Description

The project “Enabling Centralized Access to Land Cover Data for Climate Change Integrated Assessment Modeling” is making selected MODIS land cover data available as global mosaics at resolutions suitable for use with global models. This project is part of NASA’s Advancing Collaborative Connections for Earth System Science (ACCESS) program. The Joint Global Change Research Institute, a joint activity of the Pacific Northwest National Laboratory and the University of Maryland, and the Global Land Cover Facility of the University of Maryland collaborate in this activity.

Land use is an essential human activity to meet demands for food, fiber, shelter, and natural resources. Additionally, biomass grown specifically to meet energy demands is increasingly important as an alternative energy source, and maintaining ecosystems with higher carbon stocks is an option for mitigating climate change. But land cover changes to meet human demands for resources and land use in themselves cause greenhouse gas emissions and affect the climate through biophysical interactions. Thus, a variety of global, integrated assessment and Earth system models represent not only the commitments of land and terrestrial natural resources to meeting human demands, but also the corresponding changes in terrestrial carbon pools and carbon exchanges with the atmosphere, other greenhouse gas emissions due to land use, and changes in land surface characteristics that affect climate.

Global Model Requirements

In terms of temporal stability and consistent spatial coverage, the standard land cover data products derived from Moderate Resolution Imaging Spectroradiometer (MODIS) observations by NASA’s Terra and Aqua spacecraft provide one of the highest quality representations of global land cover over a sufficient period to investigate change.

But a number of factors complicate the use of standard MODIS data products for representing land attributes within global models:

  • Retrieving an attribute from a set of tiles or iterating over tiles in order to represent a land attribute globally is inefficient and prone to error;

  • Within global models, locations typically are specified in terms of latitude and longitude making spatial reference to MODIS land product tiles in a sinusoidal projection incompatible with model coordinates;

  • The storage required to represent a land attribute globally at the native spatial resolutions of MODIS standard data products is prohibitive in both complex and more simplified models; and

  • Many Earth system models are computationally demanding and require efficient interfaces.

While moderate spatial resolution land cover data are useful in studies at local to regional scales, to be manageable within Earth system models and applications, global geospatial land cover data need to be at coarser resolutions than are nominal for standard remote sensing data products (e.g., 500 m MODIS land cover data).

For example, a single byte attribute of the standard MODIS land cover type data product at the 500 m resolution requires approximately 3 Gbytes of storage for global coverage. As a result, within integrated assessment models and more complex Earth system models, land attributes typically are represented at resolutions of 5' latitude × 5' longitude or 2.5' × 2.5'. Simplified models may represent land attributes at even coarser resolutions such as 0.5° latitude × 0.5° longitude.

Storage requirements for 1 Byte attributes at typical resolutions.

Resolution Rows Columns Storage
500 m Pixels 35,520 86,400 3.07 Gbytes
5' x 5' 1,776 4,320 7.67 Mbytes
0.5° x 0.5° 296 720 213 Kbytes

The complexity of data structures is also an issue. The data structures required to manipulate a collection of tiles are significantly more complicated than a single array of uniform pixels covering the Earth’s surface. While in many cases operations involving a single array can take advantage of matrix representations and efficient algorithms for array manipulation and processing, iterating over a set of tiles while maintaining their geographic context can be inefficient, complicated to implement, and error prone. For example, a simple operation on a single array such as extracting a two-dimensional slice of data can be complicated with a set of separate tiles where the slice spans parts of multiple tiles.

Global Land Cover Mosaics

Global mosaics of the standard MODIS data product MCD12Q1, Version 5.1, in the IGBP classification of land cover types were derived from the standard MODIS data product MCD12Q1 Version 5.1 collection. The project processed data for each year in the period 2001-2012.

The standard MODIS land cover data product is produced at 500 m spatial resolution in a sinusoidal projection. In order to provide data in convenient file sizes, the Earth’s surface is divided into a grid of non-overlapping tiles, of which 460 contain land cover data. At the Equator, each tile covers approximately 10° latitude x 10° longitude (Figure 1).

MODIS-Sinusoidal-Grid

Figure 1. Grid of non-overlapping tiles that organize the standard MODIS land cover type data product in a sinusoidal projection.

In order to facilitate the use of MODIS land cover data with other observations, especially imagery collected by Landsat satellites, we reprojected MODIS tiles containing data into geographic coordinates of latitude and longitude and created mosaics of adjacent tiles sufficient to cover areas in geographic coordinates that align well with Landsat tracks (Figure 2).

