High Resolution Optical Satellite Imagery Research Articles

Orientation of Spaceborne SAR Stereo Pairs Employing the RPC Adjustment Model

Since the adoption of the rational polynomial coefficient (RPC) adjustment model as a preferred sensor orientation model for high-resolution optical satellite imagery, it has been demonstrated to be effective and robust. However, no publication discusses the application of the RPC adjustment model to the 3-D intersection from SAR stereoscopic pairs. This paper aims to validate the RPC adjustment model for spaceborne SAR stereoscopic orientation. Initially, a brief summary of the mathematical background of the RPC model is presented. Then, the SAR orientation errors are analyzed, namely, the orientation parameters, having the same net effect on the object-image relationship, and combined into a single adjustment parameter. The required adjustment is then discussed, and the formulation of the adjustment model is outlined. Finally, a number of designed adjustment experiments controlled via well-surveyed corner reflectors and an existing digital elevation model plus a digital orthophotograph map at the scale of 1:10 000 are performed. Multisensor images of TerraSAR-X, COnstellation of small Satellites for the Mediterranean basin Observation (COSMO-SkyMed), and Satellite Pour l'Observation de la Terre-5 (SPOT-5) over the Guangzhou area are used as test data. The results demonstrate that the proposed method can be generally applied to different imaging systems or the stereoscopic fusion of combined data and can achieve high orientation accuracy.

ACCURATE EVALUATION OF BUILDING DAMAGE IN THE 2003 BOUMERDES, ALGERIA EARTHQUAKE FROM QUICKBIRD SATELLITE IMAGES

Using QuickBird satellite images of Boumerdes city obtained following the 21 May 2003 Algeria earthquake, our study examined the applicability of high-resolution optical imagery for the visual detection of building damage grade based on the ground-truth data on the urban nature, typology of a total of 2,794 buildings, and the real damage observed. The results are presented as geographical information system (GIS) damage mapping of buildings obtained from field surveys and QuickBird images. In general, totally collapsed buildings, partially collapsed buildings, and buildings surrounded by debris can be identified by using only post-event pan-sharpened images. However, due to the nature of the damage observed, some buildings may be judged incorrectly even if preevent images are employed as a reference to evaluate the damage status. Hence, in this study, we clarify the limitations regarding the applicability of high-resolution optical satellite imagery in building damage-level mapping.

Describing coral reef bleaching using very high spatial resolution satellite imagery: experimental methodology

This paper proposes an experimental methodology toward describing and quantifying coral reef bleaching using very high spatial resolution optical satellite imagery. Sea surface temperature-based bleaching alerts issued by NOAA's Coral Reef Watch triggered image acquisition and served as an indication for high bleaching probability. Images of suspected coral reef bleaching events and reference images of the same reefs during previous unbleached conditions were coregistered and radiometrically normalized for change detection. An experimental methodology was developed to describe the severity and extent of the bleaching. The methodology hinges on the creation of the Coral Bleaching Index (CBI), constructed from change detected in the green, blue, and red wavelength bands. Results are provided in the form of colorized difference images showing areas of observed bleaching in gold, as well as CBI images, visualizing varying bleaching intensities. Comparison of the CBI with available field validation data yielded a correlation, however additional reference data would be needed for more detailed quality assessment. This technique is seen as a step toward the routine detection and long-term monitoring of coral reef bleaching from space and serves as a proposed tool for detecting bleaching in remote areas where observers cannot be deployed.

Satellite-based prediction of rainfall interception by tropical forest stands of a human-dominated landscape in Central Sulawesi, Indonesia

Summary Rainforest conversion to other land use types drastically alters the hydrological cycle in which changes in rainfall interception contribute significantly to the observed differences. However, little is known about the effects of more gradual changes in forest structure and at regional scales. We studied land use types ranging from natural forest over selectively-logged forest to cacao agroforest in a lower montane region in Central Sulawesi, Indonesia, and tested the suitability of high-resolution optical satellite imagery for modeling observed interception patterns. Investigated characteristics indicating canopy structure were mean and standard deviation of reflectance values, local maxima, and self-similarity measures based on the grey level co-occurrence matrix and geostatistical variogram analysis. Previously studied and published rainfall interception data comprised twelve plots and median values per land use type ranged from 30% in natural forest to 18% in cacao agroforests. A linear regression model with local maxima, mean contrast and normalized digital vegetation index (NDVI) as regressors was able to explain more than 84% ( R adj 2 ) of the variation encountered in the data. Other investigated characteristics did not prove significant in the regression analysis. The model yielded stable results with respect to cross-validation and also produced realistic values and spatial patterns when applied at the landscape level (783.6 ha). High values of interception were rare and localized in natural forest stands distant to villages, whereas low interception characterized the intensively used sites close to settlements. We conclude that forest use intensity significantly reduced rainfall interception and satellite image analysis can successfully be applied for its regional prediction, and most forest in the study region has already been subject to human-induced structural changes.

Developing A Method For Tsunami Damage Detection Using High-Resolution Optical Satellite Imagery

The authors propose a method to detect the structural damage due to tsunami inundation flow, by using the visual interpretation and analysis of high-resolution optical satellite imagery. The method is implemented to the recent tsunami event, the 2007 Solomon Islands earthquake tsunami, to identify the damage probabilities within the inundation zone and construct the fragility function (damage probability to tsunami height), combined with the post-tsunami survey data.

