An information adaptive system study report and development plan

The purpose of the information adaptive system (IAS) study was to determine how some selected Earth resource applications may be processed onboard a spacecraft and to provide a detailed preliminary IAS design for these applications. Detailed investigations of a number of applications were conducted with regard to IAS and three were selected for further analysis. Areas of future research and development include algorithmic specifications, system design specifications, and IAS recommended time lines

SPATIAL PRIORITIZATION FOR INVASIVE PLANT MANAGEMENT

Invasive exotic plant species have been recognized as serious threats to ecosystems. Extensive research on invasive exotic plant species has primarily focused on the impacts, characteristics, and potential treatments. Decision tools and management models that incorporate these findings often lack input from managers and have limited use in differing invasion scenarios. Therefore, in this study, I created a scientifically-driven framework that incorporates expert input to prioritize watersheds for management within the Inner Bluegrass region of Kentucky. The widely distributed invasive exotic plant Amur honeysuckle (Lonicera maackii) was used as an example species. The framework is built around the Analytic Hierarchy Process and highlights areas in most need of invasive exotic plant management by incorporating weighted landscape variables associated with the invasion process. Results of the prioritization provide useful information for natural resource managers by aiding in the development of control strategies while also creating a valuable framework that can be adapted to various invasive exotic plant species

Assessing potential locations for flood-based farming using satellite imagery: a case study of Afar region, Ethiopia

The dry lowlands of Ethiopia are seasonally affected by long periods of low rainfall and, coinciding with rainfall in the Amhara highlands, flood waters which flow onto the lowlands resulting in damage to landscapes and settlements. In an attempt to convert water from storm generated floods into productive use, this study proposes a methodology using remote sensing data and geographical information system tools to identify potential sites where flood spreading weirs may be installed and farming systems developed which produce food and fodder for poor rural communities. First, land use land cover maps for the study area were developed using Landsat-8 and MODIS temporal data. Sentinel-1 data at 10 and 20m resolution on a 12-day basis were then used to determine flood prone areas. Slope and drainage maps were derived from Shuttle RADAR Topography Mission Digital Elevation Model at 90m spatial resolution. Accuracy assessment using ground survey data showed that overall accuracies (correctness) of the land use/land cover classes were 86% with kappa 0.82. Coinciding with rainfall in the uplands, March and April are the months with flood events in the short growing season (belg) and June, July and August have flood events during the major (meher) season. In the Afar region, there is potentially >0.55m ha land available for development using seasonal flood waters from belg or meher seasons. During the 4 years of monitoring (2015–2018), a minimum of 142,000 and 172,000 ha of land were flooded in the belg and meher seasons, respectively. The dominant flooded areas were found in slope classes of <2% with spatial coverage varying across the districts. We concluded that Afar has a huge potential for flood-based technology implementation and recommend further investigation into the investments needed to support new socio-economic opportunities and implications for the local agro-pastoral communities

Towards Automated Analysis of Urban Infrastructure after Natural Disasters using Remote Sensing

