Ben Gorte

Three-dimensional reconstruction of stems for assessment of taper, sweep and lean based on laser scanning of standing trees

A method and algorithm for reconstructing the three-dimensional (3D) surface of stems based on terrestrial laser scanner data from standing trees is presented. Laser scanning delivers a dense cloud of points, and this raw point data are filtered for deriving a digital terrain model and subsequent fitting of a parametric stem model. The stem model is made up of a sequence of successive cylinders that overlap in space; each cylinder is parameterized by its orientation and radius. The model is estimated iteratively from a given starting point and by adding cylinder segments. Successive segments are added whenever criteria on deviation in orientation and radius relative to the previous cylinder and a fit statistic to the point data are met. The method has proven applicable when applied to a European beech tree and a wild cherry tree from dense forest stands. The use of the resulting 3D reconstruction of tree stems in respect to diameter in breast height and height of crown base calculation, as well as taper, sweep and lean assessment of standing trees, is described. Finally, desirable future improvements to the basic algorithm are discussed.

Spatial statistics for remote sensing

Preface. Contributors and editors. Introduction. I. 1. Description of the data B. Gorte. 2. Some basic elements of statistics A. Stein. 3. Physical principles of optical R.S. F. van der Meer. 4. Remote Sensing and GIS S. de Bruin, M. Molenaar. II. 5. Spatial Statistics P.M. Atkinson. 6. Spatial prediction by linear kriging A. Papritz, A. Stein. 7. Issues of scale and optimal pixel size P.J. Curran, P. M. Atkinson. 8. Conditional Simulation J.L. Dungan. 9. Supervised image classification B. Gorte. 10. Unsupervised class detection C.H.M. van Kemenade, et al. 11. Spectral unmixing F. van der Meer. III. 12. Accuracy assessment A.K. Skidmore. 13. Spatial sampling schemes J. de Gruijter. 14. Decision support systems A. Sharifi. Bibliography.

A method for object-oriented land cover classification combining Landsat TM data and aerial photographs

Object-oriented classification techniques based on image segmentation are gaining interest as methods for producing output maps directly storable into Geophysical Information System (GIS) databases. A limitation in efficiently applying image segmentation is often represented by the spatial resolution of the image. This contribution proposes a method for overcoming this problem, based on the integrated use of images of different resolution. A high-resolution black and white (b/w) orthophoto and a subscene of a Landsat Thematic Mapper (TM) image have been used to obtain an object-oriented classification of the land cover of a study area in northern Italy. The method consists of a sequential application of segmentation and classification techniques. First, the TM image was classified using the maximum likelihood classifier and additional empirical rules. Subsequently, the orthophoto was segmented by applying a region-based segmentation algorithm. Finally, the classification of the segmented images was performed using as a reference the TM image previously classified. The resulting land cover map was tested for accuracy and the results are dicusssed.

Interdriver Differences in Car-Following: A Vehicle Trajectory–Based Study

This paper examines the car-following behavior of individual drivers in real traffic on the basis of vehicle trajectory data extracted from high-resolution digital images collected at a high frequency from a helicopter. These data are used to cross-compare seven car-following models regarding their average performances as well as their specific performances for all individual drivers observed using a simulation approach. The prime objective of this cross-comparison is to study interdriver differences; both optimal parameter settings and model performances are compared between drivers. Average model performances reveal that the simplest models are generally not able to capture the dynamics of car-following behavior correctly, whereas individual estimates show that the performances of more elaborate models differ between drivers. The most important contribution of this paper is that analysis shows that interdriver differences cannot be caught by different parameter settings alone; driving styles of individual drivers appear to be inherently different in that various car-following models are needed to model them satisfactorily.

