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 /

Pavement Surface Evaluation Using Mobile Terrestrial LiDAR Scanning Systems

Periodic measurement of pavement surfaces for pavement management system (PMS) data collection is vital for state transportation agencies. Vehicle-based mobile light detection and ranging (LiDAR) systems can be used as a versatile tool to collect point data throughout a roadway corridor. The overall goal of this research is to investigate if mobile terrestrial LiDAR Scanning (MTLS) systems can be used as an efficient and effective method to create accurate digital pavement surfaces for. LiDAR data were collected by five MTLS vendors. In particular, the research is interested in three things: 1) how accurate MTLS is for collecting roadway cross slopes; 2) what is the potential for using MTLS digital pavement surfaces to do materials calculations for pavement rehabilitation projects; and 3) examine the benefit of using MTLS to identify pavement rutting locations. Cross slopes were measured at 23 test stations using traditional surveying methods (conventional leveling served as ground-truth) and compared with adjusted and unadjusted MTLS extracted cross slopes. The results indicate that both adjusted and unadjusted MTLS derived cross slopes meet suggested cross slope accuracies (±0.2%). Application of unadjusted MTLS instead of post-processed MTLS point clouds may decrease/eliminate the cost of a control surveys. The study also used a novel approach to process the MTLS data in a geographic information system (GIS) environment to create a 3-dimension raster representation of a roadway surface. MTLS data from each vendor was evaluated in terms of the accuracy and precision of their raster surface. The resultant surfaces were compared between vendors and with a raster surface created from a centerline profile and 100-ft. cross-section data obtained using traditional surveying methods. When comparing LiDAR data between compliant MTLS vendors, average raster cell height differences averaged 0.21 inches, indicating LiDAR data has considerable potential for creating accurate pavement material volume estimates. The application of MTLS data was also evaluated in terms of the accuracy of collected transverse profiles. Transverse profiles captured from MTLS systems have been compared to 2-inch interval field data collection using partial curve mapping (PCM), Frechet distance, area, curve length, and Dynamic Time Warping (DTW) techniques. The results indicated that there is potential for MTLS systems for use in creating an accurate transverse profile for potential identification of pavement rut areas. This research also identified a novel approach for determining pavement rut areas based on the shape of grid cells. This rather simplistic approach is easily implementable on a network wide basis depending on MTLS point cloud availability. The method does not require the calculation/estimation of an ideal surface to determine rut depths/locations

Land of 10,000 pixels: applications of remote sensing & geospatial data to improve forest management in northern Minnesota, USA

2018 Summer.Includes bibliographical references.The use of remote sensing and geospatial data has become commonplace in a wide variety of ecological applications. However, the utility of these applications is often limited by field sampling design or the constraints on spatial resolution inherent in remote sensing technology. Because land managers require map products that more accurately reflect habitat composition at local, operational levels there is a need to overcome these limitations and improve upon currently available data products. This study addresses this need through two unique applications demonstrating the ability of remote sensing to enhance operational forest management at local scales. In the first chapter, remote sensing products were evaluated to improve upon regional estimates of the spatial configuration, extent, and distribution of black ash from forest inventory and analysis (FIA) survey data. To do this, spectral and topographic indices, as well as ancillary geospatial data were combined with FIA survey information in a non-parametric modeling framework to predict the presence and absence of black ash dominated stands in northern Minnesota, USA. The final model produced low error rates (Overall: 14.5%, Presence: 14.3%, Absence: 14.6%; AUC: 0.92) and was strongly informed by an optimized set of predictors related to soil saturation and seasonal growth patterns. The model allowed the production of accurate, fine-scale presence/absence maps of black ash stand dominance that can ultimately be used in support of invasive species risk management. In the second chapter, metrics from low-density LiDAR were evaluated for improving upon estimates of forest canopy attributes traditionally accessed through the LANDFIRE program. To do this, LiDAR metrics were combined with a Landsat time-series derived canopy cover layer in random forest k-nearest neighbor imputation approach to estimate canopy bulk density, two measures of canopy base height, and stand age across the Boundary Waters Canoe Area in northern Minnesota, USA. These models produced strong relationships between the estimates of canopy fuel attributes and field-based data for stand age (R2 = 0.82, RMSE = 10.12 years), crown fuel base height (R2 = 0.79, RMSE = 1.10 m.), live crown base height (R2 = 0.71, RMSE 1.60 m.), and canopy bulk density (R2 = 0.58, RMSE 0.09 kg/m3). An additional standard randomForest model of canopy height was less successful (R2 = 0.33, RMSE 2.08 m). The map products generated from these models improve upon the accuracy of national available canopy fuel products and provide local forest managers with cost-efficient and operationally ready data required to simulate fire behavior and support management efforts

