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

Xiaolin Zhu,Elvia J Meléndez-Ackerman,Sean Fleming,Jiaqi Tian,David Gwenzi,Eileen H Helmer,Humfredo Marcano-Vega,Melissa Collin,Jess K Zimmerman

doi:10.3390/rs13234736

Abstract

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.

Highlights

This article is an open access articleClimate change is lengthening dry seasons in many tropical regions [1,2] and changing the vegetation phenology [3], and it is intensifying drought, heat, fires, storms, and flooding [1]
This study proposed a framework of reconstructing high-quality Landsat time series for monitoring dry-season phenology of tropical forests
Fifteen dry-season phenology metrics were defined. These metrics were extracted from vegetation index (VI) time series derived from reconstructed Landsat imagery through a robust phenology-detection method

Summary

Introduction

Climate change is lengthening dry seasons in many tropical regions [1,2] and changing the vegetation phenology [3], and it is intensifying drought, heat, fires, storms, and flooding [1]. Tree mortality from drought, heat, and storms is increasing worldwide [8,9,10], in more seasonal tropical forests and savannas [5,9,11]. These changes could cause tropical distributed under the terms and conditions of the Creative Commons. Drought-related changes in the seasonal phenology of greenness patterns in satellite imagery, i.e., dryseason phenology, can yield insights into how tropical forests may respond to climate change [15,16,17,18]

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Conclusion