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

Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method

Forest fire risk has increased globally during the previous decades. The Mediterranean region is traditionally the most at risk in Europe, but continental countries like Serbia have experienced significant economic and ecological losses due to forest fires. To prevent damage to forests and infrastructure, alongside other societal losses, it is necessary to create an effective protection system against fire, which minimizes the harmful effects. Forest fire probability mapping, as one of the basic tools in risk management, allows the allocation of resources for fire suppression, within a fire season, from zones with a lower risk to those under higher threat. Logistic regression (LR) has been used as a standard procedure in forest fire probability mapping, but in the last decade, machine learning methods such as fandom forest (RF) have become more frequent. The main goals in this study were to (i) determine the main explanatory variables for forest fire occurrence for both models, LR and RF, and (ii) map the probability of forest fire occurrence in Eastern Serbia based on LR and RF. The most important variable was drought code, followed by different anthropogenic features depending on the type of the model. The RF models demonstrated better overall predictive ability than LR models. The map produced may increase firefighting efficiency due to the early detection of forest fire and enable resources to be allocated in the eastern part of Serbia, which covers more than one-third of the country's area

Exploring Spatially Varying Relationships between Forest Fire and Environmental Factors in Fujian, China

In recent decades, the occurrence of forest fires has risen in the world and led to significant, long-lasting impacts on ecological, social, and economic systems. Along with the traditional tools for fire prediction, statistical modeling has been playing an important role in understanding the nature of forest fires and providing guidelines for decision making of fire prevention and management. In this dissertation, a large data set was collected from 2001 to 2016 in Fujian province, China, including the occurrence of forest fires and many environmental factors. I developed spatial generalized linear models and spatial quantile models under the framework of geographically weighted regression (GWR) to investigate the relationships between the counts and proportion or rate of forest fires and driving topographical, meteorological, human, vegetation, and land coverage factors. The corresponding global models were used as the benchmarks for model comparisons. These spatial models included: (1) geographically weighted Poisson and geographically weighted negative binomial models designed for the counts of forest fires; (2) geographically weighted quantile models for the counts of forest fires at different quantiles or risk levels; and (3) geographically weighted beta model for the proportion or rate of forest fires. The results indicated that the observed forest fires were highly likely to occur in lower elevation, smaller aspect index (meaning stronger sunlight), heavier precipitation, smaller population density, less vegetation, wider grassland, and/or less cropland, while other environmental factors varied greatly with the forest fire occurrence. This study showed the great superiority of these GWR models to the corresponding global models in terms of characterizing the spatial nonstationary relationships, producing better model fitting and prediction, providing a more complete view on the spatial distribution of forest fires, and highlighting the risky local “hot spots” of forest fires as well as environmental factors across the Fujian province, China. Hopefully, the more detailed and localized information would help and assist the forest and fire managers to better understand the behavior of forest fires and influences of the environmental factors across the study area. Thus, the government agencies can make wiser and better decisions on where and what the fire management and prevention should be focused on with reduced economic expenses and improved the efficiency of forest fire management

A review of machine learning applications in wildfire science and management

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table

Mapping and Risk Assessment of Juniper Encroachment Into a Prairie Landscape

Juniper encroachment is a considerable threat to the prairie ecosystems of the Great Plains because it has the potential to alter native grasslands by changing soil characteristics, limiting herbaceous biomass, and hindering native community regeneration. Accurate maps of juniper cover and predictions of areas at risk for future expansion are needed to support proactive management measures. Therefore, our objectives are to: (1) Develop a practical workflow for large-scale juniper mapping using Landsat 8 Operational Land Imager (OLI) imagery and partial unmixing techniques, (2) Compare the classification accuracies from the resulting map based on different juniper density thresholds and different types of imagery, (3) Develop a predictive spatial model for the distribution of low-density juniper based on distance to seed source and environmental covariates and determine the prediction accuracy, and (4) Use the resulting maps to evaluate the extent of current juniper establishment and the risk of future encroachment. The study area encompasses counties bordering the Missouri River in southeastern South Dakota and northeastern Nebraska and covering approximately 23,000 km2. We applied a matched filtering technique to classify juniper with snowcovered and snow-free winter imagery (December-March) and snow-free spring imagery (April-June). We found that using the snow-covered winter images suppressed background spectral signatures and resulted in a higher overall classification accuracy of 93.7% for juniper densities above 15 percent, compared to snow-free winter imagery and spring imagery. When characterizing juniper densities below 10 percent our 30-meter pixel level classification map was unreliable, with an 11% probability of correctly classifying juniper. Therefore, we used Random Forests, a machine-learning algorithm, to develop a model of low-density (≤ 15%) juniper based on classified juniper cover and other ecological factors. We used the receiver operating characteristics (ROC) curve to evaluate model predictions; accuracy was high with an area under the curve (AUC) of 0.884. Our susceptibility map indicated that an additional 7.7% of the study area currently contained low densities of juniper and had high to very high risk of future encroachment. This study will provide agencies and land managers with information and techniques needed to address juniper encroachment in the Northern Great Plains

