ABOVEGROUND BIOMASS ESTIMATION IN A TROPICAL FOREST WITH SELECTIVE LOGGING USING RANDOM FOREST AND LIDAR DATA

The tropical forest is characterized by expressive biomass and stores high amounts of carbon, which is an important variable for climate monitoring. Thus, studies aiming to analyze suitable methods to predict biomass are crucial, especially in the tropics, where dense vegetation makes modeling difficult. Thus, the objective of the present study was to estimate aboveground biomass (AGB) in a tropical forest area with selective logging in the Amazon forest using the Random Forest (RF) machine learning algorithm and LiDAR data. For this, 85 sample units were used at Fazenda Cauaxi, in the municipality of Paragominas, Pará State. LiDAR data were collected in 2014 and made available by the Sustainable Landscapes Project. The software R was used for data analysis. Among the LiDAR metrics, the average height was used as it had the greatest significance to compose the model. The model presented a pseudo R² of 0.69 (value obtained by the RF), Spearman's Correlation Coefficient of 0.80, RMSE of 47.05 Mg.ha-1 (19.84%), and Bias of 2.06 Mg.ha-1 (0.87%). With the results, it was possible to infer that the average height metric was enough to estimate AGB in a tropical forest with selective logging, in addition, the RF algorithm the biomass to be estimated, which can be used to assist in monitoring and action management in areas of selective logging and serve as a basis for climate change mitigation policies

Airborne laser scanning applied to eucalyptus stand inventory at individual tree level

The objective of this work was to evaluate the application of airborne laser scanning (ALS) to a large-scale eucalyptus stand inventory by the method of individual trees, as well as to propose a new method to estimate tree diameter as a function of the height obtained from point clouds. The study was carried out in a forest area of 1,681 ha, consisting of eight eucalyptus stands with ages varying from four to seven years. After scanning, tree heights were obtained using the local maxima algorithm, and total wood stock by summing up individual volumes. To determine tree diameters, regressions fit using data measured in the inventory plots were used. The results were compared with the estimates obtained from field sampling. The equation system proposed is adequate to be applied to the tree height data derived from ALS point clouds. The tree individualization approach by local maxima filters is efficient to estimate number of trees and wood stock from ALS data, as long as the results are previously calibrated with field datainfo:eu-repo/semantics/publishedVersio

Escaneamento a laser aerotransportado aplicado a inventário de povoamentos de eucalipto ao nível de árvores individuais

The objective of this work was to evaluate the application of airborne laser scanning (ALS) to a large-scale eucalyptus stand inventory by the method of individual trees, as well as to propose a new method to estimate tree diameter as a function of the height obtained from point clouds. The study was carried out in a forest area of 1,681 ha, consisting of eight eucalyptus stands with ages varying from four to seven years. After scanning, tree heights were obtained using the local maxima algorithm, and total wood stock by summing up individual volumes. To determine tree diameters, regressions fit using data measured in the inventory plots were used. The results were compared with the estimates obtained from field sampling. The equation system proposed is adequate to be applied to the tree height data derived from ALS point clouds. The tree individualization approach by local maxima filters is efficient to estimate number of trees and wood stock from ALS data, as long as the results are previously calibrated with field data.O objetivo deste trabalho foi avaliar a aplicação do escaneamento a laser aerotransportado (ALS) na realização de inventário de povoamentos de eucalipto, em larga escala, por meio do método de individualização de árvores, bem como propor um novo método para estimativa dos diâmetros das árvores em função das alturas obtidas a partir da nuvem de pontos. O estudo foi conduzido em floresta de 1,681 ha, composta por oito povoamentos de eucalipto com idades entre quatro e sete anos. Após o escaneamento, foram obtidas as alturas das árvores pelo algoritmo de máximos locais, e o estoque total de madeira, pela soma dos volumes individuais. Para determinar os diâmetros das árvores, foram utilizadas regressões ajustadas a partir de dados medidos em parcelas de inventário. Os resultados foram comparados às estimativas obtidas via amostragem de campo. O sistema de equações proposto é adequado para ser aplicado aos dados de altura derivados da nuvem de pontos do ALS. O método de individualização de árvores com o filtro de máximos locais é eficiente para estimar o número de árvores e o estoque de madeira a partir dos dados do ALS, desde que os resultados sejam previamente calibrados com o inventário de campo

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

Effects of pulse density on digital terrain models and canopy metrics using airborne laser scanning in a tropical rainforest

