Predicting stand-thinning maturity from airborne laser scanning data

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.     

Practical large-scale forest stand inventory using a small-footprint airborne scanning laser

Mean tree height, dominant height, mean diameter, stem number, basal area and timber volume of 116 georeferenced field sample plots were estimated from various canopy height and canopy density metrics derived by means of a small-footprint laser scanner over young and mature forest stands using regression analysis. The sample plots were distributed systematically throughout a 6500 ha study area, and the size of each plot was 232.9 m². Regressions for coniferous forest explained 60–97% of the variability in ground reference values of the six studied characteristics. A proposed practical two-phase procedure for prediction of corresponding characteristics of entire forest stands was tested. Fifty-seven test plots within the study area with a size of approximately 3740 m² each were divided into 232.9 m² regular grid cells. The six examined characteristics were predicted for each grid cell from the corresponding laser data using the estimated regression equations. Average values for each test plot were computed and compared with ground-based estimates measured over the entire plot. The bias and standard deviations of the differences between predicted and ground reference values (in parentheses) of mean height, dominant height, mean diameter, stem number, basal area and volume were ?0.58 to ?0.85 m (0.64–1.01 m), ?0.60 to ?0.99 m (0.67–0.84 m), 0.15–0.74 cm (1.33–2.42 cm), 34–108 ha^?1 (97–466 ha^?1), 0.43–2.51 m² ha^?1 (1.83–3.94 m² ha^?1) and 5.9–16.1 m³ ha^?1 (15.1–35.1 m³ ha^?1), respectively.     

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.     

Predicting dynamic modulus of elasticity of Norway spruce structural timber by forest inventory,airborne laser scanning and harvester-derived data

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 (E_dyn) 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.     

Predicting understory maximum shrubs cover using altitude and overstory basal area in different Mediterranean forests

In some areas of the Mediterranean basin where the understory stratum represents a critical fire hazard, managing the canopy cover to control the understory shrubby vegetation is an ecological alternative to the current mechanical management techniques. In this study, we determine the relationship between the overstory basal area and the cover of the understory shrubby vegetation for different dominant canopy species (Pinaceae and Fagaceae species) along a wide altitudinal gradient in the province of Catalonia (Spain). Analyses were conducted using data from the Spanish National Forest Inventory. At the regional scale, when all stands are analysed together, a strong negative relationship between mean shrub cover and site elevation was found. Among the Pinaceae species, we found fairly good relationships between stand basal area and the maximum development of the shrub stratum for species located at intermediate elevations (Pinus nigra, Pinus sylvestris). However, at the extremes of the elevation-climatic gradient (Pinus halepensis and Pinus uncinata stands), stand basal area explained very little of the shrub cover variation probably because microsite and topographic factors override its effect. Among the Fagaceae species, a negative relationship between basal area and the maximum development of the shrub stratum was found in Quercus humilis and Fagus sylvatica dominated stands but not in Quercus ilex. This can be due to the particular canopy structure and management history of Q. ilex stands. In conclusion, our study revealed a marked effect of the tree layer composition and the environment on the relationship between the development of the understory and overstory tree structure. More fine-grained studies are needed to provide forest managers with more detailed information about the relationship between these two forest strata.     

Mapping and modeling forest tree volume using forest inventory and airborne laser scanning

In this paper, we present a study on the efficiency of multi-return LIDAR (Light Detection Ranging) data in the estimation of forest stem volume over a multi-layered forest area in the Italian Alps. The goals of this paper are (1) to verify the usefulness of multi-return LIDAR data compared to single-return data in forest volume estimation and (2) to define the optimal resolution of a stem volume distribution raster map over the investigated area. To achieve these goals, raw data were segmented into a net, and different cell dimensions were investigated to maximize the relationship between the LIDAR data and the ground-truth information. Twenty predicting variables (e.g., mean height, coefficient of variation) have been extracted from multi-return LIDAR data, and a multiple linear regression analysis has been used for predicting tree stem volume. Experimental results found that the optimal resolutions of the net square cells were 40 m. The analysis indicated that in a mixed multi-layered forest, characterized by a complex vertical structure, the correct selection of the map spatial resolution and the inclusion of the secondary-return data were important factors for improving the effectiveness of the laser scanning approach in forest inventories. The experimental tests showed that the chosen model is effective for the estimation of stem volume over the analyzed area, providing good results on all the three considered validation methods.     

