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Special Issue "100 Years ISPRS - Advancing Remote Sensing Science"

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (1 December 2010)

Special Issue Editors

Guest Editor
Prof. Dr. Wolfgang Wagner (Website)

Research Group Remote Sensing, Department of Geodesy and Geoinformation (GEO), Vienna University of Technology (TU Wien), Gusshausstrasse 27-29, 1040 Vienna, Austria
Fax: +43 1 58801 12299
Interests: remote sensing; geophysical parameter retrieval; airborne laser scanning; full-waveform lidar; radar remote sensing; soil moisture
Guest Editor
Dr. Balazs Szekely (Website)

Institute of Photogrammetry & Remote Sensing, Vienna University of Technology, Vienna, Austria
Fax: +43 158 80112299

Special Issue Information

Dear Colleagues,

This special issue commemorates the foundation of the International Society for Photogrammetry (ISP) on July 4, 1910, in Vienna on the initiative by Prof. Eduard Doležal. The Society changed its name to the International Society for Photogrammetry and Remote Sensing (ISPRS) in 1980, reflecting the increasing integration of the two disciplines. In our modern digital age, photogrammetry and remote sensing have virtually grown together, having as their common scope the extraction of reliable information from noncontact imaging and other sensor systems about the Earth and its environment through recording, measuring, analyzing and representation. At the occasion of the ISPRS Centenary, an ISPRS Commission VII Symposium was held on July 5-7, 2010, at the Vienna University of Technology, Austria. This special issue is a collection of selected and substantially extended papers from this symposium.

Prof. Dr. Wolfgang Wagner
Dr. Balazs Szekely
Guest Editor

Keywords

  • multi-spectral and hyperspectral remote sensing
  • microwave remote sensing
  • lidar and laser scanning
  • geometric modelling
  • physical modelling and signatures
  • change detection and process modelling
  • land cover classification
  • image processing and pattern recognition
  • data fusion and data assimilation
  • earth observation programmes
  • remote sensing applications
  • operational remote sensing applications

Published Papers (14 papers)

