Análisis y estudio de independencia espectral entre sensores espaciales y aerotransportados: integración con LiDAR

En esta Tesis Doctoral se han analizado un conjunto de nuevas técnicas de explotación de la información recogida por sensores espaciales y aerotransportados, estudiando las ventajas de una explotación combinada entre sensores. Este planteamiento general se concreta en los siguientes objetivos: Analizar el concepto independencia en el procesado y explotación de imágenes. Evaluar la influencia de la curva de respuesta espectral relativa de un sensor multiespectral en los procesos de clasificación mediante operadores basados en estadística de primer orden. Analizar el parámetro intensidad registrado por sensores ALS para valorar métodos y procesos que permitan una mejor explotación y aprovechamiento. Estudiar la combinación de sensores multiespectrales y ALS estudiando su influencia en los procesos de clasificación de usos de suelo. La Tesis Doctoral aparece estructurada en 6 capítulos. El Capítulo 1, presenta el estado del arte en cuanto a sensores y procesos se refiere. En los siguientes capítulos se presentan y desarrollan los trabajos que fundamentan el núcleo de la presente Tesis. En este sentido, en el Capítulo 2 se presenta las implicaciones de trabajar en un marco de independencia con las bandas de un sensor multiespectral, presentando los beneficios de trabajar con técnicas basadas en el análisis de componentes independientes frente a técnicas más convencionales en Teledetección como el análisis de componentes principales. El Capitulo 3 presenta una metodología diseñada para mitigar los efectos del comportamiento solapado de las bandas de sensores multiespectrales en los procesos de clasificación mediante operadores de clasificación apoyados en estadística de primer orden. A lo largo del Capítulo 4 se estudia como poder aprovechar la información registrada por sensores ALS mediante técnicas de clasificación, estudiando métodos para la normalización de la variable intensidad y sus beneficios con la combinación de información multiespectral registrada por sensores aéreos. En el Capítulo 5 se aplica el concepto de independencia estudiado en el Capítulo 2 en la explotación combinada de sensores multiespectrales y ALS y sus repercusiones en los procesos de clasificación de imágenes. Por último, en el Capítulo 6 se presentan unas conclusiones generales sobre la viabilidad del uso combinado de sensores para la explotación de la información desde el punto temático

Improvement of Remote Sensing-Based Assessment of Defoliation of Pinus spp. Caused by Thaumetopoea pityocampa Denis and Schiffermüller and Related Environmental Drivers in Southeastern Spain

This study used Landsat temporal series to describe defoliation levels due to the Pine Processionary Moth (PPM) in Pinus forests of southeastern Andalusia (Spain), utilizing Google Earth Engine. A combination of remotely sensed data and field survey data was used to detect the defoliation levels of different Pinus spp. and the main environmental drivers of the defoliation due to the PPM. Four vegetation indexes were also calculated for remote sensing defoliation assessment, both inside the stand and in a 60-m buffer area. In the area of study, all Pinus species are affected by defoliation due to the PPM, with a cyclic behavior that has been increasing in frequency in recent years. Defoliation levels were practically equal for all species, with a high increase in defoliation levels 2 and 3 since 2014. The Moisture Stress Index (MSI) and Normalized Difference Infrared Index (NDII) exhibited similar overall (p < 0.001) accuracy in the assessment of defoliation due to the PPM. The synchronization of NDII-defoliation data had a similar pattern for all together and individual Pinus species, showing the ability of this index to adjust the model parameters based on the characteristics of specific defoliation levels. Using Landsat-based NDII-defoliation maps and interpolated environmental data, we have shown that the PPM defoliation in southeastern Spain is driven by the minimum temperature in February and the precipitation in June, March, September, and October. Therefore, the NDII-defoliation assessment seems to be a general index that can be applied to forests in other areas. The trends of NDII-defoliation related to environmental variables showed the importance of summer drought stress in the expansion of the PPM on Mediterranean Pinus species. Our results confirm the potential of Landsat time-series data in the assessment of PPM defoliation and the spatiotemporal patterns of the PPM; hence, these data are a powerful tool that can be used to develop a fully operational system for the monitoring of insect damage

