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Accurate and reliable mapping of fire extent and severity is critical for assessing the impact of fire on vegetation and informing post-fire recovery trajectories. Classification approaches that combine pixel-wise and neighbourhood statistics including image texture derived from high-resolution satellite data may improve on current methods of fire severity mapping. Texture is an innate property of all land cover surfaces that is known to vary between fire severity classes, becoming increasingly more homogenous as fire severity increases. In this study, we compared candidate backscatter and reflectance indices derived from Sentinel 1 and Sentinel 2, respectively, together with grey-level-co-occurrence-matrix (GLCM)-derived texture indices using a random forest supervised classification framework. Cross-validation (for which the target fire was excluded in training) and target-trained (for which the target fire was included in training) models were compared to evaluate performance between the models with and without texture indices. The results indicated that the addition of texture indices increased the classification accuracies of severity for both sensor types, with the greatest improvements in the high severity class (23.3%) for the Sentinel 1 and the moderate severity class (17.4%) for the Sentinel 2 target-trained models. The target-trained models consistently outperformed the cross-validation models, especially with regard to Sentinel 1, emphasising the importance of local training data in capturing post-fire variation in different forest types and severity classes. The Sentinel 2 models more accurately estimated fire extent and were improved with the addition of texture indices (3.2%). Optical sensor data yielded better results than C-band synthetic aperture radar (SAR) data with respect to distinguishing fire severity and extent. Successful detection using C-band data was linked to significant structural change in the canopy (i.e., partial-complete canopy consumption) and is more successful over sparse, low-biomass forest. Future research will investigate the sensitivity of longer-wavelength (L-band) SAR regarding fire severity estimation and the potential for an integrated fire-mapping system that incorporates both active and passive remote sensing to detect and monitor changes in vegetation cover and structure. [ABSTRACT FROM AUTHOR]

Aim Ecological factors that vary along spatial scales can greatly influence the outcome of evolution. However, often it is difficult for researchers to identify significant ecological variables that might be associated with the geographical distribution of phenotypes and genotypes in nature. In this paper, we use remote sensing image texture analyses to investigate breeding territories of the white-throated sparrow ( Zonotrichia albicollis), a polymorphic passerine species that has been shown to segregate spatially according to morph. Our aim was to examine how an integrative measure of landscape feature, such as image texture, could be used to distinguish subtle differences in habitat use and, presumably, habitat selection. Location A population of white-throated sparrows located 1.5 miles east of the Cranberry Lake Biological Station in the Adirondack Mountains of St Lawrence County, New York, was used in this study; the study site itself was approximately 1 km2. Methods We analysed 27 territories (16 white males; 11 tan males) to determine if image texture can be used to distinguish differences in vegetation and structure in territories of the two colour morphs. First (variance)- and second (homogeneity)-order image texture measurements with three different window sizes were derived using the green, red, and near infrared bands of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. Texture was then placed into t-tests to determine statistical differences between the territories of the two male morphs. Results Significant differences between the territories of the two morphs were found in both variance and homogeneity texture measures in the red and green ASTER bands in all three window sizes. There were no differences with any near infrared texture measures. Main conclusions Examination of first- and second-order statistical measures indicated that the territories of tan males have significantly more spectral variance than the territories of white males, while white males’ territories are significantly more homogeneous. These findings are consistent with the previous finding of the morphs settling in ‘high’ and ‘low’ neighbour density habitats that differ with respect to the amount of useable area on their borders. Previous study has also suggested that white and tan male territories differ according to overall landscape structure, not vegetation composition. Therefore, we suggest that red and green spectra may be more useful when examining differences in territory structure and possibly territory quality of similar edge-type species. Our results suggest that image texture is a useful tool for ecological and evolutionary modelling of the distributions of phenotypes/genotypes across landscapes. [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]

• 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]

Texture segmentation is one of the most difficult and important tasks in the remote sensing image processing. Through the years, several methods, based on statistical features, structural features and other image features, have been proposed for texture segmentation. This paper proposes a new feature based on Contourlet transform and Fractal analysis for texture segmentation. The performance of the proposed feature is compared with that of fractal, statistical, fuzzy and power spectrum features in presence of noise and the obtained results show superiority of the new proposed feature. [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]

The diversity of informal settlement morphologies across locales makes their mapping inherently challenging in heterogeneous urban landscapes. The aim of this study was to evaluate the potential of pansharpening techniques on Sentinel 2A data, and textural features, in enhancing informal settlement identification accuracy in a fragmented urban environment. Brovey transform, intensity, hue and saturation transform, Environmental Systems Research Institute (ESRI), simple mean, and Gram–Schmidt techniques were employed to pansharpen multispectral bands of Sentinel 2A, bands 5, 6, and 7 in the first group, and bands 8A, 11 and 12 in another, using an average of bands 4 and 8 as the panchromatic band. The main objective was to investigate the efficacy of pansharpening Sentinel 2A imagery and texture analysis in automated mapping of morphologically varied informal settlements. An evaluation of the quality of fused images was undertaken through computation of the correlation between the spectral values of the original multispectral and pansharpened image. Grey-level-co-occurrence matrix texture features were extracted from the pansharpened images, and subsequently incorporated in the classification process, using a support vector machine classifier. Our results confirm that the Gram–Schmidt fusion technique yielded the highest informal settlement identification accuracy (F-score 95.2%; overall accuracy 91.8%). The experimental results demonstrated the potential of pansharpening Sentinel 2A, and the added value of image texture for a more nuanced characterisation of informal settlements. [ABSTRACT FROM AUTHOR]

• 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]

Texture-based informal settlement (IS) mapping has gained attention in urban remote sensing (RS) research. Numerous studies conducted on the use of texture analysis for IS mapping have investigated wide-ranging sensors, algorithms, scale, classifiers, feature selection methods, and other factors of interest. However, no study has systematically investigated key factors affecting texture-based classification processes. This paper presents a detailed synthesis of scientific progress in texture based IS mapping. Results revealed that grey level co-occurrence matrix was the most popularly used algorithm.Quickbird was the widely used sensor. The use of machine-learning classifiers, particularly, support vector machine and random forest yielded, comparatively, high accuracies (>80%). Interestingly, deep learning showed potential to advance IS identification. Multi-city comparison studies demonstrated need for texture features to be locally specific in order to allow transferability. Thus, integration of RS data and field survey statistics could be crucial in enhancing understanding of morphological variations for improved IS mapping. [ABSTRACT FROM AUTHOR]

