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
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
A diachronic analysis of a changing landscape on the Duero river borderlands of Spain and Portugal combining remote sensing and ethnographic approaches
(MDPI, 2021) Hearn, Kyle Patrick; Álvarez-Mozos, Jesús; Giza eta Hezkuntza Zientziak; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Ciencias Humanas y de la Educación; Ingeniería; Gobierno de Navarra / Nafarroako Gobernua; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
The Arribes del Duero region spans the border of both Spain and Portugal along the Duero River. On both sides of the border, the region boasts unique human‐influenced ecosystems. The borderland landscape is dotted with numerous villages that have a history of maintaining and managing an agrosilvopastoral use of the land. Unfortunately, the region in recent decades has suffered from massive outmigration, resulting in significant rural abandonment. Consequently, the oncemaintained landscape is evolving into a more homogenous vegetative one, resulting in a greater propensity for wildfires. This study utilizes an interdisciplinary, integrated approach of “bottom up” ethnography and “top down” remote sensing data from Landsat imagery, to characterize and document the diachronic vegetative changes on the landscape, as they are perceived by stakeholders and satellite spectral analysis. In both countries, stakeholders perceived the current changes and threats facing the landscape. Remote sensing analysis revealed an increase in forest cover throughout the region, and more advanced, drastic change on the Spanish side of the study area marked by wildfire and a rapidly declining population. Understanding the evolution and history of this rural landscape can provide more effective management and its sustainability.
Open Access
Influence of surface roughness measurement scale on radar backscattering in different agricultural soils
(IEEE, 2017) Martínez de Aguirre Escobar, Alejandro; Álvarez-Mozos, Jesús; Lievens, Hans; Verhoest, Niko E. C.; Landa Ingeniaritza eta Proiektuak; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Proyectos e Ingeniería Rural
Soil surface roughness strongly affects the scattering of microwaves on the soil surface and determines the backscattering coefficient (σ 0 ) observed by radar sensors. Previous studies have shown important scale issues that compromise the measurement and parameterization of roughness especially in agricultural soils. The objective of this paper was to determine the roughness scales involved in the backscattering process over agricultural soils. With this aim, a database of 132 5-m profiles taken on agricultural soils with different tillage conditions was used. These measurements were acquired coinciding with a series of ENVISAT/ASAR observations. Roughness profiles were processed considering three different scaling issues: 1) influence of measurement range; 2) influence of low-frequency roughness components; and 3) influence of high-frequency roughness components. For each of these issues, eight different roughness parameters were computed and the following aspects were evaluated: 1) roughness parameters values; 2) correlation with σ 0 ; and 3) goodness-of-fit of the Oh model. Most parameters had a significant correlation with σ 0 especially the fractal dimension, the peak frequency, and the initial slope of the autocorrelation function. These parameters had higher correlations than classical parameters such as the standard deviation of surface heights or the correlation length. Very small differences were observed when longer than 1-m profiles were used as well as when small-scale roughness components (<;5 cm) or large-scale roughness components (>100 cm) were disregarded. In conclusion, the medium-frequency roughness components (scale of 5-100 cm) seem to be the most influential scales in the radar backscattering process on agricultural soils.
Open Access
Crop type mapping based on Sentinel-1 backscatter time series
(IEEE, 2018) Arias Cuenca, María; Campo-Bescós, Miguel; Álvarez-Mozos, Jesús; Ingeniería; Ingeniaritza
The high revisit time of Sentinel-1 (S1) observations enables the design of crop type mapping approaches exploiting the backscatter time series observed for the different crops. The objective of this study is to propose a supervised crop classification methodology based on the temporal signature of crops. With this aim 29 dual-pol S1 observations acquired over an agricultural area of Spain, where ground truth was available, were processed. The classification approach was based on the temporal signatures obtained for each polarization channel (VH, VV and the cross-pol ratio) for the different crops. Highest accuracies were obtained when fields were assigned to the class that minimized the RMSE, with an overall accuracy of 79% and best results for rapeseed, sunflower, alfalfa and barley.
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
Accuracy of methods for field assessment of rill and ephemeral gully erosion
(Elsevier, 2006) Casalí Sarasíbar, Javier; Loizu Maeztu, Javier; Campo-Bescós, Miguel; Santisteban Comino, Luisa María de; Álvarez-Mozos, Jesús; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak
To properly assess soil erosion in agricultural areas, it is necessary to determine precisely the volume of ephemeral gullies and rills in the field by using direct measurement procedures. However, little information is available on the accuracy of the different methods used. The main purpose of this paper is to provide information for a suitable assessment of rill and ephemeral gully erosion with such direct measurement methods. To achieve this objective: a) the measurement errors associated to three methods used for field assessment of channel cross sectional areas are explored; b) the influence of the number of cross sections used per unit channel length on the assessment accuracy, is analysed and; c) the effect of the channel size and shape on measurement errors is examined. The three methods considered to determine the cross sectional areas were: micro-topographic profile meter (1); detailed measurement of section characteristic lengths with a tape (2); measurement of cross section width and depth with a tape (3). Five reaches of different ephemeral gully types 14.0 or 30.0 m long and a set of six 20.4 to 29.4 m long rill reaches were selected. On each gully reach, the cross sectional areas were measured using the three above mentioned methods, with a separation (s) between cross sections of 1 m. For rills, the cross sectional areas were measured with methods 1 and 3, with s= 2 m. Then, the corresponding total erosion volumes were computed. The volume calculated with method 1 with s= 1 m for gullies and s= 2 m for rills was taken as the reference method. For each channel, and for each one of the possible combinations of s and measurement method (m), the relative measurement error and the absolute value of the relative measurement error (Ersm and |Ersm| ), defined with respect to the reference one, was calculated. |Ersm| much higher than 10% were obtained very easily, even for small s values and for apparently quasi prismatic channels. Channel size and shape had a great influence on measurement errors. In fact, the selection of the more suitable method for a certain gully shape and size seemed to be much more important than s, at least when s< 10 m. Method 1 always provided the most precise measurements, and its results were the less dependent on s. However, s must be <5 m to guarantee an error smaller than 10%. Method 2 is not recommended, because it is difficult, time consuming and can lead to large errors. Method 3 seems to be enough for small, wide and shallow gullies, and for small rills, but only if s is shorter than 5 m. Results obtained after the analysis of rill measurement errors were similar to those of gullies. The analysis of Ersm and |Ersm| when calculating channel volumes using a unique representative cross section highlighted the importance of correctly selecting the adequate cross section. Due to the high error values that this method can entail, it is not considered as advisable whenever accurate erosion measurements are pursued.
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
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
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.