Predicting tree heights for biomass estimates in tropical forests – a test from French Guiana

Abstract

Abstract. The recent development of REDD+ mechanisms requires reliable estimation of carbon stocks, especially in tropical forests that are particularly threatened by global changes. Even though tree height is a crucial variable for computing aboveground forest biomass (AGB), it is rarely measured in large-scale forest censuses because it requires extra effort. Therefore, tree height has to be predicted with height models.The height and diameter of all trees over 10 cm in diameter were measured in 33 half-hectare plots and 9 one-hectare plots throughout northern French Guiana, an area with substantial climate and environmental gradients. We compared four different model shapes and found that the MichaelisMenten shape was most appropriate for the tree biomass prediction. Model parameter values were significantly different from one forest plot to another, and this leads to large errors in biomass estimates.Variables from the forest stand structure explained a sufficient part of plot-to-plot variations of the height model parameters to improve the quality of the AGB predictions. In the forest stands dominated by small trees, the trees were found to have rapid height growth for small diameters. In forest stands dominated by larger trees, the trees were found to have the greatest heights for large diameters. The aboveground biomass estimation uncertainty of the forest plots was reduced by the use of the forest structure-based height model. It demonstrated the feasibility and the importance of height modeling in tropical forests for carbon mapping. When the tree heights are not measured in an inventory, they can be predicted with a height-diameter model and incorporating forest structure descriptors may improve the predictions.

Weak Environmental Controls of Tropical Forest Canopy Height in the Guiana Shield

“…In the case of Pearson's product-moment correlation coefficient, which may vary between −1 and 1, the test is two-tailed, and we consider the proportion of simulations for which the correlation r sim falls outside the interval [−r obs , r obs ] where r obs is the observed Pearson's correlation coefficient. Because hydrological and topographic variables are structured at multiple scales, and in order to assess the importance of the spatial scale in shaping the link between forest canopy height and environmental drivers, we apply the test to the grids re-sampled at six different resolutions: 1024, 512, 256, 128, 64, and 32 pixels, which correspond to 6,12,24,48,96,192, and 384 m per pixel, respectively. Conversion to larger pixel size was done by using the "aggregate" function of the Raster package, using the mean height estimate.…”

“…We first sampled 500 points from p1, p2, and p3, and for each pixel size (6,12,24,48,96,192, and 384). We chose 500 points because this number corresponds to a low-resolution case (GLAS spaceborne) of about 3 points·km −2 .…”

“…To do so, we defined a regularly spaced grid covering the study area with pixel sizes of 6,12,24,48,96,192, and 384 m. For each pixel size and for each type of prediction (with and without environment, inside or outside), 10 maps were computed, a new sampling of 500 points each time to generate confidence intervals. For each pixel size, the canopy height was predicted and the accuracy of the spatial prediction of canopy height (H) was assessed by comparing predicted values with LiDAR values (H) from DCM using the Root Mean Square Error (RMSE):…”

“…For instance, several studies observed that dendrometric (diameter and height) measurements and allometric relationships for one species could be different according to the type of soil [6], which is directly linked to the type of water drainage [11]. Indeed, in French Guiana [49], examined in detail the spatial distribution of vegetation in 19 ha of tropical forests and concluded that large soil drainage classes affect tree height.…”

“…In the global change context, precise knowledge of forest canopy height is also a prerequisite to accurately predict the aboveground forest biomass, a key component of the terrestrial carbon cycle [5]. At the individual tree level, forest canopy height is an important parameter to improve tree allometric equations [6]. Indeed, most allometric equations estimate this height value from a statistical inference procedure the relationship between forest structure and environmental variables and the effect of sampling strategy on the accuracy of the canopy height map prediction with airborne LiDAR in French Guiana.…”