Estimation of stem attributes using a combination of terrestrial and airborne laser scanning

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.     

Developing an airborne laser scanning dominant height model from a countrywide scanning survey and national forest inventory data

Abstract

An airborne laser scanning (ALS) dominant height model was developed based on data from a national scanning survey with the aim of developing a digital terrain model (DTM) for Denmark. Data obtained in the ongoing Danish national forest inventory (NFI) were used as reference data. The data comprised a total of 2072 measurements of dominant height on NFI sample plots inventoried in 2006–2007 and their corresponding ALS data. The dominant height model included four variables derived from the ALS point cloud distribution. The variables were related to canopy height, canopy density and species composition on individual plots. The RMSE of the final model was 2.25 m and the model explained 93.9% of the variation (R ²). The model was successful in predicting dominant height across a wide range of forest tree species, stand heights, stand densities, canopy cover and growing conditions. The study demonstrated how low-density ALS data obtained in a survey not specifically aimed at forest applications may be used for obtaining biophysical forest properties such as dominant height, thereby reducing the overall forest inventory costs.     

Comparing stand inventories for large areas based on photo-interpretation and laser scanning by means of cost-plus-loss analyses

Evaluations of inventory methods usually end when precision and bias are quantified. Additional information on the appropriateness of a method may be provided through cost-plus-loss analyses, where the total costs are calculated as the sum of net present value (NPV) losses, i.e. expected economic losses as a result of future incorrect decisions due to errors in measurements, and inventory costs. The aim of the study was to compare inventories of basal area, dominant height and number of trees per hectare based on photo-interpretation and laser scanning from two sites in Norway by means of cost-plus-loss analyses. In general, more precise estimates were found for laser scanning than for photo-interpretation, while the biases were about equally distributed between the two methods. On average for the two sites, the inventory costs, NPV losses and total costs for photo-interpretation were about 6, 49 and 54 euros ha^?1, respectively, while they were 11, 13 and 25 euros ha^?1 for laser scanning. The data used for the comparison were limited to two sites and 77 stands, and certain simplifying assumptions were made in the cost-plus-loss analyses. Still, there is reason to believe that the results of the study are of general validity with respect to the main conclusion when comparing the two methods.     

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.     

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.     

Using airborne laser scanning data for detecting canopy gaps and their understory type in mature boreal forest

• Introduction   

Canopy gap dynamics in old-growth boreal forests is a result of tree mortality caused by insects, diseases, or meteorological phenomena. Canopy gaps improve the possibilities of natural regeneration, and concentrations of decomposed deadwood are often found in these natural openings, which provide specific habitats for many deadwood-dependent species and organisms.     

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.     

Predicting stand damage and tree survival in burned forests in Catalonia (North-East Spain)

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.     

Automatic recognition and measurement of single trees based on data from airborne laser scanning over the richly structured natural forests of the Bavarian Forest National Park

The purpose of this study was to test a method for delineating individual tree crowns based on a fully automated recognition methodology. The study material included small-footprint time-of-flight laser scanner data acquired in the spring and summer of 2002. The data were collected with a Toposys II airborne laser system flown over the Norway spruce (Picea abies) and European beech (Fagus sylvatica) dominated forests of the Bavarian Forest National Park, Germany. The applied algorithm, which earlier had been validated for Swedish forest conditions, is a watershed algorithm that is based on the use of laser scanning data. 2584 trees in a total of 28 representative reference stands, each 0.1–0.25 ha in area, were included in the investigation. With the algorithm, 76.9% of the trees in the upper layer could be recognised. This corresponds to 85.2% of the timber volume determined by ground measurements. The results for conifers were more accurate in this respect than for deciduous trees. A negative aspect was the number of falsely identified trees, the percentage of which was 5.4%.     

