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Wu, Jianguo; Shen, Wei; Sun, Wenfeng; Tueller, Paul T.

doi:10.1023/a:1022995922992

Cited by 474 publications

(91 citation statements)

References 42 publications

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“…The effect of changing grain size was more predictable than the effect of changing extent for various North American landscapes, a finding that our results support [17]. The curves of the three samples show the same trend, and their relationships can be fit by the same functions.…”

Section: Impact Of Grain Size On Landscape Metricssupporting

confidence: 83%

“…In this paper, we calculated 12 landscape-level and six class-level landscape metrics in arid valleys and generated response curves for grain size and landscape pattern. In a previous study on this subject, the metrics were classified into three categories (Wu et al, 2002); another study identified four types of metrics: monotonic increase, monotonic decrease, no change, or erratic [61]. We followed the four-category classification and refined the predictable responses to two, either increasing or decreasing.…”

Section: Impact Of Grain Size On Landscape Metricsmentioning

confidence: 99%

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Analysis of Landscape Patterns of Arid Valleys in China, Based on Grain Size Effect

et al. 2017

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Landscape metrics are useful tools in investigating spatial structure and in describing the heterogeneity of landscapes, but are sensitive to grain size. Thus, it is necessary to determine the appropriate grain size before researching landscape patterns. However, there have been few large-scale investigations in high-precision research about the effect of grain size on landscape patterns, especially in arid valleys in China. Thus, we selected three representative sample areas according to the basic characteristics of arid valleys, and we chose 22 grain sizes from 15 to 450 m to calculate twelve landscape metrics at the landscape level and six landscape metrics at the class level to analyze the most appropriate grain size for the arid valleys. All basins in the study area were converted to an appropriate-sized grid to analyze the landscape patterns. Our results showed that the effect of grain size on landscape metrics can be categorized as: no law, increasing, decreasing, or no change. The majority of the fitted landscape index curves were good, with high R 2 values. The most appropriate grain size at both levels was 75 m. The landscape pattern of arid valleys was scale-dependent. At the landscape level, arid valley landscape patterns changed from northwest to southeast due to topography and hydrothermal conditions. While the value of aggregation for different size classes was high, the other metrics showed significant differences due to area and degree of human activity at the class level.

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Section: Impact Of Grain Size On Landscape Metricssupporting

confidence: 83%

Section: Impact Of Grain Size On Landscape Metricsmentioning

confidence: 99%

Analysis of Landscape Patterns of Arid Valleys in China, Based on Grain Size Effect

et al. 2017

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“…This difference may be a function of scale, which is defined by the resolution (i.e., grain) and extent of the available spatial data. Numerous studies (54)(55)(56)(57)(58)(59)(60)(61)(62) have found that changing the study scale changes the strength of associations and interactions. For example, in a study on habitat use by Eleodes hispilabris, McIntyre (62) found that E. hispilabris avoided shrubs at small scales but selectively occupied shrubland at larger scales, which may be due to different mechanisms influencing habitat selection at the different scales.…”

Section: Discussionmentioning

confidence: 99%

Validation of a Previously Developed Geospatial Model That Predicts the Prevalence of Listeria monocytogenes in New York State Produce Fields

