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Article

Combining Multiple Geospatial Data for Estimating Aboveground Biomass in North Carolina Forests

1
Geomatics Program, Department of Built Environment, North Carolina A&T State University, Greensboro, NC 27411, USA
2
Department of Economics, North Carolina A&T State University, Greensboro, NC 27411, USA
3
Department of Environmental, Earth and Geospatial Sciences, North Carolina Central University, Durham, NC 27707, USA
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Applied Science and Technology Program, North Carolina A&T State University, Greensboro, NC 27411, USA
*
Author to whom correspondence should be addressed.
Leila Hashemi-Beni and Lyubov A. Kurkalova two authors share the same contribution to this article.
Academic Editor: Klaus Scipal
Remote Sens. 2021, 13(14), 2731; https://doi.org/10.3390/rs13142731
Received: 10 June 2021 / Revised: 2 July 2021 / Accepted: 6 July 2021 / Published: 12 July 2021
(This article belongs to the Section Forest Remote Sensing)
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. View Full-Text
Keywords: southeastern U.S.; Crop Data Layer (CDL); National Land Cover Database (NLCD); LiDAR; Forest Inventory Analysis (FIA) southeastern U.S.; Crop Data Layer (CDL); National Land Cover Database (NLCD); LiDAR; Forest Inventory Analysis (FIA)
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MDPI and ACS Style

Hashemi-Beni, L.; Kurkalova, L.A.; Mulrooney, T.J.; Azubike, C.S. Combining Multiple Geospatial Data for Estimating Aboveground Biomass in North Carolina Forests. Remote Sens. 2021, 13, 2731. https://doi.org/10.3390/rs13142731

AMA Style

Hashemi-Beni L, Kurkalova LA, Mulrooney TJ, Azubike CS. Combining Multiple Geospatial Data for Estimating Aboveground Biomass in North Carolina Forests. Remote Sensing. 2021; 13(14):2731. https://doi.org/10.3390/rs13142731

Chicago/Turabian Style

Hashemi-Beni, Leila, Lyubov A. Kurkalova, Timothy J. Mulrooney, and Chinazor S. Azubike 2021. "Combining Multiple Geospatial Data for Estimating Aboveground Biomass in North Carolina Forests" Remote Sensing 13, no. 14: 2731. https://doi.org/10.3390/rs13142731

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