The effect of crown shape on the reflectance of coniferous stands

The Kuusk-Nilson forest reflectance model was used to study the effect of crown shape on the reflectance of Scots pine and Norway spruce stands. In the first part of the study, we examined spruce and pine stands with an age range of 20-100 years and compared their simulated hemispherical-directional reflectance factors (HDRFs) at nadir in red (661 nm), NIR (838 nm) and MIR (1677 nm) when crowns were modeled as ellipsoids or cones. In all the cases, when a stand was modeled with conical crowns, it had a smaller reflectance factor than the same stand with ellipsoidal crowns.To analyze the sensitivity of HDRF on crown shape, in the second part of the study we simulated the angular distributions of HDRF of two pine stands with different leaf area index (LAI) and canopy closure values at 661 nm assuming four different crown shapes (cone, cylinder, ellipsoid, and cylinder bottom, cone top) and separated the components forming the HDRF. Considerable difference in the HDRF between the four crown shapes was observed: The larger the crown volume, the higher the canopy reflectance at similar LAI and canopy closure. A comparison of the two stands revealed that in denser stands (with a higher canopy closure) single scattering from tree crowns was responsible for the difference in HDRF between the different crown shapes, whereas in stands with a smaller canopy closure the single scattering from ground dominated the HDRF. Finally, the role of crown shape for the retrieval of LAI by inversion from remotely sensed data is discussed.     

Comparison of three individual tree crown detection methods

Three image processing methods for single tree crown detection in high spatial resolution aerial images are presented and compared using the same image material and reference data. The first method uses templates to find the tree crowns. The other two methods uses region growing. One of them is supported by fuzzy rules while the other uses an image produced by Brownian motion. All three methods detect around 80%, or more, of the visible sunlit trees in two pine Pinus Sylvestris L.) and two spruce stands Picea abies Karst.) in a boreal forest. For all methods, large tree crowns are easier to detect than small ones.     

Radar modelling of coniferous forest using a tree growth model

The use of a tree growth model to provide statistical information about the microwave scattering components of boreal-type forests (in this case, Scots pine and Norwegian spruce), as an alternative to data obtained through intensive fieldwork, is described. The total backscatter from six test stands at C- and L-band frequency for three polarization combinations (HH, VV and HV) was predicted. Differences between measured C- and L-band data from a polarimetric airborne Synthetic Aperture Radar (EMISAR) and simulated backscatter values compare favourably with previous studies, with like- and cross-polarization differences generally less than 2.5 dB. Modelled backscatter values were consistently less than those observed. A likely explanation for such a discrepancy is the unrealistic manner in which the model incorporates the spatial distribution of tree needles.     

Airborne SAR monitoring of tree growth in a coniferous plantation

This paper examines three empirically based methods of monitoring forest growth between 1991 and 2000 from airborne synthetic aperture radar (SAR). In the first method, height change and volume change between 1991 and 2000 were estimated from the mean L‐band stand backscatter difference between the two dates. Height change and volume change over the 9‐year period were estimated with an accuracy of 0.23 m and 15 m³ ha^?1, respectively, when the stands were below saturation point for the first date. The accuracy of the results was lower for stands beyond saturation in both data sets. In the second method, the height change is calculated from the estimated stand height in 2000 minus the estimated stand height in 1991. The second method produced poorer results than the first method, but better results than predicted by the error propagation equation. The difference between the observed accuracy and the expected error (based on the error propagation equation) appears to be due to a systematic bias in both the 1991 and 2000 estimates, as the residuals are correlated for stands below 20 years old (r = 0.71 for stand volume residuals). The third experiment investigates the utility of data from two dates to classify the stands into three age classes. The results show that, with two images separated by 9 years, 85% of stands were correctly classified compared with 69% for a single date L‐HV image.     

