Clasificación orientada a objetos en fotografías aéreas digitales para la discriminación de usos del suelo

Las técnicas de clasificación tradicionales, basadas en rasgos de la imagen a nivel de píxel, presentan ciertas limitaciones, como lo son la aparición de un característico efecto “sal y pimienta” o su reducida capacidad para extraer objetos de interés. Éstas resultan especialmente problemáticas al aplicarse en imágenes de moderada o alta resolución. Una alternativa a dichos sistemas de clasificación pasa por un proceso previo de segmentación de la imagen. De esta forma se permite el trabajo con la imagen a nivel de objeto, lo cual amplía notablemente la cantidad de información que se puede extraer de la misma. En el presente estudio, el objetivo principal es obtener una clasificación digital de la interfase urbano forestal que pueda ser usada por los servicios contra incendios forestales. Para ello, se ha segmentado y clasificado una imagen aérea digital del sensor DMC, empleando el software eCognition, donde la formación de objetos tiene lugar de forma que la homogeneidad interna se mantiene constante. Los objetos resultantes sirven de base para la posterior clasificación. Se utilizaron fotografías aéreas digitales y datos de 350 parcelas en la provincia de Granada, España, para validar las clasificaciones, consiguiendo una precisión total del 90% y un excelente estadístico Kappa (85%) para la clasificación orientada a objetosTraditional classification techniques, basically pixel-based approaches, are limited. Typically, they produce a characteristic “salt and pepper” effect, and are unable to extract objects of interest. These techniques have considerable difficulties in dealing with the rich information content of medium and high-resolution images. One alternative to these classification systems can be a previous segmentation of the image to be classified. In this way, object-based classification can be performed so that a significant increase on the information that can be extracted is obtained. In the present work, the aim is to obtain a digital classification of wilderness-urban interface areas that can be used by fire management services. To this end, a digital aerial image provided by the DMC sensor was segmented and classified using eCognition software, which allows homogeneous image object extraction. The meaningful image objects obtained were then used for the classification. Segmentation before classification worked out as an efficient image analysis technique, overcoming traditional approaches limitations. Digital aerial photographs and data of 350 plots in Granada, Spain, were used to validate the classifications obtained; the overall classification accuracy of 90% and an excellent Kappa statistic (85%) for the object-based classification, proved the validity of this metho

Algorithms of expert classification applied in quickbird satellite images for land use mapping

The objective of this paper was the development of a methodology for the classification of digital aerial images, which, with the aid of object-based classification and the Normalized Difference Vegetation Index (NDVI), can quantify agricultural areas, by using algorithms of expert classification, with the aim of improving the final results of thematic classifications. QuickBird satellite images and data of 2532 plots in Hinojosa del Duque, Spain, were used to validate the different classifications, obtaining an overall classification accuracy of 91.9% and an excellent Kappa statistic (87.6%) for the algorithm of expert classificatio

Biocalcarenites as construction materials in Santa Marina de Aguas Santas Church at Cordoba, Spain

Para la caracterización litológica y determinación del grado de alteración de los materiales pétreos se han empleado las siguientes técnicas: difracción de rayos X (método del polvo), microscopía petrográfica (sobre lámina delgada) y microscopía de barrido con EDS (energía dispersiva de rayos X), para determinar la composición química. El estado de degradación del material pétreo se ha cuantificado a partir del índice químico de alteración (CIW). Se han realizado cartografías sobre la fachada oeste: a) de las litologías presentes y b) de los diferentes tipos de alteración observados. La comparación de muestras del edificio con las de antiguas canteras ha permitido identificar la del Naranjo como la posible cantera de origen.This study consisted in characterizing the materials used to build Santa Marina de Aguas Santas Church at Cordoba and locating the original quarries. The techniques used in the lithological and chemical characterization included XRD, petrographic microscopy and electron dispersive scanning microscopy. The chemical index of weathering (CIW) was used to quantify the state of stone decay. The lithology and different types of alterations observed were mapped. A comparison of the material on the building to ancient quarries identified “Naranjo” as the possible site where the stone was originally quarried

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

Assessment of the Potential of UAV Video Image Analysis for Planning Irrigation Needs of Golf Courses

Golf courses can be considered as precision agriculture, as being a playing surface, their appearance is of vital importance. Areas with good weather tend to have low rainfall. Therefore, the water management of golf courses in these climates is a crucial issue due to the high water demand of turfgrass. Golf courses are rapidly transitioning to reuse water, e.g., the municipalities in the USA are providing price incentives or mandate the use of reuse water for irrigation purposes; in Europe this is mandatory. So, knowing the turfgrass surfaces of a large area can help plan the treated sewage effluent needs. Recycled water is usually of poor quality, thus it is crucial to check the real turfgrass surface in order to be able to plan the global irrigation needs using this type of water. In this way, the irrigation of golf courses does not detract from the natural water resources of the area. The aim of this paper is to propose a new methodology for analysing geometric patterns of video data acquired from UAVs (Unmanned Aerial Vehicle) using a new Hierarchical Temporal Memory (HTM) algorithm. A case study concerning maintained turfgrass, especially for golf courses, has been developed. It shows very good results, better than 98% in the confusion matrix. The results obtained in this study represent a first step toward video imagery classification. In summary, technical progress in computing power and software has shown that video imagery is one of the most promising environmental data acquisition techniques available today. This rapid classification of turfgrass can play an important role for planning water management

