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.     

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.     

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.     

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.     

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.     

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.     

森林资源规划设计调查中小班区划方法探讨

针对目前森林资源规划设计调查中小班区划方法存在的问题，提出了连续小班区划方法，并分析了连续小班区划方法的优点及应用前景。     

Characterizing forest species composition using multiple remote sensing data sources and inventory approaches

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.     

浅析我国森林资源清查体系存在的问题与对策

全国森林资源清查是通过定期、准确查清各省的森林资源数量、质量及其消长动态,从而掌握森林生态系统的现状和变化趋势,对森林资源与生态状况进行综合评价。由于清查覆盖面积大、跨度时间长,清查结果不能有效及时利用。本文浅析了我国森林资源连续清查体系在应用中存在的不足,并提出改进措施,以期为完善我国森林资源连续清查体系提供参考。     

森林资源档案管理系统中数据更新方法的探讨

森林资源档案管理系统主要对森林资源的档案信息进行管理,是林业信息管理中的重要内容。森林资源档案数据具有易变性和时效性,森林资源档案数据的及时更新将有助于林业主管部门对森林资源状况的掌握。本文综合了时空数据模型中基态修正模型、生命周期模型和面向对象模型的特点,建立了增量存储数据更新模型,提出了增量存储数据更新方法,并以甘肃省小陇山林业局党川林场的部分数据为基础,利用增量存储数据更新方法,对数据更新实现过程进行了探讨。     

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.     

Airborne laser scanning in forest management: Individual tree identification and laser pulse penetration in a stand with different levels of thinning

This study examined the ability of an airborne laser scanner to identify individual trees in the canopy of a Chamaecyparis obtusa stand and investigated the relationship between the penetration rate of the laser pulses and stand attributes under different canopy conditions caused by different levels of thinning. Individual tree crowns were identified from a digital canopy model (DCM) derived from airborne laser scanner data by the watershed segmentation method. The identification rate of individual trees in blocks with heavy thinning (ratio of the basal area of the felled trees to the total basal area, hereinafter thinning ratio of the basal area, 38.0%), moderate thinning (30.4%), and no thinning was 95.3%, 89.2%, and 60.0%, respectively. Individual tree heights were estimated from the DCM values by local maximum filtering within identified individual crowns. Tree height in the three blocks was estimated with a root-mean-square error of 0.95, 0.65, and 0.68 m, respectively. Tree heights determined in a field survey were regressed against those estimated from the DCM, yielding coefficients of determination (r²) of 0.71, 0.87, and 0.85, respectively, for the blocks with heavy thinning, moderate thinning, and no thinning, respectively, and 0.86 overall. The respective penetration rates of the laser pulses through the canopy to the ground were 50.6%, 43.1%, and 9.2%. Regression of the laser pulse penetration rate against the thinning ratio of the basal area and against the total basal area of the remaining trees in 25 quadrats established in the blocks, yielded r² values of 0.89 and 0.74, respectively.     

浅议卫星遥感在森林资源清查中的应用

本文综述了卫星遥感在我国森林资源调查中的应用概况,并结合实践讨论了卫星遥感应用于森林资源调查的可能性.     

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.     

Accurate single-tree positions from a harvester: a test of two global satellite-based positioning systems

Accurate positioning of single trees registered automatically during harvesting operations opens up new possibilities for reducing the field sampling effort in forest inventories utilising remotely sensed data. In the present study, we propose to use a harvester to collect single-tree data during regular harvest operations and use these data to substitute or supplement traditional measurements on sample plots. Today’s harvesters are capable of recording single-tree information such as species and diameter at breast height, and a cut-to-length harvester was equipped with an integrated accurate positioning system based on real-time kinematic global satellite positioning, as well as a low-cost global navigation satellite system (GNSS) receiver mounted directly on the harvester head. Positions from 73 trees were evaluated and compared to coordinates obtained using a total station. At the single-tree level, the mean error for the integrated positioning system was 0.94?m. The low-cost GNSS receiver mounted on the harvester head yielded a mean error of 7.00?m. The sub-meter accuracy obtained with the integrated system suggests that data acquired with a harvester using this positioning system may have a great potential as a method for single-tree field data acquisition.     

