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1.
Estimating forest biomass using small footprint LiDAR data: An individual tree-based approach that incorporates training data 总被引：1，自引：0，他引：1
Zachary J. Bortolot Randolph H. Wynne 《ISPRS Journal of Photogrammetry and Remote Sensing》2005,59(6):824
A new individual tree-based algorithm for determining forest biomass using small footprint LiDAR data was developed and tested. This algorithm combines computer vision and optimization techniques to become the first training data-based algorithm specifically designed for processing forest LiDAR data. The computer vision portion of the algorithm uses generic properties of trees in small footprint LiDAR canopy height models (CHMs) to locate trees and find their crown boundaries and heights. The ways in which these generic properties are used for a specific scene and image type is dependent on 11 parameters, nine of which are set using training data and the Nelder–Mead simplex optimization procedure. Training data consist of small sections of the LiDAR data and corresponding ground data. After training, the biomass present in areas without ground measurements is determined by developing a regression equation between properties derived from the LiDAR data of the training stands and biomass, and then applying the equation to the new areas. A first test of this technique was performed using 25 plots (radius = 15 m) in a loblolly pine plantation in central Virginia, USA (37.42N, 78.68W) that was not intensively managed, together with corresponding data from a LiDAR canopy height model (resolution = 0.5 m). Results show correlations (r) between actual and predicted aboveground biomass ranging between 0.59 and 0.82, and RMSEs between 13.6 and 140.4 t/ha depending on the selection of training and testing plots, and the minimum diameter at breast height (7 or 10 cm) of trees included in the biomass estimate. Correlations between LiDAR-derived plot density estimates were low (0.22 ≤ r ≤ 0.56) but generally significant (at a 95% confidence level in most cases, based on a one tailed test), suggesting that the program is able to properly identify trees. Based on the results it is concluded that the validation of the first training data-based algorithm for determining forest biomass using small footprint LiDAR data was a success, and future refinement and testing are merited. 相似文献
2.
Carlos Alberto Silva Andrew Thomas Hudak Carine Klauberg Lee Alexandre Vierling Carlos Gonzalez-Benecke Samuel de Padua Chaves Carvalho Luiz Carlos Estraviz Rodriguez Adrián Cardil 《Carbon balance and management》2017,12(1):13
Background
LiDAR remote sensing is a rapidly evolving technology for quantifying a variety of forest attributes, including aboveground carbon (AGC). Pulse density influences the acquisition cost of LiDAR, and grid cell size influences AGC prediction using plot-based methods; however, little work has evaluated the effects of LiDAR pulse density and cell size for predicting and mapping AGC in fast-growing Eucalyptus forest plantations. The aim of this study was to evaluate the effect of LiDAR pulse density and grid cell size on AGC prediction accuracy at plot and stand-levels using airborne LiDAR and field data. We used the Random Forest (RF) machine learning algorithm to model AGC using LiDAR-derived metrics from LiDAR collections of 5 and 10 pulses m?2 (RF5 and RF10) and grid cell sizes of 5, 10, 15 and 20 m.Results
The results show that LiDAR pulse density of 5 pulses m?2 provides metrics with similar prediction accuracy for AGC as when using a dataset with 10 pulses m?2 in these fast-growing plantations. Relative root mean square errors (RMSEs) for the RF5 and RF10 were 6.14 and 6.01%, respectively. Equivalence tests showed that the predicted AGC from the training and validation models were equivalent to the observed AGC measurements. The grid cell sizes for mapping ranging from 5 to 20 also did not significantly affect the prediction accuracy of AGC at stand level in this system.Conclusion
LiDAR measurements can be used to predict and map AGC across variable-age Eucalyptus plantations with adequate levels of precision and accuracy using 5 pulses m?2 and a grid cell size of 5 m. The promising results for AGC modeling in this study will allow for greater confidence in comparing AGC estimates with varying LiDAR sampling densities for Eucalyptus plantations and assist in decision making towards more cost effective and efficient forest inventory.3.
