Vegetation structure and composition of tropical evergreen and deciduous forests in Uttara Kannada District, Western Ghats under different disturbance regimes

In the present paper, we present the structure and composition of tropical evergreen and deciduous forests in the Western Ghats monitored under a long-term programme involving Indian Institute of Science, Earthwatch and volunteer investigators from HSBC. Currently, there is limited evidence on the status and dynamics of tropical forests in the context of human disturbance and climate change. Observations made in this study show that the `more disturbed' evergreen and one of the deciduous plots have low species diversity compared to the less-disturbed forests. There are also variations in the size class structure in the more and `less disturbed' forests of all the locations. The variation is particularly noticeable in the DBH size class 10 - 15 cm category. When biomass stock estimates are considered, there was no significant difference between evergreen and deciduous forests. The difference in biomass stocks between `less disturbed' and `more disturbed' forests within a forest type is also low. Thus, the biomass and carbon stock has not been impacted despite the dependence of communities on the forests. Periodic and long-term monitoring of the status and dynamics of the forests is necessary in the context of potential increased human pressure and climate change. There is, therefore, a need to inform the communities of the impact of extraction and its effect on regeneration so as to motivate them to adopt what may be termed as ``adaptive resource management'', so as to sustain the flow of forest products.

Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks

Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8-50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass density (AGBD in Mgha(-1)) at spatial scales ranging from 5 to 250m (0.025-6.25 ha), and to evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that local spatial variability in AGBD is large for standard plot sizes, averaging 46.3% for replicate 0.1 ha subplots within a single large plot, and 16.6% for 1 ha subplots. AGBD showed weak spatial autocorrelation at distances of 20-400 m, with autocorrelation higher in sites with higher topographic variability and statistically significant in half of the sites. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGBD leads to a substantial ``dilution'' bias in calibration parameters, a bias that cannot be removed with standard statistical methods. Our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise.