Remote Sensing of Ecology, Biodiversity and Conservation: A Review from the Perspective of Remote Sensing Specialists

Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC). Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS)

Applications of Remote Sensing to Alien Invasive Plant Studies

Biological invasions can affect ecosystems across a wide spectrum of bioclimatic conditions. Therefore, it is often important to systematically monitor the spread of species over a broad region. Remote sensing has been an important tool for large-scale ecological studies in the past three decades, but it was not commonly used to study alien invasive plants until the mid 1990s. We synthesize previous research efforts on remote sensing of invasive plants from spatial, temporal and spectral perspectives. We also highlight a recently developed state-of-the-art image fusion technique that integrates passive and active energies concurrently collected by an imaging spectrometer and a scanning-waveform light detection and ranging (LiDAR) system, respectively. This approach provides a means to detect the structure and functional properties of invasive plants of different canopy levels. Finally, we summarize regional studies of biological invasions using remote sensing, discuss the limitations of remote sensing approaches, and highlight current research needs and future directions

GEO-SPATIAL MODELING OF CARBON SEQUESTRATION ASSESSMENT IN DATE PALM, ABU DHABI: AN INTEGRATED APPROACH OF FIELDWORK, REMOTE SENSING, AND GIS

The United Arab Emirates (UAE) has undertaken huge efforts to green the desert and afforestation projects (planted mainly with date palms) hence, reducing its carbon footprint, which have never been accounted for, because of lack of implemented mechanisms and tools to assess the amount of biomass and carbon stock (CS) sequestered by plants in the country. The purpose of this dissertation is to implement a new approach towards assessing the carbon sequestered by date palm (DP) plantations in Abu Dhabi, in both their biomass compartment as well as the soils under beneath, using geospatial technologies (RS and GIS) assessed by field measurements. The methodology proposed in this dissertation relied on both fieldwork and labwork, besides the intensive use of geospatial technology including, digital image processing of multi-scale, multi-resolution satellite imagery as well as Geographical Information Systems (GIS) modelling. For detecting and mapping the DP, the research proposes a framework based on using multi-source/ multi-sensor data in a hierarchical integrated approach (HIA) to map DP plantations at different age stages: young, medium, and mature. The outcomes of the implemented approach were the creation of detailed and accurate maps of DP at three age stages. The overall accuracies for mixed-ages DP the value reached up to 94.5%, with an overall Kappa statistic estimated at 0.888 with total area of DP equal to 7,588.04 ha and the total number of DP planted in the study area counted an estimated number of 8,966,826 palms.The study showed that the correlation of mature DP class alone (\u3e10 years) with single bands was significant with shorwave infrared 1 (SWIR1) and shortwave infrared 2 (SWIR2), while the correlation was significant with all tested vegetation indices (VI) except for tasseled cap transformation index for brightness (TCB) and for greenness (TCG). By using different types of regression equations, tasseled cap transformation index for wetness (TCW) showed the strongest correlation using a second-order polynomial equation to estimate the biomass of mature DP with R² equal to 0.7643 and P value equal to 0.007. The exponential regression equation that uses renormalized difference vegetation index (RDVI) as RS predictor was the best single VI and had the strongest correlation among all RS variables of Landsat 8 OLI for AGB of non-mature DP, with an R2 value of 0.4987 and P value equal 0.00002. The findings of the dissertation work are promising and can be used to estimate the amount of biomass and carbon stock in DP plantations in the country as well as in arid land in general. Therefore, it can be applied to enhance the decision-making process on sustainable monitoring and management of carbon sequestration by date palms in other similar ecosystems. The research’s approach has never been developed elsewhere for date palms in arid areas

Evaluating the impact of road infrastructure on household income in Papua New Guinea:Spatial data compilation and analysis

As part of the ADB sub‐project ‘Developing Impact Evaluation Methodologies, Approaches, and Capacities in Selected Developing Member Countries’ two closely related research projects were carried out by VU to evaluate the impact of road infrastructure on household income in Papua New Guinea. One project focused on econometric analysis (contract no. 117629‐S85196), while the other developed the spatial database needed for this analysis (contract no. 117642‐S84962). The current report documents the data collection process and provides a concise description of the data that were collected for the project and the analyses that were performed to enrich the available data sources to create meaningful variables for the statistical analysis. It serves as a background document with the scientific paper‐that concisely describes the findings of the two joint research projects

Sensitivity of high-resolution satellite sensor imagery to regenerating forest age and site preparation for wildlife habitat analysis

In west-central Alberta increased landscape fragmentation has lead to increased human use, having negative effects on wildlife such as the grizzly bear (Ursus arctos L.). Recently, grizzly bears in the Foothills Model Forest were found to select clear cuts of different age ranges as habitat and selected or avoided certain clear cuts depending on the site preparation process employed. Satellite remote sensing offers a practical and cost-effective method by which cut areas, their age, and site preparation activities can be quantified. This thesis examines the utility of spectral reflectance of SPOT-5 pansharpened imagery (2.5m spatial resolution) to identify and map 44 regenerating stands sampled in August 2005. Using object based classification with the Normalized Difference Moisture Index (NDMI), green, and short wave infrared (SWIR) bands, 90% accuracy can be achieved in the detection of forest disturbance. Forest structural parameters were used to calculate the structural complexity index (SCI), the first loading of a principal components analysis. The NDMI, first-order standard deviation and second-order correlation texture measures were better able to explain differences in SCI among the 44 forest stands (R2=0.74). The best window size for the texture measures was 5x5, indicating that this is a measure only detectable at a very high spatial resolution. Age classes of these cut blocks were analysed using linear discriminant analysis and best separated (82.5%) with the SWIR and green spectral bands, second order correlation under a 25x25 window, and the predicted SCI. Site preparation was best classified (90.9%) using the NDMI and homogeneity texture under a 5x5 window. Future applications from this research include the selection of high probability grizzly habitat for high spatial resolution imagery acquisition for detailed mapping initiatives

Modelling spatial variability of coffee (Coffea Arabica L.) crop condition with multispectral remote sensing data.

Doctor of Philosophy in Environmental Science. University of KwaZulu-Natal, Pietermaritzburg, 2017.Abstract available in PDF file

Thermal Infrared Remote Sensing for Analysis of Landscape Ecological Processes: Current Insights and Trends

NASA or NOAA Earth-observing satellites are not the only space-based TIR platforms. The European Space Agency (ESA), the Chinese, and other countries have in orbit or plan to launch TIR remote sensing systems. Satellite remote sensing provides an excellent opportunity to study land-atmosphere energy exchanges at the regional scale. A predominant application of TIR data has been in inferring evaporation, evapotranspiration (ET), and soil moisture. In addition to using TIR data for ET and soil moisture analysis over vegetated surfaces, there is also a need for using these data for assessment of drought conditions. The concept of ecological thermodynamics provides a quantification of surface energy fluxes for landscape characterization in relation to the overall amount of energy input and output from specific land cover types