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  • Rural Digital Europe
  • 2014-2023
  • Research software
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  • CemOA
  • North American Studies

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Rajab Pourrahmati, M.;

    The importance of measuring forest biophysical parameters for ecosystem health monitoring and forest management encourages researchers to find precise, yet low-cost methods especially in mountainous and large areas. In the present study Geoscience Laser Altimeter System (GLAS) on board ICESat (Ice Cloud and land Elevation Satellite) was used to estimate three biophysical characteristics of forests located in the north of Iran: 1) maximum canopy height (Hmax), 2) Lorey's height (HLorey), and 3) Forest volume (V). A large number of Multiple Linear Regressions (MLR), Random Forest (RF) and also Artificial Neural Network regressions were developed using two different sets of variables including waveform metrics and Principal Components (PCs) produced from Principal Component Analysis (PCA). To validate and compare models, statistical criteria were calculated based on a five-fold cross validation. Best model concerning the maximum height was an MLR (RMSE=5.0m) which combined two metrics extracted from waveforms (waveform extent "Wext" and height at 50% of waveform energy "H50"), and one from Digital Elevation Model (Terrain Index: TI). The mean absolute percentage error (MAPE) of maximum height estimates was 16.4%. For Lorey's height, an ANN model using PCs and waveform extent 'Wext' outperformed other models (RMSE=3.4m, MAPE=12.3%). In order to estimate forest volume, two approaches was employed: First, estimating volume using volume-height relationship while height is GLAS estimated height; Second, estimation of forest volume directly from GLAS data by developing regressions between in situ volume and GLAS metrics. The result from first approach (116.3 m3/ha) was slightly better than the result obtained by the second approach that is a PCs-based ANN model (119.9 m3/ha). But the ANN model performed better in very low ( 800 m3/ha) volume stands. In total, the relative error of estimated forest volume was about 26%. Generally, MLR and ANN models had better performance when compared to the RF models. In addition, the accuracy of height estimations using waveform metrics was better than those based on PCs. Given the suitable results of GLAS height models (maximum and Lorey's heights), production of wall to wall height maps from synergy of remote sensing (GLAS, PALSAR, SPOT5 and Landsat-TM) and environmental data (slope, aspect, classified elevation map and also geological map) was taken under consideration. Thus, MLR and RF régressions were built between all GLAS derived heights, inside of the study area, and indices extracted from mentioned remotely sensed and environmental data. The best resulted models for Hmax (RMSE=7.4m and Ra2=0.52) and HLorey (RMSE=5.5m and Ra2=0.59) were used to produce a wall to wall maximum canopy height and Lorey' height maps. Comparison of Hmax extracted from the resulted Hmax map with true height values at the location of 32 in situ plots produced an RMSE and R2 of 5.3m and 0.71, respectively. Such a comparison for HLorey led to an RMSE and R2 of 4.3m and 0.50, respectively. Regression-kriging method was also used to produce canopy height map with considering spatial correlation between canopy heights. This approach, with the aim of improving the precision of canopy height map provided from non-spatial method, was unsuccessful which could be due to the heterogeneity of the study area in case of forest structure and topography. / L'importance de mesurer les paramètres biophysiques de la forêt pour la surveillance de la santé des écosystèmes et la gestion forestière encourage les chercheurs à trouver des méthodes précises et à faible coût en particulier sur les zones étendues et montagneuses. Dans la présente étude, Le lidar satellitaire GLAS (Geoscience Laser Altimeter System) embarqué à bord du satellite ICESat (Ice Cloud and land Elevation Satellite) a été utilisé pour estimer trois caractéristiques biophysiques des forêts situées dans le nord de l'Iran: 1) hauteur maximale de la canopée (Hmax), 2) hauteur de Lorey (HLorey), et 3) le volume du bois (V). Des régressions linéaires multiples (RLM), des modèles basés sur les Forêts Aléatoires (FA : Random Forest) et aussi des réseaux de neurones (ANN) ont été développés à l'aide de deux ensembles différents de variables incluant des métriques obtenues à partir des formes d'onde GLAS et des composantes principales (CP) produites à partir de l'analyse en composantes principales (ACP) des données GLAS. Pour valider et comparer les modèles, des critères statistiques ont été calculées sur la base d'une validation croisée. Le meilleur modèle pour l'estimation de la hauteur maximale a été obtenu avec une régression RLM (RMSE = 5.0 m) qui combine deux métriques extraites des formes d'onde GLAS (étendue et hauteur pour une énergie à 50%, respectivement Wext et H50), et un paramètre issu du modèle numérique d'élévation (Indice de relief TI). L'erreur moyenne absolue en pourcentage (MAPE) sur les estimations de la hauteur maximale est de 16.4%. Pour la hauteur de Lorey, un modèle basé sur les réseaux de neurones et utilisant des CPs et le Wext fournit le meilleur résultat avec RMSE = 3.4 m et MAPE = 12.3%. Afin d'estimer le volume du bois, deux approches ont été utilisées: (1) estimation du volume à l'aide d'une relation volume-hauteur avec une hauteur estimée à partir de données GLAS et (2) estimation du volume du bois directement à partir des données GLAS en développant des régressions entre le volume in situ et les métriques GLAS. Le résultat de la première approche (RMSE=116.3 m3/ha) était légèrement meilleur que ceux obtenus avec la seconde approche. Par exemple, le réseau de neurones basé sur les PCs donnait un RMSE de 119.9 m3/ha mais avec des meilleurs résultats que l'approche basée sur la relation volume-hauteur pour les faibles ( 800 m3/ha) volumes. Au total, l'erreur relative sur le volume de bois est estimée à environ 26%. En général, les modèles RLM et ANN avaient des meilleures performances par rapport aux modèles de FA. En outre, la précision sur l'estimation de la hauteur à l'aide de métriques issues des formes d'onde GLAS est meilleure que celles basées sur les CPs. Compte tenu des bons résultats obtenus avec les modèles de hauteur GLAS (hauteurs maximale et de Lorey), la production de la carte des hauteurs d'étude par une utilisation combinée de données de télédétection lidar, radar et optique (GLAS, PALSAR, SPOT-5 et Landsat-TM) et de données environnementales (pente, aspect, et altitude du terrain ainsi que la carte géologique) a été effectuée à l'intérieur de notre zone. Ainsi, des régressions RLM et FA ont été construites entre toutes les hauteurs dérivées des données GLAS, à l'intérieur de la zone d'étude, et les indices extraits des données de télédétection et des paramètres environnementaux. Les meilleurs modèles entrainés pour estimer Hmax (RMSE = 7.4 m et Ra2=0.52) et HLorey (RMSE = 5.5 m et Ra2=0.59) ont été utilisées pour produire les cartes de hauteurs. La comparaison des Hmax de la carte obtenue avec les valeurs de Hmax in situ à l'endroit de 32 parcelles produit un RMSE de 5.3 m et un R2 de 0.71. Une telle comparaison pour HLorey conduit à un RMSE de 4.3m et un R2 de 0.50. Une méthode de régression-krigeage a également été utilisée pour produire une carte des hauteurs en considérant la corrélation spatiale entre les hauteurs. Cette approche, testée dans le but d'améliorer la précision de la carte de la hauteur du couvert fournie par la méthode non-spatiale, a échouée due à l'hétérogénéité de la zone d'étude en termes de la structure forestière et de la topographie.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ CemOAarrow_drop_down
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    CemOA
    2016
    Data sources: CemOA
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      CemOA
      2016
      Data sources: CemOA
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    Authors: De Thoisy, B.; Fayad, I.; Clement, L.; Barrioz, S.; +2 Authors

