Contrained linear spectral unmixing technique for regional land cover mapping using MODIS data

Over the last few decades, there has been a significant land cover (LC) change across the globe due to the increasing demand of the burgeoning population and urban sprawl. In order to take account of the change, there is a need for accurate and up- to-date LC maps. Mapping and monitoring of LC in India is being carried out at national level using multi-temporal IRS AWiFS data. Multispectral data such as IKONOS, Landsat- TM/ETM+, IRS-1C/D LISS-III/IV, AWiFS and SPOT-5, etc. have adequate spatial resolution (~ 1m to 56m) for LC mapping to generate 1:50,000 maps. However, for developing countries and those with large geographical extent, seasonal LC mapping is prohibitive with data from commercial sensors of limited spatial coverage. Superspectral data from the MODIS sensor are freely available, have better temporal (8 day composites) and spectral information. MODIS pixels typically contain a mixture of various LC types (due to coarse spatial resolution of 250, 500 and 1000 m), especially in more fragmented landscapes. In this context, linear spectral unmixing would be useful for mapping patchy land covers, such as those that characterise much of the Indian subcontinent. This work evaluates the existing unmixing technique for LC mapping using MODIS data, using end- members that are extracted through Pixel Purity Index (PPI), Scatter plot and N-dimensional visualisation. The abundance maps were generated for agriculture, built up, forest, plantations, waste land/others and water bodies. The assessment of the results using ground truth and a LISS-III classified map shows 86% overall accuracy, suggesting the potential for broad-scale applicability of the technique with superspectral data for natural resource planning and inventory applications.

Stream-Close: Fast mining of Closed Frequent Itemsets in high speed data streams

With the emergence of large-volume and high-speed streaming data, the recent techniques for stream mining of CFIpsilas (closed frequent itemsets) will become inefficient. When concept drift occurs at a slow rate in high speed data streams, the rate of change of information across different sliding windows will be negligible. So, the user wonpsilat be devoid of change in information if we slide window by multiple transactions at a time. Therefore, we propose a novel approach for mining CFIpsilas cumulatively by making sliding width(ges1) over high speed data streams. However, it is nontrivial to mine CFIpsilas cumulatively over stream, because such growth may lead to the generation of exponential number of candidates for closure checking. In this study, we develop an efficient algorithm, stream-close, for mining CFIpsilas over stream by exploring some interesting properties. Our performance study reveals that stream-close achieves good scalability and has promising results.

Image fusion in GRDSS for Land use Mapping

Image fusion techniques are useful to integrate the geometric detail of a high-resolution panchromatic (PAN) image and the spectral information of a low-resolution multispectral (MSS) image, particularly important for understanding land use dynamics at larger scale (1:25000 or lower), which is required by the decision makers to adopt holistic approaches for regional planning. Fused images can extract features from source images and provide more information than one scene of MSS image. High spectral resolution aids in identification of objects more distinctly while high spatial resolution allows locating the objects more clearly. The geoinformatics technologies with an ability to provide high-spatial-spectral-resolution data helps in inventorying, mapping, monitoring and sustainable management of natural resources. Fusion module in GRDSS, taking into consideration the limitations in spatial resolution of MSS data and spectral resolution of PAN data, provide high-spatial-spectral-resolution remote sensing images required for land use mapping on regional scale. GRDSS is a freeware GIS Graphic User Interface (GUI) developed in Tcl/Tk is based on command line arguments of GRASS (Geographic Resources Analysis Support System) with the functionalities for raster analysis, vector analysis, site analysis, image processing, modeling and graphics visualization. It has the capabilities to capture, store, process, analyse, prioritize and display spatial and temporal data.

Spatial Variation of Physico-Chemical and hydrological Parameters with land-use in Venkatapura Catchment,Karnataka

Social, economic and political development of a region is dependent on the health and quantity of the natural resources. Integrated approaches in the management of natural resources would ensure sustainability, which demands inventorying, mapping and monitoring of resources considering all components of an ecosystem. The monitoring of hydrological and catchment landscape of river resources have a vital role in the conservation and management of aquatic resources. This paper presents a case study Venkatapura river basin in Uttara Kannada district of Karnataka State, India based on stream hydrology and landuse analyses. The results revealed variations in dissolved oxygen and free carbon dioxide according to the flow nature of the water, and increased amount of phosphates and coliform contamination in streams closer to anthropogenic activities.

Land Surface Temperature with Land Cover Dynamics:Multi-Resolution, Spatio-Temporal Data Analysis of Greater Bangalore

Bangalore is experiencing unprecedented urbanisation in recent times due to concentrated developmental activities with impetus on IT (Information Technology) and BT (Biotechnology) sectors. The concentrated developmental activities has resulted in the increase in population and consequent pressure on infrastructure, natural resources, ultimately giving rise to a plethora of serious challenges such as urban flooding, climate change, etc. One of the perceived impact at local levels is the increase in sensible heat flux from the land surface to the atmosphere, which is also referred as heat island effect. In this communication, we report the changes in land surface temperature (LST) with respect to land cover changes during 1973 to 2007. A novel technique combining the information from sub-pixel class proportions with information from classified image (using signatures of the respective classes collected from the ground) has been used to achieve more reliable classification. The analysis showed positive correlation with the increase in paved surfaces and LST. 466% increase in paved surfaces (buildings, roads, etc.) has lead to the increase in LST by about 2 ºC during the last 2 decades, confirming urban heat island phenomenon. LSTs’ were relatively lower (~ 4 to 7 ºC) at land uses such as vegetation (parks/forests) and water bodies which act as heat sinks.

