Geospatial mapping of carbon estimates for forested areas using the InVEST model and Sentinel-2: A case study in Galicia (NW Spain)

CO2 emissions have increased exponentially in recent years, so measuring and quantifying carbon sequestration is a step towards sustainable forest management and combating climate change. The overall goal of this study is to develop an accurate model for estimating carbon storage and sequestration for forest areas of the Atlantic Biogeographic Region. Specifically, the modelling and field sampling are carried out in the municipality of Baiona (Galicia, NW Spain), which was selected as a representative biome of this region. The methodology consists of carrying out two object-based image analysis (OBIA) classifications in spring and autumn to observe possible stocks of seasonal differences. Two carbon storage and sequestration models are built up (model 1 and model 2): model 1 for forest areas only and model 2 including all other land cover in the study area. Sentinel-2 geospatial data for 2021, Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) tools and geographic information systems (GIS) are used. A Kappa index of 0.92 is obtained for both classifications, thus ruling out any notable seasonal differences in the images used. The results from both models indicate that it is land covers associated with forest uses which store the most carbon in the study area, accounting for >50 % more than the other land covers. It is concluded that the methodology and data used are very useful for quantifying ecosystem services, which will help the governance of the region by implementing measures to mitigate some of the effects of climate change and help to create silvicultural models for the sustainable management of the Atlantic Biogeographic Region.Agencia Estatal de Investigación | Ref. TED2021-130241A-I00Xunta de Galicia | Ref. GPC-ED431B 2022/12Xunta de Galicia | Ref. ED481B-2023-042Universidade de Vigo/CISU

Detection of Land Use Change in the River Tea Ecological Corridor (NW Spain)

Los cambios de uso del suelo y de la cobertura terrestre (LULCC) tienen una fuerte interrelación entre ellos, llegando a alterar los servicios ecosistémicos y contribuir negativamente al cambio climático. El objetivo general de este estudio es analizar diferentes fuentes de información para cuantificar el LULCC en el LIC Río Tea (NO de España) en el periodo 2015-2023. El área de estudio presenta múltiples coberturas con muy baja variabilidad espectral entre ellas y con una gran fragmentación del territorio, lo que dificulta la obtención de altos niveles de precisión. La clasificación de las coberturas del suelo se realizó mediante metodologías de Clasificación por Análisis de Imágenes Basadas en Objetos (OBIA) y Redes Neuronales Artificiales (ANN) a partir de imágenes de las plataformas de satélites multiespectrales Sentinel-2 y Planet Labs (RapidEye y PlanetScope). La precisión en la clasificación de las coberturas terrestres obtenida para los datos de Planet Labs y la metodología OBIA fue de un 80%, mientras que Sentinel-2 proporcionó una precisión de un 70%, empleando la metodología de ANN con los datos de Planet Labs, la precisión se sitúa en torno al 55%. Así pues, los datos utilizados y la metodología seguida influyen en los niveles de precisión obtenidos en las clasificaciones. Por último, se concluye que los datos geoespaciales disponibles son muy útiles para detectar y cuantificar los cambios en la ocupación del suelo. No se detectan grandes cambios en las coberturas terrestres, el principal cambio detectado es una transición de las coberturas vinculadas a los cultivos, las cuales en períodos de barbecho se detectan como suelo desnudo. Se confirma que la metodología utilizada contribuye a la planificación territorial y a la gestión forestal sostenible, facilitando futuras decisiones y planes de acción en la gobernanza de la regiónLand use and land cover change (LULCC) is strongly interrelated, altering ecosystem services and contributing negatively to climate change. The overall objective of this study is to analyse different sources of information to quantify LULCC in the Río Tea SCI (NW Spain) in the period 2015-2023. The study area has multiple land covers with very low spectral variability between them and with a high fragmentation of the territory, which makes it difficult to obtain high levels of accuracy. Land cover classification was carried out using Object Based Image Analysis Classification (OBIA) and Artificial Neural Network (ANN) methods based on images from the Sentinel-2 and Planet Labs multispectral satellite platforms (RapidEye and PlanetScope). The accuracy of land cover classification obtained using Planet Labs data and the OBIA method was 80%, while Sentinel-2 gave an accuracy of 70% and the ANN method with Planet Labs data gave an accuracy of around 55%. Thus, the data used and the methodology applied influence the accuracy of the classifications. Finally, it is concluded that the available geospatial data are very useful for detecting and quantifying land cover changes. No major changes in land cover are detected; the main change detected is a transition of land cover associated with crops, which in fallow periods are detected as bare soil. It is confirmed that the methodology used contributes to spatial planning and sustainable forest management, facilitating future decisions and action plans in the governance of the regionAgencia Estatal de Investigación | Ref. TED2021-130241A-I00Xunta de Galicia | Ref. GPC-ED431B 2022/1

