60 research outputs found
Moniajalliset aaltomuotolaserpiirteet metsäpuissa – fenologian, puulajien ja skannausgeometrian vaikutus
Ilmalaserkeilauksella ”airborne LiDAR” (Light Detection and Ranging) tuotetaan korkearesoluutioista 3D-tietoa
erittäin kustannustehokkaasti. Tämänhetkiset metsien inventointimenetelmät yhdistävät sekä LiDARin että
passiivisen ilmakuvauksen. Mahdollisuus pelkän LiDARin käyttöön on erittäin houkutteleva, koska se johtaisi
ainakin osittain kustannusten alenemiseen. Tässä tutkimuksessa keskitytään ns. täyden aaltomuodon havaintoihin,
mitkä sisältävät enemmän tietoa lähetetystä ja vastaanotetusta signaalista kuin ’tavanomaiset’ pistepilvet. Tässä
tutkimuksessa tarkastellaan metsän latvuston rakenteellisten ominaisuuksien ja LiDAR-signaalien välisiä
riippuvuuksia ja pyritään lisäämään ymmärrystämme LiDARin ja kasvillisuuden välisistä vuorovaikutuksista ja
tekijöistä, jotka rajoittavat nykyistä kykyä käyttää LiDAR-dataa mm. puulajitulkintaan, ja sitä, kuinka erilaisin
prosessointi ja laskentamenetelmin voimme parantaa LiDARin tulkintaa metsässä.
Tämän tutkimuksen tarkoituksena on ymmärtää, kuinka erilaisia aaltomuotopiirteitä voidaan tulkita ja kuinka piirteet
käyttäytyvät muuttuvan fenologian mukaan. Tutkimusaineisto koostuu kolmesta peräkkäisestä LiDAR- ja ilmakuva
kampanjasta, jotka on tehty alueella 38 kuukauden aikana sekä tämän ajanjakson aikana mitatuista
maastoreferenssipuista. Käytössä on monen ajankohdan dataa, mikä koostuu kolmesta toistetusta laserkeilauksesta,
jotka kaikki käyttivät samaa sensoria, lentoratoja ja keilausasetuksia. Koska LiDAR-havainnot ovat vertailukelpoisia
ja samoista puista, voidaan ns. "puutekijää" tutkia ja vaihtelua aaltomuodon ominaisuuksien välillä toistuvissa
keilauksissa seurata. Fenologiset muutokset ovat havaittavissa, koska aineistot sisältävät talven (lehdetön aika),
alkukesän (alhainen lehtialaindeksi (LAI) havupuilla) ja loppukesän (täyslehti, korkea LAI). Myös
skannauszeniittikulman (SZA) vaikutus aaltomuodon ominaisuuksiin ja piirteisiin otettiin huomioon, koska sama
puu voitiin nähdä usealta lentolinjalta.
Tulokset osoittavat, että huolellisella koeasettelulla on mahdollista havaita lajien sisäisiä ja lajien välisiä fenologisia
eroja ja muutoksia moniajallisista aaltomuotopiirteistä. SZA:lla ei ollut merkittävää vaikutusta tuloksiin.
Puulajiluokitus onnistui hyvin vaihtelevissa fenologisissa olosuhteissa ja erirakenteellisissa metsiköissä. Fenologiset
muutokset olivat hyvin ilmeisiä kausivihannoilla puilla, mutta melko pieniä ainavihannilla havupuilla.
Kokonaistarkkuudet puulajiluokituksessa olivat talvella 92 %, alkukesällä 88 % ja loppukesällä 84 %
kasvatusmetsässä ja talvella 84 %, alkukesällä 81 % ja loppukesällä 83 % vanhassa puustossa. "puutekijän"
osoitettiin olevan merkittävä. Lajien sisäinen varianssi johtuu pääasiassa puutekijästä eli lajinsisäinen
ominaisuusvarianssi edustaa luonnollista vaihtelua saman lajin puiden välillä.Airborne LiDAR (Light Detection And Ranging) produces high-resolution and cost-efficient 3D data. Currently,
forest inventories combine the use of both LiDAR and passive imaging by cameras, and the possibility of using
LiDAR only is very tempting as it would lead to cost reduction. Focus of this study is on the full-waveform
observations that extent the information content compared to conventional point clouds and are somewhat rarer to
have access to. This study explores basic dependencies between structural canopy features and LiDAR signals over
time and aims at augmenting our understanding of LiDAR-vegetation interactions and factors limiting our current
ability to use pulsed LiDAR data for species detection, and how possibilities to overcome those limitations.
