- Effects of the spatial pattern of vegetation cover on urban warming in a desert city

2012 ◽  
pp. 278-295
Keyword(s):  
Spatial Pattern ◽  
Vegetation Cover ◽  
Urban Warming

Buried propagule bank of a high subalpine site: microsite variation and comparisons with aboveground vegetation

1993 ◽  
Vol 71 (5) ◽  
pp. 712-717 ◽  
Author(s):  
Cheryl A. Ingersoll ◽  
Mark V. Wilson
Keyword(s):  
Spatial Pattern ◽  
Key Words ◽  
Seed Bank ◽  
Vegetation Cover ◽  
Bare Soil ◽  
Soil Cores ◽  
Cascade Mountains ◽  
Greenhouse Soil ◽  
Propagule Bank ◽  
Bare Soils

We assessed the composition and spatial pattern of the persistent buried propagule bank (seeds and vegetative structures) of a treeline site in the Oregon Cascade Mountains. We monitored emergence from soil cores removed from four microsite types and recorded vegetation cover and seedling abundance on the site. Over 3100 seedlings/m2 emerged from the greenhouse soil cores; the seed bank was dominated by Juncus species. Few vegetative sprouts emerged. Vegetated microsites produced significantly more emergents than did bare soils, but even bare soils contained abundant seeds. Overall site cover was low and few seedlings occurred on the site. Discrepancies between aboveground and belowground abundance were common. Phyllodoce empetriformis and Luetkea pectinata were abundant in the vegetation and produced many seeds but were poorly represented in the seed bank and as seedlings on the site. Other species were abundant in the seed bank, but rare in the vegetation. Our results indicate that despite the abundance of seeds in bare soil, colonization is likely to be extremely slow. Key words: seed bank, subalpine, seedlings, microsite, spatial pattern.

scholarly journals REMOTE SENSING-BASED DETECTION AND SPATIAL PATTERN ANALYSIS FOR GEO-ECOLOGICAL NICHE MODELING OF TILLANDSIA SPP. IN THE ATACAMA, CHILE

2016 ◽  
Vol XLI-B2 ◽  
pp. 251-256
Author(s):  
N. Wolf ◽  
A. Siegmund ◽  
C. del Río ◽  
P. Osses ◽  
J. L. García
Keyword(s):  
Remote Sensing ◽  
Spatial Pattern ◽  
Vegetation Cover ◽  
Ecological Niche Modeling ◽  
Distribution Patterns ◽  
Spatial Pattern Analysis ◽  
Gis Analysis ◽  
Stratocumulus Clouds ◽  
Coastal Fog ◽  
Topographic Constraints

In the coastal Atacama Desert in Northern Chile plant growth is constrained to so-called ‘fog oases’ dominated by monospecific stands of the genus Tillandsia. Adapted to the hyperarid environmental conditions, these plants specialize on the foliar uptake of fog as main water and nutrient source. It is this characteristic that leads to distinctive macro- and micro-scale distribution patterns, reflecting complex geo-ecological gradients, mainly affected by the spatiotemporal occurrence of coastal fog respectively the South Pacific Stratocumulus clouds reaching inlands. The current work employs remote sensing, machine learning and spatial pattern/GIS analysis techniques to acquire detailed information on the presence and state of <i>Tillandsia spp.</i> in the Tarapacá region as a base to better understand the bioclimatic and topographic constraints determining the distribution patterns of <i>Tillandsia spp.</i> Spatial and spectral predictors extracted from WorldView-3 satellite data are used to map present Tillandsia vegetation in the Tarapaca region. Regression models on Vegetation Cover Fraction (VCF) are generated combining satellite-based as well as topographic variables and using aggregated high spatial resolution information on vegetation cover derived from UAV flight campaigns as a reference. The results are a first step towards mapping and modelling the topographic as well as bioclimatic factors explaining the spatial distribution patterns of Tillandsia fog oases in the Atacama, Chile.

scholarly journals Spatial variability models of CO2 emissions from soils colonized by grass (Deschampsia antarctica) and moss (Sanionia uncinata) in Admiralty Bay, King George Island

2010 ◽  
Vol 23 (1) ◽  
pp. 27-33 ◽  
Author(s):  
Eduardo de Sá Mendonça ◽  
Newton La Scala ◽  
Alan Rodrigo Panosso ◽  
Felipe N.B. Simas ◽  
Carlos E.G.R. Schaefer
Keyword(s):  
Spatial Variability ◽  
Spatial Pattern ◽  
Carbon Cycling ◽  
Vegetation Cover ◽  
Terrestrial Ecosystems ◽  
Deschampsia Antarctica ◽  
King George Island ◽  
Maritime Antarctica ◽  
Admiralty Bay ◽  
Sanionia Uncinata

AbstractSoil CO2 emission is an important part of the terrestrial carbon cycling and is influenced by several factors, such as type and distribution of vegetation. In this work we evaluated the spatial variability of soil CO2 emission in terrestrial ecosystems of maritime Antarctica, under two contrasting vegetation covers: 1) grass areas of Deschampsia antarctica Desv., and 2) moss carpets of Sanionia uncinata (Hedw.) Loeske. Highest mean emission was obtained for the Deschampsia (4.13 μmol m-2 s-1) developed on organic-rich soil with a strong penguin influence. The overall results indicate that soil temperature is not directly related to the spatial pattern of soil CO2 emission at the sites studied. Emission adjusted models were Gaussian and exponential with ranges varying from 1.3 to 2.8 m, depending on the studied site and vegetation cover.