Integration of geospatial data and machine learning for assessment of freshwater ecosystem: a study on the Hydrographical Demarcation Galicia-Costa (NW of Spain)

El estado de la biodiversidad en los ecosistemas de agua dulce es una métrica relevante con relación al nivel de contaminación de estos. En este aspecto, resulta fundamental ser capaces de medir esta biodiversidad, identificar y cuantificar las variables ambientales que influyen en mayor medida al estado del ecosistema. En este estudio se pretende, por un lado, analizar el estado de los ecosistemas de agua dulce de la Demarcación Hidrográfica de Galicia-Costa, evaluando su estado actual y su susceptibilidad a según qué variables; por otro lado, entrenar algoritmos basados en técnicas de machine learning, capaces de predecir el estado de la biodiversidad. Se llevó a cabo tomando el índice biológico METI como variable de calidad del ecosistema, parámetros fisicoquímicos de muestras de agua in situ, métricas paisajísticas para los usos Agrícola, Forestal, Artificial y Agua, parámetros físicos de las cuencas hidrográficas y densidad de población, obtenidos a partir del SIOSE y del Modelo Digital del Terreno. Los factores ambientales más relevantes para el estado de la biodiversidad son mayoritariamente variables de usos del suelo, lo que permite el modelado del METI con poco respaldo de variables fisicoquímicas, lo cual supone una potencial optimización en la detección de cambios en el estado de los ecosistemas acuáticos. Los algoritmos Random Forest (RMSE = 0.55, R2 = 0.82) y Deep learning (RMSE = 0.56, R2 = 0.83) mostraron predicciones aceptables en el área y período de estudio. El desarrollo de herramientas basadas el conjunto de datos geoespaciales y técnicas de machine learning es esencial para la mejora de la planificación integral sostenible de las cuencas hidrográficasThe state of biodiversity in the freshwater ecosystems is a relevant metric regarding their pollution conditions. On this issue, it is fundamental to be able to measure this biodiversity, identify and quantify the environmental variables which influence in the ecosystem state to a greater extent. Thus, this study aims, on one hand, to analyse the state of the freshwater ecosystems of the Hydrographical Demarcation of Galicia-Costa, evaluating their current situation and their susceptibility to other variables. In the other hand, it aims to train machine-learning-based algorithms capable of predicting this parameter. This was carried by taking the biological index METI as the ecosystem quality variable; physicochemical parameters of in-situ water samples; and land-use variables, physical parameters of the basins and population density, provided by the SIOSE and the Digital Terrain Model. The most relevant factors for the state of biodiversity were mainly land-use related variables, which allows modelling the METRI with little support of physicochemical variables. This implies a potential optimisation on the detection of changes in the state of the freshwater ecosystems. The Random Forest (RMSE = 0.55, R2 = 0.82) and Deep Learning (RMSE = 0.56, R2 = 0.83) algorithms showed acceptable predictions within the area and period of study. The development of tools based in the combination of geospatial data and machine-learning-based techniques is essential to improve the integral sustainable planification of the basinsXunta de Galicia | Ref. ED481B-2023-042Agencia Estatal de Investigación | Ref. PID2022-138374OA-I00Agencia Estatal de Investigación | Ref. TED2021-130241A-I0

