Local-Scale Mapping of Biomass in Tropical Lowland Pine Savannas Using ALOS PALSAR

Abstract

Fine-scale biomass maps offer forest managers the prospect of more detailed and locally accurate information for measuring, reporting and verification activities in contexts, such as sustainable forest management, carbon stock assessments and ecological studies of forest growth and change. In this study, we apply a locally validated method for estimating aboveground woody biomass (AGWB) from Advanced Land Observing Satellite (ALOS) Phased Array-type L-band Synthetic Aperture Radar (PALSAR) data to produce an AGWB map for the lowland pine savannas of Belize at a spatial resolution of 100 m. Over 90% of these woodlands are predicted to have an AGWB below 60 tha −1 , with the average woody biomass of these savannas estimated at 23.5 tha −1 . By overlaying these spatial estimates upon previous thematic mapping of national land cover, we derive representative average biomass values of ~32 tha −1 and ~18 tha −1 for the previously qualitative classes of "denser" and "less dense" tree savannas. The predicted average biomass, from the mapping for savannas woodlands occurring within two of Belize's larger protected areas, agree closely with previous biomass estimates for these areas based on ground surveys and forest inventories (error ≤20%). However, biomass estimates derived for these protected areas from two biomass maps produced at coarser resolutions (500 m and 1000 m) from global datasets overestimated biomass (errors ≥275% in each dataset). OPEN ACCESSForests 2014, 5 2378The finer scale biomass mapping of both protected and unprotected areas provides evidence to suggest that protection has a positive effect upon woody biomass, with the mean AGWB higher in areas protected and managed for biodiversity (protected and passively managed (PRPM), 29.5 tha −1 ) compared to unprotected areas (UPR, 23.29 tha −1 ).These findings suggest that where sufficient ground data exists to build a reliable local relationship to radar backscatter, the more detailed biomass mapping that can be produced from ALOS and similar satellite data at resolutions of ~100 m provides more accurate and spatially detailed information that is more appropriate for supporting the management of forested areas of ~10,000 ha than biomass maps that can be produced from lower resolution, but freely available global data sets.

Potential of ALOS2 and NDVI to Estimate Forest Above-Ground Biomass, and Comparison with Lidar-Derived Estimates

“…The larger overall overestimation found in Tahoe compared to Asiago is possibly due to the sparser tree cover characterizing this site, in which the SAR backscattering is therefore more influenced by the ground signal component. Viergever [59] also reported overestimation modeling biomass with SAR data in sparse savanna woodlands, while [60] suggested that pantropical carbon maps may overestimate AGB in savanna areas. However, underestimation of tree heights has also been reported for lidar-derived tree height models, and attributed to the laser beams missing the tree tops, especially at low laser point densities [61][62][63], even if there are several examples of AGB models developed with low point density lidar [64,65].…”

Remote sensing as a tool to assess botanical composition, structure, quantity and quality of temperate grasslands

“…There are vast areas covered by grassland and rangeland ecosystems in the tropics and subtropics, including savannas, and a large body of RS research has been conducted within these systems (Michelakis, Stuart, Lopez, Linares, & Woodhouse, 2014;Mutanga & Rugege, 2006;Paruelo et al, 2000;Sarrazin et al, 2011). Although this work has resulted in important methodological progress, many of the findings apply solely to the specific conditions of these ecosystems, and in most cases, they cannot be transferred to the grassland systems of moderate climates, which are typically managed more intensively.…”

Growing Stock Volume Retrieval from Single and Multi-Frequency Radar Backscatter

“…Several modeling approaches can be used to retrieve forest GSV (or AGB) from radar backscatter coefficients, including parametric and nonparametric models [3,[8][9][10]13,16,24,[37][38][39][40][41]. Within a previous study, some of these models were evaluated using the Romanian NFI and ALOS PALSAR-2 datasets [42].…”

“…Such limitations translate into discrepancies between in situ and mapped biomass stocks. Indeed, many studies have shown differences between the global products specified accuracy and in situ samples over national to regional scales [14,18,[21][22][23][24] with locally calibrated products providing significant improvements for the estimated forest parameters [14,[23][24][25].…”