White paper – On the use of LiDAR data at AmeriFlux sites

Our aim is to inform the AmeriFlux community on existing and upcoming LiDAR technologies (atmospheric Doppler or Raman LiDAR often deployed at flux sites are not considered here), how it is currently used at flux sites, and how we believe it could, in the future, further contribute to the AmeriFlux vision. Heterogeneity in vegetation and ground properties at various spatial scales is omnipresent at flux sites, and 3D mapping of canopy, understory, and ground surface can help move the science forward

On promoting the use of lidar systems in forest ecosystem research

Forest structure is an important driver of ecosystem dynamics, including the exchange of carbon, water and energy between canopies and the atmosphere. Structural descriptors are also used in numerous studies of ecological processes and ecosystem services. Over the last 20+ years, lidar technology has fundamentally changed the way we observe and describe forest structure, and it will continue to impact the ways in which we investigate and monitor the relations between forest structure and functions. Here we present the currently available lidar system types (ground, air, and space-based), we highlight opportunities and challenges associated with each system, as well as challenges associated with a wider use of lidar technology and wider availability of lidar derived products. We also suggest pathways for lidar to further contribute to addressing questions in forest ecosystem science and increase benefits to a wider community of researchers

Exploring Data Mining Techniques for Tree Species Classification Using Co-Registered LiDAR and Hyperspectral Data

NASA Goddard’s LiDAR, Hyperspectral, and Thermal imager provides co-registered remote sensing data on experimental forests. Data mining methods were used to achieve a final tree species classification accuracy of 68% using a combined LiDAR and hyperspectral dataset, and show promise for addressing deforestation and carbon sequestration on a species-specific level

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

Improved boreal vegetation mapping using imaging spectroscopy to aid wildfire management, Interior Alaska

Thesis (Ph.D.) University of Alaska Fairbanks, 2023Wildfires are a natural and essential part of Alaska ecosystems, but excessive wildfires pose a risk to the ecosystem's health and diversity, as well as to human life and property. To manage wildfires effectively, vegetation/fuel maps play a critical role in identifying high-risk areas and allocating resources for prevention, suppression, and recovery efforts. Furthermore, vegetation/fuel maps are an important input for fire behavior models, along with weather and topography data. By predicting fire behavior, such as spread rate, intensity, and direction, fuel models allow fire managers to make informed decisions about wildfire suppression, management, and prevention. Traditionally used vegetation/fuel maps in Alaska are inadequate due to a lack of detailed information since they are primarily generated using coarser resolution (30m) multispectral data. Hyperspectral remote sensing offers an efficient approach for better characterization of forest vegetation due to the narrow bandwidth and finer spatial resolution. However, the high cost associated with data acquisition remains a significant challenge to the widespread application of hyperspectral data. The aim of this research is to create accurate and detailed vegetation maps and upscale them for the boreal region of Alaska. The study involves hyperspectral data simulation using Airborne Visible InfraRed Imaging Spectrometer - Next Generation (AVIRIS-NG) data and publicly available Sentinel-2 multispectral data, ground spectra convolved to Sentinel-2 and AVIRIS-NG using the spectral response function of each sensor. Simulated data captured the minute details found in the real AVIRIS-NG data and were classified to map vegetation. Using the ground data from Bonanza Creek Long-Term Ecological Research sites, we compared the new maps with the two existing map products (the LANDFIRE's Existing Vegetation Type (EVT) and Alaska Vegetation and Wetland Composite). The maps generated using simulated data showed an improvement of 33% in accuracy and are more detailed than existing map products. In addition to fuel maps, we performed sub-pixel level mapping to generate a needleleaf fraction map, which serves fire management needs since needleleaf species are highly flammable. However, validating the sub-pixel product was challenging. To overcome this, we devised a novel validation method incorporating high-resolution airborne hyperspectral data (1m) and ground data. The study addresses the limitations of traditional fuel/vegetation maps by providing a more detailed and accurate representation of vegetation/fuel in Alaska. The methods and findings advance fuel and vegetation mapping research in Alaska and offer a novel pathway to generate detailed fuel maps for boreal Alaska to aid wildfire management.Alaska Established Program to Stimulate Competitive Research (EPSCoR), AmericaView, and the College of Natural Science and Mathematics, National Science Foundation award OIA-1757348, State of Alaska and the U.S. Geological Survey Grant/Cooperative Agreement No. G18AP0007

