scholarly journals Does fluvial channel belt clustering predict net sand to gross rock volume? Architectural metrics and point pattern analysis of a digital outcrop model

2019 ◽  
Author(s):  
Alexander Koch ◽  
Cari Johnson ◽  
Lisa Stright
Keyword(s):  
Pattern Analysis ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Channel Belt ◽  
Digital Outcrop

scholarly journals Does fluvial channel-belt clustering predict net sand to gross rock volume? Architectural metrics and point-pattern analysis of a digital outcrop model

2019 ◽  
Vol 89 (11) ◽  
pp. 1109-1126
Author(s):  
Alexander R. Koch ◽  
Cari L. Johnson ◽  
Lisa Stright
Keyword(s):  
Large Scale ◽  
Small Scale ◽  
Point Pattern ◽  
Point Patterns ◽  
Spatial Point ◽  
Channel Belt ◽  
John Henry Member ◽  
Scale Point ◽  
Belt Width ◽  
Digital Outcrop

ABSTRACT Spatial point-pattern analyses (PPAs) are used to quantify clustering, randomness, and uniformity of the distribution of channel belts in fluvial strata. Point patterns may reflect end-member fluvial architecture, e.g., uniform compensational stacking and avulsion-generated clustering, which may change laterally, especially at greater scales. To investigate spatial and temporal changes in fluvial systems, we performed PPA and architectural analyses on extensive outcrops of the Cretaceous John Henry Member of the Straight Cliffs Formation in southern Utah, USA. Digital outcrop models (DOMs) produced using unmanned aircraft system-based stereophotogrammetry form the basis of detailed interpretations of a 250-m-thick fluvial succession over a total outcrop length of 4.5 km. The outcrops are oriented roughly perpendicular to fluvial transport direction. This transverse cross-sectional exposure of the fluvial system allows a study of the system's variation along depositional strike. We developed a workflow that examines spatial point patterns using the quadrat method, and architectural metrics such as net sand to gross rock volume (NTG), amalgamation index, and channel-belt width and thickness within moving windows. Quadrat cell sizes that are ∼ 50% of the average channel-belt width-to-thickness ratio (16:1 aspect ratio) provide an optimized scale to investigate laterally elongate distributions of fluvial-channel-belt centroids. Large-scale quadrat point patterns were recognized using an array of four quadrat cells, each with 237× greater area than the median channel belt. Large-scale point patterns and NTG correlate negatively, which is a result of using centroid-based PPA on a dataset with disparately sized channel belts. Small-scale quadrat point patterns were recognized using an array of 16 quadrat cells, each with 21× greater area than the median channel belt. Small-scale point patterns and NTG correlate positively, and match previously observed stratigraphic trends in the fluvial John Henry Member, suggesting that these are regional trends. There are deviations from these trends in architectural statistics over small distances (hundreds of meters) which are interpreted to reflect autogenic avulsion processes. Small-scale autogenic processes result in architecture that is difficult to correlate between 1D datasets, for example when characterizing a reservoir using well logs. We show that 1D NTG provides the most accurate prediction for surrounding 2D architecture.

scholarly journals Spatial point-pattern analysis as a powerful tool in identifying pattern-process relationships in plant ecology: an updated review

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Mariem Ben-Said
Keyword(s):  
Spatial Patterns ◽  
Pattern Analysis ◽  
Biotic Interactions ◽  
Plant Ecology ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Summary Statistics ◽  
Spatial Point Pattern ◽  
Spatial Point ◽  
Spatial Point Pattern Analysis

Abstract Background Ecological processes such as seedling establishment, biotic interactions, and mortality can leave footprints on species spatial structure that can be detectable through spatial point-pattern analysis (SPPA). Being widely used in plant ecology, SPPA is increasingly carried out to describe biotic interactions and interpret pattern-process relationships. However, some aspects are still subjected to a non-negligible debate such as required sample size (in terms of the number of points and plot area), the link between the low number of points and frequently observed random (or independent) patterns, and relating patterns to processes. In this paper, an overview of SPPA is given based on rich and updated literature providing guidance for ecologists (especially beginners) on summary statistics, uni-/bi-/multivariate analysis, unmarked/marked analysis, types of marks, etc. Some ambiguities in SPPA are also discussed. Results SPPA has a long history in plant ecology and is based on a large set of summary statistics aiming to describe species spatial patterns. Several mechanisms known to be responsible for species spatial patterns are actually investigated in different biomes and for different species. Natural processes, plant environmental conditions, and human intervention are interrelated and are key drivers of plant spatial distribution. In spite of being not recommended, small sample sizes are more common in SPPA. In some areas, periodic forest inventories and permanent plots are scarce although they are key tools for spatial data availability and plant dynamic monitoring. Conclusion The spatial position of plants is an interesting source of information that helps to make hypotheses about processes responsible for plant spatial structures. Despite the continuous progress of SPPA, some ambiguities require further clarifications.

scholarly journals Space-Time Point Pattern Analysis of Flavescence Dorée Epidemic in a Grapevine Field: Disease Progression and Recovery

2017 ◽  
Vol 7 ◽  
Author(s):  
Federico Maggi ◽  
Domenico Bosco ◽  
Luciana Galetto ◽  
Sabrina Palmano ◽  
Cristina Marzachì
Keyword(s):  
Disease Progression ◽  
Pattern Analysis ◽  
Space Time ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Flavescence Dorée ◽  
Time Point

Spatiotemporal Point Pattern Analysis Using Ripley's K Function

2017 ◽  
pp. 155-176
Author(s):  
Alexander Hohl ◽  
Minrui Zheng ◽  
Wenwu Tang ◽  
Eric Delmelle ◽  
Irene Casas
Keyword(s):  
Pattern Analysis ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Ripley’S K Function ◽  
Ripley’S K ◽  
Ripley's K ◽  
K Function

scholarly journals Analyzing the Spatial Distribution of Drumlins: A Two-Phase Mosaic Approach

1984 ◽  
Vol 30 (106) ◽  
pp. 302-307
Author(s):  
B. N. Boots ◽  
R. K. Burns
Keyword(s):  
Spatial Distribution ◽  
Pattern Analysis ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Statistical Bias ◽  
Two Phase ◽  
Alternative Approach ◽  
Mosaic Approach ◽  
The Individual

AbstractResearchers have analyzed various properties of drumlins within individual drumlin fields in order to provide evidence to help in identifying the processes involved in drumlin formation. One property which has been examined is the spatial distribution of drumlins within a field. Traditionally, in such endeavours the individual drumlins have been represented as points and their distribution examined using techniques of point-pattern analysis. We suggest that not only is such a representation inappropriate at this scale, it also introduces statistical bias which makes the results of such analyses questionable. Consequently, we propose an alternative approach which involves representing individual drumlins as areal phenomena and considering their pattern as a two-phase mosaic. The advantages of such an approach are discussed and it is illustrated by applying it to two different drumlin fields.

Astronomical Aspects of Multifractal Point-Pattern Analysis: Application to the DENIS/2MASS Near-Infrared and BATSE Gamma-Ray Data

2006 ◽  
pp. 499-500
Author(s):  
Roland Vavrek ◽  
Lajos G. Baláazs ◽  
Attila Mészáros ◽  
István Horváth ◽  
Zsolt Bagoly
Keyword(s):  
Near Infrared ◽  
Pattern Analysis ◽  
Gamma Ray ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Analysis Application
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