HICF: A MATLAB PACKAGE FOR HYPERSPECTRAL IMAGE CLASSIFICATION AND FUSION FOR EDUCATIONAL LEARNING AND RESEARCH

A significant surge has been observed with the development and research in remote sensing in recent years for hyperspectral applications in Earth observation. Subsequently, the development of software and tools have also experienced an unprecedented rise, both in research as well as in academia. Although commercial software and tools such as ENVI by ITT Visual Information Solutions, Boulder, CO, USA are available for visualizing and analyzing the hyperspectral images, such software are expensive. Some open source toolboxes such as the MATLAB-based Hyperspectral Image Analysis Toolbox (HIAT) are also available. However, mostly these toolboxes have not been packaged for dissemination and operation without the MATLAB software which is commercial. In this paper, we introduce the Hyperspectral Image Classification and Fusion (HICF) package which is being developed at the Geoinformatics laboratory, Department of Civil Engineering, Indian Institute of Technology Kanpur (IITK) in MATLAB that can be used by standalone installation with an open source supplementary MATLAB compiler. This software is intended to provide a collection of algorithms both conventional and those developed at the Geoinformatics laboratory that utilizes the numerical computing capability of MATLAB for the processing of hyperspectral and multispectral imagery. The HICF software comprises a simple design of the graphical user interface which can be efficiently used particularly for academic purposes

Estimation and Mapping the Rubber Trees Growth Distribution using Multi Sensor Imagery With Remote Sensing and GIS Analysis

The plantation of rubber tree in different countries throughout the world are expanded rapidly in areas that are not known before in planting such as these vegetation species. Estimating and mapping the distribution of rubber trees stand ages in these regions is very necessary to get better understanding of the effects of the changes of land cover on the Carbon and Water Cycle and also the productivity of the latex in different ages. Many remote sensing techniques that have been used to estimate the land cover / land use for mapping and monitoring the distribution of rubber trees growth based on different remote sensing classification algorithms (Maximum likelihood, SAM classification, Decision Tree and Mahalanobis Distance) with different types of data (Multispectral, Hyperspectral or statistical) by using many sensor

Using GIS to Predict Corn Yields in Colombia

Crop yield prediction can play an important role in developing the agriculture sector in Colombia. Remote sensing and GIS have proven to be an effective mechanism for this purpose in developed economies. This project created a proof-of-concept application for the Colombian Ministry of Agriculture and other related governmental institutions. The project used existing methodologies including the classification of satellite imagery, interpolation of climate data into continuous surfaces, the extraction of Normalized Difference Vegetation Index, and the computation of multiple linear regressions. ESRI ArcGIS provided the interface, software, tools and functions to build the application, and to integrate and automate the application‟s functionalities. Cloud coverage in the imagery and the lack of specialized data affected the accuracy of the crop yields estimates. Nevertheless, the application predicts corn yields with an estimated accuracy of 71% when cloud coverage is minimal. The application can use both Landsat and Spot preprocessed images, and in less than six minutes yield predictions for areas inside Cordoba, a major corn producing state in Colombia

Landscape and Impervious Surface Mapping in the Twin Cities Metropolitan Area using Feature Recognition and Decision Tree techniques

Land Use and Land Cover (LULC) and Impervious Surface Area (ISA) are important parameters for many environmental studies, and serve as an essential tool for decision makers and stakeholders in Urban & Regional planning. Newly available high spatial resolution aerial ortho-imagery and LiDAR data, in combination with specialized, object-oriented and decision-tree classification techniques, allow for accurate mapping of these features. In this study, a method was developed to first classify LULC using an object-based classifier, and then use the resulting map as input for a decision-tree model to classify ISA in the Twin Cities Metropolitan Area in Minnesota. It was found that vegetation cover classes were the most prevalent in the study area, making up over half of the land area. Water was the smallest class, followed by urban land cover, which made up 11%. Impervious surface was determined to make up 14% of the TCMA area.Overall classification accuracy for LULC cover was estimated to be 74%, and 95% for the ISA classification

Análisis temporal del uso del suelo en el Departamento de Soriano y su incidencia en la biodiversidad

Tribunal: Dr. Alberto Yanosky; Dr. Néstor Mazzeo; Prof. Daniel PanarioOrientador: Dr. Marcel Achkar.Orientador: Dr. Alejandro Brazeiro

Surficial Materials Mapping using Remote Sensing and Classification Methods: A Geological Knowledge and Statistical Approach

