ISSN: 2319-8753 International Journal of Innovative Research in Science, Engineering and Technology (An ISO 3297: 2007 Certified Organization) Vol. 3, Issue 12, December 2014 DOI: 10.15680/IJIRSET.2014.0312057 Copyright to IJIRSET www.ijirset.com 18165 Remote Sensing and GIS tools used to analyse the Floristic diversity in South Gujarat G.D. Bhatt 1 , S.P.S. Kushwaha 2 , Kiran Bargali 3 Department of Environmental Sciences & Natural Resource Management, School of Natural Sciences, Shiv Nadar University, Gautam Budh Nagar, Uttar Pradesh, India 1 Department of Forestry and Ecology, Indian Institute of Remote Sensing, Indian Space Research Organization Dehradun, Uttarakhand, India 2 Department of Botany, Kumaun University, Nainital, Uttarakhand, India 3 ABSTRACT: The present study evaluates the floristic diversity analysis in south Gujarat forest using stratify random sampling technique. The forest cover in south Gujarat is 5492.05 km 2 (17.54 %) out of total geographical area 31,495 km 2 . The maximum forest area covered by teak mixed dry deciduous forest (14.98 %) and minimum by riverain forest (0.0004 %), respectively. Based on the area of forest vegetation types 157 sample plots of 31.62 m x 31.62 m were laid in different forest vegetation types of south Gujarat. The IVI was calculated in different vegetation types and highest value was observed in teak mixed dry and moist deciduous forest. The dominant families were Leguminosace (19) followed by Poaceae (13), Compositae (12), Amranthaceae (11), Malvaceae (10), Lamiaceae (09) and Rubiaceae (08) respectively. The study demonstrates integration of stratified random sampling techniques in south Gujarat for an assessment of medicinal and economical plants. KEYWORDS: Floristic diversity, Remote sensing and GIS, South Gujarat, Phytosociology, Random sampling I. INTRODUCTION Conservation and maintaining biodiversity on this planet earth is a very important objective for sustainable natural resource management system [1]. The biodiversity indicators help to establish and to monitor levels of biodiversity in terrestrial and aquatic ecosystems[2-3]. The number of these biodiversity indicators, however, is vast and these ranges from gene to landscape level depends on different spatial scales. To simplify the monitoring of biodiversity surrogate measures have been proposed, which are closely correlated with direct measures of biodiversity, but are easier to measure[4]. These surrogate measures include indices accounting for three basic tree diversity aspects [5-6] i.e., the diversity of tree locations, species diversity and the diversity of tree dimensions e.g., stem diameters and tree heights. The surrogate role is based on the observation that a large variety of forest structures or tree species generally provides a large amount of habitats for different species[6-10]. Tree diversity indices are also good quantitative descriptions of forest structures [6-7, 9, 11-12], which is a key pre-requisite for understanding the interactions between patterns and processes in forest ecosystems. Diversity indices are also important input for the reconstruction of forest structures used in spatially explicit growth models and computer visualizations [12-17].There are two different strategies of data collection, mapping and sampling. Mapping involves the full spatial enumeration of all trees within a large observation window i.e., stem maps, while in sampling only a sub-set of trees and their spatial relationships are measured, usually in very small replicated observation windows [18]. Mapping is very common in ecological studies and the corresponding data allow the application of powerful statistics and detailed analyses of plant interactions [19-20]. However, often summary characteristics for larger geographic entities, such as forest districts, enterprises, political regions and whole countries, are required for management and political decision making. In this context sampling methods are the only feasible option. Since the observation windows, i.e., the sample plots and size used for this purpose are comparatively small, tree diversity and structural indices are naturally more suitable other than sophisticated characteristics from spatial statistics[15]. This also offers the opportunity of combining the sampling of diversity measures with existing forest resource inventories [21] and adds significant value to traditional forest inventory with a comparatively low additional effort. Such a combination of inventory objectives clearly facilitates the