Ismael Fernández

Geopositioning Accuracy Assessment of GeoEye-1 Panchromatic and Multispectral Imagery

Currently GeoEye-1 is the World’s highest resolution commercial satellite. This paper analyses the attainable geopositioning accuracy from a single GeoEye-1 Geo image, both through the sensor orientation and orthorectification phases, for panchromatic ( PAN ) and multispectral ( MS ) products. Different 3D sensor models as well as the number and distribution of the ground control points ( GCP s) used for the sensor orientation were tested. Planimetric Root Mean Square Errors ( RMSE 2D ) close to 0.7 pixels, both for PAN and MS images, were attained using the third order 3D rational functions with the vendor’s rational polynomial coefficients data afterwards being refined by a zero order polynomial adjustment ( RPC 0). Furthermore, the RPC 0 sensor model proved to be significantly independent regarding the number and distribution of the GCP s. The RPC 0 model yielded RMSE 2D close to 0.46 m and 1.56 m for the PAN and MS orthorectified images, respectively, using a very accurate lidar-derived digital elevation model.

Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery

Remote sensing technologies have been commonly used to perform greenhouse detection and mapping. In this research, stereo pairs acquired by very high-resolution optical satellites GeoEye-1 (GE1) and WorldView-2 (WV2) have been utilized to carry out the land cover classification of an agricultural area through an object-based image analysis approach, paying special attention to greenhouses extraction. The main novelty of this work lies in the joint use of single-source stereo-photogrammetrically derived heights and multispectral information from both panchromatic and pan-sharpened orthoimages. The main features tested in this research can be grouped into different categories, such as basic spectral information, elevation data (normalized digital surface model; nDSM), band indexes and ratios, texture and shape geometry. Furthermore, spectral information was based on both single orthoimages and multiangle orthoimages. The overall accuracy attained by applying nearest neighbor and support vector machine classifiers to the four multispectral bands of GE1 were very similar to those computed from WV2, for either four or eight multispectral bands. Height data, in the form of nDSM, were the most important feature for greenhouse classification. The best overall accuracy values were close to 90%, and they were not improved by using multiangle orthoimages.

Influence of Data Source and Training Size on Impervious Surface Areas Classification Using VHR Satellite and Aerial Imagery Through an Object-Based Approach

Two very-high-resolution (VHR) satellite images from the GeoEye-1 and WorldView-2 sensors have been used in order to extract impervious surface areas (ISAs) over a Mediterranean coastal area of Almeria (Spain) through an object-based image analysis (OBIA). Different feature sets (basic multispectral information, relative spectral indices, and texture indices based on local variance) were used to feed a support vector machine (SVM) classifier in order to determine the most suitable information for ISAs classification. The classification results coming from both satellite images were compared to each other and also against those provided by a previous similar work carried out on an archival orthoimage. An estimation of the most appropriate number of training samples was performed for each data source by a sampling size reduction procedure. The accuracy assessment of the classification results showed that texture based on local variance was a valuable feature to improve ISA classification accuracy. When texture based on variance was included, the classification accuracy results provided by the archival orthoimage experiment (overall accuracy: 88.1% and KHAT: 0.760) were similar to those obtained from the VHR-satellite images (overall accuracy: 90.4% and 89.7%, KHAT: 0.806 and 0.792 for GeoEye-1 and WorldView-2, respectively). Finally, the influence of the data source and training size on ISA classification accuracy was also proved.

A New Two-Step Robust Surface Matching Approach for Three-Dimensional Georeferencing of Historical Digital Elevation Models

In this letter, a new approach based on a two-step (coarse and fine) automatic surface matching for registering two overlapping multidate digital elevation models (DEMs) is proposed to avoid the costly and time-consuming ground-control-point acquisition. The proposed methodology was tested to georeference a historical grid DEM obtained from a photogrammetric flight taken in 1977 and located at a heavily developed coastal area of Almería (southeast Spain). The reference DEM consisted of a newer DEM produced by the Andalusia Regional Government from a photogrammetric flight taken in 2001. The results obtained from this work may be deemed as very promising, showing high efficiency and accuracy for historical-DEM georeferencing. The vertical accuracy of the finally coregistered DEM was computed over a recent LiDAR-derived DEM (validation data set) which presented relatively unaltered areas, yielding a standard deviation that is fairly similar to the estimated uncertainty of the reference DEM.

