Can models for forest attributes based on airborne laser scanning be generalized for different silvicultural management systems?

In Finland, interest in continuous cover forestry (CCF) has increased rapidly in recent years. During those years CCF has been examined from various viewpoints but not from the perspective of forest inventories. This holds especially true for applications based on remote sensing. Conversely, airborne laser scanning (ALS) data have been widely used to predict forest characteristics such as size distribution and vertical forest structure, which are closely related to the forest information needs of CCF. In this study we used the area-based approach to predict a set of stand attributes from ALS data (5 pulses per m2) in a CCF forest management experiment in Katajama & BULL;ki, eastern Finland. In addition to the CCF stands, the experiment included shelterwood stands and untreated stands. The predicted attributes included volume, biomass, basal area, number of stems, mean diameter, Lorey's height, dominant height, standing dead wood volume, parameters of the theoretical stem diameter distribution model, understory height and number of understory stems. Our main aim was to test whether the same model could be used across different management systems. The accuracy of the attributes predicted for the CCF stands was compared with the predictions for the other management systems in the same experiment. We also compared and discussed our results in relation to the even-aged stand attribute predictions that were conducted by using separate operational forest data collected from sites surrounding Katajama & BULL;ki. The results showed that forest data from the different management systems could be combined into a single model of a stand attribute, i.e., ALS metrics were found to be suitable for comparing different management systems in regard to differences in forest structure. The accuracy of the predicted attributes in the CCF plots was comparable to that of the other management alternatives in the experiment. The accuracy was also comparable to that of even-aged forests. The results of this study were promising; the stand attributes of CCF-managed forests could be predicted analogously to those of other management systems. This indicates that for the purposes of forest inventories there may not be a need to stratify forest lands by management system. It should be noted, however, that the study area was relatively small, that the forest stands were harvested in the 1980 s, and that the attributes may not have been completely exhaustive for CCF

Comparing Remote Sensing and Field-Based Approaches to Estimate Ladder Fuels and Predict Wildfire Burn Severity

While fire is an important ecological process, wildfire size and severity have increased as a result of climate change, historical fire suppression, and lack of adequate fuels management. Ladder fuels, which bridge the gap between the surface and canopy leading to more severe canopy fires, can inform management to reduce wildfire risk. Here, we compared remote sensing and field-based approaches to estimate ladder fuel density. We also determined if densities from different approaches could predict wildfire burn severity (Landsat-based Relativized delta Normalized Burn Ratio; RdNBR). Ladder fuel densities at 1-m strata and 4-m bins (1–4 m and 1–8 m) were collected remotely using a terrestrial laser scanner (TLS), a handheld-mobile laser scanner (HMLS), an unoccupied aerial system (UAS) with a multispectral camera and Structure from Motion (SfM) processing (UAS-SfM), and an airborne laser scanner (ALS) in 35 plots in oak woodlands in Sonoma County, California, United States prior to natural wildfires. Ladder fuels were also measured in the same plots using a photo banner. Linear relationships among ladder fuel densities estimated at broad strata (1–4 m, 1–8 m) were evaluated using Pearson’s correlation (r). From 1 to 4 m, most densities were significantly correlated across approaches. From 1 to 8 m, TLS densities were significantly correlated with HMLS, UAS-SfM and ALS densities and UAS-SfM and HMLS densities were moderately correlated with ALS densities. Including field-measured plot-level canopy base height (CBH) improved most correlations at medium and high CBH, especially those including UAS-SfM data. The most significant generalized linear model to predict RdNBR included interactions between CBH and ladder fuel densities at specific 1-m stratum collected using TLS, ALS, and HMLS approaches (R2 = 0.67, 0.66, and 0.44, respectively). Results imply that remote sensing approaches for ladder fuel density can be used interchangeably in oak woodlands, except UAS-SfM combined with the photo banner. Additionally, TLS, HMLS and ALS approaches can be used with CBH from 1 to 8 m to predict RdNBR. Future work should investigate how ladder fuel densities using our techniques can be validated with destructive sampling and incorporated into predictive models of wildfire severity and fire behavior at varying spatial scales

LiDAR-Based Estimates of Canopy Base Height for a Dense Uneven-Aged Structured Forest