Motivation is to understand how different waveform features can be interpreted and how the features behave over
time with changing vegetation phenology. The study material consists of three consecutive LiDAR campaigns and
aerial imaging surveys done in the area during a 38-month period and field reference trees that have been measured
during this period. I use multi-temporal data that comprise three repeated acquisitions, which all applied same
sensor, trajectories, as well as sensor and acquisition settings. As I had repeated LiDAR observations of the same
trees where the acquisition settings are comparable, I could study the so-called ‘tree effect’ and overall co-variation
between waveform features in the repeated acquisitions. Phenological changes are available as the data comprises
winter (leaf-off), early summer (low LAI in conifers) and late summer data (full leaf, high LAI). The influence of
scan zenith angle (SZA) on waveform features and attributes is also considered, as the same tree can be seen from
multiple strips.
The results showed that by using careful experimentation it is possible to detect intra- and interspecies phenological
changes from multitemporal full-waveform data, while SZA did not have markable effect on the WF features. I was
also able to perform well with the tree species classification task in varying phenological conditions. The
phenological changes were very apparent on deciduous trees, but rather small on evergreen conifers. In a 45-year-old
stand, the overall accuracies in tree species classification were 92, 87 and 88 % for winter, early summer, and late
summer, respectively. These figures were 84, 81, and 83 % for in an old growth forest. The ‘tree effect’ was shown
to be significant, i.e., many of the WF features of trees were correlated over time. The intra-species feature variance
that is due to the tree effect represents natural variation between trees of the same species
Airborne dual-wavelength waveform LiDAR improves species classification accuracy of boreal broadleaved and coniferous trees
Funding Information: This study was conducted on course FOR-254 ‘Advanced Forest Inventory and Management Project’ at the University of Helsinki. Plots IM and OG were measured by students and assistants on course FOR110B with the kind permission of Prof. Pauline Stenberg. Dr. Pekka Kaitaniemi provided phenological observations during LiDAR campaigns, and support by Dr. Antti Uotila was crucial in finding aspen, alder and larch samples in Hyytiälä. The LiDAR and field data in 2013 were collected and processed with funding from the Academy of Finland and Metsämiesten säätiö. Other work by made possible by the University of Helsinki. Publisher Copyright: © 2022, Finnish Society of Forest Science. All rights reserved.Tree species identification constitutes a bottleneck in remote sensing applications. Waveform LiDAR has been shown to offer potential over discrete-return observations, and we assessed if the combination of two-wavelength waveform data can lead to further improvements. A total of 2532 trees representing seven living and dead conifer and deciduous species classes found in Hyytiälä forests in southern Finland were included in the experiments. LiDAR data was acquired by two single-wavelength sensors. The 1064-nm and 1550-nm data were radiometrically corrected to enable range-normalization using the radar equation. Pulses were traced through the canopy, and by applying 3D crown models, the return waveforms were assigned to individual trees. Crown models and a terrain model enabled a further split of the waveforms to strata representing the crown, understory and ground segments. Different geometric and radiometric waveform attributes were extracted per return pulse and aggregated to tree-level mean and standard deviation features. We analyzed the effect of tree size on the features, the correlation between features and the between-species differences of the waveform features. Feature importance for species classification was derived using F-test and the Random Forest algorithm. Classification tests showed significant improvement in overall accuracy (74→83% with 7 classes, 88→91% with 4 classes) when the 1064-nm and 1550-nm features were merged. Most features were not invariant to tree size, and the dependencies differed between species and LiDAR wavelength. The differences were likely driven by factors such as bark reflectance, height growth induced structural changes near the treetop as well as foliage density in old trees.Peer reviewe
Complexity and Dynamics of Semi-Arid Vegetation Structure, Function and Diversity Across Spatial Scales from Full Waveform Lidar
Semi-arid ecosystems cover approximately 40% of the earth’s terrestrial landscape and show high dynamicity in ecosystem structure and function. These ecosystems play a critical role in global carbon dynamics, productivity, and habitat quality. Semi-arid ecosystems experience a high degree of disturbance that can severely alter ecosystem services and processes. Understanding the structure-function relationships across spatial extents are critical in order to assess their demography, response to disturbance, and for conservation management. In this research, using state-of-the-art full waveform lidar (airborne and spaceborne) and field observations, I developed a framework to assess the complexity and dynamics of vegetation structure, function and diversity across spatial scales in a semi-arid ecosystem.
