Open Access
Automatic detection of uprooted orchards based on orthophoto texture analysis
(MDPI, 2017) Ciriza Labiano, Raquel; Sola Torralba, Ion; Albizua, Lourdes; Álvarez-Mozos, Jesús; González de Audícana Amenábar, María; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak
Permanent crops, such as olive groves, vineyards and fruit trees, are important in European agriculture because of their spatial and economic relevance. Agricultural geographical databases (AGDBs) are commonly used by public bodies to gain knowledge of the extension covered by these crops and to manage related agricultural subsidies and inspections. However, the updating of these databases is mostly based on photointerpretation, and thus keeping this information up-to-date is very costly in terms of time and money. This paper describes a methodology for automatic detection of uprooted orchards (parcels where fruit trees have been eliminated) based on the textural classification of orthophotos with a spatial resolution of 0.25 m. The textural features used for this classification were derived from the grey level co-occurrence matrix (GLCM) and wavelet transform, and were selected through principal components (PCA) and separability analyses. Next, a Discriminant Analysis classification algorithm was used to detect uprooted orchards. Entropy, contrast and correlation were found to be the most informative textural features obtained from the co-occurrence matrix. The minimum and standard deviation in plane 3 were the selected features based on wavelet transform. The classification based on these features achieved a true positive rate (TPR) of over 80% and an accuracy (A) of over 88%. As a result, this methodology enabled reducing the number of fields to photointerpret by 60–85%, depending on the membership threshold value selected. The proposed approach could be easily adopted by different stakeholders and could increase significantly the efficiency of agricultural database updating tasks.
Open Access
Monitoring rainfed alfalfa growth in semiarid agrosystems using Sentinel-2 imagery
(MDPI, 2021) Echeverría Obanos, Andrés; Urmeneta, Alejandro; González de Audícana Amenábar, María; González de Andrés, Ester; Zientziak; Ingeniaritza; Institute for Multidisciplinary Research in Applied Biology - IMAB; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Ciencias; Ingeniería; Gobierno de Navarra / Nafarroako Gobernua
The aim of this study was to assess the utility of Sentinel-2 images in the monitoring of the fractional vegetation cover (FVC) of rainfed alfalfa in semiarid areas such as that of Bardenas Reales in Spain. FVC was sampled in situ using 1 m2 surfaces at 172 points inside 18 alfalfa fields from late spring to early summer in 2017 and 2018. Different vegetation indices derived from a series of Sentinel-2 images were calculated and were then correlated with the FVC measurements at the pixel and parcel levels using different types of equations. The results indicate that the normalized difference vegetation index (NDVI) and FVC were highly correlated at the parcel level (R 2 = 0.712), where as the correlation at the pixel level remained moderate across each of the years studied. Based on the findings, another 29 alfalfa plots (28 rainfed; 1 irrigated) were remotely monitored operationally for 3 years (2017–2019), revealing that location and weather conditions were strong determinants of alfalfa growth in Bardenas Reales. The results of this study indicate that Sentinel-2 imagery is a suitable tool for monitoring rainfed alfalfa pastures in semiarid areas, thus increasing the potential success of pasture management.
Open Access
Inter-comparison of atmospheric correction methods on Sentinel-2 images applied to croplands
(IEEE, 2018) Sola Torralba, Ion; Álvarez-Mozos, Jesús; González de Audícana Amenábar, María; Ingeniería; Ingeniaritza
Atmospheric correction of high resolution satellite scenery is a necessary preprocessing step for applications where bottom of atmosphere (BOA) reflectances are needed. The selection of the best atmospheric correction method to use on images acquired from new platforms, such as Sentinel-2, is essential to provide accurate BOA reflectances. In this work the performance of three atmospheric correction methods (6S, MAJA and SEN2COR) applied to Sentinel-2 scenes are compared by evaluating the resultant spectral signatures of six crop types on two specific dates, and their NDVI time series along a complete year. Although SEN2COR introduced greater corrections, especially in the infrared bands, the results suggest a varying performance of the methods depending on the land cover and the atmospheric conditions. Further research, particularly incorporating ground truth data, is recommended to rigorously validate the different atmospheric methods.
