The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

Peter Reinartz - One of the best experts on this subject based on the ideXlab platform.

  • investigating the applicability of Cartosat 1 dems and topographic maps to localize large area urban mass concentrations
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014
    Co-Authors: Michael Wurm, Pablo Dangelo, Peter Reinartz, Hannes Taubenbock
    Abstract:

    Building models are a valuable information source for urban studies and in particular for analyses of urban mass con- centrations (UMCS). Most commonly, light detection and ranging (LiDAR) is used for their generation. The trade-off for the high geometric detail of these data is the low spatial coverage, compa- rably high costs and low actualization rates. Spaceborne stereo data from Cartosat-1 are able to cover large areas on the one hand, but hold a lower geometric resolution on the other hand. In this paper, we investigate to which extent the geometric shortcom- ings of Cartosat-1 can be overcome integrating building footprints from topographic maps for the derivation of large-area building models. Therefore, we describe the methodology to derive digital surface models (DSMs) from Cartosat-1 data and the derivation of building footprints from topographic maps at 1:25 000 (DTK- 25). Both data are fused to generate building block models for four metropolitan regions in Germany with an area of ∼ 16 000 km 2 . Building block models are further aggregated to 1 × 1 km grid cells and volume densities are computed. Volume densities are classified to various levels of UMCs. Performance evaluation of the building block models reveals that the building footprints are larger in the DTK-25, and building heights are lower with a mean absolute error of 3.21 m. Both factors influence the building vol- ume, which is linearly lower than the reference. However, this error does not affect the classification of UMC, which can be clas- sified with accuracies between 77% and 97%.

  • quality assessment of tandem x raw dems oriented to a fusion with Cartosat 1 dems
    2013
    Co-Authors: Cristian Rossi, Pablo Dangelo, Michael Eineder, Thomas Fritz, Peter Reinartz
    Abstract:

    This paper addresses a quality assessment of TanDEM-X standard Raw DEMs, with a resolution of 12 meters, for three different terrain configurations: urban areas, moderate topography and complex topography. The analysis is performed in the geospatial and in the spectral domain. Beside TanDEM-X, the same analysis is also carried out for Cartosat-1 and LiDAR DEMs. The latter one is used as a reference. Nevertheless, the focus is centered on TanDEM-X, whose geometric limitations and their impacts on the DEM are analyzed here in detail. How the DEM appears in layover, shadow and phase unwrapping error areas is one of the objectives of the paper. The chosen test site is around Terrassa/Barcelona (Spain), offering all kinds of terrain variations. The final scope of the analysis is to learn about the potentials and the limitations of the two systems, radar (TanDEM-X) and optical (Cartosat-1), in a way to optimally fuse them and to create an enhanced DEM. A simple fusion processing chain, based on a weighted average depending on the quality of the DEMs adapted to the local geometry, is tested. First results show that in urban and complex terrain areas the improvements are limited, mainly due to the previously analyzed geometrical issues, whereas in moderate terrain areas the enhancement is significant, with a drop in the RMSE of about 25% for TanDEM-X and 30% for Cartosat-1.

  • assessment of Cartosat 1 and worldview 2 stereo imagery in combination with a lidar dtm for timber volume estimation in a highly structured forest in germany
    Forestry, 2013
    Co-Authors: Christoph Straub, Jiaojiao Tian, Rudolf Seitz, Peter Reinartz
    Abstract:

