3 research outputs found
Forestry and Arboriculture Applications Using High-Resolution Imagery from Unmanned Aerial Vehicles (UAV)
Forests cover over one-third of the planet and provide unmeasurable benefits to the ecosystem. Forest managers have collected and processed countless amounts of data for use in studying, planning, and management of these forests. Data collection has evolved from completely manual operations to the incorporation of technology that has increased the efficiency of data collection and decreased overall costs. Many technological advances have been made that can be incorporated into natural resources disciplines. Laser measuring devices, handheld data collectors and more recently, unmanned aerial vehicles, are just a few items that are playing a major role in the way data is managed and collected. Field hardware has also been aided with new and improved mobile and computer software. Over the course of this study, field technology along with computer advancements have been utilized to aid in forestry and arboricultural applications. Three-dimensional point cloud data that represent tree shape and height were extracted and examined for accuracy. Traditional fieldwork collection (tree height, tree diameter and canopy metrics) was derived from remotely sensed data by using new modeling techniques which will result in time and cost savings. Using high resolution aerial photography, individual tree species are classified to support tree inventory development. Point clouds were used to create digital elevation models (DEM) which can further be used in hydrology analysis, slope, aspect, and hillshades. Digital terrain models (DTM) are in geographic information system (GIS), and along with DEMs, used to create canopy height models (CHM). The results of this study can enhance how the data are utilized and prompt further research and new initiatives that will improve and garner new insight for the use of remotely sensed data in forest management
Prediction of Forest Structural Parameters Using Airborne Full-Waveform LiDAR and Hyperspectral Data in Subtropical Forests
Accurate acquisition of forest structural parameters, which is essential for the parameterization of forest growth models and understanding forest ecosystems, is also crucial for forest inventories and sustainable forest management. In this study, simultaneously acquired airborne full-waveform (FWF) LiDAR and hyperspectral data were used to predict forest structural parameters in subtropical forests of southeast China. The pulse amplitude and waveform shape of airborne FWF LiDAR data were calibrated using a physical process-driven and a voxel-based approach, respectively. Different suites of FWF LiDAR and hyperspectral metrics, i.e., point cloud (derived from LiDAR-waveforms) metrics (DPC), full-waveform (geometric and radiometric features) metrics (FW) and hyperspectral (original reflectance bands, vegetation indices and statistical indices) metrics (HS), were extracted and assessed using correlation analysis and principal component analysis (PCA). The selected metrics of DPC, FW and HS were used to fit regression models individually and in combination to predict diameter at breast height (DBH), Lorey’s mean height (HL), stem number (N), basal area (G), volume (V) and above ground biomass (AGB), and the capability of the predictive models and synergetic effects of metrics were assessed using leave-one-out cross validation. The results showed that: among the metrics selected from three groups divided by the PCA analysis, twelve DPC, eight FW and ten HS were highly correlated with the first and second principal component (r > 0.7); most of the metrics selected from DPC, FW and HS had weak relationships between each other (r < 0.7); the prediction of HL had a relatively higher accuracy (Adjusted-R2 = 0.88, relative RMSE = 10.68%), followed by the prediction of AGB (Adjusted-R2 = 0.84, relative RMSE = 15.14%), and the prediction of V had a relatively lower accuracy (Adjusted-R2 = 0.81, relative RMSE = 16.37%); and the models including only DPC had the capability to predict forest structural parameters with relatively high accuracies (Adjusted-R2 = 0.52⁻0.81, relative RMSE = 15.70⁻40.87%) whereas the usage of DPC and FW resulted in higher accuracies (Adjusted-R2 = 0.62⁻0.87, relative RMSE = 11.01⁻31.30%). Moreover, the integration of DPC, FW and HS can further improve the accuracies of forest structural parameters prediction (Adjusted-R2 = 0.68⁻0.88, relative RMSE = 10.68⁻28.67%)
