The National Land Cover Database (NLCD) is an operational land cover monitoring program providing updated land cover and related information for the United States at five-year intervals. NLCD2016 ...extends temporal coverage to 15 years (2001–2016). We collected land cover reference data for the 2011 and 2016 nominal dates to report land cover accuracy for the NLCD2016 database 2011 and 2016 land cover components at Level II and Level I and for Level I 2011–2016 land cover change using two definitions of agreement. For both the 2011 and 2016 land cover components, single-date Level II overall accuracies (OA) were 72% (standard error of ±0.9%) when agreement was defined as match between the map label and primary reference label only and 86% (± 0.7%) when agreement also included the alternate reference label. The corresponding level I OA for both dates were 79% (± 0.9%) and 91% (± 1.0%). The 2011–2016 user's and producer's accuracies (UA and PA) were ~ 75% for forest loss and PA for water loss, grassland loss, and grass gain were > 70% when agreement included a match between the map label and either the primary or alternate reference label. Depending on agreement definition and level of the classification hierarchy, OA for the 2011 land cover component of the NLCD2016 database was about 4% to 7% higher than OA for the 2011 land cover component of the NLCD2011 database, suggesting that the changes in mapping methodologies initiated for production of the NLCD2016 database have led to improved product quality. Additionally, we used the reference dataset collected for assessment of the NLCD2011 database to assess the 2001 and 2006 land cover components of the NLCD2016 database. OA for the 2001 and 2006 land cover components was 1% - 5% lower than OA for the 2011 and 2016 land cover components of the NLCD2016 database. Higher OA for 2011 and 2016 land cover components of the NLCD2016 database relative to OA for its 2001 and 2006 components may be attributable to differences in reference data quality.
•Level II & I Overall accuracy (OA) of NLCD2016 land cover (LC) was 86.4% & 90.6%.•NLCD2016 database Level II 2011 LC OA improved by 5% over its prior release.•Forest loss user's Accuracies (UA) and producer's accuracies PA were 75%.•PA was >70% for 2011–2016 grassland loss, grassland gain, and water loss.•More research on reference data quality is needed.
Release of NLCD 2006 provides the first wall-to-wall land-cover change database for the conterminous United States from Landsat Thematic Mapper (TM) data. Accuracy assessment of NLCD 2006 focused on ...four primary products: 2001 land cover, 2006 land cover, land-cover change between 2001 and 2006, and impervious surface change between 2001 and 2006. The accuracy assessment was conducted by selecting a stratified random sample of pixels with the reference classification interpreted from multi-temporal high resolution digital imagery. The NLCD Level II (16 classes) overall accuracies for the 2001 and 2006 land cover were 79% and 78%, respectively, with Level II user's accuracies exceeding 80% for water, high density urban, all upland forest classes, shrubland, and cropland for both dates. Level I (8 classes) accuracies were 85% for NLCD 2001 and 84% for NLCD 2006. The high overall and user's accuracies for the individual dates translated into high user's accuracies for the 2001–2006 change reporting themes water gain and loss, forest loss, urban gain, and the no-change reporting themes for water, urban, forest, and agriculture. The main factor limiting higher accuracies for the change reporting themes appeared to be difficulty in distinguishing the context of grass. We discuss the need for more research on land-cover change accuracy assessment.
► We assess the thematic accuracy of NLCD 2006 land cover and impervious surface. ► Nationally, NLCD 2001 and 2006 overall accuracies were 79% and 78%, respectively. ► Land cover change accuracies were high for forest loss, urban gain, and water flux. ► More research on land-cover change reference label assignment is needed.
Pixels, blocks of pixels, and polygons are all potentially viable spatial assessment units for conducting an accuracy assessment. We develop a population-based statistical framework to examine how ...the spatial unit chosen affects the outcome of an accuracy assessment. The population is conceptualized as a difference map created by overlaying a complete coverage reference classification and the target map being evaluated. The per-class areas of agreement and disagreement derived from this population are summarized by a population error matrix and accuracy parameters (e.g., overall, user's and producer's accuracies). The population and values of the accuracy parameters are strongly affected by the protocols implemented for the response design which include the choice of spatial unit, how within-unit homogeneity is addressed when assigning class labels, and the definition of agreement between the reference and map classification. Several complete coverage populations are used to illustrate how accuracy results are affected by the spatial unit chosen for the assessment and also to evaluate how spatial misregistration of the map and reference locations impacts accuracy results for different spatial units. The sampling design implemented for accuracy assessment does not change the population or values of the accuracy parameters, but the choice of spatial unit will influence decisions regarding use of strata and clusters in the design. A universally best spatial assessment unit does not exist, so it is critical to recognize how the population, values of the accuracy parameters, and sampling design are impacted by the choice of spatial unit.
