SAR texture to multispectral data showed strong correlations with above ground biomass (r =0.79, 0.79 and 0.84 for Thailand, Malaysia and Brazil respectively). Also, when networks were trained and tested with data from all three sites, the strength of correlation (r =0.55) was stronger than previously reported results from the same sites that used multispectral data only. Uncertainty in estimating AGB from different allometric equations was also tested but found to have little effect on the strength of the relationships observed. The results suggest that the inclusion of SAR texture with multispectral data can go someway towards providing relationships that are transferable across time and space, but that further work is required if satellite remote sensing is to provide robust and reliable methodologies for initiatives such as Reducing Emissions from Deforestation and Degradation (REDD+). [Copyright &y& Elsevier]

Abstract: Remote sensing techniques have been seen as valuable and low-cost tools for frequent forest inventory purposes. However, estimation errors of relevant forest structure variables remain too high for operational use of high spatial resolution satellite imagery, such as Landsat TM/ETM and SPOT HRV, in temperate forests. Very high spatial resolution images that have been acquired by new commercial satellites, such as IKONOS-2 or QuickBird, are expected to reduce estimation errors to a level that is acceptable by foresters. This study assessed the capability of 1-m resolution IKONOS-2 imagery to estimate the five main forest variables—age, top height, circumference, stand density and basal area—in even-aged common spruce stands. They were estimated on the basis of texture features that were derived from the grey-level co-occurrence matrix (GLCM). The coefficients of determination, R 2, of the best models ranged from 0.76 to 0.82 for top height, circumference, stand density and age variables. Basal area was found to be weakly correlated to texture variables (R 2 =0.35). Relative prediction errors of four out of the five studied forest variables were comparable to the usual sampling inventory errors (top height: 10%; circumference: 15%; basal area: 16%; age: 18%), but the stand density estimation error (29%) remained too high for use in forest planning. The sensitivity analysis to the GLCM parameters showed that the most important parameters were the texture feature, the displacement and the window size. The orientation parameter had minimal effects on the R 2 values, even if it influenced the values of the texture features. [Copyright &y& Elsevier]

Abstract: Texture analysis has been used extensively in the computer-assisted interpretation of digital imagery. A popular texture feature extraction approach is the grey level co-occurrence probability (GLCP) method. Most investigations consider the use of the GLCP texture features for classification purposes only, and do not address segmentation performance. Specifically, for segmentation, the pixels in an image located near texture boundaries have a tendency to be misclassified. Boundary preservation when using the GLCP texture features for image segmentation is important. An advancement which exploits spatial relationships has been implemented. The generated features are referred to as weighted GLCP (WGLCP) texture features. In addition, an investigation for selecting suitable GLCP parameters for improved boundary preservation is presented. From the tests, WGLCP features provide improved boundary preservation and segmentation accuracy at a computational cost. As well, the GLCP correlation statistical parameter should not be used when segmenting images with high contrast texture boundaries. [Copyright &y& Elsevier]

Calculation of co-occurrence probabilities is a popular method for determining texture features within remotely sensed digital imagery. Typically, the co-occurrence features are calculated by using a grey level co-occurrence matrix (GLCM) to store the co-occurring probabilities. Statistics are applied to the probabilities in the GLCM to generate the texture features. This method is computationally intensive since the matrix is usually sparse leading to many unnecessary calculations involving zero probabilities when applying the statistics. An improvement on the GLCM method is to utilize a grey level co-occurrence linked list (GLCLL) to store only the non-zero co-occurring probabilities. The GLCLL suffers since, to achieve preferred computational speeds, the list should be sorted. An improvement on the GLCLL is to utilize a grey level co-occurrence hybrid structure (GLCHS) based on an integrated hash table and linked list approach. Texture features obtained using this technique are identical to those obtained using the GLCM and GLCLL.The GLCHS method is implemented using the C language in a Unix environment. Based on a Brodatz test image, the GLCHS method is demonstrated to be a superior technique when compared across various window sizes and grey level quantizations. The GLCHS method required, on average, 33.4% (σ=3.08%) of the computational time required by the GLCLL. Significant computational gains are made using the GLCHS method. [Copyright &y& Elsevier]

• Cities are dominated by private lands which presents a challenge for field study. • LiDAR data characterizes indices of avifaunal habitat on private residential lands. • NDVI, image texture, and land cover data are weaker predictors of urban avifauna. • LiDAR combined with street tree data has the strongest predictive power for urban birds. • High-resolution remote sensing data should be utilized in urban avifaunal studies. Urban ecosystems are dominated by private lands which poses a significant hurdle to performing field-based assessment of wildlife. An alternative approach is to characterize indices of animal habitat in difficult-to-access areas using data from airborne remote sensing platforms. Characterizing indices of wildlife habitat using remotely sensed data is common in natural systems but has received less attention within urban ecosystems. We tested the utility of using remotely sensed data from high-resolution airborne sensors, including LiDAR, a measure of vertical habitat structure, NDVI, a measure of greenness, image texture, a measure of horizontal habitat structure, and parcel level land-cover data, along with field-based street-tree measurements to predict bird abundance and richness across Greater Los Angeles, California, USA. We surveyed birds and gathered street-tree data on public lands of residential neighborhoods and processed the remote sensing data in 50-m and 300-m circular buffers of bird survey locations to capture data primarily on private, residential land across three winter field seasons (2016–18, 2019/20) at 23 locations along a tree-canopy cover gradient. Data from LiDAR processed as an index for the density of trees summarized in the 50-m and 300-m extents were the strongest univariate predictors of avifaunal abundance and richness explaining 75 % and 74 % of the likelihood in fitted models. NDVI, image texture, land cover, and street-tree density measures were weaker univariate predictors than models fitted with LiDAR data. Models including LiDAR and ground-based street-tree measurements accounted for upwards of 80 % of the variability in avifaunal abundance and richness, particularly for bird species associated with trees and shrubs. We recommend the prioritization of high-resolution remote sensing data, particularly LiDAR, in combination with field-based habitat measures e.g., street trees, to characterize indices of avifaunal habitat on public and private lands of cities, which could help to improve our understanding of the distribution of birds across urban areas. [ABSTRACT FROM AUTHOR]

