NEW, MULTI-SCALE APPROACHES TO CHARACTERIZE PATTERNS IN VEGETATION, FUELS, AND WILDFIRE

Pattern and scale are key to understanding ecological processes. My dissertation research aims for novel quantification of vegetation, fuel, and wildfire patterns at multiple scales and to leverage these data for insights into fire processes. Core to this motivation is the 3-dimensional (3-D) characterization of forest properties from light detection and ranging (LiDAR) and structure-from-motion (SfM) photogrammetry. Analytical methods for extracting useable information currently lag the ability to collect such 3-D data. The chapters that follow focus on this limitation blending interests in machine learning and data science, remote sensing, wildland fuels (vegetation), and wildfire. In Chapter 2, forest canopy structure is characterized from multiple landscapes using LiDAR data and a novel data-driven framework to identify and compare structural classes. Motivations for this chapter include the desire to systematically assess forest structure from landscape to global scales and increase the utility of data collected by government agencies for landscape restoration planning. Chapter 3 endeavors to link 3-D canopy fuels attributes to conventional optical remote sensing data with the goal of extending the reach of laser measurements to the entire western US while exploring geographic differences in LiDAR-Landsat relationships. Development of predictive models and resulting datasets increase accuracy and spatial variation over currently used canopy fuel datasets. Chapters 4 and 5 characterize fire and fuel variability using unmanned aerial systems (UAS) and quantify trends in the influence of fuel patterns on fire processes

Benchmarking environmental machine-learning models: methodological progress and an application to forest health

Geospatial machine learning is a versatile approach to analyze environmental data and can help to better understand the interactions and current state of our environment. Due to the artificial intelligence of these algorithms, complex relationships can possibly be discovered which might be missed by other analysis methods. Modeling the interaction of creatures with their environment is referred to as ecological modeling, which is a subcategory of environmental modeling. A subfield of ecological modeling is SDM, which aims to understand the relation between the presence or absence of certain species in their environments. SDM is different from classical mapping/detection analysis. While the latter primarily aim for a visual representation of a species spatial distribution, the former focuses on using the available data to build models and interpreting these. Because no single best option exists to build such models, different settings need to be evaluated and compared against each other. When conducting such modeling comparisons, which are commonly referred to as benchmarking, care needs to be taken throughout the analysis steps to achieve meaningful and unbiased results. These steps are composed out of data preprocessing, model optimization and performance assessment. While these general principles apply to any modeling analysis, their application in an environmental context often requires additional care with respect to data handling, possibly hidden underlying data effects and model selection. To conduct all in a programmatic (and efficient) way, toolboxes in the form of programming modules or packages are needed. This work makes methodological contributions which focus on efficient, machine-learning based analysis of environmental data. In addition, research software to generalize and simplify the described process has been created throughout this work

The Monitoring and Assessment Plan (MAP) Greater Everglades Wetlands Module- Landscape Pattern- Ridge, Slough, and Tree Island Mosaics: Year 1 Annual Report

In the current managed Everglades system, the pre-drainage, patterned mosaic of sawgrass ridges, sloughs and tree islands has been substantially altered or reduced largely as a result of human alterations to historic ecological and hydrological processes that sustained landscape patterns. The pre-compartmentalization ridge and slough landscape was a mosaic of sloughs, elongated sawgrass ridges (50-200m wide), and tree islands. The ridges and sloughs and tree islands were elongated in the direction of the water flow, with roughly equal area of ridge and slough. Over the past decades, the ridge-slough topographic relief and spatial patterning have degraded in many areas of the Everglades. Nutrient enriched areas have become dominated by Typha with little topographic relief; areas of reduced flow have lost the elongated ridge-slough topography; and ponded areas with excessively long hydroperiods have experienced a decline in ridge prevalence and shape, and in the number of tree islands (Sklar et al. 2004, Ogden 2005)

Global Forest Monitoring from Earth Observation

Covering recent developments in satellite observation data undertaken for monitoring forest areas from global to national levels, this book highlights operational tools and systems for monitoring forest ecosystems. It also tackles the technical issues surrounding the ability to produce accurate and consistent estimates of forest area changes, which are needed to report greenhouse gas emissions and removals from land use changes. Written by leading global experts in the field, this book offers a launch point for future advances in satellite-based monitoring of global forest resources. It gives readers a deeper understanding of monitoring methods and shows how state-of-art technologies may soon provide key data for creating more balanced policies

