Search Menu
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information
Search Scholarly Publications

Title: An ecologically-constrained deep learning model for tropical leaf phenology monitoring using PlanetScope satellites

Abstract

In tropical forests, leaf phenology signals leaf-on/off status and exhibits considerable variability across scales from a single tree-crown to the entire forest ecosystem. Such phenology signals importantly regulate large-scale biogeochemical cycles and regional climate. PlanetScope CubeSats data with a 3-m resolution and near-daily global coverage provide an unprecedented opportunity to monitor both fine- and ecosystem-scale phenology variability along large environmental gradients. However, a scalable method that accurately characterizes leaf phenology from PlanetScope with biophysically meaningful metrics remains lacking. Here we developed an index-guided, ecologically constrained autoencoder (IG-ECAE) method to automatically derive a deciduousness metric (i.e. percentage of tree canopies with leaf-off status within an image pixel) from PlanetScope. The IG-ECAE first estimated the reflectance spectra of leafy/leafless canopies based on their spectral indices characteristics, then used the derived reflectance spectra to guide an autoencoder deep learning method with additional ecological constraints to refine the reflectance spectra, and finally used linear spectral unmixing to estimate the relative abundance of leafless canopies (or deciduousness) per PlanetScope image pixel. We tested the IG-ECAE method at 16 tropical forest sites spanning multiple continents and a large precipitation gradient (1470-2819 mm year-1). Among these sites, we evaluated the PlanetScope-derived deciduousness with corresponding measures derived frommore » WorldView-2 (n=9 sites) and local phenocams (n=9 sites). Our results show that PlanetScope-derived deciduousness agrees: 1) with that derived from WorldView-2 at the patch level (90m×90m) with r2=0.89 across all sites; and 2) with that derived from phenocams to quantify ecosystem-scale seasonality with r2 ranging from 0.62-0.96. These results demonstrate the effectiveness and scalability of IG-ECAE in characterizing the wide variability in deciduousness across scales from pixels to forest ecosystems, and from a single date to the full annual cycle, indicating the potential for using high-resolution satellites to track the large-scale phenological patterns and response of tropical forests to climate change.« less

Authors:
 [1];  [2];  [3];  [4];  [2]; ORCiD logo [5];  [6];  [7];  [8];  [2];  [2];  [2];  [2];  [9];  [10];  [11]
+ Show Author Affiliations
  1. Sun Yat-Sen Univ., Shenzhen, Guangzhou (China); University of Hong Kong (China)
  2. University of Hong Kong (China)
  3. James Cook University, Cairns, QLD (Australia)
  4. São Paulo State University (UNESP), Rio Claro, SP (Brazil)
  5. Brookhaven National Lab. (BNL), Upton, NY (United States)
  6. São Paulo State University (UNESP), Rio Claro, SP (Brazil); Instituto Tecnologico Vale, Belem, PA (Brazil)
  7. Princeton Univ., NJ (United States)
  8. Lanzhou Univ. (China); International Research Center of Big Data for Sustainable Development Goals, Beijing (China); East China Normal Univ. (ECNU), Shanghai (China)
  9. National Institute for Amazon Research (INPA), Manaus (Brazil)
  10. Univ. of Technology Sydney, NSW (Australia)
  11. Univ. of Hong Kong, Pokfulam, Hong Kong (China)
Publication Date:
Research Org.:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER); National Natural Science Foundation of China (NSFC); Hong Kong Research Grant Council; São Paulo Research Foundation (FAPESP); Shenzhen Science and Technology Program; Natural Science Foundation of Gansu Province, China; International Research Center of Big Data for Sustainable Development Goals
OSTI Identifier:
1913816
Report Number(s):
BNL-223881-2023-JAAM
Journal ID: ISSN 0034-4257
Grant/Contract Number:  
SC0012704; 31922090; 17316622; 2013/50155-0; 2019/11835-2; 2019/16191-6; 202011159154; 42171305; 21JR7RA499; CBAS2022DF006; 12300519; 17201020; 17300021; C1013-21GF; C7004-21GF; NSFC-RGC N-HKU76921
Resource Type:
Accepted Manuscript
Journal Name:
Remote Sensing of Environment
Additional Journal Information:
Journal Volume: 286; Journal ID: ISSN 0034-4257
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; tropical forests; carbon cycles; environmental gradient; deciduousness; multi-scale remote sensing; machine learning

