Successful implementation of projects under the REDD+ mechanism, securing payment for storing forest carbon as an ecosystem service, requires quantification of biomass. Airborne laser scanning (ALS) is a relevant technology to enhance estimates of biomass in tropical forests. We present the analysis and results of modeling aboveground biomass (AGB) in a Tanzanian rainforest utilizing data from a small-footprint ALS system and 153 field plots with an area of 0.06–0.12 ha located on a systematic grid. The study area is dominated by steep terrain, a heterogeneous forest structure and large variation in AGB densities with values ranging from 43 to 1147 Mg·ha⁻¹, which goes beyond the range that has been reported in existing literature on biomass modeling with ALS data in the tropics. Root mean square errors from a 10-fold cross-validation of estimated values were about 33% of a mean value of 462 Mg·ha⁻¹. Texture variables derived from a canopy surface model did not result in improved models. Analyses showed that (1) variables derived from echoes in the lower parts of the canopy and (2) canopy density variables explained more of the AGB density than variables representing the height of the canopy.

Airborne laser scanning (ALS) has recently emerged as a promising tool to acquire auxiliary information for improving aboveground biomass (AGB) estimation in sample-based forest inventories. Under design-based and model-assisted inferential frameworks, the estimation relies on a model that relates the auxiliary ALS metrics to AGB estimated on ground plots. The size of the field plots has been identified as one source of model uncertainty because of the so-called boundary effects which increases with decreasing plot size. Recent research in tropical forests has aimed to quantify the boundary effects on model prediction accuracy, but evidence of the consequences for the final AGB estimates is lacking. In this study we analyzed the effect of field plot size on model prediction accuracy and its implication when used in a model-assisted inferential framework. The results showed that the prediction accuracy of the model improved as the plot size increased. The adjusted R2 increased from 0.35 to 0.74 while the relative root mean square error decreased from 63.6 to 29.2%. Indicators of boundary effects were identified and confirmed to have significant effects on the model residuals. Variance estimates of model-assisted mean AGB relative to corresponding variance estimates of pure field-based AGB, decreased with increasing plot size in the range from 200 to 3000 m2. The variance ratio of field-based estimates relative to model-assisted variance ranged from 1.7 to 7.7. This study showed that the relative improvement in precision of AGB estimation when increasing field-plot size, was greater for an ALS-assisted inventory compared to that of a pure field-based inventory.

Airborne laser scanning (ALS) is increasingly being used to enhance the accuracy of biomass estimates in tropical forests. Although the technological development of ALS instruments has resulted in ever-greater pulse densities, studies in boreal and sub-boreal forests have shown consistent results even at relatively small pulse densities. The objective of the present study was to assess the effects of reduced pulse density on (1) the digital terrain model (DTM), and (2) canopy metrics derived from ALS data collected in a tropical rainforest in Tanzania. We used a total of 612 coordinates measured with a differential dual frequency Global Navigation Satellite System receiver to analyze the effects on DTMs at pulse densities of 8, 4, 2, 1, 0.5, and 0.025 pulses·m⁻². Furthermore, canopy metrics derived for each pulse density and from four different field plot sizes (0.07, 0.14, 0.21, and 0.28 ha) were analyzed. Random variation in DTMs and canopy metrics increased with reduced pulse density. Similarly, increased plot size reduced variation in canopy metrics. A reliability ratio, quantifying replication effects in the canopy metrics, indicated that most of the common metrics assessed were reliable at pulse densities >0.5 pulses·m⁻² at a plot size of 0.07 ha.

Accurate positioning of single trees registered automatically during harvesting operations opens up new possibilities for reducing the field sampling effort in forest inventories utilising remotely sensed data. In the present study, we propose to use a harvester to collect single-tree data during regular harvest operations and use these data to substitute or supplement traditional measurements on sample plots. Today’s harvesters are capable of recording single-tree information such as species and diameter at breast height, and a cut-to-length harvester was equipped with an integrated accurate positioning system based on real-time kinematic global satellite positioning, as well as a low-cost global navigation satellite system (GNSS) receiver mounted directly on the harvester head. Positions from 73 trees were evaluated and compared to coordinates obtained using a total station. At the single-tree level, the mean error for the integrated positioning system was 0.94 m. The low-cost GNSS receiver mounted on the harvester head yielded a mean error of 7.00 m. The sub-meter accuracy obtained with the integrated system suggests that data acquired with a harvester using this positioning system may have a great potential as a method for single-tree field data acquisition.