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1.

Information about the state of the forest is of vital importance in forest management planning. To enable high-precision modelling, many forest planning systems demand input data at the single-tree level. The conventional strategy for collecting such data is a plot-wise field inventory. This is expensive and, thus, cost-efficient alternatives are of interest. During recent years, the focus has been on remote sensing techniques. The k nearest neighbour (kNN) estimation method is a way to assign plot-wise data to all stands in a forest area, using remotely sensed data in connection with a sparse sample of field reference plots. Plot-wise aerial photograph interpretations combined with information from a stand register were used in this study. Nearness to a reference plot was decided upon using a regression transform distance. Standing stem volume was estimated with a relative root mean square error (RMSE) equal to 20% at the stand level, while age could be estimated with a RMSE equal to 15%. A cost-efficient data-capturing strategy could be to assign plot data with the presented kNN method to some types of forest, while using traditional field inventories in other, more valuable, stands.  相似文献   

2.
There is a trend to continuously update forest data in forest management planning systems. Thus, changes in forest stands caused by, e.g. operations and storm damages should be detected in order to ensure the accuracy of forest data and beneficial decisions related to the treatments of the stands. This justifies the application of aerial photographs in change detection as being reasonable because they are already used in forest management planning. This study presents a semi-automatic method based on bi-temporal aerial photographs and registration at the stand and segment levels for the detection of changes in boreal forests. Linear stepwise discriminant analysis and the non-linear k-nearest neighbour (k-NN) method were tested and statistically compared in classification. The classification results at the stand level were found to be better than at the segment level. Compared to previous studies, the results of this study demonstrate remarkable improvement in the classification accuracy of moderate changes. The results showed that change detection substantially improved when the registration at the stand level was used, especially in the detection of thinned stands. To some extent, the method can be already applied operationally.  相似文献   

3.
This paper tests the reliability of a biomass prediction procedure which combines aerial data collection, biometric models and optimisation for forest management planning. Tree stock information is obtained by predicting species-specific diameter and height distributions by a combination of field sampling, ALS data and aerial photographs. The subsequent steps in the chain are (1) assignment of the plots to forestry operation classes by means of remote sensing-based tree stock estimates, (2) estimation of the biomass components removed by simulating forestry operations, and (3) estimation of forest owners’ income flow from optimised bucking of the species-specific diameter distributions. The error effects caused by these steps are analysed, and the applicability of remote sensing–based data collection for biomass inventories and planning is assessed. The approach used for assigning the plots to operation classes resulted in moderate accuracies (75%). The reliability estimates indicated quite poor performance when predicting the biomass components removed in forest treatments, with RMSEs of 33.0–69.4% in the case of final cutting and 76.9–228.0% in the case of thinning. The relative RMSEs of the above-ground biomass estimates of the standing stock were about 19%. The relative bias for the biomasses removed was 10.0–88.6% and that for the standing stock biomasses 0.0%. When optimising bucking, the bucked assortments were larger and the incomes enhanced with this estimation method relative to the reference. This explains why the estimation of forest owner’s incomes in the energy wood thinning simulations led to suboptimal decisions and income losses.  相似文献   

4.
The sample plot data of National Forest Inventories (NFI) are widely used in the analysis of forest production and utilization possibilities to support national and regional forest policy. However, there is an increasing interest for similar impact and scenario analyses for strategic planning at the local level. As the fairly sparse network of field plots only provides calculations for large areas, satellite image data have been applied to produce forest information for smaller areas. The aim of this study was to test the feasibility of generating forest data for a Finnish forest analysis tool, the MELA system, by means of the Landsat satellite imagery and the NFI sample plot data. The study was part of the preparation of a local forestry programme, where a strategic scenario analysis for the forest area of two villages (ca 8000 ha) was carried out. Management units that approximate forest stands were delineated by image segmentation. Stand volume and other parameters for each forest segment were estimated from weighted means of the NFI sample plots, where the individual sample plot weights were estimated by the k nearest neighbour (kNN) method. Two different spectral features were tested: single pixel values and average pixel values within a segment. The estimated forest data were compared with the forest data based on independent stand-level field assessments in two subareas, a national park and an area of forest managed for timber production.In the national park, the estimated mean volume of the growing stock from both spectral feature sets (about 160 m3 ha−1) was clearly lower than that obtained from stand-level field assessment (186 m3 ha−1). Using average pixel values within a segment resulted in a higher proportion of pine and a lower proportion of spruce volume than using single pixel values. It also resulted in an estimated felling potential nearly 10% higher over the first 10-year period in the scenario analysis of the area dedicated to timber production. However, the maximum long-term sustainable removal was at the same level (about 30,000 m3 year−1) for both feature sets over the simulated 30-year period. The resulting annual felling area in the first 10-year period was 12% lower when the segment averages were applied, but the difference subsequently levelled off. The kNN approach in estimating initial forest data for scenario analyses at the local level was found promising.  相似文献   

