Evaluating TIFFS (Toolbox for Lidar Data Filtering and Forest Studies) in Deriving Forest Measurements from Lidar Data

Recent advances in LiDAR (Light Detection and Ranging) technology have allowed for the remote sensing of important forest characteristics to be more reliable and commercially available. Studies have shown that this technology can adequately estimate forest characteristics such as individual tree locations, tree heights, and crown diameters. These values are then used to estimate biophysical properties of forests, such as basal area and timber volume. This study assessed the capability of a commercially available program, Tiffs (Toolbox for Lidar Data Filtering and Forest Studies), to accurately estimate forest characteristics, as compared to data collected at the plot level using traditional timber sampling methods. We found a high, positive correlation coefficient (r)of 0.8223 for tree heights, between the LiDAR-derived measurements and the field measurements, which is somewhat promising. However, we found low correlations in tree count per plot (r = 0.1777) and tree crown radius (r = 0.1517), between the LiDAR-derived measurements and the field measurements, results which are far from satisfactory

Using lidar to approximate keystone structure and evaluate management practices in potential habitats of the endangered Karner blue butterfly (Lycaeides melissa samuelis)

Keystone structure is the spatial structure required by a given species, at a scale that is determined by that species’ needs and mobility. The endangered Karner blue butterfly (Lycaeides melissa samuelis, hereafter KBB) has a keystone structure that incorporates trees and bushes to provide the mixture of sun and shade required to fulfil its life functions. Airborne light detection and ranging (lidar) is a potentially invaluable tool for characterizing keystone structures. However, lidar has yet to be utilized to evaluate structural suitability of KBB habitats. Therefore, I investigated the use of lidar for characterizing critical attributes of KBB habitat structure, and its use in the evaluation of management practices. Structural diversity was summarized from lidar using two approaches: one that attempted to test the canopy cover criteria used in the field-based Glacial Lake Albany habitat mapping (hereafter GLA heterogeneity), and a second based on the texture of the lidar-derived canopy cover imagery. These lidar-derived measures were calculated at five scales, using kernels (moving windows) with areas of 0.05 ha to 19.2 ha. The lidar heterogeneity measures derived at 0.9 ha or less were highly correlated with density of field observations of KBB presence, with the highest correlation at 0.2 ha. Larger kernels were poorly correlated with KBB presence. Notably, the 0.9 ha scale corresponds to more than 75% of KBB mobility range observations, as reported in a previous field study. GLA heterogeneity was also found to be consistently more correlated with KBB observations than the texture measure. The criteria used to establish the four GLA heterogeneity classes appear to be useful, based on rank correlation relationships with the classes were combined or evaluated individually. The 0.2 ha kernel GLA heterogeneity was used to evaluate the effects of prescribed burning on structural suitability, and was found to be significantly correlated with burn intensity

Integrating Hands-On Undergraduate Research in an Applied Spatial Science Senior Level Capstone Course

A senior within a spatial science Ecological Planning capstone course designed an undergraduate research project to increase his spatial science expertise and to assess the hands-on instruction methodology employed within the Bachelor of Science in Spatial Science program at Stephen F Austin State University. The height of 30 building features estimated remotely with LiDAR data, within the Pictometry remotely sensed web-based interface, and in situ with a laser rangefinder were compared to actual building feature height measurements. A comparison of estimated height with actual height indicated that all three estimation techniques tested were unbiased estimators of height. An ANOVA, conducted on the absolute height errors resulting in a p-value of 0.035, concluded the three height estimating techniques were statistically different at the 95% confidence interval. A Tukey pair-wise test found the remotely sensed Pictometry web-based interface was statistically more accurate than LiDAR data, while the laser range finder was not different from the others. The results indicate that height estimates within the Pictometry web-based interface could be used in lieu of time consuming and costly in situ height measurements. The findings also validate the interactive hands-on instruction methodology employed by Geographic Information Systems faculty within the Arthur Temple College of Forestry and Agriculture in producing spatial science graduates capable of utilizing spatial science technology to accurately quantify, qualify, map, and monitor natural resources

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

The Assessment of habitat condition and consevation status of lowland British woodlands using earth observation techniques.

