Images

Annual NLCD land cover confidence product provides the level of confidence that Annual NLCD land cover class per pixel values that matches the training data. The image shown is around Marysville, WA.

Depicts the day-of-year (DOY) on which a significant deviation in Landsat surface reflectance was detected within the calendar year of an area around Marysville, WA

Depicts the day-of-year (DOY) on which a significant deviation in Landsat surface reflectance was detected within the calendar year.  Area around Marysville, WA is depicted in the image.

Annual NLCD generates a product suite of six land use and land cover themes from 1985-2023. The Annual NLCD Land Cover product Depicts the predominant thematic land cover class within the mapping year with respect to broad categories of artificial or natural surface cover. The image shows an area around Marysville, WA.

Annual NLCD land cover change product shows the change between the current year’s primary land cover and the prior year’s land cover when there is a change in primary class. The image shows an area around Marysville, WA.

Depicts the fractional surface area of the map unit (pixel) that is covered with artificial substrate or structures. The image shows an area around Marysville, WA.

Depicts the fractional surface area of the map unit (pixel) that is covered with artificial substrate or structures in an area around Marysville, WA. 

Annual NLCD generates a product suite of six land use and land cover themes from 1985-2023. The Annual NLCD Land Cover product Depicts the predominant thematic land cover class within the mapping year with respect to broad categories of artificial or natural surface cover. The image shows an area around Marysville, WA.

The Annual National Land Cover Database (NLCD) impervious descriptor product generates a raster layer that differentiates between urban and road surfaces. The area depicted is around Marysville, WA

The Annual National Land Cover Database (NLCD) impervious descriptor product generates a raster layer that differentiates between urban and road surfaces. The image shows an area around Marysville, WA.

The Annual National Land Cover Database (NLCD) impervious descriptor product generates a raster layer that differentiates between urban and road surfaces. 

Annual National Land Cover Database (Annual NLCD) land cover legend contains 16 land cover classes based on the Anderson Level II classification system. 

The Annual National Land Cover Database (NLCD) land cover change legend contains all of the annual NLCD land cover classes plus the land cover classification change classes. Annual NLCD Land Cover Change land cover change product contains a total of 241 land cover classes.

Annual NLCD land cover change product shows the change between the current year’s primary land cover and the prior year’s land cover when there is a change in primary class. The area shown is around Marysville, WA.

An animation of images from the Annual National Land Cover Database (NLCD) for every year from 1985 through 2023 showing land cover changes and the progression of growth of urban areas (in red) in the northern Dallas-Fort Worth, Texas, metropolitan area.

An Annual NLCD Land Cover map of centered around Lake Stevens, WA depicting 16 land cover classes.

The Annual NLCD product suite offers six different land use and land cover products. The Annual NLCD Land Cover product depicts the predominant thematic land cover of 16 Anderson Level II classes within the current mapping year. The Annual NLCD Land Cover of the conterminous United States shows all Anderson Level II classes.

Western and Eastern region eVIIRS Remote Sensing Phenology Start of Season for 2023.

To assess the accuracy of Annual NLCD, Collection 1.0, a team of image interpreters review Landsat imagery, high resolution aerial photography, and other supporting data to make annual land cover classifications for thousands of 30-meter by 30-meter plot locations for the full 1985-2023 time series. This process is broken down into two phases.

Rangeland managers need information on the current vegetation condition and how the landscape has changed through time. To address this need, scientsist from the USGS and Bureau of Land Management (BLM) developed the Rangeland Condition Monitoring Assessment and Projection (RCMAP) project. These data classify U.S.

Clearcutting in a forest in the Ouachita Mountains of Arkansas and Oklahoma.  Heavy machinery is being used to cut trees and haul lumber away to another location.