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The paper focuses on automatic extraction building stock information for quantifying physical exposure and its vulnerability from High and Very High Resolution (VHR) optical satellite imagery. We use two case studies. In Sana’a (Yemen) we use automatic techniques to extract the building stock as well as building height that is used to characterize its vulnerability. In Port-au-Prince (Haiti) – in the area affected by the 2010 earthquake -we map the building stock based on a pre-disaster imagery – and we show the added value of area based information when added to point based damage assessment from visual change interpretation of post disaster aerial images. The paper shows that VHR imagery can be used to locate and quantify the building stock and its height. This paper also shows that damages measured from changes detected from pre-and post-disaster imagery can in principle map and provide vulnerability information related to the structural fragility of the building stock., JRC.G.2-Global security and crisis management

This work addresses the use of remote sensing imagery to quantify the built environment and its spatial and temporal changes. It identifies building footprint map, building location map and built-up area map as information products that can be used to quantify physical exposure, one of the variables required in disaster risk assessments. The paper also reviews urban land use maps and urban classes in land cover maps as potential source for deriving exposure information. The paper focuses on the latest generation of satellite-borne remote sensing imaging systems that deliver high-resolution optical imagery able to resolve buildings and other three-dimensional man-made constructions. This work also reviews the semantics, the spatial unit used to define physical exposure, image processing procedures and change techniques.

With methods for processing remote sensing data becoming widely available, the ability to quantify changes in spatial data and to evaluate the distribution of diverse landforms across target areas in datasets becomes increasingly important. One way to approach this problem is through satellite image processing. In this paper, we primarily focus on the methods of the unsupervised classification of the Landsat OLI/TIRS images covering the region of the Qena governorate in Upper Egypt. The Qena Bend of the Nile River presents a remarkable morphological feature in Upper Egypt, including a dense drainage network of wadi aquifer systems and plateaus largely dissected by numerous valleys of dry rivers. To identify the fluvial structure and stream network of the Wadi Qena region, this study addresses the problem of interpreting the relevant space-borne data using R, with an aim to visualize the land surface structures corresponding to various land cover types. To this effect, high-resolution 2D and 3D topographic and geologic maps were used for the analysis of the geomorphological setting of the Qena region. The information was extracted from the space-borne data for the comparative analysis of the distribution of wadi streams in the Qena Bend area over several years: 2013, 2015, 2016, 2019, 2022, and 2023. Six images were processed using computer vision methods made available by R libraries. The results of the k-means clustering of each scene retrieved from the multi-temporal images covering the Qena Bend of the Nile River were thus compared to visualize changes in landforms caused by the cumulative effects of geomorphological disasters and climate–environmental processes. The proposed method, tied together through the use of R scripts, runs effectively and performs favorably in computer vision tasks aimed at geospatial image processing and the analysis of remote sensing data.

In this paper, we propose an advanced scripting approach using Python and R for satellite image processing and modelling terrain in Côte d’Ivoire, West Africa. Data include Landsat 9 OLI/TIRS C2 L1 and the SRTM digital elevation model (DEM). The EarthPy library of Python and ‘raster’ and ‘terra’ packages of R are used as tools for data processing. The methodology includes computing vegetation indices to derive information on vegetation coverage and terrain modelling. Four vegetation indices were computed and visualised using R: the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index 2 (EVI2), Soil-Adjusted Vegetation Index (SAVI) and Atmospherically Resistant Vegetation Index 2 (ARVI2). The SAVI index is demonstrated to be more suitable and better adjusted to the vegetation analysis, which is beneficial for agricultural monitoring in Côte d’Ivoire. The terrain analysis is performed using Python and includes slope, aspect, hillshade and relief modelling with changed parameters for the sun azimuth and angle. The vegetation pattern in Côte d’Ivoire is heterogeneous, which reflects the complexity of the terrain structure. Therefore, the terrain and vegetation data modelling is aimed at the analysis of the relationship between the regional topography and environmental setting in the study area. The upscaled mapping is performed as regional environmental analysis of the Yamoussoukro surroundings and local topographic modelling of the Kossou Lake. The algorithms of the data processing include image resampling, band composition, statistical analysis and map algebra used for calculation of the vegetation indices in Côte d’Ivoire. This study demonstrates the effective application of the advanced programming algorithms in Python and R for satellite image processing.