Influence of Microcystis sp. and freshwater algae on pH: changes in their growth associated with sediment

Samples from two reservoirs with eutrophication problems, located in Pontevedra and Ourense (Northwestern Spain), were cultured, along with a third crop from a reservoir with no problems detected in Ourense (Northwestern Spain). The samples were grown under the same conditions (with an average temperature of 21 ± 2 °C, and a 3000 lux light intensity) in triplicate, and their growth, absorbance and pH were studied. High correlation values were obtained for pH and cellular growth (R2 ≥ 95%). The water from Salas showed the greatest microalgal growth (0.15 × 106 cells/ml to 31.70 × 106 cells/ml of "Microcystis sp." for the last day of culturing) and the greatest increase in pH (5.72–9.02). In all the cultures studied here, the main species that reproduced was "Microcystis sp.", which can produce neurotoxins and hepatotoxins. In addition, water samples were cultured with sediments of their own reservoir and with others to observe their evolution. The sediments studied in this case were rich in biotites, which can lead phosphate to be a limiting factor for phytoplankton due to the formation and sedimentation of insoluble salts of ferric phosphate. In crops grown with sediments from the Salas reservoir, actinobacteria developed which can inhibit microalgal growth. The study of the growth of cyanobacteria and possible methods of inhibiting them directly concerns the quality of water and its ecosystems, avoiding pollution and impact on ecosystems

Water toxicity in reservoirs after freshwater algae harvest

Blooms of microalgae and cyanobacteria increase every year, presenting great problems for the environment. Finding a way of harvesting these microalgae could be useful for water governance. Furthermore, the method should not cause cell lysis and should thus avoiding discharging toxins into the water. Three reservoirs were studied, two of them with eutrophication problems (As Conchas and A Baxe) and another (Salas) with no such problems. Three different harvesting methods were studied; electroflocculation (EF) with the application time being varied; centrifugation, with application times and speeds being varied; and finally, natural sedimentation. The highest efficiency was obtained in the culture from A Baxe, which had a higher initial absorbance value (1.664), using EF (90.64% for an application time of 2 min and 30 s) and centrifugation at 4000 rpm (92.25% for2 minutes, 92.73% for 5 min). Electrofloculation can obtain up to 84% more biomass than natural sedimentation alone. Sample toxicity was studied before and after harvesting using Microcystest and found to be higher after harvesting. It was observed that for the same sample, the higher the yield was the greater the toxicity was. For the A Baxe culture with an application time of 2 min, a speed of 2000 rpm and a yield of 87.02%, a toxicity figure of 0.94 μg/L was obtained, while for a speed of 4000 rpm the yield was 92.25% and the toxicity was 1.05 μg/L. The toxicity limit set by the World Health Organization (WHO) is 1 μg/L, and this small difference seems to be key. With these results, this study concludes that chlorophyll levels may interfere with the test used. Future tests or analyses should be developed so as to avoid such interference, which may alter the toxin values. Electroflocculation seems to be a promising method since it does not cause the lysis of "Microcystis aeruginosa", whereas the centrifugation method could give problems. Finally, it is worth highlighting the importance of performing toxin measurements after harvesting the microalgae to check that the method is viable in natural ecosystems

