Using ICESAT\u27s geoscience laser altimeter system to assess large scale forest disturbance caused by Hurricane Katrina

We assessed the use of GLAS data as a tool to quantify large-scale forest damage. GLAS data for the year prior to and following Hurricane Katrina were compared to wind speed, forest cover, and MODIS NPV maps to analyze senor sampling, and changes in mean canopy height. We detected significant losses in mean canopy height post-Katrina that increased with wind intensity, from ∼.5m in forests hit by tropical storm winds to ∼4m in forests experiencing category two force winds. Season of data acquisition was shown to influence calculations of mean canopy height. There was insufficient sampling to adequately detect changes at one degree resolution and less. We observed a strong relationship between delta NPV and post storm mean canopy heights. Changes in structure were converted into loss of standing carbon estimates using a height structured ecosystem model, yielding above ground carbon storage losses of ∼30Tg over the domain

Extraction of Vegetation Biophysical Structure from Small-Footprint Full-Waveform Lidar Signals

The National Ecological Observatory Network (NEON) is a continental scale environmental monitoring initiative tasked with characterizing and understanding ecological phenomenology over a 30-year time frame. To support this mission, NEON collects ground truth measurements, such as organism counts and characterization, carbon flux measurements, etc. To spatially upscale these plot-based measurements, NEON developed an airborne observation platform (AOP), with a high-resolution visible camera, next-generation AVIRIS imaging spectrometer, and a discrete and waveform digitizing light detection and ranging (lidar) system. While visible imaging, imaging spectroscopy, and discrete lidar are relatively mature technologies, our understanding of and associated algorithm development for small-footprint full-waveform lidar are still in early stages of development. This work has as its primary aim to extend small-footprint full-waveform lidar capabilities to assess vegetation biophysical structure. In order to fully exploit waveform lidar capabilities, high fidelity geometric and radio-metric truth data are needed. Forests are structurally and spectrally complex, which makes collecting the necessary truth challenging, if not impossible. We utilize the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model, which provides an environment for radiometric simulations, in order to simulate waveform lidar signals. The first step of this research was to build a virtual forest stand based on Harvard Forest inventory data. This scene was used to assess the level of geometric fidelity necessary for small-footprint waveform lidar simulation in broadleaf forests. It was found that leaves have the largest influence on the backscattered signal and that there is little contribution to the signal from the leaf stems and twigs. From this knowledge, a number of additional realistic and abstract virtual “forest” scenes were created to aid studies assessing the ability of waveform lidar systems to extract biophysical phenomenology. We developed an additive model, based on these scenes, for correcting the attenuation in backscattered signal caused by the canopy. The attenuation-corrected waveform, when coupled with estimates of the leaf-level reflectance, provides a measure of the complex within-canopy forest structure. This work has implications for our improved understanding of complex waveform lidar signals in forest environments and, very importantly, takes the research community a significant step closer to assessing fine-scale horizontally- and vertically-explicit leaf area, a holy grail of forest ecology

AUTOMATED TREE-LEVEL FOREST QUANTIFICATION USING AIRBORNE LIDAR

Traditional forest management relies on a small field sample and interpretation of aerial photography that not only are costly to execute but also yield inaccurate estimates of the entire forest in question. Airborne light detection and ranging (LiDAR) is a remote sensing technology that records point clouds representing the 3D structure of a forest canopy and the terrain underneath. We present a method for segmenting individual trees from the LiDAR point clouds without making prior assumptions about tree crown shapes and sizes. We then present a method that vertically stratifies the point cloud to an overstory and multiple understory tree canopy layers. Using the stratification method, we modeled the occlusion of higher canopy layers with respect to point density. We also present a distributed computing approach that enables processing the massive data of an arbitrarily large forest. Lastly, we investigated using deep learning for coniferous/deciduous classification of point cloud segments representing individual tree crowns. We applied the developed methods to the University of Kentucky Robinson Forest, a natural, majorly deciduous, closed-canopy forest. 90% of overstory and 47% of understory trees were detected with false positive rates of 14% and 2% respectively. Vertical stratification improved the detection rate of understory trees to 67% at the cost of increasing their false positive rate to 12%. According to our occlusion model, a point density of about 170 pt/m² is needed to segment understory trees located in the third layer as accurately as overstory trees. Using our distributed processing method, we segmented about two million trees within a 7400-ha forest in 2.5 hours using 192 processing cores, showing a speedup of ~170. Our deep learning experiments showed high classification accuracies (~82% coniferous and ~90% deciduous) without the need to manually assemble the features. In conclusion, the methods developed are steps forward to remote, accurate quantification of large natural forests at the individual tree level

