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Remote Sens. 2017, 9(2), 123; doi:10.3390/rs9020123

Predictions of Tropical Forest Biomass and Biomass Growth Based on Stand Height or Canopy Area Are Improved by Landsat-Scale Phenology across Puerto Rico and the U.S. Virgin Islands

1
Department of Environmental Science and Management, Humboldt State University, Arcata, CA 95521, USA
2
International Institute of Tropical Forestry, USDA Forest Service, Río Piedras, PR 00926, USA
3
Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, Hung Hom, Hong Kong, China
4
Department of Ecosystem Science and Sustainability, Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA
5
Southern Research Station, USDA Forest Service, Knoxville, TN 37919, USA
*
Author to whom correspondence should be addressed.
Received: 22 November 2016 / Accepted: 25 January 2017 / Published: 1 February 2017
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Abstract

Remotely-sensed estimates of forest biomass are usually based on various measurements of canopy height, area, volume or texture, as derived from LiDAR, radar or fine spatial resolution imagery. These measurements are then calibrated to estimates of stand biomass that are primarily based on tree stem diameters. Although humid tropical forest seasonality can have low amplitudes compared with temperate regions, seasonal variations in growth-related factors like temperature, humidity, rainfall, wind speed and day length affect both tropical forest deciduousness and tree height-diameter relationships. Consequently, seasonal patterns in spectral measures of canopy greenness derived from satellite imagery should be related to tree height-diameter relationships and hence to estimates of forest biomass or biomass growth that are based on stand height or canopy area. In this study, we tested whether satellite image-based measures of tropical forest phenology, as estimated by indices of seasonal patterns in canopy greenness constructed from Landsat satellite images, can explain the variability in forest deciduousness, forest biomass and net biomass growth after already accounting for stand height or canopy area. Models of forest biomass that added phenology variables to structural variables similar to those that can be estimated by LiDAR or very high-spatial resolution imagery, like canopy height and crown area, explained up to 12% more variation in biomass. Adding phenology to structural variables explained up to 25% more variation in Net Biomass Growth (NBG). In all of the models, phenology contributed more as interaction terms than as single-effect terms. In addition, models run on only fully-forested plots performed better than models that included partially-forested plots. For forest NBG, the models produced better results when only those plots with a positive growth, from Inventory Cycle 1 to Inventory Cycle 2, were analyzed, as compared to models that included all plots View Full-Text
Keywords: Landsat; phenology; forest deciduousness; forest biomass; forest biomass growth; forest carbon stocks Landsat; phenology; forest deciduousness; forest biomass; forest biomass growth; forest carbon stocks
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Gwenzi, D.; Helmer, E.H.; Zhu, X.; Lefsky, M.A.; Marcano-Vega, H. Predictions of Tropical Forest Biomass and Biomass Growth Based on Stand Height or Canopy Area Are Improved by Landsat-Scale Phenology across Puerto Rico and the U.S. Virgin Islands. Remote Sens. 2017, 9, 123.

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