Efficient methods to convert LiDAR-derived ellipsoid heights to orthometric heights

Abstract Nowadays cartographic products are usually obtained from data sources which provide large amount of data. LiDAR acquisition system is a good example of the great quantity of data obtained, such as points with spatial coordinates in a determined reference system. The height of these points is usually related to a global ellipsoid (e.g. WGS84), but the local vertical reference system, and so the corresponding orthometric heights, are usually measured from a local geoid which is adjusted for a country or region. Orthometric height determination can be performed for each point by knowing the undulation value which relates the ellipsoid to the geoid for each position. However, this operation may not be necessary for all points if we take into account the LiDAR specifications. Thus we can use a simplification which minimizes the processing time for this calculation. In this paper we present the results obtained by applying several simplifications to drastically shorten the number of point-to-point computations to obtain the orthometric height from the raw LiDAR point cloud data.

Assessing Shoreline Change Rates in Mediterranean Beaches

Shoreline change rate constitutes an essential parameter for coastal areas management and monitoring in order to map erosion/accretion areas and to forecast the future shoreline position. Here, shoreline rates were assessed in a heavily human influenced coastal sector of the Mediterranean coast located at Almeria province, Spain. In order to evaluate shoreline rate change assessment in Mediterranean beaches, a comparison was carried out between three methods applied throughout 2009 to 2011 period. In this sense, two kinds of sources were used in order to derive shoreline positions: (i) digitizing the high water line (HWL) through orthoimage interpretation and (ii) automatically extracting a contour level from a LiDAR-derived coastal elevation model (CEM). Shoreline extraction quality was studied by comparing HWL and two datum-based contours, one extrapolated up to 0 m and the other interpolated at 0.75 m above mean sea level (Spanish altimetric datum). It was found a significant bias between HWL and datum-based shoreline positions which had been qualified as negligible in other previous studies carried out in microtidal areas. Since HWL and 0.75 m contour-based shorelines showed a similar distribution, although presenting an added offset, and the 0 m contour was too noisy because of extrapolation errors, it was concluded that the 0.75 m contour-based shoreline was the most stable and accurate proxy datum for multitemporal shorelines comparison. Finally, a high variability of shoreline position could be tested when HWL was used as a proxy for shoreline, being HWL less accurate than CEM-derived shorelines except for the case of using poorly accurate photogrammetrically derived CEMs (e.g. based on very old aerial flights).

3D Coastal Monitoring from very dense UAV-Based Photogrammetric Point Clouds

In the present study, the potential use of unmanned aerial vehicles (UAVs) as a platform to flexibly obtain sequence of images along coastal areas from which producing high quality SfM-MVS based geospatial data is tested. A flight campaign was conducted over a coastal test site covering an area of around 4 has near Malaga (Spain). Images were taken on 1st December 2015 at a height above the ground ranging from 113.5 to 118 meters by using a Sony α6000® consumer camera mounted on a UFOCAM XXL v2® octocopter. 40 RTK-GPS surveyed ground points were evenly distributed over the whole working area. Furthermore, a very dense and accurate point cloud was collected by using a FARO Focus 3D X-130 terrestrial laser scanner (TLS). The photogrammetric block was computed by using two widely known SfM-MVS commercial software implementations such as Inpho UASMaster® and PhotoScan Professional®. PhotoScan provided a highly accurate bundle adjustment with errors of 1.5 cm, 1.5 cm and 6.1 cm along X, Y and Z axis respectively. The triangulation errors computed from UASMaster turned out to be slightly poorer along Z axis. In this sense, the very high resolution Surface Model built up from the corresponding photogrammetric point cloud depicted higher Z-differences with respect to the reference TLS derived surface model in the case of the UASMaster workflow. Summing up, the high degree of automation and efficient data acquisition provided by UAV-based digital photogrammetry makes this approach competitive and useful to be applied in high resolution 3D coastal mapping.