This work proposes a new method to classify multi-spectral satellite images based on multivariate adaptive regression splines (MARS) and compares this classification system with the more common parallelepiped and maximum likelihood (ML) methods. We apply the classification methods to the land cover classification of a test zone located in southwestern Spain. The basis of the MARS method and its associated procedures are explained in detail, and the area under the ROC curve (AUC) is compared for the three methods. The results show that the MARS method provides better results than the parallelepiped method in all cases, and it provides better results than the maximum likelihood method in 13 cases out of 17. These results demonstrate that the MARS method can be used in isolation or in combination with other methods to improve the accuracy of soil cover classification. The improvement is statistically significant according to the Wilcoxon signed rank test.

This paper presents a complete analysis of the positional errors of terrestrial laser scanning (TLS) data based on spherical statistics and 3D graphs. Spherical statistics are preferred because of the 3D vectorial nature of the spatial error. Error vectors have three metric elements (one module and two angles) that were analyzed by spherical statistics. A study case has been presented and discussed in detail. Errors were calculating using 53 check points (CP) and CP coordinates were measured by a digitizer with submillimetre accuracy. The positional accuracy was analyzed by both the conventional method (modular errors analysis) and the proposed method (angular errors analysis) by 3D graphics and numerical spherical statistics. Two packages in R programming language were performed to obtain graphics automatically. The results indicated that the proposed method is advantageous as it offers a more complete analysis of the positional accuracy, such as angular error component, uniformity of the vector distribution, error isotropy, and error, in addition the modular error component by linear statistics.

Studies the accuracy and reliability of digital elevation models (DEMs) generated from two different satellite sources, the Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Systeme Pour l'Observation de la Terre (SPOT) High Resolution Visible (HRV) stereoscopic images, using three different photogrammetric softwares. The main reason for the study is the heterogeneity and absence of agreement found in previous research concerning several significant aspects of DEM generation methods. A set of 91 DEMs were generated from SPOT data and 55 DEMs from ASTER data. Error control was performed with 315 check points determined by differential global positioning systems. Results of Terra ASTER DEMs show that elevation RMSE (root mean square error) equals 13.0 m. The corresponding RMSE value for SPOT HRV DEM is 7.3 m. In both cases, the error is less than the pixel size. Furthermore, this communication proposes a technique to improve DEM structure, based on an objective criterion to cleanse redundancy in DEMs without a significant loss of accuracy. This criterion is based on removing all points with a correlation value below a threshold value.

The main objective of the research consists of quantifying the degree of sustainability of the city of Cáceres in terms of the inhabitant’s accessibility to public services through the use of GIS tools and urban indicators, taking into account two areas of study: The Historic Centre (PCH) and the city as a whole. The methodology applied is based on the criteria proposed by the Spanish Government derived from the Spanish Strategy for Urban and Local Sustainability (EESUL), which suggests suitable indicators for analysing urban environments. The degree of sustainability of the study areas, applied to the field of mobility and accessibility to public services, is evaluated through numerical calculations complementing the study with accessibility maps obtained using Geographic Information Systems (GIS) tools. The results show that the city of Cáceres is sustainable in terms of accessibility to bus stops, organic waste containers, household waste recycling centre, schools and education, health centres, and public administration. However, bike parking coverage and lanes, clothes and oil collection, and sports centres need to be further enhanced. In conclusion, there is little disparity in the results between the PCH and the city as a whole, not influenced by the fact that one of the areas is a consolidated historic area. This research has allowed some gaps in the topic to be addressed. However, the main limitation of this methodology consists in the need to have a considerable amount of initial starting data to be able to carry out the research. Finally, the sustainability analysis using urban indicators is considered a valuable source of information for the local manager, becoming a real planning tool in medium-sized cities.

This paper introduces a new method to analyze the positional accuracy of georeferenced satellite images without the use of ground control points. Compared to the traditional method used to carry out this kind of analysis, our approach provides a semiautomatic way to obtain a larger number of control points that satisfy the requirements of current standards regarding the size of the set of sample points, the positional accuracy of such points, the distance between points, and the distribution of points in the sample. Our methodology exploits high quality orthoimages, such as those provided by the Aerial Orthography National Plan (PNOA)—developed by the Spanish National Geographic Institute—and has been tested on spatial data from Landsat 8. Our method works under the current international standard (ASPRS 2014) and exhibits similar performance than other well-known methods to analyze the positional accuracy of georeferenced images based on the use of independent ground control points. More specifically, the positional accuracy achieved for a Landsat 8 dataset evaluated by the traditional method is 5.22 ± 1.95 m, and when evaluated with the proposed method, it exhibits a typical accuracy of 5.76 ± 0.50 m. Our experimental results confirm that the method is equally effective and less expensive than other available methods to analyze the positional accuracy of satellite images.