Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using Machine Learning Methods Trained with Radiative Transfer Simulations

Satellite remote sensing has been widely used in the last decades for agricultural applications, {both for assessing vegetation condition and for subsequent yield prediction.} Existing remote sensing-based methods to estimate gross primary productivity (GPP), which is an important variable to indicate crop photosynthetic function and stress, typically rely on empirical or semi-empirical approaches, which tend to over-simplify photosynthetic mechanisms. In this work, we take advantage of all parallel developments in mechanistic photosynthesis modeling and satellite data availability for advanced monitoring of crop productivity. In particular, we combine process-based modeling with the soil-canopy energy balance radiative transfer model (SCOPE) with Sentinel-2 {and Landsat 8} optical remote sensing data and machine learning methods in order to estimate crop GPP. Our model successfully estimates GPP across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites. This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms

Assessing the long-term urban heat island in San Antonio, Texas based on moderate resolution imaging spectroradiometer/Aqua Data

Urban environmental conditions are strongly dependent on the land use and land cover properties. Urban and rural areas normally exhibit obvious difference in land surface temperature (LST). The Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua (PM satellite) MYD11A1 temperature products (daily and 1 km spatial resolution) for the period from June 1 to September 30 between 2002 and 2008 were used to screen the existence of urban heat island (UHI) phenomena for the city of San Antonio, TX. 8-day MYD11A2 temperature products between 2002 and 2008 were also retrieved to map the temperature climatology at the 1:30 a.m. for the region. The UHI effect was detected in both satellite surface- temperature and meteorological station air- temperature record. The existence of an UHI of the San Antonio downtown area was clearly shown in about 90% of the available cloud- free (or cloudless) data from June 1-September 30 each year. It is especially prevalent in the night- time imagery due to less cloud contamination. During nighttime, the heat island (HI) is about 4 - 5 degrees K (6 - 8 degrees F) higher than the average temperature of the study area and 6 7 degrees K (8 - 12 degrees F) higher than the rural area. Surprisingly, the HI phenomenon is found not only in the downtown area, but also several other small areas in the northern corner. Finally, the long- term UHI effect of San Antonio and its relationship with normalized difference vegetation index (NDVI) were discussed. USGS rainfall data were also used to discuss the possible connections between the UHI and several local storm events

The role of remote sensing in assessing the impact of climate variability on vegetation dynamics in Europe

Tese de doutoramento em Ciências Geofísicas e da Geoinformação (Detecção Remota), apresentada à Universidade de Lisboa através da Faculdade de Ciências, 2008The study aims at investigating the relationship between climate variability and vegetation dynamics by combining meteorological and remote-sensed information. The vegetation response to both precipitation and temperature in two contrasting areas (Northeastern Europe and the Iberian Peninsula) of the European continent is analysed and special attention is devoted to the impact of the North Atlantic Oscillation (NAO) on the vegetative cycle in the two regions which is assessed taking into account the different land cover types and the respective responses to climate variability. An analysis is performed of the impact of climate variability on wheat yield in Portugal and. the role of NAO and of relevant meteorological variables (net solar radiation, temperature and precipitation) is investigated. Using spring NDVI and NAO in June as predictors, a simple regression model of wheat yield is built up that shows a general good agreement between observed and modelled wheat yield values. The severity of a given drought episode in Portugal is assessed by evaluating the cumulative impact over time of negative anomalies of NDVI. Special attention is devoted to the drought episodes of 1999, 2002 and 2005. While in the case of the drought episode of 1999 the scarcity of water in the soil persisted until spring, the deficit in greenness in 2005 was already apparent at the end of summer. Although the impact of dry periods on vegetation is clearly noticeable in both arable land and forest, the latter vegetation type shows a higher sensitivity to drought conditions. Persistence of negative anomalies of NDVI was also used to develop a procedure aiming to identify burned scars in Portugal and then assess vegetation recovery over areas stricken by large wildfires. The vulnerability of land cover to wildfire is assessed and a marked contrast is found between forest and shrubland vs. arable land and crops. Vegetation recovery reveals to strongly depend on meteorological conditions of the year following the fire event, being especially affected in case of a drought event.Fundação para a Ciência e Tecnologia (FCT), (SFRH/BD/32829/2006