Improving Tree Crown Mapping using Airborne LiDAR with Genetic Algorithms

Landscape-scale mapping of individual trees derived from LiDAR (Light Detection And Ranging) data have been found to be valuable for a wide range of environmental analyses including carbon inventories; fuel estimations for wildfire risk assessment and management. These mapping efforts use individual tree crown (ITC) recognition algorithms applied to LiDAR point clouds, which have complex parameter sets. Genetic algorithms (GA) have been demonstrated to be excellent function optimizers for very complex search spaces and perform well for parameter tuning. Here, we use GAs to identify the best of a set of published ITC models and their optimal parameters for airborne LiDAR of forested plots in the Sierra Nevada Mountains of California. We assessed the accuracy of these ITC models in terms of the F-score and percentage bias for tree crown prediction. GA-optimization generally improved on ITC default parameters and showed that these models typically perform better for detecting overstory trees

Using Low Density, Small Footprint LIDAR in Forest Inventory Applications in the Southeastern U.S.

Light Detection and Ranging (LIDAR) is becoming a widely used tool in forestry and natural resource fields. The availability of free and low cost datasets gives LIDAR the ability to save time and money over traditional forest inventory practices. In this study, the effectiveness of low density, small footprint LIDAR compared to forest field inventory measurements from the Clemson Experimental Forest was determined. LIDAR based estimates were analyzed to determine if LIDAR is a viable tool for estimating particular forest inventory features in the Southeastern U.S. and whether a transition could be made to a more GIS based analysis. Standard field inventory methods were used to assess forest stand measurements throughout the Clemson Experimental Forest. Processed LIDAR data was used in conjunction with Treevaw, a LIDAR software application, to extract forest inventory features at the individual tree level. Statistical correlation and regression comparisons were made between the data at the plot level. Comparisons were also made between stand types to determine the type of effects that leaf-off conditions could have on the LIDAR data analysis. Overall, results of the entire sample comparing tree heights, diameter at breast height, and above ground biomass were varied. Correlations between inventory and LIDAR measurements were high, with a minimum value of 0.70. Dividing the plots by stand cover type showed variations in the dataset. Pine plantation plots achieved the best overall results, followed by pine-hardwood plots. Natural pine, upland hardwood, and cove hardwood plots each produced similar results, but were not as accurate as the stands mentioned previously. Results show that low density, small footprint LIDAR can be used to accurately estimate certain features of individual trees in particular forest stand types. The use of higher density LIDAR would most likely provide a more accurate analysis across a broader range of forest types

The Role of Patch Size, Isolation, and Forest Condition on Pileated Woodpecker Occupancy in Southwestern Ohio

No studies of Pileated Woodpeckers (Dryocopus pileatus) have been done in southwestern Ohio where agriculture is prevalent and forests are significantly fragmented. The objective of this study was to determine the forest fragment size, isolation, and structure preferred by D. pileatus for breeding habitat. I sampled 37 forest fragments varying in size and isolation for D. pileatus cavities and forest characteristics and used LiDAR remote sensing data to analyze forest complexity. I hypothesized that D. pileatus relative abundance would increase with forest fragment size, density of dead trees, and forest vertical complexity but decrease with isolation. The hypotheses that size and isolation of a forest fragment influence D. pileatus habitat choices were rejected. However, snag density, directly relating food and shelter requirements for D. pileatus , showed the predicted association with woodpecker activity as did forest height and forest complexity

Remote detection of forest structure in the White Mountains of New Hampshire: An integration of waveform lidar and hyperspectral remote sensing data

