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

Análisis de minería de datos para la clasificación de imágenes aéreas

[EN] This project tries to improve the land cover classification of aerial images using data mining. A comparison of some data mining techniques is done so as to classify urban and peri-urban areas, using the image segmentation in order to differentiate the objects that we want to classify. The next step is to analyse if adding and selecting attributes, the classification accuracy can be improved. Finally, we investigate whether the models that have been generated can classify a new geographical area with similar features.[ES] Este proyecto trata de mejorar la clasificación de los usos del suelo con imágenes aéreas utilizando la minería de datos. Para ello se comparan diferentes técnicas de minería de datos en clasificaciones de zonas urbanas y periurbanas, utilizando la segmentación de imágenes para poder separar los diferentes objetos a clasificar. A partir de los resultados obtenidos, se estudia el problema de la agregación y selección de atributos para este tipo de imágenes, y se analiza el comportamiento de los modelos generados cuando se aplican a otras zonas geográficas con características similares.Crespo Peremarch, P. (2014). Análisis de minería de datos para la clasificación de imágenes aéreas. http://hdl.handle.net/10251/51835Archivo delegad

A full-waveform airborne laser scanning metric extraction tool for forest structure modelling. Do scan angle and radiometric correction matter?

[EN] In the last decade, full-waveform airborne laser scanning (ALSFW) has proven to be a promising tool for forestry applications. Compared to traditional discrete airborne laser scanning (ALSD), it is capable of registering the complete signal going through the different vertical layers of the vegetation, allowing for a better characterization of the forest structure. However, there is a lack of ALSFW software tools for taking greater advantage of these data. Additionally, most of the existing software tools do not include radiometric correction, which is essential for the use of ALSFW data, since extracted metrics depend on radiometric values. This paper describes and presents a software tool named WoLFeX for clipping, radiometrically correcting, voxelizing the waves, and extracting object-oriented metrics from ALSFW data. Moreover, extracted metrics can be used as input for generating either classification or regression models for forestry, ecology, and fire sciences applications. An example application of WoLFeX was carried out to test the influence of the relative radiometric correction and the acquisition scan angle (1) on the ALSFW metric return waveform energy (RWE) values, and (2) on the estimation of three forest fuel variables (CFL: canopy fuel load, CH: canopy height, and CBH: canopy base height). Results show that radiometric differences in RWE values computed from different scan angle intervals (0°¿5° and 15°¿20°) were reduced, but not removed, when the relative radiometric correction was applied. Additionally, the estimation of height variables (i.e., CH and CBH) was not strongly influenced by the relative radiometric correction, while the model obtained for CFL improved from R2 = 0.62 up to R2 = 0.79 after applying the correction. These results show the significance of the relative radiometric correction for reducing radiometric differences measured from different scan angles and for modelling some stand-level forest fuel variables.This research was funded by the Spanish Ministerio de Economia y Competitividad and FEDER, in the framework of the projects ForeStructure (CGL2013-46387-C2-1-R) and FIRMACARTO (CGL2016-80705-R).Crespo-Peremarch, P.; Ruiz Fernández, LÁ. (2020). A full-waveform airborne laser scanning metric extraction tool for forest structure modelling. Do scan angle and radiometric correction matter?. Remote Sensing. 12(2):1-17. https://doi.org/10.3390/rs12020292S11712

Characterizing understory vegetation in Mediterranean forests using full-waveform airborne laser scanning data

