A Framework for Multi-Dimensional Assessment of Wildfire Disturbance Severity from Remotely Sensed Ecosystem Functioning Attributes

Wildfire disturbances can cause modifications in different dimensions of ecosystem functioning, i.e., the flows of matter and energy. There is an increasing need for methods to assess such changes, as functional approaches offer advantages over those focused solely on structural or compositional attributes. In this regard, remote sensing can support indicators for estimating a wide variety of effects of fire on ecosystem functioning, beyond burn severity assessment. These indicators can be described using intra-annual metrics of quantity, seasonality, and timing, called Ecosystem Functioning Attributes (EFAs). Here, we propose a satellite-based framework to evaluate the impacts, at short to medium term (i.e., from the year of fire to the second year after), of wildfires on four dimensions of ecosystem functioning: (i) primary productivity, (ii) vegetation water content, (iii) albedo, and (iv) sensible heat. We illustrated our approach by comparing inter-annual anomalies in satellite-based EFAs in the northwest of the Iberian Peninsula, from 2000 to 2018. Random Forest models were used to assess the ability of EFAs to discriminate burned vs. unburned areas and to rank the predictive importance of EFAs. Together with effect sizes, this ranking was used to select a parsimonious set of indicators for analyzing the main effects of wildfire disturbances on ecosystem functioning, for both the whole study area (i.e., regional scale), as well as for four selected burned patches with different environmental conditions (i.e., local scale). With both high accuracies (area under the receiver operating characteristic curve (AUC) > 0.98) and effect sizes (Cohen’s |d| > 0.8), we found important effects on all four dimensions, especially on primary productivity and sensible heat, with the best performance for quantity metrics. Different spatiotemporal patterns of wildfire severity across the selected burned patches for different dimensions further highlighted the importance of considering the multi-dimensional effects of wildfire disturbances on key aspects of ecosystem functioning at different timeframes, which allowed us to diagnose both abrupt and lagged effects. Finally, we discuss the applicability as well as the potential advantages of the proposed approach for more comprehensive assessments of fire severity.Portuguese national funds through FCT-Foundation for Science and Technology, I.P., under the GreenRehab project PCIF/RPG/0077/2017Junta de Andalucia P18-RT-1927Project DETECTOR A-RNM-256-UGR18European Union Funds for Regional DevelopmentPortuguese Foundation for Science and Technology European CommissionMinistry of Education, Culture, Sports, Science and Technology, Japan (MEXT) European CommissionEuropean Social Fund, within the 2014-2020 EU Strategic Framework, through FCT SFRH/BD/99469/2014Individual Scientific Employment Stimulus Program (2017), through FCT CEECIND/02331/201

Toward a better understanding of changes in Northern vegetation using long-term remote sensing data

Cascading consequences of recent changes in the physical environment of northern lands associated with rapid warming have affected a broad range of ecosystem processes, particularly, changes in structure, composition, and functioning of vegetation. Incomplete understanding of underlying processes driving such changes is the primary motivation for this research. We report here the results of three studies that use long-term remote sensing data to advance our knowledge of spatiotemporal changes in growing season, greenness and productivity of northern vegetation. First, we improve the remote sensing-based detection of growing season by fusing vegetation greenness, snow and soil freeze/thaw condition. The satellite record reveals extensive lengthening trends of growing season and enhanced annual total greenness during the last three decades. Regionally varying seasonal responses are linked to local climate constraints and their relaxation. Second, we incorporate available land surface histories including disturbances and human land management practices to understand changes in remotely sensed vegetation greenness. This investigation indicates that multiple drivers including natural (wildfire) and anthropogenic (harvesting) disturbances, changing climate and agricultural activities govern the large-scale greening trends in northern lands. The timing and type of disturbances are important to fully comprehend spatially uneven vegetation changes in the boreal and temperate regions. In the final part, we question how photosynthetic seasonality evolved into its current state, and what role climatic constraints and their variability played in this process and ultimately in the carbon cycle. We take the ‘laws of minimum’ as a basis and introduce a new framework where the timing of peak photosynthetic activity (DOYPmax) acts as a proxy for plants adaptive state to climatic constraints on their growth. The result shows a widespread warming-induced advance in DOYPmax with an increase of total gross primary productivity across northern lands, which leads to an earlier phase shift in land-atmosphere carbon fluxes and an increase in their amplitude. The research presented in this dissertation suggests that understanding past, present and likely future changes in northern vegetation requires a multitude of approaches that consider linked climatic, social and ecological drivers and processes

