Abstract

Mangroves are one of the productive and highly adaptive ecosystems on the Earth, and provide invaluable services to the coastal communities. Ecological and sustainable management of the mangrove ecosystem requires crucial knowledge of variability and dynamics over a time and space. There are no reliable recent spatial extent estimates of mangroves or trend of coverage in West coast. The mapping from medium spatial resolution remote sensing data often leads to the underestimation of spatial extent. An attempt has been made to provide detailed information of mangroves species distribution using remote sensing data of high spatial resolution integrated with the other collateral field information through GIS. The coastline changes and the mangrove dynamics during 1989–2010 were assessed using supervised classifier technique provided the spatial distribution of mangrove species namely Rhizophora mucronata, Sonneratia caseolaris, Avicennia officinalis, Sonneratia alba, and Kandelia candel with classification good accuracy. Delineation of mangroves at species wise records based on extensive field data will be invaluable for appropriate management (eg plantation, eco-tourism) and conservation measures for estuaries of Central Western Ghats. Mangroves play pivotal role in providing vital ecosystem goods and services, but are under threat due to anthropogenic activities, affecting habitat for specialised fauna and food resources for humans, birds and fish. This necessitates interventions of regulatory authorities to partner with the local communities in the restoration and conservation of mangrove habitats.

Abstract

One of the major challenges in the current scenario for ecological conservation is to quantify the forest landscape in its spatio-temporal domain and understand further implications of those. While the detailed study of the forest ecosystems may provide insights into biodiversity, carrying capacity and productive nature, most of the studies are restricted to single time/event inventory and focused on assessment of tree diversity patterns. Through the adoption of geospatial technologies like remote sensing and Geographical Information System (GIS), though forest monitoring has been possible, the linkages to the biodiversity distribution and its patterns are still at an empirical level, thus supporting broad measures of protection and preservation without accounting for the local/regional variability.

Abstract

In the recent past, remotely sensed data with high spectral resolution has been made available and has been explored for various agricultural and geological applications. While these spectral signatures of the objects of interest provide important clues, the relatively poor spatial resolution of these hyperspectral images limits their utility and performance. In this context, hyperspectral image enhancement using multispectral data has been actively pursued to improve spatial resolution of such imageries and thus enhancing its use for classification and composition analysis in various applications. But, this also poses a challenge in terms of managing the trade-off between improved spatial detail and the distortion of spectral signatures in these fused outcomes. This paper proposes a strategy of using vector decomposition, as a model to transfer the spatial detail from relatively higher resolution data, in association with sensor simulation to generate a fused hyperspectral image while preserving the inter band spectral variability. The results of this approach demonstrates that the spectral separation between classes has been better captured and thus helped improve classification accuracies over mixed pixels of the original low resolution hyperspectral data. In addition, the quantitative analysis using a rank-correlation metric shows the appropriateness of the proposed method over the other known approaches with regard to preserving the spectral signatures.

Abstract

The land use changes in forested landscape are highly complex and dynamic, affected by the natural, socio-economic, cultural, political and other factors. The remote sensing (RS) and geographical information system (GIS) techniques coupled with multi-criteria evaluation functions such as Markov-cellular automata (CA–Markov) model helps in analysing intensity, extent and future forecasting of human activities affecting the terrestrial biosphere. Karwar taluk of Central Western Ghats in Karnataka state, India has seen rapid transitions in its forest cover due to various anthropogenic activities, primarily driven by major industrial activities. A study based on Landsat and IRS derived data along with CA–Markov method has helped in characterizing the patterns and trends of land use changes over a period of 2004–2013, expected transitions was predicted for a set of scenarios through 2013-2022. The analysis reveals the loss of pristine forest cover from 75.51% to 67.36%(1973 to 2013) and increase in agriculture land as well as built-up area of 8.65%(2013), causing impact on local flora and fauna. The other factors driving these changes are the aggregated level of demand for land, local and regional effects of land use activities such as deforestation, improper practices in expansion of agriculture and infrastructure development, deteriorating natural resources availability. The spatio temporal models helped in visualizing on-going changes apart from prediction of likely changes. The CA-Markov based analysis provides us insights into the localized changes impacting these regions and can be useful in developing appropriate mitigation management …

