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Coastal communities, land covers, and intertidal habitats are vulnerable receptors of erosion, flooding or both in combination. This vulnerability is likely to increase with sea level rise and greater storminess over future decadal-scale time periods. The accurate, rapid, and wide-scale determination of shoreline position, and its migration, is therefore imperative for future coastal risk adaptation and management. This paper develops and applies an automated tool, VEdge_Detector, to extract the coastal vegetation line from high spatial resolution (Planet's 3 to 5 m) remote-sensing imagery, training a very deep convolutional neural network (holistically nested edge detection), to predict sequential vegetation line locations on annual to decadal timescales. Red, green, and near-infrared (RG-NIR) was found to be the optimum image spectral band combination during neural network training and validation. The VEdge_Detector outputs were compared with vegetation lines derived from ground-referenced positional measurements and manually digitized aerial photographs, which were used to ascertain a mean distance error of <6 m (two image pixels) and >84% producer accuracy (PA) at six out of the seven sites. Extracting vegetation lines from Planet imagery of the rapidly retreating cliffed coastline at Covehithe, Suffolk, United Kingdom, has identified a landward retreat rate >3 m year−1 (2010–2020). Plausible vegetation lines were successfully retrieved from images in The Netherlands and Australia, which were not used to train the neural network, although significant areas of exposed rocky coastline proved to be less well recovered by VEdge_Detector. The method therefore promises the possibility of generalizing to estimate retreat of sandy coastlines from Planet imagery in otherwise data-poor areas, which lack ground-referenced measurements. Vegetation line outputs derived from VEdge_Detector are produced rapidly and efficiently compared to more traditional non-automated methods. These outputs also have the potential to inform upon a range of future coastal risk management decisions, incorporating future shoreline change. [ABSTRACT FROM AUTHOR]

Airborne laser scanning (ALS) is generally known as the most accurate and primary source providing the 3D structure of forest canopy model (CHM) information; however, in many countries, it is not updated as needed for continuous forest management planning due to its high costs. Image-based point clouds derived from airborne stereo aerial photographs, space-borne stereo very high-resolution WorldView-2, and TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X) can also be used for the generation of the CHM. In this study, we compared the performance of three types of 3D data, i.e. stereo aerial photographs, stereo very high-resolution WorldView-2, and TanDEM-X for the retrieval of forest CHM. Digital surface models (DSMs) generated from image-based point clouds produced from stereo aerial photographs and stereo WorldView-2 using image-matching algorithms and from TanDEM-X data using interferometry. Finally, three types of CHMs generated by subtracting ALS-based digital terrain model (DTM) from the above three DSMs. Plot-level heights and density metrics extracted from the CHMs and regressed with the field-based Lorey's mean, and maximum height. Stereo aerial photographic CHM showed the most accurate results with root-mean-square error (RMSE) = 1.71 m, followed by high-resolution stereo WorldView-2 with RMSE = 2.04 m, and TanDEM-X with RMSE = 2.13 m, respectively. Also, for maximum height, we obtained higher accuracy for image-based point clouds with RMSE = 2.33 m, followed by high-resolution stereo WorldView-2 with RMSE = 2.71 m, and TanDEM-X with RMSE = 2.81 m, respectively. Our overall finding indicated that stereo aerial photography is the most accurate option followed by stereo very high-resolution WorldView-2 and TanDEM-X data for estimating forest heights. The study shares valuable information for the generation and regular update of forest structure information in the presence of pre-existing ALS DTM. [ABSTRACT FROM AUTHOR]

Accurately identified trees can serve as a basis of estimating forest variables through the individual tree-based approach. Increasing richness of remote sensing data also provides opportunities to explore the potential uses of various types of data sources. This study adopted three widely used remote sensing data, including airborne light detection and ranging (LiDAR), unmanned aerial vehicle (UAV) photography and traditional digital aerial photos (DAP), and aimed to investigate their potentials on estimating tree heights and extracting individual tree information in four forested sites in Hong Kong with different tree compositions. Image-based point clouds were generated through photogrammetry. Local maxima and region growing methods were adopted to identify treetops and delineate tree crowns, respectively, with different fixed and variable window size settings. Tree heights obtained from remote sensing datasets resulted in correlation coefficients (r) = 0.58–0.94 and root-mean-square errors (RMSE) = 1.33–3.78 m compared to field-measured values and similar levels of correspondences among the datasets. Point cloud characteristics were highlighted through point-based and profile-based analysis. The highest F-scores for treetop detections in each site ranged from 0.53 to 0.69 with variations caused by different window sizes and data sources. Matched rates of reference trees were positively correlated (r = 0.19–0.49) with geometric properties including diameter at breast height (DBH), tree height, crown area, and distance to the nearest neighbour. No single remote sensing dataset was clearly superior in all methodologies in this study, but unique properties were demonstrated in terms of both data acquisitions and analysis. Knowledge and testing on both characteristics of study areas and data sources were important when deciding the best window size parameters. Heterogeneity of forest environment could be a major factor hindering the delineation performance with further influences on plot-level difference and tree-level detectability. [ABSTRACT FROM AUTHOR]

A landslide inventory is indispensable for determination of landslide susceptibility, hazard, risk assessment and disaster mitigation strategies. These inventories were traditionally developed using manual digitization of remote sensing images and aerial photographs, and pixel-based image classification. Recently, Object-Based Image Analysis (OBIA) supersedes visual interpretation and pixel-based methods. OBIA utilizes spectral, textural, contextual, morphological and topographical information in remote sensing images. However, OBIA-based landslide detection methods are often designed for specific areas and remote sensing dataset. The aim of this study is to evaluate the transferability of three published OBIA landslide detection methods for semi-automated landslide detection in the Himalaya mountainous region of northern Pakistan. A SPOT-6 multi-spectral image with Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) Digital Elevation Model (DEM) derivatives, i.e. slope, aspect, hill-shade, relief, elevation and stream network are used for landslide detection using eCognition developer software. The three published methods scale parameters for image segmentation and parameter thresholds are evaluated first. It is observed that the aforementioned methods are not directly applicable to our study area and remote sensing datasets. Therefore, an alternate (proposed) method is developed for semi-automated landslide detection. Accuracy assessment of the selected methods and proposed method is assessed by Precision, Recall and F1 measures. Using the proposed method, a total of 357 landslides are detected with 91.46% Precision, 93.31% Recall and 92.38% F1 measure accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a processing scheme whose aim is to provide a tool for a rapid measurement of pancake ice size distribution from aerial photographs. The test images used in this study were collected during the flights of the Twin Otter of the Naval Research Laboratory (NRL) which assisted the cruise of the research ship 'Sikuliaq' in carrying out an extensive study of autumn sea ice in the southern Beaufort Sea in 2015. The processing scheme is composed of the following steps: i) image enhancement, ii) non-linear support vector machine (SVM) analysis, iii) marker-controlled watershed segmentation, and iv) ice size distribution computation. The results demonstrate the usefulness of having immediate information on pancake ice size distribution for the subsequent tasks of the field campaign. [ABSTRACT FROM AUTHOR]

