A Comparison of Foliage Profiles in the Sierra National Forest Obtained with a Full-Waveform Under-Canopy EVI Lidar System with the Foliage Profiles Obtained with an Airborne Full-Waveform LVIS Lidar System

Foliage profiles retrieved froma scanning, terrestrial, near-infrared (1064 nm), full-waveformlidar, the Echidna Validation Instrument (EVI), agree well with those obtained from an airborne, near-infrared, full-waveform, large footprint lidar, the Lidar Vegetation Imaging Sensor (LVIS). We conducted trials at 5 plots within a conifer stand at Sierra National Forest in August, 2008. Foliage profiles retrieved from these two lidar systems are closely correlated (e.g., r = 0.987 at 100 mhorizontal distances) at large spatial coverage while they differ significantly at small spatial coverage, indicating the apparent scanning perspective effect on foliage profile retrievals. Alsowe noted the obvious effects of local topography on foliage profile retrievals, particularly on the topmost height retrievals. With a fine spatial resolution and a small beam size, terrestrial lidar systems complement the strengths of the airborne lidars by making a detailed characterization of the crowns from a small field site, and thereby serving as a validation tool and providing localized tuning information for future airborne and spaceborne lidar missions

First operational BRDF, albedo nadir reflectance products from MODIS

With the launch of NASA’s Terra satellite and the MODerate Resolution Imaging Spectroradiometer (MODIS), operational Bidirectional Reflectance Distribution Function (BRDF) and albedo products are now being made available to the scientific community. The MODIS BRDF/Albedo algorithm makes use of a semiempirical kernel-driven bidirectional reflectance model and multidate, multispectral data to provide global 1-km gridded and tiled products of the land surface every 16 days. These products include directional hemispherical albedo (black-sky albedo), bihemispherical albedo (white-sky albedo), Nadir BRDF-Adjusted surface Reflectances (NBAR), model parameters describing the BRDF, and extensive quality assurance information. The algorithm has been consistently producing albedo and NBAR for the public since July 2000. Initial evaluations indicate a stable BRDF/Albedo Product, where, for example, the spatial and temporal progression of phenological characteristics is easily detected in the NBAR and albedo results. These early beta and provisional products auger well for the routine production of stable MODIS-derived BRDF parameters, nadir reflectances, and albedos for use by the global observation and modeling communities

The Use of Prior Probabilities in Maximum Likelihood Classification

The use of prior information about the expected distribution of classes in a final classification map can be used to improve classification accuracies. Prior information is incorporated through the use of prior probabilities--that is, probabilities of occurrence of classes which are based on separate, independent knowledge concerning the area to be classified. The use of prior probabilities in a classification system is sufficiently versatile to allow (1) prior weighting of output classes based on their-anticipated sizes; (2) the merging of continuously varying measurements (multispectral signatures) with discrete collateral information data sets (e.g., rock type, soil type); and (3) the construction of time-sequential classification systems in which an earlier classification modifies the outcome of a later one. The prior probabilities are incorporated by modifying the maximum likelihood decision rule employed in a Bayesian-type classifier to calculate 3 posteriori probabilities of class membership which are based not only on the resemblance of a pixel to the class signature, but also on the weight of the class which is estimated for the final output classification. In the merging of discrete collateral information with continuous spectral values into a single classification, a set of prior probabilities (weights) is estimated for each value which the discrete collateral variable may assume (e.g., each rock type or soil type). When maximum likelihood calculations are performed, the prior probabilities appropriate to the particular pixel are used in classification. For time-sequential classification, the prior classification of a pixel indexes a set of appropriate conditional probabilities reflecting the confidence of the investigator in the prior classification

Instructor's manual for modern physical geography

iv, 238 p.; 28 cm

Forest Stand Delineation from Unsupervised Classification of Optimal Landsat Spectral, Landsat Texture and Topographic Channels

Landsat data, in conjunction with collateral data sources such as synthesized texture channels and digital terrain models, can be used to delineate forest stands and other ecological land units found in the coniferous North American forest environment. Incorporation of texture and terrain channels enhances site-specific stratification of Landsat data, promoting delineation of forest stand units of a size and homogeneity approaching those found on manually prepared maps used in the management of timber, range, wildlife, watershed and recreational resources. The procedure is a joint research effort between the Jet Propulsion Laboratory of the California Institute of Technology and the University of California at Santa Barbara. The classification approach includes 1) compressing Landsat spectral data into one or two new channels of data using ratio and principle components techniques; 21 generating two texture measures where one channel emphasizes tonal contrast derived from statistical texture techniques and the other emphasizes spatial extent and shape using image segmentation procedures; 3) processing National Cartographic Information Center -- U.S. Geological Survey Digital Terrain information into elevation, slope and aspect channels; 4) reducing the number of synthesized channels by using divergence analysis to identify channels not contributing significantly to the separation of preliminary training classes; 5) introducing spatial constraints by including line and sample coordinates into the unsupervised classification algorithm; and 6) properly weighting selected spectral, texture and terrain channels such that no single data set overpowers the others in unsupervised classification. The combination of spectral tone, tonal texture, spatial texture, topographic data and line and sample location coordinates, is likely to be sufficient for the stand delineation task because each contributes a separate, independent piece of information towards the stand delineation problem. Spectral tone is most important for recognizing the existence of a feature and combines with the topographic data to provide species information. Tonal texture measures the neighborhood contrast of spectral tones providing an indication of relative timber volume. Spatial texture stratifies tone to quantify the spatial extent and shape of tonal patterns. The topographic information provides a powerful independent parameter well known to improve forest classification accuracies because of its ecological predictive effect. Inclusion of line and sample coordinates introduces a strong spatial constraint designed to permit analyst regulation over the automatic merging of distant and unrelated, but similar appearing features. Target area for generation of maps delineating forest stands and related ecological land units is the 220 square kilometer Doggett Creek watershed located in the Klamath National Forest of northern California. The mountainous topography ranges from 500 to 2100 meters in elevation, and bears a variety of important coniferous timber types including douglas fir, ponderosa pine, white and red fir, and several miscellaneous hardwoods such as black oak and madrone

Environmental geoscience : Interaction between natural systems and man

ix, 575 p.; 27 cm

Introducing physical geography : Version 1.0

1 CD-ROM ; 4 3/4 in

Introducing physical geography

xix, 684 p. : ill. (some col.), col. maps ; 29 cm + 1 CD (4 3/4 in)

Study guide for modern physical geography

335 p.; 28 cm