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

Characterizing Dryland Ecosystems Using Remote Sensing and Dynamic Global Vegetation Modeling

Drylands include all terrestrial regions where the production of crops, forage, wood and other ecosystem services are limited by water. These ecosystems cover approximately 40% of the earth terrestrial surface and accommodate more than 2 billion people (Millennium Ecosystem Assessment, 2005). Moreover, the interannual variability of the global carbon budget is strongly regulated by vegetation dynamics in drylands. Understanding the dynamics of such ecosystems is significant for assessing the potential for and impacts of natural or anthropogenic disturbances and mitigation planning, and a necessary step toward enhancing the economic and social well-being of dryland communities in a sustainable manner (Global Drylands: A UN system-wide response, 2011). In this research, a combination of remote sensing, field data collection, and ecosystem modeling were used to establish an integrated framework for semi-arid ecosystems dynamics monitoring. Foliar nitrogen (N) plays an important role in vegetation processes such as photosynthesis and there is wide interest in retrieving this variable from hyperspectral remote sensing data. In this study, I used the theory of canopy spectral invariants (AKA p-theory) to understand the role of canopy structure and soil in the retrieval of foliar N from hyperspectral data and machine learning techniques. The results of this study showed the inconsistencies among different machine learning techniques used for estimating N. Using p-theory, I demonstrated that soil can contribute up to 95% to the total radiation budget of the canopy. I suggested an alternative approach to study photosynthesis is the use of dynamic global vegetation models (DGVMs). Gross primary production (GPP) is the apparent ecosystem scale photosynthesis that can be estimated using DGVMs. In this study, I performed a thorough sensitivity analysis and calibrated the Ecosystem Demography (EDv2.2) model along an elevation gradient in a dryland study area. I investigated the GPP capacity and activity by comparing the EDv2.2 GPP with flux towers and remote sensing products. The overall results showed that EDv2.2 performed well in capturing GPP capacity and its long term trend at lower elevation sites within the study area; whereas the model performed worse at higher elevations likely due to the change in vegetation community. I discussed that adding more heterogeneity and modifying ecosystem processes such as phenology and plant hydraulics in ED.v2.2 will improve its application to higher elevation ecosystems where there is more vegetation production. And finally, I developed an integrated hyperspectral-lidar framework for regional mapping of xeric and mesic vegetation in the study area. I showed that by considering spectral shape and magnitude, canopy structure and landscape features (riparian zone), we can develop a straightforward algorithm for vegetation mapping in drylands. This framework is simple, easy to interpret and consistent with our ecological understanding of vegetation distribution in drylands over large areas. Collectively, the results I present in this dissertation demonstrate the potential for advanced remote sensing and modeling to help us better understand ecosystem processes in drylands

Plant spectra as integrative measures of plant phenotypes

Spectroscopy at the leaf and canopy scales has attracted considerable interest in plant ecology over the past decades. Using reflectance spectra, ecologists can infer plant traits and strategies—and the community- or ecosystem-level processes they correlate with—at individual or community levels, covering more individuals and larger areas than traditional field surveys. Because of the complex entanglement of structural and chemical factors that generate spectra, it can be tricky to understand exactly what phenotypic information they contain. We discuss common approaches to estimating plant traits from spectra—radiative transfer and empirical models—and elaborate on their strengths and limitations in terms of the causal influences of various traits on the spectrum. Many chemical traits have broad, shallow and overlapping absorption features, and we suggest that covariance among traits may have an important role in giving empirical models the flexibility to estimate such traits. While trait estimates from reflectance spectra have been used to test ecological hypotheses over the past decades, there is also a growing body of research that uses spectra directly, without estimating specific traits. By treating positions of species in multidimensional spectral space as analogous to trait space, researchers can infer processes that structure plant communities using the information content of the full spectrum, which may be greater than any standard set of traits. We illustrate this power by showing that co-occurring grassland species are more separable in spectral space than in trait space and that the intrinsic dimensionality of spectral data is comparable to fairly comprehensive trait datasets. Nevertheless, using spectra this way may make it harder to interpret patterns in terms of specific biological processes. Synthesis. Plant spectra integrate many aspects of plant form and function. The information in the spectrum can be distilled into estimates of specific traits, or the spectrum can be used in its own right. These two approaches may be complementary—the former being most useful when specific traits of interest are known in advance and reliable models exist to estimate them, and the latter being most useful under uncertainty about which aspects of function matter most

Going hyperspectral: the 'unseen' captured?