Xiuxiao Yuan Jianhong Fu Hongxing Sun Charles Toth 《ISPRS Journal of Photogrammetry and Remote Sensing》2009,64(6):541-550
In traditional GPS-supported aerotriangulation, differential GPS (DGPS) positioning technology is used to determine the 3-dimensional coordinates of the perspective centers at exposure time with an accuracy of centimeter to decimeter level. This method can significantly reduce the number of ground control points (GCPs). However, the establishment of GPS reference stations for DGPS positioning is not only labor-intensive and costly, but also increases the implementation difficulty of aerial photography. This paper proposes aerial triangulation supported with GPS precise point positioning (PPP) as a way to avoid the use of the GPS reference stations and simplify the work of aerial photography.Firstly, we present the algorithm for GPS PPP in aerial triangulation applications. Secondly, the error law of the coordinate of perspective centers determined using GPS PPP is analyzed. Thirdly, based on GPS PPP and aerial triangulation software self-developed by the authors, four sets of actual aerial images taken from surveying and mapping projects, different in both terrain and photographic scale, are given as experimental models. The four sets of actual data were taken over a flat region at a scale of 1:2500, a mountainous region at a scale of 1:3000, a high mountainous region at a scale of 1:32000 and an upland region at a scale of 1:60000 respectively. In these experiments, the GPS PPP results were compared with results obtained through DGPS positioning and traditional bundle block adjustment. In this way, the empirical positioning accuracy of GPS PPP in aerial triangulation can be estimated. Finally, the results of bundle block adjustment with airborne GPS controls from GPS PPP are analyzed in detail.The empirical results show that GPS PPP applied in aerial triangulation has a systematic error of half-meter level and a stochastic error within a few decimeters. However, if a suitable adjustment solution is adopted, the systematic error can be eliminated in GPS-supported bundle block adjustment. When four full GCPs are emplaced in the corners of the adjustment block, then the systematic error is compensated using a set of independent unknown parameters for each strip, the final result of the bundle block adjustment with airborne GPS controls from PPP is the same as that of bundle block adjustment with airborne GPS controls from DGPS. Although the accuracy of the former is a little lower than that of traditional bundle block adjustment with dense GCPs, it can still satisfy the accuracy requirement of photogrammetric point determination for topographic mapping at many scales. 相似文献
4.
Forest canopy cover (CC) and above-ground biomass (AGB) are important ecological indicators for forest monitoring and geoscience applications. This study aimed to estimate temperate forest CC and AGB by integrating airborne LiDAR data with wall-to-wall space-borne SPOT-6 data through geostatistical modeling. Our study involved the following approach: (1) reference maps of CC and AGB were derived from wall-to-wall LiDAR data and calibrated by field measurements; (2) twelve discrete LiDAR flights were simulated by assuming that LiDAR data were only available beneath these flights; (3) training/testing samples of CC and AGB were extracted from the reference maps inside and outside the simulated flights using stratified random sampling; (4) The simple linear regression, ordinary kriging and regression kriging model were used to extend the sparsely sampled CC/AGB data to the entire study area by incorporating a selection of SPOT-6 variables, including vegetation indices and texture variables. The regression kriging model was superior at estimating and mapping the spatial distribution of CC and AGB, as it featured the lowest mean absolute error (MAE; 11.295% and 18.929 t/ha for CC and AGB, respectively) and root mean squared error (RMSE; 17.361% and 21.351 t/ha for CC and AGB, respectively). The predicted and reference values of both CC and AGB were highly correlated for the entire study area based on the estimation histograms and error maps. Finally, we concluded that the regression kriging model was superior and more effective at estimating LiDAR-derived CC and AGB values using the spatially-reduced samples and the SPOT-6 variables. The presented modeling workflow will greatly facilitate future forest growth monitoring and carbon stock assessments for large areas of temperate forest in northeast China. It also provides guidance on how to take full advantage of future sparsely collected LiDAR data in cases where wall-to-wall LiDAR coverage is not available from the perspective of geostatistics. 相似文献
5.