The use of very high resolution (VHR) aerial imagery for quantitative remote sensing has been limited by unwanted radiometric variation over temporal and spatial extents. In this paper we propose a simple yet effective technique for the radiometric homogenisation of the digital numbers of aerial images. The technique requires a collocated and concurrent, well-calibrated satellite image as surface reflectance reference to which the aerial images are calibrated. The bands of the reference satellite sensor should be spectrally similar to those of the aerial sensor. Using radiative transfer theory, we show that a spatially varying local linear model can be used to approximate the relationship between the surface reflectance of the reference image and the digital numbers of the aerial images. The model parameters for each satellite pixel location are estimated using least squares regression inside a small sliding window. The technique was applied to a set of aerial images captured over multiple days with an Intergraph Digital Mapping Camera (DMC) system. A near-concurrent Moderate Resolution Imaging Spectroradiometer (MODIS) nadir bidirectional reflectance distribution function (BRDF) adjusted reflectance image was used as the reflectance reference dataset. The resulting DMC mosaic was compared to a near-concurrent Satellite Pour l'Observation de la Terre (SPOT) 5 reflectance image of a portion of the same area, omitting the blue channel from the DMC mosaic due to its absence in the SPOT 5 data. The mean absolute reflectance difference was found to be 3.43% and the mean coefficient of determination (R2) over the bands was 0.84. The technique allows the production of seamless mosaics corrected for coarse scale atmospheric and BRDF effects and does not require the manual acquisition (or provision) of ground reflectance references. The accuracy of corrections is limited by the resolution of the reference image, which is generally significantly coarser than VHR imagery. The method cannot correct for small scale BRDF or other variations not captured at the reference resolution. Nevertheless, results show a significant improvement in homogeneity and correlation with SPOT 5 reflectance. [ABSTRACT FROM AUTHOR]

Hurricanes and tropical storms are severe threats to coastal properties, settlements, and infrastructure. Airborne light detection and ranging (lidar) surveys conducted before and after storm events allow detailed analysis of coastal geomorphologic and sediment volumetric changes and have been proved very useful in the study of coastal changes. Traditionally, most studies use the pixel-based differencing method to quantify the spatial extent and magnitude of coastal changes based on sequential lidar surveys. This research presents a graph theory-based approach and associated software tools for representing and quantifying storm-induced damages to buildings, beaches and sand dunes, coastal vegetation canopy, and infrastructure. Generation of elevation difference grids, construction of local contour trees, and derivation of semantic properties are key components of the new algorithm for change object detection and extraction. An ontology and taxonomy are proposed to classify change objects into different types of coastal damages in terms of their semantic properties. This method has been successfully applied to assess damages of Hurricane Ike to the Bolivar Peninsula on the Texas Gulf Coast based on pre- and post-storm airborne lidar data and colour infrared aerial photographs. [ABSTRACT FROM PUBLISHER]

Remote sensing, together with geographic information systems (GISs), provide a powerful tool for monitoring environmental processes. Aerial and satellite photographs enable the analysis of an entire landscape and, using multi-temporal sets of photographs, one is able to follow processes over time. In our study, we used multi-temporal sets of aerial photographs, taken between 1944 and 2005, to observe the phenomenon of woody plant encroachment in four neighbouring study sites in northern KwaZulu-Natal, South Africa. We also analysed rainfall and temperature trends. Woody plant encroachment was evident in all sites. We suggest possible drivers behind the bush encroachment; in particular, climatic trends. However, encroachment did not relate directly to rainfall and temperature trends. We also speculate on the effects of herbivory and fire, as well as rising atmospheric CO2concentrations and its effect on C3and C4plants. We suggest that increasing atmospheric CO2concentrations could be the overriding driver of woody plant encroachment in our study area, with other drivers, such as rainfall, temperature, fire, herbivory, and land use, playing a modifying role. [ABSTRACT FROM AUTHOR]

Geospatial objects change over time and this necessitates periodic updating of the cartography that represents them. Currently, this updating is done manually, by interpreting aerial photographs, but this is an expensive and time-consuming process. While several kinds of geospatial objects are recognized, this article focuses on buildings. Specifically, we propose a novel automatic approach for detecting buildings that uses satellite imagery and laser scanner data as a tool for updating buildings for a vector geospatial database. We apply the support vector machine (SVM) classification algorithm to a joint satellite and laser data set for the extraction of buildings. SVM training is automatically carried out from the vector geospatial database. For visualization purposes, the changes are presented using a variation of the traffic-light map. The different colours assist human operators in performing the final cartographic updating. Most of the important changes were detected by the proposed method. The method not only detects changes, but also identifies inaccuracies in the cartography of the vector database. Small houses and low buildings surrounded by high trees present significant problems with regard to automatic detection compared to large houses and taller buildings. In addition to visual evaluation, this study was checked for completeness and correctness using numerical evaluation and receiver operating characteristic curves. The high values obtained for these parameters confirmed the efficacy of the method. [ABSTRACT FROM AUTHOR]

Change detection of objects, such as buildings, is essential for map updating. Traditionally, detection is usually performed through spectral analysis of multi-temporal images. This article proposes a method that employs multi-temporal interpolated lidar data. The objective of this study is to perform change detection and change-type determination via geometric analysis. A shape difference map is generated between the digital surface models in two different time periods. The areas with small shape differences are treated as non-changed areas and are excluded from the segmentation. The object's properties are then applied to determine the change types. Experimental results demonstrate that the proposed scheme achieves accuracy as high as 80%. Most of the errors from this study occurred in small or vegetation areas. [ABSTRACT FROM PUBLISHER]

The aim of this study is to extract landslide-related factors from remote-sensing data, such as Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery, and to examine their applicability to landslide susceptibility near Boun, Korea, using a geographic information system (GIS). Landslide was mapped from interpretation of aerial photographs and field surveying. Factors that influence landslide occurrence were extracted from ASTER imagery. The slope, aspect and curvature were calculated from the digital elevation model (DEM) with 25.77 m root mean square error (RMSE), which was derived from ASTER imagery. Lineaments, land-cover and normalized difference vegetation index (NDVI) layers were also estimated from ASTER imagery. Landslide-susceptible areas were analysed and mapped using the occurrence factors by a frequency ratio and logistic regression model. Validation results were 84.78% in frequency ratio and 84.20% in logistic regression prediction accuracy for the susceptibility map with respect to ground-truth data. [ABSTRACT FROM AUTHOR]