All objects, name them soil, water, trees, vegetation, structures, metals, paints or fabrics, create a unique spectral fingerprint. A sensor determines these fingerprints by measuring reflected light, most of which registers in wavelengths, or bands, invisible to humans. This is what the crime scene investigation (CSI) television programs have popularized how DNA or fingerprints can be used to solve crimes. Similarly, forest CSI of “seeing” the trees in the deep high mountain tropical forest is now a major focus in the air and spaceborne hyperspectral sensing technology and in other different applications such as agriculture, environment, geology, transportation, security, and several others. The availability of sub-meter resolution colour imagery from satellites coupled with internet based services like Google Earth and Microsoft Virtual Earth have resulted in an enormous interest in remote sensing among the general public. The ability to see one’s home or familiar landmarks in an image taken from hundreds of kilometers above the earth elicits wonder and awe. Deciding where, when, what and how to sense or measure the DNA of individual trees from the air or space is a crucial question in the sustainable development and management of our Malaysian tropical forest ecosystems. However, to monitor, quantify, map and understand the content and nature of our forest, one would ideally like to monitor it everywhere and all the time too. This is impossible, and consequently, forest engineers must select relatively very high to high near to real time resolution sensors with the ability to transcend boundaries, capabilities, features and interfacing realms for such measurement. The dynamic interplay of these elements is precisely coordinated by signaling networks that orchestrate their interactions. High-throughput experimental and analytical techniques now provide forest engineers with incredibly rich and potentially revealing datasets from both air and spaceborne hyperspectral sensors (also known as imaging spectrometers). However, it is impossible to exhaustively explore the full experimental and operational hyperspectral sensors available in the market out there and so forest engineers must judiciously choose which one is the best to perform and fulfill their project objectives and missions. The complexity and high-dimensionality of these systems makes it incredibly difficult for forest engineers and other users alone to manage and optimize sensing processes. In order to add or derive value from a hyperspectral remotely sensed image several factors such as resolution, swath, and signal to noise ratio, amongst others need to be considered. A grand challenge for the forest engineer’s scientific discovery in the 21st Century is therefore, to devise very high real-time ultra-spatial and spectral air and space borne sensors that automatically measure and adapt sensing operations in large-scale and economical systems with the unseen captured. This lecture therefore focuses on the emerging theory, origin of the hyperspectral sensors, research, practice, limitations and identifies future challenge and outlook of hyperspectral sensing systems in the quest towards a sustainable Malaysian forestry context and other different applications to capture the “unseen”. It is quite certain that advances in hyperspectral remote sensing and more sophisticated analytical methods will resolve any “unseen” issues in time with the best approach of transcending boundaries and interfacing remote sensing data with precise information from the field plots. Unfortunately, as a relatively new analytical technique, the full potential of air and spaceborne hyperspectral imaging has not yet been realized in Malaysi

Leaf and canopy responses to forest degradation and extreme climatic events: a remote sensing perspective