Mapping the geology of Northern regions in Canada is an essential step in providing key knowledge for resource development and economic prosperity of northern communities. However, mapping this large remote region presents a major challenge both in terms of financial resources and the time required to cover such a large area. With convenient access to remotely sensed imagery, new automatic and remote approaches are emerging that support the surficial geological mapping of vast northern regions at scales appropriate for mineral exploration and related land-use management. An approach using LANDSAT 7 TM imagery, field-based data and a maximum likelihood classification algorithm is employed to produce remote predictive maps of the surficial materials in the Repulse Bay area, Nunavut (NTS 46M-SW, 46L-W and S and 46K-SW). Two approaches in the remote predictive mapping (RPM) process are used to determine the optimal class combination and resultant maps. The first approach employs general and field knowledge from Quaternary geologists to the map evaluation. This approach allows training areas to be grouped and merged based on Quaternary geology principles. The second approach uses statistical techniques to produce classified maps based on training areas along with measures of classification accuracy. These qualitative (geological knowledge-based) and quantitative (statistical-based) methods are used and compared to determine optimal class combinations. Four classification maps that offer the highest overall classification accuracies - through analysis of a confusion matrix and associated variability maps - were produced (two for each approach). Exposed marine sediments, carbonate-rich tills, organics and boulder terrains are the most accurately (>75%) classified of the surficial materials classes; confusion occurs between remaining till, sand and gravel, and bedrock units. Variability maps were produced using these optimal class combinations and corresponding classifications, through which it is found that the geological knowledge-based approach is more suitable for remotely mapping surficial materials in this study area. A comparison to surficial materials maps derived from surficial geology maps was conducted with results of classification outputs using the most optimal class combinations with LANDSAT and SPOT 4/5 imagery. This visual and GIS analysis comparison allowed for evaluation of the classification products, while an overlay analysis compared a pixel-to-pixel correspondence between the maps. Although it is found that both imageries are useful for mapping marine and alluvial sediments, it has limitations in mapping organic materials, till and bedrock. It is apparent that LANDSAT imagery is more appropriate for general mapping while SPOT is better suited for mapping marine sediments

Connaissances expertes et modélisation pour l'exploitation d'images d'observation de la Terre à hautes résolutions spatiale, spectrale et temporelle

Les futures missions spatiales d'observation de la Terre, Venµs et Sentinelle (1 et 2), fourniront un flot de données inédit en termes de résolution spatiale, revisite temporelle et richesse spectrale. Afin d'exploiter de façon efficace ces données pour la réalisation de cartes d'occupation des sols ou de détection de changements, des approches rapides, robustes et le moins supervisées possibles seront nécessaires. Un exemple d'utilisation de ces données pourrait être d'identifier, dès le mois de mai, les surfaces couvertes par du maïs dans tout le Sud-ouest de la France. Ou encore d'obtenir une carte d'occupation des sols mensuelle, dans un délai très court, à l'échelle de grandes régions. On constate que les images seules ne permettent pas d'obtenir de telles données. Nous avons cependant d'autres types d'informations à notre disposition, qui ont jusqu'alors été très peu exploitées. Ce travail de thèse a consisté à identifier les informations dites a priori disponibles, évaluer leur pertinence, et les introduire dans les chaînes de traitement déjà existantes pour chiffrer leur apport. Nous nous sommes intéressés en particulier au domaine du suivi de l'agriculture. Les informations que nous avons utilisées sont, entre autres, les connaissances sur les pratiques agricoles (rotations de culture, irrigation, alternances de catégories de cultures, etc.), les tailles des parcelles et la topographie. Nous avons principalement travaillé avec 2 sources de connaissances a priori : * Celles contenues dans des bases de données telles que le Registre Parcellaire Graphique (RPG). Nous avons utilisé des méthodes d'apprentissage automatique sur les données pour les extraire. * Celles fournies par des experts. Nous les avons modélisées à l'aide de règles de la logique de 1er ordre. Une des contributions de cette thèse est la sélection et l'évaluation d'un outil qui permette d'extraire l'information et de la traiter, de manière à ce qu'elle soit introduite de façon efficace dans les algorithmes de classification déjà existants. Pour cela, nous avons utilisé la Logique de Markov, un outil statistique capable de travailler à la fois sur des informations issues de bases de données, et sur des informations modélisées sous la forme de règles logiques. Nous avons montré que l'utilisation de ces données permet d'améliorer la qualité des cartes d'occupation du sol. Nous avons de plus montré que ces informations permettent d'obtenir des cartes en quasi-temps-réel, dont la qualité va crescendo avec l'arrivé de nouvelles informations. En conclusion de ce travail de thèse, nous donnons des pistes pour appliquer la même méthodologie à d'autres domaines, en particulier au suivi des forêts tropicales et à la cartographie générique de l'occupation du sol.The future Earth observation space missions, Venµs and Sentinel (1 and 2), will provide us with a flow of data unseen in terms of spatial, spectral and temporal resolution. To use these data efficiently for the generation of land cover maps or change detection, we need fast, robust approaches that require as little supervision as possible. For instance, a concrete use of these data could be the identification, as early as May, of the area growing corn in all the South-West part of France. Or obtaining a monthly land cover map, in a slight delay, on large areas. Images alone don't allow us to reach such goals. Nevertheless, other information is available, which hasn't been really used. The main goal of this thesis is to identify available prior information, evaluate its revelance, and introduce it in preexisting processing chains to assess its contribution. We focused on agriculture monitoring. The information we used is knowledge on farming practices (crop rotations, irrigation, crop class alternation, etc) and the size and the topography of the fields. We mainly worked with 2 sources of prior knowledge: * Knowledge contained in databases such as the Registre Parcellaire Graphique (RPG). We used data mining methods to extract it. * Knowledge provided by experts. We modeled it with 1\up{st} order logic rules. One contribution of this thesis is the selection and assessment of a tool allowing us to extract and process information in a way that we can introduce it efficiently in preexisting classification algorithms: Markov Logic. Markov Logic is a statistical tool able to work with both information from databases and information modeled with logic rules. We show that using these data increases the quality of the land cover maps. We also show that this information allows us to obtain real time maps, whose quality increases with the arrival of new information. As a conclusion of this thesis work, we provide outlooks for applying the same methodology to other areas, such as the monitoring of tropical forests dans generic land cover mapping