Vurderer skogbrannen i Chitwan nasjonal park, Nepal

Although fire has long been an Essent foremost of the forest ecosystem and has a significant impact on the flora and fauna, it is also widely believed to be one of the main causes of biodiversity loss and environmental deterioration. Furthermore, little study has been conducted on the timing and location of wildfires in Nepal. Because of this, Chitwan National Park is highly susceptible to wildfires (DFRS, 2015). For wildfire monitoring, detection, and management, geographic information systems (GIS) and remote sensing (RS) are frequently used. Quick and affordable solutions are produced through RS and GIS. United States Geological Survey Earth Explorer website was used to retrieve the Landsat image and digital elevation module. The ICIMOD website was used to acquire information on the study area's land use, land cover, road network, and population. A difference-normalized burn ratio (dNBR) was calculated using geographic information software to assess the severity of the burns and A multi-criteria weighted-overlay analysis was performed to determine the wildfire risk zone. Throughout the research period, 3617 fire events were reported in CNP, with 3135 of them taking place in the core region and 482 in the buffer zone. The variance in the mean value of fire frequency was examined using one-way ANOVA, and it was found that the number of wildfire occurrences during the summer months was substantially high (p-value less than 0.05 at the 5% level of significance). Since 2021 saw the most fire events in CNP from 2001 and 2021 (384 fire incidents), the severity of the year's burns was calculated. A total of 76558.68 hectares of forest were burned in CNP in 2021, per the burn severity study. The research indicates that there is a high risk of wildfire for 6391.6 hectares in CNP, a moderate risk for 154054.4 hectares, and a low risk for 7754.02 hectares. Most events took place in the core area, which was the consequence of deliberate fire used to manage grasslands and slow down succession. However, prevention is advised since it might impair the species that depend on a specific grassland ecosystem

Analisis Perubahan Suhu Permukaan Daratan di Kecamatan Ternate Tengah Menggunakan Google Earth Engine Berbasis Cloud Computing

Suhu permukaan daratan di Kecamatan Ternate Tengah mengalami peningkatan dari tahun 2013-2023, salah satu faktor penyebabnya yaitu terjadinya perkembangan lahan terbangun yang semakin meningkat setiap tahunnya. Penelitian ini menggunakan data citra Landsat 8 Collection 1 Tier 2 TOA Reflectance pada google earth engine berbasis cloud computing. Hasil penelitian menunjukan bahwa nilai suhu permukaan daratan tertinggi di tahun 2013 yaitu 24,41ᵒ C dan mengalami peningkatan di tahun 2023 menjadi 28,63ᵒ C. Hasil peneltian diharapkan dapat memberikan manfaat yang besar bagi Pemerintah setempat dalam merencanakan dan mengambil keputusan dalam berbagai sector diantaranya pengembangan sektor pertanian, pengelolaa

Pyrogeography of the Southeast USA: Exploring the Relationships between Wildfire and Climate

Wildfire plays a contradictory role as both a hazard and a necessary ecological process in certain ecosystems. A variety of factors influence wildfire, including fuel type and quantity, land management policies, and patterns of human activity. Climate, however, can often play a dominant role. Wildfires are often thought to occur only in the western United States; however, fires are common on the southeastern U.S. landscape. Despite the abundance of fires, limited fire climatology work has been performed in this region. This dissertation addresses a knowledge gap in Southeast fire climatology by examining how gradients in precipitation regimes, in particular precipitation variability, influence spatial patterns of wildfire. In addition, modern synoptic climatology techniques are used to examine the relationships between atmospheric circulation patterns and wildfire ignitions in the central Gulf Coast sub-region of the Southeast. Chapter II characterizes precipitation regimes in Southeast national forests and associates mean annual ignition density and mean annual area burned with various precipitation metrics. Weak positive correlations were observed between daily precipitation variability and mean annual ignition density. Chapter III employs the Spatial Synoptic Classification (SSC) scheme to examine weather types associated with wildfire ignitions in the central Gulf Coast. Results show that dry tropical (DT) days occurred more often during years with higher numbers of ignitions in central Gulf Coast national forests, as well as in the 180, 90, and 30-day periods prior to a fire. DT weather types occurred most commonly in the fall and spring corresponding with peak fire seasons in much of the region. Particularly in the spring, DT variability was influenced by positive phases of the North Atlantic Oscillation (NAO), presumably increasing westerly flow and driving DT weather types farther east from their general source region. Finally, chapter IV developed eleven synoptic types using the Synoptic Typer Tool (STT). Principal Component Analysis (PCA) was applied to daily (18z) 500 mb geopotential height grids. Synoptic types were then linked with wildfire ignitions in the central Gulf Coast. Results suggested that troughs were associated with wildfire activity, as well as zonal flow and high pressure systems