    Stereo satellites provide height information of the earth’s surface with increasing accuracy. High temporal resolution and wide regional coverage are the great advantages of satellites compared with aerial surveys. There is currently little experience of how accurate forest attributes can be modelled using high-resolution stereo satellite data, especially for highly structured forests in Central Europe. Thus, the potential of Cartosat-1 and WorldView-2 was assessed for timber volume estimation in a complex forest in Germany. Digital surface models were generated using Semi-Global Matching. Canopy height models (CHMs) were computed by subtracting a Light detection and ranging (LiDAR) terrain model. The CHMs were co-registered with field plots of a forest inventory. Explanatory variables were derived from the CHMs for timber volume estimation using regressions. Accuracies were evaluated at plot and stand levels. Results were compared with estimations based on a LiDAR-CHM. At plot level the following root mean squared errors (RMSEs) for timber volume estimation were obtained: 50.26 per cent for Cartosat-1, 44.40 per cent for WorldView-2 and 38.02 per cent for LiDAR. The RMSEs were smaller than the standard deviation of the observed timber volume. The RMSEs at a stand level yielded 21.49 per cent for Cartosat-1, 19.59 per cent for WorldView-2 and 17.14 per cent for LiDAR. The study demonstrates the potential of satellite stereo images for regionalization of sample plot inventories.

  • region based automatic building and forest change detection on Cartosat 1 stereo imagery
    Isprs Journal of Photogrammetry and Remote Sensing, 2013
    Co-Authors: Jiaojiao Tian, Pablo Dangelo, Peter Reinartz, Manfred Ehlers
    Abstract:

    Abstract In this paper a novel region-based method is proposed for change detection using space borne panchromatic Cartosat-1 stereo imagery. In the first step, Digital Surface Models (DSMs) from two dates are generated by semi-global matching. The geometric lateral resolution of the DSMs is 5 m × 5 m and the height accuracy is in the range of approximately 3 m (RMSE). In the second step, mean-shift segmentation is applied on the orthorectified images of two dates to obtain initial regions. A region intersection following a merging strategy is proposed to get minimum change regions and multi-level change vectors are extracted for these regions. Finally change detection is achieved by combining these features with weighted change vector analysis. The result evaluations demonstrate that the applied DSM generation method is well suited for Cartosat-1 imagery, and the extracted height values can largely improve the change detection accuracy, moreover it is shown that the proposed change detection method can be used robustly for both forest and industrial areas.

  • region based forest change detection from Cartosat 1 stereo imagery
    ISPRS - International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, 2012
    Co-Authors: Jiaojiao Tian, Jens Leitloff, Thomas Kraus, Peter Reinartz
    Abstract:

    Tree height is a fundamental parameter for describing the forest situation and changes. The latest development of automatic Digital Surface Model (DSM) generation techniques allows new approaches of forest change detection from satellite stereo imagery. This paper shows how DSMs can support the change detection in forest area. A novel region based forest change detection method is proposed using single-channel Cartosat-1 stereo imagery. In the first step, DSMs from two dates are generated based on automatic matching technology. After co-registration and normalising by using LiDAR data, the mean-shift segmentation is applied to the original pan images, and the images of both dates are classified to forest and non-forest areas by analysing their histograms and height differences. In the second step, a rough forest change detection map is generated based on the comparison of the two forest map. Then the GLCM texture from the nDSM and the Cartosat-1 images of the resulting regions are analyzed and compared, the real changes are extracted by SVM based classification.

Xiao Xiang Zhu - One of the best experts on this subject based on the ideXlab platform.

  • fusion of tandem x and Cartosat 1 elevation data supported by neural network predicted weight maps
    Isprs Journal of Photogrammetry and Remote Sensing, 2018
    Co-Authors: Hossein Bagheri, Michael Schmitt, Xiao Xiang Zhu
    Abstract:

    Abstract Recently, the bistatic SAR interferometry mission TanDEM-X provided a global terrain map with unprecedented accuracy. However, visual inspection and empirical assessment of TanDEM-X elevation data against high-resolution ground truth illustrates that the quality of the DEM decreases in urban areas because of SAR-inherent imaging properties. One possible solution for an enhancement of the TanDEM-X DEM quality is to fuse it with other elevation data derived from high-resolution optical stereoscopic imagery, such as that provided by the Cartosat-1 mission. This is usually done by Weighted Averaging (WA) of previously aligned DEM cells. The main contribution of this paper is to develop a method to efficiently predict weight maps in order to achieve optimized fusion results. The prediction is modeled using a fully connected Artificial Neural Network (ANN). The idea of this ANN is to extract suitable features from DEMs that relate to height residuals in training areas and then to automatically learn the pattern of the relationship between height errors and features. The results show the DEM fusion based on the ANN-predicted weights improves the qualities of the study DEMs. Apart from increasing the absolute accuracy of Cartosat-1 DEM by DEM fusion, the relative accuracy (respective to reference LiDAR data) of DEMs is improved by up to 50% in urban areas and 22% in non-urban areas while the improvement by the HEM-based method does not exceed 20% and 10% in urban and non-urban areas respectively.