Biofuel production and implications for land use, food production and environment in India

There is a large interest in biofuels in India as a substitute to petroleum-based fuels, with a purpose of enhancing energy security and promoting rural development. India has announced an ambitious target of substituting 20% of fossil fuel consumption by biodiesel and bioethanol by 2017. India has announced a national biofuel policy and launched a large program to promote biofuel production, particularly on wastelands: its implications need to be studied intensively considering the fact that India is a large developing country with high population density and large rural population depending upon land for their livelihood. Another factor is that Indian economy is experiencing high growth rate, which may lead to enhanced demand for food, livestock products, timber, paper, etc., with implications for land use. Studies have shown that area under agriculture and forest has nearly stabilized over the past 2-3 decades. This paper presents an assessment of the implications of projected large-scale biofuel production on land available for food production, water, biodiversity, rural development and GHG emissions. The assessment will be largely focused on first generation biofuel crops, since the Indian program is currently dominated by these crops. Technological and policy options required for promoting sustainable biofuel production will be discussed. (C) 2010 Elsevier Ltd. All rights reserved.

Sensitivity of terrestrial water and energy budgets to CO(2)-physiological forcing: an investigation using an offline land model

Increasing concentrations of atmospheric carbon dioxide (CO(2)) influence climate by suppressing canopy transpiration in addition to its well- known greenhouse gas effect. The decrease in plant transpiration is due to changes in plant physiology (reduced opening of plant stomata). Here, we quantify such changes in water flux for various levels of CO(2) concentrations using the National Center for Atmospheric Research's (NCAR) Community Land Model. We find that photosynthesis saturates after 800 ppmv (parts per million, by volume) in this model. However, unlike photosynthesis, canopy transpiration continues to decline at about 5.1% per 100 ppmv increase in CO(2) levels. We also find that the associated reduction in latent heat flux is primarily compensated by increased sensible heat flux. The continued decline in canopy transpiration and subsequent increase in sensible heat flux at elevated CO(2) levels implies that incremental warming associated with the physiological effect of CO(2) will not abate at higher CO(2) concentrations, indicating important consequences for the global water and carbon cycles from anthropogenic CO(2) emissions.

mRegistry: a registry representation for fault diagnosis

Microsoft Windows uses the notion of registry to store all configuration information. The registry entries have associations and dependencies. For example, the paths to executables may be relative to some home directories. The registry being designed with faster access as one of the objectives does not explicitly capture these relations. In this paper, we explore a representation that captures the dependencies more explicitly using shared and unifying variables. This representation, called mRegistry exploits the tree-structured hierarchical nature of the registry, is concept-based and obtained in multiple stages. mRegistry captures intra-block, inter-block and ancestor-children dependencies (all leaf entries of a parent key in a registry put together as an entity constitute a block thereby making the block as the only child of the parent). In addition, it learns the generalized concepts of dependencies in the form of rules. We show that mRegistry has several applications: fault diagnosis, prediction, comparison, compression etc.

Association rule mining activity : A general formalism

Mining association rules from a large collection of databases is based on two main tasks. One is generation of large itemsets; and the other is finding associations between the discovered large itemsets. Existing formalism for association rules are based on a single transaction database which is not sufficient to describe the association rules based on multiple database environment. In this paper, we give a general characterization of association rules and also give a framework for knowledge-based mining of multiple databases for association rules.

A survey of temporal data mining

Data mining is concerned with analysing large volumes of (often unstructured) data to automatically discover interesting regularities or relationships which in turn lead to better understanding of the underlying processes. The field of temporal data mining is concerned with such analysis in the case of ordered data streams with temporal interdependencies. Over the last decade many interesting techniques of temporal data mining were proposed and shown to be useful in many applications. Since temporal data mining brings together techniques from different fields such as statistics, machine learning and databases, the literature is scattered among many different sources. In this article, we present an overview of techniques of temporal data mining.We mainly concentrate on algorithms for pattern discovery in sequential data streams.We also describe some recent results regarding statistical analysis of pattern discovery methods.

Root cause diagnostics using temporal data mining

A method, system, and computer program product for fault data correlation in a diagnostic system are provided. The method includes receiving the fault data including a plurality of faults collected over a period of time, and identifying a plurality of episodes within the fault data, where each episode includes a sequence of the faults. The method further includes calculating a frequency of the episodes within the fault data, calculating a correlation confidence of the faults relative to the episodes as a function of the frequency of the episodes, and outputting a report of the faults with the correlation confidence.

System and Method for mining of Temporal Data

A system for temporal data mining includes a computer readable medium having an application configured to receive at an input module a temporal data series having events with start times and end times, a set of allowed dwelling times and a threshold frequency. The system is further configured to identify, using a candidate identification and tracking module, one or more occurrences in the temporal data series of a candidate episode and increment a count for each identified occurrence. The system is also configured to produce at an output module an output for those episodes whose count of occurrences results in a frequency exceeding the threshold frequency.