Citation Formats

Wang, Jing, Song, Guangqin, Liddell, Michael, Morellato, Patricia, Lee, Calvin K. F., Yang, Dedi, Alberton, Bruna, Detto, Matteo, Ma, Xuanlong, Zhao, Yingyi, Yeung, Henry C. H., Zhang, Hongsheng, Ng, Michael, Nelson, Bruce W., Huete, Alfredo, and Wu, Jin. An ecologically-constrained deep learning model for tropical leaf phenology monitoring using PlanetScope satellites. United States: N. p., 2023. Web. doi:10.1016/j.rse.2022.113429.
Copy to clipboard
Wang, Jing, Song, Guangqin, Liddell, Michael, Morellato, Patricia, Lee, Calvin K. F., Yang, Dedi, Alberton, Bruna, Detto, Matteo, Ma, Xuanlong, Zhao, Yingyi, Yeung, Henry C. H., Zhang, Hongsheng, Ng, Michael, Nelson, Bruce W., Huete, Alfredo, & Wu, Jin. An ecologically-constrained deep learning model for tropical leaf phenology monitoring using PlanetScope satellites. United States. https://doi.org/10.1016/j.rse.2022.113429
Copy to clipboard
Wang, Jing, Song, Guangqin, Liddell, Michael, Morellato, Patricia, Lee, Calvin K. F., Yang, Dedi, Alberton, Bruna, Detto, Matteo, Ma, Xuanlong, Zhao, Yingyi, Yeung, Henry C. H., Zhang, Hongsheng, Ng, Michael, Nelson, Bruce W., Huete, Alfredo, and Wu, Jin. Fri . "An ecologically-constrained deep learning model for tropical leaf phenology monitoring using PlanetScope satellites". United States. https://doi.org/10.1016/j.rse.2022.113429. https://www.osti.gov/servlets/purl/1913816.
Copy to clipboard
@article{osti_1913816,
title = {An ecologically-constrained deep learning model for tropical leaf phenology monitoring using PlanetScope satellites},
author = {Wang, Jing and Song, Guangqin and Liddell, Michael and Morellato, Patricia and Lee, Calvin K. F. and Yang, Dedi and Alberton, Bruna and Detto, Matteo and Ma, Xuanlong and Zhao, Yingyi and Yeung, Henry C. H. and Zhang, Hongsheng and Ng, Michael and Nelson, Bruce W. and Huete, Alfredo and Wu, Jin},
abstractNote = {In tropical forests, leaf phenology signals leaf-on/off status and exhibits considerable variability across scales from a single tree-crown to the entire forest ecosystem. Such phenology signals importantly regulate large-scale biogeochemical cycles and regional climate. PlanetScope CubeSats data with a 3-m resolution and near-daily global coverage provide an unprecedented opportunity to monitor both fine- and ecosystem-scale phenology variability along large environmental gradients. However, a scalable method that accurately characterizes leaf phenology from PlanetScope with biophysically meaningful metrics remains lacking. Here we developed an index-guided, ecologically constrained autoencoder (IG-ECAE) method to automatically derive a deciduousness metric (i.e. percentage of tree canopies with leaf-off status within an image pixel) from PlanetScope. The IG-ECAE first estimated the reflectance spectra of leafy/leafless canopies based on their spectral indices characteristics, then used the derived reflectance spectra to guide an autoencoder deep learning method with additional ecological constraints to refine the reflectance spectra, and finally used linear spectral unmixing to estimate the relative abundance of leafless canopies (or deciduousness) per PlanetScope image pixel. We tested the IG-ECAE method at 16 tropical forest sites spanning multiple continents and a large precipitation gradient (1470-2819 mm year-1). Among these sites, we evaluated the PlanetScope-derived deciduousness with corresponding measures derived from WorldView-2 (n=9 sites) and local phenocams (n=9 sites). Our results show that PlanetScope-derived deciduousness agrees: 1) with that derived from WorldView-2 at the patch level (90m×90m) with r2=0.89 across all sites; and 2) with that derived from phenocams to quantify ecosystem-scale seasonality with r2 ranging from 0.62-0.96. These results demonstrate the effectiveness and scalability of IG-ECAE in characterizing the wide variability in deciduousness across scales from pixels to forest ecosystems, and from a single date to the full annual cycle, indicating the potential for using high-resolution satellites to track the large-scale phenological patterns and response of tropical forests to climate change.},
doi = {10.1016/j.rse.2022.113429},
journal = {Remote Sensing of Environment},
number = ,
volume = 286,
place = {United States},
year = {Fri Jan 06 00:00:00 EST 2023},
month = {Fri Jan 06 00:00:00 EST 2023}
}
Copy to clipboard
Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1016/j.rse.2022.113429
Copyright Statement
Other availability
Search WorldCat Search WorldCat to find libraries that may hold this journal
Save / Share:
Export Metadata
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.