GLCF-tile-scheme

Figure 2. GLCF tile scheme used to organize land cover data reprojected into geographic coordinates.

Using the Geospatial Data Abstraction Library (GDAL), continental mosaics of tiles on the native sinusoidal grid (Figure 1) were created (gdal_merge). The gdalwarp utility was used to reproject these continental mosaics into geographic coordinates of latitude and longitude and to crop these reprojected continental mosaics to GLCF tiles (Figure 2). Insofar as possible, native resolution was preserved.

Global mosaics of the re-projected tiles were created using the gdal_merge utility. At the native 500 m spatial resolution of the MODIS data, each global mosaic comprised approximately 3 gigabyte of 1 byte data in GeoTIFF format.

Land cover types were assigned to coarser resolution elements by assuming the most frequently occurring type at the higher resolution of the source data. A Python script was used to aggregate the native resolution elements contained within global mosaics. A frequency distribution of land cover types assigned to native resolution pixels within each coarser resolution element was derived using the bincount function of the NumPy package for scientific computing with Python. The most frequently occurring type was assigned to the coarser resolution element.

Global Mosaics of Vegetation Continuous Fields

Processing of vegetation continuous fields data (e.g., the MOD44B standard MODIS product describing fraction of tree cover), is identical to that for land cover type; however, the native resolution of the VCF data is 250 m, and processing contends with unassigned pixels.

Each pixel of the arrays containing spatially aggregated data is assigned a value in the range 0 <= z <= 100 expressing the fraction of the area of the pixel in tree cover as [%]. The value z = 200 indicates a pixel containing water, and the value z = 253 designates a pixel not assigned data.

Where a coarser resolution pixel (5' x 5' or 0.5° x 0.5°) contains native resolution (250 m) pixels that are not assigned a value (z = 253), a value is estimated for the coarser resolution only if the ration of native resolution pixels not assigned data to the total number of native resolution pixels within the coarser resolution pixel is less than a threshold:

  • Coarse resolution of 5' x 5': 5%
  • Coarse resolution of 0.5° x 0.5°: 20% .

Where the fraction of pixels not assigned data is less than the threshold, percent tree cover is estimated as the ration of tree cover area represented by pixels assigned data to the total area of pixels assigned data.

In order to provide compatibility with land cover data, all elements assigned as water in the land cover data were also assigned as water in the corresponding VCF data. Since the land cover data begin in 2001, the 2001 data were used to assign water in the 2000 VCF data.

References

Friedl, M. A., D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley,, and X. Huang. 2010.
MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets.
Remote Sensing of Environment 114:168–182.

García-Mora, T. J., J.-F. Mas, and E. A. Hinkley. 2012. Land cover mapping applications with MODIS: A
literature review. International Journal of Digital Earth 5:63–87.

Hansen, M. C., R. S. DeFries, J. R. G. Townshend, M. Carroll, C. DiMiceli, and R. A. Sohlberg. 2003. Global percent tree cover at a spatial resolution of 500 meters: First results of the MODIS Vegetation Continuous Fields algorithm. Earth Interactions 7:1-15.

Strahler, A., D. Muchoney, J. Borak, M. Friedl, S. Gopal, E. Lambin, and A. Moody. 1999. MODIS Land
Cover Product Algorithm Theoretical Basis Document (ATBD) Version 5.0: MODIS Land Cover and
Land-Cover Change. Boston University, Boston, Massachusetts.

Townshend, J., M. Hansen, M. Carroll, C. DiMiceli, R. Sohlberg, C. Huang. User Guide for the MODIS Vegetation Continuous Fields Product Collection 5, Version 1.

Zhan, X., R. DeFries, M. Hansen, J. Townshend, C. DiMiceli, R. Sohlberg, and C. Huang. 1999. MODIS Enhanced Land Cover and and Cover Change Product Algorithm Theoretical Basis Documents (ATBD) Version 2.0. Department of Geography, University of Maryland, College Park, Maryland. 

Geospatial Data Abstraction Library (GDAL). http://www.gdal.org/.

MODIS Land Data Product Grids. http://modis-land.gsfc.nasa.gov/MODLAND_grid.html.

NumPy Fundamental Package for Scientific Computing with Python. http://www.numpy.org/.

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