Cetacean Diversity and Mixed-Species Associations off Southern Sri Lanka

March 8-9, 2011, BANGKOK, THAILANDSri Lanka, in the northern Indian Ocean island, has a relatively narrow continental shelf and an abundance of cetacean fauna in her waters. A few vessel surveys have produced data on cetacean occurrence off the east and west coast but no similar data exists for the south. To fill this data gap vessel-based transects were carried out in 2008/2009 off a selected segment of the south coast. A high sighting rate was recorded and nine species were documented: Balaenoptera musculus, Balaenoptera brydei, Physeter macrocephalus, Stenella longirostris, Tursiops truncates, Pseudorca crassidens, Feresa attenuata, Orcinus orca and Globicephala macrorhynchus. Significantly the first scientifically documented sighting of O. orca anywhere in Sri Lanka's waters was recorded. Additionally blue whale feeding aggregations including mother-calf pairs were documented off southern Sri Lanka in the Austral summer. Mixed species associations involving five species of cetaceans were also recorded. The coastal waters off southern Sri Lanka are therefore an important cetacean habitat with high diversity and mixing of coastal and usually pelagic species. The implications of the importance of the area for blue whales also warrants further study and more detailed studies are recommended to generate data that can inform future management and conservation decisions

A Higher-Order VOF Interface Reconstruction Scheme for Non-Orthogonal Structured Grids - with Application to Surface Tension Modelling

The volume-of-fluid (VOF) method [24] is widely used to track the interface for the purpose of simulating liquid-gas interfacial flows numerically. The key strength of VOF is its mass conserving property. However, interface reconstruction is required when geometric properties such as curvature need to be accurately computed. For surface tension modelling in particular, computing the interface curvature accurately is crucial to avoiding so-called spurious or parasitic currents. Of the existing VOF-based schemes, the height-function (HF) method [10, 16, 18, 42, 46, 53] allows accurate interface representation on Cartesian grids. No work has hitherto been done to extend the HF philosophy to non-orthogonal structured grids. To this end, this work proposes a higher-order accurate VOF interface reconstruction method for non-orthogonal structured grids. Higher-order in the context of this work denotes up to 4 th-order. The scheme generalises the interface reconstruction component of the HF method. Columns of control volumes that straddle the interface are identified, and piecewise-linear interface constructions (PLIC) are computed in a volume-conservative manner in each column. To ensure efficiency, this procedure is executed by a novel sweep-plane algorithm based on the convex decomposition of the control volumes in each column. The PLIC representation of the interface is then smoothed by iteratively refining the PLIC facet normals. Rapid convergence of the latter is achieved via a novel spring-based acceleration procedure. The interface is then reconstructed by fitting higher-order polynomial curves/surfaces to local stencils of PLIC facets in a least squares manner [29]. Volume conservation is optimised for at the central column. The accuracy of the interface reconstruction procedure is evaluated via grid convergence studies in terms of volume conservation and curvature errors. The scheme is shown to achieve arbitrary-order accuracy on Cartesian grids and up to fourth-order accuracy on non-orthogonal structured grids. The curvature computation scheme is finally applied in a balanced-force continuum-surface-force (CSF) [4] surface tension scheme for variable-density flows on nonorthogonal structured grids in 2D. Up to fourth-order accuracy is demonstrated for the Laplace pressure jump in the simulation of a 2D stationary bubble with a high liquid-gas density ratio. A significant reduction in parasitic currents is demonstrated. Lastly, second-order accuracy is achieved when computing the frequency of a 2D inviscid oscillating droplet in zero gravity. The above tools were implemented and evaluated using the Elemental®multi-physics code and using a vertex-centred finite volume framework. For the purpose of VOF advection the algebraic CICSAM scheme (available in Elemental®) was employed

Complexity and Dynamics of Semi-Arid Vegetation Structure, Function and Diversity Across Spatial Scales from Full Waveform Lidar

