Shijo Joseph

Monitoring conservation effectiveness in a global biodiversity hotspot: the contribution of land cover change assessment.

Tropical forests, which play critical roles in global biogeochemical cycles, radiation budgets and biodiversity, have undergone rapid changes in land cover in the last few decades. This study examines the complex process of land cover change in the biodiversity hotspot of Western Ghats, India, specifically investigating the effects of conservation measures within the Indira Gandhi Wildlife Sanctuary. Current vegetation patterns were mapped using an IRS P6 LISS III image and this was used together with Landsat MSS data from 1973 to map land cover transitions. Two major and divergent trends were observed. A dominant degradational trend can be attributed to agricultural expansion and infrastructure development while a successional trend, resulting from protection of the area, showed the resilience of the system after prolonged disturbances. The sanctuary appears susceptible to continuing disturbances under the current management regime but at lower rates than in surrounding unprotected areas. The study demonstrates that remotely sensed land cover assessments can have important contributions to monitoring land management strategies, understanding processes underpinning land use changes and helping to inform future conservation strategies.

Mapping and Inventory of Forest Fires in Andhra Pradesh, India: Current Status and Conservation Needs

Analyzing the spatial extent and distribution of forest fires is essential for sustainable forest management. The present study appraises the distribution of forest fires in one of the largest states in India, Andhra Pradesh, using satellite remote sensing. Advanced Wide Field Sensor (AWiFS) onboard on Indian Remote Sensing Satellite (IRS P6) was used for mapping and analyzing the spatial extent of burnt areas. Comparative analysis was carried out with respect to different forest types, protected areas and across elevation zones to demarcate and identify the fire-affected areas. The results show that about 19% (8594 km2) of forest area were burnt in the state during 2009. Burnt area statistics for Protected Areas reveal that 24% of forest cover was affected by fire. Nagarjunasagar Srisailam Tiger Reserve, the largest tiger reserve of the country, shows an area of 793 km2 (22%) under forest fire. Higher elevation areas which are predominantly dominated by savannah and woodlands experienced higher fire occurrence in comparison with lower elevation areas. Similarly, fires were prevalent near edges compared to core forest. Results of the study suggested that forests of Andhra Pradesh are prone to high fire occurrences and current fire regime poses a severe conservation threat to biodiversity both within and outside the Protected Areas.

Rainfall and Elevation Influence the Local-Scale Distribution of Tree Community in the Southern Region of Western Ghats Biodiversity Hotspot (India)

The present study characterises the tree communities with respect to topographic and climatic variables and identifies the most important environmental correlate of species richness in the southern region of Western Ghats Biodiversity Hotspot, India. Digitally derived environmental variables in combination with tree species richness information were analysed using Canonical Correspondence Analysis (CCA) to characterise the communities. Multiple regression technique based on stepwise backward elimination was used to identify the most important environment correlate of species richness. Canonical correspondence analysis results in six major tree communities along the first and second axes. Rainfall is the dominant environmental gradient influencing vegetation patterns on the first CCA axis while elevation showed the highest correlation with the second CCA axis. Backward elimination regression technique yielded rainfall as the most important environmental correlate of species richness. Results were in agreement with the observations in the Neotropics that rainier areas maintain high species diversity.

Comparison of carbon assimilation estimates over tropical forest types in India based on different satellite and climate data products

Carbon assimilation defined as the overall rate of fixation of carbon through the process of photosynthesis is central to the climate change research. The present study compares the two well-known algorithms in satellite based carbon assimilation estimation, the Vegetation Photosynthesis Model (VPM) and the MOD 17A2 GPP Model, over the tropical forest types in India for a period of two years (September, 2006-August, 2008). The results indicate that the evergreen forest assimilate carbon at a higher rate while the rate is lower for montane grasslands. The comparison between the model results shows that there are large differences between these estimates, and that the spatial resolution of the input datasets plays a larger role than the algorithms of the models. The comparison exercise will be helpful for the refinement and development of the existing and future GPP models by incorporating the empirical environmental conditions

Spatial interpolation of carbon stock: a case study from the Western Ghats biodiversity hotspot, India

Carbon stock distribution in different tropical forest types in India is rarely studied although India is a country with mega-diversity. The present study estimates the biomass and carbon stock of major tropical forest types in India, and attempts to identify suitable interpolation techniques for mapping carbon stock. Empirically derived allometric equations and carbon conversion coefficients were used to estimate the aboveground biomass and carbon stock, respectively. The point estimates were interpolated to spatial surface using different interpolation techniques. Two main modelling approaches were implemented: deterministic modelling and stochastic modelling. Deterministic modelling was to interpolate point information using similarities between measured points (inverse distance weighted (IDW) interpolation), and fitting a smoothing curve along the measured points (polynomial interpolation). In stochastic modelling, ordinary kriging (OK) was employed using parameters derived from semivariograms. The results showed that the average carbon stock in the study area was 84 t/ha. The highest carbon stock was in evergreen forest and the lowest in thorny scrub forest. Validation of the model using the mean and RMS errors indicated that ordinary kriging performs better than IDW and polynomial interpolations.