The capability of waveform lidar, used singly and through integration with high-resolution spectral data, to describe and predict various aspects of the structure of a northern temperate forest is explored. Waveform lidar imagery was acquired in 1999 and 2003 over Bartlett Experimental Forest in the White Mountains of central New Hampshire using NASA\u27s airborne Laser Vegetation Imaging Sensor (LVIS). High-resolution spectral imagery from 1997 and 2003 was likewise acquired using NASA\u27s Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). USDA Forest Service Northeastern Research Station (USFS NERS) 2001-2003 inventory data was used to define basal area, above-ground biomass, quadratic mean stem diameter and proportional species abundances within each of over 400 plots. Field plots scaled to LVIS footprints were also established. At the smallest scale, metrics derived from single LVIS footprints were strongly correlated with coincident forest measurements. At the larger scale of USFS NERS plots, strong correlations encompassing the full variability of the Forest Service data could not be established. Restrictions set by species composition and land-use, however, significantly improved both the descriptive and predictive power of the regression analyses. Higher amplitude values of 1999 LUIS ground return metrics obtained within two years of the January 1998 ice storm, were found to provide a spatial record of higher levels of canopy damage within older, unmanaged forest tracts. Subjected to repeated disturbance of intermediate severity over the time frame of decades, these particular tracts, predominately found on southeastern aspects, simultaneously support by levels of sugar maple abundance and low levels of sugar maple coarse woody debris. LVIS height metrics were used here to establish a statistical relationship with coarse woody debris data. The integration of waveform lidar with hyperspectral data did enhance the ability to remotely describe a number of common measures of forest structure. Compositional abundance patterns, however, were not improved over use of AVIRIS data alone. Maps predicting species abundance patterns (primarily derived from AVIRIS data) with coincident patterns of stem size (derived from LVIS data) can be created for several of the dominant tree species of this region. The results are the near equivalent of a field-based forest inventory

