föra forest technologies
Recent publications
Impacts of climate warming on forests vigour are forecasted to increase in magnitude. Yet it remains unclear how stand characteristics and competition modulate the relationship between tree growth and gross primary production with drought. Here, we studied how the spatial variation in stand density, basal area and height modulates tree growth (Basal Area Increment, BAI and stand growth), summer NDVI, as well as their responses to drought (Standardized Precipitation-Evapotranspiration Index, SPEI) in 56 Aleppo pine ( Pinus halepensis Mill.) planted forests located in Northeast Spain. Long-term BAI responses to SPEI were strongly determined by stand density, suggesting that competition modulates Aleppo pine growth responses to drought. Along this, summer NDVI also displayed strong associations with SPEI. NDVI was mostly related with stand growth, suggesting canopy densification drives NDVI pattern and trends. Short-term BAI and NDVI responses to severe droughts were mainly independent of stand characteristics. In the studied region, drought is a universal factor limiting Aleppo pine secondary growth and canopy greening. However, the results suggest that stand density modulates Aleppo pine growth responses to drought on the long-term, reducing the growth in densest stands. Denser stands with larger trees are the ones that present higher NDVI values, suggesting that canopy activity depends more on stand canopy coverage than on secondary growth rate and its response to drought. In these Mediterranean pines, canopy activity and secondary growth are temporally coupled but spatially decoupled.
Forest certification is a private, voluntary and market-driven instrument designed to promote responsible forest management. This paper focused on the FSC and the NOM NMX-AA-143-SCFI-2008 schemes used in Mexico for the certification of sustainable forest management. In this paper we used the analytical hierarchical process (AHP) to study the factors that determine the main impacts of forest certification in México. A panel of 30 experts was selected as decision-makers to find which principles, criteria and indicators are considered as the most relevant while implementing forest certification. For decision-makers, the environmental principle occupied the first place with 40.26% of importance in the implementation of forest certification, followed by the social principle, and the economic principle with 32.15% and 27.59% of importance, respectively. Regarding the criteria, forest management and production, biodiversity, and forest protection were considered to be the most relevant. Regarding the indicators, the results indicated that forest certification in Mexico can have a positive impact on the existence of educational institutions, community services such as water, energy, medical services and drainage, the quality of the forest management plans, investment in forest management, machinery and equipment, environmental services, recreation, tourism, research, development and community education, planning for the conservation of biological diversity, and planning for biodiversity conservation.
Forest fungi provide recreational and economic services, as well as ecosystem biodiversity. Wild mushroom yields are difficult to estimate; climatic conditions are known to trigger temporally localised yields, and forest structure also affects productivity. In this work, we analyse the capacity of remotely sensed variables to estimate wild mushroom biomass production in Mediterranean Pinus pinaster forests in Soria (Spain) using generalised additive mixed models (GAMMs). In addition to climate variables, multitemporal NDVI derived from Landsat data, as well as structural variables measured with mobile Terrestrial Laser Scanner (TLS), are considered. Models are built for all mushroom species as a single pool and for Lactarius deliciosus individually. Our results show that, in addition to autumn precipitation, the interaction of multitemporal NDVI and vegetation biomass are most explanatory of mushroom productivity in the models. When analysing the productivity models of Lactarius deliciosus, in addition to the interaction between canopy cover and autumn minimum temperature, basal area (BA) becomes relevant, indicating an optimal BA range for the development of this species. These findings contribute to the improvement of knowledge about wild mushroom productivity, helping to meet Goal 15 of the 2030 UN Agenda.