Aim: To evaluate the transferability between fire recurrence scenarios of post‐fire vegetation cover models calibrated with satellite imagery data at different spatial resolutions within two Mediterranean pine forest sites affected by large wildfires in 2012. Location: The northwest and east of the Iberian Peninsula. Methods: In each study site, we defined three fire recurrence scenarios for a reference period of 35 years. We used image texture derived from the surface reflectance channels of WorldView‐2 and Sentinel‐2 (at a spatial resolution of 2 m × 2 m and 20 m × 20 m, respectively) as predictors of post‐fire vegetation cover in Random Forest regression analysies. Percentage vegetation cover was sampled in two sets of field plots with a size roughly equivalent to the spatial resolution of the imagery. The plots were distributed following a stratified design according to fire recurrence scenarios. Model transferability was assessed within each study site by applying the vegetation cover model developed for a given fire recurrence scenario to predict vegetation cover in other scenarios, iteratively. Results: For both wildfires, the highest model transferability between fire recurrence scenarios was achieved for those holding the most similar vegetation community composition regarding the balance of species abundance according to their plant‐regenerative traits (root mean square error [RMSE] around or lower than 15%). Model transferability performance was highly improved by fine‐grained remote‐sensing data. Conclusions: Fire recurrence is a major driver of community structure and composition so the framework proposed in this study would allow land managers to reduce efforts in the context of post‐fire decision‐making to assess vegetation recovery within large burned landscapes with fire regime variability. [ABSTRACT FROM AUTHOR]

Faced with the problem of incompatibility between traditional information acquisition mode and spaceborne earth observation tasks, starting from the general mathematical model of compressed sensing, a theoretical model of block compressed sensing was established, and a full-process adaptive coding and decoding compressed sensing framework for remote sensing images was proposed, which includes five parts: mode selection, feature factor extraction, adaptive shape segmentation, adaptive sampling rate allocation and image reconstruction. Unlike previous semi-adaptive or local adaptive methods, the advantages of the adaptive encoding and decoding method proposed in this paper are mainly reflected in four aspects: (1) Ability to select encoding modes based on image content, and maximizing the use of the richness of the image to select appropriate sampling methods; (2) Capable of utilizing image texture details for adaptive segmentation, effectively separating complex and smooth regions; (3) Being able to detect the sparsity of encoding blocks and adaptively allocate sampling rates to fully explore the compressibility of images; (4) The reconstruction matrix can be adaptively selected based on the size of the encoding block to alleviate block artifacts caused by non-stationary characteristics of the image. Experimental results show that the method proposed in this article has good stability for remote sensing images with complex edge textures, with the peak signal-to-noise ratio and structural similarity remaining above 35 dB and 0.8. Moreover, especially for ocean images with relatively simple image content, when the sampling rate is 0.26, the peak signal-to-noise ratio reaches 50.8 dB, and the structural similarity is 0.99. In addition, the recovered images have the smallest BRISQUE value, with better clarity and less distortion. In the subjective aspect, the reconstructed image has clear edge details and good reconstruction effect, while the block effect is effectively suppressed. The framework designed in this paper is superior to similar algorithms in both subjective visual and objective evaluation indexes, which is of great significance for alleviating the incompatibility between traditional information acquisition methods and satellite-borne earth observation missions. [ABSTRACT FROM AUTHOR]

Spatial resolution is an important indicator that measures the quality of remote sensing images. Image texture has been successfully recovered by generative adversarial networks in deep learning super‐resolution (SR) methods. However, the existing methods are prone to image texture distortion. To solve the above problems, this paper proposes an improved generative adversarial network to enhance the super‐resolution reconstruction effect of medium‐ and low‐resolution (LR) remote sensing images. This network is based on the Super Resolution Generative Adversarial Network (SRGAN), which makes great improvements in the structure of the connection between the inside and outside of the residual block and the design of the model loss function. At the same time, the G1–G2–G3 structure between residuals effectively combines the image information of the three scales of small, medium and large. The model loss function can be designed based on the Charbonnier loss function to narrow the pixel distance between the reconstructed remote sensing image and the original image. Furthermore, targeted perceptual loss can direct the network to restore the texture details of the image according to the semantic category. The subjective and objective evaluation of the generated images and the ablation experiments prove that compared with SRGAN and other networks, our method can generate more realistic and reliable textures. Additionally, the indicators [peak signal‐to‐noise ratio (PSNR), structural similarity (SSIM), multiscale structural similarity (MS‐SSIM)] used to measure the quality of the reconstructed image obtain improved objective quantitative evaluation. [ABSTRACT FROM AUTHOR]

The article provides a summary of a doctoral research by Catherine Simpson on the role of remotely sensed image texture in predictive vegetation modelling. Sympathetic management of remnant vegetation on private lands is an important factor in the effective conservation of native vegetation. Remotely sensed image texture provides an alternative data source for predictive vegetation modelling, and is suited for rapid, repeated measurements over large areas and lands with restricted access. The important consequences of the role of image texture indices in representing unexplained spatial patterning were also addressed.

Unmanned Aerial Systems (UASs) are increasingly vital in precision agriculture, offering detailed, real-time insights into plant health across multiple spectral domains. However, this technology's precision in estimating plant traits associated with Nitrogen Use Efficiency (NUE), and the factors affecting this precision, are not well-documented. This review examines the capabilities of UASs in assessing NUE in crops. Our analysis specifically highlights how different growth stages critically influence NUE and biomass assessments in crops and reveals a significant impact of specific signal processing techniques and sensor types on the accuracy of remote sensing data. Optimized flight parameters and precise sensor calibration are underscored as key for ensuring the reliability and validity of collected data. Additionally, the review delves into how different canopy structures, like planophile and erect leaf orientations, uniquely influence spectral data interpretation. The study also recognizes the untapped potential of image texture features in UAV-based remote sensing for detailed analysis of canopy micro-architecture. Overall, this research not only underscores the transformative impact of UAS technology on agricultural productivity and sustainability but also demonstrates its potential in providing more accurate and comprehensive insights for effective crop health and nutrient management strategies. [ABSTRACT FROM AUTHOR]