  • fusion of tandem x and Cartosat 1 dems using tv norm regularization and ann predicted weights
    International Geoscience and Remote Sensing Symposium, 2017
    Co-Authors: Hossein Bagheri, Michael Schmitt, Xiao Xiang Zhu
    Abstract:

    This paper deals with TanDEM-X and Cartosat-1 DEM fusion over urban areas with support of weight maps predicted by an artificial neural network (ANN). Although the TanDEM-X DEM is a global elevation dataset of unprecedented accuracy (following HRTI-3 standard), its quality decreases over urban areas because of artifacts intrinsic to the SAR imaging geometry. DEM fusion techniques can be used to improve the TanDEM-X DEM in problematic areas. In this investigation, Cartosat-1 elevation data were fused with the TanDEM-X DEM by weighted averaging and total variation (TV)-based regularization, resorting to weight maps derived by a specifically trained ANN. The results show that the proposed fusion strategy can significantly improve the final DEM quality.

  • uncertainty assessment and weight map generation for efficient fusion of tandem x and Cartosat 1 dems
    ISPRS - International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, 2017
    Co-Authors: Hossein Bagheri, Michael Schmitt, Xiao Xiang Zhu
    Abstract:

    Abstract. Recently, with InSAR data provided by the German TanDEM-X mission, a new global, high-resolution Digital Elevation Model (DEM) has been produced by the German Aerospace Center (DLR) with unprecedented height accuracy. However, due to SAR-inherent sensor specifics, its quality decreases over urban areas, making additional improvement necessary. On the other hand, DEMs derived from optical remote sensing imagery, such as Cartosat-1 data, have an apparently greater resolution in urban areas, making their fusion with TanDEM-X elevation data a promising perspective. The objective of this paper is two-fold: First, the height accuracies of TanDEM-X and Cartosat-1 elevation data over different land types are empirically evaluated in order to analyze the potential of TanDEM-XCartosat- 1 DEM data fusion. After the quality assessment, urban DEM fusion using weighted averaging is investigated. In this experiment, both weight maps derived from the height error maps delivered with the DEM data, as well as more sophisticated weight maps predicted by a procedure based on artificial neural networks (ANNs) are compared. The ANN framework employs several features that can describe the height residual performance to predict the weights used in the subsequent fusion step. The results demonstrate that especially the ANN-based framework is able to improve the quality of the final DEM through data fusion.

Pablo Dangelo - One of the best experts on this subject based on the ideXlab platform.

  • investigating the applicability of Cartosat 1 dems and topographic maps to localize large area urban mass concentrations
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014
    Co-Authors: Michael Wurm, Pablo Dangelo, Peter Reinartz, Hannes Taubenbock
    Abstract:

    Building models are a valuable information source for urban studies and in particular for analyses of urban mass con- centrations (UMCS). Most commonly, light detection and ranging (LiDAR) is used for their generation. The trade-off for the high geometric detail of these data is the low spatial coverage, compa- rably high costs and low actualization rates. Spaceborne stereo data from Cartosat-1 are able to cover large areas on the one hand, but hold a lower geometric resolution on the other hand. In this paper, we investigate to which extent the geometric shortcom- ings of Cartosat-1 can be overcome integrating building footprints from topographic maps for the derivation of large-area building models. Therefore, we describe the methodology to derive digital surface models (DSMs) from Cartosat-1 data and the derivation of building footprints from topographic maps at 1:25 000 (DTK- 25). Both data are fused to generate building block models for four metropolitan regions in Germany with an area of ∼ 16 000 km 2 . Building block models are further aggregated to 1 × 1 km grid cells and volume densities are computed. Volume densities are classified to various levels of UMCs. Performance evaluation of the building block models reveals that the building footprints are larger in the DTK-25, and building heights are lower with a mean absolute error of 3.21 m. Both factors influence the building vol- ume, which is linearly lower than the reference. However, this error does not affect the classification of UMC, which can be clas- sified with accuracies between 77% and 97%.