The paper presents the application of ArcGIS for environmental modelling of the landscapes in northern Iceland (17.00°W–23.00°W, 64.30°N–67.00°N). The aim was to explore the vegetation distribution by NDVI and ISOCLUST classification of the land cover types. Data include the Landsat TM image. Freely available satellite remote sensing data from the Landsat mission have been processed by GIS to deliver information on land cover types from image classification and NDVI vegetation index. Landsat products provide geospatial data on regional scale with moderate temporal (weekly) and spatial (30–10 m) resolution, making them useful for environmental monitoring and landscape studies. The tools include the ArcGIS software used for raster processing. Data processing was performed in the three steps: 1) comparative analysis of the visualized sixteen band combinations to assess the distinguishability of vegetation and other land cover types in colour composites; 2) computed NDVI standardized vegetation index; 3) unsupervised classification of the Landsat TM by the ISOCLUST algorithm. Large glaciers Hofsjökull and Langjökull were detected on various colour composites, and the visibility of the water/land borders is assessed (Blöndulón lake), agricultural areas near the Varmahlíð, vegetated areas around the Akrahreppur municipality. Computing the NDVI and using ISOCLUST by ArcGIS software enabled to distinguish various land cover types and map landscapes in the study area. The computed NDVI shown the presence and condition of vegetation, that is, a relative biomass in the area of northern Iceland. The NDVI was used based on the contrast of the two channels from a multispectral Landsat TM raster data.

This paper approaches the implementation of an algorithm that automatically detects features of interest on the Martian surface known as Dark Slope Streaks (DSS), and the application of an inpainting technique to improve the results obtained by the detection. The greatest difficulty for the development of this algorithm is the false positives caused by shadows, depressions or fading of DSS, after a period. To overcome these problems, techniques such as image segmentation and custom filtering routines were used. Sometimes, the extraction algorithm obtains partially detected features, culminating in a loss of quality. However, in order to remove occlusions and restore lost features, the resulting image goes through an inpainting process proposed by Galerne et al (2017). To understand if the inpainting technique improves the quality of the extraction process, we created a reference image and compared with both the image obtained from the extraction algorithm and the resulting image of the inpainting algorithm. This process was repeated for four interest areas. Comparing the mean of pre-reconstruction SSIM and the mean of post-reconstruction SSIM, the inpainting algorithm shows an improvement of 6.13% in quality. Therefore, both algorithms may contribute greatly to the increasing quality of extraction of DSS in the area of cartography.

Video systems have become widely used all around the world in coastal monitoring strategies, allowing both high temporal and spatial sampling frequency, with low logistic and costs efforts. The present paper deals with a new tool for coastal images processing, aimed at the automatic shoreline detection and data analysis. The tool is composed by a shoreline detection routine implemented in a web-application, addressed at images processing (i.e. shoreline extraction and geo-rectification), data analysis and sharing results about beach actual state and shore evolution in quasi-real time. The Shoreline Detection Model (SDM) is based on a new algorithm, implementing image-processing procedures, which allows extracting the sea/land boundary from automatic segmented Timex images. The SDM calibration and validation has been performed on different coastal images derived from a video monitoring system installed at Alimini (Lecce, IT) in 2005, by comparing automatic shoreline contours with the manual detected ones. Moreover, in December 2015, new video monitoring systems were installed in South Italy (Porto Cesareo and Torre Canne, Apulia region), at sandy beaches affected by erosion phenomena. The application of the SDM on images recorded by the new systems has allowed testing the model feasibility at sites characterized by different morphological features and geographical exposition. The present describes in detail the SDM algorithm and the image processing procedures used. The results of the model calibration and validation performed at Alimini and the tests performed at Porto Cesareo on first images are reported.

This paper presents a lineament detection method using multi-band remote sensing images. The main objective of this work is to design an automatic image processing tool for lineament mapping from Landsat-7 ETM + satellite data. Five procedures were involved: 1) The Principal Component Analysis; 2) image enhancement using histogram equalization technique 3) directional Sobel filters of the original data; 4) histogram segmentation and 5) binary image generation. The applied methodology was contributed in identifying several known large-scale faults in the Northeast of Tunisia. The statistical and spatial analyses of lineament map indicate a difference of morphological appearance of lineaments in the satellite image. Indeed, all the lineaments present a specific organization. Five groups were classified based on three orientations: NE-SW, E-W and NW-SE. The overlapping of lineament map with the geologic map confirms that these lineaments of diverse directions can be identified and recognized on the field as a fault. The identified lineaments were linked to a deep faults caused by tectonic movements in Tunisia. This study shows the performance of the satellite image processing in the analysis and mapping of the accidents in the northern Atlas.