    Tropical forests with a low human population and absence of large-scale deforestation provide unique opportunities to study successful conservation strategies, which should be based on adequate monitoring tools. This study explored the conservation status of a large predator, the jaguar, considered an indicator of the maintenance of how well ecological processes are maintained. We implemented an original integrative approach, exploring successive ecosystem status proxies, from habitats and responses to threats of predators and their prey, to canopy structure and forest biomass. Niche modeling allowed identification of more suitable habitats, significantly related to canopy height and forest biomass. Capture/recapture methods showed that jaguar density was higher in habitats identified as more suitable by the niche model. Surveys of ungulates, large rodents and birds also showed higher density where jaguars were more abundant. Although jaguar density does not allow early detection of overall vertebrate community collapse, a decrease in the abundance of large terrestrial birds was noted as good first evidence of disturbance. The most promising tool comes from easily acquired LiDAR data and radar images: a decrease in canopy roughness was closely associated with the disturbance of forests and associated decreasing vertebrate biomass. This mixed approach, focusing on an apex predator, ecological modeling and remote-sensing information, not only helps detect early population declines in large mammals, but is also useful to discuss the relevance of large predators as indicators and the efficiency of conservation measures. It can also be easily extrapolated and adapted in a timely manner, since important open-source data are increasingly available and relevant for large-scale and real-time monitoring of biodiversity.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ CemOAarrow_drop_down
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    CemOA
    2016
    Data sources: CemOA
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ CemOAarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      CemOA
      2016
      Data sources: CemOA
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Ogilvie, A.; Belaud, G.; Massuel, S.; Mulligan, M.; +3 Authors

