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.     

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.     

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.     

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.     

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.     

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.     

Spatial occurrence of major tree species groups in Europe derived from multiple data sources

Pan-European distribution maps have been compiled for six main species groups in Europe. The aim was to combine detailed tree species information from plot data of the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests) with other existing spatial and statistical information on forests. The applied method involves the interpolation of the plot data using the geo-statistical interpolation method kriging and a scaling and calibration procedure which makes the maps correspond both to the Forest map of Europe on pixel level and to national forest inventory statistics at regional or national level. Output is a set of European-wide maps with a resolution of 1 km, showing the proportion of the main tree species groups as a percent of the total land area. The maps are considered to be potentially valuable input for various applications in the forestry and biodiversity field at a pan-European scale.     

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.     

Laser scanning of forest resources: the nordic experience

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.     

Estimating and mapping forest biomass in northeast China using joint forest resources inventory and remote sensing data

Being able to accurately estimate and map forest biomass at large scales is important for a better understanding of the terrestrial carbon cycle and for improving the effectiveness of forest management. In this study, forest plot sample data, forest resources inventory(FRI) data, and SPOT Vegetation(SPOT-VGT) normalized difference vegetation index(NDVI) data were used to estimate total forest biomass and spatial distribution of forest biomass in northeast China(with 1 km resolution). Total forest biomass at both county and provincial scales was estimated using FRI data of 11 different forest types obtained by sampling 1156 forest plots, and newly-created volume to biomass conversion models. The biomass density at the county scale and SPOT-VGT NDVI data were used to estimate the spatial distribution of forest biomass. The results suggest that the total forest biomass was 2.4 Pg(1 Pg = 10~(15) g), with an average of 77.2 Mg ha~(-1), during the study period. Forests having greater biomass density were located in the middle mountain ranges in the study area. Human activities affected forest biomass at different elevations, slopes and aspects. The results suggest that the volume to biomass conversion models that could be developed using more plot samples and more detailed forest type classifications would be better suited for the study area and would provide more accurate biomass estimates. Use of both FRI and remote sensing data allowed the down-scaling of regional forest biomass statistics to forest cover pixels to produce a relatively fineresolution biomass map.     

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.     

Uncertainties of mapping aboveground forest carbon due to plot locations using national forest inventory plot and remotely sensed data

Abstract

Forest carbon sinks significantly contribute to mitigation of atmospheric concentrations of carbon dioxide. Thus, estimating forest carbon is becoming important to develop policies for mitigating climate change and trading carbon credits. However, a great challenge is how to quantify uncertainties in estimation of forest carbon. This study investigated uncertainties of mapping aboveground forest carbon due to location errors of sample plots for Lin-An County of China. National forest inventory plot data and Landsat TM images were combined using co-simulation algorithm. The findings show that randomly perturbing plot locations within 10 distance intervals statistically did not result in biased population mean predictions of aboveground forest carbon at a significant level of 0.05, but increased root mean square errors of the maps. The perturbations weakened spatial autocorrelation of aboveground forest carbon and its correlation with spectral variables. The perturbed distances of 800 m or less did not obviously change the spatial distribution of predicted values. However, when the perturbed distances were 1600 m or larger, the correlation coefficients of the predicted values from the perturbed locations with those from the true plot locations statistically did not significantly differ from zero at a level of 0.05 and the spatial distributions became random.     

Modeling individual tree height growth of Norway spruce and Scots pine from national forest inventory data in Norway

We developed individual tree height growth models for Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.) in Norway based on national forest inventory data. Potential height growth is based on existing dominant height growth models and reduced due to competition by functions developed in this study. Three spatially explicit and two spatially non-explicit competition indices were tested. Distance effects and diameter ratio effects were estimated from the data simultaneously with parameters of the potential modifier functions. Large height measurement errors in the national forest inventory data caused large residual variation of the models. However, the effects of competition on height growth were significant and plausible. The potential modifier functions show that height growth of dominant trees is largely unaffected by competition. Only at higher levels of competition, height growth is reduced as a consequence of competition. However, Scots pine also reduced height growth at very low levels of competition. Distance effects in the spatially explicit competition indices indicated that the closest neighbors are most important for height growth. However, for Scots pine also competitors at larger distance affected height growth. The five competition indices tested in this study explained similar proportions of the variation in relative height growth. Given that unbiased predictions can only be expected for the same plot size, we recommend a spatially explicit index, which describes the distance function with a negative exponential, for use in growth simulators.     

