The Nexus of Biogeography and GIScience: Utilizing Emerging Big Data Sources and Multiscale Analysis for Species Distribution Models

Species Distribution Models (SDMs) are important tools for biological conservation and wildlife management as they detail the distributions of biota across landscapes. In this dissertation I explored two emerging Big Data sources that can be used to enhance SDMs, lidar and Voluntary Geographic Information (VGI). Lidar data can be used in ecological models as explanatory variables that provide information about 3D attributes of space (i.e., structural ecology), and observation data from VGI projects (like eBird) can help inform models about species presence across spatial and temporal scales. In my first research study, I employ a multiscale analysis to address the challenges associated with developing SDMs with high-resolution data from lidar. I present an approach, SBBS, in which the output of SDMs developed with variables that had aspatial resolution of 30-m were used to improve SDMs developed with variables that had a 10-m resolution. This approach produced better models than both a model developed with the default Maxent background sampling area, and a model developed using the conventional approach of resampling environmental data to a common resolution. In my second study I focused on model thresholds to explore the differences between an SDM developed with data from citizen scientists through eBird and one developed with data from wildlife professionals. Results corroborated past research that found SDMs developed with citizen science favor anthropogenic landscapes, but also found factors related to elevation and habitat fragmentation contributed to the mismatch between these models. In my third study, I used inferences from an SDM developed with a scientific occurrence dataset and the statistical concept of influence to evaluate, categorize, and filter eBird points. Through my methods, I was able to isolate species presence locations from eBird that best matched the environmental characteristics of observation locations from the scientific dataset and analyze attributes of points that differed from that profile. This research contributes to knowledge at the nexus of Biogeography and GIScience, as spatial data methods are used to better understand species distributions, while knowledge about ecological relationships across space serves as a basis to better understand these two emerging spatial data sources

Marine species distributions : from data to predictive models

The increased anthropogenic pressure on the marine environment through over-use and overfishing, invasion of species and global climate change has led to an urgent need for more knowledge on the marine ecosystem. Marine species distribution modelling is an important element of marine ecosystem management. It is relied upon by marine spatial planning for i.e. predicting biological resources, the design of marine protected areas, the designation of essential fish habitats, the assessment of species invasion risk, pest control, human-animal conflict prevention, ….This study aims to improve and contribute to the process and understanding of marine species distribution modelling in order to facilitate an in depth study of the trends, vectors and distribution of introduced seaweeds in Europe. More specifically we wanted to 1) provide quality indicators for the marine species distribution data available in the Ocean Biogeographic Information System (OBIS), 2) make global datasets for species distribution modelling in the past, current and future climate more accessible in R, 3) explore the relevance of different predictors of marine species distributions with MarineSPEED, a marine benchmark dataset of more than 500 species, 4) investigate the introduction history and trends in introduced seaweeds in Europe, 5) evaluate the risk of aquarium trade as a vector for future introductions of seaweeds and 6) study the ability of species distribution modelling to predict the introduction and spread of introduced seaweeds and propose a method for identifying candidate areas for further spreading under climate change. The first part of this thesis concerns general aspects of marine species distributions, the environmental data used for modelling and the relevance of marine predictors of species distributions

Modelling the Impacts of Predicted Environmental Change on the Frequency and Magnitude of Rainfall Induced Landslides in Central Kenya