Highlights • Plant species richness and diversity were estimated from high resolution imagery and LiDAR. • Image texture measures were used as indicators of habitat heterogeneity. • LiDAR metrics were used as indicators of vegetation structure complexity. • Image texture provided better estimations of species richness and LiDAR of diversity. • Combining image texture and LiDAR metrics improved estimation accuracy of diversity. Abstract Tropical dry forests provide goods and ecosystem services that rely on their diversity and are vital for human wellbeing. However, they are among the most threatened ecosystems due mainly to conversion to agriculture. Accurate estimations of species diversity in tropical secondary forests are needed for effective conservation and forest management. We assessed the separate and combined performance of remotely-sensed surrogates of habitat heterogeneity and vegetation structure complexity for predicting and mapping woody plant species richness and diversity in tropical dry forests. Here, we used image texture measures to calculate spectral variability from RapidEye imagery as an indicator of habitat heterogeneity as well as height and cover metrics from LiDAR data as surrogates of the complexity of vegetation structure. Separately, image texture measures and LiDAR metrics were used to explain variation in species richness and exp Shannon diversity calculated in 48 plots using multiple regression analysis. We also evaluated the relative importance of two sets of indicators to estimate species diversity using variation partitioning analyses. Habitat heterogeneity (image texture metrics) contributed most to explain variation in species richness (R2: 0.72–0.87), whereas complexity of vegetation structure (LiDAR metrics) was more important for diversity (R2: 0.68–0.74). However, a large percentage of variance of richness and diversity (58%–67%) was jointly explained by both factors and using models that combine them provided similar or higher prediction accuracy (R2: 0.68–0.89). We conclude that using image texture of high resolution imagery as an indicator of habitat heterogeneity allows precise and cost-effective estimations of species richness, while LiDAR metrics as a surrogate of vegetation structure complexity allow better estimations of diversity and that combining image texture and LiDAR provides the best estimates of species richness and diversity. [ABSTRACT FROM AUTHOR]

In order to monitor natural and anthropogenic disturbance effects to wetland ecosystems, it is necessary to employ both accurate and rapid mapping of wet graminoid/sedge communities. Thus, it is desirable to utilize automated classification algorithms so that the monitoring can be done regularly and in an efficient manner. This study developed a classification and accuracy assessment method for wetland mapping of at-risk plant communities in marl prairie and marsh areas of the Everglades National Park. Maximum likelihood (ML) and Support Vector Machine (SVM) classifiers were tested using 30.5 cm aerial imagery, the normalized difference vegetation index (NDVI), first and second order texture features and ancillary data. Additionally, appropriate window sizes for different texture features were estimated using semivariogram analysis. Findings show that the addition of NDVI and texture features increased classification accuracy from 66.2% using the ML classifier (spectral bands only) to 83.71% using the SVM classifier (spectral bands, NDVI and first order texture features). [ABSTRACT FROM AUTHOR]

Abstract: Information on the spatial distribution and composition of biological communities is essential in designing effective strategies for biodiversity conservation and management. Reliable maps of species richness across the landscape can be useful tools for these purposes. Acquiring such information through traditional survey techniques is costly and logistically difficult. The kriging interpolation method has been widely used as an alternative to predict spatial distributions of species richness, as long as the data are spatially dependent. However, even when this requirement is met, researchers often have few sampled sites in relation to the area to be mapped. Remote sensing provides an inexpensive means to derive complete spatial coverage for large areas and can be extremely useful for estimating biodiversity. The aim of this study was to combine remotely sensed data with kriging estimates (hybrid procedures) to evaluate the possibility of improving the accuracy of tree species richness maps. We did this through the comparison of the predictive performance of three hybrid geostatistical procedures, based on tree species density recorded in 141 sampling quadrats: co-kriging (COK), kriging with external drift (KED), and regression kriging (RK). Reflectance values of spectral bands, computed NDVI and texture measurements of Landsat 7 TM imagery were used as ancillary variables in all methods. The R 2 values of the models increased from 0.35 for ordinary kriging to 0.41 for COK, and from 0.39 for simple regression estimates to 0.52 and 0.53 when using simple KED and RK, respectively. The R 2 values of the models also increased from 0.60 for multiple regression estimates to 0.62 and 0.66 when using multiple KED and RK, respectively. Overall, our results demonstrate that these procedures are capable of greatly improving estimation accuracy, with multivariate RK being clearly superior, because it produces the most accurate predictions, and because of its flexibility in modeling multivariate relationships between tree richness and remotely sensed data. We conclude that this is a valuable tool for guiding future efforts aimed at conservation and management of highly diverse tropical forests. [Copyright &y& Elsevier]

