Management Recommendation Generation for Areas Under Forest Restoration Process through Images Obtained by UAV and LiDAR

Evaluating and monitoring forest areas during a restoration process is indispensable to estimate the success or failure of management intervention and to correct the restoration trajectory through adaptive management. However, the field measurement of several indicators in large areas can be expensive and laborious, and establishing reference values for indicators is difficult. The use of supervised classification techniques of high resolution images, combined with an expert system to generate management recommendations, can be considered promising tools for monitoring and evaluating restoration areas. The objective of the present study was to elaborate an expert system of management recommendation generation for areas under restoration, which were monitored by two different remote sensors: UAV (Unmanned Aerial Vehicle) and LiDAR (Light Detection and Ranging). The study was carried out in areas under restoration with about 54 ha and five years of implementation, owned by Fibria Celulose S.A. (recently acquired by Suzano S.A.), in the southern region of Bahia State, Brazil. We used images from Canon S110 NIR (green, red, near infrared) on UAV and LiDAR data compositions (intensity image, digital surface model, digital terrain model, normalized digital surface model). The monitored restoration indicator entailed land cover separated into three classes: Canopy cover, bare soil and grass cover. The images were classified using the Random Forest (RF) and Maximum Likelihood (ML) algorithms and the area occupied by each land cover classes was calculated. An expert system was developed in ArcGIS to define management recommendations according to the land cover classes, and then we compared the recommendations generated by both algorithms and images. There was a slight difference between the recommendations generated by the different combinations of images and classifiers. The most frequent management recommendation was “weed control + plant seedlings” (34%) for all evaluated methods. The image monitoring methods suggested by this study proved to be efficient, mainly by reducing the time and cost necessary for field monitoring and increasing the accuracy of the generated management recommendations

Improving 3-PG calibration and parameterization using artificial neural networks

Understanding how the physiological processes of trees are affected by the environment or silvicultural practices is important for forest management, which requires process-based models. It enables the evaluation of the growth of a forest under different scenarios. The 3-PG model has been widely used all over the world, justified by its simplicity and efficiency, as it uses a more accessible language and fewer parameters than other process-based models. It is a model of greatest interest for forest management because it enables the use of allometric equations to calculate variables of interest in this area, such as the average diameter at 1.30 m height (DBH), total height and stand volume. The 3-PG parameterization is essential to guarantee the model's good performance; however, in some cases, when observed data are not available, values from the literature is used or calibration is performed. In general, there is a mixture of these alternatives in the same parameterization, but some of the parameters generate greater sensitivity in some outputs or change according to site characteristics. In the present work, we analyzed the efficiency of artificial neural networks to predict some of the parameters pointed out in the literature as being of the greatest importance for 3-PG using climate and process variables as inputs. For this, a simulated database was generated, using 16 parameterizations of 3-PG, for different regions of Brazil. The parameters values of the DBH function (as and ns), minimum and maximum fraction of biomass allocated to the root (ηRn and ηRx), and age at full canopy cover (tc) were associated with this database. The Artificial Neural Networks (ANNs) were trained using the database with parameter repetition over time and with the average condition of each site. In the second case, training was performed using 100% of the data, and validation was performed using a simulated database. The efficiency of neural networks has been proven in predicting the parameters as, ns and ηRx, with validation root mean squared error (RMSE) of 6.9%, 6.9% and 4.8%, in the first training approach, respectively. For training based on sites average condition RMSE was 20.7%, 3.0% and 8.8%, for as, ns and ηRx, respectively. The study showed the need for more scientific investigation for the other parameters, including information and input variables such as soil characteristics. As demonstrated in this study, the possibility of parameterizing 3-PG with ANNs or any machine learning technique may contribute to the broader use of this process-based model. In addition, artificial neural networks have great potential to assist in the calibration process of the 3-PG model, making this process more efficient by integrating environmental conditions and allowing the association between parameters. It is recommended to apply these ANNs for the conditions tested here

Beyond trees: Mapping total aboveground biomass density in the Brazilian savanna using high-density UAV-lidar data