Current studies on large-scale remotely sensed images are of great national importance for monitoring and evaluating global climate and ecological changes. In particular, real time distributed high-performance visualization and computation have become indispensable research components in facilitating the extraction of remotely sensed image textures to enable mining spatiotemporal patterns and dynamics of landscapes from massive geo-digital information collected from satellites. Remotely sensed images are usually highly correlated with rich landscape features. By exploiting the structures of these images and extracting their textures, fundamental insights of the landscape can be derived. Furthermore, the interdisciplinary collaboration on the remotely sensed image analysis demands multifarious expertise in a wide spectrum of fields including geography, computer science, and engineering. This paper develops a Distributed Computing System for Remotely Sensed Images framework (DCSRI) to support distributed and high performance computing for geospatial images. A new algorithm supporting parallel computing with dynamic workload balance for large images, namely Variogram-based Image Texture Extractor (VITE) for extracting image texture from massive and dynamic digital remotely sensed images is presented. The VITE algorithm is used to represent and transform the original data into image textures. The DCSRI framework has the capacity to perform high performance computing on Linux clusters or supercomputers to address the intensive computing challenges arising from large and multiple images. Advanced web technology is also exploited to enable interactive user experience with prompt visual feedback as well as good interoperability. Users can also dynamically steer the visualization and computation process by adjusting the computing parameters on-the-fly. This system leads to a great reduction of image data and provides useful information for knowledge discovery and digital image classification in a user friendly and computing efficient way. [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 obtain high-quality images, the application of low-illumination image enhancement techniques plays a vital role in enhancing the overall visual appeal. However, it is particularly difficult to enhance an image while maintaining the original information of the scene. The augmentation method based on Retinex theory is widely considered as one of the representative techniques for such problems, but this method still has some limitations. First of all, noise is easily ignored in the process of model building, and the robustness of the model needs to be improved. Secondly, the image decomposition is less effective, so that part of the image information is not effectively presented. Finally, the optimization procedure is computationally complicated. This paper introduces a novel approach for enhancing low-illumination images by utilizing the relative deviation of local gradients. The proposed method aims to address the challenges associated with low-illumination images and offers a solution to these issues. In this paper, local gradient relative deviation is used as a constraint term and a noise term is added to highlight the image texture and structure and improve the robustness of the models, considering that L P achieves piecewise smoothing with better sparsity compared to the sum norm commonly used by L 1 and L 2 norms. In this paper, the L 2 − L P norm is used to constrain the model, which smooths the illumination component and better preserves the details of the reflectance component. In addition, to efficiently solve the optimization problem, the alternating direction multiplier method is chosen to transform the optimization process into the solution of several sub-problems. In comparison to traditional Retinex models, the proposed method excels in its ability to simultaneously enhance the image and suppress noise effectively. The experimental outcomes demonstrate the effectiveness of the proposed model in enhancing both simulated and real data. This approach can be applied to low-illumination remote sensing images to obtain high-quality remote sensing image data. [ABSTRACT FROM AUTHOR]

Ultra-high-resolution aerial images obtained from Unmanned Aerial Vehicles (UAVs) have widely been used to extract crop planting information in recent years. However, some high-resolution multispectral or hyperspectral images were usually costly and time-consuming for data processing. Therefore, it is very necessary to effectively use easily accessible and low-cost high-resolution RGB images, particularly to eliminate the common noises (e.g., shadows and bare land) for a better extraction accuracy of crop planting. In this study, a high-precision extraction method was proposed to obtain the maize planting information using 1.8 cm resolution UAV aerial orthophotos (i.e., RGB images). The experimental maize farm was located in Southeast Africa, where images were taken at noon during the maize growing season. The classification features were also selected from the aspects of the spectrum, color space, and image texture. Then, five types of classification were selected to extract maize planting information, including Bayes, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) algorithms). Firstly, an object-oriented interpreting platform (eCognition9.0 software) was selected to calculate the space transform of Hue, Saturation, and Intensity (HSI) color, eight types of RGB texture, and five vegetation indices, including the normalized green-red difference index, red-green ratio index, vegetation color verification index, visible-band difference vegetation index, and excess green vegetation index. Then, three types of feature space were constructed: 1) The first feature space was composed of three sub-feature spaces, i.e., vegetation indices, HSI color space features, and RGB image texture features; 2) The second feature space was composed of four sub-feature spaces, where three types of features were combined (i.e., vegetation indices, HSI color space, and RGB image texture) in pairs or total; 3) The third feature space was composed of the most optimal factors, where the dimension reduction was performed on the combination of all three types of features using RF. Subsequently, the RGB images were classified into three land-use types, including maize, bare land, and shadow. Bayes, KNN, SVM, DT, and RFs models were finally selected for the supervised classification with error matrix. The results showed that the optimal classification accuracy was obtained using neither a single feature nor all three types of features in total. More importantly, a combination of features was usually achieved higher accuracy than that of a single feature. Specifically, the best choice was the combination of HSI color and RGB image texture features using the RF, particularly with the total highest accuracy of 0.862 and a Kappa coefficient of 0.793. Additionally, the dimension reduction of features using RF models was neither significantly improved nor reduced classification accuracy (except for the SVM). However, the factors retained from the feature dimension reduction were easily explained suitable for the actual background and meaning. Furthermore, both classification efficiency and stability were improved greatly during this time. The finding can provide a specific solution for the high-precision extraction of crop planting information using UAV RGB images [ABSTRACT FROM AUTHOR]

The Sierra Madre Occidental mountain range (Durango, Mexico) is of great ecological interest because of the high degree of environmental heterogeneity in the area. The objective of the present study was to estimate the biomass of mixed and uneven-aged forests in the Sierra Madre Occidental by using Landsat- 5 TM spectral data and forest inventory data. We used the ATCOR3® atmospheric and topographic correction module to convert remotely sensed imagery digital signals to surface reflectance values. The usual approach of modeling stand variables by using multiple linear regression was compared with a hybrid model developed in two steps: in the first step a regression tree was used to obtain an initial classification of homogeneous biomass groups, and multiple linear regression models were then fitted to each node of the pruned regression tree. Cross-validation of the hybrid model explained 72.96% of the observed stand biomass variation, with a reduction in the RMSE of 25.47% with respect to the estimates yielded by the linear model fitted to the complete database. The most important variables for the binary classification process in the regression tree were the albedo, the corrected readings of the short-wave infrared band of the satellite (2.08-2.35 µm) and the topographic moisture index. We used the model output to construct a map for estimating biomass in the study area, which yielded values of between 51 and 235 Mg ha-1. The use of regression trees in combination with stepwise regression of corrected satellite imagery proved a reliable method for estimating forest biomass. [ABSTRACT FROM AUTHOR]