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Research

Open AccessArticle Detecting Mountain Peaks and Delineating Their Shapes Using Digital Elevation Models, Remote Sensing and Geographic Information Systems Using Autometric Methodological Procedures
Remote Sens. 2012, 4(3), 784-809; doi:10.3390/rs4030784
Received: 16 December 2011 / Revised: 2 February 2012 / Accepted: 2 February 2012 / Published: 21 March 2012
Cited by 7 | PDF Full-text (3057 KB) | HTML Full-text | XML Full-text
Abstract
The detection of peaks (summits) as the upper parts of mountains and the delineation of their shape is commonly confirmed by inspections carried out by mountaineers. In this study the complex task of peak detection and shape delineation is solved by autometric [...] Read more.
The detection of peaks (summits) as the upper parts of mountains and the delineation of their shape is commonly confirmed by inspections carried out by mountaineers. In this study the complex task of peak detection and shape delineation is solved by autometric methodological procedures, more precisely, by developing relatively simple but innovative image-processing and spatial-analysis techniques (e.g., developing inventive variables using an annular moving window) in remote sensing and GIS domains. The techniques have been integrated into automated morphometric methodological procedures. The concepts of peaks and their shapes (sharp, blunt, oblong, circular and conical) were parameterized based on topographic and morphologic criteria. A geomorphologically high quality DEM was used as a fundamental dataset. The results, detected peaks with delineated shapes, have been integratively enriched with numerous independent datasets (e.g., with triangulated spot heights) and information (e.g., etymological information), and mountaineering criteria have been implemented to improve the judgments. This holistic approach has proved the applicability of both highly standardized and universal parameters for the geomorphologically diverse Kamnik Alps case study area. Possible applications of this research are numerous, e.g., a comprehensive quality control of DEM or significantly improved models for the spatial planning proposes. Full article
(This article belongs to the Special Issue 100 Years ISPRS - Advancing Remote Sensing Science)
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Open AccessArticle Using Remote Sensing Products for Environmental Analysis in South America
Remote Sens. 2011, 3(10), 2110-2127; doi:10.3390/rs3102110
Received: 21 July 2011 / Revised: 2 September 2011 / Accepted: 12 September 2011 / Published: 26 September 2011
Cited by 4 | PDF Full-text (4193 KB) | HTML Full-text | XML Full-text
Abstract
Land cover plays a major role in many biogeochemical models that represent processes and connections with terrestrial systems; hence, it is a key component for public decisions in ecosystems management. The advance of remote sensing technology, combined with the emergence of new [...] Read more.
Land cover plays a major role in many biogeochemical models that represent processes and connections with terrestrial systems; hence, it is a key component for public decisions in ecosystems management. The advance of remote sensing technology, combined with the emergence of new operational products, offers alternatives to improve the accuracy of environmental monitoring and analysis. This work uses the GLOBCOVER, the Vegetation Continuous Field (VCF), MODIS Fire Radiative Power (FRP) and the Tropical Rainfall Measuring Mission (TRMM) remotely sensed databases to analyze the biomass burning distribution, the land use and land cover characteristics and the percent of tree cover in South America during the years 2000 to 2005. Initially, GLOBCOVER was assessed based on VCF product, and subsequently used for quantitative analysis of the spatial distribution of the South America fires with the fire radiative power (FRP). The results show that GLOBCOVER has a tendency to overestimate forest classes and to underestimate urban and mangroves areas. The fire quantification based on GLOBCOVER product shows that the highest incidence of fires can be observed in the arc of deforestation, located in the Amazon forest border, with vegetation cover composed mainly of broadleaved evergreen or semi-deciduous forest. A time series analysis of FRP database indicates that biomass burning occurs mainly in areas of broadleaved evergreen or semi-deciduous forest and in Brazilian Cerrado associated with grassland management, agricultural land clearing and with the deforestation of Amazon tropical rainforest. Also, variations in FRP intensity and spread can be attributed to rainfall anomalies, such as in 2004, when South America had a positive anomaly rainfall. Full article
(This article belongs to the Special Issue 100 Years ISPRS - Advancing Remote Sensing Science)
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Open AccessArticle Effects of Individual Tree Detection Error Sources on Forest Management Planning Calculations