Multiple-constraint inversion of SCOPE. Evaluating the potential of GPP and SIF for the retrieval of plant functional traits

The most recent efforts to provide remote sensing (RS) estimates of plant function rely on the combination of Radiative Transfer Models (RTM) and Soil-Vegetation-Atmosphere Transfer (SVAT) models, such as the Soil-Canopy Observation Photosynthesis and Energy fluxes (SCOPE) model. In this work we used ground spectroradiometric and chamber-based CO2 flux measurements in a nutrient manipulated Mediterranean grassland in order to: 1) develop a multiple-constraint inversion approach of SCOPE able to retrieve vegetation biochemical, structural as well as key functional traits, such as chlorophyll concentration (Cab), leaf area index (LAI), maximum carboxylation rate (Vcmax) and the Ball-Berry sensitivity parameter (m); and 2) compare the potential of the of gross primary production (GPP) and sun-induced fluorescence (SIF), together with up-welling Thermal Infrared (TIR) radiance and optical reflectance factors (RF), to estimate such parameters. The performance of the proposed inversion method as well as of the different sets of constraints was assessed with contemporary measurements of water and heat fluxes and leaf nitrogen content, using pattern-oriented model evaluation. The multiple-constraint inversion approach proposed together with the combination of optical RF and diel GPP and TIR data provided reliable estimates of parameters, and improved predicted water and heat fluxes. The addition of SIF to this scheme slightly improved the estimation of m. Parameter estimates were coherent with the variability imposed by the fertilization and the seasonality of the grassland. Results revealed that fertilization had an impact on Vcmax, while no significant differences were found for m. The combination of RF, SIF and diel TIR data weakly constrained functional traits. Approaches not including GPP failed to estimate LAI; however GPP overestimated Cab in the dry period. These problems might be related to the presence of high fractions of senescent leaves in the grassland. The proposed inversion approach together with pattern-oriented model evaluation open new perspectives for the retrieval of plant functional traits relevant for land surface models, and can be utilized at various research sites where hyperspectral remote sensing imagery and eddy covariance flux measurements are simultaneously taken

Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under short-term, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analysing multiple stress responses simultaneously (holistic view); (2) simultaneous retrieval of plant traits combining multi-domain radiative transfer models and machine learning methods; (3) assimilation of estimated plant traits from distinct spectral domains into integrated crop growth models. As a future outlook, we recommend combining multiple remote sensing data streams into crop model assimilation schemes to build up Digital Twins of agroecosystems, which may provide the most efficient way to detect the diversity of environmental and biotic stresses and thus enable respective management decisions

Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under short-term, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analysing multiple stress responses simultaneously (holistic view); (2) simultaneous retrieval of plant traits combining multi-domain radiative transfer models and machine learning methods; (3) assimilation of estimated plant traits from distinct spectral domains into integrated crop growth models. As a future outlook, we recommend combining multiple remote sensing data streams into crop model assimilation schemes to build up Digital Twins of agroecosystems, which may provide the most efficient way to detect the diversity of environmental and biotic stresses and thus enable respective management decisions

Uso de sensores remotos en el seguimiento de la vegetación de dehesa y su influencia en el balance hidrológico a escala de cuenca

