Educator's Guide for Mission to Earth: LANDSAT Views the World

This teacher's guide is specifically designed to provide information and suggestions for using LANDSAT imagery to teach basic concepts in several content areas. Content areas include: (1) Earth science and geology; (2) environmental studies; (3) geography; and (4) social and urban studies

Flood Prediction and Mitigation in Data-Sparse Environments

In the last three decades many sophisticated tools have been developed that can accurately predict the dynamics of flooding. However, due to the paucity of adequate infrastructure, this technological advancement did not benefit ungauged flood-prone regions in the developing countries in a major way. The overall research theme of this dissertation is to explore the improvement in methodology that is essential for utilising recently developed flood prediction and management tools in the developing world, where ideal model inputs and validation datasets do not exist. This research addresses important issues related to undertaking inundation modelling at different scales, particularly in data-sparse environments. The results indicate that in order to predict dynamics of high magnitude stream flow in data-sparse regions, special attention is required on the choice of the model in relation to the available data and hydraulic characteristics of the event. Adaptations are necessary to create inputs for the models that have been primarily designed for areas with better availability of data. Freely available geospatial information of moderate resolution can often meet the minimum data requirements of hydrological and hydrodynamic models if they are supplemented carefully with limited surveyed/measured information. This thesis also explores the issue of flood mitigation through rainfall-runoff modelling. The purpose of this investigation is to assess the impact of land-use changes at the sub-catchment scale on the overall downstream flood risk. A key component of this study is also quantifying predictive uncertainty in hydrodynamic models based on the Generalised Likelihood Uncertainty Estimation (GLUE) framework. Detailed uncertainty assessment of the model outputs indicates that, in spite of using sparse inputs, the model outputs perform at reasonably low levels of uncertainty both spatially and temporally. These findings have the potential to encourage the flood managers and hydrologists in the developing world to use similar data sets for flood management

Application of remote sensing to state and regional problems

There are no author-identified significant results in this report

Image Processing and Spatial Analysis of Satellite Imagery for Geobiophysical Modeling of Sources for Increased Sediment Yield in the Greenup Pool of the Ohio River

The study area for this research is the Greenup Pool of the Ohio River, with the Guyandotte River watershed used as a test case. The watershed passes through southwestern West Virginia. The objective of this research was to create and validate a model for extraction of parameters affecting sediment load from satellite imagery and spatial analysis to enrich the data available for the Ohio River. Unsupervised classification, accuracy assessment, map algebra, and suitability modeling were performed to address the research question. In the area selected for this research, extant data consisted of two points approximately 61.8 river miles apart. In many sediment yield models, adequate data is available for velocity, bathymetry, discharge, and sediment load. Results of this research show the potential for remotely sensed imagery and analysis of statistical and spatial relationships in a geobiophysical model to augment investigations of complex systems where conventional data are lacking