  • quality assessment of tandem x raw dems oriented to a fusion with Cartosat 1 dems
    2013
    Co-Authors: Cristian Rossi, Pablo Dangelo, Michael Eineder, Thomas Fritz, Peter Reinartz
    Abstract:

    This paper addresses a quality assessment of TanDEM-X standard Raw DEMs, with a resolution of 12 meters, for three different terrain configurations: urban areas, moderate topography and complex topography. The analysis is performed in the geospatial and in the spectral domain. Beside TanDEM-X, the same analysis is also carried out for Cartosat-1 and LiDAR DEMs. The latter one is used as a reference. Nevertheless, the focus is centered on TanDEM-X, whose geometric limitations and their impacts on the DEM are analyzed here in detail. How the DEM appears in layover, shadow and phase unwrapping error areas is one of the objectives of the paper. The chosen test site is around Terrassa/Barcelona (Spain), offering all kinds of terrain variations. The final scope of the analysis is to learn about the potentials and the limitations of the two systems, radar (TanDEM-X) and optical (Cartosat-1), in a way to optimally fuse them and to create an enhanced DEM. A simple fusion processing chain, based on a weighted average depending on the quality of the DEMs adapted to the local geometry, is tested. First results show that in urban and complex terrain areas the improvements are limited, mainly due to the previously analyzed geometrical issues, whereas in moderate terrain areas the enhancement is significant, with a drop in the RMSE of about 25% for TanDEM-X and 30% for Cartosat-1.

  • region based automatic building and forest change detection on Cartosat 1 stereo imagery
    Isprs Journal of Photogrammetry and Remote Sensing, 2013
    Co-Authors: Jiaojiao Tian, Pablo Dangelo, Peter Reinartz, Manfred Ehlers
    Abstract:

    Abstract In this paper a novel region-based method is proposed for change detection using space borne panchromatic Cartosat-1 stereo imagery. In the first step, Digital Surface Models (DSMs) from two dates are generated by semi-global matching. The geometric lateral resolution of the DSMs is 5 m × 5 m and the height accuracy is in the range of approximately 3 m (RMSE). In the second step, mean-shift segmentation is applied on the orthorectified images of two dates to obtain initial regions. A region intersection following a merging strategy is proposed to get minimum change regions and multi-level change vectors are extracted for these regions. Finally change detection is achieved by combining these features with weighted change vector analysis. The result evaluations demonstrate that the applied DSM generation method is well suited for Cartosat-1 imagery, and the extracted height values can largely improve the change detection accuracy, moreover it is shown that the proposed change detection method can be used robustly for both forest and industrial areas.

  • iterative approach for efficient digital terrain model production from Cartosat 1 stereo images
    Journal of Applied Remote Sensing, 2011
    Co-Authors: Hossein Arefi, Pablo Dangelo, Helmut Mayer, Peter Reinartz
    Abstract:

    This paper proposes a new algorithm for automatic digital terrain model (DTM) generation from high resolution Cartosat-1 satellite images. It consists of two major steps: generation of digital surface models (DSM) from stereo scenes and hierarchical image filtering for DTM generation. Automatic georeferencing, dense stereo matching, and interpolation into a regular grid yields a DSM. In the second step, the DSM pixels are classified into ground and nonground regions using an algorithm motivated from gray-scale image reconstruction to suppress unwanted elevated pixels. Nonground regions, i.e., 3D objects as well as outliers are iteratively separated from the ground regions. The generated DTM is qualitatively and quantitatively evaluated. Height profiles and comparisons between the generated DSM, derived DTM, and ground truth data are presented. The evaluation indicates that almost all nonground objects regardless of their size are eliminated and appropriate results are archived in hilly as well as smooth residential areas. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).

