Open Access
Evaluación de la aplicabilidad de la teledetección radar a la estimación de la humedad superficial del suelo en cuencas agrícolas
(2006) Álvarez-Mozos, Jesús; Casalí Sarasíbar, Javier; González de Audícana Amenábar, María; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak
En esta tesis doctoral se aborda el tema de la estimación de la humedad superficial del suelo mediante teledetección radar. La humedad superficial del suelo es una variable que interviene en multitud de procesos que tienen lugar en la superficie terrestre. Su estimación a partir de sensores espaciales resultaría muy atractiva para disciplinas como la hidrología, agronomía, meteorología, etc. Los sensores de teledetección radar emiten un pulso de radiación hacia la superficie del suelo y reciben la proporción del mismo que retorna al sensor, de este modo permiten calcular el coeficiente de retrodispersión σ0 de la superficie. Este coeficiente depende de las características dieléctricas de la superficie del suelo, que, a su vez, se encuentran íntimamente relacionadas con su contenido de humedad. Sin embargo, existen otras variables que influyen en las observaciones radar, como la rugosidad superficial, lo que complica la estimación de la humedad superficial del suelo a partir de este tipo de imágenes. Se han propuesto diferentes técnicas para estimar la humedad superficial del suelo a partir de imágenes radar. Entre éstas la aplicación e inversión de modelos de retrodispersión constituye la opción más adecuada. Otras técnicas, como los modelos de regresión lineal o las técnicas de detección de cambios, requieren de condiciones homogéneas de rugosidad y ángulo de incidencia, por lo que su aplicabilidad es más reducida. Entre los diferentes modelos de retrodispersión que se han propuesto el Integral Equation Model (IEM) es el más adecuado. Existen otros que pueden resultar interesantes por su sencillez o porque incorporan una descripción más simple de la rugosidad, pero el IEM tiene una sólida base teórica y ha sido exitosamente validado en condiciones de laboratorio. En esta tesis doctoral, se evalúa la aplicabilidad de estos métodos a la estimación de la humedad superficial del suelo en una cuenca agrícola de Navarra. Para ello se cuenta con observaciones adquiridas por dos sensores espaciales, RADARSAT-1 y ENVISAT/ASAR, en sendas campañas experimentales llevadas a cabo en los años 2003 y 2004. En estas campañas experimentales se realizaron mediciones de campo de la humedad del suelo y de la rugosidad superficial. Estas mediciones se emplean para estimar los parámetros de rugosidad necesarios en los modelos de retrodispersión y para proporcionar mediciones de humedad de referencia con las que comparar las estimaciones que se realicen. La existencia de vegetación complica el estudio de la humedad mediante esta técnica. En esta tesis se corrige la influencia que ejerce una cubierta de cereal mediante el empleo de un modelo semi-empírico denominado Water Cloud Model (WCM). Este método constituye una herramienta útil y sencilla para corregir la atenuación que ejerce la vegetación. Entre los diferentes modelos de retrodispersión evaluados, el IEM es el que proporciona unos resultados más adecuados. El modelo empírico de Oh et al. (1992) no funciona correctamente en condiciones de ángulo de incidencia bajo y superficies poco rugosas. El modelo posterior de Oh (2004), en cambio, proporciona unos resultados adecuados. La fiabilidad de las estimaciones mejora cuanto mayor sea el nivel de agregación o escala a la que se estima la humedad. En este trabajo se han obtenido estimaciones a escala de cuenca con un error del 0,06 cm3cm-3, comparable al obtenido con métodos de medición de humedad en campo. Las estimaciones a escala puntual no resultan tan adecuadas debido a la influencia de la rugosidad espacial y su variabilidad espacial. La rugosidad superficial es el principal escollo de la estimación de la humedad mediante teledetección radar. Su alta variabilidad espacial, por un lado, y la sensibilidad del coeficiente de retrodispersión a sus parámetros, por otro, hacen que sea necesario caracterizarla de forma muy detallada. En este contexto, se han empleado esquemas iterativos basados en el método propuesto por Pauwels et al. (2002) que permiten estimar tanto los parámetros de rugosidad como la humedad superficial a partir de dos observaciones adquiridas en condiciones homogéneas de rugosidad. Estos esquemas se basan en el uso combinado de dos modelos de retrodispersión que forman un sistema que se resuelve de forma iterativa. Si bien el fundamento de esta metodología resulta interesante, los resultados obtenidos varían con la rugosidad, ángulo de incidencia y condiciones de humedad haciendo que tales esquemas no sean generalizables. Por otro lado, se ha tratado de estimar el parámetro de rugosidad longitud de correlación l, cuya