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.

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Traditional forest restoration (FR) monitoring methods employ spreadsheets and photos taken at the ground level. Since remotely piloted aircraft (RPA) generate a panoramic high resolution and georeferenced view of the entire area of interest, this technology has high potential to improve the traditional FR monitoring methods. This study evaluates how low-cost RPA data may contribute to FR monitoring of the Brazilian Atlantic Forest by the automatic remote measurement of Tree Density, Tree Height, Vegetation Cover (area covered by trees), and Grass Infestation. The point cloud data was processed to map the Tree Density, Tree Height, and Vegetation Cover parameters. The orthomosaic was used for a Random Forest classification that considered trees and grasses as a single land cover class. The Grass Infestation parameter was mapped by the difference between this land cover class (which considered trees and grasses) and the Vegetation Cover results (obtained by the point cloud data processing). Tree Density, Vegetation Cover, and Grass Infestation parameters presented F_scores of 0.92, 0.85, and 0.64, respectively. Tree Height accuracy was indicated by the Error Percentage considering the traditional fieldwork and the RPA results. The Error Percentage was equal to 0.13 and was considered accurate because it estimated a 13% shorter height for trees that averaged 1.93 m tall. Thus, this study showed that the FR structural parameters were accurately measured by the low-cost RPA, a technology that contributes to FR monitoring. Despite accurately measuring the structural parameters, this study reinforced the challenge of measuring the Biodiversity parameter via remote sensing because the classification of tree species was not possible. After all, the Brazilian Atlantic Forest is a biodiversity hotspot, and thus different species have similar spectral responses in the visible spectrum and similar geometric forms. Therefore, until improved automatic classification methods become available for tree species, traditional fieldwork remains necessary for a complete FR monitoring diagnostic.

Throughout the world, restoration of degraded areas (RDA) is not only a global but also a local challenge. In this context, the Brazilian government committed itself to restore 12 million hectares of forests by 2030. RDA monitoring customarily depends on extensive fieldwork to collect data on all individuals planted. As remotely piloted aircrafts (RPAs) can reduce costs and time of fieldwork activities, studying this technology is therefore timely given. A crucial metric for RDA is the number of trees established in the area. Methods using RPAs on automatic tree counting showed good accuracy using algorithms based on the canopy height model (CHM), which is the difference between a digital surface model (DSM) and a digital terrain model (DTM). However, obtaining a DTM demands an extra computational processing step and may require field control points or manually delimiting objects on the surface. The study presented here proposes and evaluates a semi-automated methodology for counting trees directly on DSM in RDAs in the Amazon using RPA coupled with a red–green–blue standard photographic sensor. The DSM method obtained good overall accuracy and F-score indexes, superior to the CHM method for all study areas even when overall accuracy was low for both methods.

Monitoring the vegetation structure and species composition of forest restoration (FR) in the Brazilian Amazon is critical to ensuring its long-term benefits. Since remotely piloted aircrafts (RPAs) associated with deep learning (DL) are becoming powerful tools for vegetation monitoring, this study aims to use DL to automatically map individual crowns of Vismia (low resilience recovery indicator), Cecropia (fast recovery indicator), and trees in general (this study refers to individual crowns of all trees regardless of species as All Trees). Since All Trees can be accurately mapped, this study also aims to propose a tree crown heterogeneity index (TCHI), which estimates species diversity based on: the heterogeneity attributes/parameters of the RPA image inside the All Trees results; and the Shannon index measured by traditional fieldwork. Regarding the DL methods, this work evaluated the accuracy of the detection of individual objects, the quality of the delineation outlines and the area distribution. Except for Vismia delineation (IoU = 0.2), DL results presented accurate values in general, as F1 and IoU were always greater than 0.7 and 0.55, respectively, while Cecropia presented the most accurate results: F1 = 0.85 and IoU = 0.77. Since All Trees results were accurate, the TCHI was obtained through regression analysis between the canopy height model (CHM) heterogeneity attributes and the field plot data. Although TCHI presented robust parameters, such as p-value < 0.05, its results are considered preliminary because more data are needed to include different FR situations. Thus, the results of this work show that low-cost RPA has great potential for monitoring FR quality in the Amazon, because Vismia, Cecropia, and All Trees can be automatically mapped. Moreover, the TCHI preliminary results showed high potential in estimating species diversity. Future studies must assess domain adaptation methods for the DL results and different FR situations to improve the TCHI range of action.

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Este estudo teve por objetivo estabelecer predições do impacto das mudanças climáticas em cenários futuros sobre Dicksonia sellowiana (Pres.) Hook, por meio de modelos de distribuição de espécies e uso de técnicas que diminuam o grau de incerteza nesse tipo de modelagem. Para a geração dos modelos foram utilizados cinco algoritmos disponíveis no programa Openmodeller. A modelagem incluiu o modelo climático HadCM3, com projeções para o ano 2050, em dois cenários de emissão de CO2: pessimista (A2) e otimista (B2). Todos os algoritmos apresentaram eficiência nas projeções para o clima atual, o que foi validado pelos valores da área sobre a curva ROC e pelo índice de Kappa. Considerando os remanescentes de habitats disponíveis para a espécie, os resultados apresentaram diferenças significativas entre as projeções de área com o clima atual e em relação às áreas em cenários climáticos futuros. Esses modelos indicaram reduções de área potencial superiores a 50% para a espécie. Mesmo considerando os valores bons e excelentes de acurácia com os dados de clima atual, a variabilidade das projeções de distribuição para o clima no futuro indica a necessidade do uso de técnicas de consenso para a redução das incertezas associadas a este tipo de modelagem

Remotely piloted aircrafts (RPAs) are becoming well-known for monitoring forest restoration (FR). Canopy cover percentage on a restoration site is a major indicator for the early phases of FR. We present a protocol to evaluate canopy cover using low-cost RPA and a friendly computational environment. We tested the protocol in six FR projects in the Brazilian Atlantic Forest. After processing the images automatically to generate the digital surface model and the digital terrain model, the canopy cover is measured in QGIS by considering height thresholds in the canopy height model. The results had F1 values higher than 0.9, which confirms a robust methodology that does not require specific computer science or remote sensing skills. Thus, low-cost RPA proved to be accurate and user-friendly in measuring the canopy cover, which improves monitoring during most FR stages. Such finding plays an important role in FR policies and future works must study a user-friendly protocol for measuring tree height via low-cost drones.