Flood area mapping is an integral part of disaster management operation which gets value when the details about inundated region has been made available in real time mode as well as when the much required temporal information is shared to the disaster mitigation authorities at right time. The challenges of such real time flood area mapping operations can be met by spaceborn Synthetic Aperture Radar (SAR) technology which is capable of capturing the critical information of large and hard-to-reach territories during all weather and all time situations. Mapping the flood related information of SAR images require much attention as the pixels associated with the inundated regions exhibit similar reflectance with major part of the pixels associated with high altitude region, shadow, runway and broad road networks. Such challenges have been addressed by worldwide researchers with the help of image processing functions. Many such SAR image based flood area mapping models take the advantages of various image classification approaches as well as in integrating multiple image processing functions mainly to differentiate the inundated pixels from other pixels which exhibits similar reflectance by which the mapping accuracy is enhanced. This paper is dedicated, in understanding and documenting various such significant SAR image based flood area mapping models by highlighting its strengths. Significant SAR image bases flood area mapping models from 1990’s to 2015 has been discussed. The respective references can be used by young researchers who are interested and willing to work in SAR image based flood area mapping techniques.

In this paper, we demonstrate an approach to align aerial images to airborne lidar data by using common object features (tree tops) from both data sets under the condition that conventional correlation-based approaches are challenging due to the fact that the spatial pattern of pixel gray-scale values in aerial images hardly exist in lidar data. We extracted tree tops by using an image processing technique called extended-maxima transformation from both aerial images and lidar data. Our approach was tested at the Angelo Coast Range Reserve on the South Fork Eel River forests in Mendocino County, California. Although the aerial images were acquired simultaneously with the lidar data, the images had only approximate exposure point locations and average flight elevation information, which mimicked the condition of limited information availability about the aerial images. Our results showed that this approach enabled us to align aerial images to airborne lidar data at the single-tree level with reasonable accuracy. With a local transformation model (piecewise linear model), the RMSE and the median absolute deviation (MAD) of the registration were 9.2 pixels (2.3 meters) and 6.8 pixels (1.41 meters), respectively. We expect our approach to be applicable to fine scale change detection for forest ecosystems and may serve to extract detailed forest biophysical parameters.

High-alpine areas are subject to rapid topographic changes, mainly caused by natural processes like glacial retreat and other geomorphological processes, and also due to anthropogenic interventions like construction of slopes and infrastructure in skiing resorts. Consequently, the demand for highly accurate digital terrain models (DTMs) in alpine environments has arisen. Public administrations often have dedicated resources for the regular monitoring of glaciers and natural hazard processes. In case of glaciers, traditional monitoring encompasses in-situ measurements of area and length and the estimation of volume and mass changes. Next to field measurements, data for such monitoring programs can be derived from DTMs and digital ortho photos (DOPs). Skiing resorts, on the other hand, require DTMs as input for planning and – more recently – for RTK-GNSS supported ski-slope grooming. Although different in scope, the demand of both user groups is similar: high-quality and up-to-date terrain data for extended areas often characterised by difficult accessibility and large elevation ranges. Over the last two decades, airborne laser scanning (ALS) has replaced photogrammetric approaches as state-of-the-art technology for the acquisition of high-resolution DTMs also in alpine environments. Reasons include the higher productivity compared to (manual) stereo-photogrammetric measurements, canopy-penetration capability, and limitations of photo measurements on sparsely textured surfaces like snow or ice. Nevertheless, the last few years have shown strong technological advances in the field of aerial camera technology, image processing and photogrammetric software which led to new possibilities for image-based DTM generation even in alpine terrain. At Vermessung AVT, an Austrian-based surveying company, and its subsidiary Terra Messflug, very promising results have been achieved for various projects in high-alpine environments, using images acquired by large-format digital cameras of Microsoft’s UltraCam series and the in-house processing chain centred on the Dense-Image-Matching (DIM) software SURE by nFrames. This paper reports the work carried out at AVT for the surface- and terrain modelling of several high-alpine areas using DIM- and ALS-based approaches. A special focus is dedicated to the influence of terrain morphology, flight planning, GNSS/IMU measurements, and ground-control distribution in the georeferencing process on the data quality. Based on the very promising results, some general recommendations for aerial photogrammetry processing in high-alpine areas are made to achieve best possible accuracy of the final 3D-, 2.5D- and 2D products.

