The Global Ecosystem Dynamics Investigation LiDAR (GEDI) is a new full waveform (FW) based LiDAR system that presents a new opportunity for the observation of forest structures globally. The backscattered GEDI signals, as all FW systems, are distorted by topographic conditions within their footprint, leading to uncertainties on the measured forest variables. In this study, we explore how well several approaches based on waveform metrics and ancillary digital elevation model (DEM) data perform on the estimation of stand dominant heights ($H_{dom}$) and wood volume (V) across different sites of Eucalyptus plantations with varying terrain slopes. In total, five models were assessed on their ability to estimate $H_{dom}$ and four models for V. Results showed that the models using the GEDI metrics, such as the height at different energy quantiles with terrain data from the shuttle radar topography mission’s (SRTM) digital elevation model (DEM) were still dependent on the topographic slope. For $H_{dom}$, an RMSE increase of 14% was observed for data acquired over slopes higher than 20% in comparison to slopes between 10 and 20%. For V, a 74% increase in RMSE was reported between GEDI data acquired over slopes between 0–10% and those acquired over slopes higher than 10%. Next, a model relying on the height at different energy quantiles of the entire waveform ($H{T}_n$) and the height at different energy quartiles of the bare ground waveform ($H{G}_n$) was assessed. Two sets of the $H{G}_n$ metrics were generated, the first one was obtained using a simulated waveform representing the echo from a bare ground, while the second one relied on the actual ground return from the waveform by means of Gaussian fitting. Results showed that both the simulated and fitted models provide the most accurate estimates of $H_{dom}$ and V for all slope ranges. The simulation-based model showed an RMSE that ranged between 1.39 and 1.66 m (between 26.76 and 39.26 m³·ha⁻¹ for V) while the fitting-based method showed an RMSE that ranged between 1.26 and 1.34 m (between 26.78 and 36.29 m³·ha⁻¹ for V). Moreover, the dependency of the GEDI metrics on slopes was greatly reduced using the two sets of metrics. As a conclusion, the effect of slopes on the 25-m GEDI footprints is rather low as the estimation on canopy heights from uncorrected waveforms degraded by a maximum of 1 m for slopes between 20 and 45%. Concerning the wood volume estimation, the effect of slopes was more pronounced, and a degradation on the accuracy (increased RMSE) of a maximum of 20 m³·ha⁻¹ was observed for slopes between 20 and 45%.

Brazil is one of the world’s wood short-fiber producers, cultivating 7.5 million hectares of eucalypt trees. Foresters and resource managers often face difficulties in surveying reliable Eucalyptus productivity levels for the purpose of purchasing and prospecting lands. Spatial data science (DS) and machine learning (ML) provide powerful approaches to make the best use of the large datasets available today. Agriculture has made great use of these approaches, and in this paper, we explore how forestry can benefit as well. We hypothesized that both DS and ML techniques can be used to improve Eucalyptus productivity zoning based on multiple operational datasets of tree growth and environment. Based on more than 12,000 permanent forest inventory plots of commercial Eucalyptus plantations and the climate, soil, and altitude variables associated with them, a supervised ML approach was adjusted to model the forest plantation productivity. A multi-tuning of the decision-tree (DT) algorithm hyperparameters was prepared to yield 450 DT models, with a better one delivering an RMSE of 53.5 m³ ha⁻¹, split in 35 terminal nodes, here interpreted as Eucalyptus productivity zones. The DT model showed an optimum performance index of 0.83, a coefficient of determination of 0.91, a root mean squared error of 12.3 m³ ha⁻¹, and a mean absolute percentage error only of 3.1% in predicting the testing dataset throughout the study area. The DT rule set was interpreted in a user-friendly table and was prepared to classify any location within the study area in each one of the 35 productivity zones based on the required environment variables of the DT algorithm. The high quality of the model obtained made it possible to spatialize the DT rules, providing a reliable cartographic visualization of the probability levels of true Eucalyptus productivity for a huge region of forest-based industries in Brazil. These data-science techniques also provided a yield gap analysis using a very down-to-earth approach. We estimated a yield gap by an amount of 4.2 × 10⁷ m³, representing a few more than 113,000 ha, or 15% of the current forest base. This is the amount of avoided area expansion to accumulate the same wood stock in case the productivity is raised to the attainable level in each zone. This present study provided deeper analysis and reproducible tools to manage forest assets sustainably.

O avanço das pragas exóticas do gênero Eucalyptus, no Brasil, está cada vez maior. A vespa-da-galha Leptocybe invasa Fisher & La Salle (Hymenoptera: Eulophidae) já é encontrada em nove estados brasileiros. O objetivo do trabalho foi avaliar a eficiência de inseticidas sistêmicos aplicados no viveiro de mudas, via rega, para controle de L. invasa. O experimento foi conduzido no viveiro da ESALQ/USP, em Piracicaba–SP, com mudas de Eucalyptus camaldulensis. O teste utilizou os inseticidas Imidacloprid e Tiametoxam nas dosagens 4,1, 8,3 e 12,5 g.m-2 e 37,5, 75 e 112,5 g.m-2, respectivamente. A eficiência foi determinada pelo total de galhas encontradas nas mudas. A eficicácia foi de 84,41% e 61,03%, nas doses de 12,5 g.m-2 e 112,5 g.m-2, respectivamente. O efeito residual dos inseticidas variou de quatro a oito semanas, sem manifestação de fitotoxidade. Os melhores resultados foram obtidos com o Imidacloprid, quando comparado com o Tiametoxam, tanto em eficiência quanto no efeito residual.

Background: Brazilian planted forests play a critical role in global wood and fiber production but face significant productivity challenges from pests and diseases. Leaf-cutting ants (Atta spp. and Acromyrmex spp.) are the main pest in the Brazilian planted forests and cause significant productivity losses every year. Identifying areas most susceptible to colony establishment and growth is crucial for implementing effective management strategies. This study aims to assess the influence of edaphoclimatic and landscape variables on the establishment and expansion of leaf-cutting ant colonies and identify the key factors driving their occurrence. Results: Based on a decade of monitoring data from 33,000 Eucalyptus stands across five regions, Random Forest models reached accuracies of 83% for predicting initial nests and 78% for predicting large nests. Conclusion: The machine learning models effectively detected both initial and large nests, revealing that edaphoclimatic and landscape conditions exert varying levels of influence across macro-regions.