2. GLAD Landsat ARD

Multi-temporal metrics are a standard method of time-series data transformation. They simplify the analysis of land surface phenology, facilitate land cover mapping and change detection. The metrics approach was developed in the mid-1980s to extract phenology features from time-series of Normalized Difference Vegetation Index (NDVI) (Badwhar, 1984; Maligreau, 1986). At the same time, the idea of using NDVI time-series to extract spectral reflectance corresponding to certain phenological stages was proposed by Holben (1986). Later, both approaches were merged together by researchers from the Laboratory for Global Remote Sensing Studies at the University of Maryland (DeFries, Hansen, and Townsend, 1995). In their work, metrics were calculated by extracting spectral information for specific phenological stages defined by the NDVI annual dynamics. Later, the multi-temporal metrics were widely used for forest extent and structure monitoring at continental and global scales using MODIS (Hansen et al., 2010) and Landsat data (Hansen et al., 2013; Potapov et al., 2012; 2017; 2019;). The purpose of metrics is to create consistent inputs for annual vegetation mapping and change detection models and to overcome the inconsistency of clear-sky data availability that is typical for sensors with low observation frequency, like Landsat.

 

Implementation of the multi-temporal metric approach requires processing the entire archive of the Landsat observations. To simplify metric processing, the Global Land Analysis and Discovery team (GLAD) developed a Landsat Analysis Ready Data (ARD) product. The Landsat ARD produced by the GLAD team using an automated image processing system. The essence of the GLAD ARD approach is to convert individual Landsat images into a time-series of 16-day normalized surface reflectance composites with minimal atmospheric contamination. The Landsat data processing algorithms were prototyped by Hansen et al. (2008) and Potapov et al. (2012) and was significantly improved in the recent years (Potapov et al., 2019). The ARD serve as a data source to generate metrics suitable for annual land cover, land cover change, vegetation composition and structure mapping.

 

The following sections provide an overview of the Landsat processing methodology, explain ARD structure, and provide data download instructions.

    2.1. Landsat ARD Methodology

    2.2. Landsat ARD Structure

    2.3. Landsat ARD Download

    2.4. Topography Data Download

    2.5. License and Disclaimer

    2.6. User Registration

 

References

  1. Badwhar, G. D. (1984) Use of LANDSAT-derived profile features for spring small-grains classification, Int. J. Remote Sens. 5(5):783-797.
  2. Holben, B. N. (1986), Characteristics of maximum-value composite images from temporal AVHRR data, Int. J. Remote Sens. 12:1147-1163.
  3. Malingreau, J.-P. (1986) Global vegetation dynamics: satellite observations over Asia, Int. J. Remote Sens. 7:1121-1146.
  4. DeFries, R., Hansen, M., Townshend, J. (1995) Global discrimination of land cover types from metrics derived from AVHRR pathfinder data. Remote Sens. Environ. 54:209–222.
  5. Hansen, M., Stehman, S., Potapov, P. (2010) Quantification of global gross forest cover loss. Proc. Natl. Acad. Sci. USA. 107(19):8650–8655.
  6. Potapov, P.V., Turubanova, S.A., Hansen, M.C., Adusei, B., Broich, M., Altstatt, A., Mane, L., Justice, C.O. (2012) Quantifying forest cover loss in Democratic Republic of the Congo, 2000–2010, with Landsat ETM+ data. Remote Sens. Environ. 122:106-116.
  7. Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A.A., Tyukavina, A., Thau, D., Stehman, S.V., Goetz, S.J., Loveland, T.R., Kommareddy, A. (2013) High-resolution global maps of 21st-century forest cover change. Science, 342(6160):850-853.
  8. Potapov, P. V., Turubanova, S.A., Tyukavina, A., Krylov, A.M., McCarty, J.L., Radeloff, V.C., Hansen, M.C. (2015) Eastern Europe’s forest cover dynamics from 1985 to 2012 quantified from the full Landsat archive. Remote Sens. Environ. 159:28–43.
  9. Potapov, P., Tyukavina, A., Turubanova, S., Talero, Y., Hernandez-Serna, A., Hansen, M.C., Saah, D., Tenneson, K., Poortinga, A., Aekakkararungroj, A. and Chishtie, F., 2019. Annual continuous fields of woody vegetation structure in the Lower Mekong region from 2000‐2017 Landsat time-series. Remote Sensing of Environment, 232, p.111278.