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International audience; Current research presents study of the hydrological characteristics in the surroundings of the Laguna de Gallocanta (Spain) using remote sensing methods and combination of two GIS: ArcGIS and Idrisi GIS. The methodology includes following workflow: 1) selection of the multi-temporal Landsat images and creating color composites; 2) Masking and overlaying study area out of the whole image; 3) Calculation of the direction and length of stream flow in the lake basin; 4) assessment of possible risks of floods and stability of coastal slopes. The research has aim of hydrological assessment testing GIS tools for environmental monitoring. The images are presented by Landsat ETM+, MSS and TM scenes acquired on years 1976, 1987, 1992 and 2000, respectively, with a cloud cover of 0%. The images were georeferenced in ArcGIS and rectified to the Grid Coordinate System using available vector layers. The georeferenced rectified image was then re-projected to the UTM projection WGS84 datum. The combinations of various bands (channels 2-1-4, 5-4-7, and 2-4-7) were tested and visualized. Second step included processing DEM (Digital Elevation Model) Data acquired by the SRTM providing high quality elevation data. The pre-processing included mosaicing images and cutting out study area. The hydrologically conditioning the DEM was performed: the elevations are modified in order to model flow directions. Modelling flow direction and flow accumulation was performed on the conditioned DEM using ArcGIS. The next step included extracting hydrologic features such as stream directions, length and basin extents from a DEM. The pit removal tool was used to remove error pits from a DEM. Final step included calculations of the flow direction and flow length that demonstrate the direction of flow out of each cell. Finally, using Basin function all cells belonging to the watersheds of one or more target cells were calculated. The results shown that area has sensitive hydrological settings which explains its prone to the environmental change. The research demonstrated successful use of GIS for the purposes of hydrological modeling in the environmental studies.

Current poster presents remote sensing and GIS workflow applied for environmental monitoring of the Páramo National Park. Páramo National Park, located in Ecuador, is known for its unique natural resources in high altitude grasslands. The ecosystems of Páramo consist mostly of rare species and are the key protected area for exceptionally high endemism. ENVI software enables to make an analysis of the area and to produce a map based on 2 criteria: vegetation amount and altitude. We need to show vegetation growing on different heights and to create 3D-Visualization of our analysis. The workflow consists in following 9 steps: 1) displaying the Landsat TM satellite image. 2) Enhancing the Contrast of the Image (Enhance-Gaussian). 3) Uploading the SRTM-Data. 4) 3 D-Visualising the Landscape. 5) Calculating the Greenness Index. 6) Creating the Vegetation Layer ROI – Region of Interest. 7) Creating the Altitude Layer. 8) Mapping and Design. 9) 3D-Mapping (Final Map + DEM).

Digital Elevation Models (DEMs) are a central information source for scene analysis, including specific tasks such as building localization and reconstruction. Whatever application is envisaged, DEM segmentation is a critical step, due to the great variability of landscapes and above-ground structures in urban areas. Moreover, a DEM may contain erroneous isolated 3D points which have to be identified before any interpretation process can start. Designing an automatic DEM segmentation method that is successful under all circumstances can hardly be envisaged. To facilitate the segmentation process, a user-friendly, interactive software environment, called ReconLab, has been developed. Its 3D viewing and editing capabilities allow to easily detect and remove erroneous 3D points from the initial data, to efficiently smooth the DEM and perform the segmentation in real time. ReconLab’s usefulness for urban scene interpretation is demonstrated by applying it to the estimation of a Digital Terrain Model (DTM) from the DEM. In particular, Recon-Lab is used to perform a fast, semi-automatic segmentation of the DEM and to provide a significant and representative sample region consisting of ground points. These points are then used to initialize a parametric model for the terrain, which is iteratively refined by a robust algorithm. The preprocessing by ReconLab reduces computation time by a factor 3, without loss of accuracy, as is demonstrated by experiments on synthetic data and on real world DEMs obtained by airborne laser altimetry as well as by stereo correspondence from imagery. ispartof: pages:134-139 ispartof: Proceedings of the ISPRS Congress 2004 pages:134-139 ispartof: ISPRS Congress 2004 location:Istanbul, Turkey date:12 Jul - 17 Jul 2004 status: published