Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators

A conceptual model describing why laser height metrics derived from airborne discrete return laser scanner data are highly correlated with above ground biomass is proposed. Following from this conceptual model, the concept of canopy-based quantile estimators of above ground forest biomass is introduced and applied to an uneven-aged, mature to overmature, tolerant hardwood forest. Results from using the 0th, 25th, 50th, 75th and 100th percentiles of the distributions of laser canopy heights to estimate above ground biomass are reported. A comparison of the five models for each dependent variable group did not reveal any overt differences between models with respect to their predictive capabilities. The coefficient of determination (r ² ) for each model is greater than 0.80 and any two models may differ at most by up to 9%. Differences in root-mean-square error (RMSE) between models for above ground total, stem wood, stem bark, live branch and foliage biomass were 8.1, 5.1, 2.9, 2.1 and 1.1 Mg ha^?1, respectively.     

Economic utility of 3D remote sensing data for estimation of site index in Nordic commercial forest inventories: a comparison of airborne laser scanning,digital aerial photogrammetry and conventional practices

ABSTRACT

Forest productivity is a crucial variable in forest planning, usually expressed as site index (SI). In Nordic commercial forest inventories, SI is commonly estimated by a combination of aerial image interpretation, field assessment and information obtained from previous inventories. Airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) data can alternatively be used for SI estimation, however the economic utilities of the inventory methods have not been compared. We compared seven methods of SI estimation in a cost-plus-loss analysis, by which we added the expected economic losses due to sub-optimal treatment decisions to the inventory costs. The methods comprised direct and indirect estimation from combinations of ALS, DAP and stand register data, and manual interpretation from aerial imagery supported by field assessment and information from previous inventories (conventional practices). The choice of method had great impact on both the accuracy and the economic value of the produced estimates. Direct methods using bitemporal ALS and DAP data gave the best accuracy and the smallest total cost. DAP was a suitable and low-cost data source for SI estimation. Estimation from single-date ALS and DAP data and age obtained from the stand register provided practical alternatives when applied to even-aged stands.     

Accuracy of forest inventory using airborne laser scanning: evaluating the first nordic full-scale operational project

This research reports the major results from an evaluation of the first Nordic operational stand-based forest inventory using airborne laser scanner data. Laser data from a forest area of 250 km² were used to predict six biophysical stand variables used in forest planning. The predictions were based on regression equations estimated from 250 m² field training plots distributed systematically throughout the forest area. Test plots with an approximate size of 0.1–0.4 ha were used for validation. The testing revealed standard deviations between ground-truth values and predicted values of 0.36–1.37 m (1.9–7.6%) for mean height, 0.70–1.55 m (3.0–7.6%) for dominant height, 2.38–4.88 m² ha^?1 (7.8–14.2%) for basal area and 13.9–45.9 m³ ha^?1 (6.5–13.4%) for stand volume. No serious bias was detected.     

Prediction of tree height,basal area and stem volume in forest stands using airborne laser scanning

The aim of this study was to develop prediction models using laser scanning for estimation of forest variables at plot level, validate the estimations at stand level (area 0.64 ha) and test the effect of different laser measurement densities on the estimation errors. The predictions were validated using 29 forest stands (80×80 m²), each containing 16 field plots with a 10 m radius. For the best tested case, mean tree height, basal area and stem volume were predicted with a root mean square error of 0.59 m (3% of average value), 2.7 m² ha^?1 (10% of average value) and 31 m³ ha^?1 (11% of average value), respectively, at stand level. There were small differences in terms of prediction errors for different measuring densities. The results indicate that mean tree height, basal area and stem volume can be estimated in small stands with low laser measurement densities producing accuracies similar to traditional field inventories.     

Using pre-classification to improve the accuracy of species-specific forest attribute estimates from airborne laser scanner data and aerial images

The aim of this study was to examine whether pre-classification (stratification) of training data according to main tree species and stand development stage could improve the accuracy of species-specific forest attribute estimates compared to estimates without stratification using k-nearest neighbors (k-NN) imputations. The study included training data of 509 training plots and 80 validation plots from a conifer forest area in southeastern Norway. The results showed that stratification carried out by interpretation of aerial images did not improve the accuracy of the species-specific estimates due to stratification errors. The training data can of course be correctly stratified using field observations, but in the application phase the stratification entirely relies on auxiliary information with complete coverage over the entire area of interest which cannot be corrected. We therefore tried to improve the stratification using canopy height information from airborne laser scanning to discriminate between young and mature stands. The results showed that this approach slightly improved the accuracy of the k-NN predictions, especially for the main tree species (2.6% for spruce volume). Furthermore, if metrics from aerial images were used to discriminate between pine and spruce dominance in the mature plots, the accuracy of volume of pine was improved by 73.2% in pine-dominated stands while for spruce an adverse effect of 12.6% was observed.     

