Modelling forest stand maturity from National Forest Inventory and terrestrial laser scanning data

Estimation of forest maturity linked to the biomass (or carbon) stock capacity is considered one of the most important issues regarding forest management and planning. This doctoral thesis explores such relationships and considers the use of stand level maximum biomass stock as a proxy for forest maturity through site quality gradient, as a measure that can feasibly be estimated from National Forest Inventory data. The research study was conducted in Spain for maritime pine (Pinus pinaster ssp. atlantica H. de Vill.), Scots pine (Pinus sylvestris L.), beech (Fagus sylvatica L.), beech-fir and silver fir (Abies alba Mill.) forests. Finally, the potential of terrestrial laser scanning (TLS) technology for estimating forest features in mature stands was also explored. For this purpose, the R package FORTLS was developed

Thermodynamic characterization of LF, H, and mineral soil layers from oak forest ecosystems: exploring the role of proximate analysis

Studying the thermodynamic properties of soil organic matter is a developing field that involves the measurement of the energy stored by the soil. Quantifying soil energy content is still challenging despite different methodological approaches are available to calculate that value. One of the options is the proximate analysis following the guidelines for the energetic characterization of biomass. However, proximate analyses are still unexplored for soils. In this paper, we investigate the potential of this analysis to contribute to study soil from a thermodynamic perspective. With that goal, 31 soil samples collected in mature oak forests following a depth transect were used for elemental, thermal and proximate analysis. Proximate analyses and energetic characterization were performed by simultaneous thermogravimetry and differential scanning calorimetryThe authors thank Verónica Piñeiro and Montse Gómez of the RIAIDT analytical services at the University of Santiago de Compostela (Spain) for elemental and thermal analysis. Authors also thank Ken Byrne from the department of Biological Sciences, University of Limerick (Ireland), Eva Vanguelova from the Alice Holt Forest Research Station (UK) and Ander Arias González from Neiker-Tecnalia Basque Institute for Agricultural Research and Development (Spain) for the soil samples supply. This work has been developed under the project CONGESTION, funded by the by the Spanish Ministery of Science and Innovation (PID 2022-119204RB-C22)S

Assessing site form as an indicator of site quality in even-aged Pinus radiata D. Don stands in north-western Spain

[EN] Key message: Site form and site index have shown similar precision for estimating site quality in even-agedPinus radiataD. Don stands in north-western Spain. Additionally, SF presents the advantage that it does not require stand age information and can therefore be used in a wider set of situations in the forestry practice. Context: Estimation of site quality is essential for characterizing, monitoring and predicting forest resources. Site index (i.e. the dominant height of the stand at a reference age) is ordinarily used to estimate site quality; however, this index is only useful for even-aged stands of known age. By contrast, SF is age-independent as it uses the dominant height of the stand at a reference dominant diameter. Aims: The aim of this study was to compare the performance of SF and SI for site quality estimation in even-aged P. radiata stands. Methods: Dynamic equations derived with the Generalized Algebraic Difference Approach (GADA) from the Hossfeld IV base model were fitted to predict site quality with both SI and SF. SF predictions were compared with SI regarding variability within the same plot and consistency in site quality predictions, using the observed maximum mean annual volume increment (MAI) as a direct measure of site quality. Results: Both approaches showed good performance in model fitting and provided similar goodness-of-fit statistics and variability in the predictions. However, SI performed slightly stronger when related to MAISIThis study was funded by the Spanish Ministry of Science, Innovation and Universities (AGL2016-76769-C2-2-R). JAMV was funded by Ministry of Education through the FPU program (FPU16/03057). CPC was funded by the Spanish Secretariat of State for Research, Development and Innovation through the JdC-I fellowship and by the European Commission thorough the MSCA-EF QUAFOR

Assessing site form as an indicator of site quality in even-aged Pinus radiata D. Don stands in north-western Spain

