Simbad Conferences & Publications

Conferences and Scientific Jounal publications from the SIMBAD team or with contribution from members of the SIMBAD team.

The 7th International Fire Behaviour and Fuels Conference

Space-time Universal Kriging, vegetation indices and meteorological variables to predict Live Fuel Moisture Content in Mediterranean shrubs

A. Viñuales et al. 2024

Live fuel moisture content (LFMC), is a crucial factor influencing fire behaviour, rendering its precise estimation indispensable for effective fire risk assessment and management. However, its accurate estimation over large areas still remains a significant challenge, given the dynamic nature of live forest fuels.

During the past decade, substantial research on this topic has been conducted by diverse research groups worldwide, employing different approaches such as empirical methods or radiative transfer models.  The use of satellite-derived information has greatly contributed to understanding the dynamics of LFMC, facilitated by the widespread availability of these products.

One promising technique for LFMC estimation over large areas is space-time Universal Kriging (UK), an interpolation technique that leverages spatial autocorrelation to increase accuracy, considering trends from auxiliary variables. However, to our knowledge, there needs to be more research when it comes to applying UK interpolation to the study of LFMC.

The aim of this study was to establish a robust method for estimating and monitoring LFMC by employing spatio-temporal modelling with a universal kriging approach. This research was conducted in the Sierra Morena region of Andalusia, Spain, focusing on Cistus ladanifer shrub patches, well-known for their high fire risk. A total of 38 sampling plots were established to monitor LFMC over a 15-month period (June 2021 – September 2022), with destructive sampling techniques used to determine LFMC in the laboratory. The auxiliary variables included the Enhanced Vegetation Index (EVI) derived from Sentinel-2 imagery, temperature, and the day of the year to account for seasonality.

The variogram was fitted using the IRWLS (Iterated Reweighted Least Squares) method,  resulting in a model with an RMSE (Root Mean Squared Error) score of 11.78%. In terms of LFMC variability explained by the model, 45.70% was attributed to auxiliary variables, 43.76% to temporal autocorrelation, and 4.20% to spatial autocorrelation. The remaining variability unexplained by the model was 6.34%. Estimation maps for monospecific stands of C. ladanifer were generated within the study area. The data from the last field campaign (September 2022) were excluded from the initial analysis to serve as a validation dataset, achieving an RMSE of 20.15%. Lastly, a prediction map was produced.

These findings have practical implications for forest fuel modelling, fire risk evaluation, and operational decision-making concerning fire prevention and management not only in the study area but also in potentially similar regions.

13th EARSeL Workshop on Imaging Spectroscopy 2024

From hyper- to multi-spectral databases: training machine learning models for turbidity estimation

M. Chowdhury et al. 2024

Turbidity measurements are a way to assess water clarity based on scattered light. Higher turbidity implies more scattered light due to various materials, including nutrients, bacteria, algae, and organic/inorganic particles. Higher turbidity affects marine ecosystems by impeding phytoplankton growth and indicates nutrient loading that can initiate eutrophication. Estimations of turbidity are essential for complying with the EU Marine Strategy Framework Directive. In this study, the GLORIA global dataset and Sentinel-2 (S2) satellite data are used to develop new algorithms for the estimation of turbidity using machine-learning approaches. Hyperspectral and turbidity in situ measurements, together with S2-Multipectral Imager (MSI) measurements are combined in a new dataset, which comprises representative data from where the models can learn and predict medium to very high turbidity up to 2200 FNU.

Nature Scientific Reports

M. Chowdhury et al. 2024

Seagrasses are undergoing widespread loss due to anthropogenic pressure and climate change. Since 1960, the Mediterranean seascape lost 13–50% of the areal extent of its dominant and endemic seagrass- Posidonia oceanica , which regulates its ecosystem. Many conservation and restoration projects failed due to poor site selection and lack of long-term monitoring. Here, we present a fast and efficient operational approach based on a deep-learning artificial intelligence model using Sentinel-2 data to map the spatial extent of the meadows, enabling short and long-term monitoring, and identifying the impacts of natural and human-induced stressors and changes at different timescales. We apply ACOLITE atmospheric correction to the satellite data and use the output to train the model along with the ancillary data and map the extent of the meadows. We apply noise-removing filters to enhance the map quality. We obtain 74–92% of overall accuracy, 72–91% of user’s accuracy, and 81–92% of producer’s accuracy, where high accuracies are observed at 0-25m depth. Our model is easily adaptable to other regions and can produce maps in in-situ data-scarce regions, providing a first-hand overview. Our approach can help restoration practitioners, conservationists and ecosystem managers to make rational decisions to protect this species and promote sustainability.

