Characterization of Dry-Season Phenology in Tropical Forests by Reconstructing Cloud-Free Landsat Time Series

Remote Sensing

Sean Fleming¹  David Gwenzi¹  Melissa Collin¹  Jess K. Zimmerman²  Elvia J. Meléndez-Ackerman²  Xiaolin Zhu³  Jiaqi Tian³  Eileen H. Helmer⁴  Humfredo Marcano-Vega⁵ 

[1] Department of Environmental Science & Management, Humboldt State University, Arcata, CA 95521, USA;Department of Environmental Sciences, University of Puerto Rico, Río Piedras, San Juan, PR 00926, USA;Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, China;International Institute of Tropical Forestry, USDA Forest Service, Río Piedras, San Juan, PR 00926, USA;Southern Research Station, USDA Forest Service, Knoxville, TN 37919, USA;

关键词: Landsat;    time series;    tropical forests;    phenology;    dry season;    PhenoCam;   

DOI  :  10.3390/rs13234736

来源: DOAJ

【 摘 要 】

Fine-resolution satellite imagery is needed for characterizing dry-season phenology in tropical forests since many tropical forests are very spatially heterogeneous due to their diverse species and environmental background. However, fine-resolution satellite imagery, such as Landsat, has a 16-day revisit cycle that makes it hard to obtain a high-quality vegetation index time series due to persistent clouds in tropical regions. To solve this challenge, this study explored the feasibility of employing a series of advanced technologies for reconstructing a high-quality Landsat time series from 2005 to 2009 for detecting dry-season phenology in tropical forests; Puerto Rico was selected as a testbed. We combined bidirectional reflectance distribution function (BRDF) correction, cloud and shadow screening, and contaminated pixel interpolation to process the raw Landsat time series and developed a thresholding method to extract 15 phenology metrics. The cloud-masked and gap-filled reconstructed images were tested with simulated clouds. In addition, the derived phenology metrics for grassland and forest in the tropical dry forest zone of Puerto Rico were evaluated with ground observations from PhenoCam data and field plots. Results show that clouds and cloud shadows are more accurately detected than the Landsat cloud quality assessment (QA) band, and that data gaps resulting from those clouds and shadows can be accurately reconstructed (R² = 0.89). In the tropical dry forest zone, the detected phenology dates (such as greenup, browndown, and dry-season length) generally agree with the PhenoCam observations (R² = 0.69), and Landsat-based phenology is better than MODIS-based phenology for modeling aboveground biomass and leaf area index collected in field plots (plot size is roughly equivalent to a 3 × 3 Landsat pixels). This study suggests that the Landsat time series can be used to characterize the dry-season phenology of tropical forests after careful processing, which will help to improve our understanding of vegetation–climate interactions at fine scales in tropical forests.

【 授权许可】

Unknown