Measuring the spatial arrangement of urban vegetation and its impacts on seasonal surface temperatures

2015 ◽  
Vol 39 (2) ◽  
pp. 199-219 ◽  
Author(s):  
Chao Fan ◽  
Soe W. Myint ◽  
Baojuan Zheng
Keyword(s):  
Surface Temperature ◽  
Spatial Pattern ◽  
Spatial Autocorrelation ◽  
Spatial Patterns ◽  
Vegetation Cover ◽  
Land Surface ◽  
Spatial Arrangement ◽  
Urban Vegetation ◽  
Surface Temperatures ◽  
Green Vegetation

Urban forestry is an important component of the urban ecosystem that can effectively ameliorate temperatures by providing shade and through evapotranspiration. While it is well known that vegetation abundance is negatively correlated to land surface temperature, the impacts of the spatial arrangement (e.g. clustered or dispersed) of vegetation cover on the urban thermal environment requires further investigation. In this study, we coupled remote sensing techniques with spatial statistics to quantify the configuration of vegetation cover and its variable influences on seasonal surface temperatures in central Phoenix. The objectives of this study are to: (1) determine spatial arrangement of green vegetation cover using continuous spatial autocorrelation indices combined with high-resolution remotely-sensed data; (2) examine the role of grass and trees, especially their spatial patterns on seasonal and diurnal land surface temperatures by controlling the effects of vegetation abundance; (3) investigate the sensitivity of the vegetation–temperature relationship at varying geographical scales. The spatial pattern of urban vegetation was measured using a local spatial autocorrelation index—the local Moran’s Iv. Results show that clustered or less fragmented patterns of green vegetation lower surface temperature more effectively than dispersed patterns. The relationships between the local Moran’s Iv and surface temperature are evidenced to be strongest during summer daytime and lowest during winter nighttime. Results of multiple regression analyses demonstrate significant impacts of spatial arrangement of vegetation on seasonal surface temperatures. Our analyses of vegetation spatial patterns at varying geographical scales suggest that an area extent of ˜200 m is optimal for examining the vegetation–temperature relationship. We provide a methodological framework to quantify the spatial pattern of urban features and to examine their impacts on the biophysical characteristics of the urban environment. The insights gained from our study results have significant implications for sustainable urban development and resource management.

scholarly journals REMOTE SENSING-BASED DETECTION AND SPATIAL PATTERN ANALYSIS FOR GEO-ECOLOGICAL NICHE MODELING OF TILLANDSIA SPP. IN THE ATACAMA, CHILE

2016 ◽  
Vol XLI-B2 ◽  
pp. 251-256 ◽  
Author(s):  
N. Wolf ◽  
A. Siegmund ◽  
C. del Río ◽  
P. Osses ◽  
J. L. García
Keyword(s):  
Remote Sensing ◽  
Spatial Pattern ◽  
Vegetation Cover ◽  
Ecological Niche Modeling ◽  
Distribution Patterns ◽  
Spatial Pattern Analysis ◽  
Gis Analysis ◽  
Stratocumulus Clouds ◽  
Coastal Fog ◽  
Topographic Constraints

In the coastal Atacama Desert in Northern Chile plant growth is constrained to so-called ‘fog oases’ dominated by monospecific stands of the genus Tillandsia. Adapted to the hyperarid environmental conditions, these plants specialize on the foliar uptake of fog as main water and nutrient source. It is this characteristic that leads to distinctive macro- and micro-scale distribution patterns, reflecting complex geo-ecological gradients, mainly affected by the spatiotemporal occurrence of coastal fog respectively the South Pacific Stratocumulus clouds reaching inlands. The current work employs remote sensing, machine learning and spatial pattern/GIS analysis techniques to acquire detailed information on the presence and state of &lt;i&gt;Tillandsia spp.&lt;/i&gt; in the Tarapacá region as a base to better understand the bioclimatic and topographic constraints determining the distribution patterns of &lt;i&gt;Tillandsia spp.&lt;/i&gt; Spatial and spectral predictors extracted from WorldView-3 satellite data are used to map present Tillandsia vegetation in the Tarapaca region. Regression models on Vegetation Cover Fraction (VCF) are generated combining satellite-based as well as topographic variables and using aggregated high spatial resolution information on vegetation cover derived from UAV flight campaigns as a reference. The results are a first step towards mapping and modelling the topographic as well as bioclimatic factors explaining the spatial distribution patterns of Tillandsia fog oases in the Atacama, Chile.

Spatial Pattern Discrimination in Rats

2003 ◽  
Author(s):  
Kelly A. Digian ◽  
Michael Brown
Keyword(s):  
Spatial Pattern ◽  
Pattern Discrimination

Visual cues and spatial pattern learning

2001 ◽  
Author(s):  
Michael F. Brown ◽  
Sue Yang ◽  
Kelly Digian
Keyword(s):  
Spatial Pattern ◽  
Visual Cues ◽  
Pattern Learning
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