Javier Cardenal

Analysis of Landslide Evolution Affecting Olive Groves Using UAV and Photogrammetric Techniques

This paper deals with the application of Unmanned Aerial Vehicles (UAV) techniques and high resolution photogrammetry to study the evolution of a landslide affecting olive groves. The last decade has seen an extensive use of UAV, a technology in clear progression in many environmental applications like landslide research. The methodology starts with the execution of UAV flights to acquire very high resolution images, which are oriented and georeferenced by means of aerial triangulation, bundle block adjustment and Structure from Motion (SfM) techniques, using ground control points (GCPs) as well as points transferred between flights. After Digital Surface Models (DSMs) and orthophotographs were obtained, both differential models and displacements at DSM check points between campaigns were calculated. Vertical and horizontal displacements in the range of a few decimeters to several meters were respectively measured. Finally, as the landslide occurred in an olive grove which presents a regular pattern, a semi-automatic approach to identifying and determining horizontal displacements between olive tree centroids was also developed. In conclusion, the study shows that landslide monitoring can be carried out with the required accuracy—in the order of 0.10 to 0.15 m—by means of the combination of non-invasive techniques such as UAV, photogrammetry and geographic information system (GIS).

Methodology for Landslide Susceptibility and Hazard Mapping Using GIS and SDI

In this work a methodology for preparing landslides susceptibility and hazard maps is presented, based in a bivariate analysis between past movements and determinant factors. The methodology for determining the susceptibility is an adaptation of the matrix method to a GIS, and it has been tested and validated in different zones and environments of Andalusia (southern Spain). The text also discusses the availability of information layers in Spanish SDI to developing these susceptibility maps. For the hazard evaluation, we propose a methodology of determining the susceptibility in different return periods from inventories of landslides that show activity in these considered periods. The activity was estimated from stereoscopic and monoscopic analysis of aerial photographs from different dates, using geological and geomorphic criteria, and the study of rainfall time series. Since all, four periods were considered in a logarithmic scale of 10 years (approximate return period of rainfall generating instability in the area), 100, 1000 and 10000 years. After determining the susceptibility, it was transformed into annual hazard dividing by the number of years of the return period. Finally, a total hazard map was obtained by determining at each point the maximum value of hazard of the different periods and it is expressed in several intervals.

Using PCA transformation to remove the tenuous cloudiness effect in multispectral satellite sensor images

An algorithm using no external data is proposed for removing the inhomogeneous effect of thin cloudiness and other aerosols on multispectral satellite sensor images such as Landsat Enhanced Thematic Mapper (ETM) images. The method consists of a series of digital processing operations and is based on principal component analysis (PCA). The goal is to generate, for every original band, a new band whose digital number (DN) values are related only to the atmospheric intensity effect. An example is shown and some limitations are discussed.

A Simple Photogrammetric Method to Improve Quantitative Image Analysis in Geoscience Research

Sedimentary field surveying uses 2D outcrop images to rapidly and easily obtain quantitative information. Although photogrammetry is capable of a complete and accurate 3D analysis, this technique requires great technical expertise, as well as intricate and expensive software and hardware. Rectified digital images, instead, represent a cost-effective and easier method to obtain accurate 2D images for outcrop modeling and mapping. The present methodology describes a simple technique to improve quantitative image analysis for outcrop analysis, based on widespread conventional PC software and inexpensive hardware. The proposed methodology treats outcrop digital images with a software developed by us in order to solve for colinearity equations, which calculate coordinates of terrain points. Thus, the geometric correction on the terrain-point coordinates to obtain the rectified images can be done with some commercial and widely distributed G.I.S., remote sensing, or photogrammetric programs. The final rectified image represents a metrically consistent perpendicular view of the outcrop that favors the quantitative image analysis. This technique can also be used to improve the photo-mosaicking of different perspective-corrected images of the same outcrop, avoiding the usual misfit of the image frames.