Airborne laser scanning (ALS) is increasingly being used to enhance the accuracy of biomass estimates in tropical forests. Although the technological development of ALS instruments has resulted in ever-greater pulse densities, studies in boreal and sub-boreal forests have shown consistent results even at relatively small pulse densities. The objective of the present study was to assess the effects of reduced pulse density on (1) the digital terrain model (DTM), and (2) canopy metrics derived from ALS data collected in a tropical rainforest in Tanzania. We used a total of 612 coordinates measured with a differential dual frequency Global Navigation Satellite System receiver to analyze the effects on DTMs at pulse densities of 8, 4, 2, 1, 0.5, and 0.025 pulses·m−2. Furthermore, canopy metrics derived for each pulse density and from four different field plot sizes (0.07, 0.14, 0.21, and 0.28 ha) were analyzed. Random variation in DTMs and canopy metrics increased with reduced pulse density. Similarly, increased plot size reduced variation in canopy metrics. A reliability ratio, quantifying replication effects in the canopy metrics, indicated that most of the common metrics assessed were reliable at pulse densities >0.5 pulses·m−2 at a plot size of 0.07 ha

Uso do LiDAR na estimativa de atributos florestais: uma revisão.

Estudos com o LiDAR (Light Detection and Ranging) têm revelado precisão satisfatória nas medições da estrutura de árvores, o que tem contribuído para a compreensão dos ecossistemas florestais, bem como para o fornecimento de dados necessários para a investigação de propriedades biofísicas da floresta. Frente à importância dessa ferramenta, o presente estudo teve como objetivo apresentar uma revisão sobre o LiDAR direcionado para aplicações florestais, e especificamente apresentar suas possibilidades e uso na Caatinga. A varredura a laser tem diversas classificações, uma delas baseia-se na plataforma onde o scanner está instalado. Assim, tem-se o laser scanner terrestre, que pode ser usado em uma base fixa ou móvel na superfície terrestre; o laser scanner aéreo, com o uso de aeronaves tripuladas e não-tripuladas; e o laser scanner orbital, localizado em plataformas espaciais. Em escala refinada, a estrutura tridimensional das árvores pode ser detectada pelo LiDAR, e assim se obter importantes informações de traços de espécies vegetais, bem como permitir sua identificação. Ainda, a medição de atributos florestais promovida pelos diferentes tipos de LiDAR tem fornecido dados consistentes de biomassa e carbono florestais, importantes para o desenvolvimento de estudos e monitoramento de estoque de carbono terrestre, o que tem colaborado com as estratégias de redução de efeitos das mudanças climáticas. Na caatinga, o LiDAR tem permitido a contabilização de árvores e a determinação de sua altura e diâmetro da copa, e por conseguinte, a aplicação de equações alométricas para estimativa dos estoques de carbono na vegetação

Estimativas do estoque e dinâmica de biomassa acima do solo utilizando diferentes abordagens estatístcas e dados LiDAR em floresta tropical

