The Multi-Resolution Land Characteristics (MRLC) Consortium — 20 Years of Development and Integration of USA National Land Cover Data

Wickham, James D.; Homer, Collin G.; Vogelmann, James E.; McKerrow, Alexa J.; Mueller, Rick; Herold, Nate; Coulston, John W.

doi:10.3390/rs6087424

Cited by 127 publications

(79 citation statements)

References 46 publications

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“…Mean maximum temperature in previous winter Landscape factors CA_X 1 Total area of a land cover class X PLAND_X 1 Percentage of area of a land cover class X TE_X 1 Total edge length of a land cover X at the region ED_X 1 Edge density of a land cover X at the region DIST_O Distance to the origin area of Lyme disease 1 Land cover data of 2013 was accessed from the National Land Cover Database (NLCD) [34]. Following a previous study [3], we focused on seven particular land cover classes: deciduous forest (class 41), evergreen forest (class 42), mixed forest (class 43), developed-open space (class 21), developed-low intensity space (class 22), developed-medium intensity space (class 23), and developed-high intensity space (class 24).…”

Section: Climatic Predictors Pre_1mentioning

confidence: 99%

Comparing the Climatic and Landscape Risk Factors for Lyme Disease Cases in the Upper Midwest and Northeast United States

Dong

Huang

Yong

et al. 2020

IJERPH

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Lyme disease, recognized as one of the most important vector-borne diseases worldwide, has been increasing in incidence and spatial extend in United States. In the Northeast and Upper Midwest, Lyme disease is transmitted by Ixodes scapularis. Currently, many studies have been conducted to identify factors influencing Lyme disease risk in the Northeast, however, relatively few studies focused on the Upper Midwest. In this study, we explored and compared the climatic and landscape factors that shape the spatial patterns of human Lyme cases in these two regions, using the generalized linear mixed models. Our results showed that climatic variables generally had opposite correlations with Lyme disease risk, while landscape factors usually had similar effects in these two regions. High precipitation and low temperature were correlated with high Lyme disease risk in the Upper Midwest, while with low Lyme disease risk in the Northeast. In both regions, size and fragmentation related factors of residential area showed positive correlations with Lyme disease risk. Deciduous forests and evergreen forests had opposite effects on Lyme disease risk, but the effects were consistent between two regions. In general, this study provides new insight into understanding the differences of risk factors of human Lyme disease risk in these two regions.

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Section: Climatic Predictors Pre_1mentioning

confidence: 99%

Comparing the Climatic and Landscape Risk Factors for Lyme Disease Cases in the Upper Midwest and Northeast United States

Dong

Huang

Yong

et al. 2020

IJERPH

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“…The raster formatted LUDA data were the basis of the research reported by O'Neill et al (1988) and others (e.g., Riitters et al 1995;Turner et al 1989). By the late 1990s, through the formation of the multi resolution land characteristics (MRLC) consortium (Homer et al 2004;Wickham et al 2014;Yang et al 2018), remote sensing of land cover had advanced to producing land cover for the conterminous United States from the Landsat satellite series (Vogelmann et al 2001;Homer et al 2007), which had a native resolution of 0.09 ha pixel -1 . The 4500% increase in spatial resolution increased confidence in the assumption of homogeneity for a pixel's land cover class label and obviated the need for ''mixed'' classes (e.g., cropland and natural vegetation) that were typically necessary at coarser spatial resolutions (e.g., Loveland et al 2000).…”