The 5th International Electronic Conference on Remote Sensing

A. Viñuales et al. 2023

Fuel moisture content (FMC) is a crucial factor that influences fire behavior, rendering its precise estimation indispensable for effective fire risk assessment and management. However, despite the widespread availability of remotely sensed imagery, which offers valuable insights into live fuel moisture content (LFMC) estimation, it remains a significant challenge, especially given the dynamic nature of live forest fuels.

The aim of this study was to establish a robust method for estimating and monitoring LFMC by employing spatio-temporal modelling with a universal kriging approach, integrating remote sensing data and field measurements. This research was conducted in the Sierra Morena region of Andalusia, Spain, focusing on Cistus ladanifer shrub patches, well-known for their high fire risk. A total of 38 sampling plots were established to monitor LFMC over a 15-month period, with destructive sampling techniques used to determine LFMC in the laboratory.

The universal kriging model was enriched by incorporating variables derived from Sentinel-2 and MODIS products to estimate and validate the moisture content, resulting in an RMSE (Root Mean Squared Error) score of 12%. These findings have practical implications for forest fuel modeling, fire risk evaluation, and operational decision-making concerning fire prevention and management not only in the study area but also in potentially similar regions.

Special Issue Use of Satellite Imagery in Agriculture Journal

L. Recuero et al. 2023

Multiple cropping systems constitute an essential agricultural practice that will ensure food security within the increasing demand of basic cereals as a consequence of global population growth and climate change effects. In this regard, there is a need to develop new methodologies to adequately monitor cropland intensification. The main objective of this research was to assess cropland intensification by means of spectral analysis of MODIS NDVI time series in a high cloudiness tropical area such as Ecuador. A surface of 89,225 ha of the main staple crops in this country, which are rice and maize crops, was monitored to assess the evolution of the number of crop cycles. The 20-year period of NDVI time series was used to calculate the periodograms across four subperiods (2001–2005, 2006–2010, 2011–2015, 2016–2020). The maximum ordinate value of each periodogram was used as an indicator of the number of growing crop cycles per year identifying single-, double-, and triple-cropping systems in each subperiod. Cropland intensification was assessed by comparing the cropping system between the subperiods. Results reveal that more than half of the studied croplands experienced changes in the cropping systems, and 40% showed positive trends in terms of the number of growing crop cycles, being principally located near the main rivers where irrigation facilitates crop development during the dry season. Therefore, the area under single cropping decreased from over 60,000 ha in the first subperiod to less than 50,000 ha in the last two subperiods. The cropland surface subjected to multi-cropping practices increased during the second decade of the study period, with a double-cropping system being more widely used than growing three crops per year, reaching surfaces of 24,400 ha and 10,450 ha in the last subperiod, respectively. The robust results obtained in this research show the great potential of the periodogram approach for the discrimination of cropping systems and for mapping intensification areas in tropical regions where dealing with noisy remote sensing time series as a consequence of high cloudiness is a great challenge.

"Time Series Analysis - Recent Advances, New Perspectives and Applications". IntechOpen, Londres, pp. 1-18. ISBN 978-0-85466-053-7

C. Saez et al. 2023

Rainfed crops occupy 76% of the cultivated area of Spain being distributed throughout the whole country. The yield of these crops depends on the great interannual variability of meteorological factors. The monitoring and prediction of crop dynamics is a key factor for their sustainable management from an environmental and socioeconomic point of view. Long time series of remote sensing data, such as spectral indices, allow monitoring vegetation dynamics at different spatial and temporal scales and provide valuable information to predict these dynamics through time series analysis. The objectives of this study are as follows: (1) To assess the dynamics of rainfed crops in a typical dryland area of Spain and (2) to build dynamic models to explain and predict the evolution of these crops. The NDVI time series of a rainfed cereal crop area of central Spain have been analyzed using statistical time series methods and their values were predicted using the Box-Jenkins approach. At the model identification stage, the evaluation of their utocorrelation functions, periodogram, and stationarity tests has revealed that most of these series are stationary
and that their dynamics are dominated by annual seasonality. The selected preliminary dynamic model presents a good degree of adjustment for a 30% of the studied pixels.