DRY LIGHTNING IN THE WESTERN UNITED STATES METEOROLOGICAL CONDITIONS, WILDFIRE IGNITION, AIR QUALITY IMPACTS, AND FUTURE PROJECTIONS

Cloud-to-ground lightning occurring with little to no rainfall – typically referred to as “dry lightning” – is a major source of wildfire ignition in the western United States (WUS) during summer months. Although lightning-caused wildfires occur naturally and are generally ecologically beneficial, changing climatic conditions are increasing the risk of large and severe wildfires. Motivated by these impacts, my dissertation aims to advance our understanding of dry lightning in the WUS and its associated meteorological conditions, wildfire ignitions, air quality impacts, and future projections. In the first chapter, I provide an overview of the motivations for undertaking this dissertation. In the second chapter, I use gridded air pollutant and population data to examine compound air pollution episodes in the WUS. This study demonstrates an increase in the co-occurrence of two harmful air pollutants (fine particulate matter and ground-level ozone) during the WUS wildfire season in the past two decades, and increasing population exposure to these co-occurrences of 25 million person-days per year largely driven by increasing wildfire activity. I show that the largest population exposure to co-occurring air pollution was associated with the major outbreak of dry lightning that ignited hundreds of wildfires in California during August, 2020. To better understand dry lightning in this fire-prone region, in the third chapter I examine the meteorological and geographical factors associated with dry lightning in central and northern California. I apply k-means clustering to atmospheric reanalysis data to identify four types of meteorological patterns associated with the largest dry lightning outbreaks over this region, and quantify the spatial patterns of enhanced dry lightning risk associated with each pattern. In the fourth chapter, I use radar-derived rainfall data and gridded climatological variables to investigate the precipitation amounts and biophysical factors associated with lightning-caused wildfire ignitions across the WUS. Critically, my results refine the widely-used <2.5mm precipitation amount to define dry lightning by demonstrating that substantial regional variation exists in ignition-relevant precipitation amounts depending on local topography, vegetation, and climate. In the fifth chapter, I use Convolutional Neural Networks (CNNs) to predict cloud-to-ground lightning in the WUS at the grid cell level using a suite of reanalysis-derived meteorological variables as predictors. The CNNs are skillful at predicting lightning (domain-median AUC = 0.8) and realistically capture the year-to-year variation of lightning activity across the WUS (domain-median interannual correlation = 0.87). The CNN-based predictive models developed in this study can be applied to output from global climate models, thus enabling the ability to project future lightning and lightning-caused wildfires. In the final chapter, I summarize my findings from the four studies that comprise my dissertation. The outcomes of my research can be useful to forecasters and fire managers to anticipate possible wildfire ignitions in the present climate, and can be used to inform planning, management, and policy decisions around future lightning-caused wildfires in the WUS

Spatial and temporal characteristics of historical surface climate over the Northwest Territories, Canada

