Results and Discussion

ESRs in the district are prioritized considering biological, ecological, geo-climatic, renewable energy and social prospects. Weightages are assigned to the grids for prioritizing eco-sensitiveness based on the relative significance of themes based on the aggregate metric score as ESR 1 (regions of highest sensitivity), ESR2 (regions of higher sensitivity), ESR3 (regions of high sensitivity) and ESR4 (regions of moderate sensitivity), respectively. Land use of 2013 is assessed using remote sensing data of Landsat ETM+ sensor 30 m resolution. Land-use analysis reveals that the region has about 32.08% under evergreen–semi-evergreen forests (Table 2), and higher forest cover (>80%) is confined to the grids in Sahyadri region (Supa, Yellapura, Ankola, Sirsi taluks). The coastal taluks are having forest cover in the range 60–80% towards eastern part, whereas western side totally degraded due to higher pressure. The plains show least cover (<20%) reflecting higher degradation, and the natural forest cover in the district is only 542,475 Ha. The land clearing and subsequent Table 2 Land use and fragmentation of forests in Uttara Kannada

agricultural expansion, exotic plantations resulted in the degradation of large forest patches at temporal scale. Weightages were assigned to the grids based on the extent of forest cover, and grids in Sahyadri region have highest ranking (10) compared to plains (1). Fragmentation analysis considering the spatial extent of forests reveals that contiguous forests (interior forests) cover only 25.62%, and land use under non- forest categories (cropland, plantations, built-up, etc.) covers 47.29% of the landscape across coast, Sahyadri and plains.

Flora and fauna of terrestrial and aquatic ecosystems have been studied through field investigations and compilation of information from published literature. These strategies helped in documenting 1068 species of flowering plants, representing 138 families. Grids in Honnavar, Kumta, Sirsi, Bhatkal, Siddapur are with higher weights, and Mundgod and Haliyal show least endemism [29].

Analysis of faunal distribution shows that tiger (Panthera tigris), leopard, wild dog (dhole) and sloth bear are the main predators. The district is a paradise for birds; 272 birds are listed in the Dandeli, out of which 19 are considered to be endemic [36]. The distribution of freshwater fishes is highly correlated to terrestrial landscape elements, of which quantity and quality of evergreen forests are more important [2]. Higher weightages (10) are assigned to the grids with endemic species, and least (3) were assigned for grids with non-endemic fauna.

Biomass is estimated grid-wise, based on the spatial extent of forest and per hectare basal area. The total biomass of the district is 113823.58 Gg, with Sahyadrian taluks such as Supa, Sirsi and Yellapura having greater biomass (>1200 Gg) followed by the coastal taluks (Karwar, Ankola, Kumta, Honnavar). Grids with higher standing biomass regions were assigned higher weightages [26, 39], as these regions help in maintaining global carbon through sequestration. Tree diversity is computed through the Shannon diversity index which shows that most evergreen to semi-evergreen forests with diversity values ranging between 3 and 4. Uttara Kannada district has two important protected areas, namely Anshi National Park and Dandeli Wildlife Sanctuary, which are assigned higher weights as they are key eco-sensitive regions with diverse biodiversity [40].

Geo-climatic variables such as altitude, slope and rainfall are analysed to identify sensitive zones. Highest elevation is 758 m in Supa taluk. Grids with elevations

>600 m as higher priority for conservation and > 400 m is moderate and rest is of least concern. Rainfall pattern shows that the district falls in the high rainfall zone, except Mundgod and eastern parts of Haliyal, Yellapura. Grids are assigned weights based on the quantum and duration of rainfall [40]. The sub-basin-wise field investigations were carried out to account perennial, seasonal flows of the region. Hydrological regime analysis reveals the existence of perennial streams in the catchment dominated by diverse forests with native vegetation (>60% cover) compared to the streams in the catchments of either degraded forests or dominated by monoculture plantations [32]. Grids in Sahyadri regions show 12-month water availability in the streams and were assigned higher weightages. Streams in Haliyal, Mundgod, eastern part of Yellapura have flow of only 4 months due to scarce rainfall and monoculture plantations.