  • automatic generation of high quality dsm based on irs p5 Cartosat 1 stereo data
    ESA Living Planet Symposium, 2010
    Co-Authors: Pablo Dangelo, Andreas Uttenthaler, Sebastian Carl, Frithjof Barner, Peter Reinartz
    Abstract:

    IRS-P5 Cartosat-1 high resolution stereo satellite imagery is well suited for the creation of digital surface models (DSM). A system for highly automated and operational DSM and orthoimage generation based on IRS-P5 Cartosat-1 imagery is presented, with an emphasis on automated processing and product quality. The proposed system processes IRS-P5 level-1 stereo scenes using the rational polynomial coefficients (RPC) universal sensor model. The described method uses an RPC correction based on DSM alignment instead of using reference images with a lower lateral accuracy, this results in improved geolocation of the DSMs and orthoimages. Following RPC correction, highly detailed DSMs with 5 m grid spacing are derived using Semiglobal Matching. The proposed method is part of an operational Cartosat-1 processor for the generation of a high resolution DSM. Evaluation of 18 scenes against independent ground truth measurements indicates a mean lateral error (CE90) of 6.7 meters and a mean vertical accuracy (LE90) of 5.1 meters.

Marco Gianinetto - One of the best experts on this subject based on the ideXlab platform.

  • evaluation of Cartosat 1 multi scale digital surface modelling over france
    Sensors, 2009
    Co-Authors: Marco Gianinetto
    Abstract:

    On 5 May 2005, the Indian Space Research Organization launched Cartosat-1, the eleventh satellite of its constellation, dedicated to the stereo viewing of the Earth's surface for terrain modeling and large-scale mapping, from the Satish Dhawan Space Centre (India). In early 2006, the Indian Space Research Organization started the Cartosat-1 Scientific Assessment Programme, jointly established with the International Society for Photogrammetry and Remote Sensing. Within this framework, this study evaluated the capabilities of digital surface modeling from Cartosat-1 stereo data for the French test sites of Mausanne les Alpilles and Salon de Provence. The investigation pointed out that for hilly territories it is possible to produce high-resolution digital surface models with a root mean square error less than 7.1 m and a linear error at 90% confidence level less than 9.5 m. The accuracy of the generated digital surface models also fulfilled the requirements of the French Reference 3D®, so Cartosat-1 data may be used to produce or update such kinds of products.

  • automatic digital terrain model generation using Cartosat 1 stereo images
    Sensor Review, 2008
    Co-Authors: Marco Gianinetto
    Abstract:

    Purpose – Cartosat‐1 is the first Indian Remote Sensing satellite, developed for topographic mapping, able to collect in‐track high‐resolution stereo images with a 2.5 m pixel size. In the framework of the Cartosat‐1 Scientific Assessment Programme (C‐SAP), the Politecnico di Milano University (Italy) evaluated the performances of the Cartosat‐1 satellite in the generation of digital terrain models (DTMs) from stereo‐couples. The purpose of this paper is to describe in detail the outcomes for the Salon de Provence (France) test site, with respect to existing standards and products actually used in France and also to provide a comparison with the global Shuttle Radar Topography Mission's DTM freely available from by NASA.Design/methodology/approach – The Cartosat‐1 data processing was done using the commercial off‐the‐shelf software ENVI®, selected for investigating the capabilities and limits of the system using standard image processing tools, so from the point of view of a typical remote sensing user. T...