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)

Individualization of Pinus radiata Canopy from 3D UAV Dense Point Clouds Using Color Vegetation Indices

The location of trees and the individualization of their canopies are important parameters to estimate diameter, height, and biomass, among other variables. The very high spatial resolution of UAV imagery supports these processes. A dense 3D point cloud is generated from RGB UAV images, which is used to obtain a digital elevation model (DEM). From this DEM, a canopy height model (CHM) is derived for individual tree identification. Although the results are satisfactory, the quality of this detection is reduced if the working area has a high density of vegetation. The objective of this study was to evaluate the use of color vegetation indices (CVI) in canopy individualization processes of Pinus radiata. UAV flights were carried out, and a 3D dense point cloud and an orthomosaic were obtained. Then, a CVI was applied to 3D point cloud to differentiate between vegetation and nonvegetation classes to obtain a DEM and a CHM. Subsequently, an automatic crown identification procedure was applied to the CHM. The results were evaluated by contrasting them with results of manual individual tree identification on the UAV orthomosaic and those obtained by applying a progressive triangulated irregular network to the 3D point cloud. The results obtained indicate that the color information of 3D point clouds is an alternative to support individualizing trees under conditions of high-density vegetation

The Development of an Open Hardware and Software System Onboard Unmanned Aerial Vehicles to Monitor Concentrated Solar Power Plants

Concentrated solar power (CSP) plants are increasingly gaining interest as a source of renewable energy. These plants face several technical problems and the inspection of components such as absorber tubes in parabolic trough concentrators (PTC), which are widely deployed, is necessary to guarantee plant efficiency. This article presents a system for real-time industrial inspection of CSP plants using low-cost, open-source components in conjunction with a thermographic sensor and an unmanned aerial vehicle (UAV). The system, available in open-source hardware and software, is designed to be employed independently of the type of device used for inspection (laptop, smartphone, tablet or smartglasses) and its operating system. Several UAV flight missions were programmed as follows: flight altitudes at 20, 40, 60, 80, 100 and 120 m above ground level; and three cruising speeds: 5, 7 and 10 m/s. These settings were chosen and analyzed in order to optimize inspection time. The results indicate that it is possible to perform inspections by an UAV in real time at CSP plants as a means of detecting anomalous absorber tubes and improving the effectiveness of methodologies currently being utilized. Moreover, aside from thermographic sensors, this contribution can be applied to other sensors and can be used in a broad range of applications where real-time georeferenced data visualization is necessary

Effect of Lockdown Measures on Atmospheric Nitrogen Dioxide during SARS-CoV-2 in Spain

The disease caused by SARS-CoV-2 has affected many countries and regions. In order to contain the spread of infection, many countries have adopted lockdown measures. As a result, SARS-CoV-2 has negatively influenced economies on a global scale and has caused a significant impact on the environment. In this study, changes in the concentration of the pollutant Nitrogen Dioxide (NO2) within the lockdown period were examined as well as how these changes relate to the Spanish population. NO2 is one of the reactive nitrogen oxides gases resulting from both anthropogenic and natural processes. One major source in urban areas is the combustion of fossil fuels from vehicles and industrial plants, both of which significantly contribute to air pollution. The long-term exposure to NO2 can also cause severe health problems. Remote sensing is a useful tool to analyze spatial variability of air quality. For this purpose, Sentinel-5P images registered from January to April of 2019 and 2020 were used to analyze spatial distribution of NO2 and its evolution under the lockdown measures in Spain. The results indicate a significant correlation between the population’s activity level and the reduction of NO2 values

Project-Based Learning Applied to Unmanned Aerial Systems and Remote Sensing

The development of unmanned aerial vehicle (UAV) technology and the miniaturization of sensors have changed the way remote sensing (RS) is used, popularizing this geoscientific discipline in other fields, such as precision agriculture. This makes it necessary to implement the use of these technologies in teaching RS alongside the classical platforms (satellite and manned aircraft). This manuscript describes how The Higher Technical School of Agricultural Engineering at the University of Córdoba (Spain) has introduced UAV RS into the academic program by way of project-based learning (PBL). It also presents the basic characteristics of PBL, the design of the subject, the description of the teacher-guided and self-directed activities, as well as the degree of student satisfaction. The teaching and learning objectives of the subject are to learn how to determine the vigor, temperature, and water stress of a crop through the use of RGB, multispectral, and thermographic sensors onboard a UAV platform. From the onset, students are motivated, actively participate in the tasks related to the realization of UAV flights, and subsequent processing and analysis of the registered images. Students report that PBL is more engaging and allows them to develop a better understanding of RS