medición en campo resulta más complicada, mediante sendas expresiones basadas en los trabajos de Davidson et al. (2003) y Baghdadi et al. (2002; 2004). Los resultados obtenidos por estos métodos resultan interesantes ya que demuestran la posibilidad de estimar el parámetro l a partir de la desviación estándar de las alturas de la superficie, parámetro considerablemente más fácil de determinar. Siendo la rugosidad superficial una variable cuyo conocimiento es vital para estimar adecuadamente la humedad del suelo a partir de teledetección radar, en esta tesis se estudia a fondo su caracterización. En el ámbito de este tema se ha diseñado un perfilómetro láser que ha demostrado ser una herramienta muy valiosa para el estudio de la rugosidad. A partir de mediciones adquiridas mediante el mismo se analiza el comportamiento de los distintos parámetros existentes para la caracterización de la rugosidad, la influencia del laboreo en tales parámetros, su variabilidad o la escala de medida que resulta adecuada para caracterizar adecuadamente la rugosidad en superficies agrícolas.
Open Access
Identifying forest harvesting practices: clear-cutting and thinning in diverse tree species using dense Landsat time series
(Elsevier, 2024-12-07) Giambelluca, Ana Laura; Hermosilla, Txomin; Álvarez-Mozos, Jesús; González de Audícana Amenábar, María; Ingeniería; Ingeniaritza; Institute on Innovation and Sustainable Development in Food Chain - ISFOOD; Gobierno de Navarra / Nafarroako Gobernua
Forest monitoring plays a critical role in achieving sustainable forest management practices. The ability to identify ongoing harvesting activities is crucial for developing targeted strategies to maintain forest health. Traditional monitoring methods, which rely on field inventories, are often expensive and time-consuming. Remote sensing offers an interesting alternative, leveraging dense time series of satellite imagery and various algorithms for disturbance detection. This study presents and assesses a novel methodology for identifying forest harvesting practices (clear-cutting and thinning) using Continuous Change Detection and Classification (CCDC) algorithm, available in Google Earth Engine. The methodology comprises two steps. In the first step, performed at the pixel level, the CCDC algorithm was used to detect changes in the vegetation cover by considering Landsat 8 spectral bands, vegetation indices, and different combinations thereof. In the second step, two optimal thresholds were determined to identify forest harvesting practices based on the proportion of pixels flagged as change. This study was conducted in forest stands consisting of different conifer and broadleaf species. Accuracy was assessed using an independent set of photo-interpreted samples. The results indicated that the short-wave infrared 2 was the best individual band for forest harvesting practices identification, with an average F-score of 0.77 ± 0.06, overperforming vegetation indices. The combination of all spectral bands was the most effective to identify both clear-cuts and thinning (F-score = 0.85 ± 0.05). This combination was used to evaluate the accuracy of this approach for identifying harvesting practices over different tree species. Poplar (Populus sp.) had the highest identification rate (F-score = 0.99 ± 0.02), while black pine (Pinus nigra J.F. Arnold) stands had the lowest F-score (0.74 ± 0.05). These results highlight the ability to accurately identify forest harvesting practices even in heterogeneous forests with a high diversity of tree species using dense time series of Landsat imagery.
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
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
On the influence of spatial resolution in soil surface roughness characterization using Tls and Sfm techniques
(IEEE, 2018) Martínez de Aguirre Escobar, Alejandro; Álvarez-Mozos, Jesús; Giménez Díaz, Rafael; Milenković, Milutin; Pfeifer, Norbert; Ingeniería; Ingeniaritza
Soil surface roughness strongly affects the scattering of microwaves and determines the backscattering coefficient observed by SAR (Synthetic Aperture Radar) sensors. The aim of this study is to analyze the influence of the spatial resolution of Terrestrial Laser Scanner (TLS) and Structure from Motion (SfM) techniques to parameterize surface roughness over agricultural soils. Three experimental plots (5 x 5 meters) representing different roughness conditions were measured by TLS and SfM techniques. Roughness parameters (s and l) were calculated from profiles obtained at different spatial resolutions in parallel and in perpendicular to the tillage direction on each plot. The results showed minor differences in the parameters values between both techniques and, in general, a decreasing trend and an increasing trend for lower spatial resolutions for parameter s and l, respectively.
