On bird species diversity and remote sensing – utilizing lidar and hyperspectral data to assess the role of vegetation structure and foliage characteristics as drivers of avian diversity

Avian diversity has long been used as a surrogate for overall diversity. In forest ecosystems, it has been assumed that vegetation structure, composition and condition have a significant impact on avian diversity. Today, these features can be assessed via remote sensing. This study examined how structure metrics from lidar data and narrowband indices from hyperspectral data relate with avian diversity. This was assessed in four deciduous-dominated woods with differing age and structure set in an agricultural matrix in eastern England. The woods were delineated into cells within which metrics of avian diversity and remote sensing based predictors were calculated. Best subset regression was used to obtain best lidar models, hyperspectral models and finally, the best models combining variables from both datasets. The aims were not only to examine the drivers of avian diversity, but to assess the capabilities of the two remote sensing techniques for the task. The amount of understorey vegetation was the best single predictor, followed by Foliage Height Diversity, reflectance at 830 nm, Anthocyanin Reflectance Index 1 and Vogelmann Red Edge Index 2. This showed the significance of the full vertical profile of vegetation, the condition of the upper canopy, and potentially tree species composition. The results thus agree with the role that vegetation structure, condition and floristics are assumed to have for diversity. However, the inclusion of hyperspectral data resulted in such minor improvements to models that its collection for these purposes should be assessed critically

Assessing biodiversity using forest structure indicators based on airborne laser scanning data

The role of forests in biodiversity assessment and planning is substantial as these ecosystems support approximately 80% of the world’s terrestrial biodiversity. Forests provide food, shelter, and nesting environments for numerous species, and deliver multiple ecosystem services. It has been widely recognised that forest vegetation structure and its complexity influence local variations in biodiversity. As forests are facing threats globally caused by human activities, there is a need to map the biodiversity of these ecosystems. The main objective of this review was to summarise the use of airborne laser scanning (ALS) data in biodiversity-related assessment of forests. We draw attention to topics related to animal ecology, structural diversity, dead wood, fragmentation and forest habitat classification. After conducting a thorough literature search, we categorised scientific articles based on their topics, which served as the basis for the section division in this paper. The majority of the research was found to be conducted in Europe and North America, only a small fraction of the study areas was located elsewhere. Topics that have received the most attention were related to animal ecology (namely richness and diversity of forest fauna), assessment of dead trees and tree species diversity measures. Not all studies used ALS data only, as it were often fused with other remote sensing data – especially with aerial or satellite images. The fusion of spectral information from optical images and the structural information provided by ALS was highly advantageous in studies where tree species were considered. Relevant ALS variables were found to be case-specific, so variables varied widely between forest biodiversity studies. We found that there was a lack of research in geographical areas and forest types other than temperate and boreal forests. Also, topics that considered functional diversity, community composition and the effect of spatial resolution at which ALS data and field information are linked, were covered to much lesser extent

Assessing functional diversity of a managed forest-savanna landscape using remote sensing techniques

Global biodiversity loss and changing disturbance patterns are having significant effects on the composition, function, and productivity of biological communities. Functional diversity, a multifaceted trait-based component of biodiversity, is emerging as an effective metric of community function, resilience, and response to environmental change. Remote sensing techniques can be used to measure the biochemical, physiological, and morphological traits of plants and assess functional diversity across a landscape. In this study, I demonstrated the use of multispectral imagery and light detection and ranging (LiDAR) to determine relationships between physiological and morphological functional diversity metrics - functional richness, functional evenness, and functional divergence - and forest management implemented at Pushmataha Forest Habitat Research Demonstration Area (FHRA). Indices of vegetation biochemistry, physiology, and morphology were affected by forest management, including combinations of prescribed fire, selective thinning, and pine timber harvest, while the effects of management on functional diversity metrics estimated from remote sensing were less defined. Morphological functional evenness and divergence differed between treatments, and fire return interval was determined to play a key role in vegetation community morphology and functional diversity. By expanding the methodology to assess metrics of functional diversity across the larger forest-savanna landscape of Pushmataha Wildlife Management Area, I examined the scale dependency of functional diversity metrics and demonstrated the potential for using multispectral imagery from satellite platforms to fill gaps in global functional diversity knowledge