The evaluation of beach hydromorphological behaviour and its classification is a complex issue. The main objective of this study was to develop new methodologies to identify coastal features/patterns. Pixel-based and object-oriented classification algorithms were used and a new approach was developed based on Principal Components Analysis and Histogram (PCAH) segmentation, to identify and analyse morphological features and hydrodynamic patterns. The PCAH method consists of three stages: preprocessing, PCA and histogram-based segmentation. Both manual and automatic approaches were addressed regarding the identification of the classes obtained from the segmentation stage. The dataset was composed from two aerial photographs and one IKONOS-2 image. The supervised classification algorithms present good results for both aerial photographs and the IKONOS-2 image. For the two aerial photographs the best results were found for the maximum likelihood classifier and for the IKONOS-2 image the best result was achieved with the parallelepiped classifier. The object-oriented classification performance for the aerial photographs and for the IKONOS-2 image also presented good results. The PCAH method led to promising results, with proportions of correctly classified pixels greater than 90% for the classes ‘Sea’, ‘Sediments+breaking zone’ and ‘Beach’. [ABSTRACT FROM AUTHOR]

The conservation of Jordan's Mediterranean forest requires the use of remote sensing. Among the most important parameters needed are the crown-cover percentage (C) and above-ground biomass (A). This study aims to: (1) identify the best predictor(s) of C using Landsat Enhanced Thematic Mapper (ETM) bands and the derived transformed normalized difference vegetation index (TNDVI); (2) determine if C is a good predictor of A, volume (V), Shannon diversity index (S) and basal area (B); and (3) generate maps of all these parameters. A Landsat ETM image, aerial photographs and ground surveys are used to model C using multiple regression. C is then modelled to A, V, S and B using linear regression. The relationship between C and Landsat ETM bands (1 and 7) plus the TNDVI is significantly high (coefficient of determination R2 = 0.8) and is used to produce the C map. The generated C map is used to predict A (R2 = 0.56), V (R2 = 0.58), S (R2 = 0.50) and B (R2 = 0.43). Cross validation for the predicted C map (cross-validation error = 5.3%) and for the predicted forest-parameter maps (cross-validation error = 13.7%-19.9%) shows acceptable error levels. Results indicate that Jordan's east Mediterranean forest parameters can be mapped and monitored for biomass accumulation and carbon dioxide (CO2) flux using Landsat ETM images. [ABSTRACT FROM AUTHOR]

Land use/cover change (LUCC) is a major indicator of the impact of climate change and human activity, particularly in the Sahel, where the land cover has changed greatly over the past 50 years. Aerial and satellite sensors have been taking images of the Earth's surface for several decades. These data have been widely used to monitor LUCC, but many questions remain concerning what type of pre-processing should be carried out on image resolutions and which methods are most appropriate for successfully mapping patterns and dynamics in both croplands and natural vegetation. This study considers these methodological questions. It uses multi-source imagery from 1952 to 2003 (aerial photographs, Corona, Landsat Multispectral Scanner (MSS), Landsat Thematic Mapper (TM) and Satellite Pour l'Observation de la Terre (SPOT) 5 images) and pursues two objectives: (i) to implement and compare a number of processing chains on the basis of multi-sensor data, in order (ii) to accurately track and quantify LUCC in a 100 km2 Sahelian catchment over 50 years. The heterogeneity of the spatial and spectral resolution of the images led us to compare post-classification methods aimed at producing coherent diachronic maps based on a common land-cover nomenclature. Three main approaches were tested: pixel-based classification, vector grid-based on-screen interpretation and object-oriented classification. Within the automated approaches, we also examined the influence of spectral synthesis and spatial homogenization of the data through the use of composite bands (principal component analysis (PCA) and indices) and by resampling images at a common resolution. Classification accuracy was estimated by computing confusion matrices, by analysing overall change in the relative areas of land use/cover types and by studying the geographical coherence of the changes. These analyses indicate that on-screen interpretation is the most suitable approach for providing coherent, valid results from the multi-source images available over the study period. However, satisfactory classifications are obtained with the pixel-based and object-oriented approaches. The results also show significant sensitivity, depending on the method considered, to the combinations of bands used and to resampling. Lastly, the 50-year trends in LUCC point out a large increase in croplands and erosional surfaces with sparse vegetation and a drastic reduction in woody covers. [ABSTRACT FROM AUTHOR]

In this study, a two-dimensional continuous wavelet transform is applied to quantify the non-homogeneity from remote-sensing wave images. Our study shows that the non-homogeneity index, which is proposed here, is capable of identifying the degree of non-homogeneity from the wave-field images. However, the influence from the edges of the wave-field image should be considered in analysis. The calculated results from the simulated wave fields and from the natural wave-field images reveal that the non-homogeneity index is influenced by the bathymetry inside the area of the ocean-wave image. It conforms to the character of the natural wave non-homogeneity. After discussing the wave characteristics from the wave-field image with a high non-homogeneity index, an inhomogeneous algorithm is recommended for obtaining accurate and reasonable wave results from the wave image. [ABSTRACT FROM AUTHOR]

This research investigates information fusion approaches of high-resolution aerial images and elevation data from Light Detection and Ranging (LIDAR) for urban-environment mapping. Three feature fusion methods are proposed and compared: (1) the vector-stacking approach that combines spectral and LIDAR features in one classifier; (2) the re-classification approach that firstly processes spectral signals in a classifier and then integrates its output with LIDAR features to obtain the final result and (3) the post-processing approach that uses the LIDAR data to refine the results of spectral classification. The height features used in the above three algorithms are extracted from the LIDAR digital surface model (DSM) image; these include elevation difference, maximum and minimum values, variance and the grey-level co-occurrence matrix (GLCM) textures. In addition, the average height from object-based segmentation is also computed. In the experiments, support vector machines (SVMs) are used as classifiers for all fusion schemes due to their capability and robustness for many classification problems. The three algorithms are evaluated using a 40-cm spatial resolution digital orthophoto and the corresponding LIDAR data of Odense, Denmark. In the experiments, the vector-stacking method with the Maximum-Minimum (Max-Min) feature, the re-classification method with the Max-Min feature and the post-processing approach obtain promising results (94.7%, 95.0% and 94.6%, respectively), which are significantly higher than the spectral-only classification (82.5%). [ABSTRACT FROM AUTHOR]