Accurate canopy base height (CBH) information is essential for forest and fire managers since it constitutes a key indicator of seedling growth, wood quality and forest health as well as a necessary input in fire behavior prediction systems such as FARSITE, FlamMap and BEHAVE. The present study focused on the potential of airborne LiDAR data analysis to estimate plot-level CBH in a dense uneven-aged structured forest on complex terrain. A comparative study of two widely employed methods was performed, namely the voxel-based approach and regression analysis, which revealed a clear outperformance of the latter. More specifically, the voxel-based CBH estimates were found to lack correlation with the reference data ( R 2 = 0.15 , r R M S E = 42.36 % ) while most CBH values were overestimated resulting in an r b i a s of − 17.52 % . On the contrary, cross-validation of the developed regression model showcased an R 2 , r R M S E and r b i a s of 0 . 61 , 18.19 % and − 0.09 % respectively. Overall analysis of the results proved the voxel-based approach incapable of accurately estimating plot-level CBH due to vegetation and topographic heterogeneity of the forest environment, which however didn’t affect the regression analysis performance

LiDAR-Landsat Covariance for Predicting Canopy Fuels

Managing wildfires in the western United States is becoming increasingly complex. Visualizing and quantifying canopy structures allows fire managers to both plan for fire and track recovery. Light detecting and ranging, or LiDAR can measure forests in three dimensions, but has limited spatial and temporal coverage. LiDAR-Landsat covariance uses machine learning to fill in the spatial and temporal gaps of LiDAR coverage with supplemental Landsat imagery. However, in order to capture real forest dynamics, a model needs to be stable enough to detect long term trends, sensitive to episodic disturbance, and general enough to work on multiple landcovers. The purpose of this research is to refine the methodology behind LiDAR-Landsat covariance and assess if these predictions can yield sable and ecologically sensible time series to track forest fire recovery over time. Gradient boosted machine models (GBMs) were built to predict canopy cover, height, and base height. Then, they were tested on a series of validation sites in order to quantify the spatial and temporal sources of error associated with these models. Finally, the models were used to predict the trajectories of canopy cover, height, and base height on 164 fire scars in Montana, Idaho and Wyoming over the course of 36 years. The models were sensitive to moderate and high severity disturbance, both on an incident wide and pixel by pixel basis. Overall model R2 values were 0.89 for canopy cover, 0.84 for height, and 0.88 for base height. Year to year variability in canopy cover on validation sites was 2.3%. Height had more variability due to a sensor artifact from the transition from Landsat 5 to Landsat 8. On the Lost Fire the model found high severity fire corresponded with greater canopy fuel losses on a pixelwise basis. The models also detected canopy recovery, and found four distinct trajectories in which burned sites recover from disturbance. Seventy-seven percent of sites fully recovered canopy cover to pre-fire conditions within the 36-year time series. Further refinement of GBM based LiDAR-Landsat covariance can increase the sensitivity to smaller disturbances and reduce the impact of model error on performance

Uso combinado de técnicas de teledetección y modelización para evaluar la distribución vertical de la vegetación

El objetivo de esta tesis es diseñar metodologías que identifiquen la distribución vertical de la vegetación independientemente de los ecosistemas que componen la zona de estudio. Esta tesis, al enmarcarse en un doctorado industrial, incorpora el objetivo de crear desarrollos metodológicos que impulsen la competitividad de la empresa en el mercado laboral. Con este objetivo último, la tesis ha implementado tres metodologías ágiles, precisas y aplicables a gran escala, cuya finalidad ha sido identificar la estructura tridimensional de la vegetación que condiciona las coberturas del suelo presentes y, por tanto, la gestión aplicable en cada caso. Esta tesis incorpora tres capítulos, independientes entre sí, cada uno con un reto metodológico. El Capítulo 3 buscó detectar árboles individuales de plantaciones jóvenes de Pinus pinaster y Pinus radiata en parcelas permanentes destinadas a investigación, delinear sus copas y evaluar su altura. El Capítulo 4 incluye una metodología capaz de identificar las coberturas de suelo presentes en una interfaz urbano-forestal, las cuales están relacionadas directamente con la distribución vertical de la vegetación. El Capítulo 5 incluye el desarrollo de una metodología para identificar un umbral capaz de adaptarse a la estructura real de cada parcela. Este umbral sería una alternativa generalizable a todo un monte y replicable sin necesidad de nuevas mediciones en campo. Paralelamente a estos trabajos se ha participado en dos registros de la propiedad intelectual, easyLaz® y easySat®, cuya finalidad es el procesado de información LiDAR y multiespectral respectivamente