  • multi scale digital terrain model generation using Cartosat 1 stereo images for the mausanne les alpilles test site
    The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, 2008
    Co-Authors: Marco Gianinetto
    Abstract:

    Cartosat-1 is the first Indian Remote Sensing satellite able to collect in-track high resolution stereo images with a 2.5m pixel size. Since Cartosat-1 has no multispectral cameras, it was mainly developed for topographic mapping and Digital Terrain Model (DTM) generation. In the framework of the Cartosat-1 Scientific Assessment Programme, the Politecnico di Milano University (Italy) evaluated as Co-Investigator the performances of the Cartosat-1 satellite in the generation of DTMs from stereo-couples. This paper describes in detail the outcomes for the Mausanne les Alpilles (France) test site, with respect to existing standards and products actually used in France and also provides a comparison with the global Shuttle Radar Topography Mission’s DTM supplied by NASA and widely used in the remote sensing community.

Tapas R. Martha - One of the best experts on this subject based on the ideXlab platform.

  • debris volume estimation and monitoring of phuktal river landslide dammed lake in the zanskar himalayas india using Cartosat 2 images
    Landslides, 2017
    Co-Authors: Tapas R. Martha, Vinod K Kumar, S. Muralikrishnan, Shashivardhan P Reddy, C M Bhatt, Babu Govindha K Raj, J Nalini, Anantha E Padmanabha, B Narender, Srinivasa G Rao
    Abstract:

    On 31 December 2014, a large landslide in the Zanskar Himalayas, India created a 69-m high natural dam on the Phuktal river resulting in formation of a 17.2 km long impounded lake with 150 m width near the blockade. In this study, very high-resolution Cartosat-2 (1 m) multi-temporal images were used for geomorphic investigation, debris volume estimation and monitoring of the Phuktal river landslide-dammed lake. Geomorphic analysis carried out using Cartosat-2 images shows that the Phuktal river landslide occurred in two phases in quick succession. Rapid estimation of the volume of landslide debris was done indirectly by surface volume method using a reconstructed geometry of the debris area mapped (90,422 m2) from the Cartosat-2 image acquired on 20 January 2015. Subsequently, direct volume by fill volume method was estimated from a post-landslide digital elevation model (DEM) created by photogrammetric techniques with availability of more Cartosat-2 images (25 January 2015 and 21 February 2015) from adjacent orbits. In both the methods, a pre-existing 10-m DEM created from Cartosat-1 (2.5 m) stereoscopic images of 17 October 2010 was used as baseline elevation data. The debris volumes estimated by indirect and direct methods are 2,624,873 and 2,433,173 m3 respectively. The difference in volume is attributed to reduction of debris surface area to 78,202 m2 due to the spread of water onto the landslide dam on 21 February 2015. Indirect volume estimated by constraining the debris surface area to 21 February 2015 was found to be 2,530,125 m3, which is a deviation of 4 % with respect to volume estimated by direct method. This indicates that landslide volume can be estimated fairly accurately in gorges in the absence of a post-landslide DEM. Results of the study were useful for supporting disaster management activities until landslide dam breached on 07 May 2015.

  • object oriented analysis of multi temporal panchromatic images for creation of historical landslide inventories
    Isprs Journal of Photogrammetry and Remote Sensing, 2012
    Co-Authors: Tapas R. Martha, Norman Kerle, Cees J. Van Westen, V G Jetten, K V Kumar
    Abstract:

    Object-oriented analysis (OOA) has been demonstrated to produce more accurate results than pixel-based image processing. Studies carried out by previous researchers have shown how landslide inventories can be prepared from multispectral satellite images using OOA. However, panchromatic images are frequently the only data available after a landslide event. Furthermore, preparation of historical inventories relies on the analysis of satellite images and aerial photographs acquired over past few decades that are also mostly only available in black and white. In such cases the methodology developed using multispectral data cannot be used directly due to limited spectral information, in particular in near-infrared bands. In this paper we present a new methodology that addresses some of these issues. Using high resolution panchromatic images from Cartosat-1 (2.5 m) and IRS-1D (5.8 m), and a 10 m gridded DTM extracted from Cartosat-1, we developed a new approach which uses change detection techniques and a global contextual criteria in an object-based environment to detect and classify landslides into five different types. Continuous time series images from 1998 to 2006 were used to prepare annual landslide inventories in a highly rugged Himalayan terrain. The maximum and minimum detection percentages achieved for all landslides are 96.7% and 71.5%, respectively, with corresponding quality percentages of 88.1% and 55.3%, respectively. However, the lack of spectral information proved to be a hurdle resulting in a high branching factor that indicates that further work is required to eliminate false positives. Nevertheless, the method was able to create much needed historical landslide inventories, which are critical for landslide hazard and risk assessment studies.

