Collection 1 Hawaii Science Products

Note: Products for the final year (2020) in Collection 1.0 are considered provisional, and users are encouraged to use the Spectral Model Quality (SCMQA) product for additional information on the type of time series model available for 2020.

Sources/Usage: Public Domain. View Media Details

The need for improved 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, cover 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) algorithm (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 Hawaii Collection 1.0 Science Products include 10 annual products for the years 2000–2020. Five land surface change products were produced directly from CCDC time series models, and five land cover products were produced by the classification of the time series models. Products in the final year of the dataset, 2020, are considered provisional for the initial release (see the LCMAP Hawaii Science Product Guide for more information). 

LCMAP Hawaii Collection 1.0 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.

Visit the Collections page to learn more about the differences between the LCMAP Collections.

Product Availability and Data Access

LCMAP Hawaii Collection 1.0 Science Products are available for the conterminous United States, 2000–2020, with 2020 considered provisional. Products are currently available via EarthExplorer as well as on the LCMAP Web Viewer and LCMAP Mosaic websites and in the LCMAP WMS. LCMAP Hawaii Collection 1.0 Science Products available for download through EarthExplorer include 10 individual products for a single year packaged and delivered in a single .tar file. The .tar packages “untar” (unzip) into 10 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 in the LCMAP Hawaii Science Product Guide.

LCMAP Science Products

LCMAP Collection v1 Science Products are available for the conterminous United States from 2000–2020, with 2020 considered provisional status. 

LCMAP Ten 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 (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 previous product year and the LCPRI of the current 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 predictions.
  • Delivered file name: *_SCTIME.tif
• Change Magnitude (SCMAG): Provides information on the spectral strength or intensity of a time series model “break” where spectral observations have diverged from CCDC model predictions.
  • Delivered file name: *_SCMAG.tif
• Time Since Last Change (SCLAST): Represents the time, in days, from July 1 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 1 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 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) and *.txt files (compatible with QGIS) 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.

• SCTIME Layer File
• SCMAG Layer File
• SCLAST Layer File
• SCSTAB Layer File
• SCMQA Layer File
• SCTIME Text file
• SCMAG Text file
• SCLAST Text file
• SCSTAB Text file
• SCMQA Text file

Product Validation

A validation assessment of the LCMAP Hawaii Collection 1.0 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 land cover agreement across all years was found to be 83.4%. Annual and regional accuracies are also reported. For more information on the independent reference data set, see the LCMAP Reference Data webpage.

Caveats and Constraints

• Consistency across the LCMAP collections is nuanced and use of multiple collections in the same analysis is discouraged. The addition of new Landsat observations in later data releases can result in changes to the CCDC harmonic model fits for models that were unbroken in previous releases. These changing harmonics can then affect the land cover probabilities assigned by the classifier. For more information about differences between Collections in LCMAP, visit the Collections page.
• Products in the final year of the LCMAP Hawaii Collection 1.0 Science Products dataset release, 2020, are 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 toward the end of the modeling period. These conditions can be identified in the Spectral Model Quality (SCMQA) product. 
• 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 are less than a year apart and that would normally cause model breaks  would not be recorded in the Time of Spectral Change (SCTIME) and Change Magnitude (SCMAG) products. 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 data 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 Landsat Collection 1 U.S. 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 day of year (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 1^st, 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 1^st 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 1^st of the following product year.

For individual product caveats and constraints, see the LCMAP Hawaii Collection 1.0 Science Product Guide.

Documentation

LCMAP Hawaii Collection 1.0 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 Hawaii Collection 1.0 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 Hawaii Collection 1.0 Science Products. The ADD gives in-depth descriptions of how various components of CCDC operate and how the products and product values are derived.

LCMAP Hawaii Collection 1.0 Data Format Control Book (DFCB)

• Provides detailed information on data formats for the LCMAP Hawaii Collection 1.0 Science Products. This includes information on product and file specifications, product packaging, and metadata file examples.

LCMAP Hawaii Reference Data Product Guide

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

LCMAP Hawaii CCDC product "bundle" Digital Object Identifier (DOI): https://doi.org/10.5066/P91E8M23

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 Hawaii Collection 1.0 Science Products courtesy of the U.S. Geological Survey.

Li, C., Xian, G., Wellington, D., Smith, K., Horton, J., Zhou, Q., 2022. Development of the LCMAP annual land cover product across Hawaiʻi. Int. J. Appl. Earth Obs. Geoinf.,  113, http://doi.org/10.1016/j.jag.2022.103015.

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, v. 238, article 111356, at https://doi.org/10.1016/j.rse.2019.111356.

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

Li, C., Xian, G., Wellington, D., Smith, K., Horton, J., Zhou, Q., 2022. Development of the LCMAP annual land cover product across Hawaiʻi. Int. J. Appl. Earth Obs. Geoinf.,  113, http://doi.org/10.1016/j.jag.2022.103015.

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, v. 238, article 111356, at https://doi.org/10.1016/j.rse.2019.111356.

Dwyer, J.L., Roy, D.P., Sauer, B., Jenkerson, C.B., Zhang, H.K., and Lymburner, L., 2018, Analysis Ready Data —Enabling analysis of the Landsat archive: Remote Sensing, v. 10, no. 9, article 1363, at 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., Hughes, M.J., and Laue, B., 2017, Cloud detection algorithm comparison and validation for operational Landsat data products: Remote Sensing of  Environment, v. 194, p. 379–390, at https://doi.org/10.1016/j.rse.2017.03.026.

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