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1.
Recent development in aerial digital cameras and software facilitate the photogrammetric point cloud as a new data source in forest management planning. A total of 151 field training plots were distributed systematically within three predefined strata in a 852.6 ha study area located in the boreal forest in southeastern Norway. Stratum-specific regression models were fitted for six studied biophysical forest characteristics. The explanatory variables were various canopy height and canopy density metrics derived by means of photogrammetric matching of aerial images and small-footprint laser scanning. The ground sampling distance was 17 cm for the images and the airborne laser scanning (ALS) pulse density was 7.4 points m–2. Resampled images were assessed to mimic acquisitions at higher flying altitudes. The digital terrain model derived from the ALS data was used to represent the ground surface. The results were evaluated using 63 independent test stands. When estimating height in young forest and mature forest on poor sites, the root mean square error (RMSE) values were slightly better using data from image matching compared to ALS. However, for all other combinations of biophysical forest characteristics and strata, better results were obtained using ALS data. In general, the best results were found using the highest image resolution.  相似文献   

2.
天然林区小班森林资源数据的更新模型   总被引：22，自引：2，他引：20  
以吉林省汪清林业局为例,根据１９９７年森林经理调查的８４８块固定样地数据,与全林整体模型方法相结合,建立了适合于天然林区林业局（场）无人为干预小班森林资源数据更新的林分级生长模型组。该组模型包括林分密度指数,平均高,断面积,形高,郁闭度等林分测算因子的生长或变化模型。  相似文献   

3.
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.  相似文献   

4.
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.  相似文献   

5.
Norway spruce structural timber is one of the most important products of the Norwegian sawmilling industry, and a high grade-yield of structural timber is therefore important for the economic yield. Presorting of logs suited for production of structural timber might be one option to increase the grade yield. In this study, dynamic modulus of elasticity (Edyn) of structural timber was predicted based on forest inventory data at site level and single-tree data from airborne laser scanning (ALS) and harvester. The models were based on 611 boards from 4 sites in southeastern Norway. Important variables at site level were elevation, site index (SI), and mean stand age. However, when combining data from all information sources, mean stand age and site index were the only significant variables at site level. Tree height and variables describing the crown, like crown length and crown volume, were important vaiables extracted from ALS data. Stem diameter measures and tapering were important variables measured by the harvester. The combined model with variables from all three information sources reduced the variance the most, especially when using individual tree age instead of average stand age. However, combining all these data requires accurate positioning of the trees by the harvester.  相似文献   

6.
This article reviews the research and application of airborne laser scanning for forest inventory in Finland, Norway and Sweden. The first experiments with scanning lasers for forest inventory were conducted in 1991 using the FLASH system, a full-waveform experimental laser developed by the Swedish Defence Research Institute. In Finland at the same time, the HUTSCAT profiling radar provided experiences that inspired the following laser scanning research. Since 1995, data from commercially operated time-of-flight scanning lasers (e.g. TopEye, Optech ALTM and TopoSys) have been used. Especially in Norway, the main objective has been to develop methods that are directly suited for practical forest inventory at the stand level. Mean tree height, stand volume and basal area have been the most important forest mensurational parameters of interest. Laser data have been related to field training plot measurements using regression techniques, and these relationships have been used to predict corresponding properties in all forest stands in an area. Experiences from Finland, Norway and Sweden show that retrieval of stem volume and mean tree height on a stand level from laser scanner data performs as well as, or better than, photogrammetric methods, and better than other remote sensing methods. Laser scanning is, therefore, now beginning to be used operationally in large-area forest inventories. In Finland and Sweden, research has also been done into the identification of single trees and estimation of single-tree properties, such as tree position, tree height, crown width, stem diameter and tree species. In coniferous stands, up to 90% of the trees represented by stem volume have been correctly identified from canopy height models, and the tree height has been estimated with a root mean square error of around 0.6 m. It is significantly more difficult to identify suppressed trees than dominant trees. Spruce and pine have been discriminated on a single-tree level with 95% accuracy. The application of densely sampled laser scanner data to change detection, such as growth and cutting, has also been demonstrated.  相似文献   