An Innovative Neural-Net Method to Detect Temporal Changes in High-Resolution Optical Satellite Imagery

The advent of new high spatial resolution optical satellite imagery has greatly increased our ability to monitor land cover changes from space. Satellite observations are carried out regularly and continuously, and provide a great deal of insight into the temporal changes of land cover use. High spatial resolution imagery better resolves the details of these changes and makes it possible to overcome the "mixed-pixel" problem that is inherent with more moderate resolution satellite sensors. At the same time, high-resolution imagery presents a new challenge over other satellite systems, in that a relatively large amount of data must be analyzed and corrected for registration and classification errors to identify the land cover changes. To obtain the accuracies that are required by many applications to large areas, very extensive manual work is commonly required to remove the classification errors that are introduced by most methods. To improve on this situation, we have developed a new method for land surface change detection that greatly reduces the human effort that is needed to remove the errors that occur with many classification methods that are applied to high-resolution imagery. This change detection algorithm is based on neural networks, and it is able to exploit in parallel both the multiband and the multitemporal data to discriminate between real changes and false alarms. In general, the classification errors are reduced by a factor of 2-3 using our new method over a simple postclassification comparison based on a neural-network classification of the same images.

Detecting Man-Made Structures and Changes in Satellite Imagery With a Content-Based Information Retrieval System Built on Self-Organizing Maps

The increasing amount and resolution of satellite sensors demand new techniques for browsing remote sensing image archives. Content-based querying allows an efficient retrieval of images based on the information they contain, rather than their acquisition date or geographical extent. Self-organizing maps (SOMs) have been successfully applied in the PicSOM system to content-based image retrieval in databases of conventional images. In this paper, we investigate and extend the potential of PicSOM for the analysis of remote sensing data. We propose methods for detecting man-made structures, as well as supervised and unsupervised change detection, based on the same framework. In this paper, a database was artificially created by splitting each satellite image to be analyzed into small images. After training the PicSOM on this imagelet database, both interactive and off-line queries were made to detect man-made structures, as well as changes between two very high resolution images from different years. Experimental results were both evaluated quantitatively and discussed qualitatively, and suggest that this new approach is suitable for analyzing very high resolution optical satellite imagery. Possible applications of this work include interactive detection of man-made structures or supervised monitoring of sensitive sites

Towards Rapid Citywide Damage Mapping Using Neighborhood Edge Dissimilarities in Very High-Resolution Optical Satellite Imagery—Application to the 2003 Bam, Iran, Earthquake

Remote sensing technology is increasingly recognized as a valuable post-earthquake damage assessment tool. Recent studies performed by research teams in the United States, Japan, and Europe have demonstrated that building damage sustained in urban environments can be identified through analysis of optical imagery and synthetic aperture radar (SAR) data. Damage detection using automated change detection algorithms will soon facilitate the scaling and prioritization of relief efforts, as well as the monitoring of the recovery operations. This paper introduces the use of an edge dissimilarity algorithm to quantify the extent of building damage.

Increased damage from fires in logged forests during droughts caused by El Niño.

In 1997-98, fires associated with an exceptional drought caused by the El Niño/Southern Oscillation (ENSO) devastated large areas of tropical rain forests worldwide. Evidence suggests that in tropical rainforest environments selective logging may lead to an increased susceptibility of forests to fire. We investigated whether this was true in the Indonesian fires, the largest fire disaster ever observed. We performed a multiscale analysis using coarse- and high-resolution optical and radar satellite imagery assisted by ground and aerial surveys to assess the extent of the fire-damaged area and the effect on vegetation in East Kalimantan on the island of Borneo. A total of 5.2 +/- 0.3 million hectares including 2.6 million hectares of forest was burned with varying degrees of damage. Forest fires primarily affected recently logged forests; primary forests or those logged long ago were less affected. These results support the hypothesis of positive feedback between logging and fire occurrence. The fires severely damaged the remaining forests and significantly increased the risk of recurrent fire disasters by leaving huge amounts of dead flammable wood.

Glaciological applications with Landsat-7 imagery: Early assessments

Enhanced Thematic Mapper Plus (ETM+) data from Landsat-7 are providing glaciologists with an ever-expanding data set that makes a comprehensive monitoring of the global cryosphere feasible for the first time in history. Examples of ETM+ data illustrate its ability to satisfy major scientific needs in the glaciological subdisciplines of sea-ice, glacier, ice-cap, and ice-sheet research with high-resolution optical satellite imagery. Examples shown include use as proxy ground-truth, positioning glacier termini and snowlines, and determining snow facies boundaries. The additional ETM+ panchromatic band, at a higher spatial resolution of 15 m, improves the spatial accuracy of these applications. The glaciological aspects of the Landsat-7 Long-Term Acquisition Plan are discussed to show how the timing and location of the image acquisitions will generate a cryospheric data set of unprecedented utility for future research.

Synergism of SAR and Landsat TM imagery for thematic investigations in complex terrain

Abstract Possibilities and techniques for the combined use of SAR and high resolution optical satellite imagery are discussed. Differences in imaging geometry and scene illumination complicate the analysis of multi-sensor data sets over complex terrain. For accurate geometric rectification digital elevation data are needed as reference for the surface topography. The illumination effects can be compensated by means of synthetic images. Geometric and radiometric characteristics of SAR and Landsat Thematic Mapper (TM) imagery are described using two data sets over mixed terrain, including glacier areas and mountains, in Iceland and Austria. As an example, a detailed comparison of the information content of SAR and TM is given for the application of glacier monitoring. A method for thematic mapping in mountain areas by digital classification techniques is shown using the comparison of simulated and real images.