[EN] The use of laser scanning acquired from the air, or ground, holds great potential for the assessment of forest structural attributes, beyond conventional forest inventory. The use of full-waveform airborne laser scanning (ALSFW) data allows for the extraction of detailed information in different vertical strata compared to discrete ALS (ALSD). Terrestrial laser scanning (TLS) can register lower vertical strata, such as understory vegetation, without issues of canopy occlusion, however is limited in its acquisition over large areas. In this study we examine the ability of ALSFW to characterize understory vegetation (i.e. maximum and mean height, cover, and volume), verified using TLS point clouds in a Mediterranean forest in Eastern Spain. We developed nine full-waveform metrics to characterize understory vegetation attributes at two different scales (3.75¿m square subplots and circular plots with a radius of 15¿m); with, and without, application of a height filter to the data. Four understory vegetation attributes were estimated at plot level with high R2 values (mean height: R2¿=¿0.957, maximum height: R2¿=¿0.771, cover: R2¿=¿0.871, and volume: R2¿=¿0.951). The proportion of explained variance was slightly lower at 3.75¿m side cells (mean height: R2¿=¿0.633, maximum height: R2¿=¿0.470, cover: R2¿=¿0.581, and volume R2¿=¿0.651). These results indicate that Mediterranean understory vegetation can be estimated and accurately mapped over large areas with ALSFW. The future use of these types of predictions includes the estimation of ladder fuels, which drive key fire behavior in these ecosystems.This research was developed mainly in the Integrated Remote Sensing Studio (IRSS) of University of British Columbia (UBC) (Canada) as a result of the Erasmus + KA-107 mobility grant. The authors thank the financial support provided by the Spanish Ministerio de Economia y Competitividad and FEDER, in the framework of the project CGL2016-80705-R.Crespo-Peremarch, P.; Tompalski, P.; Coops, N.; Ruiz Fernández, LÁ. (2018). Characterizing understory vegetation in Mediterranean forests using full-waveform airborne laser scanning data. Remote Sensing of Environment. 217:400-413. https://doi.org/10.1016/j.rse.2018.08.033S40041321

Analyzing the role of pulse density and voxelization parameters on full-waveform LiDAR-derived metrics

[EN] LiDAR full-waveform (LFW) pulse density is not homogeneous along study areas due to overlap between contiguous flight stripes and, to a lesser extent, variations in height, velocity and altitude of the platform. As a result, LFW-derived metrics extracted at the same spot but at different pulse densities differ, which is called ¿side-lap effect¿. Moreover, this effect is reflected in forest stand estimates, since they are predicted from LFW-derived metrics. This study was undertaken to analyze LFW-derived metric variations according to pulse density, voxel size and value assignation method in order to reduce the side-lap effect. Thirty LiDAR samples with a minimum density of 16 pulses.m¿2 were selected from the testing area and randomly reduced to 2 pulses.m¿2 with an interval of 1 pulse.m¿2, then metrics were extracted and compared for each sample and pulse density at different voxel sizes and assignation values. Results show that LFW-derived metric variations as a function of pulse density follow a negative exponential model similar to the exponential semivariogram curve, increasing sharply until they reach a certain pulse density, where they become stable. This value represents the minimum pulse density (MPD) in the study area to optimally minimize the side-lap effect. This effect can also be reduced with pulse densities lower than the MPD modifying LFW parameters (i.e. voxel size and assignation value). Results show that LFW-derived metrics are not equally influenced by pulse density, such as number of peaks (NP) and ROUGHness of the outermost canopy (ROUGH) that may be discarded for further analyses at large voxel sizes, given that they are highly influenced by pulse density. In addition, side-lap effect can be reduced by either increasing pulse density or voxel size, or modifying the assignation value. In practice, this leads to a proper estimate of forest stand variables using LFW data.This research has been funded by the Spanish Ministerio de Economia y Competitividad and FEDER, in the framework of the project CGL2016-80705-R. The authors also thank the Bureau of Land Management and the Panther Creek Remote Sensing and Research Cooperative Program for the data provided.Crespo-Peremarch, P.; Ruiz Fernández, LÁ.; Balaguer-Beser, Á.; Estornell Cremades, J. (2018). Analyzing the role of pulse density and voxelization parameters on full-waveform LiDAR-derived metrics. ISPRS Journal of Photogrammetry and Remote Sensing. 146:453-464. https://doi.org/10.1016/j.isprsjprs.2018.10.012S45346414