Remote Sens. 2011, 3(8), 1614-1626; doi:10.3390/rs3081614
Received: 12 April 2011 / Revised: 20 May 2011 / Accepted: 8 July 2011 / Published: 25 July 2011
Cited by 22 | PDF Full-text (276 KB) | HTML Full-text | XML Full-text
Abstract
The objective was to investigate the error sources of the airborne laser scanning based individual tree detection (ITD), and its effects on forest management planning calculations. The investigated error sources were detection of trees (etd), error in tree height [...] Read more.
The objective was to investigate the error sources of the airborne laser scanning based individual tree detection (ITD), and its effects on forest management planning calculations. The investigated error sources were detection of trees (etd), error in tree height prediction (eh) and error in tree diameter prediction (ed). The effects of errors were analyzed with Monte Carlo simulations. etd was modeled empirically based on a tree’s relative size. A total of five different tree detection scenarios were tested. Effect of eh was investigated using 5% and 0% and effect of ed using 20%, 15%, 10%, 5%, 0% error levels, respectively. The research material comprised 15 forest stands located in Southern Finland. Measurements of 5,300 trees and their timber assortments were utilized as a starting point for the Monte Carlo simulated ITD inventories. ITD carried out for the same study area provided a starting point (Scenario 1) for etd. In Scenario 1, 60.2% from stem number and 75.9% from total volume (Vtotal) were detected. When the only error source was etd (tree detection varying from 75.9% to 100% of Vtotal), root mean square errors (RMSEs) in stand characteristics ranged between the scenarios from 32.4% to 0.6%, 29.0% to 0.5%, 7.8% to 0.2% and 5.4% to 0.1% in stand basal area (BA), Vtotal, mean height (Hg) and mean diameter (Dg), respectively. Saw wood volume RMSE varied from 25.1% to 0.2%, as pulp wood volume respective varied from 37.8% to 1.0% when errors stemmed only from etd. The effect of ed was most significant for Vtotal and BA and the decrease in RMSE was from 12.0% to 0.6% (BA) and from 10.9% to 0.5% (Vtotal) in the most accurate tree detection scenario when ed varied from 20% to 0%. The effect of increased accuracy in tree height prediction was minor for all the stand characteristics. The results show that the most important error source in ITD is tree detection. At stand level, unbiased predictions for tree height and diameter are enough, given the present tree detection accuracy. Full article
(This article belongs to the Special Issue 100 Years ISPRS - Advancing Remote Sensing Science)
Open AccessArticle Evaluation of a LIDAR Land-Based Mobile Mapping System for Monitoring Sandy Coasts
Remote Sens. 2011, 3(7), 1472-1491; doi:10.3390/rs3071472
Received: 1 April 2011 / Revised: 2 June 2011 / Accepted: 22 June 2011 / Published: 8 July 2011
Cited by 22 | PDF Full-text (1617 KB) | HTML Full-text | XML Full-text
Abstract
The Dutch coast is characterized by sandy beaches flanked by dunes. Understanding the morphology of the coast is essential for defense against flooding of the hinterland. Because most dramatic changes of the beach and the first dune row happen during storms, it [...] Read more.
The Dutch coast is characterized by sandy beaches flanked by dunes. Understanding the morphology of the coast is essential for defense against flooding of the hinterland. Because most dramatic changes of the beach and the first dune row happen during storms, it is important to assess the state of the coast immediately after a storm. This is expensive and difficult to organize with Airborne Laser Scanning (ALS). Therefore, the performance of a Land-based Mobile Mapping System (LMMS) in mapping a stretch of sandy Dutch coast of 6 km near the municipality of Egmond is evaluated in this research. A test data set was obtained by provider Geomaat using the StreetMapper LMMS system. Both the relative quality of laser point heights and of a derived Digital Terrain model (DTM) are assessed. First, the height precision of laser points is assessed a priori by random error propagation, and a posteriori by calculating the height differences between close-by points. In the a priori case, the result is a theoretical laser point precision of around 5 cm. In the a posteriori approach it is shown that on a flat beach a relative precision of 3 mm is achieved, and that almost no internal biases exist. In the second analysis, a DTM with a grid size of 1 m is obtained using moving least squares. Each grid point height includes a quality description, which incorporates both measurement precision and terrain roughness. Although some problems remain with the scanning height of 2 m, which causes shadow-effect behind low dunes, it is concluded that a laser LMMS enables the acquisition of a high quality DTM product, which is available within two days. Full article
(This article belongs to the Special Issue 100 Years ISPRS - Advancing Remote Sensing Science)