• A flexible orthoimage mosaicking method is developed considering the cloud coverage. • Dynamic variable patch is defined as the processing unit for orthoimage mosaicking. • This method can remove clouds and also relieve the impact of misalignments on the mosaic. • An area-weighted image blending method is presented to achieve smooth transition. Orthoimage mosaicking plays an important role in remote sensing applications, such as environment monitoring and protection. However, the cloud coverage in remote sensing images often results in substantial loss of information and degradation of data quality, bringing lots of challenges to orthoimage mosaicking. Therefore, this paper proposes a flexible multi-temporal orthoimage mosaicking method based on dynamic variable patches. Different from most existing pixel-based mosaicking methods, the proposed method defines patch as the minimal processing unit for orthoimage mosaicking, and the size and position of each patch are dynamic variable. Patches from different images are rearranged under the image texture consistency constraint to generate the mosaic image. In the proposed method, the initial positions of available patches of all images are determined first, then the initial size of each patch is determined by the cloud coverage and the effective area of image. Subsequently, considering the inevitable misalignments between different orthoimages, the texture similarity between patches is utilized to select the most suitable patch, and the size of the patch is also dynamically adjusted to make the misalignments within each patch controllable. Finally, considering the color inconsistency in the overlapping areas of neighboring images, an area-weighted image blending algorithm is also presented to achieve invisible seams and smooth transitions in the final mosaic. Not only does the proposed method produce mosaic images with minimal cloud coverage areas by excluding cloud pixels as much as possible, but it is also flexible for the mosaicking of images with varying degrees of misalignments, which is achieved by dynamically adjusting the size and position of each patch while maintaining texture consistency constraint. Both simulated experiments and real data experiments are carried out to verify the performance of the proposed method. The experimental results demonstrate that the proposed method can generate the mosaic image with higher spatial continuity and radiometric consistency. [ABSTRACT FROM AUTHOR]

Abstract Accurate estimates of fishing effort are necessary in order to assess interactions with the wider ecosystem and for defining and implementing appropriate management. In intertidal and inshore fisheries in which vessel monitoring systems (VMS) or logbook programmes may not be implemented, quantifying the distribution and intensity of fishing can be difficult. The most obvious effects of bottom-contact fishing are often physical changes to the habitat, such as scarring of the sediment following dredging or trawling. We explored the potential of applying remote sensing techniques to aerial imagery collected by an unmanned aerial vehicle, or drone, in an area of intertidal mud flat (0.52 km2) in Poole Harbour, UK, where shellfish dredging is widely carried out and conflicts between commercial fishing interests and the conservation of internationally important shorebird populations are a concern. Image classification and image texture analysis were performed on imagery collected during the open dredge season in November 2015, in order to calculate measures of fishing intensity across three areas of the harbour subject to different management measures. We found a significant correlation between results of the image texture analysis and official sightings records collected during the dredging season, indicating that this method most accurately quantified dredging disturbance. The relationship between shorebird densities and food intake rates and the results of this analysis method were then investigated to assess the potential for using remotely sensed measures of fishing effort to assess responses of overwintering shorebird populations to intertidal shellfish dredging. Our work highlights the application of such methods, providing a low-cost tool for quantifying fishing effort and predicting wildlife conflicts. Highlights • Two remote sensing techniques were used to quantify intertidal bottom contact fishing effort. • Results of unsupervised image classification and image texture analysis were not comparable. • Image texture analysis more accurately quantifies intertidal fishing effort. • The relationship between results of each method and shorebird feeding ecology are investigated. [ABSTRACT FROM AUTHOR]

Tropical forest ecosystems are undergoing rapid transformation as a result of changing environmental conditions and direct human impacts. However, we cannot adequately understand, monitor or simulate tropical ecosystem responses to environmental changes without capturing the high diversity of plant functional characteristics in the species-rich tropics. Failure to do so can oversimplify our understanding of ecosystems responses to environmental disturbances. Innovative methods and data products are needed to track changes in functional trait composition in tropical forest ecosystems through time and space. This study aimed to track key functional traits by coupling Sentinel-2 derived variables with a unique data set of precisely located in-situ measurements of canopy functional traits collected from 2434 individual trees across the tropics using a standardised methodology. The functional traits and vegetation censuses were collected from 47 field plots in the countries of Australia, Brazil, Peru, Gabon, Ghana, and Malaysia, which span the four tropical continents. The spatial positions of individual trees above 10 cm diameter at breast height (DBH) were mapped and their canopy size and shape recorded. Using geo-located tree canopy size and shape data, community-level trait values were estimated at the same spatial resolution as Sentinel-2 imagery (i.e. 10 m pixels). We then used the Geographic Random Forest (GRF) to model and predict functional traits across our plots. We demonstrate that key plant functional traits can be accurately predicted across the tropicsusing the high spatial and spectral resolution of Sentinel-2 imagery in conjunction with climatic and soil information. Image textural parameters were found to be key components of remote sensing information for predicting functional traits across tropical forests and woody savannas. Leaf thickness (R 2 = 0.52) obtained the highest prediction accuracy among the morphological and structural traits and leaf carbon content (R 2 = 0.70) and maximum rates of photosynthesis (R 2 = 0.67) obtained the highest prediction accuracy for leaf chemistry and photosynthesis related traits, respectively. Overall, the highest prediction accuracy was obtained for leaf chemistry and photosynthetic traits in comparison to morphological and structural traits. Our approach offers new opportunities for mapping, monitoring and understanding biodiversity and ecosystem change in the most species-rich ecosystems on Earth. Unlabelled Image • We use a pantropical array of precisely geolocated leaf traits from 2434 trees. • We assess the ability of Sentinel-2 imagery to map leaf traits in tropical forests. • Functional traits can be mapped at the pixel-level across forests with Sentinel-2. • Leaf photosynthetic and chemical traits show the highest detectability. • Spatial texture information makes an important contribution to mapping leaf traits. [ABSTRACT FROM AUTHOR]