Open Access
Assessment of atmospheric correction methods for Sentinel-2 images in Mediterranean landscapes
(Elsevier, 2018) Sola Torralba, Ion; García-Martín, Alberto; Sandonís Pozo, Leire; Álvarez-Mozos, Jesús; González de Audícana Amenábar, María; Ingeniería; Ingeniaritza
Atmospheric correction of optical satellite imagery is an essential pre-processing for modelling biophysical variables, multi-temporal analysis, and digital classification processes. Sentinel-2 products available for users are distributed by the European Space Agency (ESA) as Top Of Atmosphere reflectance values in cartographic geometry (Level-1C product). In order to obtain Bottom Of Atmosphere reflectance images (Level-2A product) derived from this Level-1C products, ESA provides the SEN2COR module, which is implemented in the Sentinel Application Platform. Alternatively, ESA recently distributes Level-2A products processed by SEN2COR with a default configuration. On the other hand, the conversion from Level-1C to Level-2A product can be generated using alternative atmospheric correction methods, such as MAJA, 6S, or iCOR. In this context, this paper aims to evaluate the quality of Level-2A products obtained through different methods in Mediterranean shrub and grasslands by comparing data obtained from Sentinel-2 imagery with field spectrometry data. For that purpose, six plots with different land covers (asphalt, grass, shrub, pasture, and bare soil) were analyzed, by using synchronous imagery to fieldwork (from July to September 2016). The results suggest the suitability of the applied atmospheric corrections, with coefficients of determination higher than 0.90 and root mean square error lower than 0.04 achieving a relative error in bottom of atmosphere reflectance of only 2–3%. Nevertheless, minor differences were observed between the four tested methods, with slightly varying results depending on the spectral band and land cover.
Open Access
Evaluation of 2D models for the prediction of surface depression storage using realistic reference values
(Wiley, 2016) Giménez Díaz, Rafael; Mezkiritz Barberena, Irantzu; Campo-Bescós, Miguel; Álvarez-Mozos, Jesús; González de Audícana Amenábar, María; Martínez de Aguirre Escobar, Alejandro; Casalí Sarasíbar, Javier; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak
Depression storage (DS) is the maximum storage of precipitation and runoff in the soil surface at a given slope. The DS is determined by soil roughness that in agricultural soils is largely affected by tillage. The direct measurement of DS is not straightforward because of the natural permeability of the soil. Therefore, DS has generally been estimated from 2D/3D empirical relationships and numerical algorithms based on roughness indexes and height measurements of the soil surface, respectively. The objective of this work was to evaluate the performance of some 2D models for DS, using direct and reliable measurements of DS in an agricultural soil as reference values. The study was carried out in experimental microplots where DS was measured in six situations resulting from the combination of three types of tillage carried out parallel and perpendicular to the main slope. Those data were used as reference to evaluate four empirical models and a numerical method. Longitudinal altitudinal profiles of the relief were obtained by a laser profilometer. Infiltration measurements were carried out before and after tillage. The DS was largely affected by tillage and its direction. Highest values of DS are found on rougher surfaces mainly when macroforms cut off the dominant slope. The empirical models had a limited performance while the numerical method was the most effective, even so, with an important variability. In addition, a correct hydrological management should take into account that each type of soil tillage affects infiltration rate differently.
Open Access
Analysis of fire services coverage in Spain
(DYNA, 2018) Echeverría Iriarte, Francisco Javier; González de Audícana Amenábar, María; López Maestresalas, Ainara; Arazuri Garín, Silvia; Ciriza Labiano, Raquel; Jarén Ceballos, Carmen; Ingeniería; Ingeniaritza; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Previous analysis of the locations of fire stations in Spain and the extent of the areas they cover revealed significant deficiencies with regard to the proportion of communities who would not receive fire service intervention within a reasonable time period. This article discusses and describes the use of Geographic Information Systems and related tools to determine the areas and population covered by existing fire services within a specific response time. This response time by road, is based on a survey of fire service interventions in other European countries. The analysis compares data from a statistical study with georeferenced ones and demonstrates that the areas and communities not covered within this response time is greater than previously believed. The article then describes an analysis an alternative solution to reinforce the current fire stations network with part-time firefighters to cover the areas not covered mainly in rural and remote locations.
Open Access
The added value of stratified topographic correction of multispectral images
(MDPI, 2016) Sola Torralba, Ion; González de Audícana Amenábar, María; Álvarez-Mozos, Jesús; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Satellite images in mountainous areas are strongly affected by topography. Different studies demonstrated that the results of semi-empirical topographic correction algorithms improved when a stratification of land covers was carried out first. However, differences in the stratification strategies proposed and also in the evaluation of the results obtained make it unclear how to implement them. The objective of this study was to compare different stratification strategies with a non-stratified approach using several evaluation criteria. For that purpose, Statistic-Empirical and Sun-Canopy-Sensor + C algorithms were applied and six different stratification approaches, based on vegetation indices and land cover maps, were implemented and compared with the non-stratified traditional option. Overall, this study demonstrates that for this particular case study the six stratification approaches can give results similar to applying a traditional topographic correction with no previous stratification. Therefore, the non-stratified correction approach could potentially aid in removing the topographic effect, because it does not require any ancillary information and it is easier to implement in automatic image processing chains. The findings also suggest that the Statistic-Empirical method performs slightly better than the Sun-Canopy-Sensor + C correction, regardless of the stratification approach. In any case, further research is necessary to evaluate other stratification strategies and confirm these results.