The Mediterranean region is characterized by hot summers with long dry periods, a situation that may be exacerbated by the progressive global warming. In these water-limited environments where productivity of the ecosystems depends mainly on water availability, the reduction of freshwater resources can have severe consequences. An increase in aridity may lead to low productivity, land degradation and unwanted changes in land use. To reduce the vulnerability of Mediterranean landscapes it is important to improve our knowledge of the hydrological processes conditioning the water exchanges, with evapotranspiration (ET) being a key indicator of the state of ecosystems and playing a crucial role in the basin's water and energy balances. The goal of this dissertation is to improve our understanding of the evapotranspiration dynamics over Mediterranean heterogeneous and complex vegetation covers, with a focus on the dehesa ecosystem. The final aim is to contribute to the conservation of the water resources in these regions in the medium to long term, supporting the decision-making processes with quantitative, distributed, and high-quality information. To reach this goal, in this research the evaluation of remote sensing-based soil water balance (SWB) and surface energy balance (SEB) models was proposed to monitor the water consumption and water stress of typical Mediterranean vegetation at different spatial and temporal scales. In particular, the VI-ETo methodology (SWB) and the ALEXI/DisALEXI approach (SEB) have been adapted and applied. ET modeling using the VI-ETo scheme has been improved through the assessment of the vegetation layers' effective parameters. A data fusion algorithm was applied to the ET maps produced by the SEB model over the dehesa ecosystem, and we analyzed the opportunities that this high-resolution ET product in time and space can provide for water and vegetation resource management. The results have demonstrated the feasibility of both approaches (SWB and SEB models) to accurately monitor ET dynamics over the dehesa landscape, adequately reproducing the annual bimodal behavior and the response of the vegetation in periods of water deficit. The error obtained using the SWB approach (the VI-ETo method) was RMSE = 0.47 mm day-1 over the whole dehesa system (grass + trees) and over an open grassland. The monitoring of water stress for both systems with different canopy structure, using as a proxy the ET/ETo ratio, and the stress coefficient (Ks), was successful. Improvements on the specific spectral properties of oak trees and layer-specific parameters were included into the modeling. We also analyzed the influence of the spectral properties of oak trees and another typical Mediterranean tree canopy, the olive orchard, in the VI-ETo model. We found that the use of appropriate values of the parameter SAVImax (0.51 for oak trees and 0.57 for olive trees) had notable implications in the computation of ET and water stress, in contrast to using a generic value for Mediterranean crops (SAVImax= 0.75). The accuracy of this water balance-based approach was also evaluated over two heterogeneous Mediterranean basins, with a mosaic of holm oaks and grasslands, shrubs, coniferous plantations, and irrigated horticultural crops. The annual discharge flows of both watersheds, which were determined from the modeled ET data and using a simple surface water balance, were very similar to those obtained with the HBV hydrological model, and to the values measured at the outlet of one of the basins, corroborating the usefulness of the VI-ETo methodology on these vegetation types. On the other hand, the resulting ET series (30 m, daily) derived with the SEB approach (ALEXI/DisALEXI method) and the STARFM fusion algorithm provided an RMSE value of 0.67 mm day-1, which was considered an acceptable error for management purposes. This error was slightly lower compared to using simpler interpolation methods, probably due to the high temporal frequency and better spatial representation of the flux tower footprint of the fused time series. The analysis of ET patterns over small heterogeneous vegetated patches that form the dehesa structure revealed the importance of having fine resolution information at field scale to distinguish the water consumed by the different vegetation components, which influences the provision of many ecosystem services. For example, it was key for identifying phenology dates of grasslands, or understanding the hydrological functioning of riverside dense evergreen vegetation with high ET rates during the whole year, in contrast with the herbaceous areas. Accurately modeling these different behaviors of dehesa microclimates is useful to support farmers‘ management and provide recommendations tailored for each structural component and requirements.La región mediterránea se caracteriza por veranos calurosos con largos períodos sin precipitaciones, situación que puede agravarse con el progresivo calentamiento global. En estos ambientes donde la productividad de los