Simulation of L-band and HH microwave backscattering from coniferous forest stands A comparison with SIR-B data

Abstract

Shuttle Imaging Radar-B (SIR-B) images of coniferous forest stands dominated by Ponderosa pine in the Mt. Shasta region of northern California were used to evaluate a composite L-band HH backscattering model of coniferous forest stands. Eight forest stands were employed to describe the relative trend and distribution of backscattering coefficients. It was found that (1) both SIR-B and simulated backscattering coefficients for the eight stands have similar trends and relations to average tree height and average number of trees per pixel and (2) the dispersion and distribution of simulated backscattering coefficients from each stand broadly matched SIR-B data from the same stand. Although it is difficult to draw any strong conclusions from the comparisons because the experimental data arc limited in both quantity and quality and are also undersampled, the comparisons indicate that a stand-based L-band HH composite model seems promising for explaining backscattering features. The means of the backscattering coefficients are determined by the average tree height and average number of trees per pixel in the stands. The distributions of the backscattering coefficients are modelled through random assignment of tree numbers, heights and spatial distribution within a pixel.     

Distributed Simulation and Middleware for Networked UAS

As a result of the advances in solid state, electronics, sensor, wireless communication technologies, as well as evolutions in the material science and manufacturing technologies, unmanned aerial vehicles (UAVs) and unmanned aerial systems (UAS) have become more accessible by civilian entities, industry, as well as academia. There is a high level of market driven standardisation in the electronics and mechanical components used on UAVs. However, the implemented software of a UAS does not exhibit the same level of standardisation and compartmentalisation. This is a major bottleneck limiting software maintenance, and software reuse across multiple UAS programs. This paper addresses the software development processes adapted by the Australian Centre for Field Robotics (ACFR) in major UAS projects. The presented process model promotes software reuse without sacrificing the reliability and safety of the networked UAS with particular emphasis on the role of distributed simulation and middleware in the development and maintenance processes.     

A Survey of Airworthiness and Certification for UAS

The Italian SMAT project is a civilian program supported by Piemonte Region developed for the territorial monitoring and surveillance and for the prevention of natural hazards. In a different way from the previous surveillance programs, the SMAT project is based on the use of several unmanned platforms that are able to cooperate through ground coordination and control station. The flight in non-segregated airspace, needed in order to accomplish the territorial monitoring mission, is a main issue to be investigated and defined, because of the current lack of a common consensus and agreement on the regulations of UAS. The present work outlines the international initiatives that deal with the development of the regulatory framework on airworthiness and certification of UAS and their potential impact on the SMAT project.     

Classification of coniferous tree species and age classes using hyperspectral data and geostatistical methods

Classifications of coniferous forest stands regarding tree species and age classes were performed using hyperspectral remote sensing data (HyMap) of a forest in western Germany. Spectral angle mapper (SAM) and maximum likelihood (ML) classifications were used to classify the images. Classification was performed using (i) spectral information alone, (ii) spectral information and stem density, (iii) spectral and textural information, (iv) all data together, and results were compared. Geostatistical and grey level co‐occurrence matrix based texture channels were derived from the HyMap data. Variograms, cross variograms, pseudo‐cross variograms, madograms, and pseudo‐cross madograms were tested as geostatistical texture measures. Pseudo‐cross madograms, a newly introduced geostatistical texture measure, performed best. The classification accuracy (kappa) using hyperspectral data alone was 0.66. Application of pseudo‐cross madograms increased it to 0.74, a result comparable to that obtained with stem density information derived from high spatial resolution imagery.     

Neural tree density estimation for novelty detection

In this paper, a neural competitive learning tree is introduced as a computationally attractive scheme for adaptive density estimation and novelty detection. The learning rule yields equiprobable quantization of the input space and provides an adaptive focusing mechanism capable of tracking time-varying distributions. It is shown by simulation that the neural tree performs reasonably well while being much faster than any of the other competitive learning algorithms.     

