Moisture Content and Absorption Levels of Carbon Dioxide in Binuang Bini (Octomeles sumatrana Miq) Trees For Climate Change Management

Binuang bini (Octomeles sumatrana Miq) is a fast-growing tree with numerous economic benefits, such as the provision of wood for carpentry purposes, building boards, water management, and absorption of carbon dioxide (CO2). Therefore, this tree species has great potential and needs to be included in Reducing Emission from Deforestation and Forest Degradation (REDD)+'s mitigation program to tackle climate change. In its development, REDD+ has made it possible to carry out carbon trading in the world. Therefore, countries capable of performing protective functions and carry out reforestation, afforestation, and restoration, have the opportunity to be involved in world carbon trading. This study aims to determine the moisture content and carbon absorption rate of Binuang bini trees as a first step to regulate the allometric equation using destructive and laboratory analysis. The results show that the water content in the roots, leaves, as well as the base, middle, and tip of the stem were: 73.69%, 68.39%, 65.59%, 61.22%, and 66.26%, respectively. Furthermore, the sample test results indicate a very close relationship between carbon concentration and absorbance in the O. sumatrana tree with a simple linear regression equation: Y = 0.002X + 0.0593 with R2 = 0.9896. Therefore, this regression equation can be used to calculate the carbon concentration sample for the O. sumatrana tree fraction. The carbon content in 3 tree samples with a breast height diameter of 9.24 cm, 10.08 cm, and 11.68 cm was 2,585 kg. 2,913 kg, and 4,654 kg, respectively. In addition, the carbon sequestration for each tree diameter per year is 1.581 kg year-1, 1,782 kg year-1and 2,847 kg year-1, respectively

Above-ground biomass estimation from LiDAR data using random forest algorithms

Random forest (RF) models were developed to estimate the biomass for the Pinus radiata species in a region of the Basque Autonomous Community where this species has high cover, using the National Forest Inventory, allometric equations and low-density discrete LiDAR data. This article explores the tuning for RF hyperparameters, obtaining two models with an R2 higher than 0.7 using 2-fold cross-validation. The models selected were applied in Orozko, a municipality with more than 5000 ha of this species, where the model predicts a biomass of 1.06–1.08 Mton, which is between 16–18 % higher than the biomass predicted by the Basque Government.The work reported in this paper was partially supported by FEDER funds for the MINECO project TIN2017-85827-P and project KK-202000044 of the Elkartek 2020 funding program of the Basque Government. Additional support comes from grant IT1284-19 of the Basque Autonomous Community

Fuel Mass Mapping for Forested Areas

This dissertation aims to develop methods of forest analysis capable of estimating certain characteristics of trees, such as their height or the diameter they have at chest height. For that, algorithms were developed capable of dealing with 3D point clouds - originating from a handle sensor mounted on a UAV, more commonly called a drone - and from there the estimation of tree characteristics. This implementation aims to facilitate the work of forest management and analysis, since a UAV can cover significantly greater distances than a human being and a computer can process more information than a human. A wide set of algorithms were developed, the system being able to estimate parameters of total height and basal diameter for individual trees, as well as total height for trees inserted in sections of forest. Algorithms for detecting trunks in the forest section have also been developed, capable of detecting the presence of a tree trunk in a certain area of a forest section.Esta dissertação tem como objetivo desenvolver métodos de análise florestal capazes de estimar certas características das árvores, tais como a sua altura ou o diâmetro que possuem à altura do peito. Para isso foram desenvolvidos algoritmos capazes de lidar com nuvens de pontos 3D – com origem em sensor lidar montado num UAV, mais comummente designado drone – e daí fazer a estimação de características das árvores. Esta implementação pretende facilitar o trabalho de gestão e análise florestal, visto que um UAV consegue percorrer distâncias significativamente maiores que um ser humano e que um computador consegue processar mais informação que um humano. Um amplo conjunto de algoritmos foram desenvolvidos, sendo o sistema capaz de estimar parâmetros de altura total e de diâmetro basal para árvores individuais, bem como altura total para árvores inseridas em secções de floresta. Foram também desenvolvidos algoritmos para a deteção de troncos em seio florestal, capazes de detetar a presença de uma árvore numa determinada área de uma secção florestal

Quantifying Aboveground Biomass in a Tropical Forest Using a Lidar Waveform Weighted Allometric Model

Our knowledge of the distribution and amount of terrestrial above ground biomass (AGB) has increased using lidar technology. Recent advancements in satellite lidar has enabled global mapping of forest biomass and structure. However, there are large biases in satellite lidar estimates which impacts our understanding of carbon dynamics, particularly in tropical forests. Ni-Meister et al. (2022) developed a lidar full waveform weighted height-based allometric model which produced very good results in temperate deciduous/conifer forest in the continental US. The purpose of this study was to evaluate this biomass model in an African tropical forest using the Land Vegetation and Ice Sensor (LVIS) lidar system. The results were compared with field measured AGB derived from a generalized pan-topical AGB equation (Chave et al. 2014). Our analysis shows that the biomass model outperforms two regression based biomass models using LVIS and small footprint lidar data. It performs very well (R2=0.84, RMSE=55.67), producing similar results to the best fitted RH empirical model (R2=0.87, RMSE=49.02). However, the biomass model outperforms the RH model when including the wood density parameter from field data (R2=0.91, RMSE=40.47). The height scaling exponent estimated using site-based allometric relationships from individual tree structure and literature data matches well with the optimal height scaling exponent through fitting the model prediction and field data. Testing in a disturbed/young forest site indicates a slight larger scaling exponent and provide much more accurate AGB estimates for young stands. This result implies that the allometric relationships might be different for young and mature forest stands even for the same forest species. The larger scaling exponent for young stands than mature stands also suggests strong AGBD and height dependence for young stands than mature stands. Our model captures the nature of AGBD dependence on height and crown size structure features. The large returns shown in waveforms for mature trees suggests large dependence ABGD on crown size properties for mature forest stands. Our assessment results that this biomass model can be expanded to estimate AGB density in tropical forest biomes using the GEDI satellite lidar data with good accuracies