LCMAP Collection 1 Science Products

CONUS Science Products

The LCMAP project has generated an integrated suite of annual land cover and land surface change products for the Conterminous United States based on time series data from the Landsat record from 1985–2017. LCMAP Collection 1 Science Products are based on the USGS implementation of the Continuous Change Detection and Classification (CCDC) algorithm.

 

LCMAP CCDC Ten Individual Products

LCMAP Collection 1 Science Products. See the image gallery for images of individual products.

The need for improved understanding and management of land surface change requires increased understanding of the basic drivers of change, identification of potential consequences of change on human and natural systems, and greater insight into the impacts and feedbacks of climate change. The geospatial community requires a new generation of monitoring data and information to meet this need across a wide range of applications. Land cover and land change products need to span larger geographic extents, over longer time periods, at higher spatial resolutions, and provide more systematic and consistent information on change than ever before.

LCMAP Science Products are developed by applying time-series modeling to U.S. Landsat Analysis Ready Data (ARD) to detect land surface change. An application of the Continuous Change Detection and Classification (CCDC, Zhu and Woodcock 2014) was developed by the LCMAP Science Team at the USGS Earth Resources Observation and Science (EROS) Center (Brown et al., 2020). LCMAP Collection 1 Science Products include ten annual products for the years 1985–2017. The final year of products in the dataset, 2017, are considered provisional for the initial release (see LCMAP Science Product Guide for more information). There are five land surface change products, produced directly from CCDC time series models, and five land cover products, produced by the classification of the time series models.

LCMAP Collection 1 Science Products are processed to 30-meter spatial resolution in an Albers Equal Area Conic (AEA) projection using the World Geodetic System 1984 (WGS84) datum and gridded to the Landsat ARD tiling scheme.

Product Availability and Data Access

LCMAP Collection 1 Science Products are available for the conterminous United States from 1985–2017, with 2017 considered provisional status. Products are available via EarthExplorer and the LCMAP Viewer.

EarthExplorer

LCMAP Collection 1 Science Products available for download through EarthExplorer include ten individual products for a single year packaged and delivered in a single .tar file. The .tar packages “untar” (unzip) into ten individual Georeferenced Tagged Image File Format (GeoTIFF) (.tif) files for each product and an Extensible Markup Language (XML) (.xml) metadata file. Additional product specifications are located in the LCMAP Science Product Guide.

LCMAP Viewer

The ten LCMAP Science Products are available for download through the LCMAP Viewer. The LCAMP Viewer allows users to draw a bounding box in the viewer for an area of interest to download the products, metadata, and animated graphics.

Please see the LCMAP Data Access page for more information.

LCMAP Science Products

  • Primary Land Cover (LCPRI): The most likely thematic, classified land cover for the current product year. 
    • Delivered file name: *_LCPRI.tif
  • Secondary Land Cover (LCSEC): The second most likely thematic, classified land cover for the current product year.
    • Delivered file name: *_LCSEC.tif
  • Primary Land Cover Confidence (LCPCONF): Provides a measure of confidence in the Primary Land Cover (LCPRI) label or additional information regarding the provenance of the result if that label was not produced by the initial classification method.
    • Delivered file name: *_LCPCONF.tif
  • Secondary Land Cover Confidence (LCSCONF): Provides a measure of confidence in the Secondary Land Cover Label (LCSEC) label or additional information regarding the provenance of the result if that label was not produced by the initial classification method.
    • Delivered file name: *_LCSCONF.tif
  • Annual Land Cover Change (LCACHG): A synthesis product derived from the Primary Land Cover (LCPRI) of the current product year that the LCPRI of the previous year.
    • Delivered file name: *_LCACHG.tif
  • Time of Spectral Change (SCTIME): Represents the timing of a spectral change within the current product year. Defined as a “break” in a CCDC time series model where spectral observations have diverged from model predications.
    • Delivered file name: *_SCTIME.tif
  • Change Magnitude (SCMAG): Provides information on the spectral strength of intensity of a time series model “break” where spectral observations have diverged form CCDC model predictions.
    • Delivered file name: *_SCMAG.tif
  • Time Since Last Change (SCLAST): Represents the time, in days, from July 1st of the current product year back to the most recent time series model “break” where spectral observations diverged from CCDC model predictions.
    • Delivered file name: *_SCLAST.tif
  • Spectral Stability Period (SCSTAB): Represents the length, in days, of the time series model in effect as of July 1st of the current year.
    • Delivered file name: *_SCSTAB.tif
  • Spectral Model Quality (SCMQA): Provides additional information regarding the type of time series model available in the current product year.
    • Delivered file name: *_SCMQA.tif

