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Module Overview⚓︎

Reference

Dimiceli, C., M. Carroll, R. Sohlberg, D. H. Kim, M. Kelly, and J. R. G. Townshend. "MOD44B MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250m SIN Grid V006 [Data set], NASA EOSDIS L." Process. DAAC (2015).

Asher, Sam, Tobias Lunt, Ryu Matsuura, and Paul Novosad. "Development research at high geographic resolution: an analysis of night-lights, firms, and poverty in India using the shrug open data platform." The World Bank Economic Review 35, no. 4 (2021): 845-871.

@article{dimiceli2015mod44b,
  title={MOD44B MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250 m SIN Grid V006 [Data Set]},
  author={Dimiceli, C and Carroll, M and Sohlberg, R and Kim, D and Kelly, M and Townshend, J},
  journal={NASA EOSDIS Land Process},
  year={2015}
}

@article{almn2021,
  title={Development research at high geographic resolution: an analysis of night-lights, firms, and poverty in India using the shrug open data platform},
  author={Asher, Sam and Lunt, Tobias and Matsuura, Ryu and Novosad, Paul},
  journal={The World Bank Economic Review},
  volume={35},
  number={4},
  year={2021},
  publisher={Oxford University Press}
}
About Forest cover data (2001-2020) comes from Vegetation Continuous Fields (VCF), a MODIS product that measures tree cover at 250m resolution from 2000 to 2019. VCF is predicted from a machine learning algorithm based on broad spectrum satellite images and trained with human-categorized data, which can distinguish between crops, plantations and primary forest cover.
Geographic Coverage National
Aggregations Shrid, District, Subdistrict, Village/Town
Producer NASA
Source URL https://lpdaac.usgs.gov/products/mod44bv006/
Notes
  • Data is aggregated from Vegetation Continuous Fields (VCF), which provides annual tree cover in the form of the percentage of each pixel under forest cover, generated from a machine learning model based on a combination of images from MODIS and samples from higher resolution satellites (Townshend et al., 2011) and used in Asher, Garg, Novosad 2020.
  • By using broad-spectrum measures, VCF is better at distinguishing plantation from primary and secondary forest, which tends to be characterized by cooler temperatures.
  • As with night lights, we report the maximum and total forest cover value in each geographic unit.
  • We use VCF rather than the widely used Global Forest Change for India, because GFC has limited reporting of forest cover gains, and India has gained forest over the sample period according to most accounts.

Release Details⚓︎

Release Number 2.1
Release Name pakora
Last updated December 05, 2024

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