Understorey evaluation is essential in wildlife habitat management, biomass storage and wild-fire suppression, among other areas. The lack of a standardised methodology in the field meas-urements, and in their subsequent analysis, forces researchers to look for procedures that effec-tively extract understorey data to make management decisions corresponding to actual stand conditions. In this sense, when analysing the understorey characteristics from LiDAR data, it is very usual to ask: “what value should we set the understorey height range to?” It is also usual to answer by setting a numeric value on the basis of previous research. Against that background, this research aims to identify the optimal height to canopy base (HCB) filter–LiDAR metric rela-tionship for estimating understorey height (UH) and understorey cover (UC) using LiDAR data in the Pokupsko Basin lowland forest complex (Croatia). First, several HCB values per plot were obtained from field data (measured HCBi—HCBM-i, where i ϵ (minimum, maximum, mean, percentiles)), and then they were modelled based on LiDAR metrics (estimated HCBi—HCBE-i). These thresholds, measured and estimated HCBi per plot, were used as point cloud filters to es-timate understorey parameters directly on the point cloud located under the canopy layer. In this way, it was possible to predict the UH with errors (RMSE) between 0.90 and 2.50 m and the UC with errors (RMSE) between 8.8 and 18.6 in cover percentage. Finally, the sensitivity analysis showed the HCB filter (the upper threshold to select the understorey LiDAR points) is the most important factor affecting the UH estimates, while this factor and the LiDAR metric are the most important factors affecting the UC estimates.
Resilient cities incorporate a social, ecological, and technological systems perspective through their trees, both in urban and peri-urban forests and linear street trees, and help promote and understand the concept of ecosystem resilience. Urban tree inventories usually involve the collection of field data on the location, genus, species, crown shape and volume, diameter, height, and health status of these trees. In this work, we have developed a multi-stage methodology to update urban tree inventories in a fully automatic way, and we have applied it in the city of Pamplona (Spain). We have compared and combined two of the most common data sources for updating urban tree inventories: Airborne Laser Scanning (ALS) point clouds combined with aerial orthophotographs, and street-level imagery from Google Street View (GSV). Depending on the data source, different methodologies were used to identify the trees. In the first stage, the use of individual tree detection techniques in ALS point clouds was compared with the detection of objects (trees) on street level images using computer vision (CV) techniques. In both cases, a high success rate or recall (number of true positive with respect to all detectable trees) was obtained, where between 85.07% and 86.42% of the trees were well-identified, although many false positives (FPs) or trees that did not exist or that had been confused with other objects were always identified. In order to reduce these errors or FPs, a second stage was designed, where FP debugging was performed through two methodologies: (a) based on the automatic checking of all possible trees with street level images, and (b) through a machine learning binary classification model trained with spectral data from orthophotographs. After this second stage, the recall decreased to about 75% (between 71.43 and 78.18 depending on the procedure used) but most of the false positives were eliminated. The results obtained with both data sources were robust and accurate. We can conclude that the results obtained with the different methodologies are very similar, where the main difference resides in the access to the starting information. While the use of street-level images only allows for the detection of trees growing in trafficable streets and is a source of information that is usually paid for, the use of ALS and aerial orthophotographs allows for the location of trees anywhere in the city, including public and private parks and gardens, and in many countries, these data are freely available.
The installation of research or permanent plots is a very common task in growth and forest yield research. At young ages, tree height is the most commonly measured variable, so the location of individuals is necessary when repeated measures are taken and if spatial analysis is required. Identifying the coordinates of individual trees and re-measuring the height of all trees is difficult and particularly costly (in time and money). The data used comes from three Pinus pinaster Ait. and three Pinus radiata D. Don plantations of 0.8 ha, with an age ranging between 2 and 5 years and mean heights between 1 and 5 m. Five individual tree detection (ITD) methods are evaluated, based on the Canopy Height Model (CHM), where the height of each tree is identified, and its crown is segmented. Three CHM resolutions are used for each method. All algorithms used for individual tree detection (ITD) tend to underestimate the number of trees. The best results are obtained with the R package, ForestTools and rLiDAR. The best CHM resolution for identifying trees was always 10 cm. We did not detect any differences in the relative error (RE) between Pinus pinaster and Pinus radiata. We found a pattern in the ITD depending on the height of the trees to be detected: the accuracy is lower when detecting trees less than 1 m high than when detecting larger trees (RE close to 12% versus 1% for taller trees). Regarding the estimation of tree height, we can conclude that the use of the CHM to estimate height tends to underestimate its value, while the use of the point cloud presents practically unbiased results. The stakeout of forestry research plots and the re-measurement of individual tree heights is an operation that can be performed by UAV-based LiDAR scanning sensors. The individual geolocation of each tree and the measurement of heights versus pole and/or hypsometer measurement is highly accurate and cost-effective, especially when tree height reaches 1–1.5 m.