Key message: Site form and site index have shown similar precision for estimating site quality in even-aged Pinus radiata D. Don stands in north-western Spain. Additionally, SF presents the advantage that it does not require stand age information and can therefore be used in a wider set of situations in the forestry practice. Context: Estimation of site quality is essential for characterizing, monitoring and predicting forest resources. Site index (i.e. the dominant height of the stand at a reference age) is ordinarily used to estimate site quality; however, this index is only useful for even-aged stands of known age. By contrast, SF is age-independent as it uses the dominant height of the stand at a reference dominant diameter. Aims: The aim of this study was to compare the performance of SF and SI for site quality estimation in even-aged P. radiata stands. Methods: Dynamic equations derived with the Generalized Algebraic Difference Approach (GADA) from the Hossfeld IV base model were fitted to predict site quality with both SI and SF. SF predictions were compared with SI regarding variability within the same plot and consistency in site quality predictions, using the observed maximum mean annual volume increment (MAI) as a direct measure of site quality. Results: Both approaches showed good performance in model fitting and provided similar goodness-of-fit statistics and variability in the predictions. However, SI performed slightly stronger when related to MAI. Conclusion: SF performed adequately in estimating site quality for even-aged P. radiata stands, with results comparable to those obtained using traditional SIThis study was funded by the Spanish Ministry of Science, Innovation and Universities (AGL2016-76769-C2-2-R). JAMV was funded by Ministry of Education through the FPU program (FPU16/03057). CPC was funded by the Spanish Secretariat of State for Research, Development and Innovation through the JdC-I fellowship and by the European Commission thorough the MSCA-EF QUAFORDS

Interpreting the uncertainty of model-based and design-based estimation in downscaling estimates from NFI data: a case-study in Extremadura (Spain)

[EN] Remotely sensed data are increasingly used together with National Forest Inventory (NFI) data to improve the spatial precision of forest variable estimates. In this study, we combined data from the 4th Spanish National Forest Inventory (SNFI-4) and from the 2nd nationwide Airborne Laser Scanning (ALS) survey to develop predictive forest inventory variables (total over bark volume (V), basal area (G), and annual increase in total volume (IAVC)) and aboveground biomass (AGB) models for the eight major forest strata in the region of Extremadura that are included in the Spanish Forest Map (SFM). We generated maps at 25 m resolution by applying an area‐based approach (ABA) and 758 sample plots measured with good positional accuracy within the SNFI-4 in Extremadura (Spain). Inventory performance is mainly influenced by spatial scale and vegetation structure. Therefore, in this study, we conducted a comparative analysis of statistical inference methods that can characterize forest inventory variables and AGB uncertainty across multiple spatial scales and types of vegetation structure. Predictions at pixel level were used to produce county, provincial, and regional model-based estimates, which were then compared with design-based estimates at different scales for different types of forest. We developed and tested both methods for forested area (cover, 19,744.15 km2), one province (9126.78 km2), and two counties (1594.42 km2 and 2076.76 km2, respectively) in Extremadura. The resulting relative standard error (SE) for regional level forest type-specific model-based estimates of V, G, IAVC, and AGB ranged from 3.34%–14.46%, 3.22%–12.50%, 4.46%–16.67%, and 3.63%–12.58%, respectively. The performance of the model-based approach, as assessed by the relative SE, was similar to that of the design-based approach at regional and provincial levels. However, the precision of SNFI model-based estimates was higher than that of estimates based on only the plot observations in small areas (e.g. at county level). The standard errors (SE) for model-based inferences were stable across the different scales, while SNFI design-based errors were higher due to the small sample sizes available for small areas. The findings indicate that SNFI-model based maps could be used directly to estimate forest inventory variables and AGB in the major forest strata included in the Spanish Forest Map, leading to potentially large economic savings.SIThe authors also thank to Forest Research Centre, a research unit funded by Fundação para a Ciência e aTecnologia I.P. (FCT), Portugal (UIDB/00239/2021). Postdoctoral grant Ministerio de Economía, Industria y Competitividad, Gobierno de España PTQ-13-06378 (Ministry of Economy, Industry, and Competitiveness) to Dr Juan Guerra Hernández. Grant number LISBOA-01-0145-FEDER-030391, Fundação para a Ciência e a Tecnologia PTDC/ASP-SIL/30391/2017. Project “Apoio à Contratação de Recursos Humanos Altamente Qualificados” (NORTE-06-3559-FSE-000045). under the PORTUGAL 2020 Partnership Agreement. ForestWISE - Collaborative Laboratory for Integrated Forest & Fire Management, was recognized as a CoLAB by the Foundation for Science and Technology, I.P. (FCT). This research was supported by the project “Extensión del cuarto inventario forestal nacional mediante técnicas LiDAR para la gestión sostenible de los montes de Extremadura” from the Extremadura Forest Service (FEADER nº 1952SE1FR435