    Small reservoirs represent a critical water supply to millions of farmers across semi-arid regions, but their hydrological modelling suffers from data scarcity and highly variable and localised rainfall intensities. Increased availability of satellite imagery provide substantial opportunities but the monitoring of surface water resources is constrained by the small size and rapid flood declines in small reservoirs. To overcome remote sensing and hydrological modelling difficulties, the benefits of combining field data, numerical modelling and satellite observations to monitor small reservoirs were investigated. Building on substantial field data, coupled daily rainfall-runoff and water balance models were developed for 7 small reservoirs (1'10 ha) in semi arid Tunisia over 1999'2014. Surface water observations from MNDWI classifications on 546 Landsat TM, ETM+ and OLI sensors were used to update model outputs through an Ensemble (n = 100) Kalman Filter over the 15 year period. The Ensemble Kalman Filter, providing near-real time corrections, reduced runoff errors by modulating incorrectly modelled rainfall events, while compensating for Landsat's limited temporal resolution and correcting classification outliers. Validated against long term hydrometric field data, daily volume root mean square errors (RMSE) decreased by 54% to 31 200 m3 across 7 lakes compared to the initial model forecast. The method reproduced the amplitude and timing of major floods and their decline phases, providing a valuable approach to improve hydrological monitoring (NSE increase from 0.64 up to 0.94) of flood dynamics in small water bodies. In the smallest and data-scarce lakes, higher temporal and spatial resolution time series are essential to improve monitoring accuracy.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ CemOAarrow_drop_down
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    CemOA
    2018
    Data sources: CemOA
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ CemOAarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      CemOA
      2018
      Data sources: CemOA
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Corbane, C.; Guttler, F.; Alleaume, S.; Ienco, D.; +1 Authors

    Due to their high degree of vegetation heterogeneity, fragmentation and biodiversity, Mediterranean natural habitats are difficult to assess and monitor with in-situ observations solely. Together with standardized ground plots and regular in-situ measurements, remote sensing is a powerful monitoring device that can contribute to a better understanding of the diversity of natural and semi-natural habitats and to monitor their phenology. In this paper, we implemented a systematic test of the suitability of multiseasonal remote sensing data for monitoring the phenological variations of natural habitats in a Mediterranean landscape. Six multispectral sensor signals were simulated for comparison based on their spectral response curves and in-situ averaged spectra collected at monthly intervals between February and October 2013 (IKONOS, Landsat 5 TM, Landsat 8, Pléiades, Sentinel-2, and Worldview-2). The simulations and comparisons performed in this test showed that Sentinel-2 sensor has the higher sensitivity to the variations in the coverage of photosynthetic vegetation thus offering interesting perspectives for operational monitoring of natural habitats.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ CemOAarrow_drop_down
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    CemOA
    2014
    Data sources: CemOA
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ CemOAarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      CemOA
      2014
      Data sources: CemOA
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Guttler, F.; Ienco, D.; Nin, J.; Teisseire, M.; +1 Authors

    Enhancing the frequency of satellite acquisitions represents a key issue for Earth Observation community nowadays. Repeated observations are crucial for monitoring purposes, particularly when intra-annual process should be taken into account. Time series of images constitute a valuable source of information in these cases. The goal of this paper is to propose a new methodological framework to automatically detect and extract spatiotemporal information from satellite image time series (SITS). Existing methods dealing with such kind of data are usually classification-oriented and cannot provide information about evolutions and temporal behaviors. In this paper we propose a graph-based strategy that combines object-based image analysis (OBIA) with data mining techniques. Image objects computed at each individual timestamp are connected across the time series and generates a set of evolution graphs. Each evolution graph is associated to a particular area within the study site and stores information about its temporal evolution. Such information can be deeply explored at the evolution graph scale or used to compare the graphs and supply a general picture at the study site scale. We validated our framework on two study sites located in the South of France and involving different types of natural, semi-natural and agricultural areas. The results obtained from a Landsat SITS support the quality of the methodological approach and illustrate how the framework can be employed to extract and characterize spatiotemporal dynamics.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ CemOAarrow_drop_down
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    CemOA
    2017
    Data sources: CemOA
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ CemOAarrow_drop_down
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      CemOA
      2017
      Data sources: CemOA
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    Authors: Alatrista Salas, H.; Azé, J.; Bringay, S.; Cernesson, F.; +2 Authors

    Rapid population growth and human activity (such as agriculture, industry, transports,...) development have increased vulnerability risk for water resources. Due to the complexity of natural processes and the numerous interactions between hydro-systems and human pressures, water quality is difficult to be quantified. In this context, we present a knowledge discovery process applied to hydrological data. To achieve this objective, we combine successive methods to extract knowledge on data collected at stations located along several rivers. Firstly, data is pre-processed in order to obtain different spatial proximities. Later, we apply a standard algorithm to extract sequential patterns. Finally we propose a combination of two techniques (1) to filter patterns based on interest measure, and; (2) to group and present them graphically, to help the experts. Such elements can be used to assess spatialized indicators to assist the interpretation of ecological and river monitoring pressure data.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ CemOAarrow_drop_down
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    CemOA
    2015
    Data sources: CemOA
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ CemOAarrow_drop_down
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      CemOA
      2015
      Data sources: CemOA
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    Authors: Gorrab, A.; Simonneaux, V.; Zribi, M.; Saadi, S.; +3 Authors