Positional Quality Assessment of Orthophotos Obtained from Sensors Onboard Multi-Rotor UAV Platforms

In this study we explored the positional quality of orthophotos obtained by an unmanned aerial vehicle (UAV). A multi-rotor UAV was used to obtain images using a vertically mounted digital camera. The flight was processed taking into account the photogrammetry workflow: perform the aerial triangulation, generate a digital surface model, orthorectify individual images and finally obtain a mosaic image or final orthophoto. The UAV orthophotos were assessed with various spatial quality tests used by national mapping agencies (NMAs). Results showed that the orthophotos satisfactorily passed the spatial quality tests and are therefore a useful tool for NMAs in their production flowchart

Wavelength Selection Method Based on Partial Least Square from Hyperspectral Unmanned Aerial Vehicle Orthomosaic of Irrigated Olive Orchards

Identifying and mapping irrigated areas is essential for a variety of applications such as agricultural planning and water resource management. Irrigated plots are mainly identified using supervised classification of multispectral images from satellite or manned aerial platforms. Recently, hyperspectral sensors on-board Unmanned Aerial Vehicles (UAV) have proven to be useful analytical tools in agriculture due to their high spectral resolution. However, few efforts have been made to identify which wavelengths could be applied to provide relevant information in specific scenarios. In this study, hyperspectral reflectance data from UAV were used to compare the performance of several wavelength selection methods based on Partial Least Square (PLS) regression with the purpose of discriminating two systems of irrigation commonly used in olive orchards. The tested PLS methods include filter methods (Loading Weights, Regression Coefficient and Variable Importance in Projection); Wrapper methods (Genetic Algorithm-PLS, Uninformative Variable Elimination-PLS, Backward Variable Elimination-PLS, Sub-window Permutation Analysis-PLS, Iterative Predictive Weighting-PLS, Regularized Elimination Procedure-PLS, Backward Interval-PLS, Forward Interval-PLS and Competitive Adaptive Reweighted Sampling-PLS); and an Embedded method (Sparse-PLS). In addition, two non-PLS based methods, Lasso and Boruta, were also used. Linear Discriminant Analysis and nonlinear K-Nearest Neighbors techniques were established for identification and assessment. The results indicate that wavelength selection methods, commonly used in other disciplines, provide utility in remote sensing for agronomical purposes, the identification of irrigation techniques being one such example. In addition to the aforementioned, these PLS and non-PLS based methods can play an important role in multivariate analysis, which can be used for subsequent model analysis. Of all the methods evaluated, Genetic Algorithm-PLS and Boruta eliminated nearly 90% of the original spectral wavelengths acquired from a hyperspectral sensor onboard a UAV while increasing the identification accuracy of the classification

Spatial Quality Evaluation of Resampled Unmanned Aerial Vehicle-Imagery for Weed Mapping

Unmanned aerial vehicles (UAVs) combined with different spectral range sensors are an emerging technology for providing early weed maps for optimizing herbicide applications. Considering that weeds, at very early phenological stages, are similar spectrally and in appearance, three major components are relevant: spatial resolution, type of sensor and classification algorithm. Resampling is a technique to create a new version of an image with a different width and/or height in pixels, and it has been used in satellite imagery with different spatial and temporal resolutions. In this paper, the efficiency of resampled-images (RS-images) created from real UAV-images (UAV-images; the UAVs were equipped with two types of sensors, i.e., visible and visible plus near-infrared spectra) captured at different altitudes is examined to test the quality of the RS-image output. The performance of the object-based-image-analysis (OBIA) implemented for the early weed mapping using different weed thresholds was also evaluated. Our results showed that resampling accurately extracted the spectral values from high spatial resolution UAV-images at an altitude of 30 m and the RS-image data at altitudes of 60 and 100 m, was able to provide accurate weed cover and herbicide application maps compared with UAV-images from real flights