A “hybrid”, high throughput image processing system is described to provide updated urban information on Canadian topographic maps from moderate resolution image sources such as SPOT. Two features are of particular interest; Buildings to Scale (BTS) (i.e., large buildings to be represented by individual symbols), and Built-up Areas (BUA), the majority of urban developed encompassing smaller structures (typically residential areas) that are represented on maps in a generalized way by a shading or tint. This paper addresses mapping of the latter, hereafter referred to as BUA mapping. A data-driven approach that utilizes a linear vegetation index to reduce data dimensionality and facilitate automation was developed. Full scene interpretation can be completed in approximately 2–3 minutes even on low-cost desktop computers. Accuracy of the BUA mapping output is comparable with that previously provided through traditional labour-intensive means (i.e., visual interpretation of aerial photography). Our process al...

This paper reviews the potential applications of satellite remote sensing to regional science research in urban settings. Regional science is the study of social problems that have a spatial dimension. The availability of satellite remote sensing data has increased significantly in the last two decades, and these data constitute a useful data source for mapping the composition of urban settings and analyzing changes over time. The increasing spatial resolution of commercial satellite imagery has influenced the emergence of new research and applications of regional science in urban settlements because it is now possible to identify individual objects of the urban fabric. The most common applications found in the literature are the detection of urban deprivation hot spots, quality of life index assessment, urban growth analysis, house value estimation, urban population estimation and urban social vulnerability assessment. The satellite remote sensing imagery used in these applications has medium, high or very high spatial resolution, such as images from Landsat MSS, Landsat TM and ETM+, SPOT, ASTER, IRS, Ikonos and QuickBird. Consistent relationships between socio-economic variables derived from censuses and field surveys and proxy variables of vegetation coverage measured from satellite remote sensing data have been found in several cities in the US. Different approaches and techniques have been applied successfully around the world, but local research is always needed to account for the unique elements of each place. Spectral mixture analysis, object-oriented classifications and image texture measures are some of the techniques of image processing that have been implemented with good results. Many regional scientists remain skeptical that satellite remote sensing will produce useful information for their work. More local research is needed to demonstrate the real potential and utility of satellite remote sensing for regional science in urban environments.

Deforestation is one of the crucial issues in Indonesia. In 2012, deforestation rate in Indonesia reached 0.84 million hectares, exceeding Brazil. According to the 2009 Guinness World Records, Indonesia's deforestation rate was 1.8 million hectares per year between 2000 and 2005. An interesting view is the fact that Indonesia government denied the deforestation rate in those years and said that the rate was only 1.08 million hectares per year in 2000 and 2005. The different problem is on the technique how to deal with the deforestation rate. In this paper, we proposed a new approach for automatically identifying the deforestation area and measuring the deforestation rate. This approach involves differential image processing for detecting Spatio-temporal nature changes of deforestation. It consists series of important features extracted from multiband satellite images which are considered as the dataset of the research. These data are proceeded through the following stages: (1) Automatic clustering for multiband satellite images, (2) Reinforcement Programming to optimize K-Means clustering, (3) Automatic interpretation for deforestation areas, and (4) Deforestation measurement adjusting with elevation of the satellite. For experimental study, we applied our proposed approach to analyze and measure the deforestation in Mendawai, South Borneo. We utilized Landsat 7 to obtain the multiband images for that area from the year 2001 to 2013. Our proposed approach is able to identify the deforestation area and measure the rate. The experiment with our proposed approach made a temporal measurement for the area and showed the increasing deforestation size of the area 1.80 hectares during those years.

This paper presents the results of the application of mathematical morphology for the automatic quantification of the number of tents and the detection of changes in the Menik Farm IDP camp in Sri Lanka. The tents were retrieved using an area-constraint top-hat opening applied to WorldView-1 data. The counting of tents was based on the centroids of the automatically detected structures indicating an overall number of 25,150 tents on the 26 June 2009. The comparison with a visual interpretation produced an R 2 of 0.97 with an error of 1.25%. In addition, an automated detection of changes inside a camp area was conducted. The comparison of the satellite image of 26 June 2009 (WorldView-1) and an image of 28 February 2010 (GeoEye-1) is based on mutual (mixed) information metric, after using morphological image processing techniques and previously specified criterion. Changes are observed on a terrain of around 15.2% of the total camp area and 3813 of previously detected structures disappeared in a period of ...