Monitoring the responses of leaf functional traits and vegetation structure to environmental change is key to understanding the responses of ecological systems to anthropogenic global change. Advancement in earth observation technologies provide new opportunities to answer complex ecological questions at scales ranging from the community to the landscape level. This thesis investigates the impacts of environmental change on foliar traits and forest structure. In particular, it investigates the impacts of El Niño on leaf traits using hyperspectroscopy and on the structure of forests modified by oil palm plantations using airborne LiDAR. The first chapter explores the potential and limitations of using hyperspectral data to estimate leaf traits remotely. Working with six temperate tree species in contrasting soil types, I explore the relationship between leaf structure and chemistry and spectral characteristics. We show that interspecific differences in leaf traits were generally much stronger than intraspecific differences related to soil type. We highlight the difficulties that can arise in detecting within-species variation owing to “constellation effects” but demonstrate the power of spectroscopy to predict traits. The second chapter investigates the effects of drought on leaf traits and spectra, by taking measurements during and after an El Niño event. Pigments were particularly lower after the drought, when rain was more frequent, sunshine duration was shorter and radiation was lower, indicating increased greenness and forest resilience to climatic variation. Spectral information was also shown to be effective at detecting the impacts of droughts on leaf traits. Upscaling to the landscape level, the third chapter focuses on methods for estimating aboveground carbon stocks in oil palm plantations using airborne LiDAR. We show that an area-based approach is more accurate than tree-centric methods, although the latter may be useful to detect, extract and count individual oil palm trees from images. The fourth chapter explores the effects of El Niño drought on the canopy structure of tropical forests in Borneo using repeat airborne LiDAR. Our results reveal extensive leaf shedding caused by extreme high temperatures and Vapour-Pressure Deficit. Regenerating short forests on ridges were particularly more vulnerable to climatic variation owing to greater heat and atmospheric dryness. In conclusion, this thesis integrates field measurements and remote sensing to narrow down the uncertainties of vegetation responses to environmental changes.Brazilian National Council for Scientific and Technological Development (CNPq

Remotely sensing ecological genomics

Solar radiation is the prime energy source on Earth. It reaches any object in the form of electromagnetic radiation that may be absorbed, transmitted or reflected. The magnitude of these optical processes depends on the optical properties of each object, which in the case of plants relate to their biochemical and structural traits. These plant phenotypic traits result from gene expression underpinned by an individual’s genotype constrained by phylogeny, the environment the individual is exposed to, and the interaction between genotype and the environment. Remote observations of plant phenotypes across space and time may thus hold information about the composition and structure of genetic variation, if a link between spectral and genetic information can be established. This dissertation encompasses studies linking information derived from imaging spectrometer acquisitions under natural conditions with in situ collected information about genetic variation within a tree species, the European beech Fagus sylvatica. It presents the correlation between spectral and genetic information by sequentially expanding temporal, spatial and genetic aspects, and simultaneously accounting for environmental contexts that impact gene expression. By evaluating spectral-genetic similarities across decadal airborne imaging spectrometer acquisitions and accounting for spectral phenotypes and whole-genome sequences of tree individuals from across the species range, the studies provide a proof that observed reflectance spectra hold information about genetic variation within the species. Further, by accounting on uncertainties of spectral measurements and deriving genetic structure of the most abundant tree species in Europe, the dissertation advances the current remote sensing approaches and the knowledge on intraspecific genetic variation. The studies focus particularly on the genetic relatedness between the trees of the test species, whereas the acquired data may allow to establish direct associations between genes and spectral features. The methods used may be expanded to other tree species or applied to spectral data acquired by upcoming spaceborne imaging spectrometers, which overcome current spatiotemporal limitations of data collection, and demonstrate further paths towards the association of genetic variation with variation in spectral phenotypes. The thesis presents the potential of spectral derivation of intraspecific genetic variation within tree species and discusses associated limitations induced by spectral, temporal, spatial and genetic scopes of analysis. This sets a stage towards establishing a means of remote observations of spectral signatures to contribute to monitoring biological variation at the fundamental genetic level, which correlates with ecosystem performance and is an insurance mechanism for populations to adapt to global change

Hyperspectral, thermal and LiDAR remote sensing for red band needle blight detection in pine plantation forests