Semi-arid ecosystems cover approximately 40% of the earth’s terrestrial landscape and show high dynamicity in ecosystem structure and function. These ecosystems play a critical role in global carbon dynamics, productivity, and habitat quality. Semi-arid ecosystems experience a high degree of disturbance that can severely alter ecosystem services and processes. Understanding the structure-function relationships across spatial extents are critical in order to assess their demography, response to disturbance, and for conservation management. In this research, using state-of-the-art full waveform lidar (airborne and spaceborne) and field observations, I developed a framework to assess the complexity and dynamics of vegetation structure, function and diversity across spatial scales in a semi-arid ecosystem. Difficulty in differentiating low stature vegetation from bare ground is the key remote sensing challenge in semi-arid ecosystems. In this study, I developed a workflow to differentiate key plant functional types (PFTs) using both structural and biophysical variables derived from the full waveform lidar and an ensemble random forest technique. The results revealed that waveform lidar pulse width can clearly distinguish shrubs from bare ground. The models showed PFT classification accuracy of 0.81–0.86% and 0.60–0.70% at 10 m and 1 m spatial resolutions, respectively. I found that structural variables were more important than the biophysical variables to differentiate the PFTs in this study area. The study further revealed an overlap between the structural features of different PFTs (e.g. shrubs from trees). Using structural features, I derived three main functional traits (canopy height, plant area index and foliage height diversity) of shrubs and trees that describe canopy architecture and light use efficiency of the ecosystem. I evaluated the trends and patterns of functional diversity and their relationship with non-climatic abiotic factors and fire disturbance. In addition to the fine resolution airborne lidar, I used simulated large footprint spaceborne lidar representing the newly launched Global Ecosystem Dynamics Investigation system (GEDI, a lidar sensor on the International Space Station) to evaluate the potential of capturing functional diversity trends of semi-arid ecosystems at global scales. The consistency of diversity trends between the airborne lidar and GEDI confirmed GEDI’s potential to capture functional diversity. I found that the functional diversity in this ecosystem is mainly governed by the local elevation gradient, soil type, and slope. All three functional diversity indices (functional richness, functional evenness and functional divergence) showed a diversity breakpoint near elevations of 1500 m – 1700 m. Functional diversity of fire-disturbed areas revealed that the fires in our study area resulted in a more even and less divergent ecosystem state. Finally, I quantified aboveground biomass using the structural features derived from both the airborne lidar and GEDI data. Regional estimates of biomass can indicate whether an ecosystem is a net carbon sink or source as well as the ecosystem’s health (e.g. biodiversity). Further, the potential of large footprint lidar data to estimate biomass in semi-arid ecosystems are not yet fully explored due to the inherent overlapping vegetation responses in the ground signals that can be affected by the ground slope. With a correction to the slope effect, I found that large footprint lidar can explain 42% of variance of biomass with a RMSE of 351 kg/ha (16% RMSE). The model estimated 82% of the study area with less than 50% uncertainty in biomass estimates. The cultivated areas and the areas with high functional richness showed the highest uncertainties. Overall, this dissertation establishes a novel framework to assess the complexity and dynamics of vegetation structure and function of a semi-arid ecosystem from space. This work enhances our understanding of the present state of an ecosystem and provides a foundation for using full waveform lidar to understand the impact of these changes to ecosystem productivity, biodiversity and habitat quality in the coming decades. The methods and algorithms in this dissertation can be directly applied to similar ecosystems with relevant corrections for the appropriate sensor. In addition, this study provides insights to related NASA missions such as ICESat-2 and future NASA missions such as NISAR for deriving vegetation structure and dynamics related to disturbance

Cultural and core borrowings reclassified: A corpus-based study of Sri Lankan English vocabulary

World Englishes/ Varieties of English show variation from British English (BrE) through distinct linguistic processes that highlight their uniqueness. Borrowing is one such process that enhances the vocabulary of a distinct English variety used in a particular country due to the effect of the local languages. Literature on borrowing proposes that they can be classified as cultural and core borrowings. This classification encapsulates the reasons for borrowing words from a different language by its users. The term cultural borrowings denote words that are transferred from another language to fill a lexical gap, while the term core borrowings are words that already occur in the language. This paper, a part of an ongoing PhD study, explores whether this binary classification adequately accounts for the types of borrowings found in Sri Lankan English (SLE) recorded in the Sri Lankan component of the International Corpus of English (ICE-SL). The study first extracted a word list using a corpus analysis software, from which the borrowings were manually selected. This was followed by a Google search for the etymology of the words to ascertain the origin of the borrowings that could help to identify whether they filled a lexical gap or duplicated words that already exist. The data indicated that words were borrowed from Sinhala and Tamil, the two official languages of Sri Lanka, as well as other languages. Based on the analysis, this paper proposes that the binary categorization of core and cultural borrowings should be extended to four categories in order to capture the local and regional borrowings that exist within cultural borrowings, as well as to reflect the complexity of meanings identified within core borrowings. KEYWORDS:   Borrowings, core borrowings, cultural borrowings, World Englishes, corpus linguistic

Semi-Arid Ecosystem Plant Functional Type and LAI from Small Footprint Waveform Lidar

Plant functional traits such as vegetation structure, density, and composition are indicators of ecosystem response to climate and human driven disturbances. We used small footprint waveform lidar with an ensemble random forest approach to differentiate the functional traits in a western US semi-arid ecosystem. We introduced a new gap fraction based leaf area index (LAI) estimator using lidar derived parameters. Results showed 60% - 89% accuracies discriminating plant functional types and estimating shrub LAI. These results imply the potential of waveform lidar to quantify plant functional traits in low-stature vegetation which is useful to assess climate impact in semi-arid ecosystems