AGU Annual Meeting 2023

M. Chowdhury et al. 2023

Seagrasses are undergoing widespread loss due to excessive anthropogenic pressure and climate change. Over the last sesquicentennial period, the Mediterranean basin lost 70 km of its endemic and dominant seagrass- Posidonia oceanica which regulates its ecosystem. The meadows provide shelter to about 20% of Mediterranean aquatic species, contribute to sediment deposition, attenuating currents and wave energy, and act as a significant global carbon sink. To protect this species, the European Union (EU) Habitats Directive 92/43/EEC has listed P. oceanica meadows as a priority habitat. Besides, P. oceanica is also a protected species in the Natura2000 networks within the EU. Conservation of P. oceanica, alongside with the conservation of seagrasses in general, was defined by the United Nations as the Sustainable Development Goal 14 (SDG 14) ‘Conserve and sustainably use the oceans, seas and marine resources for sustainable development’ (United Nations 2030 Agenda for Sustainable Development; UN 2017). However, many conservation and restoration projects failed or were ignored due to poor site selection and lack of long-term monitoring. Here, we present a fast and effecient approach based on deep-learning model using Sentinel-2 satellite data to map the spatial extent of P. oceanica meadows, enabling short and longterm monitoring, unlike the available approaches. We obtained 74-92% of overall accuracy, 72-91% of user’s accuracy, and 81-92% of producer’s accuracy, where high accuracies were observed at 0-25m depth. Our model is easily adaptable to other regions and can produce maps in insitu data-scarce regions, providing a first-hand idea. Our approach can help restoration practitioners, conservationists and ecosystem managers to make rational decisions to protect this species and promote sustainability.

AGU Annual Meeting 2023

M. Chowdhury et al. 2023

Multiple cropping systems constitute an essential agricultural practice that will ensure food security within the increasing demand of basic cereals as a consequence of global population growth and climate change effects. In this regard, there is a need to develop new methodologies to adequately monitor cropland intensification. The main objective of this research was to assess cropland intensification by means of spectral analysis of MODIS NDVI time series in a high cloudiness tropical area such as Ecuador. A surface of 89,225 ha of the main staple crops in this country, which are rice and maize crops, was monitored to assess the evolution of the number of crop cycles. The 20-year period of NDVI time series was used to calculate the periodograms across four subperiods (2001–2005, 2006–2010, 2011–2015, 2016–2020). The maximum ordinate value of each periodogram was used as an indicator of the number of growing crop cycles per year identifying single-, double-, and triple-cropping systems in each subperiod. Cropland intensification was assessed by comparing the cropping system between the subperiods. Results reveal that more than half of the studied croplands experienced changes in the cropping systems, and 40% showed positive trends in terms of the number of growing crop cycles, being principally located near the main rivers where irrigation facilitates crop development during the dry season. Therefore, the area under single cropping decreased from over 60,000 ha in the first subperiod to less than 50,000 ha in the last two subperiods. The cropland surface subjected to multi-cropping practices increased during the second decade of the study period, with a double-cropping system being more widely used than growing three crops per year, reaching surfaces of 24,400 ha and 10,450 ha in the last subperiod, respectively. The robust results obtained in this research show the great potential of the periodogram approach for the discrimination of cropping systems and for mapping intensification areas in tropical regions where dealing with noisy remote sensing time series as a consequence of high cloudiness is a great challenge.