Effective biodiversity management in forest ecosystems relies on the assessment of environmental indicators (surrogates) when attempting to measure total biodiversity. Bird species (BS) richness and micro-habitat (MH) diversity are two key features that are easily measured and can be utilized as biodiversity surrogates. Remote sensing technologies may be employed as a cost-efficient measure as well as accurately mapping diversity features across broad geographical areas. This study examined the possibilities of predicting BS richness and MH diversity using variables derived from satellite data in a brutian pine ( Pinus brutia Ten. ) forest ecosystem located in the Southwestern Mediterranean Region of Turkey. The study utilized 40 (90 × 90 m, 0.81 ha) sample plots. We used first, and second-order image texture measures calculated from RapidEye, SPOT-5 and Aster Normalized Difference Vegetation Index (NDVI) as explanatory variables for predicting these biodiversity surrogates at alpha (α) level. Stepwise linear regression analyses showed that BS richness and MH diversity can be estimated using image texture measures. According to the cross-validation test; BS richness was best predicted using standard deviation of Gray levels (STD) and Gray-Level Co-Occurrence Matrix (GLCM) Homogeneity of RapidEye NDVI (R CV 2  = 0.73, STE CV  = 4.321), while MH diversity was best predicted using STD and GLCM Correlation of SPOT NDVI (R CV 2  = 0.73, STE CV  = 0.148). In conclusion, the satellite-based diversity maps produced in this study can provide valuable data for forest managers and assist in formulating adaptive management plans for the ecologically sustainable management of brutian pine forest ecosystems. [ABSTRACT FROM AUTHOR]

Abstract: Image classification using multispectral sensors has shown good performance in detecting macrophytes at the species level. However, species level classification often does not utilize the texture information provided by high resolution images. This study investigated whether image texture provides useful vector(s) for the discrimination of monospecific stands of three floating macrophyte species in Quickbird imagery of the South Nation River. Semivariograms indicated that window sizes of 5×5 and 13×13pixels were the most appropriate spatial scales for calculation of the grey level co-occurrence matrix and subsequent texture attributes from the multispectral and panchromatic bands. Of the 214 investigated vectors (13 Haralick texture attributes*15 bands+9 spectral bands+10 transformations/indices), feature selection determined which combination of spectral and textural vectors had the greatest class separability based on the Mann–Whitney U-test and Jefferies–Matusita distance. While multispectral red and near infrared (NIR) performed satisfactorily, the addition of panchromatic-dissimilarity slightly improved class separability and the accuracy of a decision tree classifier (Kappa: red/NIR/panchromatic-dissimilarity – 93.2% versus red/NIR – 90.4%). Class separability improved by incorporating a second texture attribute, but resulted in a decrease in classification accuracy. The results suggest that incorporating image texture may be beneficial for separating stands with high spatial heterogeneity. However, the benefits may be limited and must be weighed against the increased complexity of the classifier. [Copyright &y& Elsevier]

• Post-fire recovery under different fire regime scenarios was modeled with WorldView-2. • Image texture provides accurate predictions under high spatial heterogeneity. • Pine modeling requires a high seedling contribution on the overall plot reflectance. • Modeling of vegetation structure was accurate for each recurrence-severity scenario. • High spatial resolution satellite imagery represents an effective management tool. In the Mediterranean Basin, wildland fires are major drivers of forest ecosystem dynamics. In the current context of global change, these fires are becoming more severe and recurrent because of climatic conditions, land use changes and invasive species. In areas affected by mega-fires (burned area > 10,000 ha), the patterns of regeneration may be heterogeneous due to local variations in fire regime, community composition and environmental features. The goal of this study was to analyze the post-fire structure of both Pinus pinaster Aiton. seedlings population and understory community in a Mediterranean fire-prone ecosystem at short-term by means of high spatial resolution satellite imagery within the perimeter of a full stand replacing mega-fire that burned around 12,000 ha of a Pinus pinaster forest in NW Spain. We established 234 field plots of 2 × 2 meters to cover four recurrence-severity scenarios. In each plot, we sampled 15 vegetation structural variables at both pine seedlings population and understory community levels. From the WorldView-2 satellite imagery, we obtained three sets of spectral variables (reflectance, spectral indices and image textures) that were used as predictors of vegetation recovery in generalized linear models. At population level, the number and cover of pine seedlings were successfully modeled with spectral indices and textural information (normalized root mean square error of 16% and 17%, respectively). At understory community level, woody species cover was correlated with first order textures (normalized root mean square error of 9%). Other understory structure variables (height and richness of woody species, percentage of bare soil, necromass and leaves) were predicted with an error lower than 20%. The predictive capacity of the models was similar for all recurrence-severity scenarios. Our results highlight the usefulness of spectral indices and textural data at high spatial resolution in the analysis of post-fire recovery in large and heterogeneous burnt areas. Given the accuracy and predictive capacity of the models obtained in this study, high spatial resolution satellite imagery together with field data provide useful information in post-fire decision making in fire prone ecosystems. [ABSTRACT FROM AUTHOR]

Highlights • 1. Image texture accurately predicted vegetation recovery along a climatic gradient. • 2. Generality of recovery models between sites is crucial for management decisions. • 3. Sampling bias limits the detection of non-stationary response-predictor relationships. • 4. Models that capture more variability in the environmental factors extrapolate better. • 5. Post-fire recovery models could be extrapolated across the climatic gradient. Abstract Forest managers require reliable tools to evaluate post-fire recovery across different geographic/climatic contexts and define management actions at the landscape scale, which might be highly resource-consuming in terms of data collection. In this sense, remote sensing techniques allow for gathering environmental data over large areas with low collection effort. We aim to assess the applicability of remote sensing tools in post-fire management within and across three mega-fires that occurred in pine fire-prone ecosystems located along an Atlantic-Transition-Mediterranean climatic gradient. Four years after the wildfires, we established 120 2x2m plots in each mega-fire site, where we evaluated: (1) density of pine seedlings, (2) percentage of woody species cover and (3) percentage of dead plant material cover. These variables were modeled following a Bayesian Model Averaging approach on the basis of spectral indices and texture features derived from WorldView-2 satellite imagery at 2 m spatial resolution. We assessed model interpolation and transferability within each mega-fire, as well as model extrapolation between mega-fires along the climatic gradient. Texture features were the predictors that contributed most in all cases. The woody species cover model had the best performance regarding spatial interpolation and transferability within the three study sites, with predictive errors lower than 25% for the two approaches. Model extrapolation between the Transition and Mediterranean sites had low levels of error (from 6% to 19%) for the three field variables, because the landscape in these areas is similar in structure and function and, therefore, in spectral characteristics. However, model extrapolation from the Atlantic site achieved the weakest results (error higher than 30%), due to the large ecological differences between this particular site and the others. This study demonstrates the potential of fine-grained satellite imagery for land managers to conduct post-fire recovery studies with a high degree of generality across different geographic/climatic contexts. [ABSTRACT FROM AUTHOR]