We tested the Moment Distance Index (MDI) in combination with texture features for the summer vegetation mapping in the eastern Pamir Mountains, Tajikistan using the 2014 Landsat OLI (Operational Land Imager) image. The five major classes identified were sparse vegetation, medium-dense vegetation, dense vegetation, barren land, and water bodies. By utilizing object features in a random forest (RF) classifier, the overall classification accuracy of the land cover maps were 92% using a set of variables including texture features and MDI, and 84% using a set of variables including texture but without MDI. A decrease of the Kappa statistics, from 0.89 to 0.79, was observed when MDI was removed from the set of predictor variables. McNemar's test showed that the increase in the classification accuracy due to the addition of MDI was statistically significant (p < 0.05). The proposed method provides an effective way of discriminating sparse vegetation from barren land in an arid environment, such as the Pamir Mountains. [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]

Soil salinization in the Yellow River Delta is a difficult and miscellaneous disease to restrict the development of agri‐ cultural economy, and further hinders agricultural production. To explore the retrieval of soil salt content from remote sensing images under the condition of no vegetation coverage, the typical area of the Yellow River Delta was taken as the study area to obtain the hyperspectral of surface features, the multispectral of UAVs and the soil salt content of sample points. Three represen‐ tative experimental areas with flat terrain and obvious soil salinization characteristics were set up in the study area, and 90 sam‐ ples were collected in total. By optimizing the sensitive spectral parameters, machine learning algorithms of partial least squares regression (PLSR) and random forest (RF) for inversion of soil salt content were used in the study area. The results showed that: (1) Hyperspectral band of 1972 nm had the highest sensitivity to soil salt content, with correlation r of -0.31. The optimized spectral parameters of shortwave infrared can improve the accuracy of estimating soil salt content. (2) RF model optimized by two different data sources had better stability than PLSR model. RF model performed well in terms of generalization ability and balance error, but it had some over-fitting problems. (3) RF model based on ground feature hyperspectral (R² =0.54, verified RMSE=3.30 g/kg) was superior to the random forest model based on UAV multispectral (R² =0.54, verified RMSE=3.35 g/kg). The combination of image texture features improved the estimation accuracy of multispectral model, but the verification accuracy was still lower than that of hyperspectral model. (4) Soil salt content based on UAV multi-spectral imageries and RF model was mapped in the study area. This study demonstrates that the level of soil salinization in the Yellow River Delta region is significantly different in geographical location. The cultivated land in the study area is mainly light and moderate salinized soil with has certain restrictions on crop cultivation. Areas with low soil salt content are suitable for planting crops in low salinity fields, and farmland with high soil salt content is suitable for planting crops with high salinity tolerance. This study constructed and compared the soil salinity inversion models of the Yellow River Delta from two different sources of data, optimized them based on the advantages of each data source, explored the inversion of soil salinity content without vegetation coverage, and can provide a reference for more accurate inversion of land salinization. [ABSTRACT FROM AUTHOR]

Survey data describing land cover information such as type and diversity over several decades are scarce. Therefore, our capacity to reconstruct historical land cover using field data and archived remotely sensed data over large areas and long periods of time is somewhat limited. This study explores the relationship between CORONA texture-a surrogate for actual land cover type and complexity-with spectral vegetation indices and texture variables derived from Landsat MSS under the Spectral Variation Hypothesis (SVH) such as to reconstruct historical continuous land cover type and complexity. Image texture of CORONA was calculated using a mean occurrence measure while image textures of Landsat MSS were calculated by occurrence and co-occurrence measures. The relationship between these variables was evaluated using correlation and regression techniques. The reconstruction procedure was undertaken through regression kriging. The results showed that, as expected, texture based on the visible bands and corresponding indices indicated larger correlation with CORONA texture, a surrogate of land cover (correlation >0.65). In terms of prediction, the combination of the first-order mean of band green, second-order measure of tasseled cap brightness, second-order mean of Normalized Visible Index (NVI) and second-order entropy of NIR yielded the best model with respect to Akaike's Information Criterion (AIC), r-square, and variance inflation factors (VIF). The regression model was then used in regression kriging to map historical continuous land cover. The resultant maps indicated the type and degree of complexity in land cover. Moreover, the proposed methodology minimized the impacts of topographic shadow in the region. The performance of this approach was compared with two conventional classification methods: hard classifiers and continuous classifiers. In contrast to conventional techniques, the technique could clearly quantify land cover complexity and type. Future applications of CORONA datasets such as this one could include: improved quality of CORONA imagery, studies of the CORONA texture measures for extracting ecological parameters (e.g., species distributions), change detection and super resolution mapping using CORONA and Landsat MSS. [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]

The comparison map profile (CMP) method compares two spatially explicit data sets (original images) at each point and through several spatial scales simultaneously. The CMP combines the moving window concept with similarity indices for quantitative or qualitative data to visualize and quantify outputs: Changes in mean similarity value and its variability through scales are reported on a profile, similarities between regions are estimated on monoscale maps, and their persistence through scales assessed on a mean multiscale map. The CMP method is first illustrated using two images with slight difference in the checkered pattern. Second, two sets of comparisons related to African vegetation are conducted using the CMP method. The first set deals with quantitative data of leaf area index (LA!): Remote-sensed LA! images extracted from the AVHRR-NVDI product are compared to simulated LA! output from a dynamic global vegetation model (DGVM) using the distance and the cross-correlational coefficient for quantitative comparison of values and structure patterns, respectively. The second set of images deals with qualitative data: the remote-sensed product of land cover type by 1GBP-MOD!S is compared to the DGVM classified LA! output into land cover types using the Kappa statistics as similarity index. Results show that taking spatial patterns into account using the CMP method decreases the mean correlation by 50%, and increases the distance by 50% as compared to the global pixel-to-pixel indices. Similarly, comparison of land cover maps costs only 35% of the global Kappa value. Equatorial gradients of vegetation from forests to grassland are the most persistent similar regions between both types of data sets. Potential limits and strengths of the CMP method are discussed. [ABSTRACT FROM AUTHOR]

