Advances in Unmanned Aerial Vehicle (UAV) technology and data processing capabilities have made it feasible to obtain high-resolution imagery and three dimensional (3D) data which can be used for forest monitoring and assessing tree attributes. This study evaluates the applicability of low consumer grade cameras attached to UAVs and structure-from-motion (SfM) algorithm for automatic individual tree detection (ITD) using a local-maxima based algorithm on UAV-derived Canopy Height Models (CHMs). This study was conducted in a private forest at Cache Creek located east of Jackson city, Wyoming. Based on the UAV-imagery, we allocated 30 field plots of 20 m × 20 m. For each plot, the number of trees was counted manually using the UAV-derived orthomosaic for reference. A total of 367 reference trees were counted as part of this study and the algorithm detected 312 trees resulting in an accuracy higher than 85% (F-score of 0.86). Overall, the algorithm missed 55 trees (omission errors), and falsely detected 46 trees (commission errors) resulting in a total count of 358 trees. We further determined the impact of fixed tree window sizes (FWS) and fixed smoothing window sizes (SWS) on the ITD accuracy, and detected an inverse relationship between tree density and FWS. From our results, it can be concluded that ITD can be performed with an acceptable accuracy (F > 0.80) from UAV-derived CHMs in an open canopy forest, and has the potential to supplement future research directed towards estimation of above ground biomass and stem volume from UAV-imagery.

Submetido em 13/09/2008 Aceito para publicação em 12/11/2009 ResumoO Planalto Catarinense constitui-se em uma importante região estratégica para estudos referentes à conservação, em função da ocorrência de remanescentes de Floresta Ombrófila Mista e por ser uma área de recarga e afloramento do aquífero Guarani. Com o objetivo de avaliar a similaridade florística entre diferentes áreas amostrais e descrever a estrutura do componente arbóreo, foram alocadas, no Parque Natural Municipal de Lages, SC, quatro parcelas permanentes (40 x 40m) e cada uma foi dividida em 16 unidades amostrais de 10 x 10m. Árvores com DAP ≥ 5cm foram mapeadas, marcadas e mensuradas. Os espécimes foram coletados, identificados e depositados em herbário. Foram amostradas 46 espécies distribuídas em 39 gêneros e 27 famílias. As famílias mais ricas em espécies foram Myrtaceae, Lauraceae, Salicaceae e Sapindaceae as quais apresentaram alta densidade, assim como Dicksoniaceae e Clethraceae. Sete espécies somaram mais de 60% do total de indivíduos amostrados. A diversidade específica (H') foi de 3,05 nats.ind -1 (J'= 0,81). A similaridade entre as parcelas foi de 32 a 44%, indicando baixa semelhança entre as parcelas estudadas. A distribuição espacial da maioria das espécies é classificada como agregada, conforme o índice de Morisita. Esta floresta é considerada rica e diversa, com espécies arbóreas ameaçadas de extinção tais como Araucaria angustifolia e Dicksonia sellowiana. Devido à grande importância ecológica para a flora e fauna local e o processo de fragmentação na região, este remanescente florestal deve ser protegido e conservado, visto que ainda ocorrem interferências antrópicas negativas.

Improving management practices in industrial forest plantations may increase production efficiencies, thereby reducing pressures on native tropical forests for meeting global pulp needs. This study aims to predict stem volume (V) in plantations of fast-growing Eucalyptus hybrid clones located in southeast Brazil using field plot and airborne Light Detection and Ranging (LiDAR) data. Forest inventory attributes and LiDAR-derived metrics were calculated at 108 sample plots. The best LiDAR-based predictors of V were identified based on loadings calculated from a principal component analysis (PCA). After selecting these best predictors using PCA, we developed multiple regression models predicting V from selected LiDAR metrics. Metrics related to tree height and canopy depth were most effective for V prediction, with an overall model coefficient of determination (adj. R 2) of 0.87, and a root mean squared error (RMSE) of 27.60 m 3 ha 21 (i.e. relative RMSE ¼ 9.99 per cent). We used this model to map stem V of Eucalyptus hybrid clones across the full LiDAR data extent. The accuracy and precision of our results show that LiDAR-derived V is appropriate for updating Eucalyptus forest base maps and registries in the paper and pulp supply chain. However, further studies are necessary to evaluate and compare the cost of acquisition and processing of LiDAR data against conventional V inventory in this system.

Airborne lidar is a technology well-suited for mapping many forest attributes, including aboveground biomass (AGB) stocks and changes in selective logging in tropical forests. However, trade-offs still exist between lidar pulse density and accuracy of AGB estimates. We assessed the impacts of lidar pulse density on the estimation of AGB stocks and changes using airborne lidar and field plot data in a selectively logged tropical forest located near Paragominas, Pará, Brazil. Field-derived AGB was computed at 85 square 50 × 50 m plots in 2014. Lidar data were acquired in 2012 and 2014, and for each dataset the pulse density was subsampled from its original density of 13.8 and 37.5 pulses·m −2 to lower densities of 12, 10, 8, 6, 4, 2, 0.8, 0.6, 0.4 and 0.2 pulses·m −2 . For each pulse density dataset, a power-law model was developed to estimate AGB stocks from lidar-derived mean height and corresponding changes between the years 2012 and 2014. We found that AGB change estimates at the plot level were only slightly affected by pulse density. However, at the landscape level we observed differences in estimated AGB change of >20 Mg·ha −1 when pulse density decreased from 12 to 0.2 pulses·m −2 . The effects of pulse density were more pronounced in areas of steep slope, especially when the digital terrain models (DTMs) used in the lidar derived forest height were created from reduced pulse density data. In particular, when the DTM from high pulse density in 2014 was used to derive the forest height from both years, the effects on forest height and the estimated AGB stock and changes did not exceed 20 Mg·ha −1 . The results suggest that AGB change can be monitored in selective logging in tropical forests with reasonable accuracy and low cost with low pulse density lidar surveys if a baseline high-quality DTM is available from at least one lidar survey. We recommend the results of this study to be considered in developing projects and national level MRV systems for REDD+ emission reduction programs for tropical forests.