Difficulty in differentiating low stature vegetation from bare ground is the key remote sensing challenge in semi-arid ecosystems. In this study, I developed a workflow to differentiate key plant functional types (PFTs) using both structural and biophysical variables derived from the full waveform lidar and an ensemble random forest technique. The results revealed that waveform lidar pulse width can clearly distinguish shrubs from bare ground. The models showed PFT classification accuracy of 0.81–0.86% and 0.60–0.70% at 10 m and 1 m spatial resolutions, respectively. I found that structural variables were more important than the biophysical variables to differentiate the PFTs in this study area. The study further revealed an overlap between the structural features of different PFTs (e.g. shrubs from trees).
Using structural features, I derived three main functional traits (canopy height, plant area index and foliage height diversity) of shrubs and trees that describe canopy architecture and light use efficiency of the ecosystem. I evaluated the trends and patterns of functional diversity and their relationship with non-climatic abiotic factors and fire disturbance. In addition to the fine resolution airborne lidar, I used simulated large footprint spaceborne lidar representing the newly launched Global Ecosystem Dynamics Investigation system (GEDI, a lidar sensor on the International Space Station) to evaluate the potential of capturing functional diversity trends of semi-arid ecosystems at global scales. The consistency of diversity trends between the airborne lidar and GEDI confirmed GEDI’s potential to capture functional diversity. I found that the functional diversity in this ecosystem is mainly governed by the local elevation gradient, soil type, and slope. All three functional diversity indices (functional richness, functional evenness and functional divergence) showed a diversity breakpoint near elevations of 1500 m – 1700 m. Functional diversity of fire-disturbed areas revealed that the fires in our study area resulted in a more even and less divergent ecosystem state. Finally, I quantified aboveground biomass using the structural features derived from both the airborne lidar and GEDI data. Regional estimates of biomass can indicate whether an ecosystem is a net carbon sink or source as well as the ecosystem’s health (e.g. biodiversity). Further, the potential of large footprint lidar data to estimate biomass in semi-arid ecosystems are not yet fully explored due to the inherent overlapping vegetation responses in the ground signals that can be affected by the ground slope. With a correction to the slope effect, I found that large footprint lidar can explain 42% of variance of biomass with a RMSE of 351 kg/ha (16% RMSE). The model estimated 82% of the study area with less than 50% uncertainty in biomass estimates. The cultivated areas and the areas with high functional richness showed the highest uncertainties. Overall, this dissertation establishes a novel framework to assess the complexity and dynamics of vegetation structure and function of a semi-arid ecosystem from space. This work enhances our understanding of the present state of an ecosystem and provides a foundation for using full waveform lidar to understand the impact of these changes to ecosystem productivity, biodiversity and habitat quality in the coming decades. The methods and algorithms in this dissertation can be directly applied to similar ecosystems with relevant corrections for the appropriate sensor. In addition, this study provides insights to related NASA missions such as ICESat-2 and future NASA missions such as NISAR for deriving vegetation structure and dynamics related to disturbance
Calibration of full-waveform airborne laser scanning data for 3D object segmentation
Phd ThesisAirborne Laser Scanning (ALS) is a fully commercial technology, which has seen rapid uptake from the photogrammetry and remote sensing community to classify surface features and enhance automatic object recognition and extraction processes. 3D object segmentation is considered as one of the major research topics in the field of laser scanning for feature recognition and object extraction applications. The demand for automatic segmentation has significantly increased with the emergence of full-waveform (FWF) ALS, which potentially offers an unlimited number of return echoes. FWF has shown potential to improve available segmentation and classification techniques through exploiting the additional physical observables which are provided alongside the standard geometric information. However, use of the FWF additional information is not recommended without prior radiometric calibration, taking into consideration all the parameters affecting the backscattered energy.
