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Modelling fuel loads of understorey vegetation and forest floor components in pine stands in NW Spain

Forest Ecosystems 2022年第6期9卷 775-789页

作者： Jose A.Vega Stefano Arellano-Perez Juan Gabriel Alvarez-Gonzalez Cristina Fernandez Enrique Jimenez Pedro Cuinas Jose María Fernandez-Alonso Daniel J.Vega-Nieva Fernando Castedo-Dorado Cecilia Alonso-Rego Teresa Fontúrbel Ana Daría Ruiz-Gonzalez Centro de Investigacion Forestal de Lourizan PO Box 12736080PontevedraSpain Unidad de Gestion Ambiental y Forestal Sostenible(UXAFORES) Departamento de Ingeniería AgroforestalEscuela Politecnica Superior de IngenieríaUniversidad de Santiago de CompostelaCampus Universitario s/n27002LugoSpain Facultad de Ciencias Forestales Universidad Juarez del Estado de DurangoRío Papaloapan y Blvd.Durango s/nCol.Valle del Sur34120DurangoMexico Departamento de Ingeniería y Ciencias Agrarias Universidad de LeonCampus de Ponferrada24401PonferradaSpain

In this study,310 destructively sampled plots were used to develop two equation systems for the three main pine species in NW Spain(P.pinaster;P.radiata and P.sylvestris):one for estimating loads of understorey fuel components by size and condition(live and dead)and another one for forest floor fuels.Additive systems of equations were simultaneously fitted for estimating fuel loads using overstorey,understorey and forest floor variables as regressors.The systems of equations included both the effect of pine species and the effect of understorey compositions dominated by ferns-brambles or by woody species,due to their obvious structural and physiological differences.In general,the goodness-of-fit statistics indicated that the estimates were reasonably robust and accurate for all of the fuel fractions.The best results were obtained for total understorey vegetation,total forest floor and raw humus fuel loads,with more than 76%of the observed variability explained,whereas the poorest results were obtained for coarse fuel loads of understory vegetation with a 53%of observed variability explained.To reduce the overall costs associated with the field inventories necessary for operational use of the models,the additive systems were fitted again using only overstorey variables as potential regressors.Only relationships for fine(based only on canopy cover were fitted.These models explained only 26%-32%of the observed variability;however,their main advantage is that although understorey vegetation in forested landscapes is largely invisible to remote sensing,canopy cover can be estimated with moderate accuracy,allowing for landscape-scale estimates of total fuel lo

关键词： Area-based approach Additive equation systems Live and dead fine fuel Pinus pinaster Pinus radiata Pinus sylvestris

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On the potential to predetermine dominant tree species based on sparse-density airborne laser scanning data for improving subsequent predictions of species-specific timber volumes

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Forest Ecosystems 2016年第2期3卷 95-111页

作者： Janne Raty Jari Vauhkonen Matti Maltamo Timo Tokola University of Eastern Finland School of Forest Sciences Yliopistokatu 7 P.O.Box 111FI-80101 Joensuu Finland

Background： Tree species recognition is the main bottleneck in remote sensing based inventories aiming to produce an input for species-specific growth and yield models. We hypothesized that a stratification of the target data according to the dominant species could improve the subsequent predictions of species-specific attributes in particular in study areas strongly dominated by certain species. Methods： We tested this hypothesis and an operational potential to improve the predictions of timber volumes, stratified to Scots pine, Norway spruce and deciduous trees, in a conifer forest dominated by the pine species. We derived predictor features from airborne laser scanning （ALS） data and used Most Similar Neighbor （MSN） and Seemingly Unrelated Regression （SUR） as examples of non-parametric and parametric prediction methods, respectively Results： The relationships between the ALS features and the volumes of the aforementioned species were considerably different depending on the dominant species. Incorporating the observed dominant species inthe predictions improved the root mean squared errors by 13.3-16.4 % and 12.6-28.9 % based on MSN and SUR, respectively, depending on the species. Predicting the dominant species based on a linear discriminant analysis had an overall accuracy of only 76 % at best, which degraded the accuracies of the predicted volumes. Consequently, the predictions that did not consider the dominant species were more accurate than those refined with the predicted species. The MSN method gave slightly better results than models fitted with SUR. Conclusions： According to our results, incorporating information on the dominant species has a clear potential to improve the subsequent predictions of species-specific forest attributes. Determining the dominant species based solely on ALS data is deemed challenging, but important in particular in areas where the species composition is otherwise seemingly homogeneous except being dominated by certain species.

关键词： Forest inventory Light Detection and Ranging （LiDAR） Area-based approach Nearest neighbor estimation,Crown base height Intensity Volume model

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