Biomass estimation using LiDAR data

ABSTRACT: Forest ecosystems play a very important role in carbon cycle because they suppose one of thebiggest carbon reservoirs and sinks. Estimating the aboveground forest biomass is critical tounderstand the global carbon storage process. Different models to estimate aboveground biomassin the Pinus radiata specie in a specific region of Spain have been developed, using, exclusively,public and accessible data with low point density gathered periodically from Light Detection andRanging (LiDAR) flights. The point clouds data were processed to obtain metrics considered aspredictive variables and afterwards, the multiple regression technique has been applied togenerate the biomass estimation models. The best models explain 76% of its variability with astandard error of 0.26 ton/ha in logarithmic units. The methodology can be considered as highlyautomated and extensible to other territories with similar characteristics. Our results support theuse of this approach for more sustainable management of forest areas

Prediction of Aboveground Biomass from Low-Density LiDAR Data: Validation over P. radiata Data from a Region North of Spain

ABSTRACT: Estimation of forestry aboveground biomass (AGB) by means of aerial Light Detection and Ranging (LiDAR) data uses high-density point sampling data obtained in dedicated flights, which are often too costly for available research budgets. In this paper we exploit already existing public low-density LiDAR data obtained for other purposes, such as cartography. The challenge is to show that such low-density data allows accurate biomass estimation. We demonstrate the approach on data available from plantations of Pinus radiata in the Arratia-Nervión region, located in Biscay province located in the North of Spain. We use public data gathered from the low-density (0.5 pulse/m2) LiDAR flight conducted by the Basque Government in 2012 for cartographic production. We propose a linear regression model based on explanatory variables obtained from the LiDAR point cloud data. We calibrate the model using field data from the Fourth National Forest Inventory (NFI4), including the selection of the optimal model variables. The results revealed that the best model depends on two variables extracted from LiDAR data: One directly related with tree height and a second parameter with the canopy density. The model explained 80% of its variability with a standard error of 0.25 ton/ha in logarithmic units. We validate the predictions against the biomass measurements provided by the government institutions, obtaining a difference of 8%. The proposed approach would allow the exploitation of the periodic available low-density LiDAR data, collected with territorial and cartographic purposes, for a more frequent and less expensive control of the forestry biomass.The work reported in this paper was partially supported by FEDER funds for the MINECO project TIN2017-85827-P, and project KK-2018/00071 of the Elkartek 2018 funding program of the Basque Government

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

Delamination Fracture Behavior of Unidirectional Carbon Reinforced Composites Applied to Wind Turbine Blades

One of the materials that is used widely for wind turbine blade manufacturing are fiber-reinforced composites. Although glass fiber reinforcement is the most used in wind turbine blades, the use of carbon fiber allows larger blades to be manufactured due to their better mechanical characteristics. Some turbine manufacturers are using carbon fiber in the most critical parts of the blade design. The larger rotors are exposed to complex loading conditions in service. One of the most relevant structures on a wind turbine blade is the spar cap. It is usually manufactured by means of unidirectional laminates, and one of its major failures is the delamination. The determination of material features that influence delamination initiation and advance by appropriate testing is a fundamental topic for the study of composite delamination. The fracture behavior is studied across coupons of carbon fiber reinforcement epoxy laminates. Fifteen different test conditions have been analyzed. Fracture surfaces for different mode ratios have been explored using optical microscope and scanning electron microscope. Experimental results shown in the paper for critical fracture parameters agree with the theoretically expected values. Therefore, this experimental procedure is suitable for wind turbine blade material characterizing at the initial coupon-scale research level.e authors are grateful to the European Union Ministry of Turkey, National Agency of Turkey for the support of this project under the Project Code: 2015-1-TR01-KA203-021342 entitled Innovative European Studies on Renewable Energy Systems

Diseño y contraste de nuevos modelos de estimación del potencial energético de biomasa forestal en el Territorio de Bizkaia mediante técnicas de análisis estadístico espacial usando herramientas GIS con datos LiDAR

RESUMEN: El objetivo de esta tesis doctoral es desarrollar un modelo que permita estimar la biomasa aérea para la especie Pinus radiata en la comarca de Arratia-Nervión, (Bizkaia), a partir del uso exclusivo de los datos del vuelo LiDAR realizado por Gobierno Vasco en 2012. En el proceso, se han utilizado los datos de campo del Inventario Forestal Nacional 4 como base para estimar la biomasa de contraste. Partiendo de las nubes de puntos LiDAR y tras su procesado, se han calculado diversas métricas provenientes de las mismas que se consideran como variables predictivas. Se ha aplicado la técnica de análisis estadístico de la regresión lineal múltiple. El resultado obtenido ha revelado que el mejor modelo depende de dos variables: un parámetro relacionado con la altura LiDAR y otro con la densidad del dosel. El modelo consigue explicar el 76% de la variabilidad con un error estándar de 0.26 ton/ha en unidades logarítmicas.ABSTRACT: The main aim of this doctoral thesis is to develop a model to estimate aerial biomass in Pinus radiata D. Don species in the Arratia-Nervión region, located in Bizkaia, exclusively using data gathered from the LiDAR flight conducted by the Basque Government in 2012. In the process, field data from the National Forest Inventory 4 have been used used In order to contrast estimated biomass. The LiDAR point clouds data were processed to obtain different metrics, considered as predictive variables. Amongst them, height related variables and canopy density related variables. To obtain the model, the multiple regression analysis statistical technique has been applied. The Result obtained has revealed that the best model depends on two variables: One parameter directly related with LiDAR height and a second parameter with canopy density.The model explains 76% of its variability with a standard errorthat amounts to 0.26 ton/ha in logarithmic units

BAMS: A Tool for Supervised Burned Area Mapping Using Landsat Data

A new supervised burned area mapping software named BAMS (Burned Area Mapping Software) is presented in this paper. The tool was built from standard ArcGIS (TM) libraries. It computes several of the spectral indexes most commonly used in burned area detection and implements a two-phase supervised strategy to map areas burned between two Landsat multitemporal images. The only input required from the user is the visual delimitation of a few burned areas, from which burned perimeters are extracted. After the discrimination of burned patches, the user can visually assess the results, and iteratively select additional sampling burned areas to improve the extent of the burned patches. The final result of the BAMS program is a polygon vector layer containing three categories: (a) burned perimeters, (b) unburned areas, and (c) non-observed areas. The latter refer to clouds or sensor observation errors. Outputs of the BAMS code meet the requirements of file formats and structure of standard validation protocols. This paper presents the tool's structure and technical basis. The program has been tested in six areas located in the United States, for various ecosystems and land covers, and then compared against the National Monitoring Trends in Burn Severity (MTBS) Burned Area Boundaries Dataset