A successful mathematical description of natural landscapes relies upon a class of random processes known as fractional Brownian motions (fBms), which may exhibit correlation with long-range dependence (LRD). In remote sensing applications, the sensor observes a certain real scene B and records data I for successive signal processing tasks. Assuming that B is modeled as an fBm, does the recorded signal I preserve the LRD character of B? More in general, can we relate the Hurst coefficient (an index of LRD) of the real scene to that of the recorded data? We address the problem in a simplified setup in which the data are related to (the slope of) the original scene through a zero-memory mapping. A mathematical framework is presented in which the above questions can be answered in the asymptotic regime of infinite data size. The effect of the finite sample size is also investigated. The mathematical model is also validated by real data, which are collected by a synthetic aperture radar that is mounted onboard of ERS-l/2 satellites. [ABSTRACT FROM AUTHOR]

In order to realize the evaluation of regional comprehensive disaster reduction capacity in a complex environment, an evaluation model of regional comprehensive disaster reduction capacity in a complex environment based on remote sensing monitoring and data image feature analysis is proposed. According to the geographical location and scale of disaster spots and the parameter analysis of the model of disaster-bearing bodies around the disaster spots, the remote sensing monitoring method is adopted to extract the geographical remote sensing images of regional disaster spots in a complex environment. The collected geographical remote sensing images of regional disaster points under the complex environmental background are filtered and preprocessed, and the texture parameters of the geographical remote sensing images of regional disaster points under the complex environmental background are recognized by combining the method of image texture feature extraction. Based on the method of tone mapping, the rapid filtering and feature analysis of the geographical remote sensing images of regional disaster points under the complex environmental background are carried out, and the time, position, damage, and so on in the geographical remote sensing images of regional disaster points under the complex environmental background are analyzed. By using the method of parameter analysis and gradient operator operation, a comparison model of geographical remote sensing images of regional disaster points under the complex environmental background is established, and the reliability evaluation of regional comprehensive disaster reduction ability under the complex environmental background is realized according to the method of contrast and detail significance enhancement. The test shows that this method has high accuracy in evaluating regional comprehensive disaster reduction capability under a complex environment, high accuracy in marking the geographical location of regional disaster points under a complex environment, and good fusion performance and reliability of regional comprehensive disaster reduction capability evaluation parameters. [ABSTRACT FROM AUTHOR]

The red band and the near-infrared band are often used as sensitive bands for remote sensing retrieval of forest stock volume, however the red edge band between the two is often ignored. To investigate whether the red edge band is sensitive to the accuracy of forest stock volume retrieval, the wide imagery data of GF-6 remote sensing satellite (GF-6 WFV), the DEM, the second survey data of forest resources in 2014 of Xining city were used, and the multiple linear regression model (MLR), the random forest regression model (RF) were employed. The predicting variables were collected from spectral characteristic, vegetation index, topographic factor and image texture. These variables were divided into two groups, the one is no red edge band (No Red-Edge), and the other is red edge band added (Red-Edge Added). The results show that: (1) the selected principal components which were derived from the texture variables of two groups by PCA method mainly explained the texture features of red, near-infrared and red-edge 1 band of the image. (2) Regarding the spectral variables, the surface reflectance of red and near-infrared band were selected because of the high correlation with the forest stock volume in No Red-Edge group, and in Red-Edge Added group, the surface reflectance of the red-edge 1 band was selected. Regarding the vegetation index variables, the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) were selected in No Red-Edge group, and in Red-Edge Added group, the MERIS Terrestrial Chlorophyll Index (MTCI) was selected. (3) In Red-Edge Added group, the R² of the RF model is better than that of the MLR model, 0.6719 and 0.5487, respectively, and the root mean square error (RMSE) of the RF model is smaller than that of the MLR model, 26.3m³·ha−1 and 20.8m³·ha−1, respectively. (4) After eliminating the effect of the model on the accuracy of forest stock volume retrieval, the R² between the retrieval and the observed values in Red-Edge Added group increased by 11.6%, and the RMSE in Red-Edge Added group decreased by 9.1% compared to No Red-Edge group. Our results suggested that the red-edge band significantly improved the accuracy of the coniferous forest stock volume retrieval in Xining city. This study has high potential value in the remote sensing retrieval of forest stock volume. [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]

Applications of remote sensing for biodiversity conservation typically rely on image classifications that do not capture variability within coarse land cover classes. Here, we compare two measures derived from unclassified remotely sensed data, a measure of habitat heterogeneity and a measure of habitat composition, for explaining bird species richness and the spatial distribution of 10 species in a semi-arid landscape of New Mexico. We surveyed bird abundance from 1996 to 1998 at 42 plots located in the McGregor Range of Fort Bliss Army Reserve. Normalized Difference Vegetation Index values of two May 1997 Landsat scenes were the basis for among-pixel habitat heterogeneity (image texture), and we used the raw imagery to decompose each pixel into different habitat components (spectral mixture analysis). We used model averaging to relate measures of avian biodiversity to measures of image texture and spectral mixture analysis fractions. Measures of habitat heterogeneity, particularly angular second moment and standard deviation, provide higher explanatory power for bird species richness and the abundance of most species than measures of habitat composition. Using image texture, alone or in combination with other classified imagery-based approaches, for monitoring statuses and trends in biological diversity can greatly improve conservation efforts and habitat management. [ABSTRACT FROM AUTHOR]