The central highlands of Kenya frequently suffer the impacts of rainfall-induced landslides resulting from the interaction of slope stability and elements of environmental change (land-use and climatic variables). The impacts of rainfall-induced landslides affect the country’s fight against poverty, bearing in mind the limited budgets to cope with the socioeconomic losses incurred by landslide hazards. On the other hand, a fast population growth rate puts pressure on the country’s resources which is majorly agricultural based, thus contributing to more people settling on steep slopes and increasing their vulnerability to rainfall landslide hazards. Thus, this research sought to contribute to the mitigation measures by mapping the landslide areas, performing landslide susceptibility assessment, and investigating the impacts of predicted environmental change on the frequency and magnitude of rainfall-induced landslides. The role of environmental change was investigated using specific objectives which assessed the impacts of land-use on slope stability, and the impact of precipitation characteristics on landslide susceptibility. Several data types ranging from topographic, soil and geology, land-use land-cover (LULC), hydrology, and precipitation landslide controlling factors were mapped and used in the modelling process. The methodology comprised of LULC change detection with Landsat multitemporal data for the years 1995, 2002, 2010 and 2014; structural geology and soil mapping; landslide inventory creation with Landsat multitemporal data for the years 1995, 2000, 2010 and 2014; landslide susceptibility mapping with Combined Hydrological and Slope stability Model (CHASM) and landslide modelling with Artificial Neural Network (ANN) model. The success of mapping and visualizing geology lineaments was owed to the digital image enhancement methods involving band ratioing, False Colour Composites (FCC), feature data transformation and data reduction methods of principal and independent component analysis. In addition to the feature data transformation and data reduction, the landslide inventory mapping was enhanced by utilizing a Normalized Difference Mid-Red (NDMIDR) spectral index involving Landsat geology and red bands. The key results of this research indicated that human activities relating to land-use (mostly agricultural) did aggravate the landslide processes on the sloppy terrain. This was confirmed by the CHASM model results where forested slopes maintained low landslide susceptibility levels. In addition, the ANN model rated LULC, rainfall, and proximity to drainage network factors high in contributing to landslide occurrence in the study area. Thus, majorly shallow types of landslides dominated, although the ANN model mapped some areas with deep-seated landslide areas along lineament features. The impacts of heavy precipitation were observed to increase slope instability, especially in bare land covers and high density drainage network areas due to rapid soil saturation, while prolonged precipitation increased infiltration thus maintaining high landslide susceptibility levels. The effects of climatic variables were associated with increased rock weathering observed on bare volcanic rocks, hence high instability rates around such areas. Landslide hazard zonation with ANN model captured several landslide types and the stability classification. The results of this study can guide targeted policies on land-use management as it has been established that rainfall induced landslides are a result of the interactions of land-use, slope and rainfall landslide conditioning factors. Moreover, creating a landslide inventory which can be updated with landslide attributes was a success since this had not been done in this geographical location to indicate the potential of landslide reactivation

Spatial analysis of invasive alien plant distribution patterns and processes using Bayesian network-based data mining techniques

Invasive alien plants have widespread ecological and socioeconomic impacts throughout many parts of the world, including Swaziland where the government declared them a national disaster. Control of these species requires knowledge on the invasion ecology of each species including how they interact with the invaded environment. Species distribution models are vital for providing solutions to such problems including the prediction of their niche and distribution. Various modelling approaches are used for species distribution modelling albeit with limitations resulting from statistical assumptions, implementation and interpretation of outputs. This study explores the usefulness of Bayesian networks (BNs) due their ability to model stochastic, nonlinear inter-causal relationships and uncertainty. Data-driven BNs were used to explore patterns and processes influencing the spatial distribution of 16 priority invasive alien plants in Swaziland. Various BN structure learning algorithms were applied within the Weka software to build models from a set of 170 variables incorporating climatic, anthropogenic, topo-edaphic and landscape factors. While all the BN models produced accurate predictions of alien plant invasion, the globally scored networks, particularly the hill climbing algorithms, performed relatively well. However, when considering the probabilistic outputs, the constraint-based Inferred Causation algorithm which attempts to generate a causal BN structure, performed relatively better. The learned BNs reveal that the main pathways of alien plants into new areas are ruderal areas such as road verges and riverbanks whilst humans and human activity are key driving factors and the main dispersal mechanism. However, the distribution of most of the species is constrained by climate particularly tolerance to very low temperatures and precipitation seasonality. Biotic interactions and/or associations among the species are also prevalent. The findings suggest that most of the species will proliferate by extending their range resulting in the whole country being at risk of further invasion. The ability of BNs to express uncertain, rather complex conditional and probabilistic dependencies and to combine multisource data makes them an attractive technique for species distribution modeling, especially as joint invasive species distribution models (JiSDM). Suggestions for further research are provided including the need for rigorous invasive species monitoring, data stewardship and testing more BN learning algorithms.Environmental SciencesD. Phil. (Environmental Science

Socio-Environmental Vulnerability Assessment for Sustainable Management

This Special Issue explores the cross-disciplinary approaches, methodologies, and applications of socio-environmental vulnerability assessment that can be incorporated into sustainable management. The volume comprises 20 different points of view, which cover environmental protection and development, urban planning, geography, public policymaking, participation processes, and other cross-disciplinary fields. The articles collected in this volume come from all over the world and present the current state of the world’s environmental and social systems at a local, regional, and national level. New approaches and analytical tools for the assessment of environmental and social systems are studied. The practical implementation of sustainable development as well as progressive environmental and development policymaking are discussed. Finally, the authors deliberate about the perspectives of social–environmental systems in a rapidly changing world