7.
利用东北林区云冷杉林、落叶松林、樟子松林、红松林、栎树林、桦树林、杨树林、榆树林、椴树林和水胡黄林10种森林类型的1947个样地的激光雷达数据和地面实测蓄积量数据,首先通过多元线性回归和非线性回归方法,分别建立基于机载激光雷达数据的森林蓄积量回归估计模型,并通过对比分析,确定统一形式的基础回归模型;然后利用哑变量建模方法,建立基于不同森林类型参数和相同激光雷达变量的蓄积量模型。结果表明,研究建立的10种森林类型的线性蓄积量回归模型的解释变量个数在2~7之间,确定系数在0.460~0.858之间;非线性蓄积量回归模型的解释变量个数在2~4之间,确定系数在0.461~0.846之间。基于点云平均高度和平均强度建立的10种森林类型的二元蓄积量模型(研究称之为标准模型),其确定系数在0.440~0.815之间,平均预估误差在2.88%~4.42%之间,平均百分标准误差在16.76%~25.52%之间,预估精度基本达到森林资源规划设计调查技术规定要求。依据研究建立的10种森林类型的蓄积量模型,可以编制基于激光雷达数据的航空林分材积表,在森林资源调查实践中推广应用。  相似文献   

8.

? Context

Forest resource projections are required as part of an appropriate framework for sustainable forest management. Suitable large-scale projection models are usually based on national forest inventory (NFI) data. However, sound projections are difficult to make for heterogeneous resources as they vary greatly with respect to the factors that are assumed to drive forest dynamics on a large spatial scale, e.g. geographically varying growth conditions (here represented by NFI regions), tree species composition (here broadleaf-dominated, conifer-dominated and broadleaf-conifer mixed stands) and stand structure (here high forest, coppice forest and high-coppice forest mixture).

? Question and objective

Our question was how does the variance of forest dynamics parameters (i.e. growth, felling and mortality, and recruitment processes) and that of 20-year forest resource projections partition between these factors (NFI region, tree species composition and stand structure), including their interactions. Our objective was to capitalise on the suitability of an existing multi-strata, diameter class matrix model for the purposes of making projections for the highly heterogeneous French forest resource.

? Methods

The model was newly calibrated for the entire territory of metropolitan France based on most recent NFI data, i.e. for years 2006?C2008. The forest resource was divided into strata by crossing the factors NFI region, tree species composition and stand structure. The variance partitioning of the parameters and projections was assessed based on a model sensitivity analysis.

? Results

Growth, felling and mortality varied mainly with NFI region and species composition. Recruitment varied mainly with NFI region and stand structure. All three factors caused variations in resource projections, but with unequal intensities. Factor impacts included first order and interaction effects.

? Conclusions

We found, by considering both first order and interaction effects, that NFI region, species composition and stand structure are ecologically relevant factors that jointly drive the dynamics of a heterogeneous forest resource. Their impacts, in our study, varied depending on the forest dynamics process under consideration. Recruitment would appear to have a particularly great impact on resource changes over time.  相似文献   

9.
Forest inventories based on airborne laser scanning (ALS) have already become common practice in the Nordic countries. One possibility for improving their cost effectiveness is to use existing field data sets as training data. One alternative in Finland would be the use of National Forest Inventory (NFI) sample plots, which are truncated angle count (relascope) plots. This possibility is tested here by using a training data set based on measurements similar to the Finnish NFI. Tree species-specific stand attributes were predicted by the non-parametric k most similar neighbour (k-MSN) approach, utilising both ALS and aerial photograph data. The stand attributes considered were volume, basal area, stem number, mean age of the tree stock, diameter and height of the basal area median tree, determined separately for Scots pine, Norway spruce and deciduous trees. The results obtained were compared with those obtained when using training data based on observations from fixed area plots with the same centre point location as the NFI plots. The results indicated that the accuracy of the estimates of stand attributes derived by using NFI training data was close to that of the fixed area plot training data but that the NFI sampling scheme and the georeferencing of the plots can cause problems in practical applications.  相似文献   

10.
The aim of this work was to examine how well species-specific stand attributes can be predicted using a combination of airborne laser scanning (ALS) and existing stand register data in urban forests. In this context, the ability of three data combinations: ALS data and stand register data, ALS data and digital aerial images and all of these combined, was tested in the prediction of species-specific basal areas. We divided tree species into seven and three different tree species strata and applied two prediction methods: (1) regression method, in which the predicted total basal area was divided into tree species based on tree species proportions from stand register data, and (2) the nearest neighbour (NN) method, in which tree species proportions were used as predictor variables for species-specific basal areas. Prediction models were built based on training data of 205 field plots, and the accuracy of the models was tested based on validation data of 52 forests stands. Our results showed that species-specific predictions of seven tree species were more accurate when tree species proportions from stand register data were used in the prediction. Both the regression and the NN method provided reasonable accuracy. This study showed that tree species information from existing stand register data could be used as an alternative for aerial images in ALS-based forests inventories. The use of ALS data together with stand register data and small field data could also be economically beneficial in an inventory of urban forests.  相似文献   

