Combining ALS and NFI training data for forest management planning: a case study in Kuortane,Western Finland

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.     

Assessing 3D point clouds from aerial photographs for species-specific forest inventories

In this study we assessed the potential of using photogrammetric data for species-specific forest inventories. The method is based on a combination of Dirichlet and ordinary linear regression models. This approach was used to predict species proportions, main tree species, total, and species-specific volume. Structural and spectral variables were used as predictors. The models were validated using 63 independent validation stands. The results from airborne laser scanning (ALS) data combined with spectral data and photogrammetric data obtained using aerial imagery with different forward overlaps of 80% and 60% were compared. The best photogrammetry-based models predicted species proportions with a relative root mean square error (RMSE) of 21.4%, classified dominant species with 79% accuracy, predicted total volume with relative RMSE of 13.4%, and predicted species-specific volume with relative RMSE of 36.6%, 46.5%, and 84.9% for spruce, pine, and deciduous species, respectively. The results were similar for the three point cloud datasets obtained from aerial imagery and ALS and the accuracies of the predictions were comparable to methods used in operational FMI. The study highlights the effectiveness of forest inventories carried out using photogrammetric data, which – differently from ALS, can include species-specific information without relying on multiple data sources.     

Aerial photo-interpretation using Z/I DMC images for estimation of forest variables

Abstract

Interpretation and tree height measurements in aerial photographs using photogrammetric workstations are frequently performed in standwise forest inventory. Images acquired by digital aerial cameras are now replacing the traditional film-based aerial photographs. In this study, digital images from the airborne Z/I DMC system for standwise estimation of stem volume, tree height and tree species composition were investigated at a 1200 ha forest area located in southern Sweden (58°30′N, 13°40′E). The 56 selected stands were dominated by Norway spruce [Picea abies (L.) Karst.] and Scots pine (Pinus sylvestris L.) with stem volume in the range of 30–630 m³ ha^?1 (average 300 m³ ha^?1) and tree height in the range of 6–28 m (average 20 m). The large-format pansharpened colour infrared images were captured at a flight altitude of 4800 m above ground level corresponding to a pixel size of 0.48 m. The photo-interpretation was conducted by four professional interpreters, independently. In particular, two different base-to-height ratios (i.e. the ratio between the ground distance between image centres at the time of exposure and the flight altitude above ground level) of 0.26 and 0.39 were evaluated, but no significant difference in the estimation accuracy for stem volume and tree height was found. The accuracy for stem volume estimation in terms of relative root mean square error, corrected for systematic errors, was on average 24% (in the range of 17–39%). The corresponding accuracy for tree height estimation was on average 1.4 m (in the range of 0.9–1.6 m). The tree species composition accuracy assessment using a fuzzy set evaluation procedure showed that 95% of the stands were correctly classified. The estimation accuracies are in agreement with previous results using conventional film-based aerial panchromatic photographs.     

Predicting species-specific basal areas in urban forests using airborne laser scanning and existing stand register data

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.     

Forest variable estimation using photogrammetric matching of digital aerial images in combination with a high-resolution DEM

Abstract

The rapid development in aerial digital cameras in combination with the increased availability of high-resolution Digital Elevation Models (DEMs) provides a renaissance for photogrammetry in forest management planning. Tree height, stem volume, and basal area were estimated for forest stands using canopy height, density, and texture metrics derived from photogrammetric matching of digital aerial images and a high-resolution DEM. The study was conducted at a coniferous hemi-boreal site in southern Sweden. Three different data-sets of digital aerial images were used to test the effects of flight altitude and stereo overlap on an area-based estimation of forest variables. Metrics were calculated for 344 field plots (10 m radius) from point cloud data and used in regression analysis. Stand level accuracy was evaluated using leave-one-out cross validation of 24 stands. For these stands the tree height ranged from 4.8 to 26.9 m (17.8 m mean), stem volume 13.3 to 455 m³ ha^?1 (250 m³ ha^?1 mean), and basal area from 4.1 to 42.9 m² ha^?1 (27.1 m² ha^?1 mean) with mean stand size of 2.8 ha. The results showed small differences in estimation accuracy of forest variables between the data-sets. The data-set of digital aerial images corresponding to the standard acquisition of the Swedish National Land Survey (Lantmäteriet), showed Root Mean Square Errors (in percent of the surveyed stand mean) of 8.8% for tree height, 13.1% for stem volume and 14.9% for basal area. The results imply that photogrammetric matching of digital aerial images has significant potential for operational use in forestry.     