The height to crown base (hcb) is a critical measure used in many investigations as an input variable to investigate the vigour of the stands, the social position of the trees, and to evaluate the behaviour of forest fires, among other uses. Though measuring height-related variables in the field is always time-consuming, the foremost benefits offered by modelling hcb are that it permits to generalize and average a very uneven attribute and, furthermore, provides insights about which tree and stand variables have a significant impact on hcb. However, there are many species in which models of the crown base height have not been developed in Croatia. The objective of this research was to develop a height to base crown model for each of the main species present in the two-layered mixed stands of this study. According to previous investigations, logistic models provide the highest precision and require the lowest inventory cost owing to less frequent measurements. Tree- and plot-level variables with distance-independent competition indexes were studied in the fitting model. In this research, we obtained models for the main stand species: Acer campestre (root mean squared error (RMSE) = 2.28 m, R2 = 82.80%); Alnus glutinosa (RMSE = 1.78 m, R2 = 85.36%); Carpinus betulus (RMSE = 2.47 m, R2 = 67.55%); Fraxinus angustifolia (RMSE = 2.46 m, R2 = 82.45%); Quercus robur (RMSE = 2.60 m, R2 = 80.57%); Tilia sp. (RMSE = 2.01 m, R 2 = 89.07%); and Ulmus laevis (RMSE = 1.71 m, R2 = 92.42%). The combination of the total height, tree, and plot-level variables with distance-independent competition indexes contributed to the prediction accuracy of proposed model significantly.
There is an increasing interest in forest biomass for energy throughout Europe, which is seen as a way of promoting forest mobilization and economic development locally, in particular in regions where forest biomass is available but its use is limited by lack of demand. This study was conducted to define, evaluate and select viable forest industry scenarios to increasing forest mobilisation in the North of Portugal using AppTitude ® , a Forest Management Decision Support Systems (FMDSS) considering spatially explicitly supply (biomass growth and yield), demand (industry), and supply-demand interactions (markets). The protocol followed combined a set of indicators of sustainable forest management to guide the selection of the best industry solutions in terms of location, dimension, forest biomass and other variables defined as objectives. The simulations allowed the selection of a small set of industry scenarios compatible with an existing plant outside the study area, increasing wood mobilization, preventing overexploitation and competition among industries but increasing value and price of forest biomass. The results of the application of this FMDSS showed that introducing new biomass plants in the region will increase sustainable forest mobilization and related local development. AppTitude ® revealed to be a powerful and reliable tool to assist forest planning.
Progress in high‐throughput phenotyping and genomics provides the potential to understand the genetic basis of plant functional differentiation. We developed a semi‐automatic methodology based on Unmanned Aerial Vehicle (UAV) imagery for deriving tree‐level phenotypes followed by genome‐wide association study (GWAS). A RGB‐based point cloud was used for tree crown identification in a common garden of Pinus halepensis in Spain. Crowns were combined with multispectral and thermal orthomosaics to retrieve growth traits, vegetation indices and canopy temperature. Thereafter, GWAS was performed to analyse the association between phenotypes and genomic variation at 235 Single Nucleotide Polymorphisms (SNPs). Growth traits were associated with 12 SNPs involved in cellulose and carbohydrate metabolism. Indices related to transpiration and leaf water content were associated with six SNPs involved in stomata dynamics. Indices related to leaf pigments and leaf area were associated with 11 SNPs involved in signalling and peroxisomes metabolism. About 16% to 20% of trait variance was explained by combinations of several SNPs, indicating polygenic control of morpho‐physiological traits. Despite a limited availability of markers and individuals, this study is a successful proof‐of‐concept for the combination of high‐throughput UAV‐based phenotyping with cost‐effective genotyping to disentangle the genetic architecture of phenotypic variation in a widespread conifer.