Influence of ashes in the use of forest biomass as source of energy

One of the main challenges using biofuels, such as pellets or wood chips in domestic boilers, is the slagging and fouling risks associated to the process, which could damage the boiler and limit its efficiency. The prediction of sintering and slagging in biomass combustion is essential to establish biofuel quality standards, and minimize their harmful effects. In this work, we analysed the chemical composition of 40 woodchips samples from different genera and origins in order to predict slagging. We studied two indexes of deposition (%B and the NaK/B) in order to limit sintering of biomass ashes. In addition, a new threshold classification was proposed for ratio-slag viscosity index. These indexes were validated with two different tests: a qualitative test, and a quantitative test (Bioslag). Our results showed that the species chosen did not have an impact in slagging and sintering. However, biomass with high concentrations of SiO2 and tree bark showed high risk of slagging. On the other hand, high CaO concentrations showed a low slagging risk. The results obtained from the validation tests showed similar results to the ones obtained from the indexes. It can be concluded that the %B and NaK/B indexes show good potential and should be considered as tools for predicting slagging in woody and herbaceous biomass

Water security and watershed management assessed through the modelling of hydrology and ecological integrity: a study in the Galicia-Costa (NW Spain)

Water management is a crucial tool for addressing the increasing uncertainties caused by climate change, biodiversity loss and the conditions of socioeconomic limits. The multiple factors affecting water resources need to be successfully managed to achieve optimal governance and thus move towards water security. This study seeks to obtain a holistic vision of the various threats that affect the ecological integrity of the basins that form the hydrological district of Galicia-Costa, through the method of partial least squares path modelling (PLS-PM). The data is analysed overall for the hydrological years from 2009 to 2015. The independent latent variables are “Anthropogenic” (comprising the percentage of water bodies with edges alongside artificial surfaces, the percentage connected to artificial land use patches, the edge density of artificial surfaces and population density) and “Nature” (edge density of forestry land uses, edge length of land water bodies alongside forested areas and the percentage of land occupied by the largest patch of forest). The dependent latent variables are “SWP”, which represents surface water parameters (biological oxygen demand, chlorides, conductivity and dissolved iron) and “Ecological Integrity” (METI Bioindicator). The connections between latent variables are uantified through path coefficients (β). From an overall perspective, the PLS-PM results reveal that 69.0% of “SWP” is predicted by the independent variables (R2 = 0.690), “Anthropogenic” contributes by increasing SWP (β = 0.471), while “Nature” decreases the concentration of SWP (β = −0.523), which indicates the polluting parameters in the water. The variables “Anthropogenic” (β = −0.351) and “SWP” (β = −0.265) lower the quality of “Ecological Integrity”. This variable must be managed through soil conservation measures for the benefit of water security. This study has been able to identify and quantify the variables that increase contaminant concentration and decrease ecological integrity, providing a promising methodology that facilitates protection and correction measures to guarantee water safety.Xunta de Galicia | Ref. R815 131H 64502Fundação para a Ciência e a Tecnologia | Ref. UID/AGR/04033/2020Fundação para a Ciência e a Tecnologia | Ref. UIDB/QUI/00616/2020Fundação para a Ciência e a Tecnologia | Ref. UIDP/00616/2020Fundação para a Ciência e a Tecnologia | Ref. SFRH/BD/146151/201

Connectivity study in Northwest Spain: barriers, impedances, and corridors

Functional connectivity between habitats is a fundamental quality for species dispersal and genetic exchange throughout their distribution range. Brown bear populations in Northwest Spain comprise around 200 individuals separated into two sub-populations that are very difficult to connect. We analysed the fragmentation and connectivity for the Ancares-Courel Site of Community Importance (SCI) and its surroundings, including the distribution area for this species within Asturias and in the northwest of Castile and León. The work analysed the territory’s connectivity by using Geographic Information Systems (GIS). The distance-cost method was used to calculate the least-cost paths with Patch Matrix. The Conefor Sensinode software calculated the Integral Connectivity Index and the Connectivity Probability. Locating the least-cost paths made it possible to define areas of favourable connectivity and to identify critical areas, while the results obtained from the connectivity indices led to the discovery of habitat patches that are fundamental for maintaining connectivity within and between different spaces. Three routes turned out to be the main ones connecting the northern (Ancares) and southern (Courel) areas of the SCI. Finally, this work shows the importance of conserving natural habitats and the biology, migration, and genetic exchange of sensitive species.Xunta de Galici