Tropical savanna ecosystems play a major role in the seasonality of the global carbon cycle. However, their ability to store and sequester carbon is uncertain due to combined and intermingling effects of anthropogenic activities and climate change, which impact wildfire regimes and vegetation dynamics. Accurate measurements of tropical savanna vegetation aboveground biomass (AGB) over broad spatial scales are crucial to achieve effective carbon emission mitigation strategies. UAV-lidar is a new remote sensing technology that can enable rapid 3-D mapping of structure and related AGB in tropical savanna ecosystems. This study aimed to assess the capability of high-density UAV-lidar to estimate and map total (tree, shrubs, and surface layers) aboveground biomass density (AGBt) in the Brazilian Savanna (Cerrado). Five ordinary least square regression models esti-mating AGBt were adjusted using 50 field sample plots (30 m × 30 m). The best model was selected under Akaike Information Criterion, adjusted coefficient of determination (adj.R2), absolute and relative root mean square error (RMSE), and used to map AGBt from UAV-lidar data collected over 1,854 ha spanning the three major vegetation formations (forest, savanna, and grassland) in Cerrado. The model using vegetation height and cover was the most effective, with an overall model adj-R2 of 0.79 and a leave-one-out cross-validated RMSE of 19.11 Mg/ha (33.40%). The uncertainty and errors of our estimations were assessed for each vegetation formation separately, resulting in RMSEs of 27.08 Mg/ha (25.99%) for forests, 17.76 Mg/ha (43.96%) for savannas, and 7.72 Mg/ha (44.92%) for grasslands. These results prove the feasibility and potential of the UAV-lidar technology in Cerrado but also emphasize the need for further developing the estimation of biomass in grasslands, of high importance in the characterization of the global carbon balance and for supporting integrated fire management activities in tropical savanna ecosystems. Our results serve as a benchmark for future studies aiming to generate accurate biomass maps and provide baseline data for efficient management of fire and predicted climate change impacts on tropical savanna ecosystems

Optimization of the geometric alignment of forest roads

Estradas desempenham diversas funções para a sociedade. No setor florestal esse meio de transporte é fundamental, pois é utilizado em praticamente todas as atividades do empreendimento. Dentre essas atividades, o transporte de madeira merece destaque pois, juntamente com a colheita, é a operação mais onerosa do processo produtivo. Diversos elementos podem influenciar o custo do transporte; no entanto, a estrada pode ser considerada como um dos mais importantes, pois possui forte relação com os demais elementos, estando associada a impactos ambientais, além de ter elevados custo de construção e manutenção. O principal desafio dos gestores é determinar o local ideal de construção de estrada, de forma a permitir o tráfego com eficiência e segurança. Neste estudo é proposto um método desenvolvido para otimização do traçado geométrico de estradas florestais, implementado em ambiente de sistema de informações geográficas (SIG). A metaheurística simulated annealing foi utilizada para implementar um algoritmo com o objetivo de minimizar o custo total da estrada, atendendo restrições técnicas dos alinhamentos horizontal e vertical. O custo total incluiu: construção, manutenção, utilização e fatores ambientais e sociais. O método desenvolvido é apropriado para a otimização do traçado geométrico de estradas, por atender as condições necessárias: considera todos os custos dominantes e sensíveis, respeitando as restrições técnicas; otimiza as atribuições horizontais e verticais de modo simultâneo; é capaz de retornar uma boa solução para um problema de grande porte em um tempo aceitável; e é compatível com um SIG, o que favorece o processo de tomada de decisão, permitindo lidar com bases de dados extensas e complexas.Roads perform many functions for society. In the forestry sector, it is a key factor, because it is used in practically all the activities of the enterprise. Among these activities, timber transportation deserves special mention because, together with harvesting, it is the most costly operation of the production process. Several elements can influence the cost of transport; however, the road can be considered as one of the most important because it has a strong relationship with other elements, for instance, being associated with environmental impacts, and also having high construction and maintenance costs. The main challenge for managers is to determine the ideal local for road construction in order to allow efficient and safe traffic. In this study, a method developed for optimization of the geometric alignment of forest roads, implemented in a geographic information system (GIS) environment, is proposed. The simulated annealing metaheuristic was used to implement an algorithm with the objective of minimizing the total cost of the road, addressing technical restrictions of the horizontal and vertical alignment. The total cost included: construction, maintenance, use and environmental and social factors. The developed method is suitable for optimization of the geometric alignment of roads, considering the needed conditions: it considers all the dominant and sensitive costs, respecting the technical restrictions; it optimizes horizontal and vertical assignments simultaneously; it is able to return a good solution to a large problem in an acceptable time; and it is compatible with a GIS, which favors the decision- making process, allowing to deal with extensive and complex databases.Conselho Nacional de Desenvolvimento Científico e Tecnológic