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Open AccessArticle Integrated Landsat Image Analysis and Hydrologic Modeling to Detect Impacts of 25-Year Land-Cover Change on Surface Runoff in a Philippine Watershed
Remote Sens. 2011, 3(6), 1067-1087; doi:10.3390/rs3061067
Received: 4 March 2011 / Revised: 4 May 2011 / Accepted: 6 May 2011 / Published: 26 May 2011
Cited by 7 | PDF Full-text (1928 KB) | HTML Full-text | XML Full-text
Abstract
Landsat MSS and ETM+ images were analyzed to detect 25-year land-cover change (1976–2001) in the critical Taguibo Watershed in Mindanao Island, Southern Philippines. This watershed has experienced historical modifications of its land-cover due to the presence of logging industries in the 1950s, [...] Read more.
Landsat MSS and ETM+ images were analyzed to detect 25-year land-cover change (1976–2001) in the critical Taguibo Watershed in Mindanao Island, Southern Philippines. This watershed has experienced historical modifications of its land-cover due to the presence of logging industries in the 1950s, and continuous deforestation due to illegal logging and slash-and-burn agriculture in the present time. To estimate the impacts of land-cover change on watershed runoff, land-cover information derived from the Landsat images was utilized to parameterize a GIS-based hydrologic model. The model was then calibrated with field-measured discharge data and used to simulate the responses of the watershed in its year 2001 and year 1976 land-cover conditions. The availability of land-cover information on the most recent state of the watershed from the Landsat ETM+ image made it possible to locate areas for rehabilitation such as barren and logged-over areas. We then created a “rehabilitated” land-cover condition map of the watershed (re-forestation of logged-over areas and agro-forestation of barren areas) and used it to parameterize the model and predict the runoff responses of the watershed. Model results showed that changes in land-cover from 1976 to 2001 were directly related to the significant increase in surface runoff. Runoff predictions showed that a full rehabilitation of the watershed, especially in barren and logged-over areas, will be likely to reduce the generation of a huge volume of runoff during rainfall events. The results of this study have demonstrated the usefulness of multi-temporal Landsat images in detecting land-cover change, in identifying areas for rehabilitation, and in evaluating rehabilitation strategies for management of tropical watersheds through its use in hydrologic modeling. Full article
(This article belongs to the Special Issue 100 Years ISPRS - Advancing Remote Sensing Science)
Open AccessArticle Comparison of Grid-Based and Segment-Based Estimation of Forest Attributes Using Airborne Laser Scanning and Digital Aerial Imagery
Remote Sens. 2011, 3(5), 945-961; doi:10.3390/rs3050945
Received: 2 February 2011 / Revised: 4 March 2011 / Accepted: 2 May 2011 / Published: 12 May 2011
Cited by 6 | PDF Full-text (938 KB) | HTML Full-text | XML Full-text
Abstract
Forest management planning in Finland is currently adopting a new-generation forest inventory method, which is based on interpretation of airborne laser scanning data and digital aerial images. The inventory method is based on a systematic grid, where the grid elements serve as [...] Read more.
Forest management planning in Finland is currently adopting a new-generation forest inventory method, which is based on interpretation of airborne laser scanning data and digital aerial images. The inventory method is based on a systematic grid, where the grid elements serve as inventory units, for which the laser and aerial image data are extracted and the forest variables estimated. As an alternative or a complement to the grid elements, image segments can be used as inventory units. The image segments are particularly useful as the basis for generation of the silvicultural treatment and cutting units since their boundaries should follow the actual stand borders, whereas when using grid elements it is typical that some of them cover parts of several forest stands. The proportion of the so-called mixed cells depends on the size of the grid elements and the average size and shape of the stands. In this study, we carried out automatic segmentation of two study areas on the basis of laser and aerial image data with a view to delineating micro-stands that are homogeneous in relation to their forest attributes. Further, we extracted laser and aerial image features for both systematic grid elements and segments. For both units, the feature set used for estimating the forest attributes was selected by means of a genetic algorithm. Of the features selected, the majority (61–79%) were based on the airborne laser scanning data. Despite the theoretical advantages of the image segments, the laser and aerial features extracted from grid elements seem to work better than features extracted from image segments in estimation of forest attributes. We conclude that estimation should be carried out at grid level with an area-specific combination of features and estimates for image segments to be derived on the basis of the grid-level estimates. Full article