Open Access
Error in radar-derived soil moisture due to roughness parameterization: an analysis based on synthetical surface profiles
(MDPI, 2009) Lievens, Hans; Vernieuwe, Hilde; Álvarez-Mozos, Jesús; Baets, Bernard de; Verhoest, Niko E. C.; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak
In the past decades, many studies on soil moisture retrieval from SAR demonstrated a poor correlation between the top layer soil moisture content and observed backscatter coefficients, which mainly has been attributed to difficulties involved in the parameterization of surface roughness. The present paper describes a theoretical study, performed on synthetical surface profiles, which investigates how errors on roughness parameters are introduced by standard measurement techniques, and how they will propagate through the commonly used Integral Equation Model (IEM) into a corresponding soil moisture retrieval error for some of the currently most used SAR configurations. Key aspects influencing the error on the roughness parameterization and consequently on soil moisture retrieval are: the length of the surface profile, the number of profile measurements, the horizontal and vertical accuracy of profile measurements and the removal of trends along profiles. Moreover, it is found that soil moisture retrieval with C-band configuration generally is less sensitive to inaccuracies in roughness parameterization than retrieval with L-band configuration.
Open Access
On the assimilation set-up of ASCAT soil moisture data for improving streamflow catchment simulation
(Elsevier, 2018) Loizu Maeztu, Javier; Massari, Christian; Álvarez-Mozos, Jesús; Tarpanelli, Angelica; Brocca, Luca; Casalí Sarasíbar, Javier; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak
Assimilation of remotely sensed surface soil moisture (SSM) data into hydrological catchment models has been identified as a means to improve stream flow simulations, but reported results vary markedly depending on the particular model, catchment and assimilation procedure used. In this study, the in fluence of key aspects, such as the type of model, re-scaling technique and SSM observation error considered, were evaluated. For this aim, Advanced SCATterometer ASCAT-SSM observations were assimilated through the ensemble Kalman filter into two hydrological models of different complexity namely MISDc and TOPLATS) run on two Mediterranean catchments of similar size (750 km2). Three different re-scaling techniques were evaluated (linear re-scaling, variance matching and cumulative distribution function matching), and SSM observation error values ranging from 0.01% to 20% were considered. Four different efficiency measures were used for evaluating the results. Increases in Nash-Sutcliffe efficiency (0.03–0.15) and efficiency indices (10–45%) were obtained, especially when linear re-scaling and observation errors within 4-6% were considered. This study found out that there is a potential to improve stream flow prediction through data assimilation of remotely sensed SSM in catchments of different characteristics and with hydrological models of different conceptualizations schemes, but for that, a careful evaluation of the observation error and re-scaling technique set-up utilized is required.
Open Access
On the soil roughness parameterization problem in soil moisture retrieval of bare surfaces from synthetic aperture radar
(MDPI, 2008) Verhoest, Niko E. C.; Lievens, Hans; Wagner, Wolfgang; Álvarez-Mozos, Jesús; Moran, M. Susan; Mattia, Francesco; Proyectos e Ingeniería Rural; Landa Ingeniaritza eta Proiektuak
Synthetic Aperture Radar has shown its large potential for retrieving soil moisture maps at regional scales. However, since the backscattered signal is determined by several surface characteristics, the retrieval of soil moisture is an ill-posed problem when using single configuration imagery. Unless accurate surface roughness parameter values are available, retrieving soil moisture from radar backscatter usually provides inaccurate estimates. The characterization of soil roughness is not fully understood, and a large range of roughness parameter values can be obtained for the same surface when different measurement methodologies are used. In this paper, a literature review is made that summarizes the problems encountered when parameterizing soil roughness as well as the reported impact of the errors made on the retrieved soil moisture. A number of suggestions were made for resolving issues in roughness parameterization and studying the impact of these roughness problems on the soil moisture retrieval accuracy and scale.
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
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.