标题：Comparative assessment of the relationship of satellite data with the above ground biomass of Sal trees (Shorea robusta) determined from phenologically different time periods
摘要：The Above Ground Biomass (AGB) estimates of vegetation comprise both the bole biomass determined through a volumetric equation and litter biomass collected from the ground. For mature trees, the AGB estimated in phenologically different time periods is directly affected by the litter biomass since the Diameter at Breast Height (DBH) and height (H) of such trees that are used in the estimation of bole biomass would remain unchanged over a reasonable time period. In the present study, we have determined the AGB of Sal trees (Shorea robusta) in two contrasting seasons: the peak green period in October being devoid of litter on the ground and the leaf shedding period in February with abundant amount of litter present on the ground. Estimation of AGB for the month of February included the litter biomass. In contrast, the AGB for October represented only the bole biomass. AGB was estimated for ten different plots selected in the study area. The AGB estimated from ten sampling plots for each time period was regressed with the individual tree parameters such as the average DBH and height of trees measured from the corresponding plots. The regression analysis exhibited a significantly stronger relationship between the AGB and DBH for the month of October as compared to February. Furthermore, the correlation between the remotely sensed derived data and AGB was also found to be significantly higher for the month of October than February. This observation indicates that inclusion of the litter biomass in AGB will tend to decrease the regression relationship between AGB and DBH and also between the remotely sensed data and AGB. Therefore, these conclusions invite careful consideration while estimating AGB from satellite data in phenologically different time periods.
关键词：above ground biomass; bole biomass; diameter at breast height; litter biomass; remotely sensed data
