Open Access
On the added value of quad-pol data in a multi-temporal crop classification framework based on RADARSAT-2 imagery
(MDPI, 2016) Larrañaga Urien, Arantzazu; Álvarez-Mozos, Jesús; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak
Polarimetric SAR images are a rich data source for crop mapping. However, quad-pol sensors have some limitations due to their complexity, increased data rate, and reduced coverage and revisit time. The main objective of this study was to evaluate the added value of quad-pol data in a multi-temporal crop classification framework based on SAR imagery. With this aim, three RADARSAT-2 scenes were acquired between May and June 2010. Once we analyzed the separability and the descriptive analysis of the features, an object-based supervised classification was performed using the Random Forests classification algorithm. Classification results obtained with dual-pol (VV-VH) data as input were compared to those using quad-pol data in different polarization bases (linear H-V, circular, and linear 45º), and also to configurations where several polarimetric features (Pauli and Cloude–Pottier decomposition features and co-pol coherence and phase difference) were added. Dual-pol data obtained satisfactory results, equal to those obtained with quad-pol data (in H-V basis) in terms of overall accuracy (0.79) and Kappa values (0.69). Quad-pol data in circular and linear 45º bases resulted in lower accuracies. The inclusion of polarimetric features, particularly co-pol coherence and phase difference, resulted in enhanced classification accuracies with an overall accuracy of 0.86 and Kappa of 0.79 in the best case, when all the polarimetric features were added. Improvements were also observed in the identification of some particular crops, but major crops like cereals, rapeseed, and sunflower already achieved a satisfactory accuracy with the VV-VH dual-pol configuration and obtained only minor improvements. Therefore, it can be concluded that C-band VV-VH dual-pol data is almost ready to be used operationally for crop mapping as long as at least three acquisitions in dates reflecting key growth stages representing typical phenology differences of the present crops are available. In the near future, issues regarding the classification of crops with small field sizes and heterogeneous cover (i.e., fallow and grasslands) need to be tackled to make this application fully operational.
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
Effective roughness modelling as a tool for soil moisture retrieval from C-and L-band SAR
(Copernicus Publications, 2011) Lievens, Hans; Verhoest, Niko E. C.; Keyser, E. de; Vernieuwe, Hilde; Matgen, P.; Álvarez-Mozos, Jesús; Baets, Bernard de; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak
Soil moisture retrieval from Synthetic Aperture Radar (SAR) using state-of-the-art backscatter models is not fully operational at present, mainly due to difficulties involved in the parameterisation of soil surface roughness. Recently, increasing interest has been drawn to the use of calibrated or effective roughness parameters, as they circumvent issues known to the parameterisation of field-measured roughness. This paper analyses effective roughness parameters derived from C- and L-band SAR observations over a large number of agricultural seedbed sites in Europe. It shows that parameters may largely differ between SAR acquisitions, as they are related to the observed backscatter coefficients and variations in the local incidence angle. Therefore, a statistical model is developed that allows for estimating effective roughness parameters from microwave backscatter observations. Subsequently, these parameters can be propagated through the Integral Equation Model (IEM) for soil moisture retrieval. It is shown that fairly accurate soil moisture results are obtained both at C-and L-band, with an RMSE ranging between 4 vol% and 6.5 vol%.
Open Access
Clasificación de usos y cubiertas del suelo y análisis de cambios en los alrededores de la Reserva Ecológica Manglares Churute (Ecuador) mediante una serie de imágenes Sentinel-1
(Universidad Politécnica de Valencia, 2020) Vélez Alvarado, Diana; Álvarez-Mozos, Jesús; Ingeniería; Ingeniaritza; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
La gestión de las áreas naturales protegidas frecuentemente obvia la importancia que tiene el territorio que rodea el perímetro del espacio protegido (zona de amortiguación). Estas zonas pueden ser el origen de impactos que amenacen el estado de conservación de los ecosistemas protegidos. En este artículo se describe un caso de estudio centrado en la Reserva Ecológica Manglares Churute (REMCh) de Ecuador, en el que se utilizó una serie temporal de imágenes Sentinel-1 para clasificar los usos y cubiertas del suelo y para analizar los cambios ocurridos en el periodo 2015-2018. Tras procesar la serie de imágenes y delinear el conjunto de zonas de entrenamiento sobre los principales usos y cubiertas se implementó un algoritmo de clasificación Random Forests (RF), cuyos parámetros fueron optimizados mediante una validación cruzada con el conjunto de datos de entrenamiento (70% de la verdad campo). El 30% restante se utilizó para validar la clasificación realizada, logrando una fiabilidad global del 84%, un coeficiente Kappa de 0,8 y unas métricas de rendimiento por clase satisfactorias para los principales cultivos y usos del suelo. Los resultados fueron peores para las clases más heterogéneas y minoritarias, no obstante, se considera que la clasificación fue lo suficientemente precisa para realizar el análisis de cambios perseguido. Entre 2015 y 2018 se constató un aumento en la superficie destinada a usos intensivos como el cultivo de camarón blanco y la caña de azúcar, en detrimento de otros cultivos tradicionales como el arroz o el banano. Aunque estos cambios se produjeron en las zonas que rodean al área natural protegida, pueden causar un deterioro de la calidad del agua debido al uso de fertilizantes y pesticidas, por tanto, se recomienda prestar atención a estas zonas de amortiguamiento a la hora de diseñar políticas e instrumentos adecuados de protección medioambiental.
Open Access
Influence of surface roughness sample size for C-band SAR backscatter applications on agricultural soils
(IEEE, 2017) Martínez de Aguirre Escobar, Alejandro; Álvarez-Mozos, Jesús; Lievens, Hans; Verhoest, Niko E. C.; Giménez Díaz, Rafael; Landa Ingeniaritza eta Proiektuak; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Proyectos e Ingeniería Rural
Soil surface roughness determines the backscatter coefficient observed by radar sensors. The objective of this letter was to determine the surface roughness sample size required in synthetic aperture radar applications and to provide some guidelines on roughness characterization in agricultural soils for these applications. With this aim, a data set consisting of ten ENVISAT/ASAR observations acquired coinciding with soil moisture and surface roughness surveys has been processed. The analysis consisted of: 1) assessing the accuracies of roughness parameters s and l depending on the number of 1-m-long profiles measured per field; 2) computing the correlation of field average roughness parameters with backscatter observations; and 3) evaluating the goodness of fit of three widely used backscatter models, i.e., integral equation model (IEM), geometrical optics model (GOM), and Oh model. The results obtained illustrate a different behavior of the two roughness parameters. A minimum of 10-15 profiles can be considered sufficient for an accurate determination of s, while 20 profiles might still be not enough for accurately estimating l. The correlation analysis revealed a clear sensitivity of backscatter to surface roughness. For sample sizes >15 profiles, R values were as high as 0.6 for s and ~0.35 for l, while for smaller sample sizes R values dropped significantly. Similar results were obtained when applying the backscatter models, with enhanced model precision for larger sample sizes. However, IEM and GOM results were poorer than those obtained with the Oh model and more affected by lower sample sizes, probably due to larger uncertainly of l.
Open Access
Multitemporal evaluation of topographic correction algorithms using synthetic images
(SPIE, 2012) Sola Torralba, Ion; Álvarez-Mozos, Jesús; González de Audícana Amenábar, María; Torres Escribano, José Luis; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak
Land cover classification and quantitative analysis of multispectral data in mountainous regions is considerably hampered by the influence of topography on the spectral response pattern. In the last years, different topographic correction (TOC) algorithms have been proposed to correct illumination differences between sunny and shaded areas observed by optical remote sensors. Although the available number of TOC methods is high, the evaluation of their performance usually relies on the existence of precise land cover information, and a standardised and objective evaluation procedure has not been proposed yet. Besides, previous TOC assessment studies only considered a limited set of illumination conditions, normally assuming favourable illumination conditions. This paper presents a multitemporal evaluation of TOC methods based on synthetically generated images in order to evaluate the influence of solar angles on the performance of TOC methods. These synthetic images represent the radiance an optical sensor would receive under specific geometric and temporal acquisition conditions and assuming a certain land-cover type. A method for creating synthetic images using state-of-the-art irradiance models has been tested for different periods of the year, which entails a variety of solar angles. Considering the real topography of a specific area a Synthetic Real image (SR) is obtained, and considering the relief of this area as being completely flat a Synthetic Horizontal image (SH) is obtained. The comparison between corrected image obtained applying a TOC method to SR image and SH image of the same area, i.e. considered the ideal correction, allows assessing the performance of each TOC algorithm.
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
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
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
Comparison of digital terrain models obtained with LiDAR and photogrammetry
(Springer, 2020) Martínez de Aguirre Escobar, Alejandro; García Morales, Víctor; Álvarez-Mozos, Jesús; Ingeniería; Ingeniaritza
Airborne LiDAR sensors capture three-dimensional information of the Earth, useful for obtaining high accuracy Digital Terrain Models (DTM). The Spanish National Plan for Aerial Orthophotography (PNOA) is an initiative of the Spanish Geographical Institute whereby nationwide LiDAR datasets are periodically acquired and made available to the public as.las files and value added products (e.g., DTM). The objective of this study is to assess the added value of PNOA LiDAR DTMs by comparing them to DTMs obtained through classical photogrammetric techniques. With this aim, four areas of interest were selected in Navarre (north of Spain), in areas with challenging characteristics such as forests, karst landforms, agricultural terraces and ravines. A 5 × 5 m DTM obtained with classical photogrammetry in 2008 was compared with a LiDAR DTM of the same pixel size obtained in 2011, assuming no significant changes occurred in this time. Height differences were evaluated, as well as slope, aspect and curvature differences. Besides, a multiresolution analysis was carried out to quantify how DTM smoothing affected height variations between neighbor pixels, measured with the standard deviation on a 5 × 5 window. The results obtained showed that the LiDAR DTMs provided an enhanced description of topography, particularly under forests and in areas with complex topography.