CONSEQUENCES OF VINE INFESTATION: LINKING ABIOTIC INFLUENCES AND BIOTIC INTERACTIONS TO SUCCESSIONAL AND STRUCTURAL CHANGES IN COASTAL COMMUNITIES

Located at the interfaces of terrestrial and marine environments, coastal habitats are inherently vulnerable to the effects of global change. Barrier island systems in particular serve not only as protective buffers against storm events, but also as sentinel ecosystems for observation of the impacts of sea level rise, and of increasing storm frequency and intensity. In the mid-Atlantic region, shrub thickets of Morella species compose the dominant forest community. The often monospecific nature of these plant community assemblages is advantageous to ecological studies and cross-scale applications; the relatively low diversity facilitates transitions between scales. My objective was to investigate the distribution and community roles of lianas in mid-Atlantic barrier island forest communities. I quantified environmental variables at two barrier habitats with differing site management histories and corresponding topography, and found that abiotic factors affected distributions of woody species, which subsequently affected vine species distributions. Some association of prevalent vine species with the common woody plants Prunus serotina and Morella cerifera was observed, though neither vines nor woody species demonstrated significant species-specific phytosociological associations. Vines demonstrated a long-lasting effect of arresting or delaying succession, and are potentially responsible for the lack of redevelopment of mature maritime forest at these sites. At Hog Island, Virginia, remotely-sensed data were utilized to determine the three-dimensional structural effects of vine infiltration in woody canopies. Vines were found to reduce canopy height and depth, and increase density, short-term diversity, and light-intercepting biomass. Significant vine infiltration can accelerate senescence of shrub thickets, but often results in persistent tangled masses of vegetation which reduce recruitment of later-successional species. These effects may represent long-term, lasting impacts of vine establishment and expansion in these habitats, affecting community succession towards diverse and stable maritime forest, and significantly altering resource dynamics in these sensitive ecosystems

Is there a solution to the spatial scale mismatch between ecological processes and agricultural management?

The major limit to develop robust landscape planning for biodiversity conservation is that the spatial levels of organization of landscape management by local actors rarely match with those of ecological processes. This problem, known as spatial scale mismatch, is recognized as a reason of lack of effectiveness of agri-environment schemes. We did a review to describe how authors identify the problem of spatial scale mismatch in the literature. The assumption is made that the solutions proposed in literature to conciliate agricultural management and conservation of biodiversity are based on theoretical frameworks that can be used to go towards an integration of management processes and ecological processes. Hierarchy Theory and Landscape Ecology are explicitly mobilized by authors who suggest multiscale and landscape scale approaches, respectively, to overcome the mismatch problem. Coordination in management is proposed by some authors but with no theoretical background explicitly mentioned. The theory of organization of biological systems and the theories of Social-Ecological Systems use the concept of coordination and integration as well as concepts of organization, adaptive capabilities and complexity of systems. These theories are useful to set up a new framework integrating ecological processes and agricultural management. Based on this review we made two hypotheses to explain difficulties to deal with spatial scale mismatch: (1) authors generally do not have an integrated approach since they consider separately ecological and management processes, and (2) an inaccurate use of terminology and theoretical frameworks partially explain the inadequacy of proposed solutions. We then specify some terms and highlight some ‘rules’ necessary to set up an integrative theoretical and methodological framework to deal with spatial scale mismatch.(Presentation des résumés n°186, p. 95-96, non paginé

Remote Sensing of Plant Biodiversity

This Open Access volume aims to methodologically improve our understanding of biodiversity by linking disciplines that incorporate remote sensing, and uniting data and perspectives in the fields of biology, landscape ecology, and geography. The book provides a framework for how biodiversity can be detected and evaluated—focusing particularly on plants—using proximal and remotely sensed hyperspectral data and other tools such as LiDAR. The volume, whose chapters bring together a large cross-section of the biodiversity community engaged in these methods, attempts to establish a common language across disciplines for understanding and implementing remote sensing of biodiversity across scales. The first part of the book offers a potential basis for remote detection of biodiversity. An overview of the nature of biodiversity is described, along with ways for determining traits of plant biodiversity through spectral analyses across spatial scales and linking spectral data to the tree of life. The second part details what can be detected spectrally and remotely. Specific instrumentation and technologies are described, as well as the technical challenges of detection and data synthesis, collection and processing. The third part discusses spatial resolution and integration across scales and ends with a vision for developing a global biodiversity monitoring system. Topics include spectral and functional variation across habitats and biomes, biodiversity variables for global scale assessment, and the prospects and pitfalls in remote sensing of biodiversity at the global scale