Comparison of estimators and feature selection procedures in forest inventory based on airborne laser scanning and digital aerial imagery

Digital maps of forest resources are a crucial factor in successful forestry applications. Since manual measurement of this data on large areas is infeasible, maps must be constructed using a sample field data set and a prediction model constructed from remote sensing materials, of which airborne laser scanning (ALS) data and aerial images are currently widely used in management planning inventories. ALS data is suitable for the prediction of variables related to the size and volume of trees, whereas optical imagery helps in improving distinction between tree species. We studied the prediction of forest attributes using field data from National Forest Inventory complemented with ad hoc field plots in combination with ALS and aerial imagery data in Aland province, Finland. We applied feature selection with genetic algorithm and greedy forward selection and compared multiple linear and nonlinear estimators. Maximally around 40 features from a total of 154 were required to achieve the best prediction performances. Tree height was predicted with normalized root mean squared error value of 0.1 and tree volume with a value around 0.25. Predicting the volumes of spruce and broadleaved trees was the most challenging due to small proportions of these tree species in the study area.     

Reconstructing forest canopy from the 3D triangulations of airborne laser scanning point data for the visualization and planning of forested landscapes

Key message

We present a data-driven technique to visualize forest landscapes and simulate their future development according to alternative management scenarios. Gentle harvesting intensities were preferred for maintaining scenic values in a test of eliciting public’s preferences based on the simulated landscapes.

Context

Visualizations of future forest landscapes according to alternative management scenarios are useful for eliciting stakeholders’ preferences on the alternatives. However, conventional computer visualizations require laborious tree-wise measurements or simulators to generate these observations.

Aims

We describe and evaluate an alternative approach, in which the visualization is based on reconstructing forest canopy from sparse density, leaf-off airborne laser scanning data.

Methods

Computational geometry was employed to generate filtrations, i.e., ordered sets of simplices belonging to the three-dimensional triangulations of the point data. An appropriate degree of filtering was determined by analyzing the topological persistence of the filtrations. The topology was further utilized to simulate changes to canopy biomass, resembling harvests with varying retention levels. Relative priorities of recreational and scenic values of the harvests were estimated based on pairwise comparisons and analytic hierarchy process (AHP).

Results

The canopy elements were co-located with the tree stems measured in the field, and the visualizations derived from the entire landscape showed reasonably realistic, despite a low numerical correspondence with plot-level forest attributes. The potential and limitations to improve the proposed parameterization are discussed.

Conclusion

Although the criteria to evaluate the landscape visualization and simulation models were not conclusive, the results suggest that forest scenes may be feasibly reconstructed based on data already covering broad areas and readily available for practical applications.

     

集体林权流转主体若干问题研究——以福建集体林权制度改革为研究对象

在分析福建省实践中的林权流转主体范围的基础上,重点对主体之一的联户法律性质及其权利行使进行了探讨,并结合《物权法》的规定,指出林权证是确定林权主体资格的法律依据。     

台州市退耕林分生态功能的初步研究

研究表明，退耕后营造枫香，木荷阔叶混交林的生态功能最强，物种多样性增加，林相景观浓度，改良土壤效果好，涵水保土功能强，调节小气候能好。其次为退耕后营造国外松林，杨梅林和毛竹林，这些林分的生态作业也较明显。再次为退耕后营造板栗林，但生态作用相对较差。     

Comparison of nearest neighbour approaches for small area estimation of tree species-specific forest inventory attributes in central Europe using airborne laser scanner data

The three nonparametric k nearest neighbour (kNN) approaches, most similar neighbour inference (MSN), random forests (RF) and random forests based on conditional inference trees (CF) were compared for spatial predictions of standing timber volume with respect to tree species compositions and for predictions of stem number distributions over diameter classes. Various metrics derived from airborne laser scanning (ALS) data and the characteristics of tree species composition obtained from coarse stand level ground surveys were applied as auxiliary variables. Due to the results of iterative variable selections, only the ALS data proved to be a relevant predictor variable set. The three applied NN approaches were tested in terms of bias and root mean squared difference (RMSD) at the plot level and standard errors at the stand level. Spatial correlations were considered in the statistical models. While CF and MSN performed almost similarly well, large biases were observed for RF. The obtained results suggest that biases in the RF predictions were caused by inherent problems of the RF approach. Maps for Norway spruce and European beech timber volume were exemplarily created. The RMSD values of CF at the plot level for total volume and the species-specific volumes for European beech, Norway spruce, European silver fir and Douglas fir were 32.8, 80.5, 99.0, 137.0 and 261.1%. These RMSD values were smaller than the standard deviation, although Douglas fir volume did not belong to the actual response variables. All three non-parametric approaches were also capable of predicting diameter distributions. The standard errors of the nearest neighbour predictions on the stand level were generally smaller than the standard error of the sample plot inventory. In addition, the employed model-based approach allowed kNN predictions of means and standard errors for stands without sample plots.     