Natural disasters, such as earthquakes and hurricanes, are an unpreventable component of the complex and changing environment we live in. Continued research and advancement in disaster mitigation through prediction of and preparation for impacts have undoubtedly saved many lives and prevented significant amounts of damage, but it is inevitable that some events will cause destruction and loss of life due to their sheer magnitude and proximity to built-up areas. Consequently, development of effective and efficient disaster response methodologies is a research topic of great interest. A successful emergency response is dependent on a comprehensive understanding of the scenario at hand. It is crucial to assess the state of the infrastructure and transportation network, so that resources can be allocated efficiently. Obstructions to the roadways are one of the biggest inhibitors to effective emergency response. To this end, airborne and satellite remote sensing platforms have been used extensively to collect overhead imagery and other types of data in the event of a natural disaster. The ability of these platforms to rapidly probe large areas is ideal in a situation where a timely response could result in saving lives. Typically, imagery is delivered to emergency management officials who then visually inspect it to determine where roads are obstructed and buildings have collapsed. Manual interpretation of imagery is a slow process and is limited by the quality of the imagery and what the human eye can perceive. In order to overcome the time and resource limitations of manual interpretation, this dissertation inves- tigated the feasibility of performing fully automated post-disaster analysis of roadways and buildings using airborne remote sensing data. First, a novel algorithm for detecting roadway debris piles from airborne light detection and ranging (lidar) point clouds and estimating their volumes is presented. Next, a method for detecting roadway flooding in aerial imagery and estimating the depth of the water using digital elevation models (DEMs) is introduced. Finally, a technique for assessing building damage from airborne lidar point clouds is presented. All three methods are demonstrated using remotely sensed data that were collected in the wake of recent natural disasters. The research presented in this dissertation builds a case for the use of automatic, algorithmic analysis of road networks and buildings after a disaster. By reducing the latency between the disaster and the delivery of damage maps needed to make executive decisions about resource allocation and performing search and rescue missions, significant loss reductions could be achieved

Assessing potential locations for flood-based farming using satellite imagery: a case study of Afar region, Ethiopia

The dry lowlands of Ethiopia are seasonally affected by long periods of low rainfall and, coinciding with rainfall in the Amhara highlands, flood waters which flow onto the lowlands resulting in damage to landscapes and settlements. In an attempt to convert water from storm generated floods into productive use, this study proposes a methodology using remote sensing data and geographical information system tools to identify potential sites where flood spreading weirs may be installed and farming systems developed which produce food and fodder for poor rural communities. First, land use land cover maps for the study area were developed using Landsat-8 and MODIS temporal data. Sentinel-1 data at 10 and 20 m resolution on a 12-day basis were then used to determine flood prone areas. Slope and drainage maps were derived from Shuttle RADAR Topography Mission Digital Elevation Model at 90 m spatial resolution. Accuracy assessment using ground survey data showed that overall accuracies (correctness) of the land use/land cover classes were 86% with kappa 0.82. Coinciding with rainfall in the uplands, March and April are the months with flood events in the short growing season (belg) and June, July and August have flood events during the major (meher) season. In the Afar region, there is potentially >0.55 m ha land available for development using seasonal flood waters from belg or meher seasons. During the 4 years of monitoring (2015–2018), a minimum of 142,000 and 172,000 ha of land were flooded in the belg and meher seasons, respectively. The dominant flooded areas were found in slope classes of <2% with spatial coverage varying across the districts. We concluded that Afar has a huge potential for flood-based technology implementation and recommend further investigation into the investments needed to support new socio-economic opportunities and implications for the local agro-pastoral communities

Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

Multispectral texture characterization: application to computer aided diagnosis on prostatic tissue images

International audienceVarious approaches have been proposed in the literature for texture characterization of images. Some of them are based on statistical properties, others on fractal measures and some more on multi-resolution analysis. Basically, these approaches have been applied on mono-band images. However, most of them have been extended by including the additional information between spectral bands to deal with multi-band texture images. In this article, we investigate the problem of texture characterization for multi-band images. Therefore, we aim to add spectral information to classical texture analysis methods that only treat gray-level spatial variations. To achieve this goal, we propose a spatial and spectral gray level dependence method (SSGLDM) in order to extend the concept of gray level co-occurrence matrix (GLCM) by assuming the presence of texture joint information between spectral bands. Thus, we propose new multi-dimensional functions for estimating the second-order joint conditional probability density of spectral vectors. Theses functions can be represented in structure form which can help us to compute the occurrences while keeping the corresponding components of spectral vectors. In addition, new texture features measurements related to (SSGLDM) which define the multi-spectral image properties are proposed. Extensive experiments have been carried out on 624 textured multi-spectral images for use in prostate cancer diagnosis and quantitative results showed the efficiency of this method compared to the GLCM. The results indicate a significant improvement in terms of global accuracy rate. Thus, the proposed approach can provide clinically useful information for discriminating pathological tissue from healthy tissue