Wetland biomass is essential for monitoring the stability and productivity of wetland ecosystems. Conventional field methods to measure or estimate wetland biomass are accurate and reliable, but expensive, time consuming and labor intensive. This research explored the potential for estimating wetland reed biomass using a combination of airborne discrete-return Light Detection and Ranging (LiDAR) and hyperspectral data. To derive the optimal predictor variables of reed biomass, a range of LiDAR and hyperspectral metrics at different spatial scales were regressed against the field-observed biomasses. The results showed that the LiDAR-derived H_p99 (99th percentile of the LiDAR height) and hyperspectral-calculated modified soil-adjusted vegetation index (MSAVI) were the best metrics for estimating reed biomass using the single regression model. Although the LiDAR data yielded a higher estimation accuracy compared to the hyperspectral data, the combination of LiDAR and hyperspectral data produced a more accurate prediction model for reed biomass (R2 = 0.648, RMSE = 167.546 g/m2, RMSEr = 20.71%) than LiDAR data alone. Thus, combining LiDAR data with hyperspectral data has a great potential for improving the accuracy of aboveground biomass estimation. 相似文献
6.
《International Journal of Digital Earth》2013,6(12):1007-1022
Remotely sensed Digital Elevation Models (DEM) can be used to augment a standalone Global Positioning System (GPS) by adding an extra range observation which measures the distance to the Earth centre. This method so called height aiding can reduce the number of GPS satellites required to get a 3D position fix from four to three and hence improve the performance of the GPS navigation algorithm in terms of accuracy, reliability and availability. Up until now, the accuracy of height aided GPS navigation using higher resolution Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR)-derived elevation data has not been fully evaluated in a broad spectrum of navigation scenarios. This article provides a robust and accurate analysis on how much range error is introduced by height aiding using 5 m spacing SAR and 1 m spacing LiDAR-derived DEMs under in-car and personal navigation situations. Based on the experimental results obtained from both dynamic and static tests, suggestions have been made on what level of vertical and positional accuracy can be achieved as well as the related DEM quality issues for navigation purposes. 相似文献
7.
The accurate estimation of leaf water content (LWC) and knowledge about its spatial variation are important for forest and agricultural management since LWC provides key information for evaluating plant physiology. Hyperspectral data have been widely used to estimate LWC. However, the canopy reflectance can be affected by canopy structure, thereby introducing error to the retrieval of LWC from hyperspectral data alone. Radiative transfer models (RTM) provide a robust approach to combine LiDAR and hyperspectral data in order to address the confounding effects caused by the variation of canopy structure. In this study, the INFORM model was adjusted to retrieve LWC from airborne hyperspectral and LiDAR data. Two structural parameters (i.e. stem density and crown diameter) in the input of the INFORM model that affect canopy reflectance most were replaced by canopy cover which could be directly obtained from LiDAR data. The LiDAR-derived canopy cover was used to constrain in the inversion procedure to alleviate the ill-posed problem. The models were validated against field measurements obtained from 26 forest plots and then used to map LWC in the southern part of the Bavarian Forest National Park in Germany. The results show that with the introduction of prior information of canopy cover obtained from LiDAR data, LWC could be retrieved with a good accuracy (R2 = 0.87, RMSE = 0.0022 g/cm2, nRMSE = 0.13). The adjustment of the INFORM model facilitated the introduction of prior information over a large extent, as the estimation of canopy cover can be achieved from airborne LiDAR data. 相似文献
8.
9.
论述GNSS多系统融合定位的数学模型、分析各项误差处理策略以及参数估计方法,基于日本东京海洋大学RTKLIB软件进行GPS、GLONASS、Galileo、BDS多系统融合定位试验,并分析其动/静态定位稳定性和精度。试验结果表明:GNSS多系统融合收敛时间与GPS单系统相比缩短30%~50%,定位精度与GPS单系统相比可以提高20%~50%。此外,在卫星高度截止角大于40°和不利观测环境条件下,单系统可见卫星数不足,从而导致无法进行连续定位,但多系统融合可视卫星可获得比较好的定位精度,在建筑物密集区、山区和卫星遮挡较为严重的恶劣条件下具有实际应用价值。 相似文献
10.