Effects of landscape patterns on species density and abundance of trees in a tropical subdeciduous forest of the Yucatan Peninsula

Most studies of the effects of fragmentation and landscape patterns on plant communities focus on particular patches and on local species richness (α-diversity), while few studies examine different patch-types at the whole landscape level and address effects on abundance and composition of species or functional groups. The present study aims to identify and characterize relationships between patch-type metrics and species density and abundance of trees using four tropical subdeciduous forest landscapes in the Yucatan Peninsula considering the entire landscape as the unit of study. Species density and abundance of different groups of tree species resulting from hierarchical clustering were related to landscape patterns of patch-types (area, edge, shape, similarity and contrast) using regression analysis and Akaike Information Criterion (AIC). The abundance of tree species in the oldest stages of succession was associated with percentage of land of a patch-type. Total area may favor the establishment of shade-tolerant tree species in the study area because as the area of forest patches increases, the area of forest interior conditions also increases. Conversely, the abundance of species at early and intermediate stages was related to total edge contrast and edge density, respectively. Fragmentation increases the proportion of edge zones of a patch-type, creating contrasting microclimate conditions that could promote the establishment of pioneer and light-demanding species. Thus, the combined effect of total area and edge length of a patch-type may enhance total tree species richness in the study area by favoring species with different life-history strategies. The appearance of area, shape, edge and contrast in most of the regression models suggests that some generalization can be made about the effects of spatial geometry of patch-types on species composition and abundance of tropical trees. Understanding associations between landscape metrics and species density and abundance of objectively derived groups or guilds of species can provide important insights on the effects of fragmentation and landscape pattern on these guilds and on overall α-diversity, as well as guidelines for their conservation and management.     

福建省连城县天然林资源分析与评价

对连城县18个乡镇的天然林面积和树种组成分析表明:全县天然林面积比例为72.64%,以针叶树种为主的天然林占57.75%,纯阔叶林仅占4.89%;不同针阔比例的天然林面积,随着阔叶树比例增加呈递减趋势;全县天然林阔叶树权重为0.4,乡镇间存在较大差异;通过聚类分析,18个乡镇被划分为4个不同天然林阔叶树比例类型;综合评价认为,全县天然林比例大,阔叶树种丰富,但干扰严重,天然林面积呈减小趋势,森林结构退化,个别地段出现逆行演替。     

Structure and composition of the understory treelets in a non-dipterocarp forest of tropical Asia

Species richness, floristic composition, and structure of understory treelets were investigated in three 1-ha plots, distributed in the undisturbed tropical seasonal rain forest of Xishuangbanna, SW China. We investigated all the woody species except lianas in these plots, compared the differences of species diversity of trees and treelets in these plots and the differences of understory structure between this forest and the typical dipterocarp forests in SE Asia. We found 5089 individuals belonging to 356 species, 189 genera, and 63 families in the three plots. The five most important families were Rubiaceae, Euphorbiaceae, Lauraceae, Meliaceae, and Annonacae. The treelet layer was much more diverse than the tree layer, indicating that result based only on trees may be not an appropriate representation of the diversity status of a particular tropical forest type. The three plots were more similar when the treelet layer was considered than when tree layer was considered. In contrast with the dipterocarp forests in SE Asia, the understory of tropical seasonal rain forest was mainly composed of the trees with small and middle stature at maturity classes. These results indicate that the structure of tropical forest understory may differ not only among continents but also within continent.     

