Remotely piloted aircraft system (RPAS) platforms are able to optimize the process of acquiring aerial images and improve the quality of the products generated in terms of spatial and temporal resolution. The exponential advance of the use of RPAS platforms in forestry, especially from the year 2010, is noteworthy. In this review, we present the global state of the art of the development and applications of RPAS technology in forestry, structured from a systematic review. Our results reveal a trend towards the use of multirotor RPAS platforms compared with fixed-wing platforms and that sensors that register in the visible spectral range are still the most widely used. More recent research has shown applications geared especially for areas such as forest inventory, with many innovations based on the detection of individual trees. Special focus has also been given to new alternatives for pest and disease mapping and phenological phenomena that occur at short intervals, as well as the monitoring of fires and postharvest areas. Therefore, there is a great potential for the use of RPAS platforms in a wide range of forest applications, whether linked to the productive sector or to the conservation of biodiversity, with great advances for spatiotemporal forest monitoring and expectations of further progress for the coming years.

The combination of machine learning and products obtained by Remote Piloted Aircraft Systems (RPAS) has stood out in the forest sector in the last decade due to the wide range of applications and contributions to the classification and modelling of spatial attributes. The literature presents an overview of these applications, their limitations, and the perspectives related to the theme. Many applications are promising and have different approaches in practices such as identifying rare or invasive trees species, quantifying biomass, early detection of diseases, monitoring deforestation and forest fires, among others. In this context of large data sets generated by RPAS surveys, observed significant influence of deep learning methods, mainly due to their ability to automatically extract spatial characteristics and design flexibility, as in the case of different Neural Networks (NN) architectures. Other algorithms such as Random Forest (RF) and Support Vector Machine (SVM) also occupy a prominent position in this context, having a proven ability to deal with complex problems. However, reduction of computational load, consolidation of analysis protocols, automated selection of predictors, use of proprietary software, and expansion of the research scope are some of the challenges that still need to be overcome to disseminate and improve the use of RPA data sets. Thus, computer vision through deep learning networks emerges as a promising approach in object-based image analysis, considering its ability to recover patterns, even when operating in less complex databases, such as RGB compositions.

This research aimed to develop statistical models to predict basal area increment (BAI) for Araucaria angustifolia using Artificial Neural Networks (ANNs). Tree species were measured for their biometric variables and identified at the species level. The data were subdivided into three groups: (1) intraspecific competition with A. angustifolia; (2) the first group of species that causes interspecific competition with A. angustifolia; and (3) the second group of species that causes interspecific competition with A. angustifolia. We calculated both the dependent and independent distance and the described competition indices, considering the impact of group stratification. Multi-layer Perceptron (MLP) ANN was structured for modeling. The main results were that: (i) the input variables size and competition were the most significant, allowing us to explain up to 77% of the A. angustifolia BAI variations; (ii) the spatialization of the competing trees contributed significantly to the representation of the competitive status; (iii) the separate variables for each competition group improved the performance of the models; and (iv) besides the intraspecific competition, the interspecific competition also proved to be important to consider. The ANN developed showed precision and generalization, suggesting it could describe the increment of a species common in native forests in Southern Brazil and with potential for upcoming forest management initiatives.

A floresta Amazônica apresenta espécies de elevado potencial para exploração sustentável de madeira, entretanto, para que sejam exploradas, há a necessidade de informações para estimativas precisas de seu volume de madeira. Assim, com a hipótese de que volumes estimados por diferentes métodos e os obtidos pela cubagem rigorosa são estatisticamente iguais entre si, este trabalho teve por objetivo comparar estimativas volumétricas obtidas através do fator de forma, modelos volumétricos e modelo de afilamento para cinco espécies comerciais da floresta amazônica. Foram cubadas 449 árvores pelo método de Smalian para desenvolvimento do fator de forma e ajuste das equações volumétricas e de afilamento. Foi utilizado o delineamento blocos ao acaso, onde cada espécie foi considerada como um bloco e cada método de obtenção volumétrica compôs um tratamento. Foi aplicada a análise de variância para verificar a existência de diferença significativa entre os tratamentos e entre os blocos, seguida do teste de Tukey para o nível crítico . Entre os modelos ajustados, o de Schumacher-Hall foi o que apresentou maior precisão para estimar o volume. O fator de forma médio estimado é 0,73. Não houve diferenças estatísticas na estimativa do volume utilizando os diferentes métodos, contudo, recomenda-se utilizar o modelo de simples entrada, pois a altura para espécies amazônicas é de difícil obtenção, fazendo deste método o mais prático.

Crown variables are used in growth and production models to increase the accuracy of estimates. These variables are costly and difficult to measure, mainly in dense native forests such as mixed omb...