Introduction

Mangroves maintain high level of biological productivity of coasts and estuaries through sequestering carbon and recycling of nutrients throughout the year. Mangroves cover about 8% of the world vegetation and play a vital role in the productivity of global ecosystem [1], found along the tropical and subtropical coasts of Australia, Asia, Africa and the America [2]. Mangrove shorelines occur in diverse environmental settings of geophysical (climate, tides and sea level) and geomorphological (dynamic history of the land surface and contemporary processes and biological components) [3]. Mangroves and its associated ecosystems are biologically most productive, socio-economically important, and aesthetically attractive while providing food and shelter for many vital biotic species some are commercially very important [4, 5]. Mangroves support local as well as global communities through a wide variety of ecosystem goods (fuel wood, medicine, construction materials) and services (shoreline stabilization, fisheries breeding and nursery grounds, sedimentation trapping, uptake of nutrients and heavy metals - phytoremediation) having immense value [6]. The value of an estuary is accounted as $19120/Ha/year by aggregation of all goods and services such as shrimps, fish, crabs, salt, mangroves, in addition to services such as fish spawning grounds, nutrient cycling, hydrology, flood control, soil protection, sink for carbon etc. [7]. As per the earlier estimates [8] economic value of mangroves varies between $200 k and $900 k per km2 per year depending on the geographical regions. Mangroves sequester and store carbon stocks and hence the global organizations have focused their attention with conservation measures [9, 10]. Mangrove soils and roots also act as a reservoir which sequesters an additional carbon.

Dependency of local communities has been traditional and sustainable; however in recent times during the post globalisation era mangroves are facing serious threats due to over harvesting of coastal resources due to the rapid growth of population, migration into the coast, industrialization the sustained inflow of sewage (domestic) and effluents (industries) [11], large scale topographical and hydrological alterations etc. [12]. In addition to the lack of appropriate regulatory mechanisms to implement the coastal zone norms and land use land cover (LULC) changes due to socio-economic consequences have contributed to the sharp decline of mangrove cover [13, 14]. Rampant expansion of aquaculture farms and the demand of hard wood in construction sector have led to the removal of mangroves [15, 16, 17]. This emphasises the need for integrated approaches involving inventorying, mapping and monitoring including sustainable resource management with land-use planning for conservation based decision making. Species and landscape level information of mangroves is required for a thorough understanding of mangrove biodiversity for an effective management [18, 19, 20]. Temporal remote sensing (RS) data acquired through space-borne sensors at regular intervals since 1970’s with geographic information system (GIS) provide a reliable and cost effective as well as alternative technique for mapping and management of the coastal habitats. Remote sensing data with GIS and collateral data add further information for the accurate, detailed and cost effective spatial information [21, 22] as well to visualize the predicted scenarios based on various policy prerogatives for economic and social sustainability in the coastal zone [23, 24]. Improvements in the development of free and open source software have emerging as an alternative approach for a wide range of applications with multiple data formats [25, 26]. Recent advances in sensor technologies (e.g., IRS, IKONOS, and QuickBird) have enhanced the ability of RS programme to provide multi-resolution data. RS data are supplemented with different types of collateral vector data and environmental attributes and spectral information [27, 28] for high precision mapping. Collection of ground control points [29] and training polygons using global positioning system (GPS) helps in the geo-registration, classification and accuracy assessment [30]. Data of Landsat series has limitations due to the coarse spatial resolution has often resulted in the underestimation of mangrove areas at locations where the spatial coverage is relatively small and fragmented [29, 31, 32, 33]. Complex tropical atmospheric conditions, high spatial variability further pose challenges in the selection of sensors and appropriate data analysis methods. The spectral separation of mangrove vegetation has been carried out through various classification algorithms [34, 35]. Among many techniques, supervised classification technique is considered as most effective scheme among all, because it is based on statistical identification function according to typical sample training methods. Application of the supervised Gaussian maximum likelihood classifier (GMLC) was proved to be the robust method for classifying mangroves [35, 36, 37, 38]. Inventorying, temporal mapping would provide valuable information pertaining to the spatial distribution, heterogeneity and its variability for effective monitoring and ecologically sound management of coastal ecosystems.

Objectives: Main objective is the inventorying and mapping of mangroves. This involves