Regional Forest Volume Estimation by Expanding LiDAR Samples Using Multi-Sensor Satellite Data

Accurate information regarding forest volume plays an important role in estimating afforestation, timber harvesting, and forest ecological services. Traditionally, operations on forest growing stock volume using field measurements are labor-intensive and time-consuming. Recently, remote sensing technology has emerged as a time-cost efficient method for forest inventory. In the present study, we have adopted three procedures, including samples expanding, feature selection, and results generation and evaluation. Extrapolating the samples from Light Detection and Ranging (LiDAR) scanning is the most important step in satisfying the requirement of sample size for nonparametric methods operation and result in accuracy improvement. Besides, mean decrease Gini (MDG) methodology embedded into Random Forest (RF) algorithm served as a selector for feature measure; afterwards, RF and K-Nearest Neighbor (KNN) were adopted in subsequent forest volume prediction. The results show that the retrieval of Forest volume in the entire area was in the range of 50–360 m3/ha, and the results from the two models show a better consistency while using the sample combination extrapolated by the optimal threshold value (2 × 10−4), leading to the best performances of RF (R2 = 0.618, root mean square error, RMSE = 43.641 m3/ha, mean absolute error, MAE = 33.016 m3/ha), followed by KNN (R2 = 0.617, RMSE = 43.693 m3/ha, MAE = 32.534 m3/ha). The detailed analysis that is discussed in the present paper clearly shows that expanding image-derived LiDAR samples helps in refining the prediction of regional forest volume while using satellite data and nonparametric models

An IPCC-Compliant Technique for Forest Carbon Stock Assessment Using Airborne LiDAR-Derived Tree Metrics and Competition Index

This study developed an IPCC (Intergovernmental Panel on Climate Change) compliant method for the estimation of above-ground carbon (AGC) in forest stands using remote sensing technology. A multi-level morphological active contour (MMAC) algorithm was employed to obtain tree-level metrics (tree height (LH), crown radius (LCR), competition index (LCI), and stem diameter (LDBH)) from an airborne LiDAR-derived canopy height model. Seven biomass-based AGC models and 13 volume-based AGC models were developed using a training dataset and validated using a separate validation dataset. Four accuracy measures, mean absolute error (MAE), root-mean-square error (RMSE), percentage RMSE (PRMSE), and root-mean-square percentage error (RMSPE) were calculated for each of the 20 models. These measures were transformed into a new index, accuracy improvement percentage (AIP), for post hoc testing of model performance in estimating forest stand AGC stock. Results showed that the tree-level AGC models explained 84% to 91% of the variance in tree-level AGC within the training dataset. Prediction errors (RMSEs) for these models ranged between 15 ton/ha and 210 ton/ha in mature forest stands, which is equal to an error percentage in the range 6% to 86%. At the stand-level, several models achieved accurate and reliable predictions of AGC stock. Some models achieved 90% to 95% accuracy, which was equal to or superior to the R-squared of the tree-level AGC models. The first recommended model was a biomass-based model using the metrics LDBH, LH, and LCI and the others were volume-based models using LH, LCI, and LCR and LDBH and LH. One metric, LCI, played a critical role in upgrading model performance when banded together with LH and LCR or LDBH and LCR. We conclude by proposing an IPCC-compatible method that is suitable for calculating tree-level AGC and predicting AGC stock of forest stands from airborne LiDAR data

Aplicación de imágenes de satélites y datos LiDAR en la modelización e inventario de Eucalyptus spp en Uruguay

La integración de información de inventarios de campo, con datos procedentes de sensores remotos y su alta correlación con la estructura de la vegetación, permite ajustar modelos precisos para la estimación de la producción forestal. Esto impacta reduciendo costos, tiempos y sesgos, generando productos que son insumos para procesos como la segmentación y la optimización 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 imágenes 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 información brindada para optimizar los procesos de gestión forestal con respecto a los sistemas de inventario tradicionales. Los resultados obtenidos demuestran la mejora en la precisión y en la calidad de los datos frente a los inventarios tradicionales. Se proporcionan herramientas que permiten mejorar la precisión en cuatro aspectos para la cuantificación y el manejo de la producción forestal: i) el uso de modelos compatibles y aditivos; ii) el modelado de las variables del rodal a gran escala empleando datos de teledetección; iii) la delimitación de zonas homogéneas dentro del rodal basada en una evaluación no supervisada; y iv) un método de programación 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 aplicación de herramientas de geomática en el sector forestal supone un cambio fundamental en las prácticas de inventarios, desde su planificación, ejecución y resolución, así como de la capacidad para generar modelos predictivos y de algoritmos de segmentación con mayor precisión. Se comprobó que el uso de datos procedentes de sensores activos y pasivos incrementa las posibilidades de automatización de inventarios forestales, aumentando la resolución espacial y la temporal de la cartografía forestal. Esto, junto con el uso de técnicas estadísticas paramétricas y no paramétricas, 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