An interdisciplinary analysis of Colorado Rocky Mountain environments using ADP techniques

The author has identified the following significant results. Good ecological, classification accuracy (90-95%) can be achieved in areas of rugged relief on a regional basis for Level 1 cover types (coniferous forest, deciduous forest, grassland, cropland, bare rock and soil, and water) using computer-aided analysis techniques on ERTS/MSS data. Cost comparisons showed that a Level 1 cover type map and a table of areal estimates could be obtained for the 443,000 hectare San Juan Mt. test site for less than 0.1 cent per acre, whereas photointerpretation techniques would cost more than 0.4 cent per acre. Results of snow cover mapping have conclusively proven that the areal extent of snow in mountainous terrain can be rapidly and economically mapped by using ERTS/MSS data and computer-aided analysis techniques. A distinct relationship between elevation and time of freeze or thaw was observed, during mountain lake mapping. Basic lithologic units such as igneous, sedimentary, and unconsolidated rock materials were successfully identified. Geomorphic form, which is exhibited through spatial and textual data, can only be inferred from ERTS data. Data collection platform systems can be utilized to produce satisfactory data from extremely inaccessible locations that encounter very adverse weather conditions, as indicated by results obtained from a DCP located at 3,536 meters elevation that encountered minimum temperatures of -25.5 C and wind speeds of up to 40.9m/sec (91 mph), but which still performed very reliably

Automated thematic mapping and change detection of ERTS-A images

The author has identified the following significant results. For the recognition of terrain types, spatial signatures are developed from the diffraction patterns of small areas of ERTS-1 images. This knowledge is exploited for the measurements of a small number of meaningful spatial features from the digital Fourier transforms of ERTS-1 image cells containing 32 x 32 picture elements. Using these spatial features and a heuristic algorithm, the terrain types in the vicinity of Phoenix, Arizona were recognized by the computer with a high accuracy. Then, the spatial features were combined with spectral features and using the maximum likelihood criterion the recognition accuracy of terrain types increased substantially. It was determined that the recognition accuracy with the maximum likelihood criterion depends on the statistics of the feature vectors. Nonlinear transformations of the feature vectors are required so that the terrain class statistics become approximately Gaussian. It was also determined that for a given geographic area the statistics of the classes remain invariable for a period of a month but vary substantially between seasons

Cluster analysis based on density estimates and its application to LANDSAT imagery

Includes bibliographical footnotes.This study was funded partly by the Federation of Rocky Mountain States, the U.S. Army Corps of Engineers, St. Paul District, Contract no. DAC 37-77-C-0133 and the Colorado State University Experiment Station 107

Drawing Clustered Graphs as Topographic Maps

The visualization of clustered graphs is an essential tool for the analysis of networks, in particular, social networks, in which clustering techniques like community detection can reveal various structural properties. In this paper, we show how clustered graphs can be drawn as topographic maps, a type of map easily understandable by users not familiar with information visu- alization. Elevation levels of connected entities correspond to the nested structure of the cluster hierarchy. We present methods for initial node placement and describe a tree mapping based algorithm that produces an area efficient layout. Given this layout, a triangular ir- regular mesh is generated that is used to extract the elevation data for rendering the map. In addition, the mesh enables the routing of edges based on the topo- graphic features of the map

Three-Dimensional Basin and Fault Structure From a Detailed Seismic Velocity Model of Coachella Valley, Southern California

The Coachella Valley in the northern Salton Trough is known to produce destructive earthquakes, making it a high seismic hazard area. Knowledge of the seismic velocity structure and geometry of the sedimentary basins and fault zones is required to improve earthquake hazard estimates in this region. We simultaneously inverted first P wave travel times from the Southern California Seismic Network (39,998 local earthquakes) and explosions (251 land/sea shots) from the 2011 Salton Seismic Imaging Project to obtain a 3‐D seismic velocity model. Earthquakes with focal depths ≤10 km were selected to focus on the upper crustal structure. Strong lateral velocity contrasts in the top ~3 km correlate well with the surface geology, including the low‐velocity (<5 km/s) sedimentary basin and the high‐velocity crystalline basement rocks outside the valley. Sediment thickness is ~4 km in the southeastern valley near the Salton Sea and decreases to <2 km at the northwestern end of the valley. Eastward thickening of sediments toward the San Andreas fault within the valley defines Coachella Valley basin asymmetry. In the Peninsular Ranges, zones of relatively high seismic velocities (~6.4 km/s) between 2‐ and 4‐km depth may be related to Late Cretaceous mylonite rocks or older inherited basement structures. Other high‐velocity domains exist in the model down to 9‐km depth and help define crustal heterogeneity. We identify a potential fault zone in Lost Horse Valley unassociated with mapped faults in Southern California from the combined interpretation of surface geology, seismicity, and lateral velocity changes in the model

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. The algorithm is based on divide-and-conquer constraint-based subroutines to learn the local structure around a target variable. We conduct two series of experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is currently the most powerful state-of-the-art algorithm for Bayesian network structure learning. First, we use eight well-known Bayesian network benchmarks with various data sizes to assess the quality of the learned structure returned by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in terms of goodness of fit to new data and quality of the network structure with respect to the true dependence structure of the data. Second, we investigate H2PC's ability to solve the multi-label learning problem. We provide theoretical results to characterize and identify graphically the so-called minimal label powersets that appear as irreducible factors in the joint distribution under the faithfulness condition. The multi-label learning problem is then decomposed into a series of multi-class classification problems, where each multi-class variable encodes a label powerset. H2PC is shown to compare favorably to MMHC in terms of global classification accuracy over ten multi-label data sets covering different application domains. Overall, our experiments support the conclusions that local structural learning with H2PC in the form of local neighborhood induction is a theoretically well-motivated and empirically effective learning framework that is well suited to multi-label learning. The source code (in R) of H2PC as well as all data sets used for the empirical tests are publicly available.Comment: arXiv admin note: text overlap with arXiv:1101.5184 by other author

Computer-aided processing of LANDSAT MSS data for classification of forestlands

There are no author-identified significant results in this report