Journal Frontiers in Marine Science

M. Chowdhury et al. 2023

The Guadalquivir estuary (southern Spain) occasionally experiences medium to high turbidity, reaching above 700 Formazin Nephelometric Unit (FNU) during extreme events, thus negatively influencing its nursery function and the estuarine community structure. Although several turbidity algorithms are available to monitor water quality, they are mainly developed for mapping turbidity ranges of 0-100 FNU. Thus, their use in a highly turbid region may not give accurate results, which is crucial for estuarine ecosystem management. To fill this gap, we developed a multi-conditional turbidity algorithm that can retrieve turbidity from 0 to 600 FNU using the Sentinel-2 red and red-edge bands. Four major steps are implemented: atmospheric and sun glint correction of the Level-1C Sentinel-2 data, spectral analysis for different water turbidity levels, regression modelling between in situ turbidity and remote sensing reflectance (Rrs) for algorithm development, and validation of the best-suited model. When turbidity was < 85 FNU, the Rrs increased firstly in the red wavelength (665 nm), but it saturated beyond a certain turbidity threshold (> 250 FNU). At this time, Rrs started to increase in the red-edge wavelength (704 nm). Considering this spectral behavior, our algorithm is designed to automatically select the most sensitive turbidity vs. Rrs, thus avoiding the saturation effects of the red bands at high turbidity levels. The model showed good agreement between the satellite derived turbidity and the in situ measurements with a correlation coefficient of 0.97, RMSE of 15.93 FNU, and a bias of 13.34 FNU. Turbidity maps derived using this algorithm can be used for routine turbidity monitoring and assessment of potential anthropogenic actions (e.g., dredging activities), thus helping the decision-makers and relevant stakeholders to protect coastal resources and human health.

Journal Frontiers in Forests and Global Change

V. Cicuéndez et al. 2023

Ecosystems are responsible for strong feedback processes that affect climate. The mechanisms and consequences of this feedback are uncertain and must be studied to evaluate their influence on global climate change. The main objective of this study is to assess the gross primary production (GPP) dynamics and the energy partitioning patterns in three different European forest ecosystems through time series analysis. The forest types are an Evergreen Needleleaf Forest in Finland (ENF_FI), a Deciduous Broadleaf Forest in Denmark (DBF_DK), and a Mediterranean Savanna Forest in Spain (SAV_SP). Buys-Ballot tables were used to study the intra-annual variability of meteorological data, energy fluxes, and GPP, whereas the autocorrelation function was used to assess the inter-annual dynamics. Finally, the causality of GPP and energy fluxes was studied with Granger causality tests. The autocorrelation function of the GPP, meteorological variables, and energy fluxes revealed that the Mediterranean ecosystem is more irregular and shows lower memory in the long term than in the short term. On the other hand, the Granger causality tests showed that the vegetation feedback to the atmosphere was more noticeable in the ENF_FI and the DBF_DK in the short term, influencing latent and sensible heat fluxes. In conclusion, the impact of the vegetation on the atmosphere influences the energy partitioning in a different way depending on the vegetation type, which makes the study of the vegetation dynamics essential at the local scale to parameterize these processes with more detail and build improved global models.

Oceanology International 2022

M. Chowdhury 2022

Posidonia oceanica is a seagrass species endemic to the Mediterranean Sea that provides a habitat for aquatic life. This seagrass is found in shallow regions, close to the shore, therefore strongly affected by human activities. 1m2 of the seagrass needs more than 100 years to form, hence, it is crucial to avoid damage and restore its meadows. Recently, satellite remote sensing approach is used to map this meadow, however, in a limited extent. Here we present a novel method of mapping Posidonia oceanica from satellite images over a large area based on automatic classification with a neural network, commonly known as deep learning. The technique requires atmospherically-corrected red-green-blue composite images, bathymetry data and in situ data to train the network. We apply the technique to Sentinel-2 images of the Balearic Islands, Spain, in the western Mediterranean where we find a mean accuracy of 98,5%, with 94% of pixels correctly classified as non-Posidonia and 4.5% correctly classified as Posidonia. Our model can automatically reproduce in detail the shape of the seagrass meadows at 10 m spatial resolution with a sensitivity of 84% for Posidonia pixels. This can escalate monitoring capabilities and allow implementing restoration programs efficiently. This can also be a seed to do evolution studies, carbon stock calculation and correlating the meadows with water quality parameters and physical oceanography.