Remote Sensing of Plant Biodiversity

At last, here it is. For some time now, the world has needed a text providing both a new theoretical foundation and practical guidance on how to approach the challenge of biodiversity decline in the Anthropocene. This is a global challenge demanding global approaches to understand its scope and implications. Until recently, we have simply lacked the tools to do so. We are now entering an era in which we can realistically begin to understand and monitor the multidimensional phenomenon of biodiversity at a planetary scale. This era builds upon three centuries of scientific research on biodiversity at site to landscape levels, augmented over the past two decades by airborne research platforms carrying spectrometers, lidars, and radars for larger-scale observations. Emerging international networks of fine-grain in-situ biodiversity observations complemented by space-based sensors offering coarser-grain imagery—but global coverage—of ecosystem composition, function, and structure together provide the information necessary to monitor and track change in biodiversity globally. This book is a road map on how to observe and interpret terrestrial biodiversity across scales through plants—primary producers and the foundation of the trophic pyramid. It honors the fact that biodiversity exists across different dimensions, including both phylogenetic and functional. Then, it relates these aspects of biodiversity to another dimension, the spectral diversity captured by remote sensing instruments operating at scales from leaf to canopy to biome. The biodiversity community has needed a Rosetta Stone to translate between the language of satellite remote sensing and its resulting spectral diversity and the languages of those exploring the phylogenetic diversity and functional trait diversity of life on Earth. By assembling the vital translation, this volume has globalized our ability to track biodiversity state and change. Thus, a global problem meets a key component of the global solution. The editors have cleverly built the book in three parts. Part 1 addresses the theory behind the remote sensing of terrestrial plant biodiversity: why spectral diversity relates to plant functional traits and phylogenetic diversity. Starting with first principles, it connects plant biochemistry, physiology, and macroecology to remotely sensed spectra and explores the processes behind the patterns we observe. Examples from the field demonstrate the rising synthesis of multiple disciplines to create a new cross-spatial and spectral science of biodiversity. Part 2 discusses how to implement this evolving science. It focuses on the plethora of novel in-situ, airborne, and spaceborne Earth observation tools currently and soon to be available while also incorporating the ways of actually making biodiversity measurements with these tools. It includes instructions for organizing and conducting a field campaign. Throughout, there is a focus on the burgeoning field of imaging spectroscopy, which is revolutionizing our ability to characterize life remotely. Part 3 takes on an overarching issue for any effort to globalize biodiversity observations, the issue of scale. It addresses scale from two perspectives. The first is that of combining observations across varying spatial, temporal, and spectral resolutions for better understanding—that is, what scales and how. This is an area of ongoing research driven by a confluence of innovations in observation systems and rising computational capacity. The second is the organizational side of the scaling challenge. It explores existing frameworks for integrating multi-scale observations within global networks. The focus here is on what practical steps can be taken to organize multi-scale data and what is already happening in this regard. These frameworks include essential biodiversity variables and the Group on Earth Observations Biodiversity Observation Network (GEO BON). This book constitutes an end-to-end guide uniting the latest in research and techniques to cover the theory and practice of the remote sensing of plant biodiversity. In putting it together, the editors and their coauthors, all preeminent in their fields, have done a great service for those seeking to understand and conserve life on Earth—just when we need it most. For if the world is ever to construct a coordinated response to the planetwide crisis of biodiversity loss, it must first assemble adequate—and global—measures of what we are losing

FROM DRONES TO SOIL CORES: COMPREHENSIVE ECOLOGICAL ASSESSMENTS FOR ENHANCING CONSERVATION MANAGEMENT OF URBAN FORESTED NATURAL AREAS