Environmentally sound alternative sources of energy resources (solar, wind, io) potential were considered for prioritization [26, 33–36]. The region receives an aver- age solar insolation of 5.42 kWh/m²/day annually and has more than 300 clear sunny days. Wind resource assessment shows wind speed varies from 1.9 m/s (6.84 km/hr.) to 3.93 m/s (14.15 km/hr.) throughout the year with a minimum in October and maxi- mum in June and July. Bioresource availability is computed based on the compilation of data on the area and productivity of agriculture and horticulture crops, forests and plantations. Sector-wise energy demand is computed based on a primary household survey of 2500 households, the National Sample Survey Organization (NSSO study) data and the information compiled from the literature. The supply/demand ratio in the district ranges from less than 0.5 to greater than 2. Sirsi, Siddapur, Yellapur, Supa and eastern hilly areas of Kumta, Honnavar and Ankola are fuelwood surplus regions. Hybridizing wind energy systems with other locally available resources (solar, bioen- ergy) would assure the reliable energy supply to meet the energy demand at decen- tralized levels, and weights were assigned based on the availability [33–36]. The location of forest-dwelling communities such as Kunbis, Siddis, Goulis, Gondas was spatially mapped, and the respective grids were assigned highest weights, because these people are directly and indirectly dependent on forest resources and have been protecting forests. Grid-wise population is computed by aggregating villages in the respective grid for 2011. Population density is computed for each grid and weigh- tages were assigned. Grids with the lowest population density (<50 persons) were assigned higher weight (considering the likely lower anthropogenic stress) and vice versa [40, 41].

Aggregation of these spatial layers corresponding to biological, ecological, geo- climatic, renewable energy and social variables aided in prioritising the grids as ESR 1, ESR 2, ESR 3 and ESR 4, respectively, (Fig. 5a) based on the composite metric score. Spatially, 52.38% of the district represents ESR 1, 14.29% of area represents ESR 2, 13.1% of area represents ESR 3 and about 20.23% of the district is in ESR 4. Figure 5b depicts ESR with taluk and gram panchayat (decentralized administrative units with a cluster of few villages) boundaries. Uttara Kannada district has 11 taluks and 209 panchayats. ESR analyses reveal that ESR 1 consists of mainly Supa, Yellapura, Ankola, Sirsi, Siddapura, Honnavar and Kumta taluks. Considering Panchayat-level analyses, 102 panchayats are in ESR 1, while 37 panchayats in ESR 2, 33 panchayats in ESR 3 and 37 panchayats in ESR 4. Sahyadri and eastern part of coastal regions represent highest ecological sensitiveness. ESR 2 is as good as ESR 1, except degradation of forest patches in some localities. ESR 3 represents moderate conservation region, and only regulated development is allowed in these areas. ESR 4 represents less sensitiveness.

The visualization is implemented through open layers by adding the WMS layer. Figure 6 visualizes layer of Western Ghats boundary, Western Ghats states and dis- tricts, Uttara Kannada Panchayats boundary on the backend layer of OpenStreetsMap and also the land-use WMS layer of Bhuvan. The user can choose different layers using the checkbox option and view accordingly. This information contributes to ana- lyzing and utilizing the resources in an efficient way, which helps the decision-maker or the concerned citizen to use the data to make better plans and policies. SDSS aids users to visualize diverse themes of land, ecology, energy, socio, hydro and estuarine variables of rich biodiversity hotspots and also provides an opportunity to integrate ecological and socio-economic aspects in decision-making. The 73rd amendment to the constitution (1992) empowers local governing bodies to make relevant plans for the socio-economic development of a region. Inclusive growth enhances social capital for the public can be achieved by ensuring the active and effective partici- pation of all sections of society at every level of governance. The implementation of SDSS at local levels would help in realizing the vision of Biodiversity act, 2002, which empowers Biodiversity Management Committees (BMC) at panchayat with the knowledge of local biodiversity richness with ecological status to take decisions towards the prudent use of natural resources.