Broom snakeweed (Gutierrezia sarothrae (Pursh) Britt. & Rusby) is one of the most widespread and abundant rangeland weeds in western North America. The objectives of this study were to evaluate airborne hyperspectral imagery and compare it with aerial colour-infrared (CIR) photography and multispectral digital imagery for mapping broom snakeweed infestations. Airborne hyperspectral imagery along with aerial CIR photographs and digital CIR images was acquired from a rangeland area in south Texas. The hyperspectral imagery was transformed using minimum noise fraction (MNF) and then classified using minimum distance, Mahalanobis distance, maximum likelihood, and spectral angle mapper (SAM) classifiers. The digitized aerial photographs and the digital images were respectively mosaicked as one photographic image and one digital image; these were then classified using the same classifiers. Accuracy assessment showed that the maximum likelihood classifier performed the best for the three types of images. The best overall accuracies for three-class classification maps (snakeweed, mixed woody and mixed herbaceous) were 91.0%, 92.5%, and 95.0%, respectively, for the CIR photographic image, the digital CIR image and the MNF-transformed hyperspectral image. Kappa analysis showed that there were no significant differences in maximum likelihood-based classifications among the three types of images. These results indicate that airborne hyperspectral imagery along with aerial photography and multispectral imagery can be used for monitoring and mapping broom snakeweed infestations on rangelands. [ABSTRACT FROM AUTHOR]

In remote sensing classification there are situations when users are only interested in classifying one specific land type without considering other classes, which is referred to as one-class classification. Traditional supervised learning requires all classes that occur in the image to be exhaustively labelled and hence is inefficient for one-class classification. In this study we investigate a maximum entropy approach (MAXENT) to one-class classification of remote sensing imagery, i.e. classifying a single land class (e.g. urban areas, trees, grasses and soils) from an aerial photograph with 0.3 m spatial resolution. MAXENT estimates the Gibbs probability distribution that is proportional to the conditional probability of being positive. A threshold for generating binary predictions can be determined based on the omission rate of a validation set. The results indicate that MAXENT provides higher classification accuracy than the one-class support vector machine (OCSVM). MAXENT does not require other land classes for training. Its input is only a set of training samples of the specific land class of interest, as well as a set of known constraints on the distribution. Therefore, the effort of manually collecting training data for classification can be significantly reduced. [ABSTRACT FROM AUTHOR]

In this paper we present an improved watershed segmentation algorithm for settlement mapping from medium resolution satellite data over plain areas in China. The algorithm can increase the computational efficiency of the fastest reported watershed segmentation algorithm by 30-40%. We apply this method to a selected study area in southern Hebei Province, China. We acquired a Landsat Enhanced Thematic Mapper Plus (ETM + ) image over this area in May 2000, two Landsat Thematic Mapper (TM) images in August 2004 and April 2005, and two Beijing-1 satellite images in May 2006 and May 2007. The three types of images have three similar spectral bands (green, red and near-infrared) with similar spatial resolution (30-32 m). Only the red and near-infrared bands were used in image segmentation for settlement area extraction. The extracted settlement results are compared with manual interpretation results by two people. We assumed the human interpretation results are of higher accuracy than the segmentation results. Our results indicated that our settlement area extraction method is effective. With high quality images, the overall accuracies are nearly 94%, the kappa coefficient can be greater than 0.85. [ABSTRACT FROM AUTHOR]

Google Earth uses aerial and satellite images from various sources as its background. It is designed to enable every person who owns a computer to easily 'fly' to aerial views of any location on the planet. However, just as with ordinary aerial or satellite images, there inevitably exist shadows in Google Earth images, making some ground objects obscured, even unidentifiable. In this paper, we describe a model we have developed for image shadow removal that uses radiative transfer theory in combination with prior knowledge to compensate for the lost information over shadow areas. We arbitrarily selected six images with severe shadows from Beijing, Shanghai, Hong Kong, Tokyo, New York and San Francisco, respectively, for algorithm testing. Our experimental results show that shadows in images can be considerably suppressed. [ABSTRACT FROM AUTHOR]

Using the panchromatic band of QuickBird high-spatial-resolution satellite images as a data source, we estimated the mean crown diameters of different stands and of stands with different canopy densities by applying the semivariogram methodology used in geostatistics. The results indicated that relatively accurate estimates of the mean crown diameters of stands with a relatively high canopy density could be obtained. The estimated errors increased proportionally with decreasing canopy density. The whole-stand-image (matrix) method we used provided fairly accurate estimates of stand structural parameters, but would not readily permit the evaluation of patterns in stand structure. The transect method exhibited periodicity as a function of distance and, although it permits the detection of directional patterns, it does not depict stand structural parameters as accurately as the matrix method. Our results suggest that this approach could provide a new reference method for solving the problem of automated estimation of mean crown diameter at the stand level. [ABSTRACT FROM AUTHOR]

The visible-near infrared (VNIR) reflectance of several mature boreo-nemoral stands in Estonia was measured in two concurrent years which differed significantly in precipitation. Custom-designed VNIR spectrometer systems, UAVSpec and UAVSpec2, mounted onboard a helicopter, were used for the measurements. We show that broadleaved and coniferous forests react to changes in water supply differently: the relative change of the NIR reflectance was equally about 10-15% in all stands, while the change of visible reflectance of coniferous stands exceeded that of broadleaved stands. The smallest change was observed for broadleaved stands in the red reflectance, i.e. chlorophyll absorption bands. Based on our results, we conclude that the change of stand reflectance caused by weather conditions, such as drought, should be taken into account in the estimation of the optical properties of the atmosphere, needed in the atmospheric correction of optical satellite images. A widely used correction method directly benefiting from our results for boreo-nemoral forests is the dark object method. [ABSTRACT FROM AUTHOR]

Bathymetric estimation can be obtained from multispectral satellite images for shallow waters. The method is based on the rotation of a pair of spectral bands. One of the resulting images is depth-dependent. Therefore several pixels corresponding to different depths are required to numerically evaluate the linear relation between the pixel values and the real depth for a training area. The aim of this study is to compare, for one bathymetric estimation method and one mesotrophic site, the results of depth estimation with a large panel of satellite and aerial images: CASI, QUICKBIRD, CHRIS PROBA, ETM, HYPERION and MeRIS. For each image the pair of spectral bands chosen to compute the bathymetry has been optimized. Error on depth estimation has been computed on two regions of the image: the training area and a validation area. This comparison is discussed to identify the influence of image parameters (spectral bands, S/N ratio, spatial resolution, and quantization) on the bathymetric results and to propose the most adapted image parameters for bathymetric estimation. For validation purposes, we compared the results obtained with a CASI image matching the optimized parameters in an oligotrophic site in the Red Sea. [ABSTRACT FROM AUTHOR]