Implementing an Agro-Environmental Information System (AEIS) Based on GIS, Remote Sensing, and Modelling -- A case study for rice in the Sanjiang Plain, NE-China

Information on agro-ecosystems is crucial for understanding the agricultural production and its impacts on the environment, especially over large agricultural areas. The Sanjiang Plain (SJP), covering an area of 108 829 km², is a critical food base located in NE-China. Rice, soya bean and maize are the major crops in the SJP which are sold as commercial grain throughout China. The aim of this study is to set up an Agro-Environmental Information System (AEIS) for the SJP by employing the technologies of geographic information systems (GIS), remote sensing (RS), and agro-ecosystem modelling. As the starting step, data carrying interdisciplinary information from multiple sources are organized and processed. For an AEIS, geospatial data have to be acquired, organized, operated, and even regenerated with good positioning conditions. Georeferencing of the multi-source data is mandatory. In this thesis, high spatial accuracy TerraSAR-X imagery was used as a reference for georeferencing raster satellite data and vector GIS topographic data. For the second step, the georeferenced multi-source data with high spatial accuracy were integrated and categorized using a knowledge-based classifier. Rice was analysed as an example crop. A rice area map was delineated based on a time series of three high resolution FORMOSAT-2 (FS-2) images and field observed GIS topographic data. Information on rice characteristics (i.e., biomass, leaf area index, plant nitrogen concentration and plant nitrogen uptake) was derived from the multi-temporal FS-2 images. Spatial variability of rice growing status on a within-field level was well detected. As the core part of the AEIS, an agro-ecosystem modelling was then applied and subsequently crops and the environmental factors (e.g., climate, soil, field management) are linked together through a series of biochemical functions inherent in the modelling. Consequently, the interactions between agriculture and the environment are better interpreted. In the AEIS for the SJP, the site-specific mode of the DeNitrification-DeComposition (DNDC) model was adapted on regional scales by a technical improvement for the source code. By running for each pixel of the model input raster files, the regional model assimilates raster data as model inputs automatically. In this study, detailed soil data, as well as the accurate field management data in terms of crop cultivation area (i.e. rice) were used as model inputs to drive the regional model. Based on the scenario optimized from field observation, rice yields over the Qixing Farm were estimated and the spatial variability was well detected. For comparison, rice yields were derived from multi-temporal FS-2 images and the spatial patterns were analysed. As representative environmental effects, greenhouse gas of nitrous oxide (N2O) and carbon dioxide (CO2) emitted from the paddy rice fields were estimated by the regional model. This research demonstrated that the AEIS is effective in providing information about (i) agriculture on the region, (ii) the impacts of agricultural practices on the environment, and (iii) simulation scenarios for sustainable strategies, especially for the regional areas (e.g. the SJP) that is lacking of geospatial data

THE DEVELOPMENT OF A HOLISTIC EXPERT SYSTEM FOR INTEGRATED COASTAL ZONE MANAGEMENT

Coastal data and information comprise a massive and complex resource, which is vital to the practice of Integrated Coastal Zone Management (ICZM), an increasingly important application. ICZM is just as complex, but uses the holistic paradigm to deal with the sophistication. The application domain and its resource require a tool of matching characteristics, which is facilitated by the current wide availability of high performance computing. An object-oriented expert system, COAMES, has been constructed to prove this concept. The application of expert systems to ICZM in particular has been flagged as a viable challenge and yet very few have taken it up. COAMES uses the Dempster- Shafer theory of evidence to reason with uncertainty and importantly introduces the power of ignorance and integration to model the holistic approach. In addition, object orientation enables a modular approach, embodied in the inference engine - knowledge base separation. Two case studies have been developed to test COAMES. In both case studies, knowledge has been successfully used to drive data and actions using metadata. Thus a holism of data, information and knowledge has been achieved. Also, a technological holism has been proved through the effective classification of landforms on the rapidly eroding Holderness coast. A holism across disciplines and CZM institutions has been effected by intelligent metadata management of a Fal Estuary dataset. Finally, the differing spatial and temporal scales that the two case studies operate at implicitly demonstrate a holism of scale, though explicit means of managing scale were suggested. In all cases the same knowledge structure was used to effectively manage and disseminate coastal data, information and knowledge