Identification of boreal forest stands with high herbaceous plant diversity using airborne laser scanning

Boreal forest stands with high herbaceous plant species diversity have been found to be one of the main habitats for many endangered species, but the locations and sizes of these herb-rich forest stands are not well known in many areas. Better identification of the stands could improve both their conservation and management. A new approach is proposed here for locating the mature herb-rich forest stands using airborne laser scanner (ALS) data and logistic regression, or the k-NN classifier. We show that ALS technology is capable of distinguishing the ecologically important herb-rich forests from those growing on less fertile site types, mainly on the basis of unique but quantifiable crown structure and vertical profile that characterise forests on high fertility sites. The study site, Koli National Park, is located on the border of the southern and middle boreal vegetation zones in Finland, and includes 63 herb-rich forest stands of varying sizes. The model and test data comprised 274 forest stands belonging to five forest site types varying from very fertile to poor. The best overall classification accuracy achieved with the k-NN method was 88.9%, the herb-rich forests being classified correctly in 65.0% of cases and the other forest site types in 95.7%. The best overall classification accuracy achieved with logistic regression was 85.6%, being 55.0% for the herb-rich forests and 94.3% for the other forest site types. Both methods demonstrated promising potential for separating herb-rich forests from other forest site types, although slightly better results were obtained with the non-parametric k-NN method, which was capable of utilising a higher number of explanatory variables. It is concluded that ALS-based data analysis techniques are applicable to the detection of mature boreal herb-rich forests in large-scale forest inventories.     

Assessment of standing wood and fiber quality using ground and airborne laser scanning: A review

Accurate information on the wood-quality characteristics of standing timber and logs is needed to optimize the forest production value chain and to assess the potential of forest resources to meet other services. Physical and chemical characteristics of wood vary with both tree and site characteristics. At the tree scale, crown development, stem shape and taper, branch size and branch location, knot size, type and placement, and age all influence wood properties. More broadly, at the stand level, stocking density, moisture, nutrient availability, climate, competition, disturbance, and stand age have also been identified as key determinants of wood quality. Such information is often captured in polygon based forest inventory data. Other terrain-related spatial information, such as elevation, slope and aspect, can improve assessments of site conditions and limitations upon plant growth which impact wood quality. Light Detection And Ranging (LiDAR) is an emerging technology, which directly measures the three-dimensional structure of forest canopies using ground or airborne laser instruments, and can provide highly accurate information on individual-tree and stand-level forest structure. In this paper, we explore the potential of LiDAR and other geospatial information sources to model and predict wood quality based on individual-tree and stand structural metrics. We identify a number of key wood quality attributes (i.e., basic wood density, cell perimeter, cell coarseness, fiber length, and microfibril angle) and demonstrate links between these properties and forest structure and site attributes. Finally, the potential for using LiDAR in combination with other geospatial information sources to predict wood quality in standing timber is discussed.     

Estimating percentile-based diameter distributions in uneven-sized Norway spruce stands using airborne laser scanner data

Abstract

A model for prediction of stand basal area and diameters at 10 percentiles of a basal area distribution was estimated from small-footprint laser scanner data from primeval conifer forest using partial least squares regression. The regression explained 44–80% and 67% of the variability of the 10 percentiles and stand basal area, respectively. The predicted percentiles, scaled by the predicted stand basal area, were used to compute diameter distributions. A cross-validation showed that the mean differences between the predicted and observed number of stems by diameter class were non-significant (p>0.05) for 22 of 29 diameter classes. Moreover, plot volume was calculated from the predicted diameter distribution and cross-validation revealed a non-significant deviation between predicted and observed volume of ?3.3% (of observed volume). An independent validation showed non-significant mean differences for 20 of 21 diameter classes for data corresponding to the model calibration data. Plot volumes calculated from the predicted diameter distributions deviated from observed volume by ?4.4%. The model reproduced diameter distributions corresponding to the model calibration data (uneven-sized forest) well. However, the model is not flexible enough to reproduce normal and uniform diameter distributions. Volume estimates derived from predicted diameter distributions were generally well determined, irrespective of the observed distribution.