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

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

doi:10.1016/j.isprsjprs.2021.10.023

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

Journal Article · Sun Oct 10 00:00:00 EDT 2021 · ISPRS Journal of Photogrammetry and Remote Sensing

DOI:https://doi.org/10.1016/j.isprsjprs.2021.10.023· OSTI ID:1830191

Song, Guangqin ^[1]; Wu, Shengbiao ^[1]; Lee, Calvin K.F. ^[1]; Serbin, Shawn P. ^[2]; Wolfe, Brett T. ^[3]; Ng, Michael K. ^[1]; Ely, Kim S. ^[2]; Bogonovich, Marc ^[1]; Wang, Jing ^[1]; Lin, Ziyu ^[1]; Saleska, Scott ^[4]; Nelson, Bruce W. ^[5]; ^[2]; Wu, Jin ^[1]

Univ. of Hong Kong (Hong Kong)
Brookhaven National Lab. (BNL), Upton, NY (United States)
Louisiana State Univ., Baton Rouge, LA (United States); Smithsonian Tropical Research Institute, Balboa (Republic of Panama)
Univ. of Arizona, Tucson, AZ (United States)
National Institute for Amazon Research (INPA), Manaus (Brazil)

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-green-blue (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. Here, 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.

View Accepted Manuscript (DOE)

Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: Hong Kong Research Grant Council; National Natural Science Foundation of China (NSFC); USDOE; USDOE Office of Science (SC)

Grant/Contract Number:: SC0012704

OSTI ID:: 1830191

Alternate ID(s):: OSTI ID: 1829928

Report Number(s):: BNL--222355-2021-JAAM

Journal Information:: ISPRS Journal of Photogrammetry and Remote Sensing, Journal Name: ISPRS Journal of Photogrammetry and Remote Sensing Vol. 183; ISSN 0924-2716

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (72)

