High-resolution Land Cover and Impervious Surface Classifications in the Twin Cities Metropolitan Area with NAIP Imagery

Nagel, Philipp; Yuan, Fei

doi:10.14358/pers.83.1.63

Cited by 22 publications

(15 citation statements)

References 17 publications

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“…It is available at the spatial resolution of 0.6 to 2 meters with very low cloud coverage and consists of repeat images during the growing season with two or three year cycles for more than 15 years [1]. It has been a unique choice for a variety of geospatial mapping applications, such as analysis of land cover and land use change [2][3][4], evaluation of ecosystem services [5][6][7], monitoring of forest health [8][9][10][11], and assessment of urban green infrastructure [12,13]. The NAIP imagery will likely continue to be the one of the best data sources for many research and operational efforts that need high-resolution multispectral imagery for feature extraction, change detection, or collection of ground truth for validate coarse-resolution satellite products.…”

Section: Introductionmentioning

confidence: 99%

A New Pseudoinvariant Near-Infrared Threshold Method for Relative Radiometric Correction of Aerial Imagery

2019

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The utilization of high-resolution aerial imagery such as the National Agriculture Imagery Program (NAIP) data is often hampered by a lack of methods for retrieving surface reflectance from digital numbers. This study developed a new relative radiometric correction method to retrieve 1 m surface reflectance from NAIP imagery. The advantage of this method lies in the adaptive identification of pseudoinvariant (PIV) pixels from a time series of Landsat images that can fully characterize the temporally spectral variations of land surface. The identified PIV pixels allow for an effective conversion of digital numbers to surface reflectance, as demonstrated through the validation at 150 sites across the contiguous United States. The results show substantial improvement in the agreement of NAIP-derived normalized difference vegetation index (NDVI) values with Landsat-derived NDVI reference. Across the sites, root mean square error and mean absolute error were reduced from 0.37 ± 0.14 to 0.08 ± 0.07 and from 0.91 ± 0.64 to 0.18 ± 0.52, respectively. Over 70% PIV pixels on average were derived from vegetated areas, while water and developed areas together contributed 27% of the PIV pixels. As the NAIP program is continuing to generate new images across the country, the advantages of its high spatial resolution, national coverage, long time series, and regular revisits will make it an increasingly crucial data source for a variety of research and management applications. The proposed method could benefit many agricultural, hydrological, and urban studies that rely on NAIP imagery to quantify land surface patterns and dynamics. It could also be applied to improve the preprocessing of high-resolution aerial imagery in other countries.

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Section: Introductionmentioning

confidence: 99%

A New Pseudoinvariant Near-Infrared Threshold Method for Relative Radiometric Correction of Aerial Imagery

2019

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“…Studies that have used NAIP imagery at large extents have addressed this challenge by limiting classification to a small number of classes that address specific land cover questions [11] in combination with the use of specialized computer hardware [10]. However, due to recent advances in image analysis software, we are now able to conduct broad extent fine resolution land cover classification using standard computer hardware more efficiently than was previously possible [34].…”

Section: Imagery and Datamentioning

confidence: 99%

“…In forest management, land cover classifications are frequently used to inform management activities such as timber harvest [6], forest restoration [7], fire risk mitigation [8], and preservation of rare habitats [9]. From land cover classification datasets, relevant objectives such as locating forested and non-forested areas [10] or determining the proportion of impervious surface occupying landscape [11] can be quickly addressed. Land cover classifications can also be used as a component of more complex analyses of landscape characteristics [12] and can be used to describe important characteristics of forest and woodland ecosystems, such as percent canopy cover, understory composition within open forests, and the degree of fragmentation.…”

Section: Introductionmentioning

confidence: 99%

Fine Resolution Probabilistic Land Cover Classification of Landscapes in the Southeastern United States

Peter

Hogland

Anderson

et al. 2018

IJGI

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Abstract:Land cover classification provides valuable information for prioritizing management and conservation operations across large landscapes. Current regional scale land cover geospatial products within the United States have a spatial resolution that is too coarse to provide the necessary information for operations at the local and project scales. This paper describes a methodology that uses recent advances in spatial analysis software to create a land cover classification over a large region in the southeastern United States at a fine (1 m) spatial resolution. This methodology used image texture metrics and principle components derived from National Agriculture Imagery Program (NAIP) aerial photographic imagery, visually classified locations, and a softmax neural network model. The model efficiently produced classification surfaces at 1 m resolution across roughly 11.6 million hectares (28.8 million acres) with less than 10% average error in modeled probability. The classification surfaces consist of probability estimates of 13 visually distinct classes for each 1 m cell across the study area. This methodology and the tools used in this study constitute a highly flexible fine resolution land cover classification that can be applied across large extents using standard computer hardware, common and open source software and publicly available imagery.

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“…Feature Analyst, an object-based classifier that makes use of not only spectral, but also spatial information in the imagery, was used. It was found that for remote sensing images with a low spectral variation, especially for the historical B&W aerial photography, the object-based approach and hierarchical learning process provided by the Feature Analyst were very useful [29,30]. Training polygons for forest and agriculture land-cover classes were carefully digitized from the high spatial resolution airborne imagery for both years, as they are the two prevailing land-cover types shown in the changed areas of stream networks.…”

Section: Field Survey and Land Cover Change Analysis In A Focused Stumentioning

confidence: 99%

Mapping and Analyzing Stream Network Changes in Watonwan River Watershed, Minnesota, USA

Yuan

Larson

Mulvihill

et al. 2017

IJGI

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Abstract:Much of the Watonwan River tributary system to the upper Mississippi River basin (UMR), and the fluvial systems to which it drains, are listed as impaired under the United States Environmental Protection Agency Clean Water Act303(d) and/or by the Minnesota Pollution Control Agency. In addition, eutrophic conditions and excessive sedimentation rates exist in Lake Pepin, a riverine lake to which the UMR drains. Thus, understanding the hydrogeomorphic change throughout the UMR is vital in order to establish appropriate efforts to mitigate environmental hazards downstream. This study attempts to evaluate hydrogeomorphic change at the watershed scale in the Watonwan River watershed between 1855 and the near present. Historical plat maps, digital elevation models (DEMs), aerial images, soil/topographic characteristics, land-use change, and field surveys are analyzed. Surficial hydrologic features digitized from historical plat maps are compared with contemporary stream networks extracted from high-resolution DEMs. Scale effects are investigated using multi-resolution (1 m, 3 m, 8.5 m, and 30 m) DEMs, with 8.5 m DEMs being ideal for watershed scale analysis, and 1-3 m DEMs being ideal for subwatershed analysis. There has been a substantial hydrogeomorphic change in the watershed since 1855, but most significantly, we interpret that the highest rates of erosion occur in the eastern watershed, where knickzone propagation has produced substantial relief.

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High-resolution Land Cover and Impervious Surface Classifications in the Twin Cities Metropolitan Area with NAIP Imagery

Cited by 22 publications

References 17 publications

A New Pseudoinvariant Near-Infrared Threshold Method for Relative Radiometric Correction of Aerial Imagery

A New Pseudoinvariant Near-Infrared Threshold Method for Relative Radiometric Correction of Aerial Imagery

Fine Resolution Probabilistic Land Cover Classification of Landscapes in the Southeastern United States

Mapping and Analyzing Stream Network Changes in Watonwan River Watershed, Minnesota, USA

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