This paper suggested simulation approaches for quantifying and reducing the effects of National Forest Inventory (NFI) plot location error on aboveground forest biomass and carbon stock estimation using the k-Nearest Neighbor (kNN) algorithm. Additionally, the effects of plot location error in pre-GPS and GPS NFI plots were compared. Two South Korean cities, Sejong and Daejeon, were chosen to represent the study area, for which four Landsat TM images were collected together with two NFI datasets established in both the pre-GPS and GPS eras. The effects of plot location error were investigated in two ways: systematic error simulation, and random error simulation. Systematic error simulation was conducted to determine the effect of plot location error due to mis-registration. All of the NFI plots were successively moved against the satellite image in 360° directions, and the systematic error patterns were analyzed on the basis of the changes of the Root Mean Square Error (RMSE) of kNN estimation. In the random error simulation, the inherent random location errors in NFI plots were quantified by Monte Carlo simulation. After removal of both the estimated systematic and random location errors from the NFI plots, the RMSE% were reduced by 11.7% and 17.7% for the two pre-GPS-era datasets, and by 5.5% and 8.0% for the two GPS-era datasets. The experimental results showed that the pre-GPS NFI plots were more subject to plot location error than were the GPS NFI plots. This study’s findings demonstrate a potential remedy for reducing NFI plot location errors which may improve the accuracy of carbon stock estimation in a practical manner, particularly in the case of pre-GPS NFI data. 相似文献
11.
Light detection and ranging (LiDAR) data are increasingly used to measure structural characteristics of urban forests but are rarely used to detect the growing problem of exotic understory plant invaders. We explored the merits of using LiDAR-derived metrics alone and through integration with spectral data to detect the spatial distribution of the exotic understory plant Ligustrum sinense, a rapidly spreading invader in the urbanizing region of Charlotte, North Carolina, USA. We analyzed regional-scale L. sinense occurrence data collected over the course of three years with LiDAR-derived metrics of forest structure that were categorized into the following groups: overstory, understory, topography, and overall vegetation characteristics, and IKONOS spectral features – optical. Using random forest (RF) and logistic regression (LR) classifiers, we assessed the relative contributions of LiDAR and IKONOS derived variables to the detection of L. sinense. We compared the top performing models developed for a smaller, nested experimental extent using RF and LR classifiers, and used the best overall model to produce a predictive map of the spatial distribution of L. sinense across our country-wide study extent. RF classification of LiDAR-derived topography metrics produced the highest mapping accuracy estimates, outperforming IKONOS data by 17.5% and the integration of LiDAR and IKONOS data by 5.3%. The top performing model from the RF classifier produced the highest kappa of 64.8%, improving on the parsimonious LR model kappa by 31.1% with a moderate gain of 6.2% over the county extent model. Our results demonstrate the superiority of LiDAR-derived metrics over spectral data and fusion of LiDAR and spectral data for accurately mapping the spatial distribution of the forest understory invader L. sinense. 相似文献
12.
机载LiDAR数据估算样地和单木尺度森林地上生物量 总被引：2，自引：0，他引：2
利用机载激光雷达点云数据,结合大量实测单木结构信息,分别从样地和单木尺度估算了森林地上生物量AGB。首先,利用局部最大值单木提取算法提取了每个样地内的单木结构参数,并针对样地和单木尺度分别计算了一组激光雷达变量。然后,利用激光雷达变量和地上生物量及其两者的对数形式,从样地和单木尺度分别构建了估算模型。最后,针对两种尺度估算过程中存在的不确定性进行了详细讨论。结果表明:(1)样地和单木尺度模型估算的森林地上生物量与地面实测值都具有明显的相关性,且对数模型估算效果要优于非对数模型;(2)样地尺度模型估算效果(R2=0.84,rRMSE=0.23)明显优于单木尺度模型(R2=0.61,rRMSE=0.46);(3)按树木类型分别进行估算可以提高单木地上生物量的估算精度;(4)不论是样地还是单木尺度地上生物量估算都存在一定的不确定性,与样地尺度相比,单木尺度估算过程的不确定性更大,这种不确定性主要来自单木识别过程。 相似文献
13.