Center for Research on Sustainable Forests 2017 Annual Report

Ongoing development within the CRSF to be the region’s research data portal and geospatial observatory for forests of the Northeastern US. In addition to updating the CRSF home website, we continue to support three online tools for forest resources professionals and the public: Northeast Forest Information System (NEFIS) – an online, opensource, web portal for applied forestry information (http://www.nefismembers.org). More than 1,000 documents were uploaded over the year on a wide range of topics, user numbers have doubled, and monthly page views have reached nearly 5,000. Maine Forest Spatial Tool – displays a wide variety of geospatial data on forest resources across the State of Maine for both forest resource professionals and the public (http://mfst.acg.maine.edu). Maine Forest Dashboard – The Dashboard was launched in Spring 2017 and can be accessed at http://www.maineforestdashboard.com. The site provides customizable forest statistics and changes using long-term data from the Maine Forest Service and has had nearly 100 page views since its release in early May. CRSF scientists continue to provide a strong return for every dollar provided by the Maine Economic Improvement Fund (MEIF) to support CRSF research. In the past year, there has been over $21 in return for every$1 invested in

Mapping plant diversity and composition across North Carolina Piedmont forest landscapes using LiDAR-hyperspectral remote sensing

Forest modification, from local stress to global change, has given rise to efforts to model, map, and monitor critical properties of forest communities like structure, composition, and diversity. Predictive models based on data from spatially-nested field plots and LiDAR-hyperspectral remote sensing systems are one particularly effective means towards the otherwise prohibitively resource-intensive task of consistently characterizing forest community dynamics at landscape scales. However, to date, most predictive models fail to account for actual (rather than idealized) species and community distributions, are unsuccessful in predicting understory components in structurally and taxonomically heterogeneous forests, and may suffer from diminished predictive accuracy due to incongruity in scale and precision between field plot samples, remotely-sensed data, and target biota of varying size and density. This three-part study addresses these and other concerns in the modeling and mapping of emergent properties of forest communities by shifting the scope of prediction from the individual or taxon to the whole stand or community. It is, after all, at the stand scale where emergent properties like functional processes, biodiversity, and habitat aggregate and manifest. In the first study, I explore the relationship between forest structure (a proxy for successional demographics and resource competition) and tree species diversity in the North Carolina Piedmont, highlighting the empirical basis and potential for utilizing forest structure from LiDAR in predictive models of tree species diversity. I then extend these conclusions to map landscape pattern in multi-scale vascular plant diversity as well as turnover in community-continua at varying compositional resolutions in a North Carolina Piedmont landscape using remotely-sensed LiDAR-hyperspectral estimates of topography, canopy structure, and foliar biochemistry. Recognizing that the distinction between correlation and causation mirrors that between knowledge and understanding, all three studies distinguish between prediction of pattern and inference of process. Thus, in addition to advancing mapping methodologies relevant to a range of forest ecosystem management and monitoring applications, all three studies are noteworthy for assessing the ecological relationship between environmental predictors and emergent landscape patterns in plant composition and diversity in North Carolina Piedmont forests.Doctor of Philosoph

Integrating trees outside forests into national forest inventories

Trees Outside Forests (TOF) offer a wide range of ecological, economic, and social services. For example, they sequester carbon, provide wood for fuel and construction, protect soils from erosion, and contribute to the conservation of biological diversity. In particular in regions with low forest cover, TOF often have a substantial role in meeting society’s demands for resources such as wood and fodder. Information about trees is required for many purposes and at many geographical scales, and it has been recognised that substantial tree resources are overseen when focussing on forests alone. At the global scale, reporting obligations linked to agreements such as the Kyoto protocol are important. However, information is also needed for policy making at national scale and for integrated management by rural and urban planners. The focus of this thesis is the provision of national level information about TOF resources. From a literature review it was concluded that many national forest inventories have widened the scope of their inventories through including TOF. However, in general there is a shortage of information about TOF resources on a global scale. Further, very few methodological studies exist on how TOF could be integrated into national forest inventories. A central question of this thesis thus is how an integrative monitoring approach such as a national tree inventory would look like. Existing data from country-level TOF inventories across three continents were re-analysed. It was found that TOF contribute substantially to national tree biomass and carbon stocks. A method for simulating the spatial distribution of TOF elements at the landscape scale was investigated at selected study sites in Skåne, in the south of Sweden. The aim was to reconstruct existing patterns by methods from material sciences that might be used for modelling TOF patterns. Finally, a sampling simulation study was conducted to assess the potential of different inventory strategies to form the basis for national tree inventories. It was found that the combination of data from field sample plots and airborne laser scanning offers great potential in connection with model-assisted estimation. The results of this thesis may serve as a starting point for moving from a forest-centred view on tree monitoring towards integrative monitoring approaches that consider all trees that grow in a study region as valuable