基于大比例尺航片的针叶树种冠幅的提取

基于凉水国家级自然保护区2009年拍摄的1：2000航空像片和同期森林资源二类调查的固定样地数据,采用子像元分类方法分别提取出红松、落叶松和云冷杉的专题影像图。在此基础上,将栅格专题影像图转换为矢量图形,采用目视解译的方法提取上层针叶林的树冠信息。通过将针叶树冠形似为圆形提取出各树种的冠幅,用固定样地实测数据进行对比分析和精度评价,并建立航片上提取冠幅与实测冠幅之间的一元线性回归模型。结果表明：红松、落叶松和云冷杉冠幅的提取精度分别达到83．50％、84．35％和82．26％,其预测精度分别达到83．60％、81．46％和83．57％。     

Determination of mean diameter by basal area in stands of picea abies and pinus sylvestris in southeastern Norway by means of aerial photographs

Mean diameter by basal area (d_g) is an important stand variable for long‐term economic forecasts of forest holdings. In order to use stand‐by‐stand surveys based on aerial photo interpretation as the data basis for forecasts, d_g has to be determined. The objective was to develop and test a regression function for d_g in mature stands of Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.) applicable in southeastern Norway. A study of 700 plots was used to estimate a function for d_g . An additive model was found to be most suitable. The independent variables were potential yield capacity, Lorey's mean tree height, crown closure determined by ocular estimation by means of aerial photographs, and the product of potential yield capacity and crown closure. The R² value was 0.604 and the coefficient of variation was 10.8%. The regression fitted most parts of the calibration data quite well, but it may overestimate the mean diameter in pure spruce stands by 1–2%, and underestimate the diameter in pure pine stands by 3%. For mixed coniferous stands the regression seems satisfactory. Testing by means of an independent data set showed systematic errors of 3–23%. The systematic errors were due partly to calibration problems in connection with the ocular crown closure estimation.     

Stand Structure in Pine,Spruce and Deciduous Forests: A Comparison Between Finnish and Russian Karelia

This study investigated the stand structure in pine, spruce and deciduous forests in the border district of Finland and Russia. A total of 46 mature forest stands was selected as pairs, the members of each pair being as similar as possible with respect to their forest site type, age, moisture and topography. The stands were then compared between the two countries by means of basal areas and number of stems. The proportions of dominating tree species were 2-12% lower, and correspondingly the proportions of secondary tree species higher, in Russian forests. The density of the forest stock was also higher in each forest type in Russia. The forests in the two countries differed most radically in terms of the abundance of dead trees. The amount was two to four times higher in Russian deciduous and spruce forests, and in pine forests the difference was 10-fold. The stand structures indicated that Russian coniferous stands, in particular, were more heterogeneous than intensively managed pine and spruce stands in Finland.     

Measuring loblolly pine crowns with drone imagery through deep learningOA

In modeling forest stand growth and yield, crown width, a measure for stand density, is among the parameters that allows for estimating stand timber volumes. However,accurately measuring tree crown size in the field, in particular for mature trees, is challenging. This study demonstrated a novel method of applying machine learning algorithms to aerial imagery acquired by an unmanned aerial vehicle(UAV) to identify tree crowns and their widths in two loblolly pine plantations in eastern Texas, US...     

Forest structure classes in central Finnish Lapland

Structure classes and changes in the vegetation attributable to the altitude of the terrain were studied in the Hammastunturi virgin coniferous forest area. The area is located in Finnish Lapland, approximately 250–350 km to the north of the Arctic Circle (68°15'N; 68°45'N). The following forest classes were formed on the basis of the ground vegetation and stand coverage: (1) Pine stands, with birch admixture (n=14), (2) Vaccinium‐vitis‐idaea‐type pine stands (n = 24), (3) Vaccinium myrtillus‐type pine stands (n = 17), (4) mixed stands, dominated by pine (n = 8), (5) mixed stands, dominated by spruce (n=18), (6) paludified spruce stands (n = 3) and (7) vigorously growing spruce stands (n = 6). The forest structure classes were depicted using DCA ordinates. The differences were more distinct than those between the normal forest type classifications of the same sample plots. The basal area and volume of pine decreased with increasing altitude, while the proportion of spruce on those sample plots where it was present increased. Birch was present at an equal frequency at all altitudes. The floral composition and between‐species abundance changed along with increasing altitude. The change in floral frequencies could not be designated to a specific altitude zone.     