Aplicaci贸n de im谩genes de sat茅lites y datos LiDAR en la modelizaci贸n e inventario de Eucalyptus spp en Uruguay
La integraci贸n de informaci贸n de inventarios de campo, con datos procedentes de
sensores remotos y su alta correlaci贸n con la estructura de la vegetaci贸n, permite
ajustar modelos precisos para la estimaci贸n de la producci贸n forestal. Esto impacta
reduciendo costos, tiempos y sesgos, generando productos que son insumos para
procesos como la segmentaci贸n y la optimizaci贸n de la cosecha. En este trabajo se
presenta una alternativa a los inventarios forestales y al procesamiento de datos,
mediante el uso de sensores LiDAR e im谩genes multiespectrales. El objetivo general
fue evaluar el uso de LiDAR y datos multiespectrales, en plantaciones de Eucalyptus
grandis y Eucalyptus dunnii en Uruguay; para mejorar la calidad y la cantidad de
informaci贸n brindada para optimizar los procesos de gesti贸n forestal con respecto a
los sistemas de inventario tradicionales. Los resultados obtenidos demuestran la
mejora en la precisi贸n y en la calidad de los datos frente a los inventarios
tradicionales. Se proporcionan herramientas que permiten mejorar la precisi贸n en
cuatro aspectos para la cuantificaci贸n y el manejo de la producci贸n forestal: i) el
uso de modelos compatibles y aditivos; ii) el modelado de las variables del rodal a
gran escala empleando datos de teledetecci贸n; iii) la delimitaci贸n de zonas
homog茅neas dentro del rodal basada en una evaluaci贸n no supervisada; y iv) un
m茅todo de programaci贸n lineal que optimiza los planes de corta basado en la
disponibilidad de madera, el secuestro de carbono y el Valor Actual Neto. Se
concluye que la aplicaci贸n de herramientas de geom谩tica en el sector forestal supone
un cambio fundamental en las pr谩cticas de inventarios, desde su planificaci贸n,
ejecuci贸n y resoluci贸n, as铆 como de la capacidad para generar modelos predictivos y
de algoritmos de segmentaci贸n con mayor precisi贸n. Se comprob贸 que el uso de
datos procedentes de sensores activos y pasivos incrementa las posibilidades de
automatizaci贸n de inventarios forestales, aumentando la resoluci贸n espacial y la
temporal de la cartograf铆a forestal. Esto, junto con el uso de t茅cnicas estad铆sticas
param茅tricas y no param茅tricas, constituyen un avance en el campo del manejo
forestal en Uruguay.The integration of information from field inventories, with data from remote sensors,
and its high correlation with the structure of the vegetation, allows to adjust precise
models for the estimation of forest production. This allows for a reduction in costs,
time and bias, producing valuable inputs for processes such as segmentation and
optimizing the harvest. Here we present an alternative to forest inventories and data
processing through the use of LiDAR and multispectral images. The main objective
was to evaluate the use of LiDAR information and high-resolution multispectral data
in Eucalyptus plantations in Uruguay, to improve the quality and quantity of
information provided to optimize forest management processes with respect to
traditional inventory systems. The results obtained demonstrate the improvement in
precision and quality of the data compared to traditional inventories. Tools that
improve precision in four fundamental aspects for the quantification and
management of forest production are provided: i) the use of compatible and additives
models; ii) modeling of stand variables on a large scale using remote sensing data;
iii) delimitation of homogeneous areas within the stand based on an unsupervised
assessment; and iv) a method for optimizing felling plans based on timber
availability, carbon prices, and harvest age. The main conclusion is that the
application of geomatic tools in the forestry sector represent a fundamental change in
inventory practices, from planning, execution and resolution, as well as the ability to
generate predictive models and segmentation algorithms with greater precision than
those obtained with field inventories. Throughout the thesis, it is shown that the use
of data from different active and passive sensors increases the possibilities for
automating forest inventories, increasing the spatial and temporal resolution of forest
cartography. This, together with the use of parametric and non-parametric statistical
techniques, constitutes an advance in the field of forest management in Uruguay