Orientadora: Prof.ª Drª Ana Paula Dalla CorteCoorientadores: Dr. Carlos Alberto Silva, Prof. Dr. Carlos Roberto Sanquetta; Prof. Dr. Sebastião do Amaral MachadoDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Agrárias, Programa de Pós-Graduação em Engenharia Florestal. Defesa : Curitiba, 25/02/2019Inclui referênciasÁrea de Concentração: Manejo FlorestalResumo As florestas tropicais são consideradas como os ecossistemas vegetais que mais estocam carbono, devido ao acúmulo de biomassa em seus tecidos durante seu desenvolvimento. A floresta Amazônica se destaca pela sua extensão, sendo considerada a maior floresta tropical do mundo. Assim, frente às principais discussões de mudanças climáticas globais, a floresta Amazônica tem sido apontada como umas das alternativas para redução de gases do efeito estufa, principais responsáveis pelas mudanças climáticas globais. Nesse sentido, esta pesquisa foi dividida em duas etapas: A primeira etapa objetivou realizar uma análise da dinâmica da biomassa acima do solo (AGB) em floresta tropical através de dados de LiDAR aerotransportado, relacionando com mudanças estruturais identificadas automaticamente. A segunda etapa teve como objetivo comparar diferentes abordagens para a estimativa de AGB em floresta tropical, bem como analisar a dinâmica da AGB em uma floresta tropical que foi seletivamente explorada. O primeiro estudo foi conduzido na Floresta Nacional (FLONA) do Jamari, em Rondônia - Brasil. Neste estudo, foram utilizados dados de LiDAR e de inventário florestal. A metodologia foi constituída de processamento dos dados LiDAR para obtenção do modelo de altura de copa (CHM) e das métricas para estimativa de AGB. Dois modelos disponíveis na literatura foram utilizados para as estimativas de AGB de campo e via métricas LiDAR. Após este procedimento, uma análise das mudanças dos estoques de AGB em nível de paisagem e também das mudanças estruturais identificadas foi realizada. O segundo estudo foi conduzido em uma floresta tropical seletivamente explorada no leste da Amazônia. Os dados de campo foram constituídos de 85 parcelas, enquanto que os dados LiDAR foram obtidos em 2012, 2014 e 2017. Modelos no nível da parcela foram primeiramente desenvolvidos usando 6 métricas baseadas na análise de PCA e quatro abordagens de aprendizado de máquina foram implementados e comparados com o modelo de regressão linear (OLS). Os resultados de ambos os capítulos mostraram que o LiDAR é uma ferramenta de grande potencial para a estimativa do estoque e da dinâmica de AGB em florestas tropicais, permitindo desta forma que diferentes análises sejam desenvolvidas. Foram mensuradas de forma automática 40 copas para cada ano no estrato superior da floresta, e com estas, observou-se que houve acréscimos estruturais positivos que não influenciaram nas estimativas dos estoques de AGB. Os resultados do segundo capítulo mostraram que entre as abordagens utilizadas, o método de regressão linear foi superior às demais abordagem, entretanto, abordagens não paramétricas como Random Forest e Support Vector Machine também mostraram potencial para estimativas de AGB e podem ser utilizadas quando necessário. Os resultados do segundo capítulo também revelaram que era possível mapear os estoques de AGB com uma precisão aceitável (RMSE <20%), dessa forma, foi possível analisar com precisão as mudanças ao longo do tempo dos estoques de biomassa em uma floresta seletivamente explorada. Palavras-chave: Amazônia, sensoriamento remoto, predição de biomassa, métodos de estimação.Abstract Tropical forests are considered the most carbon-storing plant ecosystems due to the accumulation of biomass in their tissues during their development. The Amazon rainforest stands out for its extension, being considered the largest tropical forest in the world. Faced with the main discussions of global climate change, the Amazon rainforest has been identified as an alternative to reduce greenhouse gases, which are the main cause of global climate change. Thereby, this research was divided into two stages. The first stage was to perform an analysis of the aboveground biomass (AGB) dynamics in tropical forest from airborne LiDAR data, relating them to the structural changes identified automatically. The second stage was aimed at comparing different approaches to estimate AGB in tropical forest, as well as to analyze the dynamics of AGB in a tropical forest that was selectively explored. The first study was conducted in the Jamari National Forest (FLONA), in Rondônia - Brazil. Airborne LiDAR and forest inventory were used and the methodology was consisted of processing the LiDAR data to obtain Canopy Height Models (CHM) and also the metrics for estimating AGB at plot and landscape level. Two allometric models available in the literature were used for AGB estimates. The first was used to estimate the AGB from the field and the other was used for the estimations via LiDAR metrics. After this, an analysis was made of the changes in AGB stocks at the landscape level and also of the structural changes identified. The second study was conducted in a selectively exploited tropical forest in eastern Amazonia. The field data were composed of 85 plots. LiDAR data were obtained in 2012, 2014 and 2017. Plotlevel models were first developed using 6 metrics based on PCA analysis and four machine learning approaches were implemented and compared with the linear regression model (OLS). The results of both chapters showed that the LiDAR is a tool with great potential for the estimation of the stock and the dynamics of AGB in tropical forests, thus allowing different analyzes to be developed. From two LiDAR surveys (2011-2013), 40 crowns were automatically measured for each year in the superior stratum of the forest, and with these, it was observed that there were positive structural increases that did not influence of AGB stocks. The results of the second chapter showed that among the approaches used, the linear regression method was superior to the other approaches, however, non-parametric approaches such as Random Forest and Support Vector Machine also shown potential for AGB estimates and may be used when required. The results of the second chapter also revealed that it was possible to map AGB stocks with acceptable accuracy (RMSE <20%), so it was possible to safely analyze the changes over time in biomass stocks in a forest that was selectively exploited. Keywords: Amazon, remote sensing, biomass prediction, methods

Estoque de carbono e quantificação da incerteza propagada combinando inventário florestal e sensoriamento remoto