  • Effect of sun elevation angle on DSMs derived from Cartosat - 1 data
    Photogrammetric Engineering & Remote Sensing, 2010
    Co-Authors: Tapas R. Martha, Norman Kerle, Cees J. Van Westen, Victor Jetten, K. Vinod Kumar
    Abstract:

    Along-track stereoscopic satellite data are increasingly used for automatic extraction of digital surface models (DSM) due to the reduced radiometric variation between the images. Problems remain with the quality of such DSMs, especially in steep terrain. This paper explores the accuracy of DSMs extracted from Cartosat-1 data acquired under high and low sun elevation angle conditions in High Himalayan terrain. The metric accuracy of the DSM was estimated by comparing it with check points obtained with a differential GPS . Additionally, we used spatial discrepancy of drainage lines to estimate errors in the DSM due to spatial auto- correlation. For valleys perpendicular to the satellite track, the DSM extracted from a low sun elevation angle data showed 45 percent higher spatial accuracy than the DSM extracted from high sun elevation angle data. The results indicate that the sun elevation angle and valley orientation affect the spatial accuracy of the DSM, though metric accuracy remains comparable.

  • landslide volumetric analysis using Cartosat 1 derived dems
    IEEE Geoscience and Remote Sensing Letters, 2010
    Co-Authors: Tapas R. Martha, Norman Kerle, Cees J. Van Westen, V G Jetten, K V Kumar
    Abstract:

    The monitoring of landscape changes can lead to the identification of environmental hot spots, improve process understanding, and provide means for law enforcement. Digital elevation models (DEMs) derived from stereoscopic satellite data provide a systematic synoptic framework that is potentially useful to support these issues. Along-track high-resolution stereoscopic data, provided with rational polynomial coefficients (RPCs), are ideal for the fast and accurate extraction of DEMs due to the reduced radiometric differences between images. In this letter, we assess the suitability of data from the relatively new Cartosat-1 satellite to quantify large-scale geomorphological changes, using the volume estimation of the 2007 Salna landslide in the Indian Himalayas as a test case. The depletion and accumulation volumes, estimated as 0.55 × 106 and 1.43 × 106 m3, respectively, showed a good match with the volumes calculated using DEMs generated only with RPCs and without ground control points (GCPs), indicating that the volume figures are less sensitive to GCP support. The result showed that these data can provide an important input for disaster-management activities.

  • mapping damage in the jammu and kashmir caused by 8 october 2005 mw 7 3 earthquake from the Cartosat 1 and resourcesat 1 imagery
    International Journal of Remote Sensing, 2006
    Co-Authors: Vinod K Kumar, Tapas R. Martha, Priyom Roy
    Abstract:

    A massive earthquake of Mw = 7.3 struck the western Himalaya on 8 October 2005 at 03:50:40 (UTC) causing widespread damage to property and lives. This earthquake is the result of thrusting of the Indian plate under the Eurasian plate. It is one of the deadliest earthquakes in South Asia in recent times. The recently launched Indian remote sensing satellite Cartosat–1, providing 2.5 m panchromatic along‐track stereoscopic data have been analysed for damage assessment along with Resourcesat–1 multispectral data for understanding the regional tectonics. In this study, nearly 25% of the buildings are identified as fully collapsed in Uri and Punch region of the Jammu and Kashmir, India. Other damage such as bridge collapse, road blockage owing to landslides etc. is also identified from the satellite data. The coseismic landslides show clear spatial association with the pre‐existing faults such as the Jhelum Fault and Main Boundary Thrust (MBT). A new trend in the alignment of landslides is found, which indicat...