The main focus of this research is to calibrate the additional information from FWF to develop the potential of point clouds for segmentation algorithms. Echo amplitude normalisation as a function of local incidence angle was identified as a particularly critical aspect, and a novel echo amplitude normalisation approach, termed the Robust Surface Normal (RSN) method, has been developed. Following the radar equation, a comprehensive radiometric calibration routine is introduced to account for all variables affecting the backscattered laser signal. Thereafter, a segmentation algorithm is developed, which utilises the raw 3D point clouds to estimate the normal for individual echoes based on the RSN method. The segmentation criterion is selected as the normal vector augmented by the calibrated backscatter signals. The developed segmentation routine aims to fully integrate FWF data to improve feature recognition and 3D object segmentation applications. The routine was tested over various feature types from two datasets with different properties to assess its potential. The results are compared to those delivered through utilizing only geometric information, without the additional FWF radiometric information, to assess performance over existing methods. The results approved the potential of the FWF additional observables to improve segmentation algorithms. The new approach was validated against manual segmentation results, revealing a successful automatic implementation and achieving an accuracy of 82%
Acquisition and evaluation of radiometrically comparable multi-footprint airborne LiDAR data for forest remote sensing
Forest inventories comprise observations, models and sampling. Airborne LiDAR has established its role in providing observations of canopy geometry and topography. These data are input for estimation of important forest properties to support forestry-related decision-making. A major deficiency in forest remote sensing is tree species identification. This study examines the option of using multi-footprint airborne LiDAR data. Features of such sensor design exist in recently introduced multispectral laser scanners. The first objective was to acquire radiometrically normalized, multi-footprint (11, 22, 44 and 59 cm) waveform (WF) data that characterize 1064nm backscatter reflectance on the interval scale. The second objective was to analyze and validate the data quality in order to draw the correct conclusions about the effect of footprint size on WFs from natural and man-made targets. The experiment was carried out in Finland. Footprint variation was generated by acquiring data at different flying heights and by adjusting the transmitted power. The LiDAR campaign was successful and the data were of sufficient quality, except for a 1 dB trend due to the atmosphere. Significant findings were made conceming the magnitude of atmospheric losses, the linearity of the amplitude scale and the bandwidth characteristics of the receiver, the stability of the transmitter, the precision of the amplitude data and the transmission losses in canopies and power lines, as well as the response of WF attributes to footprint size in forest canopies. Multi-footprint data are a promising approach although the tree species-specific signatures were weak. (C) 2016 Elsevier Inc. All rights reserved.Peer reviewe
Advances in measuring forest structure by terrestrial laser scanning with the Dual Wavelength ECHIDNA® LIDAR (DWEL)
Leaves in forests assimilate carbon from the atmosphere and woody components store the net production of that assimilation. Separate structure measurements of leaves and woody components advance the monitoring and modeling of forest ecosystem functions. This dissertation provides a method to determine, for the first time, the 3-D spatial arrangement and the amount of leafy and woody materials separately in a forest by classification of lidar returns from a new, innovative, lidar scanner, the Dual-Wavelength Echidna® Lidar (DWEL). The DWEL uses two lasers pulsing simultaneously and coaxially at near-infrared (1064 nm) and shortwave-infrared (1548 nm) wavelengths to locate scattering targets in 3-D space, associated with their reflectance at the two wavelengths. The instrument produces 3-D bispectral "clouds" of scattering points that reveal new details of forest structure and open doors to three-dimensional mapping of biophysical and biochemical properties of forests.
The three parts of this dissertation concern calibration of bispectral lidar returns; retrieval of height profiles of leafy and woody materials within a forest canopy; and virtual reconstruction of forest trees from multiple scans to estimate their aboveground woody biomass. The test area was a midlatitude forest stand within the Harvard Forest, Petersham, Massachusetts, scanned at five locations in a 1-ha site in leaf-off and leaf-on conditions in 2014. The model for radiometric calibration assigned accurate values of spectral apparent reflectance, a range-independent and instrument-independent property, to scattering points derived from the scans. The classification of leafy and woody points, using both spectral and spatial context information, achieved an overall accuracy of 79±1% and 75±2% for leaf-off and leaf-on scans, respectively. Between-scan variation in leaf profiles was larger than wood profiles in leaf-off seasons but relatively similar to wood profiles in leaf-on seasons, reflecting the changing spatial heterogeneity within the stand over seasons. A 3-D structure-fitting algorithm estimated wood volume by modeling stems and branches from point clouds of five individual trees with cylinders. The algorithm showed the least variance for leaf-off, woody-points-only data, validating the value of separating leafy and woody points to the direct biomass estimates through the structure modeling of individual trees
Kohti metsien laserkeilausmittausten syvällisempää ymmärrystä
This thesis presents basic research on how airborne LiDAR measurements of forest vegetation are influenced by the interplay of the geometric-optical properties of vegetation, sensor function and acquisition settings. Within the work, examining the potential of waveform (WF) recording sensors was of particular interest.