(This article belongs to the Special Issue 100 Years ISPRS - Advancing Remote Sensing Science)
Open AccessArticle Multi-Temporal Land-Cover Classification of Agricultural Areas in Two European Regions with High Resolution Spotlight TerraSAR-X Data
Remote Sens. 2011, 3(5), 859-877; doi:10.3390/rs3050859
Received: 28 January 2011 / Revised: 10 March 2011 / Accepted: 14 April 2011 / Published: 27 April 2011
Cited by 17 | PDF Full-text (2164 KB) | HTML Full-text | XML Full-text
Abstract
Functioning ecosystems offer multiple services for human well-being (e.g., food, freshwater, fiber). Agriculture provides several of these services but also can cause negative impacts. Thus, it is essential to derive up-to-date information about agricultural land use and its change. This paper describes the [...] Read more.
Functioning ecosystems offer multiple services for human well-being (e.g., food, freshwater, fiber). Agriculture provides several of these services but also can cause negative impacts. Thus, it is essential to derive up-to-date information about agricultural land use and its change. This paper describes the multi-temporal classification of agricultural land use based on high resolution spotlight TerraSAR-X images. A stack of l4 dual-polarized radar images taken during the vegetation season have been used for two different study areas (North of Germany and Southeast Poland). They represent extremely diverse regions with regard to their population density, agricultural management, as well as geological and geomorphological conditions. Thereby, the transferability of the classification method for different regions is tested. The Maximum Likelihood classification is based on a high amount of ground truth samples. Classification accuracies differ in both regions. Overall accuracy for all classes for the German area is 61.78% and 39.25% for the Polish region. Accuracies improved notably for both regions (about 90%) when single vegetation classes were merged into groups of classes. Such regular land use classifications, applicable for different European agricultural sites, can serve as basis for monitoring systems for agricultural land use and its related ecosystems. Full article
(This article belongs to the Special Issue 100 Years ISPRS - Advancing Remote Sensing Science)
Open AccessArticle Forest Assessment Using High Resolution SAR Data in X-Band
Remote Sens. 2011, 3(4), 792-815; doi:10.3390/rs3040792
Received: 18 January 2011 / Revised: 25 February 2011 / Accepted: 14 March 2011 / Published: 13 April 2011
Cited by 32 | PDF Full-text (9094 KB) | HTML Full-text | XML Full-text
Abstract
Novel radar satellite missions also include sensors operating in X-band at very high resolution. The presented study reports methodologies, algorithms and results on forest assessment utilizing such X-band satellite images, namely from TerraSAR-X and COSMO-SkyMed sensors. The proposed procedures cover advanced stereo-radargrammetric [...] Read more.
Novel radar satellite missions also include sensors operating in X-band at very high resolution. The presented study reports methodologies, algorithms and results on forest assessment utilizing such X-band satellite images, namely from TerraSAR-X and COSMO-SkyMed sensors. The proposed procedures cover advanced stereo-radargrammetric and interferometric data processing, as well as image segmentation and image classification. A core methodology is the multi-image matching concept for digital surface modeling based on geometrically constrained matching. Validation of generated surface models is made through comparison with LiDAR data, resulting in a standard deviation height error of less than 2 meters over forest. Image classification of forest regions is then based on X-band backscatter information, a canopy height model and interferometric coherence information yielding a classification accuracy above 90%. Such information is then directly used to extract forest border lines. High resolution X-band sensors deliver imagery that can be used for automatic forest assessment on a large scale. Full article
(This article belongs to the Special Issue 100 Years ISPRS - Advancing Remote Sensing Science)
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Open AccessArticle 3-Dimensional Building Details from Aerial Photography for Internet Maps
Remote Sens. 2011, 3(4), 721-751; doi:10.3390/rs3040721
Received: 5 January 2011 / Revised: 1 February 2011 / Accepted: 11 February 2011 / Published: 8 April 2011
Cited by 5 | PDF Full-text (1513 KB) | HTML Full-text | XML Full-text
Abstract
This paper introduces the automated characterization of real estate (real property) for Internet mapping. It proposes a processing framework to achieve this task from vertical aerial photography and associated property information. A demonstration of the feasibility of an automated solution builds on [...] Read more.