5.
The effects of field plot configurations on the uncertainties of plot-level forest resource estimates were analyzed using airborne laser scanner data, aerial photographs and field measurements. The aim was to select a field sample plot configuration that can be used for both large area and management inventories. Error estimates were evaluated at the plot level using six different training plot configurations. Additionally, separate plots with two different sizes were used for evaluation. Stem volume and five other forest resource characteristics were considered. The field measurement costs of the different plot configurations were also studied. RMSEs and mean deviations for airborne laser scanning ALS-assisted estimates were practically the same for the fixed radius plot, the two concentric plots and the angle count plot with a basal area factor of q = 1 for all three evaluation plot sizes. Angle count plots with basal area factors of q = 1.5 and 2 increased the RMSEs. For the former plot configurations, the RMSEs for the ALS-assisted estimates could be attributed to inaccuracy in the predicted relationships between the field data and ALS data, not to the training plot configuration. Tree measurements and costs can, therefore, be reduced from those of the Finnish management inventories without increasing RMSEs.  相似文献   

6.
The objective of forest management planning is often expressed as maximum sustainable economic yield. Methods used to collect information for forestry planning should, therefore, include variables significant for economic evaluations of management alternatives. It is important to be able to differentiate mature stands with respect to timber volumes and species mixture. In this study, digital high‐altitude aerial photographs are tested as a data source for planning. Circular plot data from a forest estate in northern Sweden were used as reference material. Global positioning system (GPS) measurements, with differential correction, were used to georeference the plots. Harvesting priorities were calculated for each plot using the Forest Management Planning Package. Volumes, species mixture and harvest priorities were estimated using regression analysis based on textural and spectral information from aerial photographs. The results show that the dependent variables could be estimated fairly well using only spectral information, e.g., R 2 = 0.44 when estimating timber volume at reference plot (10 m radius) level. Aggregated to stand level, the precision was comparable with customary field survey methods (e.g., RMSE= 13.4% for timber volume).  相似文献   

7.
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.  相似文献   

8.
Density estimators for k-tree distance sampling are sensitive to the amount of extra Poisson variance in distances to the kth tree. To lessen this sensitivity, we propose an adaptive composite estimator (COM). In simulated sampling from 16 test populations, a three-component composite density estimator (COM)–with weights determined by a multinomial logistic function of four readily available ancillary variables–was identified as superior in terms of average relative absolute bias. Results from a different set of nine validation populations–with widely different stem densities and spatial patterns of tree locations—confirmed that relative root mean squared errors (RRMSE) of COM were, on average, considerably lower than those obtained with the three-component k-tree density estimators. The RRMSE performance of COM improved with increasing values of k. With k = 6 and sample sizes of 10, 20, and 30, the average relative bias of COM was between −5 and 5% in seven validation populations but in an open low-density savanna-like population bias reached −12% (1979 data) and 7% (1996 data). For k = 6 and n = 10, the RRMSE of COM was, in six of the nine validation populations, within 3.3 percentage points of the RRMSE for sampling with fixed-area plots. Jackknife estimates of the precision of COM estimates of density were negatively biased, leading to under-coverage (7%) of computed 95% confidence intervals.  相似文献   

9.
Abstract

A total of 11 sample-based estimators of tree species richness (S) are evaluated in terms of accuracy and precision in a Monte Carlo simulated simple random sampling from 39,779 forest inventory plots with 7.8 million trees belonging to 85 species. The plots represent a 108 million hectare forested region in central and eastern Canada. Sample sizes varied from 50 to 800. A weighted index combining estimates of accuracy and precision identified Chao's first estimator (CHAO1) as overall best with an estimator based on the assumption of a gamma mixed Poisson distribution of species occurrence as a close runner-up. The observed sample species richness was almost always the most negatively biased estimate. A sample size of 400-700 conventional fixed area forest inventory plots are needed to produce results with bias <20%.  相似文献   