►Pixels, blocks of pixels, and polygons are viable accuracy assessment spatial units ►The spatial units are evaluated from the perspective of a statistical population ►Complete coverage populations illustrate quantitatively the effect of spatial unit ►The effect of spatial location error is examined ►The choice of spatial unit influences sampling design decisions
Accuracy assessment is a standard protocol of National Land Cover Database (NLCD) mapping. Here we report agreement statistics between map and reference labels for NLCD 2011, which includes land ...cover for ca. 2001, ca. 2006, and ca. 2011. The two main objectives were assessment of agreement between map and reference labels for the three, single-date NLCD land cover products at Level II and Level I of the classification hierarchy, and agreement for 17 land cover change reporting themes based on Level I classes (e.g., forest loss; forest gain; forest, no change) for three change periods (2001–2006, 2006–2011, and 2001–2011). The single-date overall accuracies were 82%, 83%, and 83% at Level II and 88%, 89%, and 89% at Level I for 2011, 2006, and 2001, respectively. Many class-specific user's accuracies met or exceeded a previously established nominal accuracy benchmark of 85%. Overall accuracies for 2006 and 2001 land cover components of NLCD 2011 were approximately 4% higher (at Level II and Level I) than the overall accuracies for the same components of NLCD 2006. The high Level I overall, user's, and producer's accuracies for the single-date eras in NLCD 2011 did not translate into high class-specific user's and producer's accuracies for many of the 17 change reporting themes. User's accuracies were high for the no change reporting themes, commonly exceeding 85%, but were typically much lower for the reporting themes that represented change. Only forest loss, forest gain, and urban gain had user's accuracies that exceeded 70%. Lower user's accuracies for the other change reporting themes may be attributable to the difficulty in determining the context of grass (e.g., open urban, grassland, agriculture) and between the components of the forest-shrubland-grassland gradient at either the mapping phase, reference label assignment phase, or both. NLCD 2011 user's accuracies for forest loss, forest gain, and urban gain compare favorably with results from other land cover change accuracy assessments.
•Overall accuracy (OA) of NLCD 2011 land cover is 83% (Level II) and 89% (Level I).•OA of NLCD 2001 & 2006 versions in NLCD 2011 improved by 4% over previous releases.•User's accuracies (UA) for forest loss & gain, & urban gain exceeded 70%.•UAs for forest loss & gain, & urban gain compare favorably with other studies.•UAs for other change themes were lower due to inherent class definition ambiguity.
The Multi-Resolution Land Characteristics (MRLC) Consortium demonstrates the national benefits of USA Federal collaboration. Starting in the mid-1990s as a small group with the straightforward goal ...of compiling a comprehensive national Landsat dataset that could be used to meet agencies' needs, MRLC has grown into a group of 10 USA Federal Agencies that coordinate the production of five different products, including the National Land Cover Database (NLCD), the Coastal Change Analysis Program (C-CAP), the Cropland Data Layer (CDL), the Gap Analysis Program (GAP), and the Landscape Fire and Resource Management Planning Tools (LANDFIRE). As a set, the products include almost every aspect of land cover from impervious surface to detailed crop and vegetation types to fire fuel classes. Some products can be used for land cover change assessments because they cover multiple time periods. The MRLC Consortium has become a collaborative forum, where members share research, methodological approaches, and data to produce products using established protocols, and we believe it is a model for the production of integrated land cover products at national to continental scales. We provide a brief overview of each of the main products produced by MRLC and examples of how each product has been used. We follow that with a discussion of the impact of the MRLC program and a brief overview of future plans.
Revisiting the Landscape Mosaic model Vogt, Peter; Wickham, James; Barredo, José Ignacio ...