    The present study highlights the potential of multi-temporal X-band Synthetic Aperture Radar (SAR) moisture products to be used for the calibration of a model reproducing soil moisture (SM) variations. We propose the MHYSAN model (Modèle de bilan HYdrique des Sols Agricoles Nus) for simulating soil water balance of bare soils. This model was used to simulate surface evaporation fluxes and SM content at daily time scale over a semi-arid, bare agricultural site in Tunisia (North Africa). Two main approaches are considered in this study. Firstly, the MHYSAN model was successfully calibrated for seven sites using continuous thetaprobe measurements at two depths. Then the possibility to extrapolate local SM simulations at distant sites, based on soil texture similarity only, was tested. This extrapolation was assessed using SAR estimates and manual thetaprobe measurements of SM recorded at these distant sites. The results reveal a bias of approximately 0.63% and 3.04%, and an RMSE equal to 6.11% and 4.5%, for the SAR volumetric SM and manual thetaprobe measurements, respectively. In a second approach, the MHYSAN model was calibrated using seven very high resolution SAR (TerraSAR-X) SM outputs ranging over only two months. The simulated SM were validated using continuous thetaprobe measurements during 15 months. Although the SM was measured on only seven different dates for the purposes of calibration, satisfactory results 30 were obtained as a result of the wide range of SM values recorded in these seven images. This led to good overall calibration of the soil parameters, thus demonstrating the considerable potential of Sentinel-1 images for daily soil moisture monitoring using simple models.

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    CemOA
    2017
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      CemOA
      2017
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    Authors: Nasrallah, A.; Baghdadi, N.; Mhawej, M.; Faour, G.; +3 Authors

    Global wheat production reached 754.8 million tons in 2017, according to the FAO database. While wheat is considered as a staple food for many populations across the globe, mapping wheat could be an effective tool to achieve the SDG2 sustainable development goal-End Hunger and Secure Food Security. In Lebanon, this crop is supported financially, and sometimes technically, by the Lebanese government. However, there is a lack of statistical databases, at both national and regional scales, as well as critical information much needed in the subsidy and compensation system. In this context, this study proposes an innovative approach, named Simple and Effective Wheat Mapping Approach (SEWMA), to map the winter wheat areas grown in the Bekaa plain, the primary wheat production area in Lebanon, in the years of 2016 and 2017. The proposed methodology is a tree-like approach relying on the Normalized Difference Vegetation Index (NDVI) values of four-month period that coincides with several phenological stages of wheat (i.e., tillering, stem extension, heading, flowering and ripening). The usage of the freely available Sentinel-2 imageries, with a high spatial (10 m) and temporal (5 days) resolutions, was necessary, particularly due to the small sized and overlapped plots encountered in the study area. Concerning the wheat areas, results show that there was a decrease from 11,063 ± 1309 ha in 2016 to 7605 ± 1184 in 2017. When SEWMA was applied using 2016 ground truth data, the overall accuracy reached 87.0% on 2017 data, whereas, when implemented using 2017 ground truth data, the overall accuracy was 82.6% on 2016 data. The novelty resides in executing early classification output (up to six weeks before harvest) as well as distinguishing wheat from other winter cereal crops with similar NDVI yearly profiles (i.e., barley and triticale). SEWMA offers a simple, yet effective and budget-saving approach providing early-season classification information, very crucial to decision support systems and the Lebanese government concerning, but not limited to, food production, trade, management and agricultural financial support.

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    CemOA
    2018
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      CemOA
      2018
      Data sources: CemOA
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    Authors: Baghdadi, N.; Bazzi, H.; El Hajj, M.; Zribi, M.;

    The objective of this paper is to evaluate the potential of Sentinel-1 Synthetic Aperture Radar "SAR" data (C-band) for monitoring agricultural frozen soils. First, investigations were conducted from simulated radar signal data using a SAR backscattering model combined with a dielectric mixing model. Then, Sentinel-1 images acquired at a study site near Paris, France were analyzed using temperature data to investigate the potential of the new Sentinel-1 SAR sensor for frozen soil mapping. The results show that the SAR backscattering coefficient decreases when the soil temperature drops below 0 °C. This decrease in signal is the most important for temperatures that ranges between 0 and -5 °C. A difference of at least 2 dB is observed between unfrozen soils and frozen soils. This difference increases under freezing condition when the temperature at the image acquisition date decreases. In addition, results show that the potential of the C-band radar signal for the discrimination of frozen soils slightly decreases when the soil moisture decreases (simulated data were used with soil moisture contents of 20 and 30 vol%). The difference between the backscattering coefficient of unfrozen soil and the backscattering coefficient of frozen soil decreases by approximately 1 dB when the soil moisture decreases from 30 to 20 vol%). Finally, the results show that both VV and VH allow a good detection of frozen soils but the sensitivity of VH is higher by approximately 1.5 dB. In conclusion, this study shows that the difference between a reference image acquired without freezing and an image acquired under freezing conditions is a good tool for detecting frozen soils.