Section: Introductionmentioning

confidence: 99%

Influence of high-resolution data on the assessment of forest fragmentation

Wickham

Riitters

2019

Landscape Ecol

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Context Remote sensing has been a foundation of landscape ecology. The spatial resolution (pixel size) of remotely sensed land cover products has improved since the introduction of landscape ecology in the United States. Because patterns depend on spatial resolution, emerging improvements in the spatial resolution of land cover may lead to new insights about the scaling of landscape patterns. Objective We compared forest fragmentation measures derived from very high resolution (1 m 2) data with the same measures derived from the commonly used (30 m 9-30 m; 900 m 2) Landsat-based data. Methods We applied area-density scaling to binary (forest; non-forest) maps for both sources to derive source-specific estimates of dominant (density C 60%), interior (C 90%), and intact (100%) forest. Results Switching from low-to high-resolution data produced statistical and geographic shifts in forest spatial patterns. Forest and non-forest features that were ''invisible'' at low resolution but identifiable at high resolution resulted in higher estimates of dominant and interior forest but lower estimates of intact forest from the high-resolution source. Overall, the high-resolution data detected more forest that was more contagiously distributed even at larger spatial scales. Conclusion We anticipate that improvements in the spatial resolution of remotely sensed land cover products will advance landscape ecology through reinterpretations of patterns and scaling, by fostering new landscape pattern measurements, and by testing new spatial pattern-ecological process hypotheses.

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“…However, existing methods to quantify urban heterogeneity differ in how they combine biophysical and anthropogenic components (Grimm et al, 2000) and in the spatial scales at which they are applied (Wu and Loucks, 1995). Coarse-scale ecosystem classifications based on physical, climatic, and biological conditions, such as the Anderson or Anderson-derived classification systems (Anderson et al, 1976) or the Multi-Resolution Land Characteristics classification system (Wickham et al, 2014) often fail to capture the dynamic social processes and varied built environments that typify urban areas and that may greatly influence species distributions (Pickett and Cadenasso, 1995). Fine-scale approaches like HERCULES (Cadenasso et al, 2007) and "ecotopes" (Ellis et al, 2000;Chan and Paelinckx, 2008), while highly detailed, combine social use and biophysical parameters within a single patch, but each of the resulting urban types/ecotopes are independent of each other, thereby obscuring how they are related in terms of physical features and ecological functions (Wiens et al, 1993;Steenberg et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

An Urban Biodiversity Assessment Framework That Combines an Urban Habitat Classification Scheme and Citizen Science Data

Parker

Pauly

et al. 2019

Front. Ecol. Evol.

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A lack of information about urban habitats, and a lack of professionally-collected species occurrence data are often cited as major impediments to completing bioassessments in urban landscapes. We developed an urban biodiversity assessment framework that addresses these challenges. The proposed framework combines a customized hierarchical urban habitat classification scheme with citizen science-generated species occurrence data, such as iNaturalist and eBird. It integrates publicly available data on the physical and anthropogenic environment with species occurrence information and serves as a novel method for conducting urban biodiversity assessments. This framework provides insights into how species occurrences within an urban landscape are associated with spatial variation in the physical and anthropogenic environment. It can also yield information useful for planning and conservation management aimed at maintaining and enhancing the abundance and diversity of native and other desirable species in urban areas. This framework requires minimal taxonomic expertise on the part of those who employ it, and it can be implemented in urban areas worldwide, wherever adequate data exist. We demonstrate the application of this framework in the highly urbanized portion of Los Angeles County, California, USA. Our demonstration used 18 physical and anthropogenic variables to classify our study area into nine urban habitat types. We then assessed relationships between these urban habitat types with species occurrences using research-grade data from iNaturalist. This analysis detected significant differences in distributions of some species between these nine urban habitat types and demonstrated that the proposed framework can be used to conduct urban biodiversity assessments. With increasing availability of remote sensing data and publiclygenerated biodiversity data, this framework may be used for analysis of urban areas around the globe.

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The Multi-Resolution Land Characteristics (MRLC) Consortium — 20 Years of Development and Integration of USA National Land Cover Data

Abstract: The Multi-Resolution

Cited by 127 publications

References 46 publications

Comparing the Climatic and Landscape Risk Factors for Lyme Disease Cases in the Upper Midwest and Northeast United States

Comparing the Climatic and Landscape Risk Factors for Lyme Disease Cases in the Upper Midwest and Northeast United States

Influence of high-resolution data on the assessment of forest fragmentation

An Urban Biodiversity Assessment Framework That Combines an Urban Habitat Classification Scheme and Citizen Science Data

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