Practicalities of Non-Destructive Methodologies in Monitoring Anthropogenic Greenhouse Gas Emissions from Tropical Forests under the Influence of Human Intervention

Abstract

We examined non-destructive methodologies for practicalities in monitoring anthropogenic greenhouse gas (GHG) emissions from tropical dry-land forest under the influence of various forms of human intervention. Spaceborne SAR withstood comparison with Landsat ETM+ in land cover classification of degraded tropical forest. For measurement of carbon stock and GHG flux per unit land area, the gain-loss method requires both growth rate and removal rate of forest carbon stock. However, the latter has rarely been obtained in tropical forest. For the stock-difference method, permanent sampling plot data can be used to estimate mean carbon stock per unit land area of each forest type. For cyclic land use that includes a clear-cutting stage such as slash-and-burn agriculture, chronosequential changes in carbon stock can be predicted by determining the time and spatial-distribution of cleared land. Changes in forest biomass by logging, storm-damage, etc., may be identified by monitoring the presence and diameter of the crowns of overstory trees. We developed five equations containing the parameter for crown diameter for estimating tree biomass. Overstory height can be a parameter for estimating ecosystem carbon stock of various plant communities, and forest height can be measured by airborne and spaceborne sensors, etc. Generic equations containing the parameter for overstory height are available for estimating community biomass of tropical and subtropical forests. PALSAR has an advantage over other remote systems by enabling frequent sensing and semi-direct biomass estimation using backscattering coefficients. However, no reasonable remote sensing methods exist for monitoring the amount of carbon loss by forest conversion and logging in forests with high biomass. To compensate for the faults of the present PALSAR methodologies and to enable practical and frequent monitoring of all types of forests by humans, it is vital to devise a new methodology to detect changes in high-biomass forests.