Preprints
https://doi.org/10.5194/essd-2022-379
https://doi.org/10.5194/essd-2022-379
02 Jan 2023
 | 02 Jan 2023
Status: a revised version of this preprint was accepted for the journal ESSD and is expected to appear here in due course.

Annual forest and evergreen forest cover maps in the Brazilian Amazon in terms of FAO's forest definition

Yuanwei Qin, Xiangming Xiao, Hao Tang, Ralph Dubayah, Russell Doughty, Diyou Liu, Fang Liu, Yosio Shimabukuro, Egidio Arai, Xinxin Wang, and Berrien Moore III

Abstract. Many forest cover maps have been generated by using optical and/or microwave images and various forest definitions, but these forest cover maps have large discrepancies. Both forest definition and validation data used for accuracy assessment of forest cover maps are often considered as two of the major factors for the discrepancy among these forest cover maps. To date, few studies have assessed forest cover maps in terms of two biophysical parameters used in forest definition: (1) tree canopy height and (2) canopy coverage. We generated annual forest cover maps from 2007 to 2010 and evergreen forest cover maps from 2000 to 2021 in the Brazilian Amazon using the images from the Phased Array type L-band Synthetic Aperture Radar and Moderate Resolution Imaging Spectroradiometer, and the forest definition of the Food and Agriculture Organization (FAO) of the United Nations (>5-m tree height and >10 % canopy coverage). The canopy height and coverage datasets from the Geoscience Laser Altimeter System during 2003–2007 were used to assess annual forest cover maps from 2007 to 2010 and evergreen forest cover maps from 2003 to 2007, and the results show high accuracy of these forest and evergreen forest cover maps in the Brazilian Amazon.

Yuanwei Qin et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-379', Anonymous Referee #1, 12 Apr 2023
    • AC1: 'Reply on RC1', Xiangming Xiao, 31 Aug 2023
  • RC2: 'Comment on essd-2022-379', Anonymous Referee #2, 11 Jun 2023
    • AC2: 'Reply on RC2', Xiangming Xiao, 31 Aug 2023
    • AC4: 'Reply on RC2', Xiangming Xiao, 31 Aug 2023
  • RC3: 'Comment on essd-2022-379', Anonymous Referee #3, 02 Jul 2023
    • AC3: 'Reply on RC3', Xiangming Xiao, 31 Aug 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-379', Anonymous Referee #1, 12 Apr 2023
    • AC1: 'Reply on RC1', Xiangming Xiao, 31 Aug 2023
  • RC2: 'Comment on essd-2022-379', Anonymous Referee #2, 11 Jun 2023
    • AC2: 'Reply on RC2', Xiangming Xiao, 31 Aug 2023
    • AC4: 'Reply on RC2', Xiangming Xiao, 31 Aug 2023
  • RC3: 'Comment on essd-2022-379', Anonymous Referee #3, 02 Jul 2023
    • AC3: 'Reply on RC3', Xiangming Xiao, 31 Aug 2023

Yuanwei Qin et al.

Data sets

Annual PALSAR/MODIS forest and evergreen forest maps in the Brazilian Amazon Yuanwei Qin, Xiangming Xiao https://doi.org/10.6084/m9.figshare.21445590

Model code and software

Codes for forest and evergreen forest mapping in the Brazilian Amazon Yuanwei Qin, Xiangming Xiao https://doi.org/10.6084/m9.figshare.21445626

Yuanwei Qin et al.

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Short summary
Forest definition has two major biophysical parameters, i.e., tree canopy height and canopy coverage. However, few studies have assessed forest cover maps in terms of these two parameters at a large scale. Here, we assessed the annual forest cover maps in the Brazilian Amazon using 1.1 million footprints of canopy height and canopy coverage. Over 93 % of our forest cover maps are consistent with the FAO forest definition, showing the high accuracy of these forest cover maps.
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