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
New methodology for wheat attenuation correction at C-Band VV-polarized backscatter time series
(IEEE, 2022) Arias Cuenca, María; Campo-Bescós, Miguel; Arregui Odériz, Luis Miguel; González de Audícana Amenábar, María; Álvarez-Mozos, Jesús; Agronomia, Bioteknologia eta Elikadura; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Agronomía, Biotecnología y Alimentación; Ingeniería
Wheat is one of the most important crops worldwide, and thus the use of remote sensing data for wheat monitoring has attracted much interest. Synthetic Aperture Radar (SAR) observations show that, at C-band and VV polarization, wheat canopy attenuates the surface scattering component from the underlying soil during a significant part of its growth cycle. This behavior needs to be accounted for or corrected before soil moisture retrieval is attempted. The objective of this paper is to develop a new method for wheat attenuation correction (WATCOR) applicable to Sentinel-1 VV time series and based solely on the information contained in the time series itself. The hypothesis of WATCOR is that without attenuation, VV backscatter would follow a stable long-term trend during the agricultural season, with short-term variations caused by soil moisture dynamics. The method relies on time series smoothing and changing point detection, and its implementation follows a series of simple steps. The performance of the method was compared by evaluating the correlation between backscatter and soil moisture content in six wheat fields with available soil moisture data. The Water Cloud Model (WCM) was also applied as a benchmark. The results showed that WATCOR successfully removed the attenuation in the time series, and achieved the highest correlation with soil moisture, improving markedly the correlation of the original backscatter. WATCOR can be easily implemented, as it does not require parameterization or any external data, only an approximate indication of the period where attenuation is likely to occur.
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
Evaluation of terrestrial laser scanner and structure from motion photogrammetry techniques for quantifying soil surface roughness parameters over agricultural soils
(Wiley, 2020) Martínez de Aguirre Escobar, Alejandro; Álvarez-Mozos, Jesús; Milenković, Milutin; Pfeifer, Norbert; Giménez Díaz, Rafael; Ingeniería; Ingeniaritza
The surface roughness of agricultural soils is mainly related to the type of tillage performed, typically consisting of oriented and random components. Traditionally, soil surface roughness (SSR) characterization has been difficult due to its high spatial variability and the sensitivity of roughness parameters to the characteristics of the instruments, including its measurement scale. Recent advances in surveying have greatly improved the spatial resolution, extent, and availability of surface elevation datasets. However, it is still unknown how new roughness measurements relates with the conventional roughness measurements such as 2D profiles acquired by laser profilometers. The objective of this study was to evaluate the suitability of Terrestrial Laser Scanner (TLS) and Structure from Motion (SfM) photogrammetry techniques for quantifying SSR over different agricultural soils. With this aim, an experiment was carried out in three plots (5 × 5 m) representing different roughness conditions, where TLS and SfM photogrammetry measurements were co‐registered with 2D profiles obtained using a laser profilometer. Differences between new and conventional roughness measurement techniques were evaluated visually and quantitatively using regression analysis and comparing the values of six different roughness parameters. TLS and SfM photogrammetry measurements were further compared by evaluating multi‐directional roughness parameters and analyzing corresponding Digital Elevation Models. The results obtained demonstrate the ability of both TLS and SfM photogrammetry techniques to measure 3D SSR over agricultural soils. However, profiles obtained with both techniques (especially SfM photogrammetry) showed a loss of high‐frequency elevation information that affected the values of some parameters (e.g. initial slope of the autocorrelation function, peak frequency and tortuosity). Nevertheless, both TLS and SfM photogrammetry provide a massive amount of 3D information that enables a detailed analysis of surface roughness, which is relevant for multiple applications, such as those focused in hydrological and soil erosion processes and microwave scattering.
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
Crop classification based on temporal signatures of Sentinel-1 observations over Navarre province, Spain
(MDPI, 2020) Arias Cuenca, María; Campo-Bescós, Miguel; Álvarez-Mozos, Jesús; Ingeniería; Ingeniaritza
Crop classification provides relevant information for crop management, food security assurance and agricultural policy design. The availability of Sentinel-1 image time series, with a very short revisit time and high spatial resolution, has great potential for crop classification in regions with pervasive cloud cover. Dense image time series enable the implementation of supervised crop classification schemes based on the comparison of the time series of the element to classify with the temporal signatures of the considered crops. The main objective of this study is to investigate the performance of a supervised crop classification approach based on crop temporal signatures obtained from Sentinel-1 time series in a challenging case study with a large number of crops and a high heterogeneity in terms of agro-climatic conditions and field sizes. The case study considered a large dataset on the Spanish province of Navarre in the framework of the verification of Common Agricultural Policy (CAP) subsidies. Navarre presents a large agro-climatic diversity with persistent cloud cover areas, and therefore, the technique was implemented both at the provincial and regional scale. In total, 14 crop classes were considered, including different winter crops, summer crops, permanent crops and fallow. Classification results varied depending on the set of input features considered, obtaining Overall Accuracies higher than 70% when the three (VH, VV and VH/VV) channels were used as the input. Crops exhibiting singularities in their temporal signatures were more easily identified, with barley, rice, corn and wheat achieving F1-scores above 75%. The size of fields severely affected classification performance, with ~14% better classification performance for larger fields (>1 ha) in comparison to smaller fields (<0.5 ha). Results improved when agro-climatic diversity was taken into account through regional stratification. It was observed that regions with a higher diversity of crop types, management techniques and a larger proportion of fallow fields obtained lower accuracies. The approach is simple and can be easily implemented operationally to aid CAP inspection procedures or for other purposes. © 2020 by the authors.