Beyond leaf color: Comparing camera-based phenological metrics with leaf biochemical, biophysical, and spectral properties throughout the growing season of a temperate deciduous forest: Seasonality of leaf properties Yang, Xi; Tang, Jianwu; Mustard, John F. Journal of Geophysical Research: Biogeosciences, Vol. 119, Issue 3 https://doi.org/10.1002/2013JG002460	journal	March 2014
Using the U‐net convolutional network to map forest types and disturbance in the Atlantic rainforest with very high resolution images Wagner, Fabien H.; Sanchez, Alber; Tarabalka, Yuliya Remote Sensing in Ecology and Conservation, Vol. 5, Issue 4 https://doi.org/10.1002/rse2.111	journal	February 2019
Deciduousness in a seasonal tropical forest in western Thailand: interannual and intraspecific variation in timing, duration and environmental cues Williams, Laura J.; Bunyavejchewin, Sarayudh; Baker, Patrick J. Oecologia, Vol. 155, Issue 3 https://doi.org/10.1007/s00442-007-0938-1	journal	January 2008
Superpixel based land cover classification of VHR satellite image combining multi-scale CNN and scale parameter estimation Chen, Yangyang; Ming, Dongping; Lv, Xianwei Earth Science Informatics, Vol. 12, Issue 3 https://doi.org/10.1007/s12145-019-00383-2	journal	April 2019
A review of assessing the accuracy of classifications of remotely sensed data Congalton, Russell G. Remote Sensing of Environment, Vol. 37, Issue 1 https://doi.org/10.1016/0034-4257(91)90048-B	journal	July 1991
First operational BRDF, albedo nadir reflectance products from MODIS Schaaf, Crystal B.; Gao, Feng; Strahler, Alan H. Remote Sensing of Environment, Vol. 83, Issue 1-2 https://doi.org/10.1016/S0034-4257(02)00091-3	journal	November 2002
Monitoring vegetation phenology using MODIS Zhang, Xiaoyang; Friedl, Mark A.; Schaaf, Crystal B. Remote Sensing of Environment, Vol. 84, Issue 3 https://doi.org/10.1016/S0034-4257(02)00135-9	journal	March 2003
Digital repeat photography for phenological research in forest ecosystems Sonnentag, Oliver; Hufkens, Koen; Teshera-Sterne, Cory Agricultural and Forest Meteorology, Vol. 152 https://doi.org/10.1016/j.agrformet.2011.09.009	journal	January 2012
What drives the seasonality of photosynthesis across the Amazon basin? A cross-site analysis of eddy flux tower measurements from the Brasil flux network Restrepo-Coupe, Natalia; da Rocha, Humberto R.; Hutyra, Lucy R. Agricultural and Forest Meteorology, Vol. 182-183 https://doi.org/10.1016/j.agrformet.2013.04.031	journal	December 2013
Using phenological cameras to track the green up in a cerrado savanna and its on-the-ground validation Alberton, Bruna; Almeida, Jurandy; Helm, Raimund Ecological Informatics, Vol. 19 https://doi.org/10.1016/j.ecoinf.2013.12.011	journal	January 2014
Usability of time-lapse digital camera images to detect characteristics of tree phenology in a tropical rainforest Nagai, Shin; Ichie, Tomoaki; Yoneyama, Aogu Ecological Informatics, Vol. 32 https://doi.org/10.1016/j.ecoinf.2016.01.006	journal	March 2016
Deciduousness in tropical trees and its potential as indicator of climate change: A review Singh, K. P.; Kushwaha, C. P. Ecological Indicators, Vol. 69 https://doi.org/10.1016/j.ecolind.2016.04.011	journal	October 2016
Spectral analysis of amazon canopy phenology during the dry season using a tower hyperspectral camera and modis observations de Moura, Yhasmin Mendes; Galvão, Lênio Soares; Hilker, Thomas ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 131 https://doi.org/10.1016/j.isprsjprs.2017.07.006	journal	September 2017
Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning Kemker, Ronald; Salvaggio, Carl; Kanan, Christopher ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 145 https://doi.org/10.1016/j.isprsjprs.2018.04.014	journal	November 2018
A light and faster regional convolutional neural network for object detection in optical remote sensing images Ding, Peng; Zhang, Ye; Deng, Wei-Jian ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 141 https://doi.org/10.1016/j.isprsjprs.2018.05.005	journal	July 2018
Deep multi-task learning for a geographically-regularized semantic segmentation of aerial images Volpi, Michele; Tuia, Devis ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 144 https://doi.org/10.1016/j.isprsjprs.2018.06.007	journal	October 2018
Superpixel-enhanced deep neural forest for remote sensing image semantic segmentation Mi, Li; Chen, Zhenzhong ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 159 https://doi.org/10.1016/j.isprsjprs.2019.11.006	journal	January 2020