Study I focused upon discrete return LiDAR measurements of understory trees. It showed that transmission losses influenced the intensity of observations and echo triggering probabilities, and also skewed the distribution of echoes towards those triggered by highly reflective or dense targets. The intensity data were of low value for species identification, but the abundance of understory trees could be predicted based on echo height distributions. In study II, a method of close-range terrestrial photogrammetry was developed. Images were shown as being useful for visualizations and even the geometric quality control of LiDAR data. The strength of backscattering was shown to correlate with the projected area extracted from the images.
In study III, a LiDAR simulation model was developed and validated against real measurements. The model was able to be used for sensitivity analyses to illustrate how plant structure or different pulse properties influence the WF data. Both simulated and real data showed that WF data were able to capture small-scale variations in the structural and optical properties of juvenile forest vegetation.
Study IV illustrated the potential of WF data in the species classification of larger trees. The WF features that separated tree species were also dependent on other variables such as tree size and phenology. Inherent between-tree differences in structure were quantified and the effects of pulse density on the features were examined.
Overall, the thesis provides basic findings on how LiDAR pulses interact with forest vegetation, and serves to link theory with real observations. The results contribute to an improved understanding of LiDAR measurements and their limitations, and thus provide support for further improvements in both data interpretation methods and specific sensor design.Väitöskirja käsittelee metsien mittausta ilma-aluksesta tehdyn laserkeilauksen avulla. Perustutkimusluonteisessa työssä selvitettiin, miten metsän rakenne ja heijastusominaisuudet sekä keilain- ja keilauskohtaiset parametrit vaikuttavat laserkeilaimella tehtyihin mittauksiin. Lisäksi selvitettiin aaltomuotolaserkeilainten käyttömahdollisuuksia verrattuna yleisemmin käytettyihin kaikulaserkeilaimiin.
Osajulkaisussa I tutkittiin alikasvospuustosta kaikulaserkeilaimella tehtyjä mittauksia. Energiahäviöt ylempiin latvuskerroksiin vaikuttivat todennäköisyyteen saada kaikuja alikasvospuista ja vääristivät kaikujen jakaumaa siten, että kaikuja saatiin eniten voimakkaasti heijastavista kohteista. Laserkaikujen intensiteetti ei soveltunut alikasvoksen puulajin tunnistukseen, mutta alikasvospuuston määrää pystyttiin ennustamaan kaikujen korkeusjakauman avulla.
Osajulkaisussa II kehitettiin maastofotogrammetriaan perustuva menetelmä laserkeilaustutkimuksen tueksi. Maastossa otettujen digikuvien avulla pystyttiin visualisoimaan laserkaikuja ja -aaltomuotoja sekä tutkimaan niiden geometrista tarkkuutta. Kuvilta laskettu kasvillisuuden silhuettiala oli yhteydessä lasersignaalin voimakkuuteen.
Osajulkaisussa III kehitettiin simulointimalli lasermittausten mallintamiseen ja verrattiin simuloituja aineistoja taimikkokasvillisuudesta aaltomuotolaserkeilaimella tehtyihin mittauksiin. Simuloimalla näytettiin, miten kasvillisuuden rakenne ja laserkeilaimen ominaisuudet vaikuttavat mittauksiin. Tulokset osoittivat, että aaltomuotolaserkeilaimella tehdyt mittaukset kuvaavat taimikkokasvillisuuden rakennetta ja niitä on mahdollista hyödyntää taimikkokasvillisuuden kartoituksessa.