Abstract

Mangroves are high productive estuarine ecosystem, protect barrage and defend the impact of big storms. Numerous studies attempted to map the locations of mangroves and analyse changes using different sensors including optical sensors: LANDSAT, IRS, SPOT, and other from past four decades. The present work attempts to identify the mangroves at species level using high resolution remote sensing data with field based measurements. The lack of spatial quantitative mangroves maps for the west coast has led to overlooking of the importance of mangroves and also the implementation of restoration programmes. Mangroves planting in the small estuaries of the Uttara Kannada district of Karnataka State during the recent years have yielded desired results of better ecology and higher biological production. The RS data using 2010 IRS p6 L4 MX having resolution of 5 M is useful in mapping the distribution of adult mangroves. However for mapping juveniles and shrubby forms require higher spectral resolution data. Using open source GIS software (QGIS) and remote sensing software (GRASS) and IRS imageries mangrove areas and potential areas available for planting in three river estuaries in the Honnavar Forest Division of Uttara Kannada were mapped. Mangrove species are recommended for planting in suitable areas based on salinity regimes. The effective forest management in the region shows mangroves have expanded in several regions due to the conservation measures and supervision. The net gain in mangroves could be attributed to new plantations by the State Forest Department and effective protection; agriculture to mangroves; mudflat to mangroves. The overall accuracy of satellite imagery processing was 90.89% and the Kappa index was 0.89. The work demonstrates the potential of high resolution multi-temporal satellite remote sensing data with ground based measurements for effective mapping mangrove forests in coastal regions.

Abstract

In studies like change analysis, the availability of very high resolution (VHR)/high resolution (HR) imagery for a particular period and region is a challenge due to the sensor revisit times and high cost of acquisition. Therefore, most studies prefer lower resolution (LR) sensor imagery with frequent revisit times, in addition to their cost and computational advantages. Further, the classification techniques provide us a global estimate of the class accuracy, which limits its utility if the accuracy is low. In this work, we focus on the sub-classification problem of LR images and estimate regions of higher confidence than the global classification accuracy within its classified region. The spectrally classified data was mined into spatially clustered regions and further refined and processed using statistical measures to arrive at local high confidence regions (LHCRs), for every class. Rabi season MODIS data of January 2006 & 2007 …

Abstract

Land use and land cover (LULC) changes induced by human or natural processes drive biogeochemistryof the Earth influencing the climate at global as well as regional scales. Drastic changes in the land cover with the decline in vegetation and water bodies due to anthropogenic activities enhances the heat emission from land surface and atmospheric temperatures Increased land surface temperature (LST) is mainly due to increase in concentrated human activities, paved land cover or barren lands. Due to complexity of landscapes the sampling was difficult to derive LST and environmental response relationships. Temporal data acquired through space borne remote sensors has bridged the gap of temporal data for the entire earth surface. The current study explores the relation between surface biophysical parameters to sub-pixel thermal variations. The thermal infrared bands of remote sensing data help to retrieve LST, which are supplemented byground based measurements. Analysis of LST with LULC indicates a negative correlation between vegetation indices and LST. The general trend in the ambient temperature of Uttara Kannada over the 31 year period was established. Itclarifies that there was a fundamental drift of temperature rise in recent years, especially during the last decade.

Abstract

: Railway is the largest undertaking in the country. A retrospective study of prevailing conditions is necessary to assimilate the progress of the sector and how far have the changes introduced in previous few years been successful. The paper examines how the proposed changes in the Indian railways have changed the face of it through rigorous spatial analysis in diversified fields. A balanced analysis of shortcomings as well as achievements has been tried to be achieved through this paper for few selected regions of India.Each year there has to be more emphasis on enhancing the services provided. The rail budgets play a vital role and are responsible for taking care of it. The numerous promises made in terms of introduction of trains, electrification of routes, gauge conversion, doubling and increasing frequency of trains are made every year, in order to keep track of fulfillment of those decisions the spatial analysis is conducted rigorously. Railway has also witnessed a number of disasters in past few years; the spatial analysis conducted brings out few major reasons of those. A section of study also covers the topographic conditions in the state of Jammu and Kashmir that have not allowed the progress of railways in these regions.

Abstract

In this work, Spatio-Temporal Data Mining of disease surveillance data is done, to describe the underlying patterns in disease occurrences across populations and to identify possible causes that could explain them; for better disease core prediction, detection and management. MiSTIC algorithm is used to determine spatial spread of disease core regions (scale of disease prevalence), and the frequency & regularity of occurrence of different locations in space as disease cores. The results show good correlation between the etiologic factors of Salmonellosis and the detected core locations, in addition to the significant observation of highly localized nature of disease prevalence.

Abstract

With major aerospace companies showing interest in certifying UAV systems for civilian airspace, their use in commercial remote sensing applications like traffic monitoring, map refinement, agricultural data collection, etc., are on the rise. But ambitious requirements like real-time geo-referencing of data, support for multiple sensor angle-of-views, smaller UAV size and cheaper investment cost have lead to challenges in platform stability, sensor noise reduction and increased onboard processing. Especially in small UAVs the geo-referencing of data collected is only as good as the quality of their localization sensors. This drives a need for developing methods that pickup spatial features from the captured video/image and aid in geo-referencing. This paper presents one such method to identify road segments and intersections based on traffic flow and compares well with the accuracy of manual observation. Two test video datasets, one each from moving and stationary platforms were used. The results obtained show a promising average percentage difference of 7.01% and 2.48% for the road segment extraction process using moving and stationary platform respectively. For the intersection identification process, the moving platform shows an accuracy of 75% where as the stationary platform data reaches an accuracy of 100%.