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    CemOA
    2018
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      CemOA
      2018
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Rajab Pourrahmati, M.;

    The importance of measuring forest biophysical parameters for ecosystem health monitoring and forest management encourages researchers to find precise, yet low-cost methods especially in mountainous and large areas. In the present study Geoscience Laser Altimeter System (GLAS) on board ICESat (Ice Cloud and land Elevation Satellite) was used to estimate three biophysical characteristics of forests located in the north of Iran: 1) maximum canopy height (Hmax), 2) Lorey's height (HLorey), and 3) Forest volume (V). A large number of Multiple Linear Regressions (MLR), Random Forest (RF) and also Artificial Neural Network regressions were developed using two different sets of variables including waveform metrics and Principal Components (PCs) produced from Principal Component Analysis (PCA). To validate and compare models, statistical criteria were calculated based on a five-fold cross validation. Best model concerning the maximum height was an MLR (RMSE=5.0m) which combined two metrics extracted from waveforms (waveform extent "Wext" and height at 50% of waveform energy "H50"), and one from Digital Elevation Model (Terrain Index: TI). The mean absolute percentage error (MAPE) of maximum height estimates was 16.4%. For Lorey's height, an ANN model using PCs and waveform extent 'Wext' outperformed other models (RMSE=3.4m, MAPE=12.3%). In order to estimate forest volume, two approaches was employed: First, estimating volume using volume-height relationship while height is GLAS estimated height; Second, estimation of forest volume directly from GLAS data by developing regressions between in situ volume and GLAS metrics. The result from first approach (116.3 m3/ha) was slightly better than the result obtained by the second approach that is a PCs-based ANN model (119.9 m3/ha). But the ANN model performed better in very low ( 800 m3/ha) volume stands. In total, the relative error of estimated forest volume was about 26%. Generally, MLR and ANN models had better performance when compared to the RF models. In addition, the accuracy of height estimations using waveform metrics was better than those based on PCs. Given the suitable results of GLAS height models (maximum and Lorey's heights), production of wall to wall height maps from synergy of remote sensing (GLAS, PALSAR, SPOT5 and Landsat-TM) and environmental data (slope, aspect, classified elevation map and also geological map) was taken under consideration. Thus, MLR and RF régressions were built between all GLAS derived heights, inside of the study area, and indices extracted from mentioned remotely sensed and environmental data. The best resulted models for Hmax (RMSE=7.4m and Ra2=0.52) and HLorey (RMSE=5.5m and Ra2=0.59) were used to produce a wall to wall maximum canopy height and Lorey' height maps. Comparison of Hmax extracted from the resulted Hmax map with true height values at the location of 32 in situ plots produced an RMSE and R2 of 5.3m and 0.71, respectively. Such a comparison for HLorey led to an RMSE and R2 of 4.3m and 0.50, respectively. Regression-kriging method was also used to produce canopy height map with considering spatial correlation between canopy heights. This approach, with the aim of improving the precision of canopy height map provided from non-spatial method, was unsuccessful which could be due to the heterogeneity of the study area in case of forest structure and topography. / L'importance de mesurer les paramètres biophysiques de la forêt pour la surveillance de la santé des écosystèmes et la gestion forestière encourage les chercheurs à trouver des méthodes précises et à faible coût en particulier sur les zones étendues et montagneuses. Dans la présente étude, Le lidar satellitaire GLAS (Geoscience Laser Altimeter System) embarqué à bord du satellite ICESat (Ice Cloud and land Elevation Satellite) a été utilisé pour estimer trois caractéristiques biophysiques des forêts situées dans le nord de l'Iran: 1) hauteur maximale de la canopée (Hmax), 2) hauteur de Lorey (HLorey), et 3) le volume du bois (V). Des régressions linéaires multiples (RLM), des modèles basés sur les Forêts Aléatoires (FA : Random Forest) et aussi des réseaux de neurones (ANN) ont été développés à l'aide de deux ensembles différents de variables incluant des métriques obtenues à partir des formes d'onde GLAS et des composantes principales (CP) produites à partir de l'analyse en composantes principales (ACP) des données GLAS. Pour valider et comparer les modèles, des critères statistiques ont été calculées sur la base d'une validation croisée. Le meilleur modèle pour l'estimation de la hauteur maximale a été obtenu avec une régression RLM (RMSE = 5.0 m) qui combine deux métriques extraites des formes d'onde GLAS (étendue et hauteur pour une énergie à 50%, respectivement Wext et H50), et un paramètre issu du modèle numérique d'élévation (Indice de relief TI). L'erreur moyenne absolue en pourcentage (MAPE) sur les estimations de la hauteur maximale est de 16.4%. Pour la hauteur de Lorey, un modèle basé sur les réseaux de neurones et utilisant des CPs et le Wext fournit le meilleur résultat avec RMSE = 3.4 m et MAPE = 12.3%. Afin d'estimer le volume du bois, deux approches ont été utilisées: (1) estimation du volume à l'aide d'une relation volume-hauteur avec une hauteur estimée à partir de données GLAS et (2) estimation du volume du bois directement à partir des données GLAS en développant des régressions entre le volume in situ et les métriques GLAS. Le résultat de la première approche (RMSE=116.3 m3/ha) était légèrement meilleur que ceux obtenus avec la seconde approche. Par exemple, le réseau de neurones basé sur les PCs donnait un RMSE de 119.9 m3/ha mais avec des meilleurs résultats que l'approche basée sur la relation volume-hauteur pour les faibles ( 800 m3/ha) volumes. Au total, l'erreur relative sur le volume de bois est estimée à environ 26%. En général, les modèles RLM et ANN avaient des meilleures performances par rapport aux modèles de FA. En outre, la précision sur l'estimation de la hauteur à l'aide de métriques issues des formes d'onde GLAS est meilleure que celles basées sur les CPs. Compte tenu des bons résultats obtenus avec les modèles de hauteur GLAS (hauteurs maximale et de Lorey), la production de la carte des hauteurs d'étude par une utilisation combinée de données de télédétection lidar, radar et optique (GLAS, PALSAR, SPOT-5 et Landsat-TM) et de données environnementales (pente, aspect, et altitude du terrain ainsi que la carte géologique) a été effectuée à l'intérieur de notre zone. Ainsi, des régressions RLM et FA ont été construites entre toutes les hauteurs dérivées des données GLAS, à l'intérieur de la zone d'étude, et les indices extraits des données de télédétection et des paramètres environnementaux. Les meilleurs modèles entrainés pour estimer Hmax (RMSE = 7.4 m et Ra2=0.52) et HLorey (RMSE = 5.5 m et Ra2=0.59) ont été utilisées pour produire les cartes de hauteurs. La comparaison des Hmax de la carte obtenue avec les valeurs de Hmax in situ à l'endroit de 32 parcelles produit un RMSE de 5.3 m et un R2 de 0.71. Une telle comparaison pour HLorey conduit à un RMSE de 4.3m et un R2 de 0.50. Une méthode de régression-krigeage a également été utilisée pour produire une carte des hauteurs en considérant la corrélation spatiale entre les hauteurs. Cette approche, testée dans le but d'améliorer la précision de la carte de la hauteur du couvert fournie par la méthode non-spatiale, a échouée due à l'hétérogénéité de la zone d'étude en termes de la structure forestière et de la topographie.

