Discriminating wetland vegetation species in an African savanna using hyperspectral data.

Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2010.Wetland vegetation is of fundamental ecological importance and is used as one of the vital bio-indicators for early signs of physical or chemical degradation in wetland systems. Wetland vegetation is being threatened by expansion of extensive lowland areas of agriculture, natural resource exploitation, etc. These threats are increasing the demand for detailed information on vegetation status, up-to-date maps as well as accurate information for mitigation and adaptive management to preserve wetland vegetation. All these requirements are difficult to produce at species or community level, due to the fact that some parts of the wetlands are inaccessible. Remote sensing offers nondestructive and real time information for sustainable and effective management of wetland vegetation. The application of remote sensing in wetland mapping has been done extensively, but unfortunately the uses of narrowband hyperspectral data remain unexplored at an advanced level. The aim of this study is to explore the potential of hyperspectral remote sensing for wetland vegetation discrimination at species level. In particular, the study concentrates on enhancing or improving class separability among wetland vegetation species. Therefore, the study relies on the following two factors; a) the use of narrowband hyperspectral remote sensing, and b) the integration of vegetation properties and vegetation indices to improve accuracy. The potential of vegetation indices and red edge position were evaluated for vegetation species discrimination. Oneway ANOVA and Canonical variate analysis were used to statistically test if the species were significantly different and to discriminate among them. The canonical structure matrix revealed that hyperspectral data transforms can discriminate vegetation species with an overall accuracy around 87%. The addition of biomass and water content variables improved the accuracy to 95.5%. Overall, the study demonstrated that hyperspectral data and vegetation properties improve wetland vegetation separability at species level

The remote sensing of papyrus vegetation (Cyperus papyrus L.) in swamp wetlands of South Africa.

Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2010.Papyrus (Cyperus papyrus .L) swamp is the most species rich habitat that play vital hydrological, ecological, and economic roles in central tropical and western African wetlands. However, the existence of papyrus vegetation is endangered due to intensification of agricultural use and human encroachment. Techniques for modelling the distribution of papyrus swamps, quantity and quality are therefore critical for the rapid assessment and proactive management of papyrus vegetation. In this regard, remote sensing techniques provide rapid, potentially cheap, and relatively accurate strategies to accomplish this task. This study advocates the development of techniques based on hyperspectral remote sensing technology to accurately map and predict biomass of papyrus vegetation in a high mixed species environment of St Lucia- South Africa which has been overlooked in scientific research. Our approach was to investigate the potential of hyperspectral remote sensing at two levels of investigation: field level and airborne platform level. First, the study provides an overview of the current use of both multispectral and hyperspectral remote sensing techniques in mapping the quantity and the quality of wetland vegetation as well as the challenges and the need for further research. Second, the study explores whether papyrus can be discriminated from each one of its coexistence species (binary class). Our results showed that, at full canopy cover, papyrus vegetation can be accurately discriminated from its entire co-existing species using a new hierarchical method based on three integrated analysis levels and field spectrometry under natural field conditions. These positive results prompted the need to test the use of canopy hyperspectral data resampled to HYMAP resolution and two machine learning algorithms in identifying key spectral bands that allowed for better discrimination among papyrus and other co-existing species (n = 3) (multi-class classification). Results showed that the random forest algorithm (RF) simplified the process by identifying the minimum number of spectral bands that provided the best overall accuracies. Narrow band NDVI and SR-based vegetation indices calculated from hyperspectral data as well as some vegetation indices published in literature were investigated to test their potential in improving the classification accuracy of wetland plant species. The study also evaluated the robustness and reliability of RF as a variables selection method and as a classification algorithm in identifying key spectral bands that allowed for the successful classification of wetland species. Third, the focus was to upscale the results of field spectroscopy analysis to airborne hyperspectral sensor (AISA eagle) to discriminate papyrus and it co-existing species. The results indicated that specific wavelengths located in the visible, red-edge, and near-infrared region of the electromagnetic spectrum have the highest potential of discriminating papyrus from the other species. Finally, the study explored the ability of narrow NDVI-based vegetation indices calculated from hyperspectral data in predicting the green above ground biomass of papyrus. The results demonstrated that papyrus biomass can be modelled with relatively low error of estimates using a non-linear RF regression algorithm. This provided a basis for the algorithm to be used in mapping wetland biomass in highly complex environments. Overall, the study has demonstrated the potential of remote sensing techniques in discriminating papyrus swamps and its co-existing species as well as in predicting biomass. Compared to previous studies, the RF model applied in this study has proved to be a robust, accurate, and simple new method for variables selection, classification, and modelling of hyperspectral data. The results are important for establishing a baseline of the species distributions in South African swamp wetlands for future monitoring and control efforts

