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.

Influence of land use changes in river basins on diversity and distribution of amphibians

Land-use changes influence local biodiversity directly, and also cumulatively, contribute to regional and global changes in natural systems and quality of life. Consequent to these, direct impacts on the natural resources that support the health and integrity of living beings are evident in recent times. The Western Ghats being one of the global biodiversity hotspots, is reeling under a tremendous pressure from human induced changes in terms of developmental projects like hydel or thermal power plants, big dams, mining activities, unplanned agricultural practices,monoculture plantations, illegal timber logging, etc. This has led to the once contiguous forest habitats to be fragmented in patches, which in turn has led to the shrinkage of original habitat for the wildlife, change in the hydrological regime of the catchment, decreased inflow in streams,human-animal conflicts, etc. Under such circumstances, a proper management practice is called for requiring suitable biological indicators to show the impact of these changes, set priority regions and in developing models for conservation planning. Amphibians are regarded as one of the best biological indicators due to their sensitivity to even the slightest changes in the environment and hence they could be used as surrogates in conservation and management practices. They are the predominating vertebrates with a high degree of endemism (78%) in Western Ghats. The present study is an attempt to bring in the impacts of various land-uses on anuran distribution in three river basins. Sampling was carried out for amphibians during all seasons of 2003-2006 in basins of Sharavathi, Aghanashini and Bedthi. There are as many as 46 species in the region, one of which is new to science and nearly 59% of them are endemic to the Western Ghats. They belong to nine families, Dicroglossidae being represented by 14 species,followed by Rhacophoridae (9 species) and Ranidae (5 species). Species richness is high in Sharavathi river basin, with 36 species, followed by Bedthi 33 and Aghanashini 27. The impact of land-use changes, was investigated in the upper catchment of Sharavathi river basin. Species diversity indices, relative abundance values, percentage endemics gave clear indication of differences in each sub-catchment. Karl Pearson’s correlation coefficient (r) was calculated between species richness, endemics, environmental descriptors, land-use classes and fragmentation metrics. Principal component analysis was performed to depict the influence of these variables. Results show that sub-catchments with lesser percentage of forest, low canopy cover, higher amount of agricultural area, low rainfall have low species richness, less endemic species and abundant non-endemic species, whereas endemism, species richness and abundance of endemic species are more in the sub-catchments with high tree density, endemic trees, canopy cover, rainfall and lower amount of agriculture fields. This analysis aided in prioritising regions in the Sharavathi river basin for further conservation measures.

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.

Hierarchical Clustering Algorithm for Land Cover Mapping Using Satellite Images

This paper presents hierarchical clustering algorithms for land cover mapping problem using multi-spectral satellite images. In unsupervised techniques, the automatic generation of number of clusters and its centers for a huge database is not exploited to their full potential. Hence, a hierarchical clustering algorithm that uses splitting and merging techniques is proposed. Initially, the splitting method is used to search for the best possible number of clusters and its centers using Mean Shift Clustering (MSC), Niche Particle Swarm Optimization (NPSO) and Glowworm Swarm Optimization (GSO). Using these clusters and its centers, the merging method is used to group the data points based on a parametric method (k-means algorithm). A performance comparison of the proposed hierarchical clustering algorithms (MSC, NPSO and GSO) is presented using two typical multi-spectral satellite images - Landsat 7 thematic mapper and QuickBird. From the results obtained, we conclude that the proposed GSO based hierarchical clustering algorithm is more accurate and robust.

Is land really a constraint for the utilization of solar energy in India?

This article compares the land use in solar energy technologies with conventional energy sources. This has been done by introducing two parameters called land transformation and land occupation. It has been shown that the land area transformed by solar energy power generation is small compared to hydroelectric power generation, and is comparable with coal and nuclear energy power generation when life-cycle transformations are considered. We estimate that 0.97% of total land area or 3.1% of the total uncultivable land area of India would be required to generate 3400 TWh/yr from solar energy power systems in conjunction with other renewable energy sources.

Albedo enhancement over land to counteract global warming: impacts on hydrological cycle

A recent modelling study has shown that precipitation and runoff over land would increase when the reflectivity of marine clouds is increased to counter global warming. This implies that large scale albedo enhancement over land could lead to a decrease in runoff over land. In this study, we perform simulations using NCAR CAM3.1 that have implications for Solar Radiation Management geoengineering schemes that increase the albedo over land. We find that an increase in reflectivity over land that mitigates the global mean warming from a doubling of CO2 leads to a large residual warming in the southern hemisphere and cooling in the northern hemisphere since most of the land is located in northern hemisphere. Precipitation and runoff over land decrease by 13.4 and 22.3%, respectively, because of a large residual sinking motion over land triggered by albedo enhancement over land. Soil water content also declines when albedo over land is enhanced. The simulated magnitude of hydrological changes over land are much larger when compared to changes over oceans in the recent marine cloud albedo enhancement study since the radiative forcing over land needed (-8.2 W m(-2)) to counter global mean radiative forcing from a doubling of CO2 (3.3 W m(-2)) is approximately twice the forcing needed over the oceans (-4.2 W m(-2)). Our results imply that albedo enhancement over oceans produce climates closer to the unperturbed climate state than do albedo changes on land when the consequences on land hydrology are considered. Our study also has important implications for any intentional or unintentional large scale changes in land surface albedo such as deforestation/afforestation/reforestation, air pollution, and desert and urban albedo modification.

A new method for fracture toughness determination of graded (Pt,Ni)Al bond coats by microbeam bend tests

A novel method is proposed for fracture toughness determination of graded microstructurally complex (Pt,Ni)Al bond coats using edge-notched doubly clamped beams subjected to bending. Micron-scale beams are machined using the focused ion beam and loaded in bending under a nanoindenter. Failure loads gathered from the pop-ins in the load-displacement curves combined with XFEM analysis are used to calculate K-c at individual zones, free from substrate effects. The testing technique and sources of errors in measurement are described and possible micromechanisms of fracture in such heterogeneous coatings discussed.

Quasi-based hierarchical clustering for land cover mapping using satellite images

This paper presents an improved hierarchical clustering algorithm for land cover mapping problem using quasi-random distribution. Initially, Niche Particle Swarm Optimization (NPSO) with pseudo/quasi-random distribution is used for splitting the data into number of cluster centers by satisfying Bayesian Information Criteria (BIC). Themain objective is to search and locate the best possible number of cluster and its centers. NPSO which highly depends on the initial distribution of particles in search space is not been exploited to its full potential. In this study, we have compared more uniformly distributed quasi-random with pseudo-random distribution with NPSO for splitting data set. Here to generate quasi-random distribution, Faure method has been used. Performance of previously proposed methods namely K-means, Mean Shift Clustering (MSC) and NPSO with pseudo-random is compared with the proposed approach - NPSO with quasi distribution(Faure). These algorithms are used on synthetic data set and multi-spectral satellite image (Landsat 7 thematic mapper). From the result obtained we conclude that use of quasi-random sequence with NPSO for hierarchical clustering algorithm results in a more accurate data classification.