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    CemOA
    2016
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      CemOA
      2016
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    Authors: De Thoisy, B.; Fayad, I.; Clement, L.; Barrioz, S.; +2 Authors

    Tropical forests with a low human population and absence of large-scale deforestation provide unique opportunities to study successful conservation strategies, which should be based on adequate monitoring tools. This study explored the conservation status of a large predator, the jaguar, considered an indicator of the maintenance of how well ecological processes are maintained. We implemented an original integrative approach, exploring successive ecosystem status proxies, from habitats and responses to threats of predators and their prey, to canopy structure and forest biomass. Niche modeling allowed identification of more suitable habitats, significantly related to canopy height and forest biomass. Capture/recapture methods showed that jaguar density was higher in habitats identified as more suitable by the niche model. Surveys of ungulates, large rodents and birds also showed higher density where jaguars were more abundant. Although jaguar density does not allow early detection of overall vertebrate community collapse, a decrease in the abundance of large terrestrial birds was noted as good first evidence of disturbance. The most promising tool comes from easily acquired LiDAR data and radar images: a decrease in canopy roughness was closely associated with the disturbance of forests and associated decreasing vertebrate biomass. This mixed approach, focusing on an apex predator, ecological modeling and remote-sensing information, not only helps detect early population declines in large mammals, but is also useful to discuss the relevance of large predators as indicators and the efficiency of conservation measures. It can also be easily extrapolated and adapted in a timely manner, since important open-source data are increasingly available and relevant for large-scale and real-time monitoring of biodiversity.