Urban natural areas are vegetated areas within cities that exhibit characteristics of non-urban natural areas in that they have relatively high levels of self-regulation (low or no level of management) of natural ecosystem processes and exhibit high taxonomic, genetic, and structural diversity. When these areas take the form of urban parkland, they are often managed for their social benefits to urban residents, while their ecological potential remains underutilized. Growing interest in enhancing biodiversity conservation in cities has highlighted the importance of improving the ecological planning and management of urban natural areas, particularly forested natural areas. For the variety of agencies and non-profit organizations governing and managing urban natural areas, achieving conservation goals relies on comprehensive ecological data, but this information is often lacking in spatial resolution or altogether absent in most city parks and recreation departments across the U.S. Acquiring necessary data depends on extensive and time-consuming ground surveys, where city budgets and time constraints can present considerable obstacles. With over 2,000 acres of urban natural areas, government agencies and nonprofits in Knoxville, Tennessee are facing these same obstacles. The objectives of this research study were to 1) use drone remote sensing and traditional ecological field methods to quantify and characterize key indicators of urban forest health (vegetation, soils, and ecological impacts) of a 42-acre parcel of urban forested area in Knoxville, Tennessee, 2) investigate statistical relationships between forest health indicators and 4 vegetation indices derived from drone imagery to assess (“ground-truth”) a novel drone application in urban forest conservation management, and 3) investigate statistical relationships between forest health indicators and soil physical, textural, and chemical attributes. Key findings of the comprehensive ecological assessment reveal the dominance of 129 native plant species, invasion by 11 non-native plant species, acidic high-carbon soils sufficient in most plant available macro- and micro- nutrients, and significant relationships between both drone vegetation indices and soil attributes and key indicators of urban forest health. Findings from this study establish necessary baseline ecological and soils data and demonstrate a novel application of drone remote sensing in the conservation management of an urban forested natural area

Analysis of vegetation fragmentation and impacts using remote sensing techniques in the Eastern Arc Mountains of Tanzania.

Ph. D. University of KwaZulu-Natal, Pietermaritzburg 2014.The Eastern Arc Mountains of Tanzania forms part of the Eastern Afromontane Biodiversity Hotspots, listed among the global World Wide Fund for Nature's (WWF) priority ecoregions. However, the region is threatened by fragmentation and habitat modification resulting from competing forms of land uses, which is in turn threatening biodiversity conservation, planning and management efforts. To determine vulnerability that can inform long-term conservation and management of the biodiversity hotspots, an in-depth understanding of the qualitative and quantitative nature of ecosystems is a pre-requisite. The overall goal of this study was to quantify fragmentation, investigate its impacts on tree species diversity, abundance and biomass and to identify management interventions in the Eastern Arc Mountains of Tanzania. Using ecological field based measurements and a series of LANDSAT and RapidEye satellite imagery, fragstats metrics showed dynamic fragmentation patterns at both spatial and temporal scales. Furthermore, species diversity was predicted better with customized environmental variables using the Generic Algorithm for Rule-Set Prediction (GARP) model, which recorded an Area under Curve (AUC) of 0.89. In addition, Poisson regression results showed different responses by individual tree species to patch area dynamics, habitat status and soil nitrogen. Partial Least Squares and Random Forest models were used to determine above ground biomass prediction based on a combination of edaphic variables and vegetation indices. Total biomass estimations decreased from 1162 ton ha-1 in 1980 to 285.38 ton ha-1 in 2012. As a reference point in formulation of policy insights based on strong scientific and empirical knowledge, socio-economic factors influencing vulnerability of ecosystems and management interventions were examined using remotely sensed and empirical data from 335 households. The multiple logistic regression model indicated habitat fragmentation and forest burning as key conservation threats while low income level (54.62%) and limited knowledge on environmental conservation (18.51%) were identified as major catalysts to ecosystem vulnerability. The study identified livelihood diversification, effective institutional frameworks and afforestation programmes as major intervention measures. The overall study shows the effectiveness of remote sensing techniques in ecological studies and how results can be used to inform decisions for addressing complex ecological challenges in the tropics