Osajulkaisussa IV tutkittiin aaltomuotolaserkeilaimella tehtyjen mittausten käyttöä puulajin tunnistuksessa. Aaltomuotolaserkeilaus paransi tuloksia verrattuna kaikulaserin tallentaman intensiteetin käyttöön. Lisäksi selvitettiin, mitkä muut tekijät puulajin lisäksi vaikuttavat lasermittauksiin. Tunnetuista tekijöistä puuyksilöiden välistä lasersignaalin vaihtelua selittivät parhaiten puun pituus ja fenologinen tila, mutta aineistoon jäi paljon puuyksilöstä riippuvaa selittämätöntä vaihtelua. Väitöskirjan tulokset lisäävät ymmärrystä metsäkasvillisuudesta tehtyhin laserkeilausmittauksiin vaikuttavista tekijöistä ja luovat perustaa keilainlaitteiden sekä aineistojen tulkintamenetelmien jatkokehitykselle
Processing and analysis of airborne fullwaveform laser scanning data for the characterization of forest structure and fuel properties
Tesis por compendio[ES] Esta tesis aborda el desarrollo de métodos de procesado y análisis de datos ALSFW para la caracterización de la estructura vertical del bosque y, en particular, del sotobosque. Para responder a este objetivo general, se establecieron seis objetivos específicos: En primer lugar, se analiza la influencia de la densidad de pulso, de los parámetros de voxelización (tamaño de vóxel y valor de asignación) y de los métodos de regresión sobre los valores de las métricas ALSFW y sobre la estimación de atributos de estructura del bosque. Para ello, se redujo aleatoriamente la densidad de pulsos y se modificaron los parámetros de voxelización, obteniendo los valores de las métricas ALSFW para las diferentes combinaciones de parámetros. Estas mismas métricas ALSFW se emplearon para la estimación de atributos de la estructura del bosque mediante diferentes métodos de regresión. En segundo lugar, se integran métodos de procesado y análisis de datos ALSFW en una nueva herramienta llamada WoLFeX (Waveform Lidar for Forestry eXtraction) que incluye los procesos de recorte, corrección radiométrica relativa, voxelización y extracción de métricas a partir de los datos ALSFW, así como nuevas métricas descriptoras del sotobosque. En tercer lugar, se evalúa la influencia del ángulo de escaneo utilizado en la adquisición de datos ALS y la corrección radiométrica en la extracción de métricas ALSFW y en la estimación de atributos de combustibilidad forestal. Para ello, se extrajeron métricas ALSFW con y sin corrección radiométrica relativa y empleando diferentes ángulos de escaneo. En cuarto lugar, se caracteriza la oclusión de la señal a lo largo de la estructura vertical del bosque empleando y comparando tres tipos diferentes de láser escáner (ALSFW, ALSD y láser escáner terrestre: TLS, por sus siglas en inglés), determinando así sus limitaciones en la detección de material vegetativo en dos ecosistemas forestales diferenciados: el boreal y el mediterráneo. Para cuantificar la oclusión de la señal a lo largo de la estructura vertical del bosque se propone un nuevo parámetro, la tasa de reducción del pulso, basada en el porcentaje de haces láser bloqueados antes de alcanzar una posición dada. En quinto lugar, se evalúa la forma en que se detectan y determinan las clases de densidad de sotobosque mediante los diferentes tipos de ALS. Se compararon los perfiles de distribución vertical en los estratos inferiores descritos por el ALSFW y el ALSD con respecto a los descritos por el TLS, utilizando este último como referencia. Asimismo, se determinaron las clases de densidad de sotobosque aplicando la curva Lorenz y el índice Gini a partir de los perfiles de distribución vertical descritos por ALSFW y ALSD. Finalmente, se aplican y evalúan las nuevas métricas ALSFW basadas en la voxelización, utilizando como referencia los atributos extraídos a partir del TLS, para estimar la altura, la cobertura y el volumen del sotobosque en un ecosistema mediterráneo.[EN] This thesis addresses the development of ALSFW processing and analysis methods to characterize the vertical forest structure, in particular, the understory vegetation. To answer this overarching goal, a total of six specific objectives were established: Firstly, the influence of pulse density, voxel parameters (i.e., voxel size and assignation value) and regression methods on ALSFW metric values and on estimates of forest structure attributes are analyzed. To do this, pulse density was randomly reduced and voxel parameters modified, obtaining ALSFW metric values for the different parameter combinations. These ALSFW metrics were used to estimate forest structure attributes with different regression methods. Secondly, a set of ALSFW data processing and analysis methods are integrated in a new software named WoLFeX (Waveform Lidar for Forestry eXtraction), including clipping, relative radiometric correction, voxelization and ALSFW metric extraction, and proposing new metrics for understory vegetation. Thirdly, the influence of the scan angle of ALS data acquisition and radiometric correction on the extraction of ALSFW metrics and on modeling forest fuel attributes is assessed. To do this, ALSFW metrics were extracted applying and without applying relative radiometric correction and using