ecosistemas depende principalmente de la disponibilidad de agua, la reducción de los recursos hídricos puede tener graves consecuencias. Un aumento de la aridez puede conducir a una baja productividad, degradación de la tierra y cambios no deseados en el uso del suelo. Para reducir la vulnerabilidad de las zonas mediterráneas es importante profundizar en el estudio de los procesos hidrológicos que condicionan los intercambios de agua, siendo la evapotranspiración (ET) un indicador clave del estado de los ecosistemas y jugando un papel crucial en los balances hídricos y energéticos de la cuenca. El objetivo de esta tesis es mejorar nuestro conocimiento sobre la dinámica de la evapotranspiración en cubiertas mediterráneas heterogéneas y complejas, con el foco en el ecosistema de dehesa. El objetivo final es contribuir a la conservación de los recursos hídricos de estas regiones en el medio-largo plazo, apoyando en los procesos de toma de decisiones con información cuantitativa, distribuida y de calidad. Para alcanzar este objetivo, en esta investigación se propuso evaluar modelos de balance de agua en el suelo (SWB) y balance de energía en superficie (SEB) basados en el uso de sensores remotos, para el seguimiento del consumo de agua y el estrés hídrico de la vegetación mediterránea a diferentes escalas espaciales y temporales. En particular, se ha adaptado y aplicado la metodología VI-ETo (SWB) y el enfoque ALEXI/DisALEXI (SEB). Se ha mejorado el modelado de ET utilizando el esquema VI-ETo mediante la evaluación de los parámetros efectivos de las capas de vegetación. Se aplicó un algoritmo de fusión de datos remotos a los mapas de ET generados por el modelo SEB sobre el ecosistema de dehesa, y estudiamos las oportunidades que este producto de ET con alta resolución espacial y temporal puede aportar en la gestión de los recursos hídricos y de los ecosistemas. Los resultados han demostrado la viabilidad de ambos enfoques (modelos SWB y SEB) para monitorear con precisión la dinámica de la ET sobre el ecosistema de dehesa, reproduciendo adecuadamente el comportamiento bimodal anual y la respuesta de la vegetación en períodos de déficit hídrico. El error obtenido usando el enfoque SWB (el método VI-ETo) fue RMSE = 0.47 mm día-1, tanto para el sistema dehesa (pasto + árboles) como para una zona de pastizal. El seguimiento del estrés hídrico para ambos sistemas con diferente estructura de vegetación, utilizando la relación ET/ETo y el coeficiente de estrés (Ks), fue satisfactorio. Se incluyeron en el modelado mejoras sobre las propiedades espectrales específicas de las encinas y los parámetros específicos de los diferentes estratos de vegetación. También analizamos la influencia de las propiedades espectrales de las encinas y otra cubierta mediterránea, el olivar, en el modelo VI-ETo. Encontramos que el uso de valores apropiados del parámetro SAVImax (0,51 para robles y 0,57 para olivos) tuvo un efecto significativo en la determinación del consumo de agua y estrés hídrico, en comparación con usar un valor genérico para cultivos mediterráneos (SAVImax = 0,75). La precisión de este enfoque basado en el balance hídrico también se evaluó en dos cuencas mediterráneas heterogéneas, con un mosaico de encinas y pastizales, arbustos, plantaciones de coníferas y cultivos hortícolas de regadío. Los caudales de descarga anual de ambas cuencas, determinados a partir de los datos de ET modelados y utilizando un balance hídrico superficial muy simple, fueron muy similares a los obtenidos con el modelo hidrológico HBV, y a los valores medidos en la salida de una de las cuencas, corroborando la utilidad de la metodología VI-ETo sobre estas formaciones vegetales. Por otra parte, la serie final de ET (30 m, diaria) derivada del enfoque SEB (método ALEXI/DisALEXI) y del algoritmo de fusión STARFM proporcionó un valor de RMSE de 0,67 mm día-1, considerado un error aceptable para fines de manejo. Este error fue ligeramente inferior a los obtenidos usando métodos de interpolación más simples, debido probablemente a la alta frecuencia temporal y una mejor representación espacial del footprint de la torre de medida de flujos en la serie temporal fusionada. El análisis de los patrones de la ET sobre pequeñas manchas de vegetación heterogéneas, que forman la estructura de la dehesa, reveló la importancia de tener información con alta resolución a escala de campo para distinguir el agua consumida por los diferentes componentes de la vegetación, que tienen influencia en el aprovisionamiento de muchos servicios ecosistémicos. Por ejemplo, fue clave para identificar ciertas fechas fenológicas de los pastizales, o entender el funcionamiento hidrológico de la vegetación densa de hoja perenne en zonas de ribera con altas tasas de ET durante todo el año, en comparación con zonas de especies herbáceas. Modelar con precisión estos comportamientos diferentes de los microclimas de la dehesa es útil para apoyar la gestión de los agricultores y ofrecer recomendaciones adaptadas a cada componente y necesidades estructurales