Exploring and Describing the Spatial and Temporal Dynamics of Medusahead in the Channeled Scablands of Eastern Washington Using Remote Sensing Techniques

Medusahead is a harmful weed that is invading public lands in the West. The invasion is a serious concern to the public because it can reduce forage for livestock and wildlife, increase fire frequency, alter important ecosystem cycles (like water), reduce recreational activities, and produce landscapes that are aesthetically unpleasing. Invasions can drive up costs that generally require taxpayer’s dollars. Medusahead seedlings typically spread to new areas by attaching itself to passing objects (e.g. vehicles, animals, clothing) where it can quickly begin to affect plants communities. To be effective, management plans need to be sustainable, informed, and considerate to invasion levels across large landscapes. Ecological remote sensing analysis is a method that uses airborne imagery, taken from drones, aircrafts, or satellites, to gather information about ecological systems. This Thesis strived to use remote sensing techniques to identify medusahead in the landscape and its changes through time. This was done for an extensive area of rangelands in the Channel Scabland region of eastern ashington. This Thesis provided results that would benefit land managers that include: 1) a dispersal map of medusahead, 2) a time line of medusahead cover through time, 3) “high risk’ dispersal areas, 4) climatic factors showing an influence on the time line of medusahead, 5) a strategy map that can be utilized by land managers to direct management needs. This Thesis shows how remote sensing applications can be used to detect medusahead in the landscape and understand its invasiveness through time. This information can help create sustainable and effective management plans so land managers can continue to protect and improve western public lands threatened by the invasion of medusahead

Robot-assisted measurement for hydrologic understanding in data sparse regions

This article describes the field application of small, low-cost robots for remote surface data collection and an automated workflow to support water balance computations and hydrologic understanding where water availability data is sparse. Current elevation measurement approaches, such as manual surveying and LiDAR, are costly and infrequent, leading to potential inefficiencies for quantifying the dynamic hydrologic storage capacity of the land surface over large areas. Experiments to evaluate a team of two different robots, including an unmanned aerial vehicle (UAV) and an unmanned surface vehicle (USV), to collect hydrologic surface data utilizing sonar and visual sensors were conducted at three different field sites within the Arkavathy Basin river network located near Bangalore in Karnataka, South India. Visual sensors were used on the UAV to capture high resolution imagery for topographic characterization, and sonar sensors were deployed on the USV to capture bathymetric readings; the data streams were fused in an automated workflow to determine the storage capacity of agricultural reservoirs (also known as “tanks”) at the three field sites. This study suggests: (i) this robot-assisted methodology is low-cost and suitable for novice users, and (ii) storage capacity data collected at previously unmapped locations revealed strong power-type relationships between surface area, stage, and storage volume, which can be incorporated into modeling of landscape-scale hydrology. This methodology is of importance to water researchers and practitioners because it produces local, high-resolution representations of bathymetry and topography and enables water balance computations at small-watershed scales, which offer insight into the present-day dynamics of a strongly human impacted watershed

Automated Volumetric Measurements of Truckloads through Multi-View Photogrammetry and 3D Reconstruction Software

Since wood represents an important proportion of the delivered cost, it is important to embrace and implement correct measurement procedures and technologies that provide better wood volume estimates of logs on trucks. Poor measurements not only impact the revenue obtained by haulage contractors and forest companies but also might affect their contractual business relationship. Although laser scanning has become a mature and more affordable technology in the forestry domain, it remains expensive to adopt and implement in real-life operating conditions. In this study, multi-view Structure from Motion (SfM) photogrammetry and commercial 3D image processing software were tested as an innovative and alternative method for automated volumetric measurement of truckloads. The images were collected with a small UAV, which was flown around logging trucks transporting Eucalyptus nitens pulplogs. Photogrammetric commercial software was used to process the images and generate 3D models of each truckload. The levels of accuracy obtained with multi-view SfM photogrammetry and 3D reconstruction obtained in this study were comparable to those reported in previous studies with laser scanning systems for truckloads with similar logs and species. The deviations between the actual and predicted solid volume of logs on trucks ranged between –3.2% and 3.5%, with an average deviation of –0.05%. In absolute terms, the average deviation was only 0.5 m3 or 1.7%. Although several aspects must be addressed for the operational implementation of SfM photogrammetry, the results of this study demonstrate the great potential for this method to be used as a cost-effective tool to aid in the determination of the solid volume of logs on trucks

