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 /

Assessing wood properties in standing timber with laser scanning

Managed forests play crucial roles in ongoing climatic and environmental changes. Among other things, wood is capable of sinking and storing carbon in both standing timber and wood products. To promote these positive effects, more precise planning is required that will ensure sustainable forest management and maximal deposition of harvested wood for long-term applications. Information on wood properties plays a key role; i.e. the wood properties can impact the carbon stocks in forests and the suitability of wood for structural timber. With respect to the theoretical background of wood formation, stem, crown, and branching constitute potential inputs (i.e. wood quality indicators) to allometric wood property, tree biomass, and wood quality models. Due to the complex nature of wood formation, measurements of wood quality indicators that could predict wood properties along the relevant directions of variation have previously been elusive in forest inventories. However, developments in laser scanning from aerial and terrestrial platforms support more complex mapping and modeling regimes based on dense three-dimensional point clouds. The aim here was to determine how wood properties could be estimated in remote-sensing-aided forest inventories. For this purpose, methods for characterizing select wood quality indicators in standing timber, using airborne and terrestrial laser scanning (ALS and TLS, respectively) were developed and evaluated in managed boreal Scots pine (Pinus sylvestris L.) forests. Firstly, the accuracies of wood quality indicators resolved from TLS point clouds were assessed. Secondly, the results were compared with x-ray tomographic references from sawmills. Thirdly, the accuracies of tree-specific crown features delineated from the ALS data in predictive modeling of the wood quality indicators were evaluated. The results showed that the quality and density of point clouds significantly impacted the accuracies of the extracted wood quality indicators. In the assessment of wood properties, TLS should be considered as a tool for retrieving as dense stem and branching data as possible from carefully selected sample trees. Accurately retrieved morphological data could be applied to allometric wood property models. The models should use tree traits predictable with aerial remote sensing (e.g. tree height, crown dimensions) to enable extrapolations. As an outlook, terrestrial and aerial remote sensing can play an important role in filling in the knowledge gaps regarding the behavior of wood properties over different spatial and temporal extents. Further interdisciplinary cooperation will be needed to fully facilitate the use of remote sensing and spatially transferable wood property models that could become useful in tackling the challenges associated with changing climate, silviculture, and demand for wood.Hoidetuilla metsillä on useita tärkeitä rooleja muuttuvassa ilmastossa ja ympäristössä. Puu sitoo ja varastoi hiiltä niin kasvaessaan, kuin pitkäikäisiksi puutuotteiksi jalostettuna. Näiden vaikutusten huomioiminen metsänhoidossa vaatii tarkkaa suunnittelua, jolla varmistetaan metsänhoidon ja puunkäytön kestävyys. Tieto puuaineen ominaisuuksista on keskeisessä osassa, sillä ne vaikuttavat hiilivarastojen suuruuteen metsissä, sekä puun käytettävyyteen pitkäikäisenä rakennesahatavarana. Puunmuodostuksen teoreettisen taustan mukaisesti, runko, latvus ja oksarakenne ovat potentiaalisia selittäviä muuttujia (eli puun laatuindikaattoreita), kun mallinnetaan puuaineen ominaisuuksia, puubiomassaa ja puun laatua. Puunmuodostuksen monimutkaisuudesta ja moniulotteisesta vaihtelusta johtuen, tarvittavien laatuidikaattorien mittaaminen osana metsävarojen inventointia ja riittävällä yksityiskohtaisuudella on ollut aiemmin mahdotonta. Monialustaisen laserkeilauksen kehittyminen kuitenkin tukee aiempaa monipuolisempien kartoitus- ja mallinnusjärjestelmien rakentamista, jotka perustuvat tiheisiin kolmiulotteisiin pistepilviin. Tämän työn tavoitteena oli määritellä, kuinka puuaineen ominaisuuksia voidaan arvioida kaukokartoitusta hyödyntävässä metsävarojen inventoinnissa. Tätä tarkoitusta varten kehitettiin menetelmiä puun laatuindikaattorien mittaamiseksi hoidetuissa männiköissä (Pinus sylvestris L.) lento- ja maastolaserkeilauksen avulla, ja arvioitiin niiden toimivuutta. Ensin arvioitiin laatuindikaattorien mittatarkkuus pistepilvissä. Toiseksi verrattiin pistepilvimittauksia röntgentomografiamittauksiin teollisilla sahoilla. Kolmanneksi arvioitiin lentolaserkeilauksella tuotettujen latvuspiirteiden tarkkuutta laatuindikaattorien ennustamisessa. Tuloksien perusteella pistepilvien laatu ja pistetiheys vaikuttivat merkittävästi mitattujen laatuindikaattorien tarkkuuteen. Puuaineen ominaisuuksien arvioimisessa, maastolaserkeilausta tulisi käyttää työkaluna mahdollisimman yksityiskohtaisten runko- ja oksikkuustietojen keräämiseen tarkkaan valikoiduista näytepuista. Tarkasti mitatut laatuindikaattorit voivat selittää puuaineen ominaisuuksia mallinnuksessa. Käytettyjen mallien tulisi perustua laatuindikaattoreille, jotka voidaan ennustaa lentolaserkeilausaineistosta (esim. puun pituus ja latvuksen mittasuhteet), jotta ennusteet ovat yleistettävissä laajoille alueille. Tulevaisuudessa, maasta ja ilmasta tehtävällä kaukokartoituksella voi olla tärkeä rooli puuaineen ominaisuuksien aikaan ja paikkaan sidotun vaihtelun tutkimuksessa. Lisää poikkitieteellistä työtä tarvitaan, jotta kaukokartoitusta ja puuaineen ominaisuuksia ennustavia spatiaalisia malleja voidaan täysimittaisesti hyödyntää kiihtyvän ilmastonmuutoksen, muuttuvan metsänhoidon ja lisääntyvän puunkäytön tuomien haasteiden kohtaamisessa