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.
Open Access
On the influence of acquisition geometry in backscatter time series over wheat
(Elsevier, 2022) Arias Cuenca, María; Campo-Bescós, Miguel; Álvarez-Mozos, Jesús; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Ingeniería; Gobierno de Navarra / Nafarroako Gobernua
Dense time series of Sentinel-1 imagery are an invaluable information source for agricultural applications. Multiple orbits can observe a specific area and their combination could improve the temporal resolution of the time series. However, the orbits have different acquisition geometries regarding incidence and azimuth angles that need to be considered. Furthermore, crops are dynamic canopies and the influence of incidence and azimuth angles might change during the agricultural season due to different phenological stages. The main objective of this letter is to evaluate the influence of different acquisition geometries in Sentinel-1 backscatter time series over wheat canopies, and to propose a strategy for their correction. A large dataset of wheat parcels (∼40,000) was used and 344 Sentinel-1 images from three relative orbits were processed during two agricultural seasons. The first analysis was a monthly evaluation of the influence of incidence angle on backscatter (σ0) and terrain flattened backscatter (γ0). It showed that terrain flattening significantly reduced the backscatter dependence on incidence angle, being negligible in VH polarization but not completely in VV polarization. Incidence angle influence in VV backscatter changed in time due to wheat growth dynamics. To further reduce it, an incidence angle normalization technique followed by an azimuthal anisotropy correction were applied. In conclusion, γ0 enabled a reasonable combination of different relative orbits, that may be sufficient for many applications. However, for detailed analyses, the correction techniques might be implemented to further reduce orbit differences, especially in bare soil periods or winter months.
Open Access
Automatic detection of high-voltage power lines in LiDAR surveys using data mining techniques
(Springer, 2020) Chasco Hernández, Daniel; Sanz Delgado, José Antonio; García Morales, Víctor; Álvarez-Mozos, Jesús; Ingeniería; Estadística, Informática y Matemáticas; Ingeniaritza; Estatistika, Informatika eta Matematika
The correct classification of power lines in LiDAR point clouds has attracted the interest of the mapping community in the last years. The objective of this research is the detection and automatic extraction of high-voltage transmission lines from LiDAR data using data mining techniques. With this aim, a Single Photon LiDAR (SPL) survey acquired over the region of Navarre (Spain) in 2017 was used, with a mean point density of 14 pt/m2. Different data mining techniques were evaluated, including decision trees (C4.5 and CART) and ensemble learning algorithms (Random Forests, Bagging and AdaBoost). Fifteen test sites were studied corresponding to areas with high-voltage power lines over different conditions regarding the underlying vegetation and topography. For these sites 92,104 LiDAR points were identified as power lines and more than 4M points as not power lines using existing cartography. This dataset was randomly split in train and test sets and then balanced two obtain a similar amount of data for the two classes. The results obtained show the importance of balancing the training data with improvements in accuracy of ~10% with respect to the imbalanced case. Accuracies higher than 87% were obtained in all balanced cases, with particularly successful results for ensemble learning techniques, being AdaBoost the technique with the highest accuracy 91%. These results suggest that the combination of SPL surveys and data mining tools can be successfully used for the operational mapping of high voltage power lines.
Open Access
Evaluation of soil moisture estimation techniques based on Sentinel-1 observations over wheat fields
(Elsevier, 2023) Arias Cuenca, María; Notarnicola, Claudia; Campo-Bescós, Miguel; Arregui Odériz, Luis Miguel; Álvarez-Mozos, Jesús; Agronomía, Biotecnología y Alimentación; Agronomia, Bioteknologia eta Elikadura; Ingeniería; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Soil moisture (SM) is a key variable in agriculture and its monitoring is essential. SM determines the amount of water available to plants, having a direct impact on the development of crops, on the forecasting of crop yields and on the surveillance of food security. Microwave remote sensing offers a great potential for estimating SM because it is sensitive to the dielectric characteristics of observed surface that depend on surface soil moisture. The objective of this study is the evaluation of three change detection methodologies for SM estimation over wheat at the agricultural field scale based on Sentinel-1 time series: Short Term Change Detection (STCD), TU Wien Change Detection (TUWCD) and Multitemporal Bayesian Change Detection (MTBCD). Different methodological alternatives were proposed for the implementation of these techniques at the agricultural field scale. Soil moisture measurements from eight experimental wheat fields were used for validating the methodologies. All available Sentinel-1 acquisitions were processed and the eventual benefit of correcting for vegetation effects in backscatter time series was evaluated. The results were rather variable, with some experimental fields achieving successful performance metrics (ubRMSE ~ 0.05 m3 /m3 ) and some others rather poor ones (ubRMSE > 0.12 m3 / m3 ). Evaluating median performance metrics, it was observed that both TUWCD and MTBCD methods obtained better results when run with vegetation corrected backscatter time series (ubRMSE ~0.07 m3 /m3 ) whereas STCD produced similar results with and without vegetation correction (ubRMSE ~0.08 m3 /m3 ). The soil moisture content had an influence on the accuracy of the different methodologies, with higher errors observed for drier conditions and rain-fed fields, in comparison to wetter conditions and irrigated fields. Taking into account the spatial scale of this case study, results were considered promising for the future application of these techniques in irrigation management.