10.
【目的】集成多时期航片数据和由机载激光雷达数据获取的密集林区数字高程模型,估测多时期杉木人工林冠层高度,并对其生长情况进行定量监测,为多时期航片监测森林生长趋势和评价林地生产力提供可能。【方法】首先基于分类后的激光雷达点云数据获得林下高精度数字高程模型和森林数字表面模型,利用航片数据构建立体像对,通过自动立体匹配算法生成森林冠层的摄影测量数字表面模型,然后借助数字高程模型将2种数字表面模型进行高度归一化,提取研究区多时期森林冠层高度。利用1996、2004年历史航片和2014年数字航片以及激光雷达数据,构建18年内皖南杉木人工林3期森林冠层高度,并对其精度进行分析。【结果】1)由2014年数字航片和激光雷达数据获取的森林冠层高度的R^2为0. 52,RMSE为1. 79 m; 2)由2014年数字航片处理得到的森林冠层高度与对应样地实测上层木的平均高验证精度较高,平均绝对误差1. 59 m,平均相对误差15%,最大绝对误差3. 45 m,最大相对误差30. 80%,测量精度85. 00%; 3)由1996、2004、2014年航片得到3期杉木人工林冠层高度,其增长趋势与树高生长曲线预测趋势一致。【结论】在多山复杂地形条件下,利用航片可准确定量反映山脊向阳面的森林冠层高度变化,但对于山谷阴影处,则会出现冠层高度被低估情况,利用多期航片结合高精度DEM数据可定量反映上层木的冠层高度变化。  相似文献   

11.
A goal of a National Forest Inventory (NFI) is the provision of information which is relevant and required for national level decision making and monitoring in forestry, but also for related sectors.

This paper presents and discusses a pilot study from Costa Rica where in 2000/2001 a low intensity sampling approach was used to generate national level forestry information. On a 15 km × 15 km grid air photo plots were interpreted for forest and land cover type. Readily available 1997 aerial photographs were used that were, however, only available for about 70% of the country: of the 228 grid points for the whole country only 159 could be aerial photo interpreted. Out of the 15 km × 15 km base grid of sample points, a 2 × 3 subset was selected for field assessment, resulting in a sample of 40 cluster plots, each comprising of four elongated rectangular sub-plots of 150 m × 20 m located on the perimeter of a square of 500 m side length.

Two novel components were integrated into the inventory: (1) the field plots were established on all lands, so that the tree resource was not only tallied inside forests but also on all other tree-bearing lands outside forests. (2) In addition to the biophysical information gathered on the traditional field plots, interviews were carried out with forest owners on the site of the field plots, in order to obtain data on the use of the forest resource.

Field work was carried out by 6 field crews and took altogether about 3 months. Results were generated from the field samples for the entire country. Aerial photo based area estimates were compared to the corresponding estimations from field sampling for the same area. According to the field sampling the forest cover for Costa Rica in 2001 is estimated to be 48.4% (simple standard error percent 9.3%). An estimated 8.2% of the total volume (dbh > 30 cm, all species) is outside forest.

This inventory took place with support from Food and Agriculture Organization (FAO) in the framework of FAO–Forest Resources Assessment's (FRA) Program Support to National Forest Assessments; it was carried out jointly by Sistema Nacional de Áreas de Conservación (SINAC), the Costa Rican authority responsible for forestry issues, and Centro Agronómico de Investigación y Enseñanza (CATIE), an international agricultural research center. Experiences of the study were subsequently used to implement similar inventories in three more countries (Guatemala, Cameroon, The Philippines).  相似文献   