Estimating single-tree branch biomass of Norway spruce with terrestrial laser scanning using voxel-based and crown dimension features

Abstract

Many remote sensing-based methods estimating forest biomass rely on allometric biomass models for field reference data. Terrestrial laser scanning (TLS) has emerged as a tool for detailed data collection in forestry applications, and the methods have been proposed to derive, e.g. tree position, diameter-at-breast-height, and stem volume from TLS data. In this study, TLS-derived features were related to destructively sampled branch biomass of Norway spruce at the single-tree level, and the results were compared to conventional allometric models with field measured diameter and height. TLS features were derived following two approaches: one voxel-based approach with a detailed analysis of the interaction between individual voxels and each laser beam. The features were derived using voxels of size 0.1, 0.2, and 0.4 m, and the effect of the voxel size was assessed. The voxel-derived features were compared to features derived from crown dimension measurements in the unified TLS point cloud data. TLS-derived variables were used in regression models, and prediction accuracies were assessed through a Monte Carlo cross-validation procedure. The model based on 0.4 m voxel data yielded the best prediction accuracy, with a root mean square error (RMSE) of 32%. The accuracy was found to decrease with an increase in voxel size, i.e. the model based on the 0.1 m voxel yielded the lowest accuracy. The model based on crown measurements had an RMSE of 34%. The accuracies of the predictions from the TLS-based models were found to be higher than from conventional allometric models, but the improvement was relatively small.     

Effects of auxiliary data source and inventory unit size on the efficiency of sample-based coarse woody debris inventory

The efficiency of a sample-based inventory can be greatly improved if lower cost information on the study area is utilized. It has been observed that the use of airborne laser scanning (ALS) data in the design phase may improve the efficiency of dead wood (coarse woody debris, CWD) volume inventory notably, i.e. a smaller standard error of the mean is observed with the same inventory costs. In the present case, several auxiliary data sources were employed in the design phase by using ‘probability proportional to size’ sampling to select the sample units to be inventoried in the field. It was observed that a combination of ALS data with either aerial photographs or stand-register data can improve the sampling efficiency even more than the use of ALS as a single data source. Since these additional data sources are often gathered for the inventory of living trees, their use does not incur extra expenses for CWD assessment. Thus, the use of these data separately or together with ALS data can greatly improve the cost-efficiency of a CWD volume inventory. It was also observed that the size of the sample units has a slight effect on the sampling efficiency. Even though the improvement in the sampling efficiency was usually greater with larger sample unit sizes, the CWD volume inventory was most efficient with moderate grid cell sizes.     

Distribution and composition of carabid beetle (Coleoptera, Carabidae) communities across the plantation forest cycle—Implications for management

If national afforestation targets are met, the proportion of area under plantation forest cover in Ireland will almost double by the year 2030 to 17%, and will consist of mostly non-native trees. There is an urgent and vital need to assess and to maximise the biodiversity potential of these forests. This study compares carabid beetle (Coleoptera, Carabidae) composition and assemblage patterns across different stages of the forest cycle, in addition to intensive grassland habitats, the habitat the plantation forest will most likely be replacing. The clearfell habitat had the highest median species richness, while the grassland habitat had the highest species diversity, but lowest species dominance. Ordination analysis revealed that the species assemblages of all stages of the forest cycle could be clearly separated from the grassland habitat, while differences were also observed between forest stages. Canonical correspondence analysis revealed that the most significant factors influencing carabid community composition were percent cover of ground vegetation and mosses, and also percent soil moisture. Differences in species with varying physical traits and microhabitat preferences were also observed between habitats, with larger, brachypterous species positively associated with increasing canopy cover and smaller macropterous species displaying the opposite pattern. The presence and abundance of forest-associated species increased with increasing plantation age, with a corresponding decrease in open habitat-associated and generalist species. The results of this study suggest that at the landscape scale, a variety of different aged forest stands would maximise the biodiversity potential of the planned afforestation.     

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.