Identifying and mapping individual plants in a highly diverse high-elevation ecosystem using UAV imagery and deep learning Zhang, Ce; Atkinson, Peter M.; George, Charles ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 169 https://doi.org/10.1016/j.isprsjprs.2020.09.025	journal	November 2020
SceneNet: Remote sensing scene classification deep learning network using multi-objective neural evolution architecture search Ma, Ailong; Wan, Yuting; Zhong, Yanfei ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 172 https://doi.org/10.1016/j.isprsjprs.2020.11.025	journal	February 2021
Review on Convolutional Neural Networks (CNN) in vegetation remote sensing Kattenborn, Teja; Leitloff, Jens; Schiefer, Felix ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 173 https://doi.org/10.1016/j.isprsjprs.2020.12.010	journal	March 2021
Remote sensing image segmentation advances: A meta-analysis Kotaridis, Ioannis; Lazaridou, Maria ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 173 https://doi.org/10.1016/j.isprsjprs.2021.01.020	journal	March 2021
Introducing digital cameras to monitor plant phenology in the tropics: applications for conservation Alberton, Bruna; Torres, Ricardo da S.; Cancian, Leonardo F. Perspectives in Ecology and Conservation, Vol. 15, Issue 2 https://doi.org/10.1016/j.pecon.2017.06.004	journal	April 2017
Leaf flush drives dry season green-up of the Central Amazon Lopes, Aline Pontes; Nelson, Bruce Walker; Wu, Jin Remote Sensing of Environment, Vol. 182 https://doi.org/10.1016/j.rse.2016.05.009	journal	September 2016
A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach Cai, Yaping; Guan, Kaiyu; Peng, Jian Remote Sensing of Environment, Vol. 210 https://doi.org/10.1016/j.rse.2018.02.045	journal	June 2018
An object-based convolutional neural network (OCNN) for urban land use classification Zhang, Ce; Sargent, Isabel; Pan, Xin Remote Sensing of Environment, Vol. 216 https://doi.org/10.1016/j.rse.2018.06.034	journal	October 2018
Assessing spring phenology of a temperate woodland: A multiscale comparison of ground, unmanned aerial vehicle and Landsat satellite observations Berra, Elias Fernando; Gaulton, Rachel; Barr, Stuart Remote Sensing of Environment, Vol. 223 https://doi.org/10.1016/j.rse.2019.01.010	journal	March 2019
Land-cover classification with high-resolution remote sensing images using transferable deep models Tong, Xin-Yi; Xia, Gui-Song; Lu, Qikai Remote Sensing of Environment, Vol. 237 https://doi.org/10.1016/j.rse.2019.111322	journal	February 2020
Both near-surface and satellite remote sensing confirm drought legacy effect on tropical forest leaf phenology after 2015/2016 ENSO drought Gonçalves, Nathan Borges; Lopes, Aline Pontes; Dalagnol, Ricardo Remote Sensing of Environment, Vol. 237 https://doi.org/10.1016/j.rse.2019.111489	journal	February 2020
Multi-scale integration of satellite remote sensing improves characterization of dry-season green-up in an Amazon tropical evergreen forest Wang, Jing; Yang, Dedi; Detto, Matteo Remote Sensing of Environment, Vol. 246 https://doi.org/10.1016/j.rse.2020.111865	journal	September 2020
Mapping large-scale and fine-grained urban functional zones from VHR images using a multi-scale semantic segmentation network and object based approach Du, Shouhang; Du, Shihong; Liu, Bo Remote Sensing of Environment, Vol. 261 https://doi.org/10.1016/j.rse.2021.112480	journal	August 2021
Random Forests Breiman, Leo Machine Learning, Vol. 45, Issue 1, p. 5-32 https://doi.org/10.1023/A:1010933404324	journal	January 2001
Amazon rainforests green-up with sunlight in dry season Huete, Alfredo R.; Didan, Kamel; Shimabukuro, Yosio E. Geophysical Research Letters, Vol. 33, Issue 6 https://doi.org/10.1029/2005GL025583	journal	January 2006
Amazon forests did not green-up during the 2005 drought: AMAZON DROUGHT SENSITIVITY Samanta, Arindam; Ganguly, Sangram; Hashimoto, Hirofumi Geophysical Research Letters, Vol. 37, Issue 5 https://doi.org/10.1029/2009GL042154	journal	March 2010
Dry-Season Greening and Water Stress in Amazonia: The Role of Modeling Leaf Phenology Manoli, Gabriele; Ivanov, Valeriy Y.; Fatichi, Simone Journal of Geophysical Research: Biogeosciences, Vol. 123, Issue 6 https://doi.org/10.1029/2017JG004282	journal	June 2018
Deep learning LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey Nature, Vol. 521, Issue 7553 https://doi.org/10.1038/nature14539	journal	May 2015