This paper introduces the automated characterization of real estate (real property) for Internet mapping. It proposes a processing framework to achieve this task from vertical aerial photography and associated property information. A demonstration of the feasibility of an automated solution builds on test data from the Austrian City of Graz. Information is extracted from vertical aerial photography and various data products derived from that photography in the form of a true orthophoto, a dense digital surface model and digital terrain model, and a classification of land cover. Maps of cadastral property boundaries aid in defining real properties. Our goal is to develop a table for each property with descriptive numbers about the buildings, their dimensions, number of floors, number of windows, roof shapes, impervious surfaces, garages, sheds, vegetation, presence of a basement floor, and other descriptors of interest for each and every property of a city. From aerial sources, at a pixel size of 10 cm, we show that we have obtained positional accuracies in the range of a single pixel, an accuracy of areas in the 10% range, floor counts at an accuracy of 93% and window counts at 86% accuracy. We also introduce 3D point clouds of facades and their creation from vertical aerial photography, and how these point clouds can support the definition of complex facades. Full article
(This article belongs to the Special Issue 100 Years ISPRS - Advancing Remote Sensing Science)
Open AccessArticle Roughness Mapping on Various Vertical Scales Based on Full-Waveform Airborne Laser Scanning Data
Remote Sens. 2011, 3(3), 503-523; doi:10.3390/rs3030503
Received: 7 January 2011 / Revised: 28 February 2011 / Accepted: 1 March 2011 / Published: 4 March 2011
Cited by 17 | PDF Full-text (1981 KB) | HTML Full-text | XML Full-text
Abstract
Roughness is an important input parameter for modeling of natural hazards such as floods, rock falls and avalanches, where it is basically assumed that flow velocities decrease with increasing roughness. Seeing roughness as a multi-scale level concept (i.e., ranging from fine-scale [...] Read more.
Roughness is an important input parameter for modeling of natural hazards such as floods, rock falls and avalanches, where it is basically assumed that flow velocities decrease with increasing roughness. Seeing roughness as a multi-scale level concept (i.e., ranging from fine-scale soil characteristics to description of understory and lower tree layer) various roughness raster products were derived from the original full-waveform airborne laser scanning (FWF-ALS) point cloud using two different types of roughness parameters, the surface roughness (SR) and the terrain roughness (TR). For the calculation of the SR, ALS terrain points within a defined height range to the terrain surface are considered. For the parameterization of the SR, two approaches are investigated. In the first approach, a geometric description by calculating the standard deviation of plane fitting residuals of terrain points is used. In the second one, the potential of the derived echo widths are analyzed for the parameterization of SR. The echo width is an indicator for roughness and the slope of the target. To achieve a comparable spatial resolution of both SR layers, the calculation of the standard deviation of detrended terrain points requires a higher terrain point density than the SR parameterization using the echo widths. The TR describes objects (i.e., point clusters) close but explicitly above the terrain surface, with 20 cm defined as threshold height value for delineation of the surface layer (i.e., forest floor layer). Two different empirically defined vegetation layers below the canopy layer were analyzed (TR I: 0.2 m to 1.0 m; TR II: 0.2 m to 3.0 m). A 1 m output grid cell size was chosen for all roughness parameters in order to provide consistency for further integration of high-resolution optical imagery. The derived roughness parameters were then jointly classified, together with a normalized Digital Surface Model (nDSM) showing the height of objects (i.e., trees) above ground. The presented approach enables the classification of forested areas in patches of different vegetation structure (e.g., varying soil roughness, understory, density of natural cover). For validation purposes in situ reference data were collected and cross-checked with the classification results, positively confirming the general feasibility of the proposed vertical concept of integrated roughness mapping on various vertical levels. Results can provide valuable input for forest mapping and monitoring, in particular with regard to natural hazard modeling. Full article
(This article belongs to the Special Issue 100 Years ISPRS - Advancing Remote Sensing Science)
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Open AccessArticle Advantages of the Boresight Effect in Hyperspectral Data Analysis
Remote Sens. 2011, 3(3), 484-502; doi:10.3390/rs3030484