An Analysis of the Influence of Flight Parameters in the Generation of Unmanned Aerial Vehicle (UAV) Orthomosaicks to Survey Archaeological Areas

This article describes the configuration and technical specifications of a multi-rotor unmanned aerial vehicle (UAV) using a red–green–blue (RGB) sensor for the acquisition of images needed for the production of orthomosaics to be used in archaeological applications. Several flight missions were programmed as follows: flight altitudes at 30, 40, 50, 60, 70 and 80 m above ground level; two forward and side overlap settings (80%–50% and 70%–40%); and the use, or lack thereof, of ground control points. These settings were chosen to analyze their influence on the spatial quality of orthomosaicked images processed by Inpho UASMaster (Trimble, CA, USA). Changes in illumination over the study area, its impact on flight duration, and how it relates to these settings is also considered. The combined effect of these parameters on spatial quality is presented as well, defining a ratio between ground sample distance of UAV images and expected root mean square of a UAV orthomosaick. The results indicate that a balance between all the proposed parameters is useful for optimizing mission planning and image processing, altitude above ground level (AGL) being main parameter because of its influence on root mean square error (RMSE)

Graphic Engineering in the Sustainable Preservation of the Municipal Heritage of Montilla (Cordoba, Spain) from the 18th Century: Master Builder Vicente López Cardera in Montilla

The change of territorial organisation in the 18th century in Spain was strongly related to the preservation of the local heritage. Academic architects, military engineers, and master builders coexisted to carry out the design and management of municipal construction works. The evolution of the figure of the master builder and the confrontation with architects and the guilds since the creation of the Royal Academy of Fine Arts of San Fernando posed an inflection point in this aspect. The first aim of the present study was to highlight the figure of Vicente López Cardera, master builder in the Council and Diocese of Córdoba between the late 18th century and the early 19th century, through his work on the municipal interventions in the maintenance of the construction works and infrastructures in Montilla (Córdoba, Spain) around the year 1794. The second aim of the study was to emphasise the role of graphic engineering in the conservation of municipal heritage in the Modern Age through the study of drawings and plans provided by him in the analysed documentation. His thinking in the approach to these works fits with the ideas of social hygienic improvements that began with the Enlightenment as well as with the concept of sustainable development in culture; hence, his work is relevant in the sustainable development planning of cities in the present. With this study, missing heritage elements are also revealed, opening future lines of research that lead to their virtual reconstruction and the promotion of tourism in rural areas

Modeling Major Rural Land-Use Changes Using the GIS-Based Cellular Automata Metronamica Model: The Case of Andalusia (Southern Spain)

The effective and efficient planning of rural land-use changes and their impact on the environment is critical for land-use managers. Many land-use growth models have been proposed for forecasting growth patterns in the last few years. In this work; a cellular automata (CA)-based land-use model (Metronamica) was tested to simulate (1999–2007) and predict (2007–2035) land-use dynamics and land-use changes in Andalucía (Spain). The model was calibrated using temporal changes in land-use covers and was evaluated by the Kappa index. GIS-based maps were generated to study major rural land-use changes (agriculture and forests). The change matrix for 1999–2007 showed an overall area change of 674971 ha. The dominant land uses in 2007 were shrubs (30.7%), woody crops on dry land (17.3%), and herbaceous crops on dry land (12.7%). The comparison between the reference and the simulated land-use maps of 2007 showed a Kappa index of 0.91. The land-cover map for the projected PRELUDE scenarios provided the land-cover characteristics of 2035 in Andalusia; developed within the Metronamica model scenarios (Great Escape; Evolved Society; Clustered Network; Lettuce Surprise U; and Big Crisis). The greatest differences were found between Great Escape and Clustered Network and Lettuce Surprise U. The observed trend (1999–2007–2035) showed the greatest similarity with the Big Crisis scenario. Land-use projections facilitate the understanding of the future dynamics of land-use change in rural areas; and hence the development of more appropriate plans and policies