Mapping and monitoring of vegetation using airborne laser scanning

In this thesis, the utility of airborne laser scanning (ALS) for monitoring vegetation of relevance for the environmental sector was investigated. The vegetation characteristics studied include measurements of biomass, biomass change and vegetation classification in the forest-tundra ecotone; afforestation of grasslands; and detection of windthrown trees. Prediction of tree biomass for mountain birch (Betula pubescens ssp. czerepanovii) using sparse (1.4 points/m²) and dense (6.1 points/m²) ALS data was compared for a site at the forest-tundra ecotone near Abisko in northern Sweden (Lat. 68° N, Long. 19° E). The predictions using the sparse ALS data provided almost as good results (RMSE 21.2%) as the results from the dense ALS data (18.7%) despite the large difference in point densities. A new algorithm was developed to compensate for uneven distribution of the laser points without decimating the data; use of this algorithm reduced the RMSE for biomass prediction from 19.9% to 18.7% for the dense ALS data. Additional information about vegetation height and density from ALS data improved a satellite data classification of alpine vegetation, in particular for the willow and mountain birch classes. Histogram matching was shown to be effective for relative calibration of metrics from two ALS acquisitions collected over the same area using different scanners and flight parameters. Thus the difference between histogram-matched ALS metrics from different data acquisitions can be used to locate areas with unusual development of the vegetation. The height of small trees (0.3–2.6 m tall) in former pasture land near the Remnings¬torp test site in southern Sweden (Lat. 58° N, Long. 13° E) could be measured with high precision (standard deviation 0.3 m) using high point density ALS data (54 points/m2). When classifying trees taller than 1 m into the two classes of changed and unchanged, the overall classification accuracy was 88%. A new method to automatically detect windthrown trees in forested areas was developed and evaluated at the Remningstorp test site. The overall detection rate was 38% on tree-level, but when aggregating to 40 m square grid cells, at least one windthrown tree was detected in 77% of the cells that according to field data contained windthrown trees. In summary, this thesis has shown the high potential for ALS to be a future tool to map and monitor vegetation for several applications of interest for the environmental sector

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

The Use of Remote Sensing for Coral Reef Mapping in Support of Integrated Coastal Zone Management: A Case Study in the NW Red Sea - Volume I

Worldwide, coral reefs are rapidly degrading due to the combined negative effects of human activities and global change. Even though the Red Sea is a very suitable natural environment for coral reef growth, their status also has rapidly deteriorated since the 1970s. Coral reefs are especially affected in the NW Red Sea, primarily due to coastal development projects supporting the booming tourism industry. Integrated coastal zone management (ICZM), therefore, is urgently needed to protect the coral reefs and conserve these valuable natural resources for future generations. Effective ICZM necessitates sound baseline information concerning the current status of the coral reefs and the actual human activities taking place, as well as a tool to monitor changes in both elements. Fulfilling these requirements using in situ observations alone is both time- and labour-intensive and, therefore, often financially too demanding, especially for developing countries. Here, remote sensing may bring the solution as it synoptically collects data over large areas in a cost-efficient way. This work has proven the usefulness of passive, optical remote sensing from spaceborne platforms to collect and monitor the required data and support an effective ICZM. Based on Landsat 7 ETM+ and QuickBird data, accurate information has been collected on the bathymetric structure of the coral reef seabed, its geomorphological zonation, and the distribution of the main marine coastal habitats. The possibility to monitor changes in these elements as well as in the coastal development has also been confirmed. These remote sensing derived products have subsequently been analysed and integrated with auxiliary datasets in a GIS to develop valuable decision-support products such as a risk assessment map and a multi-use marine protected area zoning plan. To support ICZM, remote sensing is best integrated in a multi-level sampling approach in which detailed in situ observations are complemented with more broad-scale, regional information derived from remote sensing data analysis. As such, information-based decisions can be made, augmenting the success of the ICZM. This not only counts for the specific study area but is likely necessary for the sustainable development of coral reef coastal zones worldwide

Principal Component Analysis

This book is aimed at raising awareness of researchers, scientists and engineers on the benefits of Principal Component Analysis (PCA) in data analysis. In this book, the reader will find the applications of PCA in fields such as image processing, biometric, face recognition and speech processing. It also includes the core concepts and the state-of-the-art methods in data analysis and feature extraction

Photoacoustic image guidance and tissue characterization in cardiovascular applications

Collectively, cardiovascular diseases continue to be the leading cause of death, across nations and across decades. Improved diagnostic imaging methods offer promise to alleviate the morbidity associated with these diseases. Photoacoustic (PA) imaging is one such method, poised to make a significant impact on cardiovascular imaging, both as a research tool, as well as a clinical imaging modality. Offering the potential of molecular imaging in real-time, PA methods have been demonstrated in proof-of-concept studies tracking myocyte calcium dynamics. These results open the door to non-invasive longitudinal assessment of cardiac electrophysiological function, with implications for drug and contrast agent development. PA image guidance has also been extended to the characterization of cardiac radiofrequency ablation lesions. This method has been demonstrated to utilize endogenous chromophore changes resulting from ablation for the generation of depth-resolved tissue characterization maps, capable of assessing lesion extent. The technique has been subsequently validated by assessing high-intensity focused ultrasound ablation lesions in myocardium, with the hope for offering thermographic capabilities as well. While PA imaging offers such promise in cardiac ablation procedures, it is also in the process of clinical translation for image guidance and characterization in coronary artery disease applications. Research has shown, using Monte Carlo optical modeling, that using a simple dual-wavelength PA imaging technique has great potential for successful visualization of atherosclerotic plaques across multiple tissue types and at clinically relevant multiple millimeters of depth. Collectively these results offer a suite of PA imaging tools with the potential for molecular and thermographic imaging across a broad range of cardiovascular applications.Biomedical Engineerin

The global tree carrying capacity (keynote)

editorial reviewe

Light Field Methods for the Visual Inspection of Transparent Objects

Transparent objects play crucial roles in humans’ everyday life, must meet high quality requirements and therefore must be visually inspected. Developing automated visual inspection systems for complex-shaped transparent objects still represents a challenging task. As a solution, this book introduces light field methods for all main components of a visual inspection system: a novel light field sensor, suitable processing methods and a light field illumination approach

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