Climate-Triggered Drought as Causes for Different Degradation Types of Natural Forests: A Multitemporal Remote Sensing Analysis in NE Iran

Climate-triggered forest disturbances are increasing either by drought or by other climate extremes. Droughts can change the structure and function of forests in long-term or cause large-scale disturbances such as tree mortality, forest fires and insect outbreaks in short-term. Traditional approaches such as dendroclimatological surveys could retrieve the long-term responses of forest trees to drought conditions; however, they are restricted to individual trees or local forest stands. Therefore, multitemporal satellite-based approaches are progressing for holistic assessment of climate-induced forest responses from regional to global scales. However, little information exists on the efficiency of satellite data for analyzing the effects of droughts in different forest biomes and further studies on the analysis of approaches and large-scale disturbances of droughts are required. This research was accomplished for assessing satellite-derived physiological responses of the Caspian Hyrcanian broadleaves forests to climate-triggered droughts from regional to large scales in northeast Iran. The 16-day physiological anomalies of rangelands and forests were analysed using MODIS-derived indices concerning water content deficit and greenness loss, and their variations were spatially assessed with monthly and inter-seasonal precipitation anomalies from 2000 to 2016. Specifically, dimensions of forest droughts were evaluated in relations with the dimensions of meteorological and hydrological droughts. Large-scale effects of droughts were explored in terms of tree mortality, insect outbreaks, and forest fires using field observations, multitemporal Landsat and TerraClimate data. Various approaches were evaluated to explore forest responses to climate hazards such as traditional regression models, spatial autocorrelations, spatial regression models, and panel data models. Key findings revealed that rangelands’ anomalies did show positive responses to monthly and inter-seasonal precipitation anomalies. However, forests’ droughts were highly associated with increases in temperatures and evapotranspiration and were slightly associated with the decreases in precipitation and surface water level. The hazard intensity of droughts has affected the water content of forests higher than their greenness properties. The stages of moderate to extreme dieback of trees were significantly associated with the hazard intensity of the deficit of forests’ water content. However, the stage of severe defoliation was only associated with the hazard intensity of forests’ greenness loss. Climate hazards significantly triggered insect outbreaks and forest fires. Although maximum temperatures, precipitation deficit, availability of soil moisture and forest fires of the previous year could significantly trigger insect outbreaks, the maximum temperatures were the only significant triggers of forest fires from 2010‒2017. In addition to climate factors, environmental and anthropogenic factors could control fire severity during a dry season. The overall evaluation indicated the evidence of spatial associations between satellite-derived forest disturbances and climate hazards. Future studies are required to apply the approaches that could handle big-data, use the satellite data that have finer wavelengths for large-scale mapping of forest disturbances, and discriminate climate-induced forest disturbances from those that induced by other biotic and abiotic agents.Klimagbedingte Waldstörungen nehmen entweder durch Dürre oder durch