This paper introduces a new and general method for change detection based on the normalized difference change detection (NDCD) technique. A case study shows the use of the NDCD technique for flood mapping. Flood maps for the city of New Orleans (Louisiana, USA) resulting from the passage of Hurricane Katrina in 2005 were produced from the data processing of Satellite Pour l'Observation de la Terre (SPOT)-4 High-Resolution Visible Infra-Red (HRVIR) and Landsat-5 Thematic Mapper (TM) images and the accuracies of the maps were verified using as ground truth the flood extension map of the city of New Orleans produced at the Dartmouth Flood Observatory (Dartmouth College, USA). The potentialities and performances of the NDCD technique in flood mapping were also compared to other standard change detection methods such as: (i) the near-infrared (NIR) normalized difference, (ii) unsupervised post-classification comparison, (iii) change vector analysis (CVA) and (iv) spectral-temporal minimum noise fraction (STMNF) transformation. When using the SPOT-4 HRVIR data, the NDCD technique led to better results than the other change detection methods considered here, while for the Landsat-5 TM data processing the closeness of the post-flood image to the Katrina landfall influenced the overall performances negatively. However, with respect to flood mapping in the urban area alone, which may be of major interest in most cases, the NDCD technique also performed better than all the other change detection methods considered here when using the Landsat-5 TM data.

This paper presents a methodology for the detection of dwelling structures in Darfur camps to estimate the total number of dwellings per camp using GeoEye-1 satellite images. The method is based on a translation of the visual characterization of the searched structures into a morphological image processing chain. Two variants are described: the first variant extracts dwellings fully automatic for enumeration, while the second links the area covered by dwellings to visual interpretation results of representative samples for estimation of the total number of dwellings. Compared to the visual interpretation both produce similar results with correlation coefficients of 0.65 and 0.66 respectively leading to a mean error of 6% in the total number of dwellings. In complex camp settings, the area-based approach might be preferred, since it provides some more control due to the visual interpretation included.

Geospatial database creation for landslide susceptibility mapping is often an almost inhibitive activity. This has been the reason that for quite some time landslide susceptibility analysis was modelled on the basis of spatially related factors. This paper presents the use of frequency ratio, fuzzy logic and multivariate regression models for landslide susceptibility mapping on Cameron catchment area, Malaysia, using a Geographic Information System (GIS) and remote sensing data. Landslide locations were identified in the study area from the interpretation of aerial photographs, high resolution satellite images, inventory reports and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing tools. There were nine factors considered for landslide susceptibility mapping and the frequency ratio coefficient for each factor was computed. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land cover from TM satellite image; the vegetation index value from Landsat satellite images; and precipitation distribution from meteorological data. Using these factors the fuzzy membership values were calculated. Then fuzzy operators were applied to the fuzzy membership values for landslide susceptibility mapping. Further, multivariate logistic regression model was applied for the landslide susceptibility. Finally, the results of the analyses were verified using the landslide location data and compared with the frequency ratio, fuzzy logic and multivariate logistic regression models. The validation results showed that the frequency ratio model (accuracy is 89%) is better in prediction than fuzzy logic (accuracy is 84%) and logistic regression (accuracy is 85%) models. Results show that, among the fuzzy operators, in the case with “gamma” operator (λ = 0.9) showed the best accuracy (84%) while the case with “or” operator showed the worst accuracy (69%).

This paper presents landslide susceptibility analysis around the Cameron Highlands area, Malaysia using a geographic information system (GIS) and remote sensing techniques. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten landslide occurrence factors were selected as: topographic slope, topographic aspect, topographic curvature and distance from drainage, lithology and distance from lineament, soil type, rainfall, land cover from SPOT 5 satellite images, and the vegetation index value from SPOT 5 satellite image. These factors were analyzed using an advanced artificial neural network model to generate the landslide susceptibility map. Each factor’s weight was determined by the back-propagation training method. Then, the landslide susceptibility indices were calculated using the trained back-propagation weights, and finally, the landslide susceptibility map was generated using GIS tools. The results of the neural network model suggest that the effect of topographic slope has the highest weight value (0.205) which has more than two times among the other factors, followed by the distance from drainage (0.141) and then lithology (0.117). Landslide locations were used to validate the results of the landslide susceptibility map, and the verification results showed 83% accuracy. The validation results showed sufficient agreement between the computed susceptibility map and the existing data on landslide areas.