Anoles & Drones: revealing controls on anole abundance and mapping sub-canopy thermal habitat using remote sensing, on the island of Utila, Honduras

In these times of rapid environmental change and species extinction, understanding the drivers and mechanisms governing species’ abundance is more important than ever. The goal of this thesis was to further our understanding of what drives variation in species’ abundance and microhabitat use through space, particularly in the context of rapid land cover and climate change, using the little explored anole fauna of the Honduran island of Utila. The work uncovered that when considering structural habitat, prey availability and the thermal environment, for the endemic Anolis bicaorum, thermal habitat quality and prey biomass both had positive direct effects on anole abundance. However, thermal habitat quality also influenced prey biomass, leading to a strong indirect effect on abundance. Consequently, the later part of this thesis focuses on the thermal environment and the use of unoccupied aerial vehicles (UAVs) and satellite remote sensing platforms for mapping thermal habitat quality and availability at scales relevant to the species. Thermal habitat quality for A. bicaorum was primarily a function of canopy density, measured as leaf area index (LAI), therefore this work combined indices of canopy cover and heterogeneity derived from UAV and WorldView-2 satellite imagery to map sub canopy operative temperature (Te). Results indicate that such methods as using remote sensing imagery, when coupled with air temperature measures, are a reasonable way of mapping Te continuously across space, allowing us to quantify the availability and spatial structure of the thermal environment, at spatial scales experienced by the organism. Lastly, I used WorldView-2 imagery and the proposed methods for mapping Te to quantify available thermal habitat for A. bicaorum on Utila across land cover and climate scenarios. This work indicates the need to determine controls and niche interactions on animal abundance and the importance quantifying these niche factors at relevant spatial scales to estimate species responses to land cover and climatic change

Anoles & Drones: revealing controls on anole abundance and mapping sub-canopy thermal habitat using remote sensing, on the island of Utila, Honduras

In these times of rapid environmental change and species extinction, understanding the drivers and mechanisms governing species’ abundance is more important than ever. The goal of this thesis was to further our understanding of what drives variation in species’ abundance and microhabitat use through space, particularly in the context of rapid land cover and climate change, using the little explored anole fauna of the Honduran island of Utila. The work uncovered that when considering structural habitat, prey availability and the thermal environment, for the endemic Anolis bicaorum, thermal habitat quality and prey biomass both had positive direct effects on anole abundance. However, thermal habitat quality also influenced prey biomass, leading to a strong indirect effect on abundance. Consequently, the later part of this thesis focuses on the thermal environment and the use of unoccupied aerial vehicles (UAVs) and satellite remote sensing platforms for mapping thermal habitat quality and availability at scales relevant to the species. Thermal habitat quality for A. bicaorum was primarily a function of canopy density, measured as leaf area index (LAI), therefore this work combined indices of canopy cover and heterogeneity derived from UAV and WorldView-2 satellite imagery to map sub canopy operative temperature (Te). Results indicate that such methods as using remote sensing imagery, when coupled with air temperature measures, are a reasonable way of mapping Te continuously across space, allowing us to quantify the availability and spatial structure of the thermal environment, at spatial scales experienced by the organism. Lastly, I used WorldView-2 imagery and the proposed methods for mapping Te to quantify available thermal habitat for A. bicaorum on Utila across land cover and climate scenarios. This work indicates the need to determine controls and niche interactions on animal abundance and the importance quantifying these niche factors at relevant spatial scales to estimate species responses to land cover and climatic change