分别选取资源三号(ZY-3)卫星平地和山地区域的前后视影像,量测20个GPS点作为控制点和检查点,对卫星传感器校正产品定位精度进行验证。通过区域网平差算法对传感器校正产品(SC)自带的有理函数模型(RPC)进行优化,消除系统误差。实验结果表明:在地形平坦地区ZY-3卫星SC产品的平面定位精度可达3.275 m,高程定位精度可达1.686 m;在地形起伏较大的山区平面定位精度可达4.335 m,高程定位精度可达3.628 m,满足1∶50 000地形图测绘的要求。 相似文献
14.
机载激光雷达平均树高提取研究 总被引：16，自引：3，他引：13
为了研究机载激光雷达(LiDAR)树高提取技术,以山东省泰安市徂徕山林场为实验区,于2005年5月进行了机载LiDAR数据获取和外业测量.通过对LiDAR点云数据的分类处理,分别得到了试验区的地面点云子集、植被点云子集和高程归一化的植被点云子集.基于高程归一化的植被点云子集计算了上四分位数处的高度,与实地测量的数据进行了比较,并结合中国森林调查规程进行了实用性分析.结果表明:对于较低密度的点云数据,使用分位数法可以较好地进行林分平均高的估计;机载激光雷达技术对树高估计是可行的,精度都高于87%,总体平均精度为90.59%,其中阔叶树的精度高于针叶树.该试验精度可以满足中国二类森林调查规程中平均树高因子的一般商品林和生态公益林的精度要求,对国有商品林小班的调查精度要求(5%)存在一点差距,需要在国有商品林区进一步开展验证工作.对本试验区而言,已经可以满足其作为森林公园生态公益林的调查要求. 相似文献
15.
This study investigated the combined use of multispectral/hyperspectral imagery and LiDAR data for habitat mapping across parts of south Cumbria, North West England. The methodology adopted in this study integrated spectral information contained in pansharp QuickBird multispectral/AISA Eagle hyperspectral imagery and LiDAR-derived measures with object-based machine learning classifiers and ensemble analysis techniques. Using the LiDAR point cloud data, elevation models (such as the Digital Surface Model and Digital Terrain Model raster) and intensity features were extracted directly. The LiDAR-derived measures exploited in this study included Canopy Height Model, intensity and topographic information (i.e. mean, maximum and standard deviation). These three LiDAR measures were combined with spectral information contained in the pansharp QuickBird and Eagle MNF transformed imagery for image classification experiments. A fusion of pansharp QuickBird multispectral and Eagle MNF hyperspectral imagery with all LiDAR-derived measures generated the best classification accuracies, 89.8 and 92.6% respectively. These results were generated with the Support Vector Machine and Random Forest machine learning algorithms respectively. The ensemble analysis of all three learning machine classifiers for the pansharp QuickBird and Eagle MNF fused data outputs did not significantly increase the overall classification accuracy. Results of the study demonstrate the potential of combining either very high spatial resolution multispectral or hyperspectral imagery with LiDAR data for habitat mapping. 相似文献
16.
An improved tree crown delineation method based on live crown ratios from airborne LiDAR data 总被引：2，自引：0，他引：2
Individual tree crowns are one of the basic forest inventory data, which can be used in various forest-related studies such as biomass and carbon stock estimation. High-resolution remote-sensing data including airborne LiDAR-derived surfaces have been widely used for delineating tree crowns. This study proposes an improved tree crown delineation algorithm that can be effectively applied to a range of forests with a limited number of parameters considering its operational use with airborne LiDAR data. The proposed algorithm integrates morphological operators, Otsu’s method, marker-controlled watershed segmentation, and the concept of crown ratios. The proposed algorithm was compared with the region growing method, a widely used tree crown delineation algorithm. The two algorithms were evaluated over 10 plots in rugged terrain located in Kangwon Province in South Korea. Results show that the proposed approach produced much better performance (~87% matched on average) for 10 plots with a range of tree densities than the region growing method (~60% matched on average). The proposed algorithm worked better for sparse plots than dense ones. It also worked well for deciduous plots (plots 1 and 4). On the other hand, the region growing method produced relatively low accuracy with many merged crowns, which requires additional postprocessing such as a resplit step. 相似文献
17.