Temporal trends in the foliar nutritional status of the French, Walloon and Luxembourg broad-leaved plots of forest monitoring

? Two decades after the launching of the monitoring program of forest ecosystems in Europe (ICP forests), a unique data set is now available regarding the foliar nutritional status of the main broad-leaved species growing in much-diversified sites (soil, climate).

? This study focuses on the foliar concentration time series (1993 to 2005) of the French (RENECOFOR), Walloon and Luxemburg broad-leaved plots. The aim is to show long-term trends while taking the inter-plot and inter-annual variability into account.

? Two kinds of statistical processing were used to analyze the data on foliar chemistry: principal component analysis (PCAs) and linear mixed models. In general, the main temporal trends reveal a decrease in the foliar P concentration and a decrease in the foliar Ca concentration, except for European beech on acid soils.

? These temporal patterns confirm the trends previously observed and could be explained by the joint actions of several processes that influence tree nutrition in the long term: tree age, N and S deposition, harvesting and climate.

     

Assessing carbon sequestration in plantation forests of important conifers based on the system of permanent sample plots across Taiwan

ABSTRACT

Plantation forests play a critical role in forest management due to their high productivity and large contribution to carbon sequestration (CSE). The purpose of this study was to assess the CSE of plantations containing four important conifer species distributed across Taiwan, namely, the China fir (Cunninghamia lanceolata), Japanese cedar (Cryptomeria japonica), Taiwania (Taiwania cryptomerioides) and Taiwan red cypress (Chamaecyparis formosensis). Data regarding the plantations were obtained from a survey of permanent sample plots (PSPs). We used these data to calculate the CSE in each PSP and adopted CSE_mean and CSE_period as indicators to assess the CSE of the four conifers. According to the CSE_mean obtained from analysis of variance and the least significant difference method, two groups were identified among these four conifers: the Japanese cedar (4.03 Mg ha^?1 yr^?1) and Taiwania (3.52 Mg ha^?1 yr^?1) yielded higher CSE_mean values and the China fir (1.79 Mg ha^?1 yr^?1) and Taiwan red cypress (2.36 Mg ha^?1 yr^?1) yielded lower CSE_mean values. The same patterns were observed in the CSE_period values; however, no significant difference in CSE_period was observed between Taiwan red cypress and either of the two groups. Therefore, Japanese cedar and Taiwania have high CSE potential among conifers.     

Estimates of forest biomass in the Brazilian Amazon: New allometric equations and adjustments to biomass from wood-volume inventories

Uncertainties in biomass estimates in Amazonian forests result in a broad range of possible magnitude for the emissions of carbon from deforestation and other land-use changes. This paper presents biomass equations developed from trees directly weighed in open forest on fertile soils in the southern Amazon (SA) and allometric equations for bole-volume estimates of trees in both dense and open forests. The equations were used to improve the commonly used biomass models based on large-scale wood-volume inventories carried out in Amazonian forest. The biomass estimates from the SA allometric equation indicate that equations developed in forests on infertile soils in central Amazonia (CA) result in overestimates if applied to trees in the open forests of SA. All aboveground components of 267 trees in open forests of SA were cut and weighed, and the proportion of the biomass stored in the crowns of trees in open forest was found to be higher than in dense forest. In the case of inventoried wood volume, corrections were applied for indentations and hollow trunks and it was determined that no adjustment is needed for the form factor used in the RadamBrasil volume formula. New values are suggested for use in models to convert wood volume to biomass estimates. A biomass map for Brazilian Amazonia was produced from 2702 plots inventoried by the RadamBrasil Project incorporating all corrections for wood density and wood volume and in factors used to add the bole volume of small trees and the crown biomass. Considering all adjustments, the biomass map indicates total biomass of 123.1 Gt (1 Gt = 1 billion tons) dry weight (aboveground + belowground) for originally forested areas in 1976 in the Brazilian Legal Amazon as a whole (102.3 Gt for aboveground only) at the time of the RadamBrasil inventories, which were carried out before intensive deforestation had occurred in the region. Excluded from this estimate are 529,000 km² of forest lacking sufficient RadamBrasil inventory data. After forest losses of 676,000 km² by 2006 – not counting 175,000 km² of this deforested area lacking RadamBrasil data – the estimated dry biomass stock was reduced to 105.4 and 87.6 Gt (aboveground + belowground and only above-ground). Thus, in 2006 the carbon storage in forested areas in Brazilian Amazonia as a whole will be around 51.1 Gt (assuming 1 Mg dry biomass = 0.485 Mg C). Biomass estimates by forest type (aggregated into 12 vegetation classes) are provided for each state in the Brazilian Legal Amazon.     