While small, fragmented wooded elements do not represent a large surface area in agricultural landscape, their role in the sustainability of ecological processes is recognized widely. Unfortunately, landscape ecology studies suffer from the lack of methods for automatic detection of these elements. We propose a hybrid approach using both aerial photographs and ancillary data of coarser resolution to automatically discriminate small wooded elements. First, a spectral and textural analysis is performed to identify all the planted-tree areas in the digital photograph. Secondly, an object-orientated spatial analysis using the two data sources and including a multi-resolution segmentation is applied to distinguish between large and small woods, copses, hedgerows and scattered trees. The results show the usefulness of the hybrid approach and the prospects for future ecological applications. [ABSTRACT FROM AUTHOR]

Remote sensing and geographic information systems (GIS) are effective tools for acquiring fast and accurate information about the Earth's resources and for providing a medium for the efficient flow of information. Moreover, these systems are particularly suitable for determining land use types and monitoring their variation through time. In this study, changes in land cover and land use types in the last 23 years were determined for the town of Kemer (in the province of Antalya) by means of remote sensing and GIS. Kemer is located within the western Mediterranean region of Turkey and is of national and international importance due to its natural, cultural, historical and tourism significance. Stereoscopic interpretation of aerial photographs and visual interpretation of high resolution IKONOS images were conducted and land cover type changes over 23 years were determined using the Coordination of Information on the Environment (CORINE) methodology. In this context, eight different land cover types were examined: and their changes were analysed using data from 1981, 1992 and 2004. Results indicate that urban land use increased approximately 10 times, from 86.3 ha to 931.0 ha, and tourism and secondary residence land use increased nearly 140 times, from 5.3 ha to 715.8 ha. In contrast, decreases of 65.8% in complex cultivation patterns, 37.2% in coastal dunes and beaches, and 2.1% in permanent crops were observed. This land use change reflects the large scale net loss of natural resources in the area and this trend does not suggest sustainable natural resource management. Over the last quarter of a century, the most significant reason for changes in land use in the Kemer area appears to be caused by the pressure from the tourism and secondary residence sectors. [ABSTRACT FROM AUTHOR]

Comprehensive and accurate information on the conditions of the road infrastructure is essential for effective management and planning of the road network, especially in cities facing serious traffic congestion problems, as in the city of Istanbul, Turkey. One of the most important services of the local authorities is the rehabilitation of road surfaces. Asphalt resurfacing is carried out to renew the road surface and restore its resistance to weathered and traffic worn pavements. Today, the determination of pavement surface conditions is usually carried out through visual inspections by the experts and interpretation of in situ photographs/videos. In this study, an investigation has been made to understand the spectral behaviour of the asphalt in terms of its contents and age, also to discover the feasibility of using widely available satellite sensors in the determination of road surface conditions. Two datasets in the study area from D-100 road and TEM highway were used to examine asphalt road aging and deterioration. The changes to the conditions of the roads were detected using multispectral IKONOS images and ground spectral measurements for asphalt samples were obtained using an Analytical Spectral Devices full range (ASD FR) instrument in a laboratory environment. It was found that the road deformations mainly occur in areas where vehicles reduce or increase their speeds suddenly. Road sections just before toll booths and junctions are found to be more prone to deformations. IKONOS images can be effectively used to determine the condition of the asphalt pixels. [ABSTRACT FROM AUTHOR]

Grimsvotn is located beneath Vatnajokull, Europe's largest temperate ice cap. As a part of ongoing research on heat flux, morphological changes and volcanic processes at Grimsvotn, Airborne Thematic Mapper (ATM) imagery and aerial photographs were acquired in 2001. The thermal images illuminated distinct areas of geothermal activity along the southern caldera wall. In combination with meteorological data the images were used to estimate surface temperatures and heat flux from patches of open water along the margin of the ice shelf covering the Grimsvotn subglacial lake. It was found that water temperatures varied from 0°C to ∼45°C and that the heat flux to the atmosphere from open water varied from slightly negative values (net energy gain) up to 1000 W m-2. The total heat output from the ∼0.1 km2 of open water was estimated as ∼18 MW, about 1% of the base heat output of Grimsvotn. The aerial photographs were used to produce geomorphological maps of the caldera wall, including areas that cannot be safely mapped from the ground. This work indicates that thermal imagery can be an important supplement to ground-based measurements, and that combined optical and thermal remote sensing is a useful tool for spatially detailed monitoring of inaccessible and partly ice-covered volcanoes. [ABSTRACT FROM AUTHOR]

Evidence for volcano collapse and debris avalanche deposits (DADs) at Oldoinyo Lengai (OL), Tanzania, has been obtained from mapping and fieldwork. Three major DADs have been identified, named Zebra, Cheetah and Oryx DADs. Field evidence indicates geologically young ages. On this basis a remote sensing (RS) study of the active carbonatite volcano OL and the surrounding rift plain was carried out, using Shuttle Radar Topography Mission (SRTM) digital elevation data, Landsat and ASTER imagery, geological maps and aerial photographs. The SRTM digital elevation model (DEM) allowed morphological characterization of OL and reassessment of the volcano volume to 41±5 km3. This enabled the identification of collapse scars, fields of large hummocks (>300 m across), sharp deposit edges typical of DADs, and estimation of the minimum thickness of the DADs. Multispectral and topographic RS data interpretation allowed mapping of the extent and estimation of the volume of two sector-collapse scars and three DADs. The DADs extend up to 24 km from OL and have volumes ranging from 0.1 to ∼5 km3. Striking radial ridges and grooves were identified in some parts of the DADs. The morphological variability for ridges and grooves in different DADs is attributed to contrasting flow dynamics and avalanching material. A volcano collapse and the corresponding DAD, ∼1 km3 in volume, were also characterized by RS at the nearby Kerimasi volcano. The presence of young DADs highlights the need for routine monitoring of ground deformation and seismicity at OL to anticipate hazardous events. [ABSTRACT FROM AUTHOR]

From early 2004, Lake Maracaibo (northwest Venezuela) experienced an unprecedented invasion of duckweed Lemna obscura. Recurrent blooms of the plant in the past 2 years illustrate the need for an automatic monitoring method to follow the plant cover with time and to plan contingency measures. We present an approach that allows the cover of the duckweed to be quantified through the classification of MODIS 250 m RGB composite images available from the internet. The method improves the accuracy of the results of the Support Vector Machine (SVM) algorithm for classification by including a bootstrap step during the training phase. Using only 200 pixels for training (<0.05% of the total), the bootstrapped SVM method allows a better identification of the duckweed class, reducing the number of false negatives by half and improving the KHAT statistic by almost 40% in comparison to the standard SVM method. This method has proved to be a reliable solution in cases where rapid responses are needed and only medium-resolution, free satellite imagery is available. [ABSTRACT FROM AUTHOR]