different scan angles. Fourthly, signal occlusion is characterized along the vertical forest structure using and comparing three different laser scanning configurations (ALSFW, ALSD and terrestrial laser scanning: TLS), determining their limitations in the detection of vegetative material in two contrasted forest ecosystems: boreal and Mediterranean. To quantify signal occlusion along the vertical forest structure, a new parameter based on the percentage of laser beams blocked prior to reach a given location, the rate of pulse reduction, is proposed. Fifthly, the assessment of how understory vegetation density classes are detected and determined by different ALS configurations is done. Vertical distribution profiles at the lower strata described by ALSFW and ALSD are compared with those described by TLS as reference. Moreover, understory vegetation density classes are determined by applying the Lorenz curve and Gini index from the vertical distribution profiles described by ALSFW and ALSD. Finally, the new proposed voxel-based ALSFW metrics are applied and evaluated, using TLS-based attributes as a reference, to estimate understory height, cover and volume in a Mediterranean ecosystem.[CA] Aquesta tesi aborda el desenvolupament de mètodes de processament i anàlisi de dades ALSFW per a la caracterització de l'estructura vertical del bosc i, en particular, del sotabosc. Per a respondre a aquest objectiu general, s'establiren sis objectius específics: En primer lloc, s'analitza la influència de la densitat de pols, dels paràmetres de voxelització (grandària de vóxel i valor d'assignació) i dels mètodes de regressió sobre els valors de les mètriques ALSFW i sobre l'estimació dels atributs d'estructura del bosc. Per a això, es reduí aleatòriament la densitat de polsos i es modificaren els paràmetres de voxelització, obtenint els valors de les mètriques ALSFW per a les diferents combinacions de paràmetres. Aquestes mètriques ALSFW s'empraren per a l'estimació d'atributs de l'estructura del bosc mitjançant diferents mètodes de regressió. En segon lloc, s'integraren mètodes de processament i d'anàlisi de dades ALSFW en una nova eina anomenada WoLFeX (Waveform Lidar for Forestry eXtraction) que inclou el processos de retallada, correcció radiomètrica relativa, voxelització i extracció de mètriques a partir de les dades ALSFW, així com noves mètriques descriptores del sotabosc. En tercer lloc, s'avalua la influència de l'angle de escaneig emprat en l'adquisició de les dades ALS i la correcció radiomètrica en l'extracció de mètriques ALSFW i en l'estimació d'atributs de combustibilitat forestal. Per a això, s'extragueren mètriques ALSFW amb i sense correcció radiomètrica relativa i emprant diferents angles d'escaneig. En quart lloc, es caracteritza l'oclusió del senyal al llarg de l'estructura vertical del bosc emprant i comparant tres tipus diferents de làser escàner (ALSFW, ALSD i làser escàner terrestre: TLS, per les seues sigles en anglès), determinant així les seues limitacions en la detecció de material vegetatiu en dos ecosistemes diferenciats: un boreal i un mediterrani. Per a quantificar l'oclusió del senyal al llarg de l'estructura vertical del bosc es proposa un nou paràmetre, la taxa de reducció del pols, basada en el percentatge de rajos làser bloquejats abans d'arribar a una posició donada. En cinquè lloc, s'avalua la manera en la qual es detecten i determinen les classes de densitat de sotabosc mitjançant els diferents tipus d'ALS. Es compararen els perfils de distribució vertical en estrats inferiors descrits per l'ALSFW i l'ALSD respecte als descrits pel TLS, emprant aquest últim com a referència. A més a més, es determinaren les classes de densitat de sotabosc aplicant la corba Lorenz i l'índex Gini a partir dels perfils de distribució vertical descrits per l'ALSFW i l'ALSD. Finalment, s'apliquen i avaluen les noves mètriques ALSFW basades en la voxelització, emprant com a referència els atributs extrets a partir del TLS, per a estimar l'alçada, la cobertura i el volum del sotabosc en un ecosistema mediterrani.Crespo Peremarch, P. (2020). Processing and analysis of airborne fullwaveform laser scanning data for the characterization of forest structure and fuel properties [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/153715TESISCompendi
The Use of Coincident Synthetic Aperture Radar and Visible Imagery to Aid in the Analysis of Photon-Counting Lidar Data Sets Over Complex Ice/Snow Surfaces
Qualitative and quantitative analysis of multi-sensor data is becoming increasingly useful as a method of improving our understanding of complex environments, and can be an effective tool in the arsenal to help climate scientists to predict sea level rise due to change in the mass balance of large glaciers in the Arctic and Antarctic. A novel approach to remote sensing of the continuously changing polar environment involves the use of coincident RADARSAT-2 synthetic aperture radar (SAR) imagery and Landsat 7 visible/near-infrared imagery, combined with digital elevation models (DEM) developed from Multiple Altimeter Beam Experimental Lidar (MABEL) data sets.