PhD ThesisClimate change indirectly affects the distribution and abundance of forest insect pests and pathogens, as well as the severity of tree diseases. Red band needle blight is a disease which has a particularly significant economic impact on pine plantation forests worldwide, affecting diameter and height growth. Monitoring its spread and intensity is complicated by the fact that the diseased trees are often only visible from aircraft in the advanced stages of the epidemic. There is therefore a need for a more robust method to map the extent and severity of the disease. This thesis examined the use of a range of remote sensing techniques and instrumentation, including thermography, hyperspectral imaging and laser scanning, for the identification of tree stress symptoms caused by the onset of red band needle blight. Three study plots, located in a plantation forest within the Loch Lomond and the Trossachs National Park that exhibited a range of red band needle blight infection levels, were established and surveyed. Airborne hyperspectral and LiDAR data were acquired for two Lodgepole pine stands, whilst for one Scots pine stand, airborne LiDAR and Unmanned Aerial Vehicle-borne (UAV-borne) thermal imagery were acquired alongside leaf spectroscopic measurements. Analysis of the acquired data demonstrated the potential for the use of thermographic, hyperspectral and LiDAR sensors for detection of red band needle blight-induced changes in pine trees. The three datasets were sensitive to different disease symptoms, i.e. thermography to alterations in transpiration, LiDAR to defoliation, and hyperspectral imagery to changes in leaf biochemical properties. The combination of the sensors could therefore enhance the ability to diagnose the infection.Natural Environment Research Council (NERC) for funding this PhD program (studentship award 1368552) and providing access to specialist equipment through a Field Spectroscopy Facility loan (710.114). I would like to thank NERC Airborne Research Facility for providing airborne data (grant: GB 14-04) that made the PhD a challenge, to say the least. My sincere gratitude goes to the Douglas Bomford Trust for providing additional funds, which allowed for completion of the UAV-borne part of this research

The data concept behind the data: From metadata models and labelling schemes towards a generic spectral library

Spectral libraries play a major role in imaging spectroscopy. They are commonly used to store end-member and spectrally pure material spectra, which are primarily used for mapping or unmixing purposes. However, the development of spectral libraries is time consuming and usually sensor and site dependent. Spectral libraries are therefore often developed, used and tailored only for a specific case study and only for one sensor. Multi-sensor and multi-site use of spectral libraries is difficult and requires technical effort for adaptation, transformation, and data harmonization steps. Especially the huge amount of urban material specifications and its spectral variations hamper the setup of a complete spectral library consisting of all available urban material spectra. By a combined use of different urban spectral libraries, besides the improvement of spectral inter- and intra-class variability, missing material spectra could be considered with respect to a multi-sensor/ -site use. Publicly available spectral libraries mostly lack the metadata information that is essential for describing spectra acquisition and sampling background, and can serve to some extent as a measure of quality and reliability of the spectra and the entire library itself. In the GenLib project, a concept for a generic, multi-site and multi-sensor usable spectral library for image spectra on the urban focus was developed. This presentation will introduce a 1) unified, easy-to-understand hierarchical labeling scheme combined with 2) a comprehensive metadata concept that is 3) implemented in the SPECCHIO spectral information system to promote the setup and usability of a generic urban spectral library (GUSL). The labelling scheme was developed to ensure the translation of individual spectral libraries with their own labelling schemes and their usually varying level of details into the GUSL framework. It is based on a modified version of the EAGLE classification concept by combining land use, land cover, land characteristics and spectral characteristics. The metadata concept consists of 59 mandatory and optional attributes that are intended to specify the spatial context, spectral library information, references, accessibility, calibration, preprocessing steps, and spectra specific information describing library spectra implemented in the GUSL. It was developed on the basis of existing metadata concepts and was subject of an expert survey. The metadata concept and the labelling scheme are implemented in the spectral information system SPECCHIO, which is used for sharing and holding GUSL spectra. It allows easy implementation of spectra as well as their specification with the proposed metadata information to extend the GUSL. Therefore, the proposed data model represents a first fundamental step towards a generic usable and continuously expandable spectral library for urban areas. The metadata concept and the labelling scheme also build the basis for the necessary adaptation and transformation steps of the GUSL in order to use it entirely or in excerpts for further multi-site and multi-sensor applications