The successful implementation of habitat preservation and management demands regular and spatially explicit monitoring of conservation status at a range of scales based on indicators. Woodland condition can be described in terms of compositional and structural attributes (e.g. overstorey, understorey, ground flora), evidence of natural turnover (e.g. deadwood and tree regeneration), andanthropogenic influences (e.g.disturbance, damage). Woodland condition assessments are currently conducted via fieldwork, which is hampered by cost, spatial coverage, objectiveness and repeatability.This projectevaluates the ability of airborne remote sensing (RS) techniques to assess woodland condition, utilising a sensor-fusion approach to survey a foreststudy site and develop condition indicators. Here condition is based on measures of structural and compositional diversity in the woodland vertical profile, with consideration of the presence of native species, deadwood, and tree regeneration. A 22 km2 study area was established in the New Forest, Hampshire, UK, which contained a variety of forest types, including managed plantation, semi-ancient coniferous and deciduous woodland. Fieldwork was conducted in 41 field plots located across this range of forest types, each with varying properties. The field plots were 30x30m in size and recorded a total of 39 forest metrics relating to individual elements of condition as identified in the literature. Airborne hyperspectral data (visible and near-infrared) and small footprint LiDAR capturing both discrete-return (DR) and full-waveform (FW) data were acquired simultaneously, under both leaf-on and leaf-off conditions in 2010. For the combined leaf-on and leaf-off datasets a total of 154 metrics were extracted from the hyperspectral data, 187 metrics from the DR LiDAR and 252 metrics from the FW LiDAR. This comprised both area-based and individual tree crown metrics. These metrics were entered into two statistical approaches, ordinary least squares and Akaike information criterion regression, in order to estimate each of the 39 field plot-level forest variables. These estimated variables were then used as inputs to six forest condition assessment approaches identified in the literature. In total, 35 of the 39 field plot-level forest variables could be estimated with a validated NRMSE value below 0.4 using RS data (23 of these models had NRMSE values below 0.3). Over half of these models involved the use of FW LiDAR data on its own or combined with hyperspectral data, demonstrating this to be single most able dataset. Due to the synoptic coverage of the RS data, each of these field plot variables could be estimated and mapped continuously over the entire study site at the 30x30m resolution (i.e. field plot-level scale). The RS estimated field variables were then used as inputs to six forest condition assessment approaches identified in the literature.Three of the derived condition indices were successful based on correspondence with field validation data and woodlandcompartment boundaries. The three successful condition assessment methods were driven primarily by tree size and tree size variation. The best technique for assessing woodland condition was a score-based method which combined seventeen inputs which relate to tree species composition, tree size and variability, deadwood, and understory components; all of whichwere shown to be derived successfully from the appropriate combination of airborne hyperspectral and LiDAR datasets. The approach demonstrated in this project therefore shows that conventional methods of assessing forest condition can be applied with RS derived inputs for woodland assessment purposes over landscape-scale areas

Characterizing Woody Encroachment in the Konza Prairie Using Object-Based Analysis of Aerial Photographs

Woody encroachment is a threat to the ecological integrity of tallgrass prairie in Kansas. Encroachment data that covers a large spatial and temporal scale would be valuable to managers of tallgrass prairie, but no such dataset exists. The objective of this research is to develop a replicable technique for creating woody vegetation maps from aerial photographs. Rather than using a traditional pixel-by-pixel approach to classification, this project uses an object-based approach, wherein individual pixels are grouped into meaningful image objects according to user-defined parameters and then classified. I created woody vegetation maps of eight watersheds in the Konza Prairie using imagery from 1978, 1991, 2003, 2006, and 2010. I also determined the efficacy of LIDAR data in classifying the image from 2006. Ground-based vegetation survey data exist for two of the watersheds included in the remote sensing portion of this study. I analyzed the data from the available years nearest to the imagery dates (1983, 1992, 2003, and 2007) in order to provide a measure of validation for the woody vegetation maps. The results of this research were used to determine the applicability of this mapping technique and to draw preliminary conclusions about the landscape-level factors associated with woody encroachment

Estimating and mapping forest structure diversity using airborne laser scanning data