Microscopic Traffic Data Collection by Remote Sensing

To gain insight into the behavior of drivers during congestion, and to develop and test theories and models that describe congested driving behavior, very detailed data are needed. A new data-collection system prototype is described for determining individual vehicle trajectories from sequences of digital aerial images. Software was developed to detect and track vehicles from image sequences. In addition to longitudinal and lateral position as a function of time, the system can determine vehicle length and width. Before vehicle detection and tracking can be achieved, the software handles correction for lens distortion, radiometric correction, and orthorectification of the image. The software was tested on data collected from a helicopter by a digital camera that gathered high-resolution monochrome images, covering 280 m of a Dutch motorway. From the test, it was concluded that the techniques for analyzing the digital images can be applied automatically without much problem. However, given the limited stability of the helicopter, only 210 m of the motorway could be used for vehicle detection and tracking. The resolution of the data collection was 22 cm. Weather conditions appear to have a significant influence on the reliability of the data: 98% of the vehicles could be detected and tracked automatically when conditions were good; this number dropped to 90% when the weather conditions worsened. Equipment for stabilizing the camera—gyroscopic mounting—and the use of color images can be applied to further improve the system.

Integrating spatial statistics and remote sensing

This paper presents an integrated approach towards spatial statistics for remote sensing. Using the layer concept in Geographical Information Systems we treat successively elements of spatial statistics, scale, classification, sampling and decision support. The layer concept allows to combine continuous spatial properties with classified map units. The paper is illustrated with five case studies: one on heavy metals in groundwater at different scales, one on soil variability within seemingly homogeneous units, one on fuzzy classification for a soillandscape model, one on classification with geostatistical procedures and one on thermal images. The integrated approach offers a better understanding and quantification of uncertainties in remote sensing studies.

Bayesian classification and class area estimation of satellite images using stratification

The paper describes an iterative extension to maximum a posteriori (MAP) supervised classification methods. A posteriori probabilities per class are used for classification as well as to obtain class area estimates. From these, an updated set of prior probabilities is calculated and used in the next iteration. The process converges to statistically correct area estimates. The iterative process can be combined effectively with a stratification of the image, which is made on the basis of additional map data. Moreover, it relies on the sample sets being representative. Therefore, the method is shown to be well applicable in combination with an existing GIS. The paper gives a description of the procedure and provides a mathematical foundation. An example is presented to distinguish residential, industrial, and greenhouse classes. A significant improvement of the classification was obtained.

Visual exploration of uncertainty in remote-sensing classification

Abstract Exploratory analysis of remotely-sensed data aims at acquiring insight as to the stability of possible classifications of these data and their information value for specific applications. For this purpose, knowledge of the uncertainties underlying these classifications is imperative. In this paper, we introduce various measures that summarise for a classification, in a single number per pixel, the distribution and extent of the uncertainties involved. Since exploratory analysis needs effective ways of conveying information to the user, we in addition address various ways of cartographic visualisation of uncertainty.

Probabilistic image classification using geological map units applied to land-cover change detection

This paper describes how probabilistic methods provide a means to integrate analysis of remotely sensed imagery and geo-information processing. In a case study from southern Spain, geological map units were used to improve land-cover classification from Landsat TM imagery. Overall classification accurracy improved from 76% to 90% (1984) and from 64% to 69% (1995) when using stratification according to geology combined with iterative estimation of prior probabilities. Differences between the two years were mainly due to extremely dry conditions during the 1995 growing season. Per-pixel probabilities of class successions and entropy values calculated from the classifications posterior probability vectors served to quantify uncertainty in a post-classification comparison. It is concluded that iterative estimation of prior probabilities provides a practical approach to improve classification accuracy. Posterior probabilities of class membership provide useful information about the magnitude and spatial distribution of classification uncertainty.

Skeletonization of Laser-Scanned Trees in the 3D Raster Domain

The paper introduces storage and processing of 3-dimensional point clouds, obtained by terrestrial laser scanning, in the 3D raster domain. The objects under consideration are trees in production orchards. The purpose is to automatically identify the structure of such trees in terms of the number of branches, their lengths and their thicknesses. An important step in the process is skeletonization. On the basis of a previously developed methodology, a new skeletonization algorithm is developed, which delivers improved results.