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

Benthic mapping of the Bluefields Bay fish sanctuary, Jamaica

Small island states, such as those in the Caribbean, are dependent on the nearshore marine ecosystem complex and its resources; the goods and services provided by seagrass and coral reef for example, are particularly indispensable to the tourism and fishing industries. In recognition of their valuable contributions and in an effort to promote sustainable use of marine resources, some nearshore areas have been designated as fish sanctuaries, as well as marine parks and protected areas. In order to effectively manage these coastal zones, a spatial basis is vital to understanding the ecological dynamics and ultimately inform management practices. However, the current extent of habitats within designated sanctuaries across Jamaica are currently unknown and owing to this, the Government of Jamaica is desirous of mapping the benthic features in these areas. Given the several habitat mapping methodologies that exist, it was deemed necessary to test the practicality of applying two remote sensing methods - optical and acoustic - at a pilot site in western Jamaica, the Bluefields Bay fish sanctuary. The optical remote sensing method involved a pixel-based supervised classification of two available multispectral images (WorldView-2 and GeoEye-1), whilst the acoustic method comprised a sonar survey using a BioSonics DT-X Portable Echosounder and subsequent indicator kriging interpolation in order to create continuous benthic surfaces. Image classification resulted in the mapping of three benthic classes, namely submerged vegetation, bare substrate and coral reef, with an overall map accuracy of 89.9% for WorldView-2 and 86.8% for GeoEye-1 imagery. These accuracies surpassed those of the acoustic classification method, which attained 76.6% accuracy for vegetation presence, and 53.5% for bottom substrate (silt, sand and coral reef/ hard bottom). Both approaches confirmed that the Bluefields Bay is dominated by submerged aquatic vegetation, with contrastingly smaller areas of bare sediment and coral reef patches. Additionally, the sonar revealed that silty substrate exists along the shoreline, whilst sand is found further offshore. Ultimately, the methods employed in this study were compared and although it was found that satellite image classification was perhaps the most cost-effective and well-suited for Jamaica given current available equipment and expertise, it is acknowledged that acoustic technology offers greater thematic detail required by a number of stakeholders and is capable of operating in turbid waters and cloud covered environments ill-suited for image classification. On the contrary, a major consideration for the acoustic classification process is the interpolation of processed data; this step gives rise to a number of potential limitations, such as those associated with the choice of interpolation algorithm, available software and expertise. The choice in mapping approach, as well as the survey design and processing steps is not an easy task; however the results of this study highlight the various benefits and shortcomings of implementing optical and acoustic classification approaches in Jamaica.Persons automatically associate tropical waters with spectacular views of coral reefs and colourful fish; however many are perhaps not aware that these coral reefs, as well as other living organisms inhabiting the seabed are in fact extremely valuable to our existence. Healthy coral reefs and seagrass assist in maintaining the sand on our beaches and fish populations and are thereby crucial to the tourism and fishing industries in the Caribbean. For this reason, a number of areas are protected by law and have been designated fish sanctuaries or marine protected areas. In order to understand the functioning of theses areas and effectively inform management strategy, the configuration of what exists on the seafloor is crucial. In the same vein that a motorist needs a road map to navigate unknown areas, coastal stakeholders require maps of the seafloor in order to understand what is happening beneath the water’s surface. The location of seafloor habitats within fish sanctuaries in Jamaica are currently unknown and the Government is interested in mapping them. However a myriad of methods exist that could be employed to achieve this goal. Remote sensing is a broad grouping of methods that involve collecting information about an object without being in direct physical contact with it. Many researchers have successfully mapped marine areas using these techniques and it was believed crucial to test the practicality of two such methods, specifically optical and acoustic remote sensing. The main question to be answered from this study was therefore: Which mapping approach is better for benthic habitat mapping in Jamaica and possibly the wider Caribbean? Optical remote sensing relates to the interaction of energy with the Earth’s surface. A digital photograph is taken from a satellite and subsequently interpreted. Acoustic/ sonar technology involves the recording of waveforms reflected from the seabed. Both methods were employed at a pilot site, the Bluefields Bay fish sanctuary, situated in western Jamaica. The optical remote sensing method involved the classification of two satellite images (named WorldView-2 and GeoEye-1) and this process was informed using known positions of seafloor features, this being known as supervised image classification. With regard to the acoustic method, a field survey utilising sonar equipment (BioSonics DT-X Portable Echosounder) was undertaken in order to collect the necessary sonar data. The processed field data was modelled in order to convert lines of field point data to one continuous map of the sanctuary, a process known as interpolation. The accuracy of each method was then tested using field knowledge of what exists in the sanctuary. The map resulting from the image classification revealed three seafloor types, namely submerged vegetation, coral reef and bare seafloor. The overall map accuracy was 89.9% for the WorldView-2 image and 86.8% for GeoEye-1 imagery. These accuracies surpassed those attained from the acoustic classification method (76.6% for vegetation presence and 53.5% for bottom type - silt, sand and coral reef/ hard bottom). Similar to previous studies undertaken, it was shown that the seabed of Bluefields Bay is primarily inhabited by submerged aquatic vegetation (including seagrass and algae), with contrastingly smaller areas of bare sediment and coral reef. Ultimately, the methods employed in this study were compared and the pros and cons of each were weighed in order to deem one method more suitable in Jamaica. Often, the presence of cloud and suspended matter in the water block the view of the seafloor making image classification difficult. On the contrary, acoustic surveys are capable of operating throughout cloudy conditions and attaining more detailed information of the ocean floor, otherwise not possible with optical remote sensing. A major step in the acoustic classification process however, was the interpolation of processed data, which may introduce additional limitations if careful consideration is not given to the intricacies of the process. Lastly, the acoustic survey certainly required greater financial resources than satellite image classification. In answer to the main question of this study, the most cost effective and feasible mapping method for Jamaica is satellite image classification (based on the results attained). It must be stressed however that the effective implementation of any method will depend on a number of factors, such as available software, equipment, expertise and user needs, that must be weighed in order to select the most feasible mapping method for a particular site

Decisin support system for risk assessment and management of floods

The objective of the RAMFLOOD project is to develop and validate a new decision support system (DSS) for the risk assessment and management of emergency scenarios due to severe floods. The DSS combines environmental and geo-physical data from earth observation, with advanced computer simulation and graphical visualisation methods and artificial intelligence techniques, for generating knowledge contributing to the risk prevention of floods and the design of effective response actions maximising the safety of infrastructures and human life