12.
Forest edge quantification by line intersect sampling in aerial photographs   总被引：1，自引：0，他引：1  
There is a need for accurate and efficient methods for quantification and characterisation of forest edges at the landscape level in order to understand and mitigate the effects of forest fragmentation on biodiversity. We present and evaluate a method for collecting detailed data on forest edges in aerial photographs by using line intersect sampling (LIS). A digital photogrammetric system was used to collect data from scanned colour infrared photographs in a managed boreal forest landscape. We focused on high-contrast edges between forest (height ≥ 10 m) and adjoining open habitat or young, regenerating forest (height ≤ 5 m). We evaluated the air photo interpretation with respect to accuracy in estimated edge length, edge detection, edge type classification and structural variables recorded in 20 m radius plots, using detailed field data as reference. The estimated length of forest edge in the air photo interpretation (52 ± 8.8 m ha−1; mean ± standard error) was close to that in the field survey (58 ± 9.3 m ha−1). The accuracy in edge type classification (type of open habitat) was high (88% correctly classified). Both tree height and canopy cover showed strong relationships with the field data in the forest, but tree height was underestimated by 2.3 m. Data collection was eight times faster and five times more cost-effective in aerial photographs than in field sampling. The study shows that line intersect sampling in aerial photographs has large potential application as a general tool for collecting detailed information on the quantity and characteristics of high-contrast edges in managed forest ecosystems.  相似文献   

13.
Discrimination of deciduous trees using spectral information from aerial images has only been partly successfully due to the complexity of the reflectance at different view angles, times of acquisition, phenology of the trees and inter-tree radiance. Therefore, the objective was to evaluate the accuracy of estimating the proportion of deciduous stem volume (P) utilizing change detection between canopy height models (CHMs) generated by digital photogrammetry from leaf-on and leaf-off aerial images instead of using spectral information. The study was conducted at a hemi-boreal study area in Sweden. Using aerial images from three seasons, CHMs with a resolution of approximately 0.5?m were generated using semi-global matching. For training plots, metrics describing the change between leaf-on and leaf-off conditions were calculated and used to model the continuous variable P, using the Random Forest approach. Validated at sub-stands, the estimation accuracy of P in terms of root mean square error and bias was found to be 18% and ?6%, respectively. The overall classification accuracy, using four equally wide classes, was 83% with a kappa value of 0.68. The validation plots in classes of high proportion of coniferous or deciduous stem volume were well classified, whereas the mixed forest classes showed lower classification accuracies.  相似文献   

14.
The k-nearest-neighbour (knn) method is known as a robust nonparametric method. It is used to estimate unknown values of data sets by means of similarity to reference data sets with known values. The spectral information of satellite remote sensing data can be used to provide the common characteristics in the knn estimation process. In forest sciences, the knn method is studied for its application potential. Some application examples are: (1) the estimation of parameters such as basal area, stem volume, number of trees per diameter class and tree species; (2) the estimation of forest debris and non-wood goods and services; (3) the production of wall-to-wall information for modelling, risk management and logistics. On the other hand, different limitations with respect to methodological characteristics as well as the selection of suitable parameters must be taken into consideration. The scope of this article concentrates on the discussion of the application potential and limits of the knn method in forestry with particular emphasis on management planning needs. The study is based on data taken from a forest inventory (FI) covering a test site near Rottenburg, in southwest Germany. Analysis results are compared with the traditional outcome of inventory data analysis and partly presented in thematic maps, which show identical spatial distribution patterns. For the map of six tree species, a map accuracy of 52.2% was found. The user’s accuracy for the prevailing tree species was between 52.6% for Picea abies and 69.4% for Quercus sp. A timber volume map for Quercus sp. clearly visualises the bias at the extreme ends of the volume distribution. The root mean square error (RMSE) for the total timber volume estimate was 30.9% for k = 5 and could be reduced to 22.6% for k = 20. For Quercus sp., however, the respective RMSE values were between 106.5 and 84.8%. Significant differences between FI and knn estimates were mainly found for rare classes with minor representation in the reference data.  相似文献   