Characteriation of Mediterranean Aleppo pine forest using low-density ALS data

Los espacios forestales son una fuente de servicios, tanto ambientales como económicos, de gran importancia para la sociedad. La caracterización de estos ambientes ha requerido tradicionalmente de un laborioso trabajo de campo. La aplicación de técnicas de teledetección ha proporcionado una visión más amplia a escala espacial y temporal, a la par que ha generado una reducción de los costes. La utilización de sensores óptico-pasivo multiespectrales y de sensores radar posibilita la estimación de parámetros forestales, si bien el desarrollo de sensores LiDAR, como el caso de los escáneres láser aeroportados (ALS), ha mejorado la caracterización tridimensional de la estructura de los bosques. La disponibilidad pública de dos coberturas LiDAR, generadas en el marco del Plan Nacional de Ortofotografía Aérea (PNOA), ha abierto nuevas líneas de investigación que permiten proporcionar información útil para la gestión forestal. La presente tesis utiliza datos LiDAR aeroportados de baja densidad para estimar diversas variables forestales, con ayuda de trabajo de campo, en masas forestales de Pino carrasco (Pinus halepensis Miller) en Aragón. La investigación aborda dos cuestiones relevantes como son la exploración de las metodologías más adecuadas para estimar variables forestales considerando escalas locales y regionales, teniendo en cuenta las posibles fuentes de error en el modelado; y, además, analiza la potencialidad de los datos LiDAR del PNOA para el desarrollo de aplicaciones forestales que valoricen las áreas forestales como recursos socio-económicos. La tesis se ha desarrollado según la modalidad de compendio de publicaciones, incluyendo cuatro trabajos que dan respuesta a los objetivos planteados. En primer lugar, se realiza un análisis comparativo de distintos modelos de regresión, paramétricos y no paramétricos, para estimar la pérdida de biomasa y las emisiones de CO2 en un incendio, mediante la utilización de datos LiDAR-PNOA y datos ópticos del satélite Landsat 8. En segundo lugar, se explora la idoneidad de distintos métodos de selección de variables para estimar biomasa total en masas de Pino carrasco utilizando datos LiDAR de baja densidad. En tercer lugar, se cuantificó y cartografió la biomasa residual forestal en el conjunto de masas de Pino carrasco de Aragón y se evaluó el efecto de diversas características de la tecnología LiDAR y de las variables ambientales en la precisión de los modelos. Finalmente, se analiza la transferibilidad temporal de modelos para estimar a escala regional siete variables forestales, utilizando datos LiDAR-PNOA multi-temporales. A este respecto, se compararon dos enfoques que permiten analizar la transferibilidad temporal: en primer lugar, el método directo ajusta un modelo para un determinado punto en el tiempo y estima las variables forestales para otra fecha; por otra parte, el método indirecto ajusta dos modelos diferentes para cada momento en el tiempo, estimando las variables forestales en dos fechas distintas. Los resultados obtenidos y las conclusiones derivadas de la investigación indican que la técnica basada en coeficientes de correlación de Spearman y el método de selección por todos los subconjuntos constituyen los métodos de selección de métricas LiDAR más apropiados para la modelización. El análisis de métodos de regresión para la estimación de variables forestales indicó que su idoneidad variaba de acuerdo con el tamaño y complejidad de la muestra. El método de regresión linear multivariante arrojó mejores resultados que los métodos no-paramétricos en el caso de muestras pequeñas. Por el contrario, el método Support Vector Machine produjo los mejores resultados con muestras grandes. El incremento de la densidad de puntos y de los valores de penetración de los pulsos LiDAR en el dosel, así como la presencia de ángulos de escaneo pequeños, incrementó la exactitud de los modelos. De forma similar, el incremento de la pendiente y la presencia de arbustos en el sotobosque implican una reducción en la exactitud de los modelos. En la estimación de variables forestales utilizando datos LiDAR multi-temporales, aunque la utilización del enfoque indirecto arrojó generalmente una mayor precisión en los modelos, se obtuvieron resultados similares con el enfoque directo, el cual constituye una alternativa óptima para reducir el tiempo de modelado y los costes de realización de trabajo de campo. La fusión de datos LiDAR y datos óptico-pasivos ha evidenciado la conveniencia de los métodos aplicados para cuantificar las emisiones de CO2 a la atmósfera generadas por un incendio. Esta metodología constituye una alternativa adecuada cuando no existen datos multi-temporales LiDAR. La estimación de variables de inventario forestal, así como de diversas fracciones de biomasa, como la biomasa total y la biomasa residual forestal, proporciona información valiosa para caracterizar las masas forestales mediterráneas de Pino carrasco y mejorar la gestión forestalForest ecosystems provide environmental and economic services of great importance to the society. The characterization of these environments has been traditionally accomplished with intense field work. In comparison, the application of remote sensing tools provides a greater overview over large spatial and temporal scales while minimizing costs. Although optical data and Synthetic Aperture Radar (SAR) allow estimating forest stand variables, the development of LiDAR sensors such as Airborne Laser Scanner (ALS) have improved three-dimensional characterization of forest structure. The availability of two ALS public data coverages for the Spanish territory, provided by the National Plan for Aerial Ortophotography (PNOA), opens new research opportunities to generate useful information for forest management. This PhD Thesis used low-density ALS-PNOA data to estimate different forest variables, with support in fieldwork, in the Aleppo pine (Pinus halepensis Miller) forests of Aragón region. The addressed research is relevant mainly for two reasons: first, the examination of suitable methodologies and error sources in forest stand variables prediction at local (small area) and regional scales (large area), and second, the application of ALS data to the characterization of forest areas as a socio-economic reservoir. This PhD Thesis is a compendium of four scientific papers, which sequentially answer the objectives established. Firstly, a comparative analysis of different parametric and non-parametric models was performed to estimate biomass losses and CO2 emissions using low-density ALS and Landsat 8 data in a burnt Aleppo pine forest. Secondly, we assess the suitability of variable selection methods when estimating total biomass in Aleppo pine forest stands using low-density ALS data. In the third manuscript, the quantification and mapping of forest residual biomass in Aleppo pine forest of Aragón region and the assessment of the effect of ALS and environmental variables in model accuracy were accomplished. Finally, the temporal transferability of seven forest stands attributes modelling using multi-temporal ALS-PNOA data in Aleppo pine forest at regional scale was explored. In this case, the temporal transferability was assessed comparing two methodologies; the direct and indirect approach. The first one fits a model for one point in time and estimates the forest variable for another point in time. The indirect approach adjusts two models in different points in time to estimate the forest variables in two different dates. The results derived from this research indicated that Spearman’s rank and All Subset Selection are the most appropriate methods in the ALS metrics selection step commonly applied in modelling. The suitability of the regression methods depends on the sample size and complexity. Thus, multivariate linear regression outperformed non-parametric methods with small samples while support vector machine was the most accurate method with larger samples. Model accuracy increased with higher point density and canopy pulse penetration, while decreasing with wider scan angles. Furthermore, the presence of steep slopes and shrub reduced model performance. In the case of forest stand variables prediction using multi-temporal ALS data, although the indirect approach produced generally a higher precision, the direct approach provided similar results, constituting a suitable alternative to reduce modelling time and fieldwork costs. The fusion of ALS and passive optical data have evidenced the suitability of this information for quantifying wildfire CO2 emissions to atmosphere, constituting a good alternative when multi-temporal ALS data is not available. The estimation of forest inventory variables as well as different biomass fractions, such as total biomass and forest residual biomass, provided valuable information to characterize Mediterranean Aleppo pine forests and improve forest management.<br /