Dry-season greening of Amazon forests Saleska, Scott R.; Wu, Jin; Guan, Kaiyu Nature, Vol. 531, Issue 7594 https://doi.org/10.1038/nature16457	journal	March 2016
Photosynthetic seasonality of global tropical forests constrained by hydroclimate Guan, Kaiyu; Pan, Ming; Li, Haibin Nature Geoscience, Vol. 8, Issue 4 https://doi.org/10.1038/ngeo2382	journal	March 2015
Resource acquisition and reproductive strategies of tropical forest in response to the El Niño–Southern Oscillation Detto, Matteo; Wright, S. Joseph; Calderón, Osvaldo Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-03306-9	journal	March 2018
Improved NDVI based proxy leaf-fall indicator to assess rainfall sensitivity of deciduousness in the central Indian forests through remote sensing Singh, Beependra; Jeganathan, C.; Rathore, V. S. Scientific Reports, Vol. 10, Issue 1 https://doi.org/10.1038/s41598-020-74563-2	journal	October 2020
Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery Richardson, Andrew D.; Hufkens, Koen; Milliman, Tom Scientific Data, Vol. 5, Issue 1 https://doi.org/10.1038/sdata.2018.28	journal	March 2018
Seasonal and interannual variability of climate and vegetation indices across the Amazon Brando, P. M.; Goetz, S. J.; Baccini, A. Proceedings of the National Academy of Sciences, Vol. 107, Issue 33 https://doi.org/10.1073/pnas.0908741107	journal	August 2010
Light-driven growth in Amazon evergreen forests explained by seasonal variations of vertical canopy structure Tang, Hao; Dubayah, Ralph Proceedings of the National Academy of Sciences, Vol. 114, Issue 10 https://doi.org/10.1073/pnas.1616943114	journal	February 2017
Very high resolution remote sensing image classification with SEEDS-CNN and scale effect analysis for superpixel CNN classification Lv, Xianwei; Ming, Dongping; Chen, YangYang International Journal of Remote Sensing, Vol. 40, Issue 2 https://doi.org/10.1080/01431161.2018.1513666	journal	September 2018
Seasonal changes in photosynthesis of eight savanna tree species Eamus, D.; Myers, B.; Duff, G. Tree Physiology, Vol. 19, Issue 10 https://doi.org/10.1093/treephys/19.10.665	journal	August 1999
Training Deep Convolutional Neural Networks for Land–Cover Classification of High-Resolution Imagery Scott, Grant J.; England, Matthew R.; Starms, William A. IEEE Geoscience and Remote Sensing Letters, Vol. 14, Issue 4 https://doi.org/10.1109/LGRS.2017.2657778	journal	April 2017
Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification Maggiori, Emmanuel; Tarabalka, Yuliya; Charpiat, Guillaume IEEE Transactions on Geoscience and Remote Sensing, Vol. 55, Issue 2 https://doi.org/10.1109/TGRS.2016.2612821	journal	February 2017
Superpixel-Based Difference Representation Learning for Change Detection in Multispectral Remote Sensing Images Gong, Maoguo; Zhan, Tao; Zhang, Puzhao IEEE Transactions on Geoscience and Remote Sensing, Vol. 55, Issue 5 https://doi.org/10.1109/TGRS.2017.2650198	journal	May 2017
Superpixel-Based Multiple Local CNN for Panchromatic and Multispectral Image Classification Zhao, Wei; Jiao, Licheng; Ma, Wenping IEEE Transactions on Geoscience and Remote Sensing, Vol. 55, Issue 7 https://doi.org/10.1109/TGRS.2017.2689018	journal	July 2017
Unsupervised-Restricted Deconvolutional Neural Network for Very High Resolution Remote-Sensing Image Classification Tao, Yiting; Xu, Miaozhong; Zhang, Fan IEEE Transactions on Geoscience and Remote Sensing, Vol. 55, Issue 12 https://doi.org/10.1109/TGRS.2017.2734697	journal	December 2017
SLIC Superpixels Compared to State-of-the-Art Superpixel Methods Achanta, R.; Shaji, A.; Smith, K. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, Issue 11, p. 2274-2282 https://doi.org/10.1109/TPAMI.2012.120	journal	November 2012
Tropical phenology: Recent advances and perspectives Sakai, Shoko; Kitajima, Kaoru Ecological Research, Vol. 34, Issue 1 https://doi.org/10.1111/1440-1703.1131	journal	January 2019
Fourier analysis to detect phenological cycles using long-term tropical field data and simulations Bush, Emma R.; Abernethy, Katharine A.; Jeffery, Kathryn Methods in Ecology and Evolution, Vol. 8, Issue 5 https://doi.org/10.1111/2041-210X.12704	journal	December 2016
Do dynamic global vegetation models capture the seasonality of carbon fluxes in the Amazon basin? A data-model intercomparison Restrepo-Coupe, Natalia; Levine, Naomi M.; Christoffersen, Bradley O. Global Change Biology, Vol. 23, Issue 1 https://doi.org/10.1111/gcb.13442	journal	August 2016