This paper evaluates the applicability of multi-beam RADARSAT-1 satellite imagery for mapping surficial sediments in northwest Alberta, Canada, using simple arithmetic algorithms. The goal is to make the RADARSAT-1 imagery acquired by the Alberta Geological Survey (AGS) usable to common users who have no access to complex image classification software and (or) expertise. Unlike the Canadian Shield, northern Alberta is mantled with much thicker glacial deposits that are covered by the Northern Boreal Forest. This limits the use of conventional image classification techniques, as there is not a one-to-one relationship between vegetation and the underlying surficial sediments. The RADARSAT-1 data utilized consists of RADARSAT-1 imagery acquired using Standard Beam 1 and 7 in both ascending and descending passes. Moisture and surface roughness information can be extracted by applying algorithms that calculate the differences between images acquired from different incidence angles and look directions (ascendin...

We report on the actions of the first daily revisit satellite, FORMOSAT-2, in the recent Indian Ocean tsunami disaster. Starting from the first images of Banda Aceh and Phuket taken on 28 December 2004, FORMOSAT-2 used its unique orbit and pointable sensor system to demonstrate the extent to which it is able to respond to emergencies. A total of 137 images throughout the Indian Ocean rim countries were taken within a month. The data were immediately analysed and turned into damage-assessment maps and other information resources for humanitarian aid. This paper focuses on the image-processing procedure followed for a fast response to the South Asia tsunami event. The imageodesy technique is used to coregister the level-2 product of FORMOSAT-2 image at high accuracy and speed. A novel approach for spectral reservation data fusion has also been proposed. With the advantages of accurate coregistration and reliable spectral property, the colour composites of FORMOSAT-2 imagery have been used as the principle source of information for our tsunami hazard assessment. The potential of FORMOSAT-2 for disaster monitoring is discussed. The technique developed in this research will be adapted to produce pan sharpened images as a standard value added product of FORMOSAT-2.

This paper presents a pilot study on riparian vegetation delineation and mapping using remote sensing and geographic information systems (GIS) in the Hunter Region, Australia. The aim of the study was to develop appropriate and repeatable assessment and mapping techniques to quantify the extent of riparian vegetation within the region. Ortho-rectified digital aerial photographs, SPOT-4 multispectral (XS) and Landsat-7 ETM+ images were tested to delineate the riparian vegetation and to develop a quantifiable and repeatable method of mapping the extent of that vegetation. Image processing techniques such as parallelepiped classification, tasselled cap transformation, and vegetation index clustering were used in an attempt to delineate riparian vegetation from remotely sensed images. Specific GIS analysis techniques were used for riparian zone buffering and segmentation, vegetation cover estimation, and mapping. Specific GIS scripts were developed for those processes so that they were automatic, fast, and repeatable. Various vegetation indices (VI) were evaluated and compared for their ability to discriminate riparian vegetation and its extent. The riparian vegetation was assessed and mapped at designed segmental interval (e.g. 1 km) with polygon and lineal representations. The classification accuracy was assessed against field observation and air photo interpretation (API). The overall accuracy of the photo-based classification was about 81%, SPOT-4 63%, and Landsat-7 ETM+ 53%. Statistic analysis shows that there is little agreement between photo-based classification and that from satellite imagery.