The topic of this doctoral thesis is the investigation of the most effective approaches and techniques that can be used to predict and map indicators of forest structural diversity, in a perspective of a more comprehensive assessment, management and monitoring of biodiversity in forest environments. The thesis is subdivided in two main sections, made up of five different but interdependent and organically connected studies, represented by as many published peer-reviewed original research articles, hereafter reported in Roman numerals as Studies I-V. The first section comprises the studies I-II-III. The contents of this section set the basis of methods and know-how that are subsequently used to estimate and map forest structure diversity in Studies IV and V. Several international cooperation projects has been stipulated in order to cope with the issue of the constantly loss of biodiversity at global scale, and because of the relevant influence that forest structure has on biodiversity, forest structure diversity needs to be to assessed and monitored on large areas. In Study I is demonstrated how this achievement can be efficiently tackled coupling ground data, such as those measured during forest inventory surveys, and remotely sensed data, in particular the ones derived from airborne laser scanning (ALS), which has proved to be a reliable source to characterize forest structure. The specific case of Study I presents how ALS data support the estimates of a common forest parameter, in such case forest above ground biomass (AGB), using field data gathered in a novel two-phase tessellation stratified sampling (TSS) design. In order to be used as a valid source of information for planning conservation strategies, along with the estimation, a detailed map showing the spatial patterns of structural diversity is of great usefulness. Study II presents an extensive meta-analysis carried out during the doctoral time frame where is demonstrated that the non-parametric k-NN is, among the others, the most used and effective technique to spatial predict and map forest attributes, alone or combined together to form synthetic indices. This technique can be further improved implementing an optimization step aimed to set the k-NN parameters in order to achieve the best prediction performance possible. Study III demonstrates that, if an optimization phase is carried out before running the k-NN procedure, the performance in the predictions improved sensibly. In the second and last section, the methods experimented in the first section are applied in two different research studies. Study IV describes the use of ALS data and ground data for the areal estimate of mean values of two forest structural diversity indices in a model-assisted framework. Along with the areal estimates, the study proposes the calculation of the confidence intervals of such estimates and the mapping of the investigated indices. Study V is framed as a methodological paper that takes a step further than Study IV, showing how, using the capability of an optimized k-NN techniques in predict simultaneously different parameters, is possible to map a more comprehensive structural diversity index (SDI) combining different forest structural diversity indices.Il tema trattato in questa tesi di dottorato è l'acquisizione e applicazione degli approcci e delle tecniche più efficaci che possono essere utilizzati per stimare e mappare indicatori di diversità strutturale delle foreste, nell’ottica di una più completa valutazione, gestione e monitoraggio della biodiversità in ambienti forestali. La tesi è suddivisa in due sezioni principali, costituite da cinque diversi ma interdipendenti e organicamente collegati studi, rappresentati da altrettanti articoli pubblicati su riviste soggette al processo di referaggio, di seguito riportati in numeri romani come Studi I-V. La prima sezione comprende tre studi, Studio I-II-III. I contenuti di questa sezione forniscono le basi conoscitive che verranno successivamente applicate per la stima e la mappatura della diversità strutturale in ambito forestale negli studi della seconda sezione (Studi IV e V). Diversi progetti di cooperazione internazionale sono stati stipulati al fine di far fronte al problema della costante perdita di biodiversità a livello mondiale, e data la rilevanza che la diversità strutturale delle foreste ha in termini di diversificazione degli habitat, un monitoraggio costante del suo status su grandi aree è di indubbia necessità. Lo Studio I dimostra come questo risultato può essere affrontato in modo efficiente integrando dati a terra, come quelli rilevati durante le indagini di tipo inventariale, e da dati rilevati, in particolare quelli derivanti da scansione laser aerea (ALS), i quali hanno dimostrato di essere uno strumento affidabile nel caratterizzare la struttura del bosco. Nel caso specifico dello Studio I viene mostrato come i dati ALS vengano utilizzati nella stima di un comune attributo forestale come la biomassa epigea, utilizzando dati a terra rilevati secondo un originale schema di campionamento stratificato a due fasi. Al fine di essere utilizzato come valida fonte di informazione per la pianificazione di strategie di conservazione, congiuntamente con la stima areale del parametro di interesse, una mappattura dettagliata che mostra come la diversità strutturale si distribuisce spazialmente è di grande utilità. Lo Studio II presenta una vasta meta-analisi e analisi bibliografica, effettuata durante il periodo il dottorato, in cui è mostrato come la tecnica parametrica della k-NN è, tra gli altri, quella più utilizzata ed efficace per la stima e spazializzazione di attributi forestali, sia come singolo attributo che come combinazione di essi, atti a formare indici sintetici. Questa tecnica può essere ulteriormente migliorata implementando una fase di ottimizzazione avente lo scopo di impostare i parametri del metodo k-NN per ottenere le migliori prestazioni possibili di stima. Lo Studio III scende nel dettaglio di questa fase, confermando che se l’ottimizzazione è effettuata prima di eseguire la procedura di k-NN, la performance nelle previsioni migliorata in maniera rilevante. Nella seconda e ultima sezione, i metodi sperimentati nella prima sezione sono applicati in due diversi studi. Lo Studio IV descrive l'uso dei dati ALS e di quelli a terra per la stima del valori medi degli indici di diversità strutturali sull’area di studio, in un contesto dove le stime derivanti dal modello fungono da supporto migliorando la precisione della stima rispetto ad una stima basata solo sull’utilizzo dei dati rilevati a terra. Lo studio propone inoltre il calcolo degli intervalli di confidenza di tali stime e la mappatura degli indici esaminati. Lo Studio V è strutturato come un approccio metodologico, portandosi un passo avanti rispetto allo Studio IV. Questo è la sintesi di tutto ciò che è stato acquisito e applicato finora, e propone la mappattura e la stima di un indice sintetico di diversità strutturale (SDI) ottenuto tramite la capacità di un’ottimizzata k-NN nello stimare attributi di interesse in maniera simultanea, sintetizzandoli in un unico e più comprensivo indice di diversità strutturale.Dottorato di ricerca in Scienze agro-forestali, delle tecnologie agro-industriali e del territorio rurale. I sistemi forestali (XXVIII ciclo