Characterizing the Impacts of the Invasive Hemlock Woolly Adelgid on the Forest Structure of New England

Climate change is raising winter temperatures in the Northeastern United States, both expanding the range of an invasive pest, the hemlock woolly adelgid (HWA; Adelges tsugae), and threatening the survival of its host species, eastern hemlock (Tsuga canadensis). As a foundation species, hemlock trees underlie a distinct network of ecological, biogeochemical, and structural systems that will likely disappear as the HWA infestation spreads northward. Remote sensing can offer new perspectives on this regional transition, recording the progressive loss of an ecological foundation species and the transition of evergreen hemlock forest to mixed deciduous forest over the course of the infestation. Lidar remote sensing, unlike other remote sensing tools, has the potential to penetrate dense hemlock canopies and record HWA’s distinct impacts on lower canopy structure. Working with a series of lidar data from the Harvard Forest experimental site, these studies identify the unique signals of HWA impacts on vertical canopy structure and use them to predict forest condition. Methods for detecting the initial impacts of HWA are explored and a workflow for monitoring changes in forest structure at the regional scale is outlined. Finally, by applying terrestrial, airborne, and spaceborne lidar data to characterize the structural variation and dynamics of a disturbed forest ecosystem, this research illustrates the potential of lidar as a tool for forest management and ecological research

An experimental investigation into ALS uncertainty and its impact on environmental applications

This study takes an experimental approach to investigating the reliability and repeatability of an airborne laser scanning (ALS) survey. The ability to characterise an area precisely in 3-D using ALS is essential for multi-temporal analysis where change detection is an important application. The reliability and consistency between two ALS datasets is discussed in the context of uncertainty within a single epoch and in the context of well known point- and grid-based descriptors and metrics. The implications of repeatability, verifiability and reliability are discussed in the context of environmental applications, specifically concerning forestry where high resolution ALS surveys are commonly used for forest mensuration over large areas. The study used a regular 10-by-10 layout of standard school tables and decreased the separation from 2.5 metres apart to 0.5 metres in order to evaluate the effects of object separation on their detection. Each configuration was scanned twice using the same ALS scanning parameters and the difference between the datasets is investigated and discussed. The results quantify uncertainty in the ability of ALS to characterise objects, estimate vertical heights and interpret features / objects with certainty. The results show that repeat scanning of the same features under the same conditions result in a laser point cloud with different properties. Objects that are expected to be present in 40 points per metre2 laser point cloud are absent, and the investigation reveals that irregular point spacing and lack of consideration of the ALS footprint size and the interaction with the object of interest are significant factors in the detection and characterisation of features. The results strongly suggest that characterisation of error is important and relevant to environmental applications that use multi-epoch ALS or data with high resolution / point density for object detection and characterisatio