ADDRESSING GEOGRAPHICAL CHALLENGES IN THE BIG DATA ERA UTILIZING CLOUD COMPUTING

Processing, mining and analyzing big data adds significant value towards solving previously unverified research questions or improving our ability to understand problems in geographical sciences. This dissertation contributes to developing a solution that supports researchers who may not otherwise have access to traditional high-performance computing resources so they benefit from the “big data” era, and implement big geographical research in ways that have not been previously possible. Using approaches from the fields of geographic information science, remote sensing and computer science, this dissertation addresses three major challenges in big geographical research: 1) how to exploit cloud computing to implement a universal scalable solution to classify multi-sourced remotely sensed imagery datasets with high efficiency; 2) how to overcome the missing data issue in land use land cover studies with a high-performance framework on the cloud through the use of available auxiliary datasets; and 3) the design considerations underlying a universal massive scale voxel geographical simulation model to implement complex geographical systems simulation using a three dimensional spatial perspective. This dissertation implements an in-memory distributed remotely sensed imagery classification framework on the cloud using both unsupervised and supervised classifiers, and classifies remotely sensed imagery datasets of the Suez Canal area, Egypt and Inner Mongolia, China under different cloud environments. This dissertation also implements and tests a cloud-based gap filling model with eleven auxiliary datasets in biophysical and social-economics in Inner Mongolia, China. This research also extends a voxel-based Cellular Automata model using graph theory and develops this model as a massive scale voxel geographical simulation framework to simulate dynamic processes, such as air pollution particles dispersal on cloud

Willow Abundance and Condition Mapping in Rocky Mountain National Park

Riparian and wetland willow species have undergone serious declines in Rocky Mountain National Park as a consequence of a variety of environmental changes and, most recently, damage resulting from moose overpopulation. To address concerns about the long-term status of willows in the park, we developed remote sensing-based raster maps of riparian and wetland willow species presence, canopy cover percentage, canopy height, and leaf area index. All outputs were produced at 3-meter resolution, and represent willows as they existed in 2021. The mapping was performed via random forests classification and regression models trained on several hundred vegetation plots from a variety of sampling efforts, and making use of predictive layers derived from aerial and satellite imagery, topographic and climatic data. The maps allowed comparison of willow abundance across spatial subsets of the park, including an assessment of areas within ungulate exclosures. Riparian and wetland willow species were mapped as present on 5.45% of the park’s total area. Across these areas, most of which likely represent vegetation types where willow is not dominant but only a component, the mean mapped willow leaf area index was 0.694. Accuracy assessment relied on cross-validated model error estimates. The habitat and imagery-based presence classification models with which the willow presence map was created had error rates of 12% and 19% respectively. The regression models for prediction of canopy cover, canopy height, and leaf area index explained 50%, 56%, and 52% of the variance in the dependent variables. The maps will be used to support assessments of willow habitat in the park and (through allometric conversion of leaf area index to leaf biomass production estimates) the determination of summer seasonal moose carrying capacity

Applications of Satellite Earth Observations section - NEODAAS: Providing satellite data for efficient research

The NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS) provides a central point of Earth Observation (EO) satellite data access and expertise for UK researchers. The service is tailored to individual users’ requirements to ensure that researchers can focus effort on their science, rather than struggling with correct use of unfamiliar satellite data

Satellite monitoring of harmful algal blooms (HABs) to protect the aquaculture industry

Harmful algal blooms (HABs) can cause sudden and considerable losses to fish farms, for example 500,000 salmon during one bloom in Shetland, and also present a threat to human health. Early warning allows the industry to take protective measures. PML's satellite monitoring of HABs is now funded by the Scottish aquaculture industry. The service involves processing EO ocean colour data from NASA and ESA in near-real time, and applying novel techniques for discriminating certain harmful blooms from harmless algae. Within the AQUA-USERS project we are extending this capability to further HAB species within several European countries

A New Robust Multi focus image fusion Method

In today's digital era, multi focus picture fusion is a critical problem in the field of computational image processing. In the field of fusion information, multi-focus picture fusion has emerged as a significant research subject. The primary objective of multi focus image fusion is to merge graphical information from several images with various focus points into a single image with no information loss. We provide a robust image fusion method that can combine two or more degraded input photos into a single clear resulting output image with additional detailed information about the fused input images. The targeted item from each of the input photographs is combined to create a secondary image output. The action level quantities and the fusion rule are two key components of picture fusion, as is widely acknowledged. The activity level values are essentially implemented in either the "spatial domain" or the "transform domain" in most common fusion methods, such as wavelet. The brightness information computed from various source photos is compared to the laws developed to produce brightness / focus maps by using local filters to extract high-frequency characteristics. As a result, the focus map provides integrated clarity information, which is useful for a variety of Multi focus picture fusion problems. Image fusion with several modalities, for example. Completing these two jobs, on the other hand. As a consequence, we offer a strategy for achieving good fusion performance in this study paper. A Convolutional Neural Network (CNN) was trained on both high-quality and blurred picture patches to represent the mapping. The main advantage of this idea is that it can create a CNN model that can provide both the Activity level Measurement" and the Fusion rule, overcoming the limitations of previous fusion procedures. Multi focus image fusion is demonstrated using microscopic images, medical imaging, computer visualization, and Image information improvement is also a benefit of multi-focus image fusion. Greater precision is necessary in terms of target detection and identification. Face recognition" and a more compact work load, as well as enhanced system consistency, are among the new features

Riverine flooding using GIS and remote sensing

Floods are caused by extreme meteorological and hydrological changes that are influenced directly or indirectly by human activities within the environment. The flood trends show that floods will reoccur and shall continue to affect the livelihoods, property, agriculture and the surrounding environment. This research has analyzed the riverine flood by integrating remote sensing, Geographical Information Systems (GIS), and hydraulic and/or hydrological modeling, to develop informed flood mapping for flood risk management. The application of Hydrological Engineering Center River Analysis System (HEC RAS) and HEC HMS models, developed by the USA Hydrologic Engineering Center of the Army Corps of Engineers in a data-poor environment of a developing country were successful, as a flood modeling tools in early warning systems and land use planning. The methodology involved data collection, preparation, and model simulation using 30m Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) as a critical data input of HEC RAS model. The findings showed that modeling using HEC-RAS and HEC HMS models in a data-poor environment requires intensive data enhancements and adjustments; multiple utilization of open sources data; carrying out multiple model computation iterations and calibration; multiple field observation, which may be constrained with time and resources to get reasonable output