The prediction of tropical forest attributes using airborne laser scanning (ALS) is becoming attractive as an alternative to traditional field measurements. Area-based ALS inventories require a set of representative field plots from the study area, which may be difficult to obtain in tropical forests with limited accessibility. This study investigates the effect of sample-plot selection in Nepal, based on two accessibility factors: distance to road and degree of slope. The sparse Bayesian method was employed in the model to estimate above-ground biomass (AGB) with an independent validation dataset for model validation. Study findings showed that the sample plot distance and slope had a considerable effect on the accuracy of the AGB estimation, because the forest structure varied according to the level of accessibility. Thus, the field sample plots that are used in model construction should cover the full range of sample plot distances and slopes occurring within the area. 相似文献
18.
Hyperspectral image and full-waveform light detection and ranging (LiDAR) data provide useful spectral and geometric information for classifying land cover. Hyperspectral images contain a large number of bands, thus providing land-cover discrimination. Waveform LiDAR systems record the entire time-varying intensity of a return signal and supply detailed information on geometric distribution of land cover. This study developed an efficient multi-sensor data fusion approach that integrates hyperspectral data and full-waveform LiDAR information on the basis of minimum noise fraction and principal component analysis. Then, support vector machine was used to classify land cover in mountainous areas. Results showed that using multi-sensor fused data achieved better accuracy than using a hyperspectral image alone, with overall accuracy increasing from 83% to 91% using population error matrices, for the test site. The classification accuracies of forest and tea farms exhibited significant improvement when fused data were used. For example, classification results were more complete and compact in tea farms based on fused data. Fused data considered spectral and geometric land-cover information, and increased the discriminability of vegetation classes that provided similar spectral signatures. 相似文献
19.
亚热带森林参数的机载激光雷达估测 总被引：5，自引：2，他引：3
通过应用机载激光雷达数据,在分析云南省中部的78块样地的基础上提出2个预测森林不同生物特性的统计模型(加权平均高度的预测模型和生物量的预测模型),并讨论了预测结果及其精确性。从激光雷达数据中提取了2组变量(树冠高度变量组和植被密度变量组)作为自变量,采用逐步回归方法进行自变量选择。结果表明,激光雷达数据与森林的平均树高和地上各部分生物量有很强的相关性。对于3种不同森林类型(针叶林,阔叶林和混交林),平均树高估测均能达到比较高的精度;生物量的估测结果是针叶林优于阔叶林,混交林的生物量与激光雷达数据则没有明显相关性。最后,对回归分析的结果和影响预测精度的因素进行讨论,认为预测结果的精度可能与森林类型、激光雷达采样时间和采样密度以及坐标误差等因素有关。 相似文献
20.
M. R. Mosavi 《GPS Solutions》2006,10(2):97-107
Position information obtained from standard global positioning system (GPS) receivers has time variant errors. For effective use of GPS information in a navigation system, it is essential to model these errors. A new approach is presented for improving positioning accuracy using neural network (NN), fuzzy neural network (FNN), and Kalman filter (KF). These methods predict the position components’ errors that are used as differential GPS (DGPS) corrections in real-time positioning. Method validity is verified with experimental data from an actual data collection, before and after selective availability (SA) error. The result is a highly effective estimation technique for accurate positioning, so that positioning accuracy is drastically improved to less than 0.40 m, independent of SA error. The experimental test results with real data emphasize that the total performance of NN is better than FNN and KF considering the trade-off between accuracy and speed for DGPS corrections prediction. 相似文献