Abstract
Remotely-sensed estimates of forest biomass are usually based on various measurements of canopy height, area, volume or texture, as derived from LiDAR, radar or fine spatial resolution imagery. These measurements are then calibrated to estimates of stand biomass that are primarily based on tree stem diameters. Although humid tropical forest seasonality can have low amplitudes compared with temperate regions, seasonal variations in growth-related factors like temperature, humidity, rainfall, wind speed and day length affect both tropical forest deciduousness and tree height-diameter relationships. Consequently, seasonal patterns in spectral measures of canopy greenness derived from satellite imagery should be related to tree height-diameter relationships and hence to estimates of forest biomass or biomass growth that are based on stand height or canopy area. In this study, we tested whether satellite image-based measures of tropical forest phenology, as estimated by indices of seasonal patterns in canopy greenness constructed from Landsat satellite images, can explain the variability in forest deciduousness, forest biomass and net biomass growth after already accounting for stand height or canopy area. Models of forest biomass that added phenology variables to structural variables similar to those that can be estimated by LiDAR or very high-spatial resolution imagery, like canopy height and crown area, explained up to 12% more variation in biomass. Adding phenology to structural variables explained up to 25% more variation in Net Biomass Growth (NBG). In all of the models, phenology contributed more as interaction terms than as single-effect terms. In addition, models run on only fully-forested plots performed better than models that included partially-forested plots. For forest NBG, the models produced better results when only those plots with a positive growth, from Inventory Cycle 1 to Inventory Cycle 2, were analyzed, as compared to models that included all plots
Highlights
Forest carbon offset programs, like the United Nations program for Reducing Emissions fromDeforestation and forest Degradation (REDD), which is aimed at tropical forests, have the potential to mitigate human-caused increases in atmospheric greenhouse gases while helping to conserve the high biodiversity of tropical forests by providing financial incentives to reduce atmospheric greenhouse gas emissions from forests
The Reducing Emissions fromDeforestation and forest Degradation (REDD) program requires inventory and monitoring of tropical forest carbon stocks and their dynamics [1], the aboveground live components of which are generally estimated from tree biomass and changes in tree biomass
Forest aboveground live biomass is most commonly estimated by summing tree biomass, estimated from tree stem diameter, as measured in forest inventory plots, with allometric equations [3], though tree height or wood density may be included in calculations
Summary
Like the United Nations program for Reducing Emissions fromDeforestation and forest Degradation (REDD), which is aimed at tropical forests, have the potential to mitigate human-caused increases in atmospheric greenhouse gases while helping to conserve the high biodiversity of tropical forests by providing financial incentives to reduce atmospheric greenhouse gas emissions from forests. The REDD program requires inventory and monitoring of tropical forest carbon stocks and their dynamics [1], the aboveground live components of which are generally estimated from tree biomass and changes in tree biomass. Forest aboveground live biomass is most commonly estimated by summing tree biomass, estimated from tree stem diameter, as measured in forest inventory plots, with allometric equations [3], though tree height or wood density may be included in calculations. Seasonal patterns in spectral measures of canopy greenness derived from satellite imagery, which we refer to here as phenology, should be related to tree height-diameter relationships and, to estimates of forest biomass or biomass growth. Phenology data have the potential to improve forest biomass estimates based on canopy characteristics, such as those that might be quantified with LiDAR or very fine spatial resolution imagery
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