Actas del Workshop sobre Teledetección Próxima Terrestre para Aplicaciones Forestales

Este libro contiene las actas del Workshop sobre Teledetección Próxima Terrestre para Aplicaciones Forestales que tuvo lugar los días 1, 2 y 3 de septiembre de 2021 en la Escuela Politécnica Superior de Ingeniería del Campus Terra de la Universidade de Santiago de Compostela (Lugo, España). Este workshop tuvo como objetivo la puesta en común de resultados científicos y aplicaciones prácticas de estas tecnologías en el ámbito forestal, así como la práctica de aspectos operativos en la toma y análisis de datos. Para ello, constó de jornadas científico-técnicas con formato de congreso (híbrido presencial-telemático), sesiones prácticas de toma de datos en campo y de su posterior análisis y procesado en ordenador mediante la impartición de talleres (presencial), y de una mesa redonda de reflexión sobre el estado del arte de estas tecnologías en todos los ámbitos forestales implicados. Este evento fue organizado conjuntamente por los grupos de investigación GI-1716 Proyectos y Planificación (PROEPLA) y GI-1837 Unidad de Gestión Ambiental y Forestal Sostenible (UXAFORES) de la Universidade de Santiago de Compostela. La celebración del workshop fue financiada por la Diputación de Lugo y la Universidade de Santiago de Compostela, a través de la Convocatoria de axudas 2021 para a realización de actividades de investigación e/ou transferencia no ámbito do desenvolvemento rural no Campus de Lugo, y por la Consellería de Educación, Universidade e Formación Profesional de la Xunta de Galicia que, mediante el Programa de Consolidación y Estructuración de Unidades de Investigación Competitivas, financia los grupos de referencia competitiva PROEPLA (GRC GI-1716, ED431C 2021/27) y UXAFORES (GRC GI-1837, ED431C 2018/07). Además, el evento fue patrocinado por las empresas ÁLAVA INGENIEROS y GRAFINTA, que han participado como sponsors vinculados a dos de las tecnologías de teledetección próxima terrestre objeto de la actividad: escáner láser terrestre (TLS) y localización simultánea y mapeo (SLAM), respectivamente. En la organización del Workshop sobre Teledetección Próxima Terrestre para Aplicaciones Forestales se contó con la colaboración del Campus Terra y la Escuela Politécnica Superior de Ingeniería de la Universidade de Santiago de Compostela, que proporcionaron los espacios en los que tuvieron lugar de forma presencial las distintas sesiones y talleres del workshopDiputación de Lugo y Universidade de Santiago de Compostela: Convocatoria de axudas 2021 para a realización de actividades de investigación e/ou transferencia no ámbito do desenvolvemento rural no Campus de Lugo. Proyectos y Planificación (PROEPLA) y Unidad de Gestión Ambiental y Forestal Sostenible (UXAFORES) de la Universidade de Santiago de Compostela: Consellería de Educación, Universidade e Formación Profesional de la Xunta de Galicia. Grupos de Referencia Competitiva: PROEPLA (GRC GI-1716, ED431C 2021/27) y UXAFORES (GRC GI-1837, ED431C 2018/07

FORTLS: An R Package for Processing TLS Data and Estimating Stand Variables in Forest Inventories