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    CemOA
    2016
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      CemOA
      2016
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    Authors: Ogilvie, A.; Belaud, G.; Massuel, S.; Mulligan, M.; +3 Authors

    Small reservoirs represent a critical water supply to millions of farmers across semi-arid regions, but their hydrological modelling suffers from data scarcity and highly variable and localised rainfall intensities. Increased availability of satellite imagery provide substantial opportunities but the monitoring of surface water resources is constrained by the small size and rapid flood declines in small reservoirs. To overcome remote sensing and hydrological modelling difficulties, the benefits of combining field data, numerical modelling and satellite observations to monitor small reservoirs were investigated. Building on substantial field data, coupled daily rainfall-runoff and water balance models were developed for 7 small reservoirs (1'10 ha) in semi arid Tunisia over 1999'2014. Surface water observations from MNDWI classifications on 546 Landsat TM, ETM+ and OLI sensors were used to update model outputs through an Ensemble (n = 100) Kalman Filter over the 15 year period. The Ensemble Kalman Filter, providing near-real time corrections, reduced runoff errors by modulating incorrectly modelled rainfall events, while compensating for Landsat's limited temporal resolution and correcting classification outliers. Validated against long term hydrometric field data, daily volume root mean square errors (RMSE) decreased by 54% to 31 200 m3 across 7 lakes compared to the initial model forecast. The method reproduced the amplitude and timing of major floods and their decline phases, providing a valuable approach to improve hydrological monitoring (NSE increase from 0.64 up to 0.94) of flood dynamics in small water bodies. In the smallest and data-scarce lakes, higher temporal and spatial resolution time series are essential to improve monitoring accuracy.

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    CemOA
    2018
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      CemOA
      2018
      Data sources: CemOA
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    Authors: Corbane, C.; Guttler, F.; Alleaume, S.; Ienco, D.; +1 Authors

    Due to their high degree of vegetation heterogeneity, fragmentation and biodiversity, Mediterranean natural habitats are difficult to assess and monitor with in-situ observations solely. Together with standardized ground plots and regular in-situ measurements, remote sensing is a powerful monitoring device that can contribute to a better understanding of the diversity of natural and semi-natural habitats and to monitor their phenology. In this paper, we implemented a systematic test of the suitability of multiseasonal remote sensing data for monitoring the phenological variations of natural habitats in a Mediterranean landscape. Six multispectral sensor signals were simulated for comparison based on their spectral response curves and in-situ averaged spectra collected at monthly intervals between February and October 2013 (IKONOS, Landsat 5 TM, Landsat 8, Pléiades, Sentinel-2, and Worldview-2). The simulations and comparisons performed in this test showed that Sentinel-2 sensor has the higher sensitivity to the variations in the coverage of photosynthetic vegetation thus offering interesting perspectives for operational monitoring of natural habitats.

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    CemOA
    2014
    Data sources: CemOA
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      CemOA
      2014
      Data sources: CemOA
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Guttler, F.; Ienco, D.; Nin, J.; Teisseire, M.; +1 Authors

    Enhancing the frequency of satellite acquisitions represents a key issue for Earth Observation community nowadays. Repeated observations are crucial for monitoring purposes, particularly when intra-annual process should be taken into account. Time series of images constitute a valuable source of information in these cases. The goal of this paper is to propose a new methodological framework to automatically detect and extract spatiotemporal information from satellite image time series (SITS). Existing methods dealing with such kind of data are usually classification-oriented and cannot provide information about evolutions and temporal behaviors. In this paper we propose a graph-based strategy that combines object-based image analysis (OBIA) with data mining techniques. Image objects computed at each individual timestamp are connected across the time series and generates a set of evolution graphs. Each evolution graph is associated to a particular area within the study site and stores information about its temporal evolution. Such information can be deeply explored at the evolution graph scale or used to compare the graphs and supply a general picture at the study site scale. We validated our framework on two study sites located in the South of France and involving different types of natural, semi-natural and agricultural areas. The results obtained from a Landsat SITS support the quality of the methodological approach and illustrate how the framework can be employed to extract and characterize spatiotemporal dynamics.

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    CemOA
    2017
    Data sources: CemOA
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      CemOA
      2017
      Data sources: CemOA
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    Authors: Alatrista Salas, H.; Azé, J.; Bringay, S.; Cernesson, F.; +2 Authors

    Rapid population growth and human activity (such as agriculture, industry, transports,...) development have increased vulnerability risk for water resources. Due to the complexity of natural processes and the numerous interactions between hydro-systems and human pressures, water quality is difficult to be quantified. In this context, we present a knowledge discovery process applied to hydrological data. To achieve this objective, we combine successive methods to extract knowledge on data collected at stations located along several rivers. Firstly, data is pre-processed in order to obtain different spatial proximities. Later, we apply a standard algorithm to extract sequential patterns. Finally we propose a combination of two techniques (1) to filter patterns based on interest measure, and; (2) to group and present them graphically, to help the experts. Such elements can be used to assess spatialized indicators to assist the interpretation of ecological and river monitoring pressure data.