An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery

[EN] Mapping forest structure variables provides important information for the estimation of forest biomass, carbon stocks, pasture suitability or for wildfire risk prevention and control. The optimization of the prediction models of these variables requires an adequate stratification of the forest landscape in order to create specific models for each structural type or strata. This paper aims to propose and validate the use of an object-oriented classification methodology based on low-density LiDAR data (0.5 m−2) available at national level, WorldView-2 and Sentinel-2 multispectral imagery to categorize Mediterranean forests in generic structural types. After preprocessing the data sets, the area was segmented using a multiresolution algorithm, features describing 3D vertical structure were extracted from LiDAR data and spectral and texture features from satellite images. Objects were classified after feature selection in the following structural classes: grasslands, shrubs, forest (without shrubs), mixed forest (trees and shrubs) and dense young forest. Four classification algorithms (C4.5 decision trees, random forest, k-nearest neighbour and support vector machine) were evaluated using cross-validation techniques. The results show that the integration of low-density LiDAR and multispectral imagery provide a set of complementary features that improve the results (90.75% overall accuracy), and the object-oriented classification techniques are efficient for stratification of Mediterranean forest areas in structural- and fuel-related categories. Further work will be focused on the creation and validation of a different prediction model adapted to the various strata.This work was supported by the Spanish Ministerio de Economia y Competitividad and FEDER under [grant number CGL2013-46387-C2-1-R]; Fondo de Garantia Juvenil under [contract number PEJ-2014-A-45358].Ruiz Fernández, LÁ.; Recio Recio, JA.; Crespo-Peremarch, P.; Sapena, M. (2018). An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery. Geocarto International. 33(5):443-457. https://doi.org/10.1080/10106049.2016.1265595S44345733

Analyzing TLS Scan Distribution and Point Density for the Estimation of Forest Stand Structural Parameters

[EN] In recent decades, the feasibility of using terrestrial laser scanning (TLS) in forest inventories was investigated as a replacement for time-consuming traditional field measurements. However, the optimal acquisition of point clouds requires the definition of the minimum point density, as well as the sensor positions within the plot. This paper analyzes the effect of (i) the number and distribution of scans, and (ii) the point density on the estimation of seven forest parameters: above-ground biomass, basal area, canopy base height, dominant height, stocking density, quadratic mean diameter, and stand density index. For this purpose, 31 combinations of TLS scan positions, from a single scan in the center of the plot to nine scans, were analyzed in 28 circular plots in a Mediterranean forest. Afterwards, multiple linear regression models using height metrics extracted from the TLS point clouds were generated for each combination. In order to study the influence of terrain slope on the estimation of forest parameters, the analysis was performed by using all the plots and by creating two categories of plots according to their terrain slope (slight or steep). Results indicate that the use of multiple scans improves the estimation of forest parameters compared to using a single one, although using more than three to five scans does not necessarily improves the accuracy. Moreover, it is also shown that lower accuracies are obtained in plots with steep slope. In addition, it was observed that each forest parameter has a strategic distribution depending on the field of view of the TLS. Regarding the point density analysis, the use of 1% to 0.1% (¿136 points·m¿2) of the initial point cloud density (¿37,240.86 points·m¿2) generates an R2adj difference of less than 0.01. These findings are useful for planning more efficient forest inventories, reducing acquisition and processing time as well as costs.This research has been funded by the project PID2020-117808RB-C21 MCIN/AEI/10.13039/501100011033 and by the grant PEJ2018-002924-A Fondo de Garantia Juvenil en I+D+i ESF Investing in your future.Torralba, J.; Carbonell-Rivera, JP.; Ruiz Fernández, LÁ.; Crespo-Peremarch, P. (2022). Analyzing TLS Scan Distribution and Point Density for the Estimation of Forest Stand Structural Parameters. Forests. 13(12):1-22. https://doi.org/10.3390/f13122115122131

Analyzing the position and density distributions of the Terrestrial Laser Scanning (TLS) to retrieve forest parameters