With the continuous development of hyperspectral remote sensing technology, it has been widely used in vegetation mapping. However, sparse vegetation canopy, soil background, and spectral similarity between different types of vegetation are still the main challenges for vegetation types mapping in arid areas. As a result, it is difficult to achieve reliable classification accuracy by using spectral or texture features separately. Generalized Normal Distribution Optimization (GNDO) is a new feature optimization algorithm, with advantages in quality and stability of feature extraction results, comparing to traditional optimization algorithms. But it has not yet been applied to select bands of hyperspectral data. In order to validate the feasibility of combining ZY-1 02D spectral and texture features to classify vegetation types in arid areas, to verify the effectiveness of the GNDO method for bands selection of hyperspectral data, and to explore the effects of feature selection methods and training sample numbers on the classification accuracy of vegetation mapping, different Wrapper Optimization methods, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and GNDO, were applied to select spectral features for vegetation mapping, taking the area around Zongjia Town, Dulan County, Qinghai Province, China as the research area, then the band selection results of these methods were analyzed. Train sample set containing 30, 50, 100, 150, and 200 pixels per class were used to select bands and to train the classifier. Different methods (ALL (without bands selection), GA, PSO, GWO, GNDO) and different sizes of the trained sample sets (30, 50, 100, 150, and 200 pixels per class) were used to obtain 25 spectral feature-based classification data sets. Simultaneously, 8 texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment) were extracted using the Gray-level co-occurrence matrix (GLCM) method, and selected on basis of distinguishability for vegetation types. Texture features (TEX) were combined with spectral feature-based classification data sets. The random forest classification method was applied to classify vegetation types for the classification data sets, and the classification accuracy of classification data sets was evaluated and compared. The results show that 1) the blue region (400-450 nm), the red edge region (700-750 nm), and the red region (600-650 nm) are the most sensitive to distinguish the vegetation types in the study area; 2) the GNDO200 achieved the highest overall classification accuracy (80.44%) among the spectral feature-based classification data sets, which was better than the classification accuracy (78.86%) using all bands (ALL200); 3) with the increase of training samples, the overall classification accuracy of each classification data set showed an increasing trend, the classification accuracy of different feature selection methods showed different reliance on the number of training samples; 4) image texture features significantly improved the classification accuracy, and the GWO200+TEX dataset had the highest overall classification accuracy (82.86%). This study could verify the potential of the ZY1-02D, the new hyperspectral satellite data, for the classification of vegetation types in arid areas, and provide an idea for the selection of spectral and texture features in hyperspectral vegetation mapping. [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]

Mangroves are salt tolerant woody plants that form highly productive intertidal ecosystems in tropical and subtropical regions. Despite the established importance of mangroves to the coastal environment, including fisheries, deforestation continues to be a major threat due to pressures for wood and forest products, land conversion to aquaculture, and coastal urban development. Over the past 15 years, remote sensing has played a crucial role in mapping and understanding changes in the areal extent and spatial pattern of mangrove forests related to natural disasters and anthropogenic forces. This paper reviews recent advancements in remote-sensed data and techniques and describes future opportunities for integration or fusion of these data and techniques for large-scale monitoring in mangroves as a consequence of anthropogenic and climatic forces. While traditional pixel-based classification of Landsat, SPOT, and ASTER imagery has been widely applied for mapping mangrove forest, more recent types of imagery such as very high resolution (VHR), Polarmetric Synthetic Aperture Radar (PolSAR), hyperspectral, and LiDAR systems and the development of techniques such as Object Based Image Analysis (OBIA), spatial image analysis (e.g. image texture), Synthetic Aperture Radar Interferometry (InSAR), and machine-learning algorithms have demonstrated the potential for reliable and detailed characterization of mangrove forests including species, leaf area, canopy height, and stand biomass. Future opportunities include the application of existing sensors such as the hyperspectral HYPERION, the application of existing methods from terrestrial forest remote sensing, investigation of new sensors such as ALOS PRISM and PALSAR, and overcoming challenges to the global monitoring of mangrove forests such as wide-scale data availability, robust and consistent methods, and capacity-building with scientists and organizations in developing countries. [ABSTRACT FROM AUTHOR]

The extraction of river planforms from remotely sensed satellite images is a task of crucial importance to many applications such as land planning, water resource monitoring, or flood prediction. In this article, we present a novel framework for the extraction of rivers from synthetic aperture radar (SAR) images, based on superpixel segmentation and subsequent classification. Superpixel segmentation is achieved by a modeling of the image pixels’ amplitudes and spatial coordinates as a finite mixture model, where the generalized Gamma distribution is used to model accurately a variety of high-resolution SAR scenes. A number of features describing image texture and statistics are extracted on a superpixel level, facilitating the identification of river superpixels—planforms are then extracted by unsupervised, agglomerative clustering, thus eliminating the need for labeled training data. We present the results of our proposed method on the ICEYE-X2 and SENTINEL-1 SAR data, demonstrating its ability to produce pixel-accurate river masks. [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]

Canopy leaf area, frequently quantified by the Leaf Area Index (LAI), serves as the dominant control over primary production, energy exchange, transpiration, and other physiological attributes related to ecosystem processes. Maps depicting the spatial distribution of LAI across the landscape are of particularly high value for a better understanding of ecosystem dynamics and processes, especially over large and remote areas. Moreover, LAI maps have the potential to be used by process models describing energy and mass exchanges in the biosphere/ atmosphere system. In this article we assess the applicability of the RapidEye satellite system, whose sensor is optimized towards vegetation analyses, for mapping LAI along a disturbance gradient, ranging from heavily disturbed shrub land to mature mountain rainforest. By incorporating image texture features into the analysis, we aim at assessing the potential quality improvement of LAI maps and the reduction of uncertainties associated with LAI maps compared to maps based on Vegetation Indexes (VI) solely. We identified 22 out of the 59 image features as being relevant for predicting LAI. Among these, especially VIs were ranked high. In particular, the two VIs using RapidEye's RED-EDGE band stand out as the top two predictor variables. Nevertheless, map accuracy as quantified by the mean absolute error obtained from a 10-fold cross validation (MAE_CV) increased significantly if VIs and texture features are combined (MAE_CV = 0.56), compared to maps based on VIs only (MAE_CV = 0.62). We placed special emphasis on the uncertainties associated with the resulting map addressing that map users often treat uncertainty statements only in a pro-forma manner. Therefore, the LAI map was complemented with a map depicting the spatial distribution of the goodness-of-fit of the model, quantified by the mean absolute error (MAE), used for predictive mapping. From this an area weighted MAE (= 0.35) was calculated and compared to the unweighted MAE of 0.29. Mapping was done using randomForest, a widely used statistical modeling technique for predictive biological mapping. [ABSTRACT FROM AUTHOR]