The impact of land use and land cover changes on wetland productivity and hydrological systems in the Limpopo transboundary river basin, South Africa

Philosophiae Doctor - PhDWetlands are highly productive systems that act as habitats for a variety of flora and fauna. Despite their ecohydrological significance, wetland ecosystems are under severe threat as a result of environmental changes (e.g. the changing temperature and rainfall), as well as pressure from anthropogenic land use activities (e.g. agriculture, rural-urban development and dam construction). Such changes result in severe disturbances in the hydrology, plant species composition, spatial distribution, productivity and diversity of wetlands, as well as their ability to offer critical ecosystem goods and services. However, wetland degradation varies considerably from place to place, with severe degradation occurring particularly in developing regions, such as sub-Saharan Africa, where Land Use and Land Cover changes impact on wetland ecosystems by affecting the diversity of plant species, productivity, as well as the wetland hydrology

Multi-temporal assessment of diversity and condition in UK semi-natural grasslands using optical reflectance

With 40% of the world’s plants estimated to be under threat of extinction and ever lowering levels of ecological intactness of biological systems, the requirement to effectively monitor plant species and diversity has never been more pressing. Globally, natural, and semi-natural grassland ecosystems are at particular risk of degradation and conversion. Semi-natural grasslands in the UK currently make up about 1-2% of the permanent lowland grassland cover. Once degraded due to agricultural additions or inappropriate management, they can be difficult and costly to restore. As these systems display high levels of plant and invertebrate diversity, there is a need to safeguard their decline. However, there are currently significant challenges to providing the data needed to assess the condition of these systems. Remote sensing could contribute by providing information on herbaceous plant diversity and vegetation state across a wide range of spatial scales and time. Optical traits are a subset of plant traits that are detectable using reflectance data from leaf to canopy scales, dependent on the configuration of the sensor employed and can be linked to taxonomic diversity and condition of vegetation. Very high spatial resolution hyperspectral imaging technologies are, for the first time, enabling in-situ grassland plant phenotyping at the leaf, individual and high-resolution canopy scale. Analyses of these spectra have demonstrated promising results in application of mapping of taxonomic units and diversity metrics. However there is little evidence of the temporal stability of these observations. At the landscape scale, openly available, higher spatial resolution satellite data is also enabling examination of smaller field parcels, which are typical of UK fragmented landscapes. In this context, spectral time-series have the potential to be used to predict the condition of vegetation communities of conservation interest. In this thesis, the use of optical remote sensing data to further our understanding of semi-natural grasslands and to safeguard their decline, is examined, with a particular focus on the exploitation of multi-temporal sampling. Firstly, spectral variation in space, as a surrogate measure for species or community type diversity (also known as the spectral variation hypothesis), is assessed via a meta-analysis of existing studies. The results of the synthesis reveal some promise for the approach, but a large amount of variation between study outcomes is observed, suggesting that methodological approaches are important in the effectiveness of the proxy. Secondly, spectral data is collected alongside botanical and phenological diversity data at high spatial resolution over a growing season to test the stability of the spectral variation hypothesis over time. The results of these experiments show that the ability to detect biodiversity using this method is seasonally, and possibly, site dependent. Next, the suitability of hyperspectral leaf reflectance for distinguishing 17 herbaceous species growing within a calcareous grassland is examined. The application of machine learning classification models to multi-temporal leaf spectra show that although species are distinguishable at most sampling times within the year, the transferability of these models is very limited between sampling dates. Finally satellite time-series of vegetation indices are used to predict favourable or unfavourable vegetation condition criteria in calcareous fields across two years. A number of indices were successful in distinguishing between the different condition criteria but there was variation in results found between the two years sampled, due to differences in intra-annual vegetation phenology. Overall the results of this thesis, show promise for remote sensing of grassland biodiversity and condition. Both high spatial resolution hyperspectral data, as well as coarser resolution multi-spectral data sets, can be useful in evaluation of these systems. However, the dynamic nature of leaves and canopies over time, will require a multi-temporal approach to model building, which should be an integral part of developing these methods in the future