National inventories of down and dead woody material forest carbon stocks in the United States: Challenges and opportunities

Concerns over the effect of greenhouse gases and consequent international agreements and regional/national programs have spurred the need for comprehensive assessments of forest ecosystem carbon stocks. Down and dead woody (DDW) materials are a substantial component of forest carbon stocks; however, few surveys of DDW carbon stocks have been conducted at national-scales around the world. This study uses the DDW survey of the United States as a case study to examine the challenges of inventorying DDW at a national scale, reviews how dead wood carbon pools are currently estimated in the National Greenhouse Gas Inventory (NGHGI), and suggests opportunities for improving such inventories. The US currently estimates national DDW carbon stocks using models with standing live tree attributes as predictor variables, calibrated using preliminary DDW field estimates. In recent years, implementation of a national DDW inventory has resulted in inventory-based DDW estimates. National field-based DDW estimates follow the national patterns of DDW carbon dispersion seen in earlier model-based estimates. Although the current DDW inventory provides fairly repeatable measurements within a statistically defensible national sample design for producing national estimates of DDW carbon stocks, improving numerous aspects of the DDW survey would may improve the accuracy and precision of C estimates reported in the NGHGI.     

Excel软件在林木采伐作业设计角规样地中的应用

利用Excel软件的函数功能,建立林木采伐作业设计角规样地表,操作规程为:1填写树种名和形高、出材率代码;2录入树种、径阶、断面积、平均高等调查数据;3填写采伐小班面积,其它数据可自动生成并进行汇总计算。通过Excel软件解决了角规样地调查内业查表、计算等工作繁琐、容易出错等问题。     

Airborne laser scanning as a method in operational forest inventory: Status of accuracy assessments accomplished in Scandinavia

Abstract

This research reports the major evaluation results from an operational stand-based forest inventory using airborne laser scanner data carried out in Norway. This is the first operational inventory in which data from two separate districts are combined. Laser data from two forest areas of 65 and 110 km² were used to predict six biophysical stand variables used in forest planning. The predictions were based on regression equations estimated from 250 m² field training plots distributed systematically throughout the two forest areas. Test plots with a size of 0.1 ha were used for validation. The testing revealed standard deviations between ground-truth values and predicted values of 0.58–0.85 m (3.4–5.6%) for mean and dominant heights, 2.62–2.87 m² ha^?1 (9.3–14.3%) for basal area, and 18.7–25.1 m³ ha^?1 (10.8–12.8%) for stand volume. No serious bias was detected. For 10 of the 12 estimated regression models there were no significant effects of district.     

Comparison of relascope and fixed-radius plots for the estimation of forest stand variables in northeast Spain: an inventory simulation approach

The Bitterlich relascope is a multiple use dendrometer widely used in forest inventory. Although it is most commonly used to estimate basal area, the relascope can also estimate other stand variables, including density and diameter distribution. However, forest stand inventories in Spain rarely use relascope plots to estimate these variables due to the belief that they lead to higher errors than fixed-radius plots due to the heterogeneity of many Mediterranean forests. This study compared the accuracy of the estimated averages of three main stand variables (basal area, stand density, and diameter class distribution) in forest stand inventories performed with relascope plots and with conventional fixed-radius circular plots, both measuring a similar number of trees (15–20). A forest stand inventory simulator (DOMO) was used (1) to generate simulated forest stands corresponding to the nine most common types in the Mediterranean region of Catalonia (NE Spain), including even-aged and uneven-aged stands, and (2) to estimate and compare the average values of these variables at the forest stand level resulting from both plot types. In general, we did not find significant accuracy differences between the inventory systems for most of the stand variables and forest types studied, as expected by established angle-count sampling theory. However, the results show that for stands with multiple strata and open structures, the Bitterlich relascope provides a more accurate estimate for basal area than for density, while the reverse occurs for fixed-radius plots.