The U.S. Fish and Wildlife Service uses the term palustrine wetland to describe vegetated wetlands traditionally identified as marsh, bog, fen, swamp, or wet meadow. Landsat TM imagery was combined with image texture and ancillary environmental data to model probabilities of palustrine wetland occurrence in Yellowstone National Park using classification trees. Model training and test locations were identified from National Wetlands Inventory maps, and classification trees were built for seven years spanning a range of annual precipitation. At a coarse level, palustrine wetland was separated from upland. At a finer level, five palustrine wetland types were discriminated: aquatic bed (PAB), emergent (PEM), forested (PFO), scrub–shrub (PSS), and unconsolidated shore (PUS). TM-derived variables alone were relatively accurate at separating wetland from upland, but model error rates dropped incrementally as image texture, DEM-derived terrain variables, and other ancillary GIS layers were added. For classification trees making use of all available predictors, average overall test error rates were 7. 8% for palustrine wetland/upland models and 17. 0% for palustrine wetland type models, with consistent accuracies across years. However, models were prone to wetland over-prediction. While the predominant PEM class was classified with omission and commission error rates less than 14%, we had difficulty identifying the PAB and PSS classes. Ancillary vegetation information greatly improved PSS classification and moderately improved PFO discrimination. Association with geothermal areas distinguished PUS wetlands. Wetland over-prediction was exacerbated by class imbalance in likely combination with spatial and spectral limitations of the TM sensor. Wetland probability surfaces may be more informative than hard classification, and appear to respond to climate-driven wetland variability. The developed method is portable, relatively easy to implement, and should be applicable in other settings and over larger extents. [Copyright &y& Elsevier]

Abstract: Modelling and mapping of hooded warbler (Wilsonia citrina) nesting habitat in forests of southern Ontario were conducted using Ikonos and Landsat data. The study began with an analysis of skyward hemispherical photography to determine canopy characteristics associated with nest sites. It showed that nest sites had significantly less overhead canopy cover and larger maximum gap size than in non-nest areas. These findings led to the hypothesis that brightness variability in high to moderate resolution remotely sensed imagery may be greater at nest sites than in non-nest areas due to larger shadows and greater shadow variability related to these gap characteristics. This was confirmed when, in addition to some spectral band brightness variables, several image texture and spectrally unmixed fraction (shadow, bare soil) variables were found to be significantly different for nest and non-nest sites in Ikonos and Landsat imagery. These significantly different variables were used in maximum likelihood classification (MLC) and logistic regression (LR) to produce maps of potential nesting habitat. Mapping was conducted with Ikonos and Landsat in a local area where most known nest sites occur, and regionally using Landsat data for almost all of the hooded warbler range in southern Ontario. For the local area mapping using Ikonos data, a posteriori probabilities for both the MLC and LR methods showed that about 62% of the nest sites set aside for validation had been classified with high probability (p >0.70) in the nest class. MLC mapping accuracy was 70% for the validation nest sites and 87% of validation nest sites were within 10 m of classified nesting habitat, a distance approximately equivalent to expected positional error in the data. LR accuracy was slightly lower. Nest site MLC mapping accuracy in the local area using Landsat data was 87% but the map was much coarser due to the larger pixel size. Regional mapping with Landsat imagery produced lower classification accuracy due to high errors of commission for the habitat class. This resulted from a poor spatial distribution and low number of observations of nest sites throughout the region compared to the local area, while the non-nest site data distribution was too narrow, having been defined and assessed (using standard accepted methods) as areas with no ground shrubs. If either of these problems can be ameliorated, regional mapping accuracy may improve. [Copyright &y& Elsevier]

Novel inversion methods are presented for active and passive satelliteborne microwave remote sensing. The objectives are biomass estimation, forest and land-cover-type recognition in boreal forests. A new adaptive inversion method for active sensors was developed for the forest block-wise stem volume estimation from satelliteborne radar images (e.g. JERS-1, ERS-1 SAR and RADARSAT). The inversion results with L-band and/or C-band synthetic aperture radar (SAR) images showed promising accuracies: the relative retrieval rms error varied from 25% to 5% as the size of the forest area varied from 5 to 30 000 h (the forest stem volume varied from 0 to 300 m3/ha). The textural information of a seasonal set of satelliteborne radar images was studied with the first- and second-order statistical measures. The multitemporal approach was beneficial for the textural measures in forest and land-cover-type recognition. Based on the SAR image texture, the overall classification accuracy for seven land-cover types was 65%, while with the SAR image intensity, the classification accuracy was 50%, respectively. In the forest-type classification based on the SAR image texture and intensity, the overall classification accuracy for four forest types was 66%, while with the intensity alone the accuracy was 40%, respectively. With the passive microwave sensor (e.g. satelliteborne SSM/I radiometer), the mixed pixel approach was employed for stem volume (biomass) and forest coverage fraction estimation. The results obtained, show that the pixel-wise fractions of water, non-forested, and forested area can be estimated with a rms errors of around 10% units. A new stem volume inversion method for wintertime SSM/I data achieved promising accuracies, the rms error was from 13 to 19 m3/ha/pixel (25 km×25 km) which was 15–16% of the mean stem volume. In the test area, the stem volume ranged from 40 to 160 m3/ha/pixel. [Copyright &y& Elsevier]

