Combining multiple geospatial data for estimating aboveground biomass in North Carolina forests

  • Authors: Hashemi-Beni, Leila; Kurkalova, Lyubov A.; Mulrooney, Timothy J.; Azubike, Chinazor S.
  • Publication Year: 2021
  • Publication Series: Scientific Journal (JRNL)
  • Source: Remote Sensing
  • DOI: 10.3390/rs13142731

Abstract

Mapping and quantifying forest inventories are critical for the management and development of forests for natural resource conservation and for the evaluation of the aboveground forest biomass (AGFB) technically available for bioenergy production. The AGFB estimation procedures that rely on traditional spatially sparse field inventory samples constitute a problem for geographically diverse regions such as the state of North Carolina in the southeastern U.S. We propose an alternative AGFB estimation procedure that combines multiple geospatial data. The procedure uses land cover maps to allocate forested land areas to alternative forest types, uses the light detection and ranging (LiDAR) data to evaluate tree heights, calculates the area total AGFB using region and tree-type-specific functions that relate the tree heights to the AGFB. We demonstrate the procedure
for a selected North Carolina region, a 2.3 km2 area randomly chosen in Duplin County. The tree diameter functions are statistically estimated based on the Forest Inventory Analysis (FIA) data and two publicly available open source land cover maps, Crop Data Layer (CDL) and National Land Cover Database (NLCD) are compared and contrasted as a source of information on the location and typology of forests in the study area. The assessment of the consistency of forestland mapping derived from the CDL and the NLCD data lets us estimate how the disagreement between the two alternative widely used maps affects the AGFB estimation. The methodology and the results we present are expected to complement and inform large scale assessments of woody biomass in the region.

  • Citation: Hashemi-Beni, Leila; Kurkalova, Lyubov A.; Mulrooney, Timothy J.; Azubike, Chinazor S. 2021. Combining Multiple Geospatial Data for Estimating Aboveground Biomass in North Carolina Forests. Remote Sensing. 13(14): 2731-. https://doi.org/10.3390/rs13142731.
  • Keywords: Southeastern U.S., Crop Data Layer (CDL), National Land Cover Database (NLCD), LiDAR, Forest Inventory Analysis (FIA)
  • Posted Date: November 16, 2021
  • Modified Date: November 17, 2021
  • Print Publications Are No Longer Available

    In an ongoing effort to be fiscally responsible, the Southern Research Station (SRS) will no longer produce and distribute hard copies of our publications. Many SRS publications are available at cost via the Government Printing Office (GPO). Electronic versions of publications may be downloaded, printed, and distributed.

    Publication Notes

    • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
    • Our online publications are scanned and captured using Adobe Acrobat. During the capture process some typographical errors may occur. Please contact the SRS webmaster if you notice any errors which make this publication unusable.
    • To view this article, download the latest version of Adobe Acrobat Reader.