Assessment of bias due to random measurement errors in stem volume growth estimation by the Swedish National Forest Inventory

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.     

天然鱼鳞云杉枯死现状调查及防治对策

针对长白山区优势树种之一的鱼鳞云杉枯死状况,对长白县境内的天然鱼鳞云杉进行踏查、清理和分析。方法是在长白县5个国有林场施业区内的天然林中随机选取标准地块,调查林内中、近成熟林立木枯死数量、树种以及风到木、濒死木株数,受侵害林分面积、蓄积等因素,从中分析立木枯死原因以及分布状况,在防治过程中找到并制定行之有效的防治措施及对策,采取先进的聚集信息素诱捕法监测、诱杀小蠹虫等危害林木虫害。探讨运用防治监测、预防以级结合伐区作业生产过程中结合营造培育复层异齡林等措施,改善林分卫生状况,进一步提高林木抗病虫害能力的途径。     

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.     

Effects of thinning on growth of six tree species in north-temperate forests of Lithuania

This paper summarises the results from 35 years-observed thinning experiments on 256 permanent sample plots in 10–60 year-old stands of ash, aspen, birch, oak, pine and spruce in Lithuania. Thinning enhanced crown projection area increment of residual trees. The largest effect was observed in stands of aspen and birch (growth increase by 200%), followed by ash and oak (over 100%), and spruce and pine (about 80%). Thinning also promoted dbh increment, especially in younger stands, and the increase of dbh increment was positively correlated with the thinning intensity. The strongest reaction was exhibited by oak and aspen, while ash, birch and conifers reacted to a lower extent. Low and moderate intensities of thinning stimulated volume production in younger stands while the opposite was observed in older stands with increasing removals. Spruce stands exhibited relatively strongest increase of volume increment and pine, –the weakest, while the effect on deciduous species was intermediate. The results demonstrate that significant increase in volume increment is achievable with thinning of only young forest stands, e.g. 10–20 year-old pine, birch and ash, or 10–30 year-old oak, aspen and spruce.     

Comparing biophysical forest characteristics estimated from photogrammetric matching of aerial images and airborne laser scanning data

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.     

The effect of quality bucking and automatic bucking on harvesting productivity and product recovery in a pine-dominated stand

On-board computers (OBC) of harvesting machines can now provide optimized bucking (task of cutting stems into different log lengths) by relying on value and demand matrices. Despite existing benefits of these systems in certain countries, they remain largely underutilized and generally poorly understood in German mechanized forest operations. The study aimed to compare and quantify the differences in harvesting productivity and value recovery between two treatments: quality bucking (OFF) and automatic bucking (ON). A mature forest stand with a high proportion of Scots pine (Pinus sylvestris L.) was divided into plots (30 m × 100 m) where initial tests of both treatments were randomly distributed and replicated 10 times for a total of 11 plots per treatment. Pre-harvest inventory was performed on each tree targeted for removal via a commercial thinning silvicultural treatment. Mechanized harvesting was performed with an excavator-based Atlas Kern T23 Königstiger single-grip harvester. The same assortment specifications and prices were used for both treatments but on-board optimized bucking solutions were applied in the ON plots, whereas the operator had full control of the products to be recovered in the OFF plots. During harvesting operations, continuous time and motion was performed in all plots. Average harvesting productivity was higher—but not statistically significant—in OFF plots compared to ON plots by 2.0 and 0.46 m³/PMH₀ for pine and spruce trees, respectively. Even if there was no difference detected in volume recovery for both treatments and tree species, value recovery was more than 1.60 € per cubic meter higher for pine in larger diameter classes when using quality bucking. This may be due to the fact that the algorithm of the OBC is designed for pine trees with a simpler crown architecture than trees harvested in this study. Results supporting quality bucking over automatic bucking in a Scots pine-dominated stands provide important forest operational information to managers.