Integrating field-based and remotely sensed data has proven valuable for assessing on-the-ground diversity of plants across a range of spatial scales. Here we assessed whether remotely sensed data is a good indicator of vegetation composition and structure in dry, Prosopis flexuosa -dominated woodlands. Our objectives were (1) to quantify on-the-ground vegetation composition and structure using (A) field-based methods and (B) remotely sensed images and analysis techniques, and (2) to evaluate how well the data extracted from remotely sensed data estimate field-based measures of vegetation composition and structure. We selected 40 individuals of P. flexuosa in Ischigualasto Provincial Park (San Juan, Argentina) and its influence zone. Each individual was the center of a plot (1500-m 2 ) where we recorded richness (compositional indicator) and abundance (structural indicator) of trees, shrubs and other plants (i.e. cacti, grasses and forbs). To assess woodland structure, we evaluated canopy area of each P. flexuosa and the proportion of adult P. flexuosa trees in a plot. In addition, we used Landsat 8 OLI to calculate SATVI (Soil Adjusted Total Vegetation Index) values from the pixel that corresponds with the center of each sample plot, and then estimated first- and second-order texture measures (in 3 × 3 and 5 × 5 moving window sizes). We fitted generalized linear models with different error distributions. Vegetation richness was significantly and directly related to range and entropy (3 × 3 and 5 × 5 windows). Both trees and shrubs, were related to SATVI values and first- and second-order means (3 × 3 and 5 × 5 windows). Moreover, shrub abundance was inversely related to range and entropy (5 × 5 window); and the “other plants” group was inversely related to first- and second-order means in the same window. Variance of the canopy area was directly related to range (5 × 5 window); however, proportion of adults was not related to remote sensing data. Our findings suggest satellite imagery-derived image texture is a valuable tool for management and conservation, and can indicate areas of high plant species richness and abundance of trees and shrubs and help differentiate areas of different canopy sizes in dry P. flexuosa -dominated woodlands of Argentina. [ABSTRACT FROM AUTHOR]

There are different research and processing methods for Image Content Retrieval. This article is described the retrieval of remote sensing images method, which is based on the image texture features of the Fourier power spectrum. Firstly, a fixed size remote sensing image unit is selected from the remote sensing image, which is converted to a spectral image by FFT, and extract the eigenvalues in the frequency domain to constitute a searchable image eigenvalue database. Secondly, the similarity between the feature and the database features are examined by the Angular Cosine Algorithm. Lastly, similarity of the resulting image are sorted to select the best match. This method converts the remote sensing image into a spectral image, and makes the selection of the eigenvalue more simple. The Angle Cosine similarity detection method with weight is used to make the similarity detection more accurate. [ABSTRACT FROM AUTHOR]

Abstract: Extraction of the flooded areas from synthetic aperture radar (SAR) and especially TerraSAR-X data is one of the most challenging tasks in the flood management and planning. SAR data due to its high spatial resolution and its capability of all weather conditions makes a proper choice for tropical countries. Texture is considered as an effective factor in distinguishing the classes especially in SAR imagery which records the backscatters that carry information of kind, direction, heterogeneity and relationship of the features. This paper put forward a computer program for texture analysis for high resolution radar data. Texture analysis program is introduced and discussed using the gray-level co-occurrence matrix (GLCM). To demonstrate the ability and correctness of this program, a test subset of TerraSAR-X imagery from Terengganu area, Malaysia was analyzed and pixel-based and object-based classification were attempted. The thematic maps derived by pixel-based method could not achieve acceptable visual interpretation and for that reason no accuracy assessment was performed on them. The overall accuracy achieved by object-based method was 83.63% with kappa coefficient of 0.8. Results on image texture classification showed that the proposed program is capable for texture analysis in TerraSAR-X image and the obtained textural analysis resulted in high classification accuracy. The proposed texture analysis program can be used in many applications such as land use/cover (LULC) mapping, hazard studies and many other applications. [Copyright &y& Elsevier]

Abstract: The spatial variability of remotely sensed image values provides important information about the arrangement of objects and their spatial relationships within the image. The characterisation of spatial variability in such images, for example, to measure of texture, is of great utility for the discrimination of land cover classes. To this end, the variogram, a function commonly applied in geostatistics, has been used widely to extract image texture for remotely sensed data classification. The aim of this study was to assess the increase in accuracy that can be achieved by incorporating univariate and multivariate textural measures of Landsat TM imagery in classification models applied to large heterogeneous landscapes. Such landscapes which difficult to classify due to the large number of land cover categories and low inter-class separability. Madogram, rodogram and direct variogram for the univariate case, and cross- and pseudocross variograms for the multivariate one, together with multi-seasonal spectral information were used in a Random Forest classifier to map land cover types. The addition of spatial variability into multi-seasonal Random Forest models leads to an increase in the overall accuracy of 8%, and to an increase in the Kappa index of 9%, respectively. The increase in per categories Kappa for the textural Random Forest model reached 30% for certain categories. This study demonstrates that the use of information on spatial variability produces a fundamental increase in per class classification accuracy of complex land-cover categories. [Copyright &y& Elsevier]