Regional Scale Dryland Vegetation Classification with an Integrated Lidar-Hyperspectral Approach

The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity, are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improved classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal, we used both spectral angle mapper (SAM) and multiple endmember spectral mixture analysis (MESMA) for optical vegetation classification. Lidar-derived maximum vegetation height and delineated riparian zones were then used to modify the optical classification. Incorporating the lidar information into the classification scheme increased the overall accuracy from 60% to 89%. Canopy structure can have a strong influence on spectral variability and the lidar provided complementary information for SAM’s sensitivity to shape but not magnitude of the spectra. Similar approaches to map large regions of drylands with low uncertainty may be readily implemented with unmixing algorithms applied to upcoming space-based imaging spectroscopy and lidar. This study advances our understanding of the nuances associated with mapping xeric and mesic regions, and highlights the importance of incorporating complementary algorithms and sensors to accurately characterize the heterogeneity of dryland ecosystems

Regional scale dryland vegetation classification with an integrated lidar-hyperspectral approach

The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity, are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improved classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal, we used both spectral angle mapper (SAM) and multiple endmember spectral mixture analysis (MESMA) for optical vegetation classification. Lidar-derived maximum vegetation height and delineated riparian zones were then used to modify the optical classification. Incorporating the lidar information into the classification scheme increased the overall accuracy from 60% to 89%. Canopy structure can have a strong influence on spectral variability and the lidar provided complementary information for SAM’s sensitivity to shape but not magnitude of the spectra. Similar approaches to map large regions of drylands with low uncertainty may be readily implemented with unmixing algorithms applied to upcoming space-based imaging spectroscopy and lidar. This study advances our understanding of the nuances associated with mapping xeric and mesic regions, and highlights the importance of incorporating complementary algorithms and sensors to accurately characterize the heterogeneity of dryland ecosystems

Remote Sensing of Drylands: Applications of Canopy Spectral Invariants

Remote sensing plays an important role in understanding the structure and function of global terrestrial ecosystems. In this project our research focus was to characterize the dryland vegetation structure and function in the western US. Sparse distribution of vegetation, low amount of leaves on the canopies and the bright soil underneath the canopy make remote sensing of drylands a challenging task. To achieve our research goal we collected aerial and ground based optical hyperspectral and lidar data concurrent to our field campaign. We studied the potential and limitations of these sensors to retrieve canopy biochemistry and structure and to map the vegetation cover at species level

Airborne and Spaceborne Lidar Reveal Trends and Patterns of Functional Diversity in a Semi-Arid Ecosystem

Assessing functional diversity and its abiotic controls at continuous spatial scales are crucial to understanding changes in ecosystem processes and services. Semi-arid ecosystems cover large portions of the global terrestrial surface and provide carbon cycling, habitat, and biodiversity, among other important ecosystem processes and services. Yet, the spatial trends and patterns of functional diversity in semi-arid ecosystems and their abiotic controls are unclear. The objectives of this study are two-fold. We evaluated the spatial pattern of functional diversity as estimated from small footprint airborne lidar (ALS) with respect to abiotic controls and fire in a semi-arid ecosystem. Secondly, we used our results to understand the capabilities of large footprint spaceborne lidar (GEDI) for future applications to semi-arid ecosystems. Overall, our findings revealed that functional diversity in this ecosystem is mainly governed by elevation, soil, and water availability. In burned areas, the ALS data show a trend of functional recovery with time since fire. With 16 months of data (April 2019-August 2020), GEDI predicted functional traits showed a moderate correlation (r = 41–61%) with the ALS predicted traits except for the plant area index (PAI) (r = 11%) of low height vegetation (<5 m). We found that the number of GEDI footprints relative to the size of the fire-disturbed areas (=< 2 km2) limited the ability to estimate the full effects of fire disturbance. However, the consistency of diversity trends between ALS and GEDI across our study area demonstrates GEDI’s potential of capturing functional diversity in similar semi-arid ecosystems. The capability of spaceborne lidar to map trends and patterns of functional diversity in this semi-arid ecosystem demonstrates its exciting potential to identify critical biophysical and ecological shifts. Furthermore, opportunities to fuse GEDI with complementary spaceborne data such as ICESat-2 or the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR), and fine scale airborne data will allow us to fill gaps across space and time. For the first time, we have the potential to monitor carbon cycle dynamics, habitats and biodiversity across the globe in semi-arid ecosystems at fine vertical scales