The conventional JSEG algorithm has a powerful detection capability on the homogeneity of regional texture features because it combines the spectral information with image texture features during the segmentation. However, the conventional JSEG method is not very accurate for the target edge localization in segmentation results. To solve this problem, this paper proposes an improved segmentation method of remotely sensed image based on JSEG algorithm and fuzzy c-means (FCM) with spatial constraints. Firstly, the FCM clustering method based on spatial neighborhood terms is used to replace the traditional HCM clustering method in the quantization step. Then the region growing method is applied to segment the class diagram after FCM clustering. Finally, the proposed method uses the improved regional merger approach to merger the over divided region after segmentation. According to the J index, the proposed algorithm is improved by 31% and 12% compared with the traditional JSEG segmentation method and improved by 17% and 8% compared with the FNEA segmentation algorithm for aerial image and the SPOT 5 image. The experimental results show that the proposed segmentation algorithm has good noise immunity because of the fuzzy clustering of spatial constraints and can extract the edge of the target more accurately. [ABSTRACT FROM AUTHOR]

The wide use of plastic greenhouses in the word has brought economic benefits, but also caused many environmental problems. Accurate and timely acquisition of spatial distribution information of plastic greenhouse is of great significance to agricultural production and soil management. The use of plastic greenhouse has changed the structure of soil, thus changing the surface spectral characteristics of the soil. Because of the particularity of the plastic film material, the reflection spectrum has strong directionality and uncertainty, so it is difficult to identify plastic greenhouse accurately only depending on the reflectance spectrum characteristics of ground objects. In this paper, Kunming City, Yunnan Province is taken as the research area, using GF-2 image as a single data source, the multi-scale segmentation method is used to obtain the image object efficiently and accurately. According to the detailed spatial information of plastic greenhouse, the applicability of three image object-based texture extraction algorithms, namely GLCM(gray-level co-occurrence matrix), LBP(local binary pattern) and PSI(pixel shape index) for plastic greenhouse identification is analysed and tested. In addition, different texture features are combined with spectral features and NDVI index to form different classification schemes, to explore which one would be the best combination of texture features for identification of plastic greenhouses. In order to explore the robustness of the method, different texture feature combination schemes are applied in the study areas. The results of SVM (support vector machine) classifier are evaluated by confusion matrix. The results show that the overall combination schemes of the two research areas with different landscape patterns have the same trend. The combination of spectral features and NDVI index can accurately identify the scattered waters in each of the two study areas. For plastic greenhouses and impervious surfaces with similar reflectance spectra, adding texture features can make up for limitation of spectral characteristics and improve the overall accuracy. The phenomena of homologous or homologous spectra in spectral features of high spatial resolution image can effectively improve the discrimination between plastic greenhouse and impervious surface. Texture features can significantly improve the identification accuracy of plastic greenhouse following the object-based image classification frame. In the classification scheme of plastic greenhouses with different spatial distribution structures, the LBP (local binary pattern) texture algorithm has the best recognition accuracy, the overall accuracy of study area A is 96.85%, Kappa coefficient is 0.95, and that of study area B is 95.58% and 0.94. Landscape fragment analysis (landscape fragmentation index area mean index, aggregation indices) of the two different study areas showed that the plastic greenhouses in study area A are more fragmented than study area B (area mean indices are 3.39 hm² and 1.37 hm², aggregation indices are 80.64 and 72.98 for plastic greenhouses in study area A and B, respectively). The results of fragmentation are consistent with those of classification, and the accuracy of landscape classification with more space fragments is lower (the highest overall classification accuracy for study area A and B are 98.13% and 96.13%, respectively, the PA(producer accuracy) and UA(user accuracy) are 96.47% and 97.93% for study area A, and 90.67%, 99.68% for study area B). The results show that texture features based on image objects can improve the recognition accuracy of small-scale plastic greenhouse. This is of great significance to the accurate mapping of the distribution of plastic greenhouses. [ABSTRACT FROM AUTHOR]

The wide use of plastic greenhouses in the word has brought economic benefits, but also caused many environmental problems. Accurate and timely acquisition of spatial distribution information of plastic greenhouse is of great significance to agricultural production and soil management. The use of plastic greenhouse has changed the structure of soil, thus changing the surface spectral characteristics of the soil. Because of the particularity of the plastic film material, the reflection spectrum has strong directionality and uncertainty, so it is difficult to identify plastic greenhouse accurately only depending on the reflectance spectrum characteristics of ground objects. In this paper, Kunming City, Yunnan Province is taken as the research area, using GF-2 image as a single data source, the multi-scale segmentation method is used to obtain the image object efficiently and accurately. According to the detailed spatial information of plastic greenhouse, the applicability of three image object-based texture extraction algorithms, namely GLCM(gray-level co-occurrence matrix), LBP(local binary pattern) and PSI(pixel shape index) for plastic greenhouse identification is analysed and tested. In addition, different texture features are combined with spectral features and NDVI index to form different classification schemes, to explore which one would be the best combination of texture features for identification of plastic greenhouses. In order to explore the robustness of the method, different texture feature combination schemes are applied in the study areas. The results of SVM (support vector machine) classifier are evaluated by confusion matrix. The results show that the overall combination schemes of the two research areas with different landscape patterns have the same trend. The combination of spectral features and NDVI index can accurately identify the scattered waters in each of the two study areas. For plastic greenhouses and impervious surfaces with similar reflectance spectra, adding texture features can make up for limitation of spectral characteristics and improve the overall accuracy. The phenomena of homologous or homologous spectra in spectral features of high spatial resolution image can effectively improve the discrimination between plastic greenhouse and impervious surface. Texture features can significantly improve the identification accuracy of plastic greenhouse following the object-based image classification frame. In the classification scheme of plastic greenhouses with different spatial distribution structures, the LBP (local binary pattern) texture algorithm has the best recognition accuracy, the overall accuracy of study area A is 96.85%, Kappa coefficient is 0.95, and that of study area B is 95.58% and 0.94. Landscape fragment analysis (landscape fragmentation index area mean index, aggregation indices) of the two different study areas showed that the plastic greenhouses in study area A are more fragmented than study area B (area mean indices are 3.39 hm2 and 1.37 hm2, aggregation indices are 80.64 and 72.98 for plastic greenhouses in study area A and B, respectively). The results of fragmentation are consistent with those of classification, and the accuracy of landscape classification with more space fragments is lower (the highest overall classification accuracy for study area A and B are 98.13% and 96.13%, respectively, the PA(producer accuracy) and UA(user accuracy) are 96.47% and 97.93% for study area A, and 90.67%, 99.68% for study area B). The results show that texture features based on image objects can improve the recognition accuracy of small-scale plastic greenhouse. This is of great significance to the accurate mapping of the distribution of plastic greenhouses. [ABSTRACT FROM AUTHOR]