Terrestrial Laser Scanning (TLS) enables rapid, automatic, and detailed 3D representation of surfaces with an easily handled scanner device. TLS, therefore, shows great potential for use in Forest Inventories (FIs). However, the lack of well-established algorithms for TLS data processing hampers operational use of the scanner for FI purposes. Here, we present FORTLS, which is an R package specifically developed to automate TLS point cloud data processing for forestry purposes. The FORTLS package enables (i) detection of trees and estimation of their diameter at breast height (dbh), (ii) estimation of some stand variables (e.g., density, basal area, mean, and dominant height), (iii) computation of metrics related to important tree attributes estimated in FIs at stand level, and (iv) optimization of plot design for combining TLS data and field measured data. FORTLS can be used with single-scan TLS data, thus, improving data acquisition and shortening the processing time as well as increasing sample size in a cost-efficient manner. The package also includes several features for correcting occlusion problems in order to produce improved estimates of stand variables. These features of the FORTLS package will enable the operational use of TLS in FIs, in combination with inference techniques derived from model-based and model-assisted approachesThis research was funded by the Spanish Ministry of Science, Innovation and Universities, AGL2016-76769-C2-2-R. JAMV was funded by the Spanish Ministry of Education through the FPU program (FPU16/03057)S

Operationalizing the use of TLS in forest inventories: the R package FORTLS

Terrestrial Laser Scanning (TLS) devices show great potential for application in Forest Inventories (FIs) as they are capable of registering high resolution point clouds rapidly and automatically. Nevertheless, operational use of TLS for FI purposes has been hampered by the absence of algorithms for processing the acquired data, particularly in the single-scan mode, as occlusions result in loss of information. The R package FORTLS has been developed to overcome this obstacle, as it automates the processing of single-scan TLS point cloud data for forestry purposes and includes several features that deal with occlusions. FORTLS makes use of the main advantage of the single-scan scenario in FI, thus improving the efficiency of data acquisition and post-processing. All of these features of the FORTLS package are potentially valuable for the operational use of TLS in FIs, in combination with inference techniques derived from model-based and model-assisted approachesThis work was supported by the Spanish Ministry of Science and Innovation [AGL2016-76769-C2-2-R; PID2020-119204RB-C22] and Galician Regional Government [2020-CP031; ED431F 2020/02]; JAMV was supported by the Spanish Ministry of Science, Innovation and Universities through the FPU program [FPU16/03057]; AMC was supported by Galician Regional Government within the framework of the agreement “Development of the Galician continuous forest inventory” [2020-CP031]; CPC was supported by the Spanish Ministry of Science and Innovation [RYC2018-024939-I]S

Estimating basal area in mature stands of Pinus Sylvestrisusing single-scan terrestrial laser scanner (TLS)