Abstract: The classification of satellite imagery into land use/cover maps is a major challenge in the field of remote sensing. This research aimed at improving the classification accuracy while also revealing uncertain areas by employing a geocomputational approach. We computed numerous land use maps by considering both image texture and band ratio information in the classification procedure. For each land use class, those classifications with the highest class-accuracy were selected and combined into class-probability maps. By selecting the land use class with highest probability for each pixel, we created a hard classification. We stored the corresponding class probabilities in a separate map, indicating the spatial uncertainty in the hard classification. By combining the uncertainty map and the hard classification we created a probability-based land use map, containing spatial estimates of the uncertainty. The technique was tested for both ASTER and Landsat 5 satellite imagery of Gorizia, Italy, and resulted in a 34% and 31% increase, respectively, in the kappa coefficient of classification accuracy. We believe that geocomputational classification methods can be used generally to improve land use and land cover classification from imagery, and to help incorporate classification uncertainty into the resultant map themes. [Copyright &y& Elsevier]

Abstract: Considerably research has been conducted on automated and semi-automated techniques that incorporate image textural information into the decision process as an alternative to improve the information extraction from images while reducing time and cost. The challenge is the selection of the appropriate texture operators and the parameters to address a specific problem given the large set of available texture operators. In this study we evaluate the optimization characteristic of an evolutionary framework to evolve solutions combining spectral and textural information in non-linear mathematical equations to improve multi-spectral image classification. Twelve convolution-type texture operators were selected and divided into three groups. The application of these texture operators to a multi-spectral satellite image resulted into three new images (one for each of the texture operator groups considered). These images were used to evaluate the classification of features with similar spectral characteristics but with distinct textural pattern. Classification of these images using a standard image classification algorithm with and without the aid of the evolutionary framework have shown that the process aided by the evolutionary framework yield higher accuracy values in two out of three cases. The optimization characteristic of the evolutionary framework indicates its potential use as a data mining engine to reduce image dimensionality as the system improved accuracy values with reduced number of channels. In addition, the evolutionary framework reduces the time needed to develop custom solutions incorporating textural information, especially when the relation between the features being investigated and the image textural information is not fully understood. [Copyright &y& Elsevier]

Abstract: Landform frame-lithologic component decomposing algorithm for remote sensing (RS) image is proposed according to the principle of optical imaging and fractal feature of landform. The algorithm is applied to decompose the enhanced thematic mapper (ETM) image of rocks for the study of α-f(α) multifractal spectra. The original ETM image, decomposed landform frame and lithologic component subimage are used to calculate the α-f(α) multifractal spectra of adamellite and metamorphic sedimentary rocks in different areas. From the original ETM image, the α-f(α) multifractal spectra do not show any relation to rock types and landform. However, using the decomposed subimage, the adamellite from different areas shows the same α-f(α) spectra feature of the lithologic component subimage, but different α-f(α) spectra features of the landform frame subimage. On the contrary, the adamellite and metamorphic sedimentary rocks in the nearby area have different α-f(α) spectra of the lithologic component subimage, but rather similar α-f(α) spectra of the land frame subimage. This means the land frame-lithologic component algorithm and α-f(α) multifratal spectra can provide a new validated method to improve lithology recognition with RS image texture. [Copyright &y& Elsevier]

Abstract: Locally computed statistics of image texture and a case-based reasoning (CBR) system were evaluated for mapping of forest attributes. Cluster analysis was preferred to regression models, as a pre-selection method of features. The best stand-based accuracy using satellite sensor images was 74.64m−3 ha−1 (36%) RMSE for stand volume, 1.98m−3 ha−1 a−1 (49%) for annual increase in stand volume, where κ =0.23 for stand growth classes and κ =0.41 for dominant tree species in stands. The top pixel-based accuracy using orthophotos was 76.54m−3 ha−1 (41%) RMSE for stand volume, 1.87m−3 ha−1 a−1 (44%) for annual increase in stand volume, where κ =0.24 for stand growth classes and κ =0.38 for dominant tree species in stands. Mean saturation in 30m radius was the most useful feature when orthophotos were used, and standard deviation of Landsat ETM 6.2 values in 80m radius was the best when satellite sensor images were used. The most valuable feature components (radii, channels and local statistics) for orthophotos were: 30m kernel radius, lightness and the mean of pixel values; for satellite sensor images: 80m kernel radius, near-infrared channel (ETM 4) and the mean of pixel values. Locally computed statistics. [Copyright &y& Elsevier]

Abstract: The interaction between river water and landfast sea ice was investigated through synthetic aperture radar (SAR) remote sensing, ice-growth modeling, and ground-based ice-core and hydrographic studies in the Laptev Sea, Siberian Arctic, in 1996/1997 and 1998/1999. Ice-core data in conjunction with ice-growth and SAR backscatter modeling demonstrated that the contrasts in dielectric and microstructural properties between freshwater/brackish (salinity<1‰) and sea ice allow a mapping of the distribution of freshwater and brackish ice as influenced by Lena River discharge. This brackish zone (surface water salinities<5) extended over 2000–3000 km2 inshore of the 10-m isobath and exhibited distinct SAR backscatter coefficients and image texture. In the nearshore zone, bottomfast ice growth could be identified and tracked over the growth season. Occupying up to 250 km2 along the Lena Delta, bottomfast ice was not as widespread as previously hypothesized, possibly due to ice being thinner by 10–20% relative to the long-term mean. In SAR and ERS-2 scatterometer data, Laptev Sea landfast ice exhibits the lowest backscatter signatures of any ice type in the Arctic Ocean, due to the lack of major deformation features. Stable-isotope data show that the landfast ice is composed of about 62% of river water, locking up 24% of the total annual Lena and Yana discharge. From ice-growth/isotopic-fractionation modeling and ice-core analysis, time series of surface water salinity have been derived, indicating freshening of under-ice waters during winter and north-/northeastward spreading of the river plume with under-ice spreading rates of 1.0–2.7 cm s− 1. A river freshwater budget for the inner Laptev shelf indicates flushing times of a year or more with cross-shelf export of 627 km3 during the winter of 1998/1999. Based on these findings, the southeastern Laptev Sea can be considered an open, seasonally frozen estuary. This system contrasts with North American shelf environments, which show a different response to climate variability and change. [Copyright &y& Elsevier]