Comparison of six individual tree crown detection algorithms evaluated under varying forest conditions

In this article, six individual tree crown (ITC) detection/delineation algorithms are evaluated, using an image data set containing six diverse forest types at different geographical locations in three European countries. The algorithms use fundamentally different techniques, including local maxima detection, valley following (VF), region-growing (RG), template matching (TM), scale-space (SS) theory and techniques based on stochastic frameworks. The structurally complexity of the forests in the aerial images used ranges from a homogeneous plantation and an area with isolated tree crowns to an extremely dense deciduous forest type. None of the algorithms alone could successfully analyse all different cases. The study shows that it is important to partition the imagery into homogeneous forest stands prior to the application of individual tree detection algorithms. It furthermore suggests a need for a common, publicly available suite of test images and common test procedures for evaluation of individual tree detection/delineation algorithms. Finally, it highlights that, for complex forest types, monoscopic images are insufficient for consistent tree crown detection, even by human interpreters.     

LIDAR-based geometric reconstruction of boreal type forest stands at single tree level for forest and wildland fire management

Vegetation structure is an important parameter in fire risk assessment and fire behavior modeling. We present a new approach deriving the structure of the upper canopy by segmenting single trees from small footprint LIDAR data and deducing their geometric properties. The accuracy of the LIDAR data is evaluated using six geometric reference targets, with the standard deviation of the LIDAR returns on the targets being as low as 0.06 m. The segmentation is carried out by using cluster analysis on the LIDAR raw data in all three coordinate dimensions. From the segmented clusters, tree position, tree height, and crown diameter are derived and compared with field measurements. A robust linear regression of 917 tree height measurements yields a slope of 0.96 with an offset of 1 m and the adjusted R² resulting at 0.92. However, crown diameter is not well matched by the field measurements, with R² being as low as 0.2, which is most certainly due to random errors in the field measurements. Finally, a geometric reconstruction of the forest scene using a paraboloid model is carried out using values of tree position, tree height, crown diameter, and crown base height.     

A cost-sensitive decision tree approach for fraud detection

With the developments in the information technology, fraud is spreading all over the world, resulting in huge financial losses. Though fraud prevention mechanisms such as CHIP&PIN are developed for credit card systems, these mechanisms do not prevent the most common fraud types such as fraudulent credit card usages over virtual POS (Point Of Sale) terminals or mail orders so called online credit card fraud. As a result, fraud detection becomes the essential tool and probably the best way to stop such fraud types. In this study, a new cost-sensitive decision tree approach which minimizes the sum of misclassification costs while selecting the splitting attribute at each non-terminal node is developed and the performance of this approach is compared with the well-known traditional classification models on a real world credit card data set. In this approach, misclassification costs are taken as varying. The results show that this cost-sensitive decision tree algorithm outperforms the existing well-known methods on the given problem set with respect to the well-known performance metrics such as accuracy and true positive rate, but also a newly defined cost-sensitive metric specific to credit card fraud detection domain. Accordingly, financial losses due to fraudulent transactions can be decreased more by the implementation of this approach in fraud detection systems.     

一种粗糙集-决策树结合的入侵检测方法

针对入侵检测系统收集数据海量、高维、检测模型复杂和检测准确率低等问题,采用粗糙集属性约简的优势寻找与判断入侵与否相关的属性,利用决策树分类算法生成模型并对网络连接进行入侵预测分类检测,从而提出了一种粗糙集属性约简和决策树预测分类相结合的网络入侵检测方法.实验结果表明,该方法在入侵检测准确率上有很大的提高,对DoS攻击、Probe攻击和R2L攻击的检测效果均有所提高,同时大大降低了检测的误报率.     

Fusion of full-waveform lidar and imaging spectroscopy remote sensing data for the characterization of forest stands

Full-waveform small-footprint laser scanning and airborne hyperspectral image data of a forest area in Germany were fused to get a detailed characterization of forest reflective properties and structure. Combining active laser scanning data with passive hyperspectral data increases the information content without adding much redundancy.