15.
Site index (SI) is one of the main measures of forest productivity in North America. For monospecific even-age stands, it is defined as the height of dominant trees at a given reference age or presented as an age–height curve. SI normally reflects the overall effect of all the environmental parameters that determine height growth locally. However, measuring SI can only be achieved though field observations and is, for this reason, limited to sample plots. In this study, we propose a new method for quantifying and mapping SI and age based on known age–height curves and time series of canopy height models (CHMs) produced using digital photogrammetry and lidar. Digital surface models (DSMs) are created by applying an automated stereo-matching algorithm to scanned aerial photographs. The canopy height is obtained by subtracting the lidar ground elevations from the DSM. Using aerial photographs covering the 1945–2003 interval and a recent lidar coverage, CHMs could be reconstructed retrospectively for a period of over 58 years. Regionally calibrated age–height curves were fitted to observations that were extracted cell-wise from the historical CHMs to estimate SI and age values for all undisturbed locations. Results demonstrate that SI and age of jack pine (Pinus banksiana [Lamb.]) stands can be quantified respectively with an average bias of 0.76 m (2.41 m root mean squared error, RMSE) and 1.86 years (7 years RMSE). The method can be used to produce quasi-continuous maps of SI and age and to estimate productivity in a spatially explicit way.  相似文献   

16.
基于大比例尺航片的针叶树种冠幅的提取   总被引：2，自引：0，他引：2  
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％。  相似文献   

17.
The k-nearest neighbors (kNN) method is widely employed in national forest inventory applications using remote sensing data. The objective of this study was to evaluate the kNN method for stand volume estimation by combining LANDSAT/ETM+ data with 622 field sample plots from the Japanese National Forest Inventory (NFI) in Kyushu, Japan. The root mean square error (RMSE) and relative RMSE of the volume estimates rapidly decreased as the number of nearest neighbors (k) increased up to five, and then it slightly declined. They were consistently smaller for the Euclidean distance than for the Mahalanobis distance. The estimation errors (RMSE and relative RMSE) were 169.2 m3/ha and 66.2%, respectively (k = 10). The relative RMSE was similar to the previous studies. The estimated values were more accurate towards the mean value of the total volume, with an overestimation of the low volumes and an underestimation of the high volumes. We found a significant linear relationship between the observed stand volumes and estimated errors, which suggests that systematic errors may be reduced using this linearity. This research concluded that the kNN method is suitable for estimating stand volumes in Kyushu.  相似文献   

18.
广西森林资源连续清查角规样地体系评价   总被引：3，自引：0，他引：3  
广西森林资源连续清查(以下简称"广西连清)"角规样地体系,是我国唯一的以点抽样理论为基础,以固定角规样地为监测载体的省(区)级森林资源连续清查体系。广西连清第7次复查,除了增设的方形样地调查以外,还对原有的角规样地进行了复查,因此本文得以用同时进行调查的方形样地调查结果作为参照对象,对角规样地体系的优点和存在的问题进行定性和定量相结合的综合分析评价。分析评价结果表明,与方形样地比较,角规样地除了具有显著的隐蔽性外,外业工作量只相当于方形样地的53%,两套体系的活立木蓄积量差异仅为3.16%,角规样地和方形样地体系总蓄积量抽样精度分别为94.47%与94.57%,均达到国家森林资源连续清查技术规定要求(≥90%)。角规样地复位率大于规定的98%,样木复位率远大于规定的95%,达97%以上,能满足林木蓄积生长量和消耗量监测的要求。角规样地体系的不足主要是由于漏测木和进测木的存在,导致森林资源的现状估计值偏低,且动态估计精度明显低于方形样地体系。  相似文献   

19.
Spatial prediction of forest stand variables   总被引：1，自引：1，他引：0  
This study aims at the development of a model to predict forest stand variables in management units (stands) from sample plot inventory data. For this purpose we apply a non-parametric most similar neighbour (MSN) approach. The study area is the municipal forest of Waldkirch, 13 km north-east of Freiburg, Germany, which comprises 328 forest stands and 834 sample plots. Low-resolution laser scanning data, classification variables as well rough estimations from the forest management planning serve as auxiliary variables. In order to avoid common problems of k-NN-approaches caused by asymmetry at the boundaries of the regression spaces and distorted distributions, forest stands are tessellated into subunits with an area approximately equivalent to an inventory sample plot. For each subunit only the one nearest neighbour is consulted. Predictions for target variables in stands are obtained by averaging the predictions for all subunits. After formulating a random parameter model with variance components, we calibrate the prior predictions by means of sample plot data within the forest stands via BLUPs (best linear unbiased predictors). Based on bootstrap simulations, prediction errors for most management units finally prove to be smaller than the design-based sampling error of the mean. The calibration approach shows superiority compared with pure non-parametric MSN predictions.  相似文献   

20.
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.  相似文献   

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