El láser escáner terrestre (TLS) ha surgido como un nuevo dispositivo de tecnología LiDAR con un gran potencial para ser implementado en inventarios forestales (IIFF). En este trabajo se ha desarrollado un algoritmo con el que se obtienen métricas capaces de estimar el área basimétrica a nivel de parcela (G) en base a una única medición del TLS. El estudio se ha realizado en masas maduras de Pinus sylvestris, inventariadas mediante una red de 40 parcelas que cubren casi por completo el área de distribución y rango de calidades de la especie en España. Este algoritmo se compone de cuatro pasos fundamentales: (1) normalización de la nube de puntos obtenida con el TLS, (2) identificación de los árboles, (3) reducción de la densidad de la nube de puntos, y (4) obtención de las métricas relacionadas con el G. Los mejores resultados se han obtenido con el G estimada en parcelas de 7 m de radio, alcanzando un coeficiente de correlación de Pearson de 0.86 significativo al 95 %. Esto ha permitido obtener modelos de regresión lineal simple con valores de R2adjy RECM de 0.75 y 10.66 m2 para toda la colección de parcelas, y 0.82 y 8.57 m2 para las parcelas agregadas por sitio. Aunque estos resultados sugieren que el TLS podría ser útil para la estimación del G en pinares de P. sylvestris, sería necesario contrastarlo en masas que cubran todos los estados de desarrollo para validar su uso en un mayor rango de estados estructuralesTerrestrial Laser Scanner (TLS) has emerged as a new LiDAR device with high potential to be implemented in forest inventories (FI). In this work has been developed an algorithm to obtain metrics related to stand basal area (G). The research has been performed in mature stands of Pinus sylvestris through 40 plots covering almost all the distribution area and range of site qualities for this species in Spain. This algorithm is based on four main steps: (1) normalisation of point clouds obtained with TLS, (2) identification of individuals trees, (3) reduction in density of the point cloud, and (4) obtaining metrics related to G. The G estimated in plots of 7 m of radio shown the best results with a Pearson correlation coefficient value of 0.86. This has enabled to achieve a linear regression model with values of 0.75 and 10.66 m2 for R2adj and RMSE respectively for all the plots. Assessing linear regression model by site, R2adj and RMSE reached higher values of 0.82 y 8.57 m2. Although these results suggest TLS as a good tool to estimate G in mature stands of P. sylvestris, further research covering all the develop stages is necessary to contrast estimated G in stands with different structuresJuan Alberto Molina-Valero ha sido financiado por el Ministerio de Ciencia, Innovación y Universidades mediante la concesión de una ayuda para la formación del profesorado universitario (FPU 16/03057). César Pérez-Cruzado ha sido financia-do por la Comisión Europea a través del programa Marie Sklodowska-Curie (QUAFORD).Este trabajo se ha desarrollado en el marco de los proyectos nacionales “Modelización del efecto de la intensidad de perturbación sobre la estructura y el stockde carbono en masas naturales a partir del Inventario Forestal Nacional”(AGL2016-76769-C2-2-R), y del proyecto “Modelización no paramétrica de dinámicas y dependencias en sistemas complejos”(MTM2016-76969-P), ambos concedidos y financiados por la Agencia Española de InvestigaciónS

Estimating Stand and Fire-Related Surface and Canopy Fuel Variables in Pine Stands Using Low-Density Airborne and Single-Scan Terrestrial Laser Scanning Data

In this study, we used data from a thinning trial conducted on 34 different sites and 102 sample plots established in pure and even-aged Pinus radiata and Pinus pinaster stands, to test the potential use of low-density airborne laser scanning (ALS) metrics and terrestrial laser scanning (TLS) metrics to provide accurate estimates of variables related to surface and canopy fires. An exhaustive field inventory was carried out in each plot to estimate the main stand variables and the main variables related to fire hazard: surface fuel loads by layers, fuel strata gap, surface fuel height, stand mean height, canopy base height, canopy fuel load and canopy bulk density. In addition, the point clouds from low-density ALS and single-scan TLS of each sample plot were used to calculate metrics related to the vertical and horizontal distribution of forest fuels. The comparative performance of the following three non-parametric machine learning techniques used to estimate the main stand- and fire-related variables from those metrics was evaluated: (i) multivariate adaptive regression splines (MARS), (ii) support vector machine (SVM), and (iii) random forest (RF). The selection of the best modeling approach was based on a comparison of the root mean square error (RMSE), obtained by optimizing the parameters of each technique and performing cross-validation. Overall, the best results were obtained with the MARS techniques for data from both sensors. The TLS data provided the best results for variables associated with the internal characteristics of canopy structure and understory fuel but were less reliable for estimating variables associated with the upper canopy, due to occlusion by mid-canopy foliage. The combination of ALS and TLS metrics improved the accuracy of estimates for all variables analyzed, except the height and the biomass of the understory shrubs. The variability demonstrated by the combined use of both types of metrics ranged from 43.11% for the biomass of duff litter layers to 94.25% for dominant height. The results suggest that the combination of machine learning techniques and metrics derived from low-density ALS data, drawn from a single-scan TLS or a combination of both metrics, may represent a promising alternative to traditional field inventories for obtaining valuable information about surface and canopy fuel variables at large scalesinfo:eu-repo/semantics/publishedVersio