Assessment of Renewable Energy Resources with Remote Sensing

The development of renewable energy sources plays a fundamental role in the transition towards a low carbon economy. Considering that renewable energy resources have an intrinsic relationship with meteorological conditions and climate patterns, methodologies based on the remote sensing of the atmosphere are fundamental sources of information to support the energy sector in planning and operation procedures. This Special Issue is intended to provide a highly recognized international forum to present recent advances in remote sensing to data acquisition required by the energy sector. After a review, a total of eleven papers were accepted for publication. The contributions focus on solar, wind, and geothermal energy resource. This editorial presents a brief overview of each contribution.About the Editor .............................................. vii Fernando Ramos Martins Editorial for the Special Issue: Assessment of Renewable Energy Resources with Remote Sensing Reprinted from: Remote Sens. 2020, 12, 3748, doi:10.3390/rs12223748 ................. 1 André R. Gonçalves, Arcilan T. Assireu, Fernando R. Martins, Madeleine S. G. Casagrande, Enrique V. Mattos, Rodrigo S. Costa, Robson B. Passos, Silvia V. Pereira, Marcelo P. Pes, Francisco J. L. Lima and Enio B. Pereira Enhancement of Cloudless Skies Frequency over a Large Tropical Reservoir in Brazil Reprinted from: Remote Sens. 2020, 12, 2793, doi:10.3390/rs12172793 ................. 7 Anders V. Lindfors, Axel Hertsberg, Aku Riihelä, Thomas Carlund, Jörg Trentmann and Richard Müller On the Land-Sea Contrast in the Surface Solar Radiation (SSR) in the Baltic Region Reprinted from: Remote Sens. 2020, 12, 3509, doi:10.3390/rs12213509 ................. 33 Joaquín Alonso-Montesinos Real-Time Automatic Cloud Detection Using a Low-Cost Sky Camera Reprinted from: Remote Sens. 2020, 12, 1382, doi:10.3390/rs12091382 ................. 43 Román Mondragón, Joaquín Alonso-Montesinos, David Riveros-Rosas, Mauro Valdés, Héctor Estévez, Adriana E. González-Cabrera and Wolfgang Stremme Attenuation Factor Estimation of Direct Normal Irradiance Combining Sky Camera Images and Mathematical Models in an Inter-Tropical Area Reprinted from: Remote Sens. 2020, 12, 1212, doi:10.3390/rs12071212 ................. 61 Jinwoong Park, Jihoon Moon, Seungmin Jung and Eenjun Hwang Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island Reprinted from: Remote Sens. 2020, 12, 2271, doi:10.3390/rs12142271 ................. 79 Guojiang Xiong, Jing Zhang, Dongyuan Shi, Lin Zhu, Xufeng Yuan and Gang Yao Modified Search Strategies Assisted Crossover Whale Optimization Algorithm with Selection Operator for Parameter Extraction of Solar Photovoltaic Models Reprinted from: Remote Sens. 2019, 11, 2795, doi:10.3390/rs11232795 ................. 101 Alexandra I. Khalyasmaa, Stanislav A. Eroshenko, Valeriy A. Tashchilin, Hariprakash Ramachandran, Teja Piepur Chakravarthi and Denis N. Butusov Industry Experience of Developing Day-Ahead Photovoltaic Plant Forecasting System Based on Machine Learning Reprinted from: Remote Sens. 2020, 12, 3420, doi:10.3390/rs12203420 ................. 125 Ian R. Young, Ebru Kirezci and Agustinus Ribal The Global Wind Resource Observed by Scatterometer Reprinted from: Remote Sens. 2020, 12, 2920, doi:10.3390/rs12182920 ................. 147 Susumu Shimada, Jay Prakash Goit, Teruo Ohsawa, Tetsuya Kogaki and Satoshi Nakamura Coastal Wind Measurements Using a Single Scanning LiDAR Reprinted from: Remote Sens. 2020, 12, 1347, doi:10.3390/rs12081347 ................. 165 Cristina Sáez Blázquez, Pedro Carrasco García, Ignacio Martín Nieto, MiguelAngel ´ Maté-González, Arturo Farfán Martín and Diego González-Aguilera Characterizing Geological Heterogeneities for Geothermal Purposes through Combined Geophysical Prospecting Methods Reprinted from: Remote Sens. 2020, 12, 1948, doi:10.3390/rs12121948 ................. 189 Miktha Farid Alkadri, Francesco De Luca, Michela Turrin and Sevil Sariyildiz A Computational Workflow for Generating A Voxel-Based Design Approach Based on Subtractive Shading Envelopes and Attribute Information of Point Cloud Data Reprinted from: Remote Sens. 2020, 12, 2561, doi:10.3390/rs12162561 ................. 207Instituto do Ma