andere Klimaextreme zu. Dürren können langfristig die Struktur und Funktion der Wälder verändern oder kurzfristig große Störungen wie Baumsterben, Waldbrände und Insektenausbrüche verursachen. Traditionelle Ansätze wie dendroklimatologische Untersuchungen könnten die langfristigen Reaktionen von Waldbäumen auf Dürrebedingungen aufzeigen, sie sind aber auf einzelne Bäume oder lokale Waldbestände beschränkt. Daher werden multitemporale satellitengestützte Ansätze zur ganzheitlichen Bewertung von klimabedingten Waldreaktionen auf regionaler bis globaler Ebene weiterentwickelt. Es gibt jedoch nur wenige Informationen über die Effizienz von Satellitendaten zur Analyse der Auswirkungen von Dürren in verschiedenen Waldbiotopen. Daher sind weitere Studien zur Analyse von Ansätzen und großräumigen Störungen von Dürren erforderlich. Diese Forschung wurde durchgeführt, um die aus Satellitendaten gewonnenen physiologischen Reaktionen der im Nordosten Irans gelegenen kaspischen hyrkanischen Laubwälder auf klimabedingte Dürren auf lokaler und regionaler Ebene zu bewerten. Auf der Grundlage der aus MODIS-Daten abgeleiteten Indizes wurden die 16-tägigen physiologischen Anomalien von Weideland und Wäldern in Bezug auf Wassergehaltsdefizit und Grünverlust analysiert und ihre Variationen räumlich mit monatlichen und intersaisonalen Niederschlagsanomalien von 2000 bis 2016 bewertet. Insbesondere wurden die Dimensionen der Walddürre in Verbindung mit den Dimensionen der meteorologischen und hydrologischen Dürre bewertet. Großräumige Auswirkungen von Dürren wurden in Bezug auf Baumsterblichkeit, Insektenausbrüche und Waldbrände mit Hilfe von Feldbeobachtungen, multitemporalen Landsat- und TerraClimate Daten untersucht. Verschiedene Ansätze wurden ausgewertet, um Waldreaktionen auf Klimagefahren wie traditionelle Regressionsmodelle, räumliche Autokorrelationen, räumliche Regressionsmodelle und Paneldatenmodelle zu untersuchen. Die wichtigsten Ergebnisse zeigten, dass die Anomalien von Weideland positive Reaktionen auf monatliche und intersaisonale Niederschlagsanomalien aufweisen. Die Dürren in den Wäldern waren jedoch in hohem Maße mit Temperaturerhöhungen und Evapotranspiration verbunden und standen in geringem Zusammenhang mit dem Rückgang von Niederschlägen und des Oberflächenwasserspiegels. Die Gefährdungsintensität von Dürren hat den Wassergehalt von Wäldern stärker beeinflusst als die Eigenschaften ihres Blattgrüns. Die Stufen mittlerer bis extremer Baumsterblichkeit waren signifikant mit der Gefährdungsintensität des Defizits des Wassergehalts der Wälder verbunden. Das Ausmaß der starken Entlaubung hing jedoch nur mit der Gefährdungsintensität des Grünverlustes der Wälder zusammen. Die Klimagefahren haben zu deutlichen Insektenausbrüchen und Waldbränden geführt. Obwohl Maximaltemperaturen, Niederschlagsdefizite, fehlende Bodenfeuchte und Waldbrände des Vorjahres deutlich Insektenausbrüche auslösen konnten, waren die Maximaltemperaturen die einzigen signifikanten Auslöser von Waldbränden von 2010 bis 2017. Neben den Klimafaktoren können auch umweltbedingte und anthropogene Faktoren den Schweregrad eines Brandes während einer Trockenzeit beeinflussen. Die Gesamtbewertung zeigt Hinweise auf räumliche Zusammenhänge zwischen aus Satellitendaten abgeleiteten Waldstörungen und Klimagefahren. Weitere Untersuchungen sind erforderlich, um Ansätze anzuwenden, die mit großen Datenmengen umgehen können, die Satellitendaten in einer hohen spektralen Auflösung für die großmaßstäbige Kartierung von Waldstörungen verwenden und die klimabedingte Waldstörungen von denen zu unterscheiden, die durch andere biotische und abiotische Faktoren verursacht werden