Open Access
Evaluation of R tools for downloading MODIS images and their use in urban growth analysis of the city of Tarija (Bolivia)
(MDPI, 2022) Campero Taboada, Milton J.; Luquin Oroz, Eduardo Adrián; Montesino San Martín, Manuel; González de Audícana Amenábar, María; Campo-Bescós, Miguel; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Ingeniería; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
The aim of this study was to compare the available tools in R for downloading and processing Moderate Resolution Imaging Spectroradiometer (MODIS) data, specifically the Enhanced Vegetation Index (EVI) product. The R tools evaluated were the MODIS package, RGISTools, MODISTools, R Google Earth Engine (RGEE) package, MODIStsp, and the Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) application. Each tool was used to download the same product (EVI) corresponding to the same day (3 December 2015), and downloaded data were used to analyze the urban growth of Tarija (Bolivia) as an interesting application. The following features were analyzed: download time and memory used during the download, additional postprocessing time, local memory occupied on the computer, and downloaded file formats. Results showed that the most efficient R tools were those that work directly in the “cloud” or use text queries (RGEE and AppEEARS, respectively) and provide, as a final product, a cropped.tif image according to the area of interest.
Open Access
Estrategia para la verificación de declaraciones PAC a partir de imágenes Sentinel-2 en Navarra
(Universidad Politécnica de Valencia, 2020) González de Audícana Amenábar, María; López Sáenz, Sandra; Sola Torralba, Ion; Álvarez-Mozos, Jesús; Ingeniería; Ingeniaritza
En junio de 2018, la Comisión Europea aprobó una modificación de la Política Agraria Común (PAC) que, entre otros aspectos, plantea el uso de imágenes del programa Copernicus para verificar que las declaraciones presentadas por los agricultores son correctas. En los últimos años distintas iniciativas investigadoras han tratado de desarrollar herramientas operativas con este fin, entre estas se encuentra el proyecto Interreg-POCTEFA PyrenEOS. En este artículo se expone la estrategia metodológica propuesta en el proyecto PyrenEOS, que se basa en la identificación del cultivo más probable utilizando el algoritmo Random Forests. Como elemento diferenciador, se propone seleccionar la muestra de entrenamiento a partir de una selección de las declaraciones PAC según su NDVI. Además, se definen una serie de reglas para determinar el grado de incertidumbre en la clasificación y los criterios para categorizar cada recinto del mapa de verificación según un código de colores a modo de semáforo, en el que el verde indica recintos con declaración correcta, el rojo recintos con declaración dudosa y el naranja recintos con una incertidumbre alta en la clasificación. Esta estrategia de verificación se aplica a dos Comarcas Agrarias de Navarra, en una campaña agrícola para la que se contó con inspecciones de campo de aproximadamente el 7% de los recintos declarados. Los resultados de esta validación, con fiabilidades globales en la clasificación próximas al 80% cuando se considera el cultivo más probable predicho por el clasificador y al 90% cuando se consideran los dos cultivos más probables, ponen de manifiesto que es posible identificar los recintos correctamente declarados (recintos verdes) con una tasa de error inferior al 1%. Los recintos naranjas y rojos, que requerirán del análisis y juicio posterior de técnicos de inspección, suponen un porcentaje reducido de las declaraciones (~6% de los recintos) y concentran la mayoría de las declaraciones incorrectas.
Open Access
Multi-criteria evaluation of topographic correction methods
(Elsevier, 2016) Sola Torralba, Ion; González de Audícana Amenábar, María; Álvarez-Mozos, Jesús; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak
In the last decades, several topographic correction methods (TOC) have been proposed, but there is not an agreement on the best method. Furthermore, different evaluation criteria have been used in the past, and there is not any simple and objective evaluation procedure to measure the quality of the correction. Consequently, a multicriteria analysis of widely used topographic correction methods is required that evaluates their performance over different sensors, terrain and temporal configurations. In this work, ten TOC methods were assessed using seven different evaluation strategies. The analysis was carried out for three SPOT5 images acquired over a mountainous area of northern Spain. The images had different acquisition dates and solar angles, so as to evaluate performance under varying illumination conditions. The results obtained showed that Statistic-Empiricalmethod, CCorrection and Sun-Canopy-Sensor+C performed the best, and differences were minor when favorable illumination conditions were considered. For the seven tested evaluation strategies, interquartile range reduction of land covers or the comparison of sunlit and shaded slopes gave very similar results, whereas there were greater contrasts among other criteria.