This work proposes a neuro-fuzzy method for suggesting alternative crop production over a region using integrated data obtained from land-survey maps as well as satellite imagery. The methodology proposed here uses an artificial neural network (multilayer perceptron, MLP) to predict alternative crop production. For each pixel, the MLP takes vector input comprising elevation, rainfall and goodness values of different existing crops. The first two components of the aforementioned input, that is, elevation and rainfall, are determined from contour information of land-survey maps. The other components, such as goodness values of different existing crops, are based on the productivity estimates of soil determined by fuzzyfication and expert opinion (on soil) along with production quality by the Normalized Difference Vegetation Index (NDVI) obtained from satellite imagery. The methodology attempts to ensure that the suggested crop will also be a high productivity crop for that region. [ABSTRACT FROM AUTHOR]

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

A rapid atmospheric correction method is proposed to be used for visible and near-infrared satellite sensor images over land. The method is based on a simplified use of a radiative transfer code (RTC), which is used only a priori, to generate Look-Up-Tables (LUTs) of the estimated surface reflectance. A typical scenario and ranges of values for the main atmospheric correction parameters are initially established. Each image pixel is treated as a slight deviation from the reference scenario defined by the vector of the typical values for the parameters. The assumption of the parameter's independence allows the use of one-dimensional LUTs. The method is suitable for near real-time processing or whenever a large number of data are to be handled rapidly. The operator intervention is minimal, and the computation time involved in the correction of a whole image is about 1000 times shorter than the full use of the base RTC. A test is performed with advanced very-high-resolution radiometer (AVHRR) visible and near-infrared data, using the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) RTC as the base code. The accuracy of the proposed method was compared with the standard use of the 6S RTC over the same dataset with resulting root mean square errors of 0.0114 and 0.0104 for AVHRR bands 1 and 2 for the estimated surface reflectance, respectively. [ABSTRACT FROM AUTHOR]

Ranging techniques such as lidar (LIght Detection And Ranging) and digital stereo-photogrammetry show great promise for mapping forest canopy height. In this study, we combine these techniques to create hybrid photo-lidar canopy height models (CHMs). First, photogrammetric digital surface models (DSMs) created using automated stereo-matching were registered to corresponding lidar digital terrain models (DTMs). Photo-lidar CHMs were then produced by subtracting the lidar DTM from the photogrammetric DSM. This approach opens up the possibility of retrospective mapping of forest structure using archived aerial photographs. The main objective of the study was to evaluate the accuracy of photo-lidar CHMs by comparing them to reference lidar CHMs. The assessment revealed that stereo-matching parameters and left-right image dissimilarities caused by sunlight and viewing geometry have a significant influence on the quality of the photo DSMs. Our study showed that photo-lidar CHMs are well correlated to their lidar counterparts on a pixel-wise basis (r up to 0.89 in the best stereo-matching conditions), but have a lower resolution and accuracy. It also demonstrated that plot metrics extracted from the lidar and photo-lidar CHMs, such as height at the 95th percentile of 20 m×20 m windows, are highly correlated (r up to 0.95 in general matching conditions). [ABSTRACT FROM AUTHOR]

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

The use of black & white (B&W) air photographs for the production of historic land cover maps can be done by image classification, using additional texture features. In this paper we evaluate the importance of a number of parameters in the image classification process based on texture, such as the window size, angle and distance used to produce the texture features, the number of features used, the image quantization level and its spatial resolution. The evaluation was performed using five photographs from the 1950s. The influence of the classification method, the number of classes searched for in the images and the post-processing tasks were also investigated. The effect of each of these parameters for the classification accuracy was evaluated by cross-validation. The selection of the best parameters was performed based on the validation results, and also on the computation load involved for each case and the end user requirements. The final classification results were good (average accuracy of 85.7%, k = 0.809) and the method has proven to be useful for the production of historic land cover maps from B&W air photographs. [ABSTRACT FROM AUTHOR]

Fires in Africa affect atmospheric emissions and carbon sequestration, landscape patterns, and regional and global climatic conditions. Studies of these effects require accurate estimation of the extent of measurable fire events. The goal of this study was to assess the influence of burned area spatial patterns on the spectral detectability of burned areas. Six Landsat-7 ETM+ images from the southern Africa were used for burned area mapping and spatial pattern analysis, while contemporaneous MODIS 500 m spatial resolution images were used to measure the spectral detectability of burned areas. Using a 15 by 15 km sample quadrats analysis, we showed that above a burned area proportion threshold of approximately 0.5 the spectral detectability of burned areas increase due to the decrease in the number of mixed pixels. This was spatially related to the coalescence of burned patches and the decrease in the total burned area perimeter. Simple burned area shapes were found at the Botswana site, where the absence of tree cover and the presence of bright surfaces (soil and dry grass) enhanced the spectral contrast of the burned surfaces, thus enabling better estimates of burned area extent. At the Zambia and Congo sites, landscape fragmentation due to human activity and the presence of a tree vegetation layer, respectively, contribute to the presence of small burned area patches, which may remain undetectable using moderate spatial resolution satellite imagery, leading to less accurate burned area extent estimates. [ABSTRACT FROM AUTHOR]

Compared to remote sensing images of medium or low spatial resolution, high-resolution remote sensing images can provide observation data containing more detailed information for georesearch. Accordingly, an important issue for current computer and geoscience experts is to develop useful methods or technology to extract information from these high-resolution satellite images. As part of a series of research into object extraction, this paper focuses mainly on the extraction of bridges over water from high-resolution panchromatic satellite images. Since bridges over water are obviously adjacent to water in remote sensing images, this paper proposes a practical knowledge-based bridge extraction method for remote sensing images of high spatial resolution. The steps involved are: water extraction based on Gauss Markov Random Field (GMRF)-Support Vector Machine (SVM) classification methods which use a SVM to classify the image based on textural features expressed by a GMRF; image thinning and removal of fragmented lines; main trunk detection by width; vectorization; and feature expression. Finally, tests are described for two pieces of panchromatic IKONOS satellite images with a 1 m resolution. The experimental results show that the proposed method is suitable for images with a single-peak histogram (contrast between water and land is sharp) or a multi-peak histogram (greyscale value of water is close to that of land). [ABSTRACT FROM AUTHOR]