Accurate forest parameters are essential for forest inventory. Traditionally, parameters such as diameter at breast height (DBH) and total height are measured in the field by level gauges and hypsometers. However, field inventories are usually based on sample plots, which, despite providing valuable and necessary information, are laborious, expensive, and spatially limited. Most of the work developed for remote measurement of DBH has used terrestrial laser scanning (TLS), which has high density point clouds, being an advantage for the accurate forest inventory. However, TLS still has a spatial limitation to application because it needs to be manually carried to reach the area of interest, requires sometimes challenging field access, and often requires a field team. UAV-borne (unmanned aerial vehicle) lidar has great potential to measure DBH as it provides much higher density point cloud data as compared to aircraft-borne systems. Here, we explore the potential of a UAV-lidar system (GatorEye) to measure individual-tree DBH and total height using an automatic approach in an integrated crop-livestock-forest system with seminal forest plantations of Eucalyptus benthamii. A total of 63 trees were georeferenced and had their DBH and total height measured in the field. In the high-density (>1400 points per meter squared) UAV-lidar point cloud, we applied algorithms (usually used for TLS) for individual tree detection and direct measurement of tree height and DBH. The correlation coefficients (r) between the field-observed and UAV lidar-derived measurements were 0.77 and 0.91 for DBH and total tree height, respectively. The corresponding root mean square errors (RMSE) were 11.3% and 7.9%, respectively. UAV-lidar systems have the potential for measuring relatively broad-scale (thousands of hectares) forest plantations, reducing field effort, and providing an important tool to aid decision making for efficient forest management. We recommend that this potential be explored in other tree plantations and forest environments.

NASA's Global Ecosystem Dynamic Investigation (GEDI) mission has been designed to measure forest structure using lidar waveforms to sample the earth's vegetation while in orbit aboard the International Space Station. In this paper, we used airborne large-footprint (LF) lidar measurements to simulate GEDI observations from which we retrieved ground elevation, vegetation height, and aboveground biomass (AGB). GEDI-like product accuracy was then assessed by comparing them to similar products derived from airborne small-footprint (SF) lidar measurements. The study focused on tropical forests and used data collected during the NASA and European Space Agency (ESA) AfriSAR ground and airborne campaigns in the Lope National Park in Central Gabon. The measurements covered a gradient of successional stages of forest development with different height, canopy density, and topography. The comparison of the two sensors shows that LF lidar waveforms and simulated waveforms from SF lidar are equivalent in their ability to estimate ground elevation (RMSE = 0.5 m, bias = 0.29 m) and maximum forest height (RMSE = 2.99 m,

Improvements in the management of pine plantations result in multiple industrial and environmental benefits. Remote sensing techniques can dramatically increase the efficiency of plantation management by reducing or replacing time-consuming field sampling. We tested the utility and accuracy of combining field and airborne lidar data with Random Forest, a supervised machine learning algorithm, to estimate stem total and assortment (commercial and pulpwood) volumes in an industrial Pinus taeda L. forest plantation in southern Brazil. Random Forest was populated using field and lidar-derived forest metrics from 50 sample plots with trees ranging from three to nine years old. We found that a model defined as a function of only two metrics (height of the top of the canopy and the skewness of the vertical distribution of lidar points) has a very strong and unbiased predictive power. We found that predictions of total, commercial, and pulp volume, respectively, showed an adjusted R 2 equal to 0.98, 0.98 and 0.96, with unbiased predictions of −0.17%, −0.12% and −0.23%, and Root Mean Square Error (RMSE) values of 7.83%, 7.71% and 8.63%. Our methodology makes use of commercially available airborne lidar and widely used mathematical tools to provide solutions for increasing the industry efficiency in monitoring and managing wood volume.

RESUMO -Araucaria angustifolia (Bertol.) Kuntze é considerada espécie ameaçada de extinção, e faltam informações sobre sua ecologia para a elaboração de técnicas eficazes para manejo e conservação. Nesse sentido, foi estudada uma população natural na Reserva Genética Florestal de Caçador, com o objetivo de gerar informações sobre a estrutura demográfica da espécie. A população estudada foi dividida em quatro classes: regeneração, juvenis, masculinas e femininas. Foram analisadas a estrutura diamétrica e de altura, a razão sexual e o padrão espacial. A regeneração natural foi baixa, e a razão sexual não diferiu de 1, de acordo com o esperado para a espécie. Na análise do padrão espacial, a agregação apareceu em todas as classes. A regeneração não esteve espacialmente associada com árvores adultas. A regeneração natural da espécie sob a floresta existe, apesar de ocorrer com baixa densidade.Palavras-chave: Estrutura populacional, regeneração natural e pinheiro-brasileiro. DEMOGRAPHIC STRUCTURE AND SPATIAL PATTERN OF ABSTRACT -The Araucaria angustifolia (Bertol.) Kuntze is a Brazilian native species considered threatened. There is a lack of information and data about the species ecology that could be used as the basis for preservation