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

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

Understanding the measurement of forests with waveform lidar

The measurement of forests is essential for monitoring and predicting the role and response of the land surface to global climate change. Globally consistent and frequent measurements can only be made by satellites; unfortunately many current system’s measurements saturate at moderate canopy densities and are not directly related to forest properties, requiring tenuous empirical relationships that are insensitive to many of the Earth’s most important, Carbon rich forests. Lidar (laser radar) is a relatively new technology that offers the potential to make direct measurements of forest height, vertical density and, when ground based, explicit measurements of structure. In addition measurements do not saturate until much higher forest densities. In recent years there has been much interest in the measurement of forests by lidar, with a number of airborne and terrestrial and one spaceborne lidar developed. Measuring a forest leaf by leaf is impractical and very tedious, so more rapid ground based methods are needed to collect data to validate satellite derived estimates. These rapid methods are themselves not directly related to forest properties causing uncertainty in any validation of remotely sensed estimates. This thesis uses Monte Carlo ray tracing to simulate the measurement of forests by full waveform lidars over explicit geometric forest models for both above and below canopy instruments. Existing methods for deriving forest properties from measurements are tested against the known truth of these simulated forests, a process impossible in reality. Causes of disagreements are explored and new methods developed to attempt to overcome any shortcomings. These new methods include dual wavelength lidar for correcting satellite based measurements for topography and a voxel based method for more directly relating terrestrial lidar signals to forest properties

The development of a temporal-BRDF model-based approach to change detection, an application to the identification and delineation of fire affected areas.

Although large quantities of southern Africa burn every year, minimal information is available relating to the fire regimes of this area. This study develops a new, generic approach to change detection, applicable to the identification of land cover change from high temporal and moderate spatial resolution satellite data. Traditional change detection techniques have several key limitations which are identified and addressed in this work. In particular these approaches fail to account for directional effects in the remote sensing signal introduced by variations in the solar and sensing geometry, and are sensitive to underlying phenological changes in the surface as well as noise in the data due to cloud or atmospheric contamination. This research develops a bi-directional, model-based change detection algorithm. An empirical temporal component is incorporated into a semi-empirical linear BRDF model. This may be fitted to a long time series of reflectance with less sensitivity to the presence of underlying phenological change. Outliers are identified based on an estimation of noise in the data and the calculation of uncertainty in the model parameters and are removed from the sequence. A "step function kernel" is incorporated into the formulation in order to detect explicitly sudden step-like changes in the surface reflectance induced by burning. The change detection model is applied to the problem of locating and mapping fire affected areas from daily moderate spatial resolution satellite data, and an indicator of burn severity is introduced. Monthly burned area datasets for a 2400km by 1200km area of southern Africa detailing the day and severity of burning are created for a five year period (2000-2004). These data are analysed and the fire regimes of southern African ecosystems during this time are characterised. The results highlight the extent of the burning which is taking place within southern Africa, with between 27-32% of the study area burning during each of the five years of observation. Higher fire frequencies are exhibited by savanna and grassland ecosystems, while more dense vegetation types such as shrublands and deciduous broadleaf forests burn less frequently. In addition the areas which burn more frequently do so with a greater severity, with a positive relationship identified between the frequency and the severity of burning