Received: 18 December 2010 / Revised: 16 February 2011 / Accepted: 18 February 2011 / Published: 1 March 2011
Cited by 3 | PDF Full-text (1157 KB) | HTML Full-text | XML Full-text
Abstract
Dual pushbroom hyperspectral sensors consist of two different instruments (covering different wavelengths) that are usually mounted on the same optical bench. This configuration leads to problems, such as co-registration of pixels and squint of the field of view, known as the boresight [...] Read more.
Dual pushbroom hyperspectral sensors consist of two different instruments (covering different wavelengths) that are usually mounted on the same optical bench. This configuration leads to problems, such as co-registration of pixels and squint of the field of view, known as the boresight effect. Determination of image-orientation parameters is due to the combination of an inertial measurement system (IMU) and global position system (GPS). The different positions of the IMU, the GPS antenna and the imaging sensors cause the orientation and boresight effect. Any small change in the correction of the internal orientation affects the co-registration between images extracted from the two instruments. Correcting the boresight effect is a key and almost automatic task performed by all dual-system users to better analyze the full spectral information of a given pixel. Thus, the boresight effect is considered to be noise in the system and a problem that needs to be corrected prior to any (thematic) data analysis. We propose using the boresight effect, prior to its correction, as a tool to monitor and detect spectral phenomena that can provide additional information not present in the corrected images. The advantage of using this effect was investigated with the AISA-Dual sensor, composed of an EAGLE sensor for the VIS-NIR (VNIR) region (400–970 nm) and HAWK for the SWIR region (980–2,450 nm). During the course of more than six years of operating this sensor, we have found that the boresight effect provides a new capacity to analyze hyperspectral data, reported herein. Accordingly, we generated a protocol to use this effect for three applications: (1) enhancing the shadow effect; (2) generating a 3-D view; and (3) better detecting spectral/spatial anomalies based on sub-pixel edge detection. This paper provides examples of these applications and suggests possible uses from an airborne platform. Full article
(This article belongs to the Special Issue 100 Years ISPRS - Advancing Remote Sensing Science)
Open AccessArticle Airborne Lidar: Advances in Discrete Return Technology for 3D Vegetation Mapping
Remote Sens. 2011, 3(3), 416-434; doi:10.3390/rs3030416
Received: 30 December 2010 / Revised: 9 February 2011 / Accepted: 10 February 2011 / Published: 25 February 2011
Cited by 20 | PDF Full-text (786 KB) | HTML Full-text | XML Full-text
Abstract
Conventional discrete return airborne lidar systems, used in the commercial sector for efficient generation of high quality spatial data, have been considered for the past decade to be an ideal choice for various mapping applications. Unlike two-dimensional aerial imagery, the elevation component [...] Read more.
Conventional discrete return airborne lidar systems, used in the commercial sector for efficient generation of high quality spatial data, have been considered for the past decade to be an ideal choice for various mapping applications. Unlike two-dimensional aerial imagery, the elevation component of airborne lidar data provides the ability to represent vertical structure details with very high precision, which is an advantage for many lidar applications focusing on the analysis of elevated features such as 3D vegetation mapping. However, the use of conventional airborne discrete return lidar systems for some of these applications has often been limited, mostly due to relatively coarse vertical resolution and insufficient number of multiple measurements in vertical domain. For this reason, full waveform airborne sensors providing more detailed representation of target vertical structure have often been considered as a preferable choice in some areas of 3D vegetation mapping application, such as forestry research. This paper presents an overview of the specific features of airborne lidar technology concerning 3D mapping applications, particularly vegetation mapping. Certain key performance characteristics of lidar sensors important for the quality of vegetation mapping are discussed and illustrated by the advanced capabilities of the ALTM-Orion, a new discrete return sensor manufactured by Optech Incorporated. It is demonstrated that advanced discrete return sensors with enhanced 3D mapping capabilities can produce data of enhanced quality, which can represent complex structures of vegetation targets at the level of details equivalent in some aspects to the content of full waveform data. It is also shown that recent advances in conventional airborne lidar technology bear the potential to create a new application niche, where high quality dense point clouds, enhanced by fully recorded intensity for multiple returns, may provide sufficient information for modeling and analysis, which have traditionally been applied mostly to full waveform data. Full article
