Knowledge Extraction from Geographical Databases for Land Use Data Production

Our study focuses on the task of land use evolution in urban environment which is fundamental in revealing the territorial planning. It refers crucially to the use of spatial data mining tools due to their high potential in handling with spatial data characteristics. The results of our knowledge discovery process are spatial and spatiotemporal association rules referring to the land use and its evolution. Three proposals based on different knowledge extraction techniques are detailed. The first approach aims to extract spatiotemporal association rules by introducing time into the attributes. The second approach forecasts the extracted rules at different dates. The third approach is devoted to the mining of spatiotemporal association rules. This proposal looks for rules that relate properties of reference objects with properties of other spatial relevant objects. The extracted patterns are relationships involving the spatial objects during time periods. To prove the applicability of each approach, experimentations are conducted on real world data. The obtained results are promising.

Leveraging Volunteered Geographic Information to Improve Disaster Resilience

Volunteered Geographic Information (VGI) has emerged as an important additional source of information for improving the resilience of cities and communities in the face of natural hazards and extreme weather events. This chapter summarizes the existing research in this area and offers an interdisciplinary perspective of the challenges to be overcome, by presenting AGORA: A Geospatial Open collaboRative Architecture for building resilience against disasters and extreme events. AGORA structures the challenges of using VGI for disaster management into three layers: acquisition, integration and application. The chapter describes the research challenges involved in each of these layers, as well as reporting on the results achieved so far and the lessons learned in the context of flood risk management in Brazil. Furthermore, the chapter concludes by setting out an interdisciplinary research agenda for leveraging VGI to improve disaster resilience.

Multi-Temporal Landsat Remote Sensing for Forest Landscape Fragmentation Analysis in the Yoko Forest, Kisangani, DRC

This study assessed the impact of human activities on deforestation and sustainability of water resources and livelihoods in the Congo Basin. It mainly aimed to assess forest degradation in the Yoko reserve from 1976 to 2015 and investigate the compatibility of Landsat imagery for forest monitoring. Digital Image processing for unsupervised classification was done using ENVI software while supervised classification was done by means of ArcGIS 10. Results show that forest landscape faced large scale human induced fragmentation over the last 40 years. If these trends continue, they will affect the sustainability of water resources and livelihoods in the Congo Basin of the Democratic Republic of Congo. Hence, policy makers need to look at key drivers and address impacts that may threaten the future of Hydrological Ecosystems Services, including water and land resources in the Congo Basin. Authorities have to apply an Integrated Management of Water, Land and Ecosystems.

GIS, Spatial Analysis, and Modeling

Breast cancer is a major health issue in all countries affecting thousands of women. Its causes are unknown and the national and international strategies to reduce its morbidity and mortality levels are based on early detection of cancer through screening and treatment according to clinical guidelines. Thus, knowledge of which women are at risk and why they are at risk is therefore essential component of disease prevention and screening. In 2015, an estimated 231,840 new cases of invasive breast cancer are expected to be diagnosed in women in the United States, along with 60,290 new cases of non-invasive (in situ) breast cancer. The purpose of this study is to provide a more detailed analysis of the breast cancer distribution in the United States by comparing the spatial distribution of breast cancer cases against physical environmental factors using Geographic Information System (GIS). Further, it gives background information to the GIS and its applications in health-related research.

Sugeno Fuzzy-Inference-System-Based Land Cover Classification of Remotely Sensed Images

Accuracy of land cover classification in remotely sensed images relies on the features extracted and the classifier used. Texture features are significant in land cover classification. Traditional texture models capture only patterns with discrete boundaries whereas fuzzy patterns need to be classified by assigning due weightage to uncertainty. When remotely sensed image contains noise, the image may have fuzzy patterns characterizing land covers and fuzzy boundaries separating land covers. So a fuzzy texture model is proposed for effective classification of land covers in remotely sensed images and the model uses Sugeno Fuzzy Inference System (FIS). Support Vector Machine (SVM) is used for precise and fast classification of image pixels. Hence it is proposed to use a hybrid of fuzzy texture model and SVM for land cover classification of remotely sensed images. In this chapter, land cover classification of IRS-P6, LISS-IV remotely sensed image is performed using multivariate version of the proposed texture model.

