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.

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.

Topography affects the fraction of direct and diffuse radiation received on a pixel and changes the sun–target–sensor geometry, resulting in variations in the observed radiance. Retrieval of surface–atmosphere properties from top of atmosphere radiance may need to account for topographic effects. This study investigates how such effects can be taken into account for top of atmosphere radiance modeling. In this paper, a system for top of atmosphere radiance modeling over heterogeneous non-Lambertian rugged terrain through radiative transfer modeling is presented. The paper proposes an extension of “the four-stream radiative transfer theory” (Verhoef and Bach 2003, 2007 and 2012) mainly aimed at representing topography-induced contributions to the top of atmosphere radiance modeling. A detailed account for BRDF effects, adjacency effects and topography effects on the radiance modeling is given, in which sky-view factor and non-Lambertian reflected radiance from adjacent slopes are modeled precisely. The paper also provides a new formulation to derive the atmospheric coefficients from MODTRAN with only two model runs, to make it more computationally efficient and also avoiding the use of zero surface albedo as used in the four-stream radiative transfer theory. The modeling begins with four surface reflectance factors calculated by the Soil–Leaf–Canopy radiative transfer model SLC at the top of canopy and propagates them through the effects of the atmosphere, which is explained by six atmospheric coefficients, derived from MODTRAN radiative transfer code. The top of the atmosphere radiance is then convolved with the sensor characteristics to generate sensor-like radiance. Using a composite dataset, it has been shown that neglecting sky view factor and/or terrain reflected radiance can cause uncertainty in the forward TOA radiance modeling up to 5 (mW/m²·sr·nm). It has also been shown that this level of uncertainty can be translated into an over/underestimation of more than 0.5 in LAI (or 0.07 in fCover) in variable retrieval.

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.