11.
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.  相似文献   

12.
Abstract

Airborne laser scanning (ALS) has been used in recent years to acquire accurate remote-sensing material for carrying out practical forest inventories. Still, much of the information needed in forest management planning must be collected in the field. For example, forest management proposals are often determined in the field by an expert. In the present study, statistical features extracted from ALS data were used in logistic regression models and in nonparametric k-MSN estimation to predict the thinning maturity of stands. The research material consisted of 381 treewise measured circular plots in young and advanced thinning stands from the vicinity of Evo, in southern Finland. Timing of thinning was determined in the field by an expert and coded as a binary variable. Models were developed (1) to locate stands that will reach thinning maturity within the next 10-year period and (2) for stands in which thinning should be done immediately. For comparison purposes, logistic regression models were formulated from accurately field-measured stand characteristics. Logistic regression models based on ALS features predicted the thinning maturity with a classification accuracy of 79% (1) and 83% (2). The respective percentages were 66% and 83% with models based on field-measured stand characteristics and 70% and 86% with k-MSN. The study showed that ALS data can be used to predict stand-thinning maturity in a practical way.  相似文献   

13.
Abstract

We evaluated the performance of two methods for estimating stem volume increment at individual tree level with respect to bias due to random measurement errors. Here, growth is either predicted as the difference between two consecutive volume estimates where single-tree volume functions are applied to data from repeated measurements or by a regression model that is applied to data from a single survey and includes radial increment. In national forest inventories (NFIs), the first method is typically used for permanent plots, the second for temporary plots. The Swedish NFI combines estimates from both plot types to assess growth at national and regional scales and it is, therefore, important that the two methods provide similar results. The accuracy of these estimates is affected by random measurement errors in the independent variables, which may lead to systematic errors in predicted variables due to model non-linearity. Using Taylor series expansion and empirical data from the Swedish NFI we compared the expected bias in stem volume growth estimates for different diameter classes of Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.). Our results indicate that both methods are fairly insensitive to random measurement errors of the size that occur in the Swedish NFI. The empirical comparison between the two methods showed greater differences for large diameter trees of both pine and spruce. A likely explanation is that the regressions are uncertain because few large trees were available for developing the models.  相似文献   

14.
In this research, we developed and tested a remote sensing-based approach for stand age estimation. The approach is based on changes in the forest canopy height measured from a time series of photo-based digital surface models that were normalized to canopy height models using an airborne laser scanning derived digital terrain model (DTM). Representing the Karelian countryside, Finland, CHMs from 1944, 1959, 1965, 1977, 1983, 1991, 2003, and 2012 were generated and allow for characterization of forest structure over a 68-year period. To validate our method, we measured stand age from 90 plots (1256?m2) in 2014, whereby producer's accuracy ranged from 25.0% to 100.0% and user's accuracy from 16.7% to 100.0%. The wide range of accuracy found is largely attributable to the quality and characteristics of archival images and intrastand variation in stand age. The lowest classification accuracies were obtained for the images representing the earliest dates. For forest managers and agencies that have access to long-term photo archives and a detailed DTM, the estimation of stand age can be performed, improving the quality and completeness of forest inventory databases.  相似文献   

15.
The study developed models for predicting the post-fire tree survival in Catalonia. The models are appropriate for forest planning purposes. Two types of models were developed: a stand-level model to predict the degree of damage caused by a forest fire, and tree-level models to predict the probability of a tree to survive a forest fire. The models were based on forest inventory and fire data. The inventory data on forest stands were obtained from the second (1989–1990) and third (2000–2001) Spanish national forest inventories, and the fire data consisted of the perimeters of forest fires larger than 20 ha that occurred in Catalonia between the 2nd and 3rd measurement of the inventory plots. The models were based on easily measurable forest characteristics, and they permit the forest manager to predict the effect of stand structure and species composition on the expected damage. According to the stand level fire damage model, the relative damage decreases when the stand basal area or mean tree diameter increases. Conversely, the relative stand damage increases when there is a large variation in tree size, when the stand is located on a steep slope, and when it is dominated by pine. According to the tree level survival models, trees in stands with a high basal area, a large mean tree size and a small variability in tree diameters have a high survival probability. Large trees in dominant positions have the highest probability of surviving a fire. Another result of the study is the exceptionally good post-fire survival ability of Pinus pinea and Quercus suber.  相似文献   

