An automated and optimized approach for online spatial biodiversity model: a case study of OGC web processing service

An online spatial biodiversity model (SBM) for optimized and automated spatial modelling and analysis of geospatial data is proposed, which is based on web processing service (WPS) and web service orchestration (WSO) in parallel computing environment. The developed model integrates distributed geospatial data in geoscientific processing workflow to compute the algorithms of spatial landscape indices over the web using free and open source software. A case study for Uttarakhand state of India demonstrates the model outputs such as spatial biodiversity disturbance index (SBDI) and spatial biological richness index (SBRI). In order to optimize and automate, an interactive web interface is developed using participatory GIS approaches for implementing fuzzy AHP. In addition, sensitivity analysis and geosimulation experiments are also performed under distributed GIS environment. Results suggest that parallel algorithms in SBM execute faster than sequential algorithms and validation of SBRI with biological diversity shows significant correlation by indicating high R2 values

Gentiana saginoides Burkill (Magnoliopsida: Gentianales: Gentianaceae) rediscovered from Sunderdhunga Valley in Uttarakhand 155 years after description: notes on its population status

Gentiana saginoides Burkill (Gentianaceae) was described by Burkill in 1906 based on a collection from Sunderdhunga, Uttarakhand by T. Anderson in 1857. The species was not collected after its type collection despite attempts by several workers in the past. A field survey in and around the type locality was conducted in June 2012 and the species was re-discovered after a lapse of 155 years since its type collection. The species is tentatively categorised as ‘Critically Endangered’ based on field observations. A full description and diagrams of the species are provided here based on new specimens collected.</p

Spatial biodiversity modeling using high-performance computing cluster: A case study to access biological richness in Indian landscape

The parallel processing and distributed GIServices provide an efficient approach to address the geocomputation challenges in biodiversity modeling. Using the widely applied Spatial Biodiversity Model (SBM) as an illustration, this study demonstrates parallelization of the spatial landscape algorithms based on Message Passing Interface (MPI) in cluster computing. The geocomputation based on MPI is performed to characterize the spatial distribution of Biological Richness (BR) for Indian landscape using developed high-performance cluster computing-based model named as SBM-HPC. In performance analysis, the execution time is reduced by 56.42%–81.41% (or the speedups of 2.29–5.38) using the parallel and cluster computing environment. Also, the spatial landscape algorithms of the model are extended to integrate large-scale geodata from online map services archives using distributed GIServices. To validate BR map, the phytosociological data is collected using participatory GIS approach. Furthermore, regression analysis between derived BR map and Shannon-Wiener index (Hˈ) represents high correlation coefficient R2 values.Highlights Development of spatial biodiversity model using parallel computing on the cluster. Geocomputation of spatial landscape indices using large-scale geospatial datasets. Distributed GIService integration in model to compute distributed data archives. Prediction of biological richness pattern and validation using participatory GIS. Characterize correlations between biological richness and bioclimatic patterns

An automated and optimized geo-computation approach for spatial fire risk modelling using geo-web service orchestration

Forest fire detection through space-based observations using thermal sensors is in operation since more than one decades. These active fire alerts are generated by using products from MODIS and SNPP–VIIRS satellites and are limited up to identification of active fire locations based on recorded temperature in satellite sensors. However, relation of fire location with forest type map, terrain profile and weather data are required for planning and monitoring of vulnerable areas. An attempt is made to develop online Spatial Decision Support System (SDSS) and spatial model for forest fire risk mapping. The developed SDSS is based on Geospatial Web Service Orchestration (GWSO) workflow which enables forest fire risk alerts and advisories as Web Map Service, Geo SMS and a Geoportal application. User can use this SDSS with minimum technical skills and limited computation resources to generate daily forest fire risk map for fire monitoring and management

ISPRS SIPT DEVELOPMENT OF BIODIVERSITY INFORMATION SYSTEM FOR NORTH EAST INDIA USING INTERNET GIS

Table of contents Table des matières Conservation of Biological Diversity will be major challenge for the ecologist in the forthcoming century. In-situ conservation, biotechnology tools for conservation and prospecting, understanding genetic variability, species habitat relationship and allowing evolutionary process of speciation are some of the challenges. India is one of the mega biodiversity centers and is also known for its traditional knowledge of conservation. The varied regions of the country, with unique floristic and faunal richness, their vastness, endemism, heterogeneity and also inaccessibility of large areas have necessitated creation of authentic baseline data on biodiversity. This information system is essential to monitor, analyze and plan action oriented programs for conserving and preserving our biological wealth. North Eastern India is one of the three mega diversity hotspots in the country. The region is referred as a cradle of flowering plants as it lies in the region of conjunction of biogeographical zones of India viz. Indo-China, Indo-Malayan and Gondwana land masses. The spatial characterization of landscape structures and its linkage with attribute information on the floristic composition, economic valuation, endemism has been developed in the form of Biodiversity Information System (BIS) on sharable environment. The BIS is integration of large databases using a concept of Internet based Geographical Information System commonly known as Internet GIS. The development of BIS involves the basic framework of concept, selection, and aggregation of fundamental and processed data. The information generated as a part of project entitled “Biodiversity Characterization at Landscap

New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities

International audienceA seamless vegetation type map of India (scale 1: 50,000) prepared using medium-resolution IRS LISS-III images is presented. The map was created using an on-screen visual interpretation technique and has an accuracy of 90%, as assessed using 15,565 ground control points. India has hitherto been using potential vegetation/forest type map prepared by Champion and Seth in 1968. We characterized and mapped further the vegetation type distribution in the country in terms of occurrence and distribution, area occupancy, percentage of protected area (PA) covered by each vegetation type, range of elevation, mean annual temperature and precipitation over the past 100 years. A remote sensing-amenable hierarchical classification scheme that accommodates natural and semi-natural systems was conceptualized, and the natural vegetation was classified into forests, scrub/shrub lands and grasslands on the basis of extent of vegetation cover. We discuss the distribution and potential utility of the vegetation type map in a broad range of ecological, climatic and conservation applications from global, national and local perspectives. We used 15,565 ground control points to assess the accuracy of products available globally (i.e., GlobCover, Holdridge’s life zone map and potential natural vegetation (PNV) maps). Hence we recommend that the map prepared herein be used widely. This vegetation type map is the most comprehensive one developed for India so far. It was prepared using 23.5 m seasonal satellite remote sensing data, field samples and information relating to the biogeography, climate and soil. The digital map is now available through a web portal (http://bis.iirs.gov.in)