     

Estimation of nutrient removals in stem-only and whole-tree harvesting of Scots pine, Norway spruce, and birch stands with generalized nutrient equations

Whole-tree harvesting (WTH), where logging residues are removed in addition to stems, is widely practised in Fennoscandian boreal forests. WTH increases the export of nutrients from forest ecosystems. The extent of nutrient removals may depend on tree species, harvesting method, and the intensity of harvesting. We developed generalized nutrient equations for Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies Karsten), and birch (Betula pendula Roth and Betula pubescens Ehrh.) stands to be able to calculate the amounts of nitrogen, phosphorus, potassium, and calcium in stems and above-ground biomass (stem and crown) as a function of stand volume. The equations were based on Fennoscandian literature data from 34 pine, 26 spruce, and 5 birch stands, and they explained, depending on the tree species and nutrient, 61–99% and 56–87% of the variation in the nutrient amounts of stems and above-ground biomass, respectively. The calculations based on the equations showed that nutrient removals caused by stem-only harvesting (SOH) and WTH per harvested stem m³ were smaller in pine than in spruce and birch stands. If the same volume of stem is harvested, nutrient removals are, in general, nearly equal at thinnings and final cuttings in SOH, but larger in thinnings than final cuttings in WTH. If the principal aim is to minimize the nutrient removals per harvested stem m³, the harvesting should be done at mature pine stands. The effect of biomass removal on overall site nutrient status depends on site-specific factors such as atmospheric deposition, weathering of minerals, and the size of the nutrient pools in the soil.     

Predicting the need for tending of conifer seedling stands in southern Finland

Assessing the need for and timing of tending in seedling stands is based on the stem numbers and heights of crop trees and competing broadleaves, as well as the expected forthcoming development of stand. The assessment is partly an outcome of field worker’s opinion and experience. The tending need of Norway spruce- and Scots pine-dominated seedling stands was modeled using the National Forest Inventory (NFI) data from southern Finland. The models predict the probabilities that the NFI field team leader’s proposal falls in the following four categories: tending is late, during the first or second 5-year period or no need for tending. The predictors such as stem numbers, tree heights, site fertility, regeneration method and accomplished tending logically explained the tending need. The overall accuracy of the models was only fair: 54% (kappa 0.27) for spruce and 55% (kappa 0.33) for pine. However, about 95% of the stands needing immediate tending were classified as stands needing immediate or first 5-year period tending. The surveyor-specific random effects were statistically significant, and the surveyors were likely to propose tending similarly in spruce and pine stands. The models can be utilized in forest planning systems and practical forest inventory.     

Tree Species Classification using Semi-automatic Delineation of Trees on Aerial Images

The purpose of this study was to develop a method for classifying tree species from remote sensing images by combining a semi-automatic pattern recognition technique and spectral properties of trees. Five stands in southern Finland were studied. Individual trees in the digital colour infrared (CIR) aerial photographs were segmented by a method based on the recognition of tree crown patterns at subpixel accuracy. The images were filtered with the Gaussian N-by-N smoothing operator and local maxima above a threshold level were segmented. The segments were classified into three tree species classes. The kappa coefficients for stands varied from 0.43 to 0.86 when the training data and test data were from the same aerial photograph. When training data from other photographs were used as reference data, the kappa coefficients ranged from 0.40 to 0.75. The method described provides an interesting approach for detecting tree species semi-automatically in digital aerial data.     

Effects of Forest Management on Field Layer Vegetation: A Comparison Between Finnish and Russian Karelia

The effects of forest management on the field layer vegetation were compared between Finnish and Russian Karelia in a total of 46 mature stands representing pine, spruce and deciduous forests. In Finland, the number of species was slightly higher in each forest type. Many grassland species, such as Galium boreale , Lathyrus pratensis , and Veronica officinalis , were more abundant in Finnish deciduous stands (14.4 species, mean cover 15.8%) than in Russian stands (9.6 species, mean cover 11.5%). Dwarf shrubs, such as Calluna vulgaris (mean cover: Fin 3.3%, Rus 7.7%) and Vaccinium vitis-idaea (Fin 25.0%, Rus 26.9%) in pine stands, and Vaccinium myrtillus (Fin 22.9%, Rus 31.5%) in spruce stands, were more abundant in Russia. Ferns and graminoids exhibited no differences between the two countries. The observed differences were likely to result from the lower tree stand density, more extensive fragmentation and fertilisation of forests in Finland, the higher frequency of forest fires and more intensive grazing in Russian forests, and from differences in natural conditions between Finnish and Russian Karelia.