Feasibility of Bi-Temporal Airborne Laser Scanning Data in Detecting Species-Specific Individual Tree Crown Growth of Boreal Forests

The tree crown, with its functionality of assimilation, respiration, and transpiration, is a key forest ecosystem structure, resulting in high demand for characterizing tree crown structure and growth on a spatiotemporal scale. Airborne laser scanning (ALS) was found to be useful in measuring the structural properties associated with individual tree crowns. However, established ALS-assisted monitoring frameworks are still limited. The main objective of this study was to investigate the feasibility of detecting species-specific individual tree crown growth by means of airborne laser scanning (ALS) measurements in 2009 (T1) and 2014 (T2). Our study was conducted in southern Finland over 91 sample plots with a size of 32 × 32 m. The ALS crown metrics of width (WD), projection area (A2D), volume (V), and surface area (A3D) were derived for species-specific individually matched trees in T1 and T2. The Scots pine (Pinus sylvestris), Norway spruce (Picea abies (L.) H. Karst), and birch (Betula sp.) were the three species groups that studied. We found a high capability of bi-temporal ALS measurements in the detection of species-specific crown growth (Δ), especially for the 3D crown metrics of V and A3D, with Cohen’s D values of 1.09–1.46 (p-value < 0.0001). Scots pine was observed to have the highest relative crown growth (rΔ) and showed statistically significant differences with Norway spruce and birch in terms of rΔWD, rΔA2D, rΔV, and rΔA3D at a 95% confidence interval. Meanwhile, birch and Norway spruce had no statistically significant differences in rΔWD, rΔV, and rΔA3D (p-value < 0.0001). However, the amount of rΔ variability that could be explained by the species was only 2–5%. This revealed the complex nature of growth controlled by many biotic and abiotic factors other than species. Our results address the great potential of ALS data in crown growth detection that can be used for growth studies at large scales

Exploring the Potential to Improve the Estimation of Boreal Tree Structural Attributes with Simple Height- and Distance-Based Competition Index

In many cases, the traditional ground-based estimates of competition between trees are not directly applicable with modern aerial inventories, due to incompatible measurements. Moreover, many former studies of competition consider extreme stand densities, hence the effect of competition under the density range in managed stands remains less explored. Here we explored the utility of a simple tree height- and distance-based competition index that provides compatibility with data produced by modern inventory methods. The index was used for the prediction of structural tree attributes in three boreal tree species growing in low to moderate densities within mixed stands. In silver birch, allometric models predicting tree diameter, crown height, and branch length all showed improvement when the effect of between-tree competition was included. A similar but non-significant trend was also present in a proxy for branch biomass. In Siberian larch, only the prediction of branch length was affected. In Scots pine, there was no improvement. The results suggest that quantification of competitive interactions based on individual tree heights and locations alone has potential to improve the prediction of tree attributes, although the outcomes can be species-specific.Peer reviewe

Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates

Landsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimation methods using both TM and LiDAR data. A case study is then presented that demonstrates the forest biomass estimation methods and uncertainty analysis. Results indicate that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems. LiDAR can overcome TM’s shortcoming providing better biomass estimation performance but has not been extensively applied in practice due to data availability constraints. The uncertainty analysis indicates that various sources affect the performance of forest biomass/carbon estimation. With that said, the clear dominate sources of uncertainty are the variation of input sample plot data and data saturation problem related to optical sensors. A possible solution to increasing the confidence in forest biomass estimates is to integrate the strengths of multisensor data