A Characterization of Human Burial Signatures using Spectroscopy and LIDAR

This study is an analysis of terrestrial remote sensing data sets collected at the University of Tennessee’s Anthropology Research Facility (ARF). The objective is to characterize human burial signatures using spectroscopy and laser scanning technologies. The development of remote human burial detection methodologies depends on basic research to establish signatures that inform forensic investigations. This dissertation provides recommendations for future research on remote sensing of human burials, and for investigators who wish to apply these technologies to case work. Data used in this study include terrestrial spectra, aerial hyperspectral imagery, satellite multispectral imagery, terrestrial light detection and ranging (LIDAR), and aerial LIDAR. In February 2013, ten individuals donated through the Forensic Anthropology Center body donation program were buried in three differently sized graves at the ARF. The graves contain one, three, and six bodies, respectively. An empty experimental control grave was also created. Terrestrial data collections were made from two-days pre-burial to 21-months post-burial. Aerial data were collected from 19 to 27-months post-burial. Satellite imagery was collected from six-months pre-burial to 23-months post-burial. Analytical emphasis is placed on the terrestrial data sets, which are of the highest spatial and spectral fidelity. Results of terrestrial data analysis reveal separable spectral and topographic signatures between the disturbed locations and surrounding undisturbed area. Aerial and satellite data were used to attempt validation of terrestrial data analysis findings, but findings were inconclusive. This study demonstrates that live vegetation spectral samples can be correctly classified as disturbed or undisturbed groups at rates from 52.0 – 78.3% using statistically-based classification models. Additionally, this study documents localized elevation change at burial surfaces as a result of initial digging activity, subsequent soil settling and subsurface decomposition. The findings of this research are significant to both researchers and practitioners. It is the first study to compare live vegetation spectra associated with human burials and is the first to document burial elevation change using LIDAR. This work contributes to a collective understanding of human burial signatures that can be used together or with other geophysical methods to assist in locating unmarked human burials

Land use change and its impact on soil properties using remote sensing, farmer decision rules and modelling in rural regions of Northern Vietnam