The Brahmaputra River is one of the largest sand-bar braided rivers in the world having a seasonal high discharge, high sediment load and characterized by frequent channel pattern changes and shift. Following natural phenomena, the river has been responding to landform/ground level changes effected by tectonic activity of the region. A satellite image mosaic and temporal Landsat data enabled detailed mapping of the Brahmaputra River revealing some prominent features with regard to change in its course, new channels and influence of landforms and tectonics. Northward shift of the river course are inferred at three different places, which are (1) in the uppermost part of Brahmaputra; (2) in the region north of the Kopili Gap (KG); (3) and north of the Mikir Hills (MH). However, the river appears to have developed a tendency to shift back in the area north of the MH. These shifts are influenced by tectonic activity in the Himalayan foothills. The Brahmaputra River shows remarkable changes downstream of Guwahati, by forming multiple channels south of the island as well as the formation of channels closer to the Shillong Plateau. The sudden and sharp south-westward to southward turn of the river just after longitude 90° E is in fact caused by a landform north of Dhubri. Development of new channels from the main river along the western and northern edges of the Shillong Massif (SM) and the shift of Kopili River towards the SM are indicators of tectonic activities that the massif suffered. [ABSTRACT FROM AUTHOR]

It is usually quite difficult to extract and recover shadow information in the urban environment from remote sensing imagery. This paper describes the study of precisely detected shadow in satellite images and recovering information from the surface covered in shadow from very high resolution (VHR) satellite imagery. [ABSTRACT FROM AUTHOR]

Many image interpolation methods have been developed to enhance the visibility of either remotely sensed or other images, such as the bilinear method and bi-cubic method As a further development of the existing approaches, an integrated interpolation method is proposed in this study, which is an integration of the bilinear and the bi-cubic interpolation methods. First, the implemented procedure of the integrated interpolation method is described. This covers (a) a low-resolution image that is interpolated by using the bilinear and the bi-cubic interpolators respectively; (b) the hybrid parameters ρ and 1-ρ endowed on the bilinear and the bi-cubic interpolation results, respectively; (c) the interpolated image that is computed according to the weighted sum of both bilinear and bi-cubic interpolation results. Second, a further discussion is given on the method and the relation between hybrid parameter and details of an image (entropy) provided from the theoretical point of view. The result demonstrates that the hybrid parameter directly affects the details of an interpolated image. Third, the effectiveness of this integrated method is verified, based on experimental studies. Here, a method for the construction of images with different entropies is developed for construct simulated images. The integrated method possesses advantages of both the bilinear method for modelling low-frequency components and the bi-cubic method of high-frequency components simultaneously. The experimental study demonstrated the effectiveness of the proposed integrated model. [ABSTRACT FROM AUTHOR]

Studies of urban ecological systems can be greatly enhanced by combining ecosystem modelling and remote sensing which often requires establishing statistical relationships between field and remote sensing data. At the Central Arizona–Phoenix Long-Term Ecological Research (CAPLTER) site in the south-western USA, we estimated vegetation abundance from Landsat ETM+ acquired at three dates by computing vegetation indices (NDVI and SAVI) and conducting linear spectral mixture analysis (SMA). Our analyses were stratified by three major land use/land covers—urban, agricultural, and desert. SMA, which provides direct measures of vegetation end member fraction for each pixel, was directly compared with field data and with the independent accuracy assessment dataset constructed from air photos. Vegetation index images with highest correlation with field data were used to construct regression models whose predictions were validated with the accuracy assessment dataset. We also investigated alternative regression methods, recognizing the inadequacy of traditional Ordinary Least Squares (OLS) in biophysical remote sensing. Symmetrical regressions—reduced major axis (RMA) and bisector ordinary least squares (OLSbisector)—were evaluated and compared with OLS. Our results indicated that SMA was a more accurate approach to vegetation quantification in urban and agricultural land uses, but had a poor accuracy when applied to desert vegetation. Potential sources of errors and some improvement recommendations are discussed. [ABSTRACT FROM AUTHOR]

Reservoir management involves allocating available water among multiple uses and users, minimizing the risks of water shortages and flooding and optimizing the beneficial use of water. Irrigation demands from a reservoir, which are generally computed by using the design cropping pattern and average rainfall conditions, may vary over the years depending on the actual cropping pattern and meteorological conditions. This study demonstrates the utility of remote sensing inputs and geographic information system (GIS) environment for determining realistic irrigation demands from a reservoir. Remote sensing data are used to map the actual cropping pattern in the command area while the GIS is used for integrating the field-level irrigation demands up to the canal system head. Ten daily irrigation demands in the command area of Samrat Ashok Sagar Reservoir in Madhya Pradesh, India have been estimated and the reservoir operation policy has been derived. Through the simulation analysis with 29 years of inflow data, rule curves have been derived for the operation of reservoir so that water deficit (if any) can be distributed in time much in advance and severe crop failure can be avoided. Inferences drawn from the analysis can guide the system operator in using the available water in a scientific and judicious manner. [ABSTRACT FROM AUTHOR]

Multi-directional satellite optical imagery collected at high spatial resolution potentially allows improving the accuracy of biophysical variable retrieval. The improvements result from the inclusion of the directional anisotropy of the target, which provides additional information related to vegetation structural properties. The research presented here analyses airborne imagery and ground reference data in order to quantify the accuracy of the retrieval methods for LAI (leaf area index). Both variables are estimated through processing of airborne POLDER (POLarization and Directionality of Earth Reflectances) sensor images from an agricultural area. In a first step, the BRDF (Bi-directional Reflectance Distribution Function) of the surface is estimated using a simple parametric model, whose parameters where derived from fitting POLDER BRF (Bi-directional Reflectance Factor) measurements. LAI estimation was performed using two different approaches, both based on an artificial neural network designed to invert a 1D soil-vegetation radiative transfer model. The difference between the two methods is that one of them uses only the isotropic component of the BRDF parametric model and the other the full BRDF information, i.e. adding the anisotropic components. The algorithm using isotropic information shows a clear improvement when compared to semi-empirical approaches. Root mean square error between estimated and ground measured LAI values is 0.87. However, the method using the full BRDF information yielded poorer estimates, pointing out the difficulty of fully exploiting the multi-directional information. The performance decrease is partially explained by the incoherence between real and modelled BRDF measurements. [ABSTRACT FROM AUTHOR]