地面三维激光扫描技术在林业中的应用与展望

以地面三维激光扫描技术的测量原理、技术特征为切入点, 重点论述了该技术在国内外林业领域的应用现状、不足及前景。与传统研究手段相比, 地面三维激光扫描技术在测树效率、三维建模、模型化精度等方面具有较大的优势, 显示出更广阔的应用前景。     

Efficiency of inventory plot patterns in quantitative analysis of vegetation: a case study of tropical woodland and dense forest in Benin

The main issue in forest inventory is the reliability of data collected, which depends on the shape and size of inventoried plots. There is also a need for harmonisation of inventoried plot patterns in West Africa. This study focused on the impact of plot patterns on the quantitative analysis of two vegetation types of West Africa based on case studies from Benin. Twenty and fifteen plots of 1 ha each were demarcated in dense forest and woodland, respectively. Each 1 ha plot was divided into 100 quadrats of 100 m² each and diameter at breast height (dbh) of trees was recorded in each quadrat. The required time to measuring trees diameter in each 1 ha plot was also recorded to compute the mean inventory effort. From the 100 quadrats in each 1 ha plot, 14 subplots of different shapes and sizes were considered by grouping together adjacent quadrats. The basal area of each subplot was computed and the relationship between estimation bias of the basal area and the size of subplots was modeled using Smith's Law (Smith 1938). The mean absolute error of the shape parameter c of Weibull distribution was computed for each of the subplot shape, size and direction. The direction and shape of subplots did not influence significantly (P > 0.05) the precision of the quantitative analysis of vegetation. However, square subplots were suitable in practice. On the contrary, plot size was significantly (P < 0.05) and inversely correlated to estimation efficiency. The optimal plot size for quantitative analysis of vegetation was 1 800 and 2 000 m2 with an inventory effort of 0.51 and 0.85 man-days per subplot in woodland and dense forest, respectively. It is concluded that use of standard sample sizes will help to harmonise a forestry database and to carry out comparisons at regional level.     

基于森林资源清样调查资料的森林生产力估算模式--以中国油松林为例

准确地评估森林净第一性生产力(NPP)对于评估全球收支有着十分重要的作用。本文充分利用森林资源清样调查资料,并动态地评估森林生产力,以油松林为例建立了反映生物因素(蓄积量V和林龄A)和气候因素(年实际蒸散E)综合影响的中国油松林生物气候生产力(NPPa)模型。基于所建模型和第四次我国油松林资源的清样调查资料(1989-1993年),估算了中国油松林的净第一性生产力,并借助于地理信息系统软件给出了中国油松林的分布格局。结果表明:我国油松林的平均净第一性生产力为7.82thm-2a-1,其变化幅度为3.32~11.87thm-2a-1。中国油松林净第一性生产力有明显的区域差异,表现为南高北低的分布趋势。山西和陕西为中国油松林的集中分布区,生产力水平处于中等,约为7.4thm-2a-1;油松林集中分布区的南部(四川、湖北、河南等省),生产力水平较高,均大于7.7thm-2a-1;而在油松林集中分布区的北部和西部(内蒙古、宁夏等省),生产力水平较低,NPP均低于5thm-2a-1。该研究为利用森林资源清样调查资料评估森林NPP的动态及研究其对气候变化的响应提供一个有效思路。图3表2参46。     

影像解译在贵州省森林资源连续清查中的应用研究

为了提高贵州省森林资源连续清查中各类土地利用类型的监测精度,在Landsat TM遥感影像处理的基础上建立贵州省的土地类型解译标志,综合利用“3S”技术建立能与图形数据库互查的属性数据库,实现全省遥感样地的计算机管理.     

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.