Wood volume estimation in three gallery forests

Foi realizada uma análise de regressão para estimar o volume de madeira de três florestas de galeria localizadas na região Centro-Oeste do Brasil. Foram analisados modelos de regressão para o volume de madeira dos fustes, dos ramos e volume total por área de estudo e pelo conjunto total. O modelo para o volume total, Vtotal (m3) = b0 + b1 DAP2H + b2nramos + b3 diamramos, apresentou um R2 em torno de 0,95. Das variáveis utilizadas, acombinação 1/DAP2H proporcionou a ponderação mais aceitável. ______________________________________________________________________________ ABSTRACTRegression functions for the wood volume were derived for three study sites in galleryforests in the Central Eastern part of Brazil. Regression models were derived for stem volume, branch volume, and total volume. The regression function for total volume was Vtotal(m3) = b0 + b1 DAP2H + b2 nramos + b3 diamramos with an R2 of 0.95. The technique ofweighted regression was applied, showing that the factor 1/DAP2H was most appropriate asweight

Evaluating the potential of ALS data to increase the efficiency of aboveground biomass estimates in tropical peat–swamp forests

Estimates of aboveground biomass (AGB) in forests are critically required by many actors including forest managers, forest services and policy makers. Because the AGB of a forest cannot be observed directly, models need to be employed. Allometric models that predict the AGB of a single tree as a function of diameter at breast height (DBH) are commonly used in forest inventories that use a probability selection scheme to estimate total AGB. However, for forest areas with limited accessibility, implementing such a field-based survey can be challenging. In such cases, models that use remotely sensed information may support the biomass assessment if useful predictor variables are available and statistically sound estimators can be derived. Airborne laser scanning (ALS) has become a prominent auxiliary data source for forest biomass assessments and is even considered to be one of the most promising technologies for AGB assessments in forests. In this study, we combined ALS and forest inventory data from a logged-over tropical peat swamp forest in Central Kalimantan, Indonesia to estimate total AGB. Our objective was to compare the precision of AGB estimates from two approaches: (i) from a field-based inventory only and, (ii) from an ALS-assisted approach where ALS and field inventory data were combined. We were particularly interested in analyzing whether the precision of AGB estimates can be improved by integrating ALS data under the particular conditions. For the inventory, we used a standard approach based on a systematic square sample grid. For building a biomass-link model that relates the field based AGB estimates to ALS derived metrics, we used a parametric nonlinear model. From the field-based approach, the estimated mean AGB was 241.38 Mgha −1 with a standard error of 11.17 Mgha −1 (SE% = 4.63%). Using the ALS-assisted approach, we estimated a similar mean AGB of 245.08 Mgha −1 with a slightly smaller standard error of 10.57 Mgha −1 (SE% = 4.30%). Altogether, this is an improvement of precision of estimation, even though the biomass-link model we found showed a large Root Mean Square Error (RMSE) of 47.43 Mgha −1 . We conclude that ALS data can support the estimation of AGB in logged-over tropical peat swamp forests even if the model quality is relatively low. A modest increase in precision of estimation (from 4.6% to 4.3%), as we found it in our study area, will be welcomed by all forest inventory planners as long as ALS data and analysis expertise are available at low or no cost. Otherwise, it gives rise to a challenging economic question, namely whether the cost of the acquisition of ALS data is reasonable in light of the actual increase in precisionWe are grateful to the Galician Government and European Social Fund (Official Journal of Galicia DOG n 52, 17 March 2014, p. 11343, exp: POS-A/2013/049) for financing the postdoctoral research stays of Eduardo González-Ferreiro at different institutionsS

The Horizontal Distribution of Branch Biomass in European Beech: A Model Based on Measurements and TLS Based Proxies

Forest biomass is currently among the most important and most researched target variables in forest monitoring. The common approach of observing individual tree biomass in forest inventory is to assign the total tree biomass to the dimensionless point of the tree position. However, the tree biomass, in particular in the crown, is horizontally distributed above the crown projection area. This horizontal distribution of individual tree biomass (HBD) has not attracted much attention—but if quantified, it can improve biomass estimation and help to better represent the spatial distribution of forest fuel. In this study, we derive a first empirical model of the branch HBD for individual trees of European beech (Fagus sylvatica L.). We destructively measured 23 beech trees to derive an empirical model for the branch HBD. We then applied Terrestrial Laser Scanning (TLS) to a subset of 17 trees to test a simple point cloud metric predicting the branch HBD. We observed similarities between a branch HBD and commonly applied taper functions, which inspired our HBD model formulations. The models performed well in representing the HBD both for the measured biomass, and the TLS-based metric. Our models may be used as first approximations to the HBD of individual trees—while our methodological approach may extend to trees of different sizes and speciesThis research was funded by the Forest Research Institute of the German Federal State of Rheinland-Pfalz (FAWF) in Trippstadt. We also thank the Marie Sklodowska-Curie Action fellow QUAFORD and the Ramón y Cajal Tenure Track awarded to C.P.-CS

The renaissance of National Forest Inventories (NFIs) in the context of the international conventions – a discussion paper on context, background and justification of NFIs

National Forest Inventories (NFI) cover whole countries and strive to put the resource forest and the ecosystem forest into a quantitative framework. While for forest management inventories it is very obvious that they shall support management decisions and contribute to making forest planning, silvicultural interventions, conservation management and timber sales more efficient, the purpose of NFIs is not immediately visible nor “measurable”: they are to support national (and sub-national) level policy processes that relate to forests. NFIs have a long history and do experience currently a boom because the availability of a science-based quantification of the forest resource and its changes is among the prerequisites for results-based payments to developing countries when they implement measures that are efficient - and evidenced by verifiable results – in reducing greenhouse gas emissions from forests. While forest monitoring science does currently focus very much on increasing precision and accuracy of forest monitoring, on integration of ever more efficient remote sensing techniques and modelling methods, surprisingly little research is being published on background, strategic justification, institutionalization and impact of NFIs

On the potential of kriging for estimation and mapping of forest plantation stock (Case study: Beneshki plantation)

This research was conducted to investigate on spatial structure and estimationof tree attributes in a forest plantation in the Caspian region of Iran. Field sampling was performed based on a 50m×125m systematic grid in a maple stand (Acer velutinum Boiss.) at age of 18 years using circular samples of 200m2 area. Overall, 96 sample plots were measured in 63 hectare and 14.25 hectare was inventoried as full census area, as well. Experimental variograms for forest stem basal area, stem density and tree height attributes were calculated and plotted using the geo-referenced inventory plots. The calculated variograms of basal area and height showed a high spatial autocorrelation, which fitted by spherical models. However, stem density showed a large amount of nugget effect. Based on variography results, optimal sampling distance for stem basal area and density obtained 165m and 350m, respectively. Estimations for basal area and height were made by ordinary block (15m×15m) kriging and cross-validation results showed that all the estimations are accurate. Furthermore, the estimated kriged mean of basal area showed no significant difference to the real mean in the full census area, while kriging in term of statistical precision was two times better than sampling. Therefore, geostatistics is able to capture and describe the spatial variability as well as estimates tree attributes (not stem density) in this kind of forest plantation, accurately