The increased synergy between neural networks (NN) and fuzzy sets has led to the introduction of granular neural networks (GNNs) that operate on granules of information, rather than information itself. The fact that processing is done on a conceptual rather than on a numerical level, combined with the representation of granules using linguistic terms, results in increased interpretability. This is the actual benefit, and not increased accuracy, gained by GNNs. The constraints used to implement the GNN are such that accuracy degradation should not be surprising. Having said that, it is well known that simple structured NNs tend to be less prone to over‐fitting the training data set, maintaining the ability to generalize and more accurately classify previously unseen data. Standard NNs are frequently found to be accurate but difficult to explain, hence they are often associated with the black box syndrome. Because in GNNs the operation is carried out at a conceptual level, the components have unambiguous meaning, revealing how classification decisions are formed. In this paper, the interpretability of GNNs is exploited using a satellite image classification problem. We examine how land use classification using both spectral and non‐spectral information is expressed in GNN terms. One further contribution of this paper is the use of specific symbolization of the network components to easily establish causality relationships. [ABSTRACT FROM AUTHOR]

A number of problems in remote sensing require the segmentation of specific spectral classes such as water bodies, clouds or forested areas. Further analysis of these classes may include the calculation of optical reflectance values such as chlorophyll concentration, absolute reflectivity or vegetation indices. To derive reliable measurements of these variables, a precise segmentation - from the rest of the image - of the spectral classes is needed. In this work, we propose a new methodology to segment open water bodies based on a variant of principal component analysis (PCA). In this variant, information about the spectral class model of the water bodies to be separated in the feature space is required. This information is input by means of a training field encircling a set of pixels representative of this spectral model. A training field for land cover is also defined. This PCA variant produces two sets of multispectral bands, one for water bodies and one for land cover types. The first two bands of each set are input into a fuzzy clustering procedure. By using a merging process, the clusters are merged into two classes: water bodies and the rest of the image. From this, a logic bitmap image is obtained. The pixels of the bitmap consist of ON for water bodies and OFF for the rest of the image. The bitmap is then used to obtain morphological parameters of the water bodies. [ABSTRACT FROM AUTHOR]

Uncertainty is imposed simultaneously with multispectral data acquisition in remote sensing. It grows and propagates in processing, transmitting and classification processes. This uncertainty affects the extracted information quality. Usually, the classification performance is evaluated by criteria such as the accuracy and reliability. These criteria can not show the exact quality and certainty of the classification results. Unlike the correctness, no special criterion has been propounded for evaluation of the certainty and uncertainty of the classification results. Some criteria such as RMSE, which are used for this purpose, are sensitive to error variations instead of uncertainty variations. This study proposes the entropy, as a special criterion for visualizing and evaluating the uncertainty of the results. This paper follows the uncertainty problem in multispectral data classification process. In addition to entropy, several uncertainty criteria are introduced and applied in order to evaluate the classification performance. [ABSTRACT FROM AUTHOR]

This paper is a presentation of the practical application of the algorithms developed in Part I of this series. It is based on the assessment of tree changes with respect to topography in a part of the Adulam Nature Reserve, Israel, between 1945 and 1996. The study is an input to ongoing ecological studies to determine the rate and pattern of tree invasion of plant species in the Adulam Nature Reserve. Datasets available for this study are images of the project site for the years 1945, 1956, 1965, 1967, 1980, 1984 and 1996. For the change detection process, a post classification image differencing method was adopted. In order to enhance the change detection and analysis processes as well as minimize errors, image differencing was applied to grid images generated from image classification products. Since ground data for the accuracy assessment of classification results were unavailable, a method of fuzzy classification and accuracy assessment of classification was employed. Results of this study show a significant general increase in percentage cover of trees in the study area between 1945 and 1996. A peak percentage range of tree cover (35–36%) was observed between 1980 and 1984. Also, a statistically significant interaction between topography and rate of change of tree cover was observed. [ABSTRACT FROM AUTHOR]

Tree locations are needed in image-based single tree forest inventories. Accurate tree-top image positions would also be useful in image matching (IM) for the estimation of a canopy surface model. We explored the performance of a local maxima (LM)-based method for detecting image positions of individual tree tops by using digitized aerial photographs in recently thinned stands in Southern Finland. The accuracy of tree detection at the single tree level was assessed using a novel 3D approach. The results indicate that the LM method works most reliably in the central parts of the aerial images. Small trees are mostly missed by the LM detector and commission errors seem unavoidable. We propose further work that would assess the applicability of the LM method as a feature detector for use in IM. [ABSTRACT FROM AUTHOR]

Tidal channel networks play an important role in the intertidal zone, exerting substantial control over the hydrodynamics and sediment transport of the region and hence over the evolution of the salt marshes and tidal flats. The study of the morphodynamics of tidal channels is currently an active area of research, and a number of theories have been proposed which require for their validation measurement of channels over extensive areas. Remotely sensed data provide a suitable means for such channel mapping. The paper describes a technique that may be adapted to extract tidal channels from either aerial photographs or LiDAR data separately, or from both types of data used together in a fusion approach. Application of the technique to channel extraction from LiDAR data has been described previously. However, aerial photographs of intertidal zones are much more commonly available than LiDAR data, and most LiDAR flights now involve acquisition of multispectral images to complement the LiDAR data. In view of this, the paper investigates the use of multispectral data for semi-automatic identification of tidal channels, firstly from only aerial photographs or linescanner data, and secondly from fused linescanner and LiDAR data sets. A multi-level, knowledge-based approach is employed. The algorithm based on aerial photography can achieve a useful channel extraction, though may fail to detect some of the smaller channels, partly because the spectral response of parts of the non-channel areas may be similar to that of the channels. The algorithm for channel extraction from fused LiDAR and spectral data gives an increased accuracy, though only slightly higher than that obtained using LiDAR data alone. The results illustrate the difficulty of developing a fully automated method, and justify the semi-automatic approach adopted. [ABSTRACT FROM AUTHOR]

Image classification of historical aerial photographs is very useful for the study of medium-to-long term (10–50 years) vegetation changes. To determine the quality of information derived from the classification process, accuracy assessment of the classification is implemented. Error matrix, which is primarily used in remote sensing for accuracy assessment, is typically based on an evaluation of the derived classification against some ‘ground truth’ or reference dataset. Regrettably ‘ground truths’ for some old historical photographs are rarely available. To solve this problem we formulate, in this Part I, methods of classification of aerial photographs and computation of accuracy assessment parameters of classification products in the absence of ground data, using the fuzzy classification technique. In addition, since point estimates of these accuracy parameters require associated standard errors, in order to be useful for statistical analysis, the method of computation of standard errors of accuracy measures using the bootstrap resampling techniques is presented. These methods are tested with historical aerial photographs, of part of Adulam Nature Reserve, Israel, spanning a period of 51 years. Results illustrate the applicability and efficiency of the proposed methods. [ABSTRACT FROM AUTHOR]