The small-footprint light detection and ranging (lidar) waveforms on the area of each 5 m × 5 m HyMap pixel were combined into quasi-large-footprint waveforms of 0.5 m vertical resolution by calculating the mean laser intensity in each voxel. As exemplary applications for this data set, we present the estimation of crown base heights and the ease of displaying vertical and horizontal slices through the three-dimensional data set.

As a consequence of the identical geometry of the voxel bases and the hyperspectral image, they could be joined as a multi-band image. The combined spectra are well suited for interpretations of pixel content. In a test classification of tree species and age classes, the joint image performed better than the hyperspectral image alone and also better than the hyperspectral image combined with lidar percentile images.     

Model-Based Design, Development and Validation for UAS Critical Software

This paper focuses on the major phases present in the development of critical software for UAS: design, development, testing and validation with flying experiments. A model-based approach is the backbone of all these development phases. The presented approach enables the researchers or engineers to work over the whole development cycle with the same tools, and produces an improvement over the classical design cycle. The model-based approach is illustrated with the development of guidance, navigation and control algorithms applied to rotary-wing UAVs. The paper discusses several implementation issues, including the integration of a hardware in the loop (HWIL) test environment within the model-based development cycle. Finally, several simulations and experimental results for this application are shown.     

无人机遥感平台技术研究与应用

作为一种新型的遥感平台,UAS(无人飞行器系统)具有优于其它遥感平台的灵活性、实时性、移动性等特点,本文介绍了UAS的发展现状及其关键技术,提出了UAS遥感平台的分类方法,从技术、市场、法律制度、成本等多方面考察了UAS应用于民用遥感领域的可行性。理论和实践证明,UAS平台在遥感技术的发展中必将发挥重要的作用,成为主流的遥感平台之一。     

Annual changes in MODIS vegetation indices of Swedish coniferous forests in relation to snow dynamics and tree phenology

Remote sensing provides spatially and temporally continuous measures of forest reflectance, and vegetation indices calculated from satellite data can be useful for monitoring climate change impacts on forest tree phenology. Monitoring of evergreen coniferous forest is more difficult than monitoring of deciduous forest, as the new buds only account for a small proportion of the green biomass, and the shoot elongation process is relatively slow. In this study, we have analyzed data from 186 coniferous monitoring sites in Sweden covering boreal, southern-boreal, and boreo-nemoral conditions. Our objective was to examine the possibility to track seasonal changes in coniferous forests by time-series of MODIS eight-day vegetation indices, testing the coherence between satellite monitored vegetation indices (VI) and temperature dependent phenology. The relationships between two vegetation indices (NDVI and WDRVI) and four phenological indicators (length of snow season, modeled onset of vegetation period, tree cold hardiness level and timing of budburst) were analyzed.The annual curves produced by two curve fitting methods for smoothening of seasonal changes in NDVI and WDRVI were to a large extent characterized by the occurrence of snow, producing stable seasonal oscillations in the northern part and irregular curves with less pronounced annual amplitude in the southern part of the country. Measures based on threshold values of the VI-curves, commonly used for determining the timing of different phenological phases, were not applicable for Swedish coniferous forests. Evergreen vegetation does not have a sharp increase in greenness during spring, and the melting of snow can influence the vegetation indices at the timing of budburst in boreal forests. However, the interannual variation in VI-values for specific eight-day periods was correlated with the phenological indicators. This relation can be used for satellite monitoring of potential climate change impacts on northern coniferous spring phenology.     

Performance characterization of the tree quorum algorithm

The tree quorum algorithm, which logically organizes the sites in a system to a tree structure, is an efficient and fault-tolerant solution for distributed mutual exclusion. In this paper, the performance characteristics of the tree quorum algorithm is analyzed. A refinement algorithm is proposed to refine a logical tree structure by eliminating nodes or subtrees which do not improve the performance. Thus the refined tree performs better than the original