Rick E. Landenberger

Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density lidar data from the eastern deciduous forest in North America

Leaf-off individual trees in a deciduous forest in the eastern USA are detected and analysed in small footprint, high sampling density lidar data. The data were acquired February 1, 2001, using a SAAB TopEye laser profiling system, with a sampling density of approximately 12 returns per square meter. The sparse and complex configuration of the branches of the leaf-off forest provides sufficient returns to allow the detection of the trees as individual objects and to analyse their vertical structures. Initially, for the detection of the individual trees only, the lidar data are first inserted in a 2D digital image, with the height as the pixel value or brightness level. The empty pixels are interpolated, and height outliers are removed. Gaussian smoothing at different scales is performed to create a three-dimensional scale-space structure. Blob signatures based on second-order image derivatives are calculated, and then normalised so they can be compared at different scale-levels. The grey-level blobs with the strongest normalised signatures are selected within the scale-space structure. The support regions of the blobs are marked one-at-a-time in the segmentation result image with higher priority for stronger blobs. The segmentation results of six individual hectare plots are assessed by a computerised, objective method that makes use of a ground reference data set of the individual tree crowns. For analysis of individual trees, a subset of the original laser returns is selected within each tree crown region of the canopy reference map. Indices based on moments of the first four orders, maximum value and number of canopy and ground returns, are estimated. The indices are derived separately for height and laser reflectance of branches for the two echoes. Significant differences (p<0.05) are detected for numerous indices for three major native species groups: oaks (Quercus spp.), red maple (Acer rubrum) and yellow poplar (Liriodendron tuliperifera). Tree species classification results of different indices suggest a moderate to high degree of accuracy using single or multiple variables. Furthermore, the maximum tree height is compared to ground reference tree height for 48 sample trees and a 1.1-m standard error (R 2 =68%

Using Remote Sensing and GIS to Teach Inquiry‐Based Spatial Thinking Skills: An Example Using the GLOBE Program's Integrated Earth Systems Science

Abstract Earth systems science and technology are essential elements of a contemporary education. Remote sensing and GIS provide a valuable spatial framework for scientific inquiry, and are highly effective as a means to integrate Earth system science components. Drawing on the GLOBE Programs K‐12 Earth science curriculum, we show how remote sensing and GIS can be used to integrate GLOBEs Land Cover‐Biology and Hydrology investigations, emphasizing the Earth as a system. Our approach uses spatial thinking, geospatial technology, and the concept of watersheds to help students develop an understanding of the basic relationships between land cover and surface hydrology, two critically important and easily observed components of the Earths surface. By starting with very simple exercises and analyses, students build upon a conceptual framework for understanding how land use pattern can influence the quantity and quality of one of the most critically important Earth resources, local fresh water. Ultimately, by understanding how terrestrial and aquatic systems interact, students begin to understand the link between science and land use policy.

Image restoration based on multiscale relationships of image structures

Aerial photographs sometimes suffer from artifacts caused by vignetting effects and changing topographic Sun-canopy-sensor geometry. We present an empirical image restoration method that is based on multiscale relationships of image structures. The fine-scale image structures depict tree crowns in a deciduous forest and serve as units in the restoration process. The color image is initially converted to the intensity, hue, saturation (IHS) system. For the I-band, two different types of variables are estimated for each segment: the local intensity difference of neighboring segments (affinity) and the mean intensity per segment. For the H- and S-bands, the mean value per segment is estimated. Regression analysis is used to model the relationship of these four variables with the coarse-scale intensity values of the corresponding segments. The correction results in new feature values that are uncorrelated with the coarse-scale intensity values. The method is evaluated on three digital aerial photographs with a ground reference dataset from the Eastern Deciduous Forest in West Virginia, USA. The image correction method is shown to result in a significant improvement for tree species classification.

Potential of digital color imagery for censusing Haleakala silverswords in Hawaii

Spatially explicit, high spatial resolution remotely sensed imagery offers a largely untapped potential for censusing and monitoring rare plant populations that exist in remote, exposed environments. Using digital color imagery acquired over the Haleakala Crater on Maui, Hawai’i, we evaluated the accuracy of photointerpretation and automated censuses by imaging nine silversword census plots characterized by individuals of known size, life cycle status, and location. Due to spatial resolution limitations, both methods tended to omit small individuals, but omissions varied by size class and type of omission. Omission rates were low for demographically important medium and large plants; however, the automated method often failed to segment and census tightly clustered plants. The photointerpreter commission error rate was lower than that of the automated method, and both methods tended to overestimate mean silversword size. These data outline the issues and challenges that will likely emerge as spatially explicit, high spatial resolution aerial censuses become more common.