MABEL is a scaled down model of the lidar altimeter that will eventually be flown on ICESat-2, and provides dense along-track and moderate slope (cross-track) elevation data over narrow (~198 m) aircraft transects. Because glacial terrain consists of steep slopes, crevices, glacial lakes, and outflow into the sea, accurate slope information is critical to our understanding of any changes that may be happening in the ice sheets. RADARSAT-2 operates in the C-band, at a wavelength of 5.55 cm, and was chosen partly for its ability to image the Earth under all atmospheric conditions, including clouds. The SAR images not only provide spatial context for the elevation data found using the lidar, but also offer key insights into the consistency of the snow and ice making up the glacier, giving us some idea of mean temperature and surface conditions on the ice sheet. Finally, Landsat 7 images provide us with information on the extent of the glacier, and additional understanding of the state of the glacial surface.
To aid in the analysis of the three data sets, proper preparation of each data set must first be performed. For the lidar data, this required the development of a new data reduction technique, based on statistical analysis, to reduce the number of received photons to those representing only the surface return. Accordingly, the raw SAR images require calibration, speckle reduction, and geocorrection, before they can be used. Landsat 7 bands are selected to provide the most contrast between rock, snow, and other surface features, and compiled into a three-band red, green, blue (RGB) image.
By qualitatively analyzing images and data taken only a short time apart using multiple imaging modalities, we are able to accurately compare glacial surface features to elevation provided by MABEL, with the goal of increasing our understanding of how the glacier is changing over time.
Quantitative analysis performed throughout this thesis has indicated that there is a strong correlation between top-of-the-atmosphere reflectance (Landsat 7), σ,0-calibrated HH and HV polarized backscatter coefficients (RADARSAT-2), elevation (MABEL), and various surface features and glacial zones on the ice sheet. By comparing data from unknown or mixed surfaces to known quantities scientists can effectively estimate the type of glacial zone the area of interest occurs in. Climate scientists can then use this data, along with long-term digital elevations models, as a measure of predicting climate change
Waveform lidar over vegetation : An evaluation of inversion methods for estimating return energy
Full waveform lidar has a unique capability to characterise vegetation in more detail than any other practical method. The reflectance, calculated from the energy of lidar returns, is a key parameter for a wide range of applications and so it is vital to extract it accurately. Fifteen separate methods have been proposed to extract return energy (the amount of light backscattered from a target), ranging from simple to mathematically complex, but the relative accuracies have not yet been assessed. This paper uses a simulator to compare all methods over a wide range of targets and lidar system parameters. For hard targets the simplest methods (windowed sum, peak and quadratic) gave the most consistent estimates. They did not have high accuracies, but low standard deviations show that they could be calibrated to give accurate energy. This may be why some commercial lidar developers use them, where the primary interest is in surveying solid objects. However, simulations showed that these methods are not appropriate over vegetation. The widely used Gaussian fitting performed well over hard targets (0.24% root mean square error, RMSE), as did the sum and spline methods (0.30% RMSE). Over vegetation, for large footprint (15 m) systems, Gaussian fitting performed the best (12.2% RMSE) followed closely by the sum and spline (both 12.7% RMSE). For smaller footprints (33 cm and 1 cm) over vegetation, the relative accuracies were reversed (0.56% RMSE for the sum and spline and 1.37% for Gaussian fitting). Gaussian fitting required heavy smoothing (convolution with an 8 m Gaussian) whereas none was needed for the sum and spline. These simpler methods were also more robust to noise and far less computationally expensive than Gaussian fitting. Therefore it was concluded that the sum and spline were the most accurate for extracting return energy from waveform lidar over vegetation, except for large footprint (15 m), where Gaussian fitting was slightly more accurate. These results suggest that small footprint (≪ 15 m) lidar systems that use Gaussian fitting or proprietary algorithms may report inaccurate energies, and thus reflectances, over vegetation. In addition the effect of system pulse length, sampling interval and noise on accuracy for different targets was assessed, which has implications for sensor design
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