This paper presents a case study on the use of features derived from remote sensing data for mapping the highly fragmented semideciduous Atlantic forest in Brazil. Innovative aspects of this research include the evaluation of different feature sets in order to improve land cover mapping. The feature sets were defined based on expert knowledge and on data mining techniques to be input to traditional and machine learning algorithms for pattern recognition, viz. maximum likelihood, univariate decision trees, multivariate decision trees, and neural networks. The results showed that the maximum likelihood classification using temporal texture descriptors as extracted with wavelet transforms was most accurate to classify the semideciduous Atlantic forest. In this study, a special accuracy measure was used: the so-called class mapping accuracy. Maximum likelihood performed relatively well, with forest mapping accuracies ranging from 34.5 to 51.3%. In contrast, accuracies for neural networks ranged from 19.0 to 45.2%. Classification confusion occurred mainly with coffee and eucalyptus plantations. Univariate trees provided the most robust results for different feature sets, with accuracies ranging from 39.6 to 46.7%. Temporal information of vegetation indices was more important than image texture, terrain topography and raw spectral information for discriminating semideciduous Atlantic forest. [Copyright &y& Elsevier]

Remote sensing of urban heat islands (UHIs) has traditionally used the Normalized Difference Vegetation Index (NDVI) as the indicator of vegetation abundance to estimate the land surface temperature (LST)–vegetation relationship. This study investigates the applicability of vegetation fraction derived from a spectral mixture model as an alternative indicator of vegetation abundance. This is based on examination of a Landsat Enhanced Thematic Mapper Plus (ETM+) image of Indianapolis City, IN, USA, acquired on June 22, 2002. The transformed ETM+ image was unmixed into three fraction images (green vegetation, dry soil, and shade) with a constrained least-square solution. These fraction images were then used for land cover classification based on a hybrid classification procedure that combined maximum likelihood and decision tree algorithms. Results demonstrate that LST possessed a slightly stronger negative correlation with the unmixed vegetation fraction than with NDVI for all land cover types across the spatial resolution (30 to 960 m). Correlations reached their strongest at the 120-m resolution, which is believed to be the operational scale of LST, NDVI, and vegetation fraction images. Fractal analysis of image texture shows that the complexity of these images increased initially with pixel aggregation and peaked around 120 m, but decreased with further aggregation. The spatial variability of texture in LST was positively correlated with those in NDVI and in vegetation fraction. The interplay between thermal and vegetation dynamics in the context of different land cover types leads to the variations in spectral radiance and texture in LST. These variations are also present in the other imagery, and are responsible for the spatial patterns of urban heat islands. It is suggested that the areal measure of vegetation abundance by unmixed vegetation fraction has a more direct correspondence with the radiative, thermal, and moisture properties of the Earth''s surface that determine LST. [Copyright &y& Elsevier]

Soil salinity is a global threat to agricultural production and necessitates the monitoring thereof. Saline conditions in South African irrigation schemes generally occur in small patches (some only a few metres in diameter) and this study, which forms part of a Water Research Commission (WRC) project, evaluates the use of very high resolution (VHR) satellite imagery, in particular those produced by the WorldView 2 (WV2) sensor, for the detection of salt accumulation in irrigated areas. A range of features derived from the WV2 image were evaluated, namely 8 WV2 bands, 10 vegetation indices (VIs), 25 texture measures and 2 principle component analysis (PCA) components. These features were generated at six spatial resolutions (0.5 m, 2 m, 6 m, 10 m, 15 m and 20 m) to investigate the value of high spatial resolution for detecting affected areas. The relationships between the image features and electric conductivity measurements of 30 soil samples were studied by means of regression analysis and classification and regression tree (CART) modelling. The regression analysis results showed that a spatial resolution of 6 m or higher is ideal when VIs are used as input. When texture measures are used, higher spatial resolution (0.5 m) produced better models. The regression results also showed that the relatively high spectral resolution of the WV2 sensor (compared to other VHR sensors) did not provide a significant improvement in accuracy. The CART results provided a categorical quantification of salt accumulation and showed that VIs generated at 0.5 m resolution were the best features to use. Because the use of WV2 images is not financially viable for operational use in very large irrigation schemes, the study concludes with some guidelines on the spectral and spatial requirements of images when monitoring salt accumulation in irrigation schemes with similar conditions. [ABSTRACT FROM AUTHOR]

Abstract: Wind disturbance events can impact spatially heterogeneous patterns in vegetation structure and disturbance severity in forested landscapes. Characterizing these patterns in forested ecosystems with remote sensing data has been a persistent challenge as variation in severity may be heterogeneous at fine spatial scales. Yet the degree and pattern of disturbance severity are an important influence on successional dynamics. This study explored how spectral and textural characteristics of high-spatial resolution IKONOS imagery reflected patterns of disturbance severity across a windstorm damaged, 121-km2 area of the Boundary Waters Canoe Area Wilderness (BWCAW) in northeastern Minnesota, USA. In this study, spectral and spatial features of high-spatial resolution (1-m panchromatic and 4-m multispectral) IKONOS satellite imagery from a single post-disturbance date are coupled with field observations of disturbance within 0.045-ha field plots to access the potential for empirically modeling disturbance severity across this heterogeneous landscape within the BWCAW. Combining textural and spectral features led to a multiple regression model that explained 68% of the variance, and predicted disturbance severity equally well for ground data not included in the model development. The results suggest the utility of combining spatial and spectral data for detecting differences in forest structure caused by ecological processes such as disturbance. [Copyright &y& Elsevier]