Tree height-diameter (H-D) relationships are important for routine forest assessment. Several H-D relationships have been developed for different species and more are still evolving. This study introduces new H-D model developed for Pinus pinaster and Pinus radiata in Spain, based on data from 184 and 96 permanent sample plots, respectively, collected in the northwest region of the country. Nonlinear mixed-effect modelling technique was used to fit the generalized H-D model. The mixed-effect H-D model was calibrated using the random effects predicted from one to three randomly selected trees per sample plot. Different indices including root mean square error (RMSE) and adjusted coefficient of determination () were used to assess the predictive performance of the model. The results showed that the new model had and RMSE of 0.906 and 1.156 m and 0.814 and 1.703 m for P. pinaster and P. radiata, respectively. The calibration response involved the selection of one tree per sample plot and resulted in a reduction of RMSE by 6.5% and 13.5% for pinaster and P. radiata, respectively.
Mushrooms play a provisioning ecosystem service as wild food. The abundance of this resource shows high annual and interannual variability, particularly in Mediterranean ecosystems. Climate conditions have been considered the main factor promoting mushroom production variability, but several evidences suggest that forest composition, age and growth play also a role. Long-term mushroom production datasets are critical to understand the factors behind mushroom productivity. We used 22 and 24 year-long time series of mushroom production in Pinus pinaster and Pinus sylvestris forests in Central Spain to evaluate the effect of climate and forest productivity on mushroom yield. We combined climatic data (precipitation and temperature) and remote sensing data (soil moisture and the Normalized Difference Vegetation Index, NDVI, a surrogate of primary productivity) to model mushroom yields for each forest and for the main edible species of economic interest (Boletus edulis and Lactarius deliciosus). We hypothesized that mushroom yield would be related to (i) forest primary productivity inferred from NDVI affects mushroom yields, that (ii) soil moisture inferred from remote sensors will equal the predictive power precipitation data, and that (iii) combining climatic and remote sensing will improve mushroom yield models. We found that (i) previous year NDVI correlated (r = 0.41–0.6) with mushroom yields; (ii) soil moisture from remote sensors rivaled the predictive power of precipitation (r = 0.63–0.72); and (iii) primary production and climate variances were independent, thus the combination of climatic and remote sensing data improved models with mean R²adj as high as 0.629. On the light of these results, we propose as a working hypothesis that mushroom production might be modelled as a two step process. Previous year primary productivity would favour resource accumulation at tree level, potentially increasing resources for mycelia growth, climatic conditions during the fruiting season control the ability of mycelia to transform available resources into fruiting bodies.
This study introduced a new height prediction model based on the modification of power function distribution. The model was fitted to the Pinus pinaster Ait (Maritime pine) data set comprised of 14339 trees measured from 155 permanent sample plots in northwest Spain. Nonlinear mixed-effect was to refit the model and calibrated with the random effects predicted from one to four sample trees per plot using validation data set (3621 trees from 29 plots). The models were evaluated based on different indices including root mean square error (RMSE), critical error (Ecrit) and adjusted coefficient of determination (R¯2). The results showed that the performance of the model was improved by the inclusion of random parameter with RMSE, Ecrit and R¯2of 1.143, 0.700 and 0.910, respectively. The calibration response of the mixed-effect model involved the selection of four trees per sample plot – each close to the 25th, 50th, 75th quartiles and maximum diameter. It resulted in a about 40 % reduction of the RMSE compared to other alternatives.
Decision-making in protected areas is often difficult due to an unclear definition of management objectives and indicators for their monitoring. This is frequently related to the coexistence of systems of protected areas at national and regional levels for which management objectives are ambiguous or that are not directly related to protected areas classification standards. In this study, we propose a participative model based on PROMETHEE II to find consensual protected areas categories based on IUCN framework. This model involves the stakeholder’s assessments of management objectives in a protected area and their aggregation based on distance comparisons with objectives of international protected areas standards. The model was tested in the Albufera de Valencia Natural Park, a strongly human-modified wetland located in Eastern Spain and it allowed the identification of consensual management priorities and the IUCN category V as the equivalent international protected area category for this regional protected area.