Land Surface Change Products Color Ramp Layer Files

The five land surface change products available for download through EarthExplorer (SCTIME, SCMAG, SCLAST, SCSTAB, and SCMQA) and the SCMAG product available through the LCMAP Viewer do not have color ramps applied to them. For the EarthExplorer distributed products, ESRI *.lyr files (compatible with ArcGIS) with the default LCMAP color ramps are provided below. For the LCMAP Viewer products, the appropriate .lyr file is included in the zip file with a README file that details how to apply the color ramps.

Product Validation

A validation assessment of the LCMAP Collection 1 annual land cover products was conducted with an independently collected reference data set. Validation analysis directly compared reference labels with annual LCMAP land cover map attributes by cross tabulation. The results of that assessment are available for download on ScienceBase as confusion matrices for land cover agreement and land cover change agreement. Overall CONUS land cover agreement across all years was found to be 82.5%. Annual and regional accuracies are also reported. For more information on the indpendent reference data set, see the LCMAP Reference Data webpage.

Caveats and Constraints

  • The final year of products in the dataset, 2017 in the case of the LCMAP Collection 1 Science Products initial release, is to be considered provisional. The time series approach can result in a high incidence of either low quality models or model absence due to a shortage of Landsat observations towards the end of the modeling period. These conditions can be identified in the Spectral Model Quality (SCMQA). Forward processing of CCDC with the addition of Landsat observations to create science products for additional years will also provide updated and improved products for the previous provisional year.
  • When more than one spectral change occurs in the span of a year (365 days), only the first change is detected/recorded and the successive changes are not detected/recorded. Time series models are fit to observations with the initial requirement of being one year in length. Final models may represent longer periods of time, but one year is the minimum. As a result, land surface changes that would normally cause model breaks that are less than a year apart would not all be recorded in the Time of Spectral Change (SCTIME) and Change Magnitude (SCMAG). For example, a forest stand may be represented by a stable time series model for several years until a management operation such as thinning causes a model break. That break is recorded in SCTIME and SCMAG. CCDC then attempts to fit a new model with subsequent observations. The same area impacted by wildfire within that first year would not cause an additional model break because a new stable model, of a minimum one year in length, had not been established.
  • CCDC is dependent on the availability of adequate Landsat observations to fit valid time series models. Locations that are persistently cloudy or snow covered will often require alternate procedures to establish a simpler time-series model. There are relatively rare sets of conditions that can cause commission errors in the cloud detection algorithm (Fmask) used to produce the PIXELQA data available in U.S. Landsat ARD (Foga et al. 2017). As CCDC uses PIXELQA to assess cloud contamination and masking, some of these conditions can result in poor models and change detection or possibly a lack of model altogether. This issue may be addressed in future science product versions.
  • Some LCMAP Science Products may exhibit patterns corresponding with Landsat-7 ETM+ SLC-off data gaps under certain conditions. The sensitivity of CCDC to detecting spectral change is impacted by the frequency of input observations. Sensitivity to small, ephemeral changes increases with observation frequency. That observation frequency is affected by data gaps in Landsat-7 ETM+ caused by the SLC-off condition existing from 2003 onwards.
  • Users should be aware of differences in the representation of time or timing between various products. Time of Spectral Change (SCTIME) and Change Magnitude (SCMAG) both correspond to specific points in time where observations diverged from values predicted by the time series models – model breaks. SCTIME reports the specific DOY of breaks observed in a given year while SCMAG is an indicator of the degree of change. Both are attributes of model breaks and are coincident in space and time. All other products represent conditions on July 1st, as a representative date, for the given year. As a result, there can be occurrences where SCTIME and SCMAG reflect a model break after July 1st of a given year which is expected to impact other products (e.g. a change in Primary Land Cover), but that would not be recorded until July 1st of the following product year.

