scholarly journals Rolling Signal-Based Ripley's K: A New Algorithm to Identify Spatial Patterns In Histological Specimens

2020 ◽  
Author(s):  
Connor Healy ◽  
Frederick R. Adler ◽  
Tara L. Deans
Keyword(s):  
Spatial Patterns ◽  
Ripley’S K ◽  
Ripley's K

Tree spatial patterns and stand development along the pine-grassland ecotone in the Colorado Front Range

1999 ◽  
Vol 29 (5) ◽  
pp. 575-584 ◽  
Author(s):  
Joy Nystrom Mast ◽  
Thomas T Veblen
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Patterns ◽  
Tree Regeneration ◽  
Colorado Front Range ◽  
Tree Age ◽  
Stand Development ◽  
Front Range ◽  
Ripley’S K ◽  
Ripley's K ◽  
Tree Spatial Patterns

Patterns of stand development may be interpreted from spatial analyses, based on variables such as tree age and size, together with past records of climate and disturbance. In the present study, our objective is to examine spatial patterns of tree age and size to determine if they are consistent with the episodic pattern of tree regeneration proposed for ponderosa pine (Pinus ponderosa Dougl. ex P. & C. Laws.) and expected changes in tree spatial patterns as cohort patches age. According to our hypothesis, internal patch structure should become less clumped as single cohort patches age due to self-thinning, with few trees attaining dominance in a small patch. In this study, tree spatial patterns in 16 stands of P. ponderosa in the Colorado Front Range are described and related to patterns of stand development. Analytical methods included Ripley's K(t) (a univariate statistic of tree spatial distribution), Ripley's K12(t) (a bivariate statistic of spatial association), and Moran's I (a measure of spatial autocorrelation). Spatial patterns imply establishment of patches of pines followed by self-thinning. Continued stand development results in strong size hierarchies as manifested by stronger spatial autocorrelation of tree age than tree size. Hence, pines exhibit a strong size class hierarchy developed within an even-aged patch.

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 Wildfires on the Mongolian Plateau: Identifying Drivers and Spatial Distributions to Predict Wildfire Probability

2019 ◽  
Vol 11 (20) ◽  
pp. 2361 ◽  
Author(s):  
Rihan ◽  
Zhao ◽  
Zhang ◽  
Guo ◽  
Ying ◽  
...  
Keyword(s):  
Climate Change ◽  
Influencing Factors ◽  
Inner Mongolia ◽  
Critical Role ◽  
Maximum Temperature ◽  
Mongolian Plateau ◽  
Ripley’S K Function ◽  
Ripley’S K ◽  
Ripley's K ◽  
K Function

With climate change, significant fluctuations in wildfires have been observed on the Mongolian Plateau. The ability to predict the distribution of wildfires in the context of climate change plays a critical role in wildfire management and ecosystem maintenance. In this paper, Ripley’s K function and a Random Forest (RF) model were applied to analyse the spatial patterns and main influencing factors affecting the occurrence of wildfire on the Mongolian Plateau. The results showed that the wildfires were mainly clustered in space due to the combination of influencing factors. The distance scale is less than 1/2 of the length of the Mongolian Plateau; that is, it does not experience boundary effects in the study area and it meets the requirements of Ripley’s K function. Among the driving factors, the fraction of vegetation coverage (FVC), land use degree (La), elevation, precipitation (pre), wet day frequency (wet), and maximum temperature (tmx) had the greatest influences, while the aspect had the lowest influence. The likelihood of fire was mainly concentrated in the northern, eastern, and southern parts of the Mongolian Plateau and in the border area between the Inner Mongolia Autonomous Region (Inner Mongolia) and Mongolian People’s Republic (Mongolia), and wildfires did not occur or occurred less frequently in the hinterland area. The fitting results of the RF model showed a prediction accuracy exceeding 90%, which indicates that the model has a high ability to predict wildfire occurrences on the Mongolian Plateau. This study can provide a reference for predictions and decision-making related to wildfires on the Mongolian Plateau.

Tear Film Classification in Interferometry Eye Images using Phylogenetic Diversity Indexes and Ripley's K Function

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Luana Batista Da Cruz ◽  
Johnatan Carvalho Souza ◽  
Anselmo Paiva ◽  
Joao Dallyson ◽  
Geraldo Braz Junior ◽  
...  
Keyword(s):  
Phylogenetic Diversity ◽  
Tear Film ◽  
Ripley’S K Function ◽  
Ripley’S K ◽  
Ripley's K ◽  
K Function
Sign in / Sign up

Export Citation Format

Share Document