Gestión de cuencas hidrográficas evaluada a través de la modelización hidrológica y la integridad ecológica: un caso de estudio en la Demarcación Hidrográfica Galicia Costa (NO de España)

La seguridad hídrica es clave para el bienestar humano y se presenta como uno de los mayores desafíos de la gobernanza ambiental. A su vez, la gobernanza forestal se encuentra estrechamente relacionada con la seguridad hídrica, por ello, resulta necesario desarrollar posibles modelos y estrategias con el fin de realizar una gestión sostenible de los recursos naturales. En el presente estudio, se han realizado modelos predictivos mediante el uso de mínimos cuadrados parciales en modelos de ruta (PLS-PM). La finalidad perseguida ha sido obtener una herramienta que pueda contribuir en la planificación sostenible e integral del agua y del territorio. Determinando y cuantificando la relación entre las variables estudiadas, como los diferentes usos del suelo, porcentaje de áreas conectadas, parámetros fisicoquímicos o índices biológicos. El área de estudio donde se ha realizado este análisis abarca 18 cuencas que forman la Demarcación Hidrográfica Galicia-Costa, con 40 puntos de muestreo divididos por los principales ríos que forman la Demarcación. Mediante este modelo se ha detectado la necesidad de realizar mejoras en la vegetación de ribera, así como de mejorar la conectividad entre ecosistemas, con el fin de beneficiar a la calidad del agua y a la integridad ecológica

Modelling of threats that affect Cyano-HABs in an eutrophicated reservoir: First phase towards water security and environmental governance in watersheds

Financiado para publicación en acceso aberto: Universidade de Vigo/CISUGCyano-HABs are proliferating around the world due to anthropogenic nutrient enrichment of freshwater bodies. This study seeks to obtain a holistic vision over the various threats that affect the Cyano-HABs of Umia basin and especially of A Baxe reservoir (Galicia, NW Spain), through the method of Partial least squares path modelling (PLS-PM). The A Baxe reservoirs is a fundamental source of drinking water supply to surrounding dwellings. This study identifies and quantify the variables that increase contaminant concentration and decrease ecological integrity, as well as how this scenario evolved over various hydrologic years. In this regard, the PLS-PM equations will be robust and powerful tools to predict changes in eutrophication and ecological integrity, as response to measures implemented in the basin that can improve water quality. The dependent latent variables are “Eutrophication” (chlorophyl-a, Microcystis sp.) and “Ecological Integrity” (METI Bioindicator). The independent latent variables are “SWP”, which represents surface water parameters (phosphorus, nitrogen and pH) and “Climatic Conditions” (temperature, precipitation). The PLS-PM results revealed that 51.0% of “Eutrophication” is predicted by the independent variables. The connections between latent variables are quantified through path coefficients (β). The “SWP” contributes by increasing “Eutrophication” (β=0.235), the same occurring with the “Climatic Conditions” (β=−0.672). The variables “Eutrophication” (β=−0.217) and “SWP” (β=−0.483) lower the “Ecological Integrity”. On the other hand, different trophic scenarios, adapted to the temperature increase predicted for the study area, were tested, and it was found that ecological integrity would improve by 46% if the oligotrophic state were reached. Therefore, it is recommended to prevent pollution by means of water control and governance plans, as well as corrective and preventive measures, which guarantee the water security of the river basins. Despite the complex mathematics behind the PLS-PM models, their user-friendly development and application through interactive graphical interfaces make them easily transposable to other eutrophic reservoirs, widening the readership of these studies focused on multiple-geosphere assessment of environmental impactsFundação para a Ciência e a Tecnologia | Ref. UIDB/00616/2020Fundação para a Ciência e a Tecnologia | Ref. UIDP/00616/ 2020.Xunta de Galicia | Ref. R815 131H 6450