Quantifying scale relationships in snow distributions

2007 Summer.Includes bibliographic references.Spatial distributions of snow in mountain environments represent the time integration of accumulation and ablation processes, and are strongly and dynamically linked to mountain hydrologic, ecologic, and climatic systems. Accurate measurement and modeling of the spatial distribution and variability of the seasonal mountain snowpack at different scales are imperative for water supply and hydropower decision-making, for investigations of land-atmosphere interaction or biogeochemical cycling, and for accurate simulation of earth system processes and feedbacks. Assessment and prediction of snow distributions in complex terrain are heavily dependent on scale effects, as the pattern and magnitude of variability in snow distributions depends on the scale of observation. Measurement and model scales are usually different from process scales, and thereby introduce a scale bias to the estimate or prediction. To quantify this bias, or to properly design measurement schemes and model applications, the process scale must be known or estimated. Airborne Light Detection And Ranging (lidar) products provide high-resolution, broad-extent altimetry data for terrain and snowpack mapping, and allow an application of variogram fractal analysis techniques to characterize snow depth scaling properties over lag distances from 1 to 1000 meters. Snow depth patterns as measured by lidar at three Colorado mountain sites exhibit fractal (power law) scaling patterns over two distinct scale ranges, separated by a distinct break at the 15-40 m lag distance, depending on the site. Each fractal range represents a range of separation distances over which snow depth processes remain consistent. The scale break between fractal regions is a characteristic scale at which snow depth process relationships change fundamentally. Similar scale break distances in vegetation topography datasets suggest that the snow depth scale break represents a change in wind redistribution processes from wind/vegetation interactions at small lags to wind/terrain interactions at larger lags. These snow depth scale characteristics are interannually consistent, directly describe the scales of action of snow accumulation, redistribution, and ablation processes, and inform scale considerations for measurement and modeling. Snow process models are designed to represent processes acting over specific scale ranges. However, since the incorporated processes vary with scale, the model performance cannot be scale-independent. Thus, distributed snow models must represent the appropriate process interactions at each scale in order to produce reasonable simulations of snow depth or snow water equivalent (SWE) variability. By comparing fractal dimensions and scale break lengths of modeled snow depth patterns to those derived from lidar observations, the model process representations can be evaluated and subsequently refined. Snow depth simulations from the SnowModel seasonal snow process model exhibit fractal patterns, and a scale break can be produced by including a sub-model that simulates fine-scale wind drifting patterns. The fractal dimensions provide important spatial scaling information that can inform refinement of process representations. This collection of work provides a new application of methods developed in other geophysical fields for quantifying scale and variability relationships

Mapping historical forest biomass for stock-change assessments at parcel to landscape scales

Understanding historical forest dynamics, specifically changes in forest biomass and carbon stocks, has become critical for assessing current forest climate benefits and projecting future benefits under various policy, regulatory, and stewardship scenarios. Carbon accounting frameworks based exclusively on national forest inventories are limited to broad-scale estimates, but model-based approaches that combine these inventories with remotely sensed data can yield contiguous fine-resolution maps of forest biomass and carbon stocks across landscapes over time. Here we describe a fundamental step in building a map-based stock-change framework: mapping historical forest biomass at fine temporal and spatial resolution (annual, 30m) across all of New York State (USA) from 1990 to 2019, using freely available data and open-source tools. Using Landsat imagery, US Forest Service Forest Inventory and Analysis (FIA) data, and off-the-shelf LiDAR collections we developed three modeling approaches for mapping historical forest aboveground biomass (AGB): training on FIA plot-level AGB estimates (direct), training on LiDAR-derived AGB maps (indirect), and an ensemble averaging predictions from the direct and indirect models. Model prediction surfaces (maps) were tested against FIA estimates at multiple scales. All three approaches produced viable outputs, yet tradeoffs were evident in terms of model complexity, map accuracy, saturation, and fine-scale pattern representation. The resulting map products can help identify where, when, and how forest carbon stocks are changing as a result of both anthropogenic and natural drivers alike. These products can thus serve as inputs to a wide range of applications including stock-change assessments, monitoring reporting and verification frameworks, and prioritizing parcels for protection or enrollment in improved management programs.Comment: Manuscript: 24 pages, 7 figures; Supplements: 12 pages, 5 figures; Submitted to Forest Ecology and Managemen

An integrated study of earth resources in the State of California using remote sensing techniques

The author has identified the following significant results. The supply, demand, and impact relationships of California's water resources as exemplified by the Feather River project and other aspects of the California Water Plan are discussed