For individual product caveats and constraints, see the LCMAP Collection 1 Science Product Guide.

Documentation

LCMAP Collection 1 Science Product Guide (SPG)

  • Provides an overview of the current LCMAP approach, descriptions of the science products and their characteristics, and other relevant information to facilitate the use of LCMAP Science Products in the land change and land cover science community.

LCMAP Collection 1 Continuous Change Detection and Classification (CCDC) Algorithm Description Document (ADD)

  • Describes the Continuous Change Detection and Classification (CCDC) algorithm that is used to produce LCMAP Collection 1 Science Products. The ADD gives in-depth descriptions of how various components of the CCDC operate and how the products and product values are derived.

LCMAP Collection 1 Data Format Control Book (DFCB)

  • Provides detailed information on data formats for the LCMAP Collection 1 Science Products. This includes information on product and file specifications, product packaging, and metadata file examples.

LCMAP Reference Data Product Guide

  • Describes the current LCMAP Reference Data collection approach, the reference data products and their characteristics, and other relevant information to facilitate use of the LCMAP Reference Data Products in the land change and land cover science community.

LCMAP Science Products Digital Object Identifier (DOI): https://doi.org/10.5066/P9W1TO6E

Citation Information

There are no restrictions on the use of LCMAP Science Products. It is not a requirement of data use, but the following citation may be used in publication or presentation materials to acknowledge the USGS as a data source and to credit the original research.

LCMAP Collection 1 Science Products courtesy of the U.S. Geological Survey.

Brown, J.F., Tollerud, H.J., Barber, C.P., Zhou, Q., Dwyer, J.L., Vogelmann, J.E., Loveland, T.R., Woodcock, C.E., Stehman, S.V., Zhu, Z., Pengra, B.W., Smith, K., Horton, J.A., Xian, G., Auch, R.F., Sohl, T.L., Sayler, K.L., Gallant, A.L., Zelenak, D., Reker, R.R., and Rover, J., (2020). Lessons learned implementing an operational continuous United States national land change monitoring capability: The Land Change Monitoring, Assessment, and Projection (LCMAP) approach. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2019.111356

Zhu, Z., Woodcock, C.E. (2014). Continuous change detection and classification of land cover using all available Landsat data: Remote Sensing of Environment 144: 152–171. https://doi.org/10.1016/j.rse.2014.01.011.

Reprints, citations of papers or oral presentations based on USGS data are welcome to help the USGS stay informed of how data are being used. These can be sent to User Services at custserv@usgs.gov

References

Brown, J.F., Tollerud, H.J., Barber, C.P., Zhou, Q., Dwyer, J.L., Vogelmann, J.E., Loveland, T.R., Woodcock, C.E., Stehman, S.V., Zhu, Z., Pengra, B.W., Smith, K., Horton, J.A., Xian, G., Auch, R.F., Sohl, T.L., Sayler, K.L., Gallant, A.L., Zelenak, D., Reker, R.R., and Rover, J., (2020). Lessons learned implementing an operational continuous United States national land change monitoring capability: The Land Change Monitoring, Assessment, and Projection (LCMAP) approach. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2019.111356

Dwyer, J., Roy, D., Sauer, B., Jenkerson, C., Zhang, H., and Lymburner, L. (2018). Analysis Ready Data: Enabling analysis of the Landsat archive. Remote Sensing. https://doi.org/10.3390/rs10091363

Foga, S., Scaramuzza, P.L., Guo, S., Zhu, Z., Dilley Jr, R.D., Beckmann, T., Schmidt, G.L., Dwyer, J.L., Joseph Hughes, M., and Laue, B., 2017. Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of  Environment, 194, 379-390, https://doi.org/10.1016/j.rse.2017.03.026

Zhu, Z., Woodcock, C.E. (2014). Continuous change detection and classification of land cover using all available Landsat data: Remote Sensing of Environment 144: 152–171. https://doi.org/10.1016/j.rse.2014.01.011.