Weller

Shiwakoti

Bergholz

et al. 2016

Appl Environ Microbiol

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dTechnological advancements, particularly in the field of geographic information systems (GIS), have made it possible to predict the likelihood of foodborne pathogen contamination in produce production environments using geospatial models. Yet, few studies have examined the validity and robustness of such models. This study was performed to test and refine the rules associated with a previously developed geospatial model that predicts the prevalence of Listeria monocytogenes in produce farms in New York State (NYS). Produce fields for each of four enrolled produce farms were categorized into areas of high or low predicted L. monocytogenes prevalence using rules based on a field's available water storage (AWS) and its proximity to water, impervious cover, and pastures. Drag swabs (n ‫؍‬ 1,056) were collected from plots assigned to each risk category. Logistic regression, which tested the ability of each rule to accurately predict the prevalence of L. monocytogenes, validated the rules based on water and pasture. Samples collected near water (odds ratio [OR], 3.0) and pasture (OR, 2.9) showed a significantly increased likelihood of L. monocytogenes isolation compared to that for samples collected far from water and pasture. Generalized linear mixed models identified additional land cover factors associated with an increased likelihood of L. monocytogenes isolation, such as proximity to wetlands. These findings validated a subset of previously developed rules that predict L. monocytogenes prevalence in produce production environments. This suggests that GIS and geospatial models can be used to accurately predict L. monocytogenes prevalence on farms and can be used prospectively to minimize the risk of preharvest contamination of produce.F resh produce presents a unique food safety challenge due to the absence of a kill step between harvest and consumption. An increase in recalls and reported outbreaks linked to fresh produce over the past decade (1-3) have been associated with consumer avoidance of products linked to outbreaks (4, 5). This trend can negatively affect growers and the produce industry (4-6). For example, following a 2011 listeriosis outbreak in the United States associated with fresh cantaloupe (7), cantaloupe consumption dropped 53% nationwide (6). The prevention of produce contamination in production environments is therefore a concern for growers, the produce industry, and public health professionals. To develop effective prevention strategies, it is important to understand the ecological processes and environmental factors that affect foodborne pathogen prevalence in produce production environments. Technological advancements, such as geographic information systems (GIS), have the potential to drastically improve our ability to examine these processes and to develop novel tools for ensuring the safety of fresh produce.Numerous studies (8-21) have examined the ecology of foodborne pathogens in agricultural environments, and several (22-27) have used GIS and geospatial analysis. For example, Chapin...

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“…Numerous landscape pattern metrics have been developed to characterize the composition and configuration of land cover types in the urban environment [49][50][51][52]. We selected the most commonly used landscape composition metrics, the percentage of landscape area (PLAND) and two other diversity metrics: Shannon's diversity index (SHDI) and Shannon's evenness index (SHEI) [51].…”

Section: Landscape Pattern Metricsmentioning

confidence: 99%

Impacts of Urbanization on Vegetation Phenology over the Past Three Decades in Shanghai, China

Qiu

Song

2017

Remote Sensing

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Vegetation phenology manifests the rhythm of annual plant life activities. It has been extensively studied in natural ecosystems. However, major knowledge gaps still exist in understanding the impacts of urbanization on vegetation phenology. This study addresses two questions to fill the knowledge gaps: (1) How does vegetation phenology vary spatially and temporally along a rural-to-urban transect in Shanghai, China, over the past three decades? (2) How do landscape composition and configuration affect those variations of vegetation phenology? To answer these questions, 30 m × 30 m mean vegetation phenology metrics, including the start of growing season (SOS), end of growing season (EOS), and length of growing season (LOS), were derived for urban vegetation using dense stacks of enhanced vegetation index (EVI) time series from images collected by Landsat 5-8 satellites from 1984 to 2015. Landscape pattern metrics were calculated using high spatial resolution aerial photos. We then used Pearson correlation analysis to quantify the associations between phenology patterns and landscape metrics. We found that vegetation in urban centers experienced advances of SOS for 5-10 days and delays of EOS for 5-11 days compared with those located in the surrounding rural areas. Additionally, we observed strong positive correlations between landscape composition (percentage of landscape area) of developed land and LOS of urban vegetation. We also found that the landscape configuration of local land cover types, especially patch density and edge density, was significantly correlated with the spatial patterns of vegetation phenology. These results demonstrate that vegetation phenology in the urban area is significantly different from its rural surroundings. These findings have implications for urban environmental management, ranging from biodiversity protection to public health risk reduction.

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Untitled

Cited by 474 publications

References 42 publications

Analysis of Landscape Patterns of Arid Valleys in China, Based on Grain Size Effect

Analysis of Landscape Patterns of Arid Valleys in China, Based on Grain Size Effect

Validation of a Previously Developed Geospatial Model That Predicts the Prevalence of Listeria monocytogenes in New York State Produce Fields

Impacts of Urbanization on Vegetation Phenology over the Past Three Decades in Shanghai, China

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