REDD+ is an instrument developed at UNFCCC conferences to financially reward developing countries for efforts to reduce deforestation and forest degradation. In 2010, the IPCC task force in Yokohama evaluated and recommended linking existing field and remote sensing works to forest emissions estimates (IPCC, 2010) in a system that should be measurable, reportable and verifiable (MRV) (UNFCCC, 2009). In developing countries these estimates are more difficult due to the lack of a field data collection system and the availability of both current and temporal images to generate their emission history. The adoption of techniques associated with mathematical modeling and computer system is necessary to reach the recommendations for REDD + projects and were applied in the Ducke Reserve of INPA. Forest inventory with ALS LiDAR and SRTM airborne data, RapidEye and Landsat 8 was used. Linear models were used to establish relationships and the Monte Carlo technique was applied to quantify the propagated error. The error in diameter measurement could be measured and controlled in forest inventories by adopting remeasurement techniques with tape and photogrammetry. The development of scripts for the processing of LiDAR data allowed us to quantify and control errors in land estimates. The georeferencing of the plots for the combination with high resolution data requires procedures that guarantee their accuracy without compromising the field activities. The Monte Carlo method was important for the estimation of the error mainly of the georeferencing of the field data, since the formula of the error propagation does not allow this type of approach. The scripts are in development and available to any user, in order to make the method replicable in different places. The models and their uncertainties demonstrated spatial variation with recognized cause and consequence effects, necessary for the reliability of the models. The most endangered mature forest areas are the plateau regions around the Ducke Reservation suitable for REDD+ project and sustainable development.O REDD+ é um instrumento elaborado nas conferências da UNFCCC para recompensar financeiramente os países em desenvolvimento que adotarem esforços para a redução do desmatamento e degradação florestal. Em 2010, a força tarefa do IPCC em Yokohama, avaliou e recomendou juntar os trabalhos existentes de campo com os de sensoriamento remoto para as estimativas das emissões florestais (IPCC, 2010) em um sistema que deve ser mensurável, reportável e verificável (MRV) (UNFCCC, 2009). Nos países em desenvolvimento estas estimativas são mais difíceis pela falta de um sistema de coleta de dados de campo e disponibilidade de imagens, tanto atuais como temporais, para gerar seu histórico de emissão. A adoção de técnicas associadas a modelagem matemática e sistema em computação é necessária para alcançar as recomendações para projetos de REDD+ e foram aplicadas na Reserva Ducke do INPA. O inventário florestal combinando com dados aerotransportados ALS, LiDAR e espaciais como SRTM, RapidEye e Landsat 8 foram utilizados. Modelos lineares foram usados para estabelecer as relações e a técnica de Monte Carlo foi aplicada para quantificação do erro propagado. O erro na medida do diâmetro pôde ser medido e controlado nos inventários florestais adotando técnicas de remedição com fita e fotogrametria. O desenvolvimento de scripts para o processamento dos dados LiDAR, permitiu quantificar e controlar os erros nas estimativas do terreno. O georreferenciamento das parcelas para a combinação com dados de alta resolução requer procedimentos que garantam sua acurácia sem comprometer as atividades de campo. O método de Monte Carlo foi importante para a estimativa do erro principalmente do georreferenciamento dos dados de campo, uma vez que a fórmula da propagação do erro não permite este tipo de abordagem. Os scripts estão em desenvolvimento e disponíveis a qualquer usuário, com intuito de tornar o método replicável em diferentes locais. Os modelos e suas incertezas demonstraram variação espacial com efeitos de causa e consequência reconhecidos, necessários para a confiabilidade dos modelos. As áreas com florestas maduras mais ameaçadas são as regiões de platô ao redor da Reserva Ducke aptas a projeto de REDD+ e ao desenvolvimento sustentável

Coupling remote sensing with wildfire spread modeling in Mediterranean areas

Wildfires are a threat to the ecosystems and in the future this threat could become stronger due to climate change. Spatially explicit fire spread models are effective tools to study fire behavior and wildfire risk. However, to run fire spread simulations, one of the most important inputs is represented by fuel models and this information is not always available. In the last decades, remote sensing technologies have offered valuable information for the classification and characterization of fuels. For this reason, in this work we created accurate maps of main fuel types for Mediterranean areas combining multispectral and LiDAR data. This information improves the current available information, which derives from the Land Use Map of Sardinia. We also enhanced the characterization of canopy fuel models using LiDAR data producing canopy layers ready to be used for wildfire spread modeling. Finally, we compared the variation in simulated wildfire spread and behavior determined by the use of fine-scale maps v. lower resolution maps. In these simulations, we assessed also the effect of using LiDAR-derived canopy layers as well. The results showed more accurate outputs when using our custom fuel and canopy layers produced in this work. In conclusion, this work suggests that the use of LiDAR and satellite imagery data can contribute to improve estimates of modeled wildfire behavior