Calculation of vegetation indices, especially Normalized Difference Vegetation Index (NDVI), has become one of the most successful, popular and traditional attempts in biogeographical research methods, because NDVI has certain advantages over other vegetation indices or band combinations. The results of calculations of vegetation indices for studies of land cover types are being repeatedly reported in various papers [1], [2], [3], [4]. Thus, NDVI is less depending on soil properties of the research area as well as to the daytime illumination comparing to simple red-infrared bands combination [5]. NDVI is well adjusted specially for the analysis of vegetation properties, which can be indirectly interpreted from the objects colors, as shown on the raster image. However, the correlation of the results received from the satellite spectral signals may not necessarily precisely correlate with real data on biomass and vegetation quality which is caused by the local climatic settings and biogeochemical background of the research area. To model the NDVI I used Map Calculation tool in command line of the Raster Operations menu in ILWIS GIS, which enables to perform spatial analysis and modeling by combination of queries, arithmetic expressions and overlays of selected raster images. The calculation of the NDVI index is read into the ILWIS spatial analysis menu using following equation: NDVI = (Band4 – Band3) / (Band4 + Band3), where Band 4 is DN values of spectral reflectance in NIR (near infra-red) and Band 3 is DN values of spectral reflectance in VIS. The NDVI was calculated automatically using arguments of images: VegIndex = NDVI (Band3, Band4). Two Landsat bands have been used: Band4, containing red reflectance and Band3 with infra-red reflectance: VegIndex=NDVI(TM_3,TM_4). Other Landsat scenes were classified individually using the same method scheme in ILWIS GIS. The resulting images shows distribution of the vegetation over the Bovanenkovo region within three (3) years: 1988, 2001 and 2011 (Fig.2). The results for the year 2001 show that vegetation has very moderate overall index, reaching value 0,500 as a maximal. The maximal NDVI values in year 2011 are 0.49, while in 1988 it was 0.76, which shows the decrease in the biomass values during the past two decades. The satellite-based monitoring of the northern ecosystems is important tool for detection of the environmental changes. This research presented ILWIS GIS based image processing of Landsat time series covering Yamal area. The results show decrease in overall NDVI values for the study area since 1988 to 2011, which can be caused by the environmental change and anthropogenic factors. The calculated NDVI indicated biomass and can be also used as indicator of “greenness” of the vegetation.

Lateritic palaeosols cover roughly 33% of Earth's land surface. In spite of this, laterite facies remain largely unmapped. This paper illustrates how spectral properties of clays and iron minerals guide the mapping of three laterite facies using remote sensing. Remotely sensed data and image processing techniques prove ideal for effective mapping of laterites over large areas and difficult terrain so that their economic importance can be evaluated. Several types of remote sensing data are considered and the most appropriate selected. A 700 km2 area in Eritrea is used as a test site. A laterite facies map derived from remotely sensed data is presented for the first time. Methods of mapping are given using Landsat Thematic Mapper (TM), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Advanced Land Imager (ALI) data. Field evidence supports observations. Remote sensing methodology developed for the test site is applied to unvisited areas in Eritrea and Saudi Arabia. The results indi...

This paper compares different image processing routines to identify suitable remote sensing variables for urban classifi- cation in the Marion County, Indiana, USA, using a Landsat 7 Enhanced Thematic Mapper Plus (ETM� ) image. The ETMmultispectral, panchromatic, and thermal images are used. Incorporation of spectral signature, texture, and surface temperature is examined, as well as data fusion techniques for combining a higher spatial resolution image with lower spatial resolution multispectral images. Results indicate that incorporation of texture from lower spatial resolution images or of a temperature image cannot improve classification accuracies. However, incorporation of textures derived from a higher spatial resolution panchromatic image improves the classification accuracy. In particular, use of data fusion result and texture image yields the best classifi- cation accuracy with an overall accuracy of 78 percent and a kappa index of 0.73 for eleven land use and land cover classes.

Understanding the temporal dynamics and spatial distribution of natural seepage phenomena in the Gulf of Mexico is fundamental for the definition of proper environmental management practices in this often cloud-covered region. RADARSAT-1 images analyzed in this paper were acquired using the wide 1 and wide 2 (W1, W2) and ScanSAR narrow 1 (SCN1) beam modes. Image processing was carried out using the unsupervised semivariogram textural classifier (USTC). In combination with RADARSAT-1 satellite images, and essential ancillary data, this technology enhances the detection of seepage slicks on the ocean surface based on radar texture. USTC classification of RADARSAT-1 data provided systematic evidence from space of the presence of prolific, present-day petroleum generation and migration in the Cantarell Complex of oil fields.