After the Indo China war in 1954, a dramatic rise in population in Northwest Vietnam led to an increased demand of agricultural land for food security requirements. Slash and burn systems which existed for many hundreds of years were replaced by intense cash crop systems, particularly maize production. Maize cropping was further expanded to steeper sloping areas, resulting in a risk of soil degradation. Therefore, investigating Land Use Change (LUC) and its impact on soil properties were considered in this study. The study aimed to identify LUC in 1954, 1973, the 1990s and 2007 in Chieng Khoi commune, Yen Chau district, Son La province, Vietnam using available remote sensing data. Furthermore, a detailed land use map classification method was developed using farmers decision rules. Based on farmers crop decision rules and, food requirement and population information, a simple LUC model was developed to simulate LUC annually from 1954 to 2007. Moreover, total soil nitrogen and carbon were determined under a chronosequence of intense cultivation. Thus, developing a modelling tool had the aim to assess the impacts of LUC on soil fertility at watershed level. The first case study (Chapter 3) presented the LUC assessment, using available remote sensing data combined with farmer information. Forest areas decreased from 1954 to 2007, except in the 1990s because of policies that aimed to encourage and support afforestation programmes to increase forest land. However, planted forest has since decreased again since 1999 whereas agricultural land has increased dramatically. Agricultural land expanded to both natural forest and planted forest areas until 2007 legally (with encouragement of agroforestry) and illegally thereafter (at the border between cultivated land and forest). The establishment of an artificial lake in Chieng Khoi commune opened the accessibility to forest land surrounding the lake, with a forest area of 929 ha remaining in 2007 compare to more than 2,500 ha in 1954. Paddy rice areas did not change because of their specific location (lower and flat lands), but production increased and was intensified by two cropping seasons per year due to irrigation improvements and a continuous water supply from the artificial lake. The second case study (Chapter 4) presented the development of a LUC model, using the outputs from the first case study comprising farmers decision rules and food requirements for an increased population. For later periods, the influence of market orientation factor was considered. The model successfully simulated the expansion of cultivation areas and replacement of forest land by agricultural land. Simulations were at accepted level of accuracy comparing actual and simulated LUC (Goodness-of-fit GOF values greater than 0.7 and Figure of merit - FOM values greater than 50%). The third case study (Chapter 5) demonstrated an investigation of the soil fertility dynamic under intense cultivation and the development of a simple dynamic and spatially-explicit modelling tool to assess the changes in soil fertility. The Dynamic of total Carbon and Nitrogen distribution (DyCNDis) model was constructed using field data combined with literature information. The field data showed that, under a decade of maize mono cultivation in slope areas, both nitrogen and carbon were largely depleted. Furthermore, the DyCNDis model showed an acceptable level of validation (modelling efficiency EF of 0.71 and root mean square error - RMSE of 0.42) to simulate nitrogen and carbon under intense maize cultivation at watershed level. Additionally, the model identified hotspot areas of 134 ha (18.9% of total upland cultivation areas) that are threatened by soil degradation through intense cultivation over a long-term period. In conclusion, the combination of qualitative and quantitative approaches allowed assessing impacts of LUC on environmental services such as soil fertility through the developed DyCNDis modeling tool. The combination of improved LUC analysis with a simple spatial dynamic soil fertility modeling tool may assist policy makers in developing alternative implementation strategies for local stakeholders in regions which face data limitations. The modelling tools developed in this study were able to successfully simulate LUC and to identify locations where soil conservation methods at watershed level need most urgently to be applied to avoid soil degradation. The model tools were able to simulate the trends rather than values of agricultural area expansion and reduction of soil nitrogen and carbon. The developed approaches could be linked and coupled to other modelling tools to economically consider benefits or ecological concerns toward sustainable crop production in remote and rural regions.Nach Ende des Indochinakrieges führten ein starkes Bevölkerungswachstum und der damit steigende Bedarf an Nahrungsmitteln in Nordwestvietnam zu einer enormen Ausweitung der landwirtschaftlich genutzten Flächen. Intensivierte Landwirtschaft, insbesondere im Maisanbau, ersetzte die traditionellen Landnutzungssysteme, die hauptsächlich auf Brandrohdung basierten und zuvor für Jahrhunderte Bestand hatten. Vor allem der Maisanbau wurde zunehmend