Watershed road network analysis with an emphasis on fire fighting management

The aim of this study is fire hazard zoning the Chehel-Chay watershed and analysis of road network in order to fire-fighting management. Using effective factors on fire occurrence, the fire hazard map of the study area produced by support vector machine algorithm and then was divided into four hazard classes. The road length and type were investigated in the each fire hazard classes. The results showed that most of occurred fires are located in the close distances of roads and forest areas. The results showed that road types and land cover are important in fire occurrences and suppression. In high dangerous zone, the roads pass through forestlands, but in low dangerous zone, the roads are passing from farmlands. The roads do not cover the half of area and do not pass at two third of high hazard class zones. Therefore, appreciate road network planning is necessary according to fire-fighting management. 

Effects of large fires on boreal forests of China : historical reconstruction and future prediction through landscape modeling

Includes vita.Boreal forests of China store about 350 Tg tree biomass carbon, which is approximately 24–31 [percent] of the total forest carbon storage in China, and thus, play an important role in maintain national carbon balance. Long-term fire exclusion and climate warming have foster larger and more severe fires. On 1987 May 6, a catastrophic fire, known as the Black Dragon Fire, occurred in this region, and burned 1.3 million ha. This fire is among the top five of such megafires ever recorded in the world, resulting in high degree of tree mortality and reset forest succession stage for most burned stands. Forests have grown back since, with much more homogeneous age classes and composition, which post new ecological risks and challenges. It is predicted that the warming will continue in the next century, and thus uncertainties exist in future fire regimes and vegetation response under novel climate. Chapter II estimate the burn severity and carbon emissions from the Black Dragon fire. I combined field and remote sensing data to map four burn severity classes and calculated combustion efficiency in terms of the biomass immediately consumed in the fire. Results of this chapter showed that 1.30 million hectares burned and 52 [percent] of that area burned with high severity. The emitted carbon dioxide equivalents (CO2e), accounted for approximately 10 [percent] of total fossil fuel emissions from China in 1987, along with CO (2 [percent] - 3 [percent] of annual anthropogenic CO emissions from China) and non-methane hydrocarbons (NMHC) contributing to the atmospheric pollutants. This study provides an important basis for carbon emission estimation and understanding the impacts of megafires. Chapter III developed a novel framework to spatially reconstruct the post-fire time-series of forest conditions after the 1987 Black Dragon fire of China by integrating a forest landscape model (LANDIS) with remote sensing and inventory data. I derived pre-fire (1985) forest composition and the megafire perimeter and severity using remote sensing and inventory data. I simulated the megafire and the post-megafire forest recovery from 1985-2015 using the LANDIS model. I calibrated the model and validated the simulation results using inventory data. I demonstrated that the framework was effective in reconstructing the post-fire stand dynamics and that it is applicable to other types of disturbances. Chapter IV investigated the effects of future fire regimes on boreal forests of China under a warming climate. I simulated species composition and distribution changes to the year 2100 using a coupled forest dynamic model (LANDIS PRO) and ecosystem process model (LINKAGES). I focused on two possible fire regimes (frequent small fires and infrequent large fires). Results of this chapter showed that climate warming and fires strongly affected tree species composition and distribution in the boreal forests of China. Climate warming promoted transitions from boreal species to pioneer and temperate species. Fire effects acted in the same direction as climate change effects on species occurrences, thereby catalyzing climate-induced transitions. Frequent small fires exerted stronger effects on the species composition shifts than infrequent large fires. The combined effects of climate warming and fire on the shifts in species composition will accumulate through time and space and can induce a complete transition of forest type, and alter forest dynamics and functions.Includes bibliographical reference

Modelling the probability of building fires

Systematic spatial risk analysis plays a crucial role in preventing emergencies.In the Czech Republic, risk mapping is currently based on the risk accumulationprinciple, area vulnerability, and preparedness levels of Integrated Rescue Systemcomponents. Expert estimates are used to determine risk levels for individualhazard types, while statistical modelling based on data from actual incidents andtheir possible causes is not used. Our model study, conducted in cooperation withthe Fire Rescue Service of the Czech Republic as a model within the Liberec andHradec Králové regions, presents an analytical procedure leading to the creation ofbuilding fire probability maps based on recent incidents in the studied areas andon building parameters. In order to estimate the probability of building fires, aprediction model based on logistic regression was used. Probability of fire calculatedby means of model parameters and attributes of specific buildings can subsequentlybe visualized in probability maps