Morphology-based landslide monitoring with an unmanned aerial vehicle

PhD ThesisLandslides represent major natural phenomena with often disastrous consequences. Monitoring landslides with time-series surface observations can help mitigate such hazards. Unmanned aerial vehicles (UAVs) employing compact digital cameras, and in conjunction with Structure-from-Motion (SfM) and modern Multi-View Stereo (MVS) image matching approaches, have become commonplace in the geoscience research community. These methods offer a relatively low-cost and flexible solution for many geomorphological applications. The SfM-MVS pipeline has expedited the generation of digital elevation models at high spatio-temporal resolution. Conventionally ground control points (GCPs) are required for co-registration. This task is often expensive and impracticable considering hazardous terrain. This research has developed a strategy for processing UAV visible wavelength imagery that can provide multi-temporal surface morphological information for landslide monitoring, in an attempt to overcome the reliance on GCPs. This morphological-based strategy applies the attribute of curvature in combination with the scale-invariant feature transform algorithm, to generate pseudo GCPs. Openness is applied to extract relatively stable regions whereby pseudo GCPs are selected. Image cross-correlation functions integrated with openness and slope are employed to track landslide motion with subsequent elevation differences and planimetric surface displacements produced. Accuracy assessment evaluates unresolved biases with the aid of benchmark datasets. This approach was tested in the UK, in two sites, first in Sandford with artificial surface change and then in an active landslide at Hollin Hill. In Sandford, the strategy detected a ±0.120 m 3D surface change from three-epoch SfM-MVS products derived from a consumer-grade UAV. For the Hollin Hill landslide six-epoch datasets spanning an eighteen-month duration period were used, providing a ± 0.221 m minimum change. Annual displacement rates of dm-level were estimated with optimal results over winter periods. Levels of accuracy and spatial resolution comparable to previous studies demonstrated the potential of the morphology-based strategy for a time-efficient and cost-effective monitoring at inaccessible areas

Planetary Science Informatics and Data Analytics Conference : April 24–26, 2018, St. Louis, Missouri

The PSIDA conference provides a forum to discuss approaches, challenges, and applications of informatics and data analytics technologies and capabilities in planetary science.Institutional Support NASA Planetary Data System Geosciences, Lunar and Planetary Institute.Chairs Tom Stein, Washington University, St. Louis, USA, Dan Crichton, Jet Propulsion Laboratory, Pasadena, USA ; Program Committee Alphan Altinok, Jet Propulsion Laboratory, Pasadena, USA … [and 8 others]PARTIAL CONTENTS: ESA Planetary Science Archive Architecture and Data Management--SPICE for ESA Planetary Missions--VESPA: Enlarging the Virtual Observatory to Planetary Science--SeaBIRD: A Flexible and Intuitive Planetary Datamining Infrastructure--Model-Driven Development for PDS4 Software and Services--The Need for a Planetary Spatial Data Clearinghouse--The Relationship Between Planetary Spatial Data Infrastructure and the Planetary Data System--Update on the NASA-USGS Planetary Spatial Data Infrastructure Inter-Agency Agreement--MoonDB - A Data System for Analytical Data of Lunar Samples--Large-Scale Numerical Simulations of Planetary Interiors--Scalable Data Processing with the LROC Processing Pipelines--PACKMAN-Net: A Distributed, Open-Access, and Scalable Network of User-Friendly Space Weather Stations