WACOMA 2021- International Conference on Water and Coastal Management

M. Chowdhury et al. 2021

The highest abundance of age-0 blue whiting Micromesistius poutassou in the Porcupine Bank since 2001 was observed in 2020. Various environmental parameters, namely chlorophyll concentration, surface salinity, temperature, ocean currents, and wind data were used to study their potential impact on the blue whiting eggs and larvae survival. Our results showed that in 2020, during the blue whiting-spawning season (March-April), the calm wind situation along with weaker ocean currents above the Porcupine Bank helped to accumulate phytoplankton biomass, thus promoting secondary productivity. The optimal salinity concentration, as well as surface temperature during this time, helped the buoyancy of eggs and larvae to the food-rich surface, thus improving the larval condition and enhanced the survival rate, which in turn resulted in the highest year-class recruitment since 2001.

Revista de Teledetección

C. Gómez et al. 2020

The Spanish Central Range hosts some of the southernmost populations of Fagus sylvatica L. (European beech). Recent cartography indicates that these populations are expanding, going up-streams and gaining ground to oak forests of Quercus pyrenaica Willd., heather-lands, and pine plantations. Understanding the spectral phenology of European beech populations””which leaf flush occurs earlier than other vegetation formations””in this Mediterranean mountain range will provide insights of the species recent dynamics, and will enable modelling its performance under future climate oscillations. Intra-annual series of 211 Landsat OLI/ETM+ images, acquired between April 2013-December 2019, and 217 Sentinel-2A/B images, acquired between April 2017-December 2019, were employed to characterize the spectral phenology of European beech populations and five other vegetation types for comparison in an area of 108000 ha. Vegetation indices (VI) including the Normalized Difference Vegetation Index (NDVI) and Tasseled Cap Angle (TCA) from Landsat, and the NDVI and Enhanced Vegetation Index (EVI) from Sentinel-2 were retrieved from sample pixels. The temporal series of these VI were modelled with Savitzky-Golay and double logistic functions, and assessed with TIMESAT software, enabling the parametric characterization of European beech spectral phenology in the area with the start, length, and end of season, as well as peak time and value. The length of beech phenological season was similar when portrayed by Landsat and Sentinel-2 NDVI time series (214 and 211 days on average for the common period 2017-2019) although start and end differed. Compared with NDVI counterparts the TCA season started and peaked later, and the EVI season was shorter. Sentinel-2 NDVI peaked higher than Landsat NDVI. The European beech had an earlier (21 days on average) start of season than competing oak forests. Joint analysis of data from the virtual constellation Landsat/ Sentinel-2 and calibration with field observations may enable more detailed knowledge of phenological traits at the landscape scale.

Journal Forest Systems

C. Gómez et al. 2019

Forest ecosystems provide a host of services and societal benefits, including carbon storage, habitat for fauna, recreation, and provision of wood or non-wood products. In a context of complex demands on forest resources, identifying priorities for biodiversity and carbon budgets require accurate tools with sufficient temporal frequency. Moreover, understanding long term forest dynamics is necessary for sustainable planning and management. Remote sensing (RS) is a powerful means for analysis, synthesis and report, providing insights and contributing to inform decisions upon forest ecosystems. In this communication we review current applications of RS techniques in Spanish forests, examining possible trends, needs, and opportunities offered by RS in a forestry context. Currently, wall-to-wall optical and LiDAR data are extensively used for a wide range of applications—many times in combination—whilst radar or hyperspectral data are rarely used in the analysis of Spanish forests. Unmanned Aerial Vehicles (UAVs) carrying visible and infrared sensors are gaining ground in acquisition of data locally and at small scale, particularly for health assessments. Forest fire identification and characterization are prevalent applications at the landscape scale, whereas structural assessments are the most widespread analyses carried out at limited extents. Unparalleled opportunities are offered by the availability of diverse RS data like those provided by the European Copernicus programme and recent satellite LiDAR launches, processing capacity, and synergies with other ancillary sources to produce information of our forests. Overall, we live in times of unprecedented opportunities for monitoring forest ecosystems with a growing support from RS technologies.