The planning of protected rural areas is usually defined by institutional decision-makers without considering the preferences of the local communities that live on the land, which frequently leads to conflicts in land management. This paper proposes a voting method based on the Borda count to rank the management goals of a protected rural area. The method was applied in a Spanish-Portuguese reserve called Meseta Ibérica with the aim of collecting the preferences of institutional decision-makers (government and scientists) and rural landowners (farmers and businesspersons). Regarding the conservation and development objectives, the results show differences in spatial planning when only the preferences of institutional decision-makers are taken into consideration, as opposed to when the preferences of landowners are included within the analysis.
In honey bee research conducted in apiaries, a large amount of information is usually generated requiring a flexible database for storing and retrieving data. Here, we developed a generic database pseudocode, based on the abstraction of the apiary system, for data collected from the colonies through time.
The estimation of forest residual biomass is of interest to assess the availability of green energy resources. This study relates the Pinus halepensis Miller forest residual biomass (FRB), estimated in 192 field plots, to several independent variables extracted from Airborne Laser Scanner (ALS) data in Aragón region (Spain). Five selection approaches and four non-parametric regression methods were compared to estimate FRB. The sample was divided into training and validation sets, composed of 144 and 48 plots, respectively. The best-fitted model was obtained using the Support Vector Machine method with the radial kernel. The model included three ALS metrics: the 70th percentile, the variance of the return heights, and the percentage of first returns above mean height. The root-mean-square error (RMSE) after validation was 8.85 tons ha−1. The influence of point density, scan angle, canopy pulse penetration, terrain slope, and shrub presence in model performance was assessed using graphical and statistical approaches. Point densities higher than 1 point m−2, scan angles lower than 15°, canopy pulse penetration over 25%, and terrain slopes under 30% generated a smaller variability in mean predictive error (MPE) values, thus increasing model accuracy in 0.56, 1.94, 1.44, and 5.47 tons ha−1, respectively. Shrub vegetation caused greater variability in MPE values but slightly decreased model accuracy (0.10 tons ha−1). No statistically significant differences were found between the categories in the influencing variables, except for canopy pulse penetration. The mapping of Pinus halepensis Miller FRB using the best-fitted model summed up a total of 3,627,021.25 tons, which equals to 1,584.91 thousand tonnes of oil (ktoe).
Key message Using a combination of remote sensing data, Pinus pinaster Ait. and Pinus pinea L. were distinguished at individual tree level in mixed Mediterranean stands with over 95% accuracy. This approach is easily applicable over large areas, enhancing the economic value of non-wood forest products, stone pine nuts, and resin, and aiding forest managers to accurately predict this production. Context The discrimination of tree species at individual level in mixed Mediterranean forest based on remote sensing is a field which has gained greater importance. In these stands, the capacity to predict the quality and quantity of non-wood forest products is particularly important due to the very different goods the two species produce. Aims To assess the potential of using low-density airborne LiDAR data combined with high-resolution Pleiades images to discriminate two different pine species in mixed Mediterranean forest (Pinus pinea L. and Pinus pinaster Ait.) at individual tree level. Methods A Random Forest model was trained using plots from the pure stand dataset, determining which LiDAR and satellite variables allow us to obtain better discrimination between groups. The model constructed was then validated by classifying individuals in an independent set of pure and mixed stands. Results The model combining LiDAR and Pleiades data provided greater accuracy (83.3% and 63% in pure and mixed validation stands, respectively) than the models which only use one type of covariables. Conclusion The automatic crown delineation tool developed allows two very similar species in mixed Mediterranean conifer forest to be discriminated using continuous spatial information at the surface: Pleiades images and open source LiDAR data. This approach is easily applicable over large areas, enhancing the economic value of non-wood forest products and aiding forest managers to accurately predict production.