Amazon drought and forest response: Largely reduced forest photosynthesis but slightly increased canopy greenness during the extreme drought of 2015/2016 Yang, Jia; Tian, Hanqin; Pan, Shufen Global Change Biology, Vol. 24, Issue 5 https://doi.org/10.1111/gcb.14056	journal	February 2018
Plant phenology and global climate change: Current progresses and challenges Piao, Shilong; Liu, Qiang; Chen, Anping Global Change Biology, Vol. 25, Issue 6 https://doi.org/10.1111/gcb.14619	journal	April 2019
Cryptic phenology in plants: Case studies, implications, and recommendations Albert, Loren P.; Restrepo‐Coupe, Natalia; Smith, Marielle N. Global Change Biology, Vol. 25, Issue 11 https://doi.org/10.1111/gcb.14759	journal	August 2019
Leaf aging of Amazonian canopy trees as revealed by spectral and physiochemical measurements Chavana-Bryant, Cecilia; Malhi, Yadvinder; Wu, Jin New Phytologist, Vol. 214, Issue 3 https://doi.org/10.1111/nph.13853	journal	February 2016
Convergence in relationships between leaf traits, spectra and age across diverse canopy environments and two contrasting tropical forests Wu, Jin; Chavana-Bryant, Cecilia; Prohaska, Neill New Phytologist, Vol. 214, Issue 3 https://doi.org/10.1111/nph.14051	journal	July 2016
Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest Wu, Jin; Kobayashi, Hideki; Stark, Scott C. New Phytologist, Vol. 217, Issue 4 https://doi.org/10.1111/nph.14939	journal	December 2017
Age-dependent leaf physiology and consequences for crown-scale carbon uptake during the dry season in an Amazon evergreen forest Albert, Loren P.; Wu, Jin; Prohaska, Neill New Phytologist, Vol. 219, Issue 3 https://doi.org/10.1111/nph.15056	journal	March 2018
Tracking seasonal rhythms of plants in diverse ecosystems with digital camera imagery Richardson, Andrew D. New Phytologist, Vol. 222, Issue 4 https://doi.org/10.1111/nph.15591	journal	December 2018
Leaf reflectance spectroscopy captures variation in carboxylation capacity across species, canopy environment and leaf age in lowland moist tropical forests Wu, Jin; Rogers, Alistair; Albert, Loren P. New Phytologist, Vol. 224, Issue 2 https://doi.org/10.1111/nph.16029	journal	June 2019
Leaf development and demography explain photosynthetic seasonality in Amazon evergreen forests Wu, J.; Albert, L. P.; Lopes, A. P. Science, Vol. 351, Issue 6276 https://doi.org/10.1126/science.aad5068	journal	February 2016
Phenology of tropical forests: patterns, causes, and consequences Reich, P. B. Canadian Journal of Botany, Vol. 73, Issue 2 https://doi.org/10.1139/b95-020	journal	February 1995
Generative adversarial networks Goodfellow, Ian; Pouget-Abadie, Jean; Mirza, Mehdi Communications of the ACM, Vol. 63, Issue 11 https://doi.org/10.1145/3422622	journal	October 2020
Panama tropical forest digital camera imagery for vegetation phenology, Dec2016-May2019, PA-SLZ, PA-PNM, PA-BCI Wu, Jin; Wolfe, Brett; Serbin, Shawn Next-Generation Ecosystem Experiments Tropics; Brookhaven National Laboratory; STRI https://doi.org/10.15486/ngt/1770776	dataset	January 2021
Near-surface remote sensing of spatial and temporal variation in canopy phenology Richardson, Andrew D.; Braswell, Bobby H.; Hollinger, David Y. Ecological Applications, Vol. 19, Issue 6 https://doi.org/10.1890/08-2022.1	journal	September 2009
Tracking forest phenology and seasonal physiology using digital repeat photography: a critical assessment Keenan, T. F.; Darby, B.; Felts, E. Ecological Applications, Vol. 24, Issue 6 https://doi.org/10.1890/13-0652.1	journal	September 2014
Quantifying Leaf Phenology of Individual Trees and Species in a Tropical Forest Using Unmanned Aerial Vehicle (UAV) Images Park, John Y.; Muller-Landau, Helene C.; Lichstein, Jeremy W. Remote Sensing, Vol. 11, Issue 13 https://doi.org/10.3390/rs11131534	journal	June 2019
Automatic Detection of Maize Tassels from UAV Images by Combining Random Forest Classifier and VGG16 Zan, Xuli; Zhang, Xinlu; Xing, Ziyao Remote Sensing, Vol. 12, Issue 18 https://doi.org/10.3390/rs12183049	journal	September 2020
Automated Extraction of Phenotypic Leaf Traits of Individual Intact Herbarium Leaves from Herbarium Specimen Images Using Deep Learning Based Semantic Segmentation Hussein, Burhan Rashid; Malik, Owais Ahmed; Ong, Wee-Hong Sensors, Vol. 21, Issue 13 https://doi.org/10.3390/s21134549	journal	July 2021
Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery Klosterman, S. T.; Hufkens, K.; Gray, J. M. Biogeosciences, Vol. 11, Issue 16 https://doi.org/10.5194/bg-11-4305-2014	journal	January 2014

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