This paper describes the use and appraises the performance of acoustic ground discrimination systems (AGDS) for marine benthic biotope mapping for those who are more familiar with satellite or airborne remote sensing. The operation of AGDS is described and the nature of the data produced compared with more conventional remotely sensed data. Analysis of the AGDS data involves the interpolation of track point data to create areal coverages that can then be interpreted using classification and image processing techniques. AGDS data have been shown to be variable both within and between surveys and systems which affects the performance of interpretation. Additionally, the marine environment imposes limitations on the way that ground sampling can be undertaken and the spatial extent of information that can be obtained. In summary, the main issues that contribute to variability and uncertainty of biotope maps based on AGDS-based methodology are: (1) errors in position fixing of both AGDS and the ground samples;...

Developments in the coastal area have significant impact on the adjacent shorelines. Mathematical modelling provides a useful tool for predicting such changes in shorelines in advance. Processing and analysis of satellite imageries of coastal area enables us to estimate and monitor the shoreline changes, which is otherwise extremely difficult, time consuming and costly by field surveying. In this paper the shoreline changes obtained by mathematical modelling and by image processing technique are compared by applying these techniques to shoreline adjacent to Ennore region. The study indicated that the cross-shore and longshore impact predicted by mathematical model and satellite information match satisfactorily. Thus the satellite information is useful for calibrating the mathematical model which can be further used for predictive purposes.

This paper presents initial tests of a novel and relatively simple methodology for near real-time operational detection of deforestation in Amazonia. Channel 3 (3.7mum) full resolution (1.1km) and geometrically corrected images of the Advanced Very High Resolution Radiometer (AVHRR) on-board the NOAAseries satellites covering the same region on different dates are digitally compared. Differences in forest cover between pairs of images are automatically selected as potential deforestation sites. In the tests, three AVHRR images (September 1989 and 1990; July 1990) were compared with 30 m high-resolution Landsat Thematic Mapper (TM) data using a geographical information system (GIS) integrated to the image processing. The percentages of correct location of newly deforested sites for September 1989-1990 and September 1990-July 1990 comparisons were, respectively: 49.1 and 56.5 per cent for about 200 deforestation sites of assorted sizes, and 82 and 90 per cent for sites larger than 3.1 km 2.

Currently, remote sensing technologies were widely employed in the dynamic monitoring of the land. This paper presented an algorithm named fuzzy nonlinear proximal support vector machine (FNPSVM) by basing on ETM(+) remote sensing image. This algorithm is applied to extract various types of lands of the city Da'an in northern China. Two multi-category strategies, namely "one-against-one" and "one-against-rest" for this algorithm were described in detail and then compared. A fuzzy membership function was presented to reduce the effects of noises or outliers on the data samples. The approaches of feature extraction, feature selection, and several key parameter settings were also given. Numerous experiments were carried out to evaluate its performances including various accuracies (overall accuracies and kappa coefficient), stability, training speed, and classification speed. The FNPSVM classifier was compared to the other three classifiers including the maximum likelihood classifier (MLC), back propagation neural network (BPN), and the proximal support vector machine (PSVM) under different training conditions. The impacts of the selection of training samples, testing samples and features on the four classifiers were also evaluated in these experiments.

The use of low cost scanning techniques in association with image processing is considered for land use/land cover mapping in part of the Sahel. This paper describes variations in the use of a Sharp JX 320 colour scanner and associated software in capturing satellite diapositive imagery (at 1:1 000 000) and thematic map data on a PC with the aim of developing a method for using scanned data in a GIS system. Optimal image scanning was achieved using PC‐Quickscan II with the MTR set to strong and RGB lightness at 100%. Scanning at 300 dpi developed a dot (pixel) size representing 86.7m on the ground. Thematic map scanning was improved by using the Image‐In program and the black and white lineart format set with no colour controls or edge correction. The spatial resolution of the digitised maps was 173.4m. Generally scanning imagery was best achieved through PC‐Quickscan because no editing was required. Since editing thematic maps was essential because extraneous lines and shadow was picked up, Imag...

The use of UAV systems for surveying archaeological sites is becoming progressively more common due to the considerable potential in terms of rapidity of survey, costs and accuracy. The paper presents the first results of the photogrammetric survey of the archaeological site of Himera in Sicily (Italy) using by UAV systems. A complete documentation of the site through the production of a DSM and an ortho image were carried out. The research further evaluated two different image processing workflows: a typical photogrammetric approach and a computer vision approach. An ortho image of the archaeological site with a very high resolution was obtained. Dept. of Civil, Environmental, Aerospace and Materials Engineering, University of Palermo, Italy. Consorzio Ticonzero, Palermo, Italy