auf Steillagen ausgeweitet, was beträchtliche Risiken der Bodendegradierung nach sich zog. Diese Dissertation befasst sich mit der Erforschung der Landnutzungsänderungen und ihren Auswirkungen auf die Bodeneigenschaften der betroffenen Flächen. Das übergeordnete Ziel ist die Analyse der Landnutzungsänderungen in der Kommune Chieng Khoi des Distrikts Son La in den Jahren 1954, 1973, den 1990er Jahren sowie 2007. Hierfür wurden verfügbare Fernerkundungsdaten verwendet. Zudem wurde eine detaillierte Klassifizierungsmethode zur Erstellung von Landnutzungskarten entwickelt. Basierend auf den Anbauentscheidungen der Kleinbauern in der Studienregion, Nahrungsmittelbedarf und demographischen Daten, wurde ein einfaches dynamisches und räumlich-explizites Modell zur Simulation der Landnutzungsänderungen im Zeitraum von 1954 bis 2007 erstellt. Gesamtstickstoff und der gesamte Kohlenstoffgehalt unter intensiver Landnutzung wurden durch Feldbeprobungen und Laboranalysen bestimmt. Somit hatte die Modellentwicklung das Ziel, den Einfluss der Landnutzungsänderungen auf die Bodenfruchtbarkeit im Wassereinzugsgebiet des Chieng Khoi-Sees zu analysieren und zu bewerten. Die erste Fallstudie dieser Dissertation behandelt die Bewertung der Landnutzungsänderungen mit Hilfe von Fernerkundungsdaten und Interviews mit Kleinbauern. Nahezu der gesamte Untersuchungszeitraum war durch Abholzung geprägt. Eine Ausnahme stellen die 1990er Jahre dar, in denen Aufforstung durch diverse politische Maßnahmen gefördert wurde. Diese kurze Phase der Aufforstung endete jedoch bereits im Jahr 1999, als mit massiver Abholzung begonnen wurde. Bis ins Jahr 2007 wurden sowohl Primärwälder, als auch wieder aufgeforstete Flächen zur Erschließung von Ackerland gerodet. Dies geschah zum einen auf legalem Weg im Rahmen von staatlich geförderten Agroforstprogrammen, zum anderen illegal an den Grenzbereichen der Ackerflächen zu Wäldern. Insbesondere die Errichtung des Chieng Khoi-Stausees ermöglichte die landwirtschaftliche Erschließung umliegender Waldgebiete. Als Konsequenz gingen die Waldgebiete von 2.500 Hektar in 1954 auf 929 Hektar in 2007 zurück. Im Gegensatz zu Hanglagen hatte die Errichtung des Stausees lediglich geringe Auswirkungen auf den Nassreisanbau in flachen Lagen und Tälern. Dennoch wurden auch im Nassreisanbau Veränderungen registriert. Die konstante Wasserverfügbarkeit durch den Stausee ermöglichte die Einführung eines zweiten Produktionszyklus pro Jahr. Außerdem konnten durch intensivere Bewirtschaftungsmethoden die Erträge gesteigert werden. In der zweiten Fallstudie wird ein Modell zur Simulation von Landnutzungsänderungen entwickelt. Hierfür werden die Ergebnisse der ersten Fallstudie, die Anbauentscheidungen der Kleinbauern in der Studienregion und der Nahrungsmittelbedarf einer wachsenden Bevölkerung herangezogen. Mit Hilfe des Modells konnte die Ausweitung der landwirtschaftlich genutzten Flächen sowie die dadurch bedingte Abholzung erfolgreich simuliert werden. Simulierte Ergebnisse lagen beim Vergleich mit historischen Daten im Toleranzbereich. Die dritte Fallstudie befasst sich mit der Untersuchung der Entwicklung der Bodenfruchtbarkeit in intensiven Ackerbausystemen. Zudem wurde ein einfaches Modellierungstool zur Bewertung der Änderungen der Bodenfruchtbarkeit entwickelt. Das Modell zur Analyse der Entwicklung der gesamten Kohlenstoff- und Stickstoffverteilung (DyCNDis) wurde mit Hilfe von im Feld erhobenen Primärdaten sowie Sekundärdaten aus relevanter Literatur entwickelt. Anhand der Primärdaten konnte gezeigt werden, dass an Hanglagen sowohl der Kohlenstoff- als auch der Stickstoffgehalt im Boden bereits nach einem Jahrzehnt Maisanbau drastisch abgenommen hatte. Zudem konnte das DyCNDis-Modell zufriedenstellend validiert werden. Demnach ist das Modell geeignet, um Kohlenstoff- und Stickstoffgehalte im Boden unter intensivem Maisanbau für Wassereinzugsgebiete zu simulieren. Außerdem konnten Standorte, welche bei dauerhafter landwirtschaftlicher Nutzung besonders von Bodendegradation bedroht waren, identifiziert werden. Diese Standorte hatten eine Gesamtfläche von 134 ha, was 18,9% der gesamten Ackerfläche an Hanglagen in der Forschungsregion entspricht. Zusammenfassend kann gesagt werden, dass die Kombination qualitativer und quantitativer Forschung eine hinreichende Bewertung der Einflüsse von Landnutzungsänderungen auf Ökosystemdienstleistungen ermöglicht. Dynamische Veränderungen der Landnutzung und Bodenfruchtbarkeit werden, wie in der Vergangenheit, auch in Zukunft auftreten. Besonders in Regionen mit beschränkter Datenverfügbarkeit bietet diese Dissertation politischen Entscheidungsträgern mögliche Umsetzungsstrategien für lokale Stakeholder. Die entwickelten Modelle konnten Landnutzungsänderungen zuverlässig modellieren und Standorte identifizieren, an denen eine Einführung von bodenkonservierenden Maßnahmen notwendig ist, um eine Bodendegradation zu verhindern. Darüber hinaus können Modelle zur Einsparung von Kosten und Arbeitszeit bei Feldversuchen verwendet werden, sowie für die Bewertung des ökonomischen Nutzens oder von ökologischen Bedenken zu einer nachhaltigeren Landbewirtschaftung in abgelegenen und ländlichen Regionen beitragen