Climate change is putting many of the Northwest Territories (NWT) ecosystems, its people and animal populations at risk due to accelerated warming, permafrost thaw, and changing precipitation regimes. As the NWT continues to warm, at disproportionately higher rates when compared to the rest of Canada, threats to the stability of NWT’s ecosystems are expected to increase. Consequently, understanding how climate warming has changed historically and its implications on natural ecosystems requires point-to-region-specific, long-term climatic data to elucidate important drivers of observed changes relevant to decision makers at community, Indigenous, Territorial and Federal government levels. However, in situ climate data are limited temporally and spatially across the NWT. Hence, the overarching goal of this research is to enhance and improve the understanding of historical surface climate variables trends and patterns (air temperature, precipitation, and shortwave radiation) and its implications at local and regional scales in the continental NWT by using interpolated, reanalysis and remote sensing climate data. Gridded climate datasets such as interpolated and reanalysis data, can provide reliable estimates for in situ observations to compensate for data scarcity, but it is critical that researchers understand how biases in these datasets can impact runoff simulation in the NWT. Thus, the objective of this dissertation was to assess the similarity between daily in situ station observations and three gridded datasets (ANUSPLIN, ERA-Interim and MERRA-2) from 1980 to 2013 to support hydrological modelling in the NWT subarctic. The ANUSPLIN maximum and minimum temperature at eight locations aligned closely to the corresponding in situ observations and had mean daily biases of less than 0.58°C and 1.33°C, respectively. Precipitation estimates showed that the alternative datasets captured year-to-year variability, but large seasonal biases mainly during spring and summer were evident when precipitation magnitudes were estimated. In addition, this study used gridded data as a substitute for in situ observations in the Cold Regions Hydrological Model (CRHM) to simulate runoff. Simulated runoff generated when using ANUSPLIN and ERA-Interim data as inputs in CRHM captures the timing and magnitude of freshet and baseflow generally well at Scotty Creek. This study suggests that gridded datasets can provide reasonable estimates of in situ climate data in data sparse regions and reinforced that the accuracy in representing in situ observations over the NWT improves as the spatial resolution of interpolated dataset increases. This research also highlighted that when comparing datasets, it is important use multiple metrics and graphical methods to discern systematic biases. The presence of oceanic-atmospheric teleconnections patterns can influence weather patterns in northern regions which may lead to an increase in climate related wildland fires. The impact of the Arctic Dipole (AD) anomaly, a northern atmospheric teleconnection, on NWT’s surface climate has not been explored. Hence, the second objective of this dissertation used the ANUSPLIN dataset to assess the effects of the AD anomaly on local climate (air temperature, precipitation, and snowmelt) during a 66-year period (1950-2015). For all seasons, from 1950 to 2015, the occurrence of 64 strong positive and 56 strong negative AD modes were identified. The AD pattern revealed significant year-to-year fluctuation, with more frequent strong negative modes observed in the 2000s. In summer, when AD is in its strong negative mode, there is increased variance in the range of local air temperature, which is amplified in the southern, lake and foothill regions of the Taiga Plains. During strong positive AD modes, local air temperature anomalies increased (\u3e0.8°C) when compared to long-term mean temperature during summer months. Positive AD modes also lead to earlier commencement of snowmelt by an average of 3 to 5 days. The air temperature/snowmelt onset north–south amplification to the AD is linked to the position and intensity of the geopotential heights ridge axis over the continental NWT. A weak correlation was found between the AD and seasonal precipitation despite high correlation association between the AD and local air temperature in summer. Finally, the spatiotemporal patterns of incoming surface shortwave radiation (SSR) were analysed and quantified for the continental NWT to enhance understanding of northern ecosystems energy balance that are undergoing environmental changes. The third objective of this dissertation addressed this knowledge gap by assessing annual and seasonal trends in SSR receipt and to explore relationship between SSR and lake surface water temperature (LSWT) during the warm season. Consequently, the quantity of SSR that reaches Earth’s surface may vary. In this study, it is observed that SSR trends display a significant temporal and spatial dependency on NWT’s ecozones between 1980 and 2020. The annual mean SSR since 1980 decreased by ~0.8 Wm-2decade-1 in the Taiga Plains and Northern Arctic ecozones, with mixture of increasing and decreasing trends in both Taiga Shield and Southern Arctic ecozones. Seasonally, SSR decreased significantly in the summer since 1980 over the majority of the Taiga Plains ecozone, with a reduction rate that ranged between 0.6 and 14.6 Wm-2decade-1. The LSWT in small lakes was positively associated with SSR, while the LSWT in medium and large lakes showed a mix of positive and negative correlation coefficients. The linkage between total cloud cover and SSR in the NWT was largely negative for spring, summer and autumn seasons, with the Taiga Plains ecozone displaying the largest negative correlation. Long-term changes in SSR in the NWT will have an impact on the seasonal and annual energy balance of the region\u27s lakes. The impact of SSR changes on lake energy balances will have a wide range of consequences, particularly for NWT communities that rely on lakes for their transportation networks. These networks are already being adversely impacted by climate change-driven alterations in warming lake ice phenology. The collective findings of this study demonstrate the feasibility of using gridded and remote sensing datasets to characterize historical changes in local and regional weather and climate, building an understanding of northern climatology and providing best estimates of long-term trends with implications for ecosystem change in the future, such as increased rates of shrubification and frequency of wildland fires. In the absence of consistent in situ climate data, these gridded and remote sensing datasets aid our understanding of the physical links between climate change and northern ecosystems, which must be accounted for in forecast models used to predict future hydroclimate scenarios and to provide enhance climate services in northern regions. Improved understanding of how local and regional climate has changed in the NWT will inform policymakers in their efforts to develop and improve climate adaptation and mitigation policies in local communities across the territory