Aim of study: In this study, both the direct and indirect methods by conditional maximum likelihood (CML) and moments for fitting Johnson’s SBB were evaluated. To date, Johnson’s SBB has been fitted by either indirect (two-stage) method using well-known procedures for the marginal diameter and heights, or direct methods, where all parameters are estimated at once. Application of bivariate Johnson’s SBB for predicting height and improving volume estimation requires a suitable fitting method. Area of study: E. globulus, P. pinaster and P. radiata stands in northwest Spain. Material and methods: The data set comprised of 308, 184 and 96 permanent sample plots (PSPs) from the aforementioned species. The suitability of the method was evaluated based on height and volume prediction. Indices including coefficient of determination (R2), root mean square Error (RMSE), model efficiency (MEF), Bayesian Information Criterion (BIC) and Hannan-Quinn Criterion (HQC) were used to assess the model predictions. Significant difference between observed and predicted tree height and volumes were tested using paired sample t-test at 5% level for each plot by species. Main results: The indirect method by CML was the most suitable method for height and volume prediction in the three species. The R2 and RMSE for height prediction ranged from 0.994 – 0.820 and 1.454 – 1.676, respectively. The percentage of plot in which the observed and predicted heights were significant was 0.32%. The direct method was the least performed method especially for height prediction in E. globulus. Research highlights: The indirect (two-stage) method, especially by conditional maximum likelihood, was the most suitable method for the bivariate Johnson’s SBB distribution. Keywords: conditional maximum likelihood; moments; two-stage method; direct method; tree volume.
This study assesses model temporal transferability using airborne laser scanning (ALS) data acquired over two different dates. Seven forest attributes (i.e. stand density, basal area, squared mean diameter, dominant diameter, tree dominant height, timber volume, and total tree biomass) were estimated using an area-based approach in Mediterranean Aleppo pine forests. Low-density ALS data were acquired in 2011 and 2016 while 147 forest inventory plots were measured in 2013, 2014, and 2016. Single-tree growth models were used to generate concomitant field data for 2011 and 2016. A comparison of five selection techniques and five regression methods were performed to regress field observations against ALS metrics. The selection of the best regression models fitted for each stand attribute, and separately for both 2011 and 2016, was performed following an indirect approach. Model performance and temporal transferability were analyzed by extrapolating the best fitted models from 2011 to 2016 and inversely from 2016 to 2011 using the direct approach. Non-parametric support vector machine with radial kernel was the best regression method with average relative % root mean square error differences of 2.13% for 2011 models and 1.58% for 2016 ones.
MCDM methods are useful to obtain information and generate knowledge useful for decision-making processes in multidisciplinary contexts. Particularly, when conflicts occur, knowledge is the key to start negotiation processes between stakeholders in order to achieve consensual solutions. The planning of protected areas is complex due to many competing uses of natural resources and the involvement of a large number of stakeholders. For the last ten years, participative MCDM methodologies have been carried out efficiently to reduce conflicts and allow to formalize stakeholder’s participation in the policy development processes. In this paper, we propose a MCDM participative methodology in three levels that seeks to define management plans in protected areas. This method allows for the definition of management plans based on three levels of criteria that define the use of the natural area and provide a ranking of the main goals according to the stakeholders’ priorities. The model was tested in two Spanish protected areas: Parque Natural de Lago de Sanabria y alrededores and Parque Natural de los Arribes del Duero. Firstly, the individual preferences of the most representative stakeholder groups were collected (Farmers, Business owners, Government and Scientists) and analysed using two multi-criteria methods: AHP and GP. Moreover, a conflict index between stakeholders’ groups was calculated. Results showed that conservation and development goals are the most preferred to consider for the planning of these areas. Furthermore, the most serious disagreement was found between farmers and scientists and between farmers and government about the wildlife conservation.
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Campus Duques de Soria, 42005, Soria, Spain
Head of institution
Francisco Rodríguez & Iñigo Lizarralde
Website
www.fora.es