PloS one,
05/2024, Letnik:
19, Številka:
5
Journal Article
Recenzirano
Odprti dostop
The landscape mosaic model quantifies and maps the spatial juxtaposition of different land uses. It provides a landscape perspective of anthropic threats posed by agriculture and urban development, ...and the spatial-temporal shifting of the landscape mosaic indicates landscapes where anthropic intensity has changed. We use the U.S. Geological Survey provided National Land Cover Database (NLCD) for the years 2001 and 2021 to derive the landscape mosaic at five analysis scales. To improve earlier implementations of the model, we introduce the heatmap, a flexible scheme providing more thematic reporting opportunities and allowing for better quantitative summary reporting across analysis scales as well as for temporal trends. The results are exemplified at regional scale for the Atlanta metropolitan area. We use the improved model to investigate the land cover context over time and at different analysis scales and show how custom color tables detail different thematic features of the landscape mosaic, including the degree and change of anthropic intensity. We conclude with a discussion of potential applications in ecology, landscape planning, and restoration to illustrate the benefits of the revised landscape mosaic model. All assessment tools are now available in open-source software packages.
To evaluate the long-term safety and efficacy of adipose-derived mesenchymal stem cell (ADMSC) therapy in the treatment of knee osteoarthritis (OA).
329 participants with knee OA underwent ...intra-articular ADMSC therapy. Participants were followed up for 24 months and were separated based on radiological OA grade.
Treatment was well tolerated with no related serious adverse events. All participant groups reported clinically and statistically significant pain improvement. Clinical outcome was not influenced by patients' age or BMI.
ADMSC therapy is an effective, safe and long-lasting treatment option for knee OA with the potential to delay total joint replacement. In addition to the observed clinical benefits, ADMSC therapy promises to reduce the global economic burden of OA. Trial registration number: ACTRN12617000638336.
Green infrastructure is a popular framework for conservation planning. The main elements of green infrastructure are hubs and links. Hubs tend to be large areas of ‘natural’ vegetation and links tend ...to be linear features (e.g., streams) that connect hubs. Within the United States, green infrastructure projects can be characterized as: (1) reliant on classical geographic information system (GIS) techniques (e.g., overlay, buffering) for mapping; (2), mainly implemented by states and local jurisdictions; and (3) static assessments that do not routinely incorporate information on land-cover change. We introduce morphological spatial pattern analysis (MSPA) as a complementary way to map green infrastructure, extend the geographic scope to the conterminous United States, and incorporate land-cover change information. MSPA applies a series of image processing routines to a raster land-cover map to identify hubs, links, and related structural classes of land cover. We identified approximately 4000 large networks (>100 hubs) within the conterminous United States, of which approximately 10% crossed state boundaries. We also identified a net loss of up to 3.59 million ha of links and 1.72 million ha of hubs between 1992 and 2001. Our national assessment provides a backbone that states could use to coordinate their green infrastructure projects, and our incorporation of change illustrates the importance of land-cover dynamics for green infrastructure planning and assessment.
The National Land Cover Database (NLCD), a product suite produced through the MultiResolution Land Characteristics (MRLC) consortium, is an operational land cover monitoring program. Starting from a ...base year of 2001, NLCD releases a land cover database every 2-3-years. The recent release of NLCD2019 extends the database to 18 years. We implemented a stratified random sample to collect land cover reference data for the 2016 and 2019 components of the NLCD2019 database at Level II and Level I of the classification hierarchy. For both dates, Level II land cover overall accuracies (OA) were 77.5% ± 1% (± value is the standard error) when agreement was defined as a match between the map label and primary reference label only, and increased to 87.1% ± 0.7% when agreement was defined as a match between the map label and either the primary or alternate reference label. At Level I of the classification hierarchy, land cover OA was 83.1% ± 0.9% for both 2016 and 2019 when agreement was defined as a match between the map label and primary reference label only, and increased to 90.3% ± 0.7% when agreement also included the alternate reference label. The Level II and Level I OA for the 2016 land cover in the NLCD2019 database were 5% higher compared to the 2016 land cover component of the NLCD2016 database when agreement was defined as a match between the map label and primary reference label only. No improvement was realized by the NLCD2019 database when agreement also included the alternate reference label. User's accuracies (UA) for forest loss and grass gain were>70% when agreement included either the primary or alternate label, and UA was generally<50% for all other change themes. Producer's accuracies (PA) were>70% for grass loss and gain and water gain and generally<50% for the other change themes. We conducted a post-analysis review for map-reference agreement to identify patterns of disagreement, and these findings are discussed in the context of potential adjustments to mapping and reference data collection procedures that may lead to improved map accuracy going forward.
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