Satellite Parametric Description to Ontology Concepts and Semantic Classification of Satellite Data

Current satellite data retrieval systems retrieves data using latitude, longitude, date, time and sensor parameters like wind, cloud etc. To achieve concept based satellite data retrieval like Storm, Hurricane, Overcast and Frost etc., requires ontological concept descriptions using satellite observation parameters and concept based classification of satellite data. The current research work has designed and implemented a two phase methodology to achieve this. The phase 1 defines ontology concepts through satellite observation parameters and phase 2 describes ontology concept based satellite data classification. The efficiency of the methodology is been tested by taking the Kalpana satellite data from MOSDAC and weather ontology. This achieves concept based retrieval of satellite data, application interoperability and strengthen the ontologies. The current methodology is implemented and results in concept based satellite data classification, storage and retrieval.

Improving the Efficiency of Image Interpretation Using Ground Truth Terrestrial Photographs

Researchers worldwide use remotely sensed imagery in their projects, in both the social and natural sciences. However, users often encounter difficulties working with satellite images and aerial photographs, as image interpretation requires specific experience and skills. The best way to acquire these skills is to go into the field, identify your location in an overhead image, observe the landscape, and find corresponding features in the overhead image. In many cases, personal observations could be substituted by using terrestrial photographs taken from the ground with conventional cameras. This chapter discusses the value of terrestrial photographs as a substitute for field observations, elaborates on issues of data collection, and presents results of experimental estimation of the effectiveness of the use of terrestrial ground truth photographs for interpretation of remotely sensed imagery. The chapter introduces the concept of GeoTruth – a web-based collaborative framework for collection, storing and distribution of ground truth terrestrial photographs and corresponding metadata.

Identification of Geospatial Objects Using Spectral Pattern

Solar radiation on hitting a target surface may be transmitted, absorbed or reflected. Different materials reflect and absorb differently at different wavelengths. The reflectance spectrum of a material is a plot of the fraction of radiation reflected as a function of the incident wavelength and serves as a unique signature for the material. In principle, a material can be identified from its spectral reflectance signature if the sensing system has sufficient spectral resolution to distinguish its spectrum from those of other materials. This premise provides the basis for multispectral remote sensing. Nguyen Dinh Duong (1997) proposed a method for decomposition of multi-spectral image into several sub-images based on modulation (spectral pattern) of the spectral reflectance curve. The hypothesis roots from the fact that different ground objects have different spectral reflectance and absorption characteristics which are stable for a given sensor. This spectral pattern can be considered as invariant and be used as one of classification rules.

3D InSAR Phase Unwrapping Within the Compressive Sensing Framework

This chapter presents a new phase unwrapping algorithm for the 3D Interferometric Synthetic Aperture Radar (3D InSAR) volumes. The proposed approach is based on the relationship between the gradient vectors of the observed wrapped phase and the true phase respectively, when the Itoh condition is satisfied. Since this relationship is violated by the residue pixels in the observed wrapped phase, a general problem formulation which takes into account the estimation error due to these residue values is proposed. This approach exploits the temporal inter correlation between the interferometric frames within a compressive sensing framework. The 3D discrete curvelet transform is used in order to ensure a suitable sparse representation of the phase volume. The performance of the proposed 3D phase unwrapping algorithm is tested on simulated and real SAR 3D datasets

Mapping Regional Landscape by Using OpenstreetMap (OSM)

A forest patterns map over a large extent at high spatial resolution is a heavily computation task but is critical to most regions. There are two major difficulties in generating the classification maps at regional scale: large training points sets and expensive computation cost in classifier modelling. As one of the most well-known Volunteered Geographic Information (VGI) initiatives, OpenstreetMap contributes not only on road network distributions, but the potential of justify land cover and land use. Google Earth Engine is a platform designed for cloud-based mapping with a strong computing power. In this study, we proposed a new approach to generating forest cover map and quantifying road-caused forest fragmentations by using OpenstreetMap in conjunction with remote sensing dataset stored in Google Earth Engine. Additionally, the landscape metrics produced after incorporating OpenStreetMap (OSM) with the forest spatial pattern layers from our output indicated significant levels of forest fragmentation in Yucatan peninsula.