16.
Abstract

The purpose of the study was to evaluate tree species composition estimated using combinations of different remotely sensed data with different inventory approaches for a forested area in Norway. Basal area species composition was estimated as both species proportions and main species by using data from airborne laser scanning (ALS) and airborne (multispectral and hyperspectral) imagery as auxiliary information in combination with three different inventory approaches: individual tree crown (ITC) approach; semi-individual tree crown (SITC) approach; and area-based approach (ABA). The main tree species classification obtained an overall accuracy higher than 86% for all ABA alternatives and for the two other inventory approaches (ITC and SITC) when combining ALS and hyperspectral imagery. The correlation between estimated species proportions and species proportions measured in the field was higher for coniferous species than for deciduous species and increased with the spectral resolution used. Especially, the ITC approach provided more accurate information regarding the proportion of deciduous species that occurred only in small proportions in the study area. Furthermore, the species proportion estimates of 83% of the plots deviated from field measured species proportions by two-tenths or less. Thus, species composition could be accurately estimated using the different approaches and the highest levels of accuracy were attained when ALS was used in combination with hyperspectral imagery. The accuracies obtained using the ABA in combination with only ALS data were encouraging for implementation in operational forest inventories.  相似文献   

17.
Properties of individual trees can be estimated from airborne laser scanning (ALS) data provided that the scanning is dense enough and the positions of field-measured trees are available as training data. However, such detailed manual field measurements are laborious. This paper presents new methods to use terrestrial laser scanning (TLS) for automatic measurements of tree stems and to further link these ground measurements to ALS data analyzed at the single tree level. The methods have been validated in six 80 × 80 m field plots in spruce-dominated forest (lat. 58°N, long. 13°E). In a first step, individual tree stems were automatically detected from TLS data. The root mean square error (RMSE) for DBH was 38.0 mm (13.1 %), and the bias was 1.6 mm (0.5 %). In a second step, trees detected from the TLS data were automatically co-registered and linked with the corresponding trees detected from the ALS data. In a third step, tree level regression models were created for stem attributes derived from the TLS data using independent variables derived from trees detected from the ALS data. Leave-one-out cross-validation for one field plot at a time provided an RMSE for tree level ALS estimates trained with TLS data of 46.0 mm (15.4 %) for DBH, 9.4 dm (3.7 %) for tree height, and 197.4 dm3 (34.0 %) for stem volume, which was nearly as accurate as when data from manual field inventory were used for training.  相似文献   

18.
We developed dominant height growth models for Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.) in Norway using national forest inventory (NFI) data. The data were collected for a different purpose which potentially causes problems for dominant height growth modelling due to short time series and large age errors. We used the generalized algebraic difference approach and fitted 15 different models using nested regression techniques. Despite the potential problems of NFI data the models fitted to these data were unbiased for most of the age and site index range covered by the NFI data when tested against independent data from long-term experiments (LTE). Biased predictions for young stands and better site indices that are better represented in the LTE data, led us to fit models to a combined data set for unbiased predictions across the total data range. The models fitted to the combined data that were unbiased with little residual variation when tested against an independent data set based on stem analysis of 73 sample trees from southeastern Norway. No indications of regional differences in dominant height growth across Norway were detected. We tested whether the better growing conditions during the short time series (22 years) of the NFI data had affected our dominant height growth models relative to long-term growing conditions, but found only minor bias. The combination with LTE data that have been collected during a longer period (91 years) reduced this potential bias. The dominant height growth models presented here can be used as potential height growth models in individual tree-based forest growth models or as site index models.  相似文献   

19.
Airborne laser scanner (ALS)-based forest inventory method usuallyadopt a laser canopy height distribution approach in which forestcharacteristics are predicted using measures such as percentilesof the distribution of laser canopy heights across a fixed area.The method requires a ground-truth sample of accurately measuredfield plots. One possibility for reducing the costs lies inthe use of existing field plots for ground-truth purposes. Themost obvious alternative in Finland would be to use truncatedangle count sample plots of the National Forest Inventory ormore locally data of checking of inventory by compartments.Due to the lack of suitable angle count ground-truth data andcorresponding laser data, we tested this possibility using dataon fixed-area sample plots, in which tree locations were simulated.The trees for a truncated angle count sample plot were thenchosen and the resulting data together with the characteristicsof an ALS-based canopy height distribution were used to constructregression models to predict stem volume, basal area, stem number,basal area median diameter and height. The accuracy of the standattributes was found to be almost as good as in the case ofmodels of fixed-area plots.  相似文献   

20.
Airborne laser scanning(ALS) has been widely applied to estimate tree and forest attributes, but it can also drive the segmentation of forest areas. Clustering algorithms are the dominant technique in segmentation but spatial optimization using exact methods remains untested. This study presents a novel approach to segmentation based on mixed integer programming to create forest management units(FMUs). This investigation focuses on using raster information derived from ALS surveys. Two mainstrea...  相似文献   

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