Improvements of Fire Fuels Attributes Maps by Integrating Field Inventories, Low Density ALS, and Satellite Data in Complex Mediterranean Forests

One of the most determining factors in forest fire behaviour is to characterize forest fuel attributes. We investigated a complex Mediterranean forest type—mountainous Abies pinsapo–Pinus–Quercus–Juniperus with distinct structures, such as broadleaf and needleleaf forests—to integrate field data, low density Airborne Laser Scanning (ALS), and multispectral satellite data for estimating forest fuel attributes. The three-step procedure consisted of: (i) estimating three key forest fuel attributes (biomass, structural complexity and hygroscopicity), (ii) proposing a synthetic index that encompasses the three attributes to quantify the potential capacity for fire propagation, and (iii) generating a cartograph of potential propagation capacity. Our main findings showed that Biomass–ALS calibration models performed well for Abies pinsapo (R2 = 0.69), Juniperus spp. (R2 = 0.70), Pinus halepensis (R2 = 0.59), Pinus spp. mixed (R2 = 0.80), and Pinus spp.–Juniperus spp. (R2 = 0.59) forests. The highest values of biomass were obtained for Pinus halepensis forests (190.43 Mg ha−1). The structural complexity of forest fuels was assessed by calculating the LiDAR Height Diversity Index (LHDI) with regard to the distribution and vertical diversity of the vegetation with the highest values of LHDI, which corresponded to Pinus spp.–evergreen (2.56), Quercus suber (2.54), and Pinus mixed (2.49) forests, with the minimum being obtained for Juniperus (1.37) and shrubs (1.11). High values of the Fuel Desiccation Index (IDM) were obtained for those areas dominated by shrubs (−396.71). Potential Behaviour Biomass Index (ICB) values were high or very high for 11.86% of the area and low or very low for 77.07%. The Potential Behaviour Structural Complexity Index (ICE) was high or very high for 37.23% of the area, and low or very low for 46.35%, and the Potential Behaviour Fuel Desiccation Index (ICD) was opposite to the ICB and ICE, with high or very high values for areas with low biomass and low structural complexity. Potential Fire Behaviour Index (ICP) values were high or very high for 38.25% of the area, and low or very low values for 45.96%. High or very high values of ICP were related to Pinus halepensis and Pinus pinaster forests. Remote sensing has been applied to improve fuel attribute characterisation and cartography, highlighting the utility of integrating multispectral and ALS data to estimate those attributes that are more closely related to the spatial organisation of vegetation

Two-phase forest inventory using very-high-resolution laser scanning

In this study, we compared a two-phase laser-scanning-based forest inventory of stands versus a traditional field inventory using sample plots. The two approaches were used to estimate stem volume (VOL), Lorey's mean height (HL), Lorey's stem diameter (DL), and VOL per tree species in a study area in Sweden. The estimates were compared at the stand level with the harvested reference values obtained using a forest harvester. In the first phase, a helicopter acquired airborne laser scanning (ALS) data with >500 points/m2 along 50-m wide strips across the stands. These strips intersected systematic plots in phase two, where terrestrial laser scanning (TLS) was used to model DL for individual trees. In total, phase two included 99 plots across 10 boreal forest stands in Sweden (lat 62.9 degrees N, long 16.9 degrees E). The single trees were segmented in both the ALS and TLS data and linked to each other. The very-high-resolution ALS data enabled us to directly measure tree heights and also classify tree species using a convolutional neural network. Stem volume was predicted from the predicted DBH and the estimated height, using national models, and aggregated at the stand level. The study demonstrates a workflow to derive forest variables and stand-level statistics that has potential to replace many manual field inventories thanks to its time efficiency and improved accuracy. To evaluate the inventories, we estimated bias, RMSE, and precision, expressed as standard error. The laser-scanning-based inventory provided estimates with an accuracy considerably higher than the field inventory. The RMSE was 17 m3/ha (7.24%), 0.9 m (5.63%), and 16 mm (5.99%) for VOL, HL, and DL respectively. The tree species classification was generally successful and improved the three species-specific VOL estimates by 9% to 74%, compared to field estimates. In conclusion, the demonstrated laser-scanning-based inventory shows potential to replace some future forest inventories, thanks to the increased accuracy demonstrated empirically in the Swedish forest study area