[EN] In the last decades, the feasibility and efficiency of using TLS (Terrestrial Laser Scanning) in forest inventories have been studied. Data acquisition requires defining the scan locations to register the largest proportion of forest parameters in the plot. In this study, 31 location combinations of TLS scanners, from a single scan in the center of the plot to nine scans, were analyzed in 28 plots in a Mediterranean forest. Multiple linear regression models were generated to estimate mean height and aboveground biomass (AGB) from point cloud elevation metrics from each combination of scan locations. Additionally, the accuracy variation of the selected models by randomly reducing the point density was also analyzed. The analysis of the number of TLS scans shows that an increment in the number of scans does not necessarily mean an improvement in the statistical models for either variable. Results show for both variables that the influence of the point density on the accuracy of the regression model is significant when its value drops below one hundredth part of the initial point density[ES] En las últimas décadas se ha investigado la viabilidad y eficiencia del uso del TLS (Terrestrial Laser Scanning) en la realización de inventarios forestales. La adquisición de las nubes de puntos en campo implica definir los puntos de ubicación del sensor para registrar la mayor proporción de parámetros forestales en la parcela. En este estudio se realizó un análisis de 31 combinaciones de posiciones de escaneo TLS, considerando desde un escaneo simple en el centro de la parcela hasta un escaneo múltiple de 9 escaneos en 28 parcelas de bosque Mediterráneo. Se generaron modelos de regresión lineal múltiple para la obtención de la altura dominante de la parcela y la biomasa aérea total, a partir de las métricas de distribución de alturas de las nubes de puntos en cada combinación. También se analizó la evolución de la precisión de los modelos seleccionados al reducir aleatoriamente la densidad de puntos. En el análisis del número de escaneos TLS se observa que el aumento del número de tomas no implica una mejora en los modelos para ninguna de las dos variables. Por otro lado, el estudio de la reducción de densidad de puntos muestra para ambas variables forestales que la precisión del modelo de regresión no comienza a decrecer significativamente hasta reducir la nube a una centésima parte de la densidad original de puntos.Torralba Pérez, J.; Ruiz Fernández, LÁ.; Carbonell-Rivera, JP.; Crespo Peremarch, P. (2019). Análisis de posiciones y densidades TLS (Terrestrial Laser Scanning) para optimizar la estimación de parámetros forestales. Ediciones Universidad de Valladolid. 443-446. http://hdl.handle.net/10251/183353S44344

RGB and multispectral point clouds of the Sierra Calderona Natural Park

This dataset contains RGB and multispectral point clouds of the Sierra Calderona Natural Park, obtained by UAV digital aerial photogrammetry.Grants BES-2017-081920 and PID2020-117808RB-C21 funded by MCIN/AEI/10.13039/501100011033 and by ESF Investing in your future.Carbonell Rivera, JP.; Torralba Pérez, J.; Crespo Peremarch, P.; Ruiz Fernández, LÁ. (2023). RGB and multispectral point clouds of the Sierra Calderona Natural Park. https://doi.org/10.4995/Dataset/10251/19419

Classification of Mediterranean Shrub Species from UAV Point Clouds

[EN] Modelling fire behaviour in forest fires is based on meteorological, topographical, and vegetation data, including species¿ type. To accurately parameterise these models, an inventory of the area of analysis with the maximum spatial and temporal resolution is required. This study investigated the use of UAV-based digital aerial photogrammetry (UAV-DAP) point clouds to classify tree and shrub species in Mediterranean forests, and this information is key for the correct generation of wildfire models. In July 2020, two test sites located in the Natural Park of Sierra Calderona (eastern Spain) were analysed, registering 1036 vegetation individuals as reference data, corresponding to 11 shrub and one tree species. Meanwhile, photogrammetric flights were carried out over the test sites, using a UAV DJI Inspire 2 equipped with a Micasense RedEdge multispectral camera. Geometrical, spectral, and neighbour-based features were obtained from the resulting point cloud generated. Using these features, points belonging to tree and shrub species were classified using several machine learning methods, i.e., Decision Trees, Extra Trees, Gradient Boosting, Random Forest, and MultiLayer Perceptron. The best results were obtained using Gradient Boosting, with a mean cross-validation accuracy of 81.7% and 91.5% for test sites 1 and 2, respectively. Once the best classifier was selected, classified points were clustered based on their geometry and tested with evaluation data, and overall accuracies of 81.9% and 96.4% were obtained for test sites 1 and 2, respectively. Results showed that the use of UAV-DAP allows the classification of Mediterranean tree and shrub species. This technique opens a wide range of possibilities, including the identification of species as a first step for further extraction of structure and fuel variables as input for wildfire behaviour models.Grants BES-2017-081920 and PID2020-117808RB-C21 funded by MCIN/AEI/10.13039/5011 00011033 and by ESF Investing in your future.Carbonell-Rivera, JP.; Torralba, J.; Estornell Cremades, J.; Ruiz Fernández, LÁ.; Crespo-Peremarch, P. (2022). Classification of Mediterranean Shrub Species from UAV Point Clouds. Remote Sensing. 14(1):1-22. https://doi.org/10.3390/rs1401019912214