Chlorophyll is an important pigment for crop light energy utilization, which directly affects the process of energy material conversion and transmission. The change of chlorophyll content directly reflects the ability of photosynthesis and the nutritional status of crop growth. When the traditional unmanned aerial vehicle (UAV) remote sensing was used for crop nutrition monitoring, most of them started from the spectral vegetation indices, ignoring the characteristics of the image itself. In this study, we estimated potato leaf chlorophyll content from a comprehensive index formed by the fusion of multi-spectral vegetation indices, texture features and comprehensive indicators of data fusion. The effect of comprehensive index model on estimating potato leaf chlorophyll content was explored. First, we used the UAV multi-spectral images during the whole potato growth period in 2018 in Xiaotangshan, Changping, and Beijing. The multi-spectral vegetation index, texture characteristics and other variables were first extracted from UVA images, then their correlation relationships with leaf chlorophyll content were analyzed. The optimal image variables were screened out, and the whole subset analysis was based on adjusted determination coefficient and 10-fold cross-validation was used to estimate the leaf chlorophyll content of potato. Finally, the vegetation index and texture features were reconstructed by principal component fusion to establish a new comprehensive index for chlorophyll content estimation. It was found that the leaf chlorophyll content estimation model based on comprehensive index was better than that based on multi-spectral vegetation indices and texture features. The main reason was that the comprehensive index contained both spectral information and image texture information. Multispectral information and model accuracy had also been significantly improved. In the bud period, compared with the vegetation indices based model and the texture feature based model, the determination coefficient (R2) of the comprehensive index model increased 0.104 and 0.136, while the normalized root mean squared error (NRMSE) reduced 1.3% and 1.6%. During the tuber formation period, the determination coefficient of comprehensive index model was increased 0.04.and 0.101, while the NRMSE was decreased 0.5% and 1.2%, compared with the other 2 models. In the tuber growth period, the determination coefficient of comprehensive index model increased 0.075 and 0.111, and the NRMSE decreased 0.9% and 1.3% compared with the vegetation index model and the texture feature model. During the starch accumulation period, the R2 of comprehensive index model increased 0.017 and 0.046, and the NRMSE decreased 0.2% and 0.6%, compared with vegetation index model and texture feature model. In the maturity period, the determination coefficient of comprehensive index model increased 0.092 and 0.057, and the NRMSE decreased 2.4% and 1.5%.Therefore, the effect of comprehensive index estimation model was the best followed by multi-spectral vegetation indices model and the texture feature model was the worst. The starch accumulation period was the best growth period for estimating chlorophyll content by multispectral vegetation index and texture characteristics, while bud period was the best growth period for estimating chlorophyll content by comprehensive index. Estimating potato chlorophyll content based on multi-spectral image from UAV platform can provide a feasible method for potato growth nutrition monitoring. It realizes low-cost, fast and high-throughput monitoring of potato growth and nutrition information, as well as provides guarantee for fine management of farmland irrigation, variable fertilization and so on. [ABSTRACT FROM AUTHOR]

Giant reed (Arundo donax) is a prevalent invasive plant in desert riparian ecosystems that threatens wildlife habitat. From 2008 to 2018, under a United States–Mexico partnership, prescribed burns and herbicide applications were used to remove giant reed and promote native revegetation along the Rio Grande—Río Bravo floodplain in west Texas, USA, and Mexico. Our goal was to explore the effects of the removal efforts on butterfly and bird communities and their habitat along the United States portion of the Rio Grande—Río Bravo floodplain in Big Bend National Park, Texas. During spring and summer, 2016–2017, we surveyed butterflies, birds, and their habitat using ground‐collected and remotely sensed data. Using a variety of generalized linear and N‐mixture modeling routines and multivariate analyses, we found that the initial giant reed removal efforts removed key components of riparian habitat leading to reduced butterfly and bird communities. Within several years following management, giant reed levels remained low, while riparian habitat conditions and butterfly and bird communities largely rebounded, including many disturbance‐sensitive butterfly species and riparian‐associated bird species. Butterflies were most consistently associated with forb and grass cover, and birds with a remotely sensed index of greenness (the normalized difference vegetation index), several vegetation cover types, and habitat heterogeneity, habitat elements that were most common in locations that had the longest time to recover following management actions. Our results suggest that prescribed burns and herbicide applications, when used following protocols to minimize risk to wildlife, can limit the spread of giant reed in desert riparian systems and introduce habitat conditions that support diverse and abundant butterfly and bird communities. [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]

Lidar (light detection and ranging) has demonstrated the ability to provide highly accurate information on forest vertical structure; however, lidar data collection and processing are still expensive. Very high spatial resolution optical remotely sensed data have also shown promising results to delineate various forest biophysical properties. In this study, our main objective is to examine the potential of Quickbird (QB) imagery to accurately estimate forest canopy heights measured from small-footprint lidar data. To achieve this, we have developed multiscale geographic object-based image analysis (GEOBIA) models from QB data for both deciduous and conifer stands. In addition to the spectral information, these models also included (1) image-texture [i.e., an internal-object variability measure and a new dynamic geographic object-based texture (GEOTEX) measure that quantifies forest variability within neighboring objects] and (2) a canopy shadow fraction measure that acts as a proxy of vertical forest structure. A novel object area-weighted error calculation approach was used to evaluate model performance by considering the importance of object size. To determine the best object scale [i.e., mean object size (MOS)] for defining the most accurate canopy height estimates, we introduce a new perspective, which considers height variability both between- and within-objects at all scales. To better evaluate the improvements resulting from our GEOBIA models, we compared their performance with a traditional pixel-based approach. Our results show that (1) the addition of image-texture and shadow fraction variables increases the model performance versus using spectral information only, especially for deciduous trees, where the average increase of R2 is approximately 23% with a further 1.47 m decrease of Root Mean Squared Error (RMSE) at all scales using the GEOBIA approach; (2) the best object scale for our study site corresponds to an MOS of 4.00 ha; (3) at most scales, GEOBIA models achieve more accurate results than pixel-based models; however, we note that inappropriately selected object scales may result in poorer height accuracies than those derived from the applied pixel-based approach. [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]

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]

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]