Pathway Optimization for Access to Forest Inventory Plots

O objetivo desse estudo foi desenvolver metodologias para otimização do acesso e do caminhamento às parcelas de inventário florestal, bem como implementá-las em um ambiente com interface gráfica para o usuário e integradas a um sistema de informações geográficas. Foram utilizados dados provenientes da empresa florestal Floresteca, de plantios de teca (Tectona grandis) situados no município de Rosário Oeste, Mato Grosso. A base de dados espaciais foi composta da localização de 80 parcelas de inventário distribuídas em 15 talhões e da rede de estradas florestais existentes no local. Normalmente as empresas florestais brasileiras adotam variações ou combinações de duas estratégias de acesso às parcelas: (1ª) caminhamento da estrada até a parcela e posteriormente da parcela até a estrada, e (2ª) caminhamento da estrada até a parcela e da parcela até uma série de outras parcelas, retornando-se, posteriormente, à estrada. Neste estudo adotou-se a 1ª estratégia. A otimização do acesso foi baseada no algoritmo do vizinho mais próximo. Este algoritmo, basicamente, determina os pontos da estrada mais próximos a cada parcela. Esses pontos são, então, considerados como a entrada da parcela. O problema do caminhamento entre as parcelas foi formulado como um problema do caixeiro-viajante (PCV) permitindo-se, contudo, a imposição da malha viária no processo de otimização. O PCV foi resolvido via formulação de programação linear inteira, sendo otimizado pelo algoritmo cutting-plane, disponível no software Concorde. Para fins de comparação, o problema foi também resolvido pelo método aproximativo do vizinho mais próximo, uma vez que o executor do inventário aplica esse algoritmo intuitivamente em campo. As metodologias foram implementadas usando-se a linguagem de programação Python e integradas ao software ArcGIS. A metodologia de acesso às parcelas otimiza o caminhamento entre a estrada e as parcelas de inventário florestal, e sua implementação computacional permite que a tarefa seja feita de forma automatizada. Com isso, obtêm-se aumento de eficiência tanto no planejamento do inventário quanto em sua execução. A metodologia de caminhamento otimiza o percurso entre as parcelas a serem inventariadas. A solução exata mostrou-se superior à solução aproximada, algo em torno de 17% em média.The aim of this study was to develop methods for optimizing access and pathway to forest inventory plots, and to implement them in an environment with graphical user interface and integrated into a geographic information system. We used data from a plantation of teak (Tectona grandis) owned by the forestry company Floresteca, located in the city of Rosário Oeste, Mato Grosso. The spatial database was composed of localization of 80 inventory plots distributed in 15 stands, as the network of forest roads located on site. Usually, the Brazilian forestry companies adopt variations or combinations of two strategies to access the portions: (1st) moving from the road to the parcel and then, from parcel to the road, and (2nd) moving from the road to the plot, then from the plot to a series of other plots, to finally return to the road. In this study we adopted the 1st strategy. The optimization of access was based on the nearest neighbor algorithm. This algorithm basically determines the points of the road closest to each plot. These points are then considered as the parcel entrance. The problem of the pathway between plots was formulated as a traveling salesman problem (TSP) allowing, however, the imposition of the road network in the optimization process. The TSP was solved via integer linear programming formulation, followed by an optimization from the algorithm cutting-plane available on Concorde software. For comparison purposes, the problem has also been solved by the method of approximate nearest neighbor, since the inventory executor intuitively applies this algorithm in the field. These methods were implemented using the Python programming language and integrated into the ArcGIS software. The parcel access methodology optimizes the path among the roads and the forest inventory plots, and its computational implementation allows the task to be done in an automated fashion. Thus, we obtain increased efficiency both in inventory planning and in execution. The methodology optimizes the traversal path between plots to be inventoried. The exact solution was superior to the approximate solution, something around 17% on average.Conselho Nacional de Desenvolvimento Científico e Tecnológic

Distribuição espacial dos danos de Heilipodus naevulus em plantio de clones de eucalipto

As espécies Heilipodus naevulus (Mannerheim, 1836) foi detectado, em 2008, no município de Barão de Cocais, MG, em plantios comerciais de clones de eucalipto, danificando o ponteiro principal e o lateral. Devido ao seu hábito noturno, o seu tamanho reduzido, coloração acinzentada, e , por se abrigar na área de serapilheira durante o dia, a sua detecção é dificultada, sendo a sua presença apenas evidenciada através dos ponteiros tombados. Este trabalho teve como objetivo conhecer a distribuição desta espécie durante o primeiro ano de crescimento clones eucalipto, a fim de fornecer subsídios para a elaboração de um plano de amostragem. O trabalho foi realizado no município de Barão de Cocais, MG, a partir de outubro/2008 a março/2009. Nos dois talhões de avaliação foram estratificados em baixada, encosta e topo. Foram distribuídas 18 porções, de 10 x 10 árvores, sendo nove por talhão, e, três por estrato. Foi adotado como indicador da presença de H.naevulus na planta a presença ponteiro recém danificado. Para o cálculo da distribuição espacial foram empregados a Razão Variância/Média, o Índice de Morisita, o Parâmetro kda distribuição binomial negativa, o Coeficiente de Green, a Distribuição de Poisson e a Lei de potência de Taylor. Ao longo do período de avaliação da presença de H. naevulus,os índices calculados apresentaram um padrão agregado de ocorrência dos danos, indicando que a espécie apresenta um padrão agregado de distribuição, mesmo em baixa incidência