While attention on logging in the tropics has been increasing, studies on the long‐term effects of silviculture on forest dynamics and ecology remain scare and spatially limited. Indeed, most of our knowledge on tropical forests arises from studies carried out in undisturbed tropical forests. This bias is problematic given that logged and disturbed tropical forests are now covering a larger area than the so‐called primary forests. A new network of permanent sample plots in logged forests, the Tropical managed Forests Observatory (TmFO), aims to fill this gap by providing unprecedented opportunities to examine long‐term data on the resilience of logged tropical forests at regional and global scales. TmFO currently includes 24 experimental sites distributed across three tropical regions, with a total of 490 permanent plots and 921 ha of forest inventories.

Tropical forests are often located in difficult-to-access areas, which make high-quality forest structure information difficult and expensive to obtain by traditional field-based approaches. LiDAR (acronym for Light Detection And Ranging) data have been used throughout the world to produce time-efficient and wall-to-wall structural parameter estimates for monitoring in native and commercial forests. In this study, we compare products and aboveground biomass (AGB) estimations from LiDAR data acquired using an aircraft-borne system in 2015 and data collected by the unmanned aerial vehicle (UAV)-based GatorEye Unmanned Flying Laboratory in 2017 for ten forest inventory plots located in the Chico Mendes Extractive Reserve in Acre state, southwestern Brazilian Amazon. The LiDAR products were similar and comparable among the two platforms and sensors. Principal differences between derived products resulted from the GatorEye system flying lower and slower and having increased returns per second than the aircraft, resulting in a much higher point density overall (11.3 ± 1.8 vs. 381.2 ± 58 pts/m²). Differences in ground point density, however, were much smaller among the systems, due to the larger pulse area and increased number of returns per pulse of the aircraft system, with the GatorEye showing an approximately 50% higher ground point density (0.27 ± 0.09 vs. 0.42 ± 0.09). The LiDAR models produced by both sensors presented similar results for digital elevation models and estimated AGB. Our results validate the ability for UAV-borne LiDAR sensors to accurately quantify AGB in dense high-leaf-area tropical forests in the Amazon. We also highlight new possibilities using the dense point clouds of UAV-borne systems for analyses of detailed crown structure and leaf area density distribution of the forest interior.

The use of photogrammetry technology for aboveground biomass (AGB) stock estimation in tropical savannas is a challenging task and is still at a preliminary stage. This work aimed to use metrics derived from point clouds, constructed using photogrammetric imaging obtained by an RGB camera on board a remotely piloted aircraft (RPA), to generate a model for estimating AGB stock for the shrubby-woody stratum in savanna areas of Central Brazil (Cerrado). AGB stock was estimated using forest inventory data and an allometric equation. The photogrammetric digital terrain model (DTM) was validated with altimetric field data, demonstrating that the passive sensor can identify topographic variations in sites with discontinuous canopies. The inventory estimated an average AGB of 18.3 (±13.3) Mg ha⁻¹ at the three sampled sites. The AGB model selected was composed of metrics used for height at the 10th and 95th percentile, with an adjusted R² of 93% and a relative root mean squared error (RMSE) of 16%. AGB distribution maps were generated from the spatialization of the metrics selected for the model, optimizing the visualization and our understanding of the spatial distribution of forest AGB. The study represents a step forward in mapping biomass and carbon stocks in tropical savannas using low-cost remote sensing platforms.

The aim of this study was to perform ecological and functional clustering of tree species in southwestern Amazon. Developed from data from 95 permanent plots of 1 ha each, all individuals with diameter at breast height (DBH) ≥10 cm were measured. The species grouping was performed in three stages: (1) cluster analysis, using the variables: diameter annual periodic increment -(IPA_DAP) considering three competition levels (high, medium and low) and the 95th percentile of the diameters (P₉₅) cumulative frequency distribution (Ward hierarchical method); (2) Discriminant analysis, using the variables P₉₅ and IPA_DAP by Fisher's method and (3) subjective stage, considering the species ecological characteristics. The Ward and Fisher methods used for discriminant and cluster analyses were effective for species grouping resulting on the formation of 10 groups. Variables: IPA_DAP and and P₉₅ were efficient on the formed groups discrimination.. Variations in the growth rates for the overall mean data were reduced wen calculated for each group of species.