(This article belongs to the Special Issue 100 Years ISPRS - Advancing Remote Sensing Science)
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Open AccessArticle Spaceborne Differential SAR Interferometry: Data Analysis Tools for Deformation Measurement
Remote Sens. 2011, 3(2), 305-318; doi:10.3390/rs3020305
Received: 30 November 2010 / Revised: 13 January 2011 / Accepted: 10 February 2011 / Published: 15 February 2011
Cited by 22 | PDF Full-text (2146 KB) | HTML Full-text | XML Full-text
Abstract
This paper is focused on spaceborne Differential Interferometric SAR (DInSAR) for land deformation measurement and monitoring. In the last two decades several DInSAR data analysis procedures have been proposed. The objective of this paper is to describe the DInSAR data processing and [...] Read more.
This paper is focused on spaceborne Differential Interferometric SAR (DInSAR) for land deformation measurement and monitoring. In the last two decades several DInSAR data analysis procedures have been proposed. The objective of this paper is to describe the DInSAR data processing and analysis tools developed at the Institute of Geomatics in almost ten years of research activities. Four main DInSAR analysis procedures are described, which range from the standard DInSAR analysis based on a single interferogram to more advanced Persistent Scatterer Interferometry (PSI) approaches. These different procedures guarantee a sufficient flexibility in DInSAR data processing. In order to provide a technical insight into these analysis procedures, a whole section discusses their main data processing and analysis steps, especially those needed in PSI analyses. A specific section is devoted to the core of our PSI analysis tools: the so-called 2+1D phase unwrapping procedure, which couples a 2D phase unwrapping, performed interferogram-wise, with a kind of 1D phase unwrapping along time, performed pixel-wise. In the last part of the paper, some examples of DInSAR results are discussed, which were derived by standard DInSAR or PSI analyses. Most of these results were derived from X-band SAR data coming from the TerraSAR-X and CosmoSkyMed sensors. Full article
(This article belongs to the Special Issue 100 Years ISPRS - Advancing Remote Sensing Science)
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Open AccessArticle Automatic Registration of Airborne and Spaceborne Images by Topology Map Matching with SURF Processor Algorithm
Remote Sens. 2011, 3(1), 65-82; doi:10.3390/rs3010065
Received: 16 November 2010 / Revised: 24 December 2010 / Accepted: 4 January 2011 / Published: 6 January 2011
Cited by 12 | PDF Full-text (1307 KB) | HTML Full-text | XML Full-text
Abstract
Image registration is widely used in remote-sensing applications. The existing automatic image registration techniques fall into two categories: Intensity-based and feature-based; the latter (which extracts structures from both images) being more suitable for multi-sensor fusion, detection of temporal changes and image mosaicking. [...] Read more.
Image registration is widely used in remote-sensing applications. The existing automatic image registration techniques fall into two categories: Intensity-based and feature-based; the latter (which extracts structures from both images) being more suitable for multi-sensor fusion, detection of temporal changes and image mosaicking. Conventional image registration algorithms have proven to be inaccurate, time-consuming, and unfeasible due to image complexity which makes it cumbersome or even impossible to discern the appropriate control points. In this study, we propose a novel method for automatic image registration based on topology (AIRTop) for change detection and multi‑sensor (airborne and spaceborne) fusion. In this algorithm, we first apply image‑processing methods (SURF—Speeded-Up Robust Features) to extract the landmark structures (roads and buildings) and convert them to a features (vector) map. The following stages are applied in GIS (Geographic Information System), where topology rules, which define the permissible spatial relationships between features, are defined. The relationships between features are established by weight-based topological map-matching algorithm (tMM). The suggested algorithm presents a robust method for image registration. The main focus in this study is on scale and image rotation, when the quality of the scanning system is constant. These seem to offer a good compromise between feature complexity and robustness to commonly occurring deformations. The skew and the anisotropic scaling are assumed to be second-order effects that are covered to some degree by the overall robustness of the sensor. Full article
(This article belongs to the Special Issue 100 Years ISPRS - Advancing Remote Sensing Science)

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