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    CemOA
    2015
    Data sources: CemOA
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      CemOA
      2015
      Data sources: CemOA
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    Authors: Gorrab, A.; Simonneaux, V.; Zribi, M.; Saadi, S.; +3 Authors

    The present study highlights the potential of multi-temporal X-band Synthetic Aperture Radar (SAR) moisture products to be used for the calibration of a model reproducing soil moisture (SM) variations. We propose the MHYSAN model (Modèle de bilan HYdrique des Sols Agricoles Nus) for simulating soil water balance of bare soils. This model was used to simulate surface evaporation fluxes and SM content at daily time scale over a semi-arid, bare agricultural site in Tunisia (North Africa). Two main approaches are considered in this study. Firstly, the MHYSAN model was successfully calibrated for seven sites using continuous thetaprobe measurements at two depths. Then the possibility to extrapolate local SM simulations at distant sites, based on soil texture similarity only, was tested. This extrapolation was assessed using SAR estimates and manual thetaprobe measurements of SM recorded at these distant sites. The results reveal a bias of approximately 0.63% and 3.04%, and an RMSE equal to 6.11% and 4.5%, for the SAR volumetric SM and manual thetaprobe measurements, respectively. In a second approach, the MHYSAN model was calibrated using seven very high resolution SAR (TerraSAR-X) SM outputs ranging over only two months. The simulated SM were validated using continuous thetaprobe measurements during 15 months. Although the SM was measured on only seven different dates for the purposes of calibration, satisfactory results 30 were obtained as a result of the wide range of SM values recorded in these seven images. This led to good overall calibration of the soil parameters, thus demonstrating the considerable potential of Sentinel-1 images for daily soil moisture monitoring using simple models.

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    CemOA
    2017
    Data sources: CemOA
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      CemOA
      2017
      Data sources: CemOA
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    Authors: Nasrallah, A.; Baghdadi, N.; Mhawej, M.; Faour, G.; +3 Authors

    Global wheat production reached 754.8 million tons in 2017, according to the FAO database. While wheat is considered as a staple food for many populations across the globe, mapping wheat could be an effective tool to achieve the SDG2 sustainable development goal-End Hunger and Secure Food Security. In Lebanon, this crop is supported financially, and sometimes technically, by the Lebanese government. However, there is a lack of statistical databases, at both national and regional scales, as well as critical information much needed in the subsidy and compensation system. In this context, this study proposes an innovative approach, named Simple and Effective Wheat Mapping Approach (SEWMA), to map the winter wheat areas grown in the Bekaa plain, the primary wheat production area in Lebanon, in the years of 2016 and 2017. The proposed methodology is a tree-like approach relying on the Normalized Difference Vegetation Index (NDVI) values of four-month period that coincides with several phenological stages of wheat (i.e., tillering, stem extension, heading, flowering and ripening). The usage of the freely available Sentinel-2 imageries, with a high spatial (10 m) and temporal (5 days) resolutions, was necessary, particularly due to the small sized and overlapped plots encountered in the study area. Concerning the wheat areas, results show that there was a decrease from 11,063 ± 1309 ha in 2016 to 7605 ± 1184 in 2017. When SEWMA was applied using 2016 ground truth data, the overall accuracy reached 87.0% on 2017 data, whereas, when implemented using 2017 ground truth data, the overall accuracy was 82.6% on 2016 data. The novelty resides in executing early classification output (up to six weeks before harvest) as well as distinguishing wheat from other winter cereal crops with similar NDVI yearly profiles (i.e., barley and triticale). SEWMA offers a simple, yet effective and budget-saving approach providing early-season classification information, very crucial to decision support systems and the Lebanese government concerning, but not limited to, food production, trade, management and agricultural financial support.

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    CemOA
    2018
    Data sources: CemOA
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      CemOA
      2018
      Data sources: CemOA
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    Authors: Baghdadi, N.; Bazzi, H.; El Hajj, M.; Zribi, M.;

    The objective of this paper is to evaluate the potential of Sentinel-1 Synthetic Aperture Radar "SAR" data (C-band) for monitoring agricultural frozen soils. First, investigations were conducted from simulated radar signal data using a SAR backscattering model combined with a dielectric mixing model. Then, Sentinel-1 images acquired at a study site near Paris, France were analyzed using temperature data to investigate the potential of the new Sentinel-1 SAR sensor for frozen soil mapping. The results show that the SAR backscattering coefficient decreases when the soil temperature drops below 0 °C. This decrease in signal is the most important for temperatures that ranges between 0 and -5 °C. A difference of at least 2 dB is observed between unfrozen soils and frozen soils. This difference increases under freezing condition when the temperature at the image acquisition date decreases. In addition, results show that the potential of the C-band radar signal for the discrimination of frozen soils slightly decreases when the soil moisture decreases (simulated data were used with soil moisture contents of 20 and 30 vol%). The difference between the backscattering coefficient of unfrozen soil and the backscattering coefficient of frozen soil decreases by approximately 1 dB when the soil moisture decreases from 30 to 20 vol%). Finally, the results show that both VV and VH allow a good detection of frozen soils but the sensitivity of VH is higher by approximately 1.5 dB. In conclusion, this study shows that the difference between a reference image acquired without freezing and an image acquired under freezing conditions is a good tool for detecting frozen soils.

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    CemOA
    2018
    Data sources: CemOA
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      CemOA
      2018
      Data sources: CemOA
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