首页|Monitoring leaf phenology in moist tropical forests by applying a superpixel-based deep learning method to time-series images of tree canopies

Monitoring leaf phenology in moist tropical forests by applying a superpixel-based deep learning method to time-series images of tree canopies

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  • NETL
  • NSTL
  • Elsevier
Tropical leaf phenology-particularly its variability at the tree-crown scale-dominates the seasonality of carbon and water fluxes. However, given enormous species diversity, accurate means of monitoring leaf phenology in tropical forests is still lacking. Time series of the Green Chromatic Coordinate (GCC) metric derived from tower-based red-greenblue (RGB) phenocams have been widely used to monitor leaf phenology in temperate forests, but its application in the tropics remains problematic. To improve monitoring of tropical phenology, we explored the use of a deep learning model (i.e. superpixel-based Residual Networks 50, SP-ResNet50) to automatically differentiate leaves from non-leaves in phenocam images and to derive leaf fraction at the tree-crown scale. To evaluate our model, we used a year of data from six phenocams in two contrasting forests in Panama. We first built a comprehensive library of leaf and non-leaf pixels across various acquisition times, exposure conditions and specific phenocams. We then divided this library into training and testing components. We evaluated the model at three levels: 1) superpixel level with a testing set, 2) crown level by comparing the model-derived leaf fractions with those derived using image-specific supervised classification, and 3) temporally using all daily images to assess the diurnal stability of the model-derived leaf fraction. Finally, we compared the model-derived leaf fraction phenology with leaf phenology derived from GCC. Our results show that: 1) the SP-ResNet50 model accurately differentiates leaves from non-leaves (overall accuracy of 93%) and is robust across all three levels of evaluations; 2) the model accurately quantifies leaf fraction phenology across tree-crowns and forest ecosystems; and 3) the combined use of leaf fraction and GCC helps infer the timing of leaf emergence, maturation and senescence, critical information for modeling photosynthetic seasonality of tropical forests. Collectively, this study offers an improved means for automated tropical phenology monitoring using phenocams.

Residual NetworksSemantic segmentationProximate remote sensingLeaf quantityLeaf qualityGreen Chromatic CoordinateTropical forestsPhenocam

Song, Guangqin、Wu, Shengbiao、Lee, Calvin K. F.、Serbin, Shawn P.、Wolfe, Brett T.、Ng, Michael K.、Ely, Kim S.、Bogonovich, Marc、Wang, Jing、Lin, Ziyu、Saleska, Scott、Nelson, Bruce W.、Rogers, Alistair、Wu, Jin

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Univ Hong Kong, Sch Biol Sci, Pokfulam Rd, Hong Kong, Peoples R China

Brookhaven Natl Lab, Dept Environm & Climate Sci, Upton, NY 11973 USA

Louisiana State Univ, Sch Renewable Nat Resources, Agr Ctr, Baton Rouge, LA 70803 USA|Smithsonian Trop Res Inst, Balboa, Panama

Univ Hong Kong, Dept Math, Hong Kong, Peoples R China

Univ Arizona, Dept Ecol & Evolutionary Biol, Tucson, AZ USA

Natl Inst Amazon Res INPA, Manaus, Amazonas, Brazil

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2022

10.1016/j.isprsjprs.2021.10.023

ISPRS journal of photogrammetry and remote sensing

ISPRS journal of photogrammetry and remote sensing

EISCI
ISSN：0924-2716
年,卷(期)：2022.183(Jan.)
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