Works referenced in this record:

Leafing patterns and leaf exchange strategies of a cerrado woody community
journal, May 2018

  • de Camargo, Maria Gabriela Gutierrez; de Carvalho, Gustavo Henrique; Alberton, Bruna de Costa
  • Biotropica, Vol. 50, Issue 3
  • DOI: 10.1111/btp.12552

Photosynthetic seasonality of global tropical forests constrained by hydroclimate
journal, March 2015

  • Guan, Kaiyu; Pan, Ming; Li, Haibin
  • Nature Geoscience, Vol. 8, Issue 4
  • DOI: 10.1038/ngeo2382

Leafing Patterns and Drivers across Seasonally Dry Tropical Communities
journal, September 2019

  • Alberton, Bruna; da Silva Torres, Ricardo; Sanna Freire Silva, Thiago
  • Remote Sensing, Vol. 11, Issue 19
  • DOI: 10.3390/rs11192267

Monitoring leaf phenology in moist tropical forests by applying a superpixel-based deep learning method to time-series images of tree canopies
journal, January 2022

Secondary Forest Detection in a Neotropical Dry Forest Landscape Using Landsat 7 ETM+ and IKONOS Imagery1
journal, December 2005

EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing
journal, January 2019

  • Ozkan, Savas; Kaya, Berk; Akar, Gozde Bozdagi
  • IEEE Transactions on Geoscience and Remote Sensing, Vol. 57, Issue 1
  • DOI: 10.1109/TGRS.2018.2856929

Variations of leaf longevity in tropical moist forests predicted by a trait-driven carbon optimality model
journal, July 2017

  • Xu, Xiangtao; Medvigy, David; Joseph Wright, Stuart
  • Ecology Letters, Vol. 20, Issue 9
  • DOI: 10.1111/ele.12804

Generation and evaluation of the VIIRS land surface phenology product
journal, October 2018

Multi-scale integration of satellite remote sensing improves characterization of dry-season green-up in an Amazon tropical evergreen forest
journal, September 2020

Process‐Guided Deep Learning Predictions of Lake Water Temperature
journal, November 2019

  • Read, Jordan S.; Jia, Xiaowei; Willard, Jared
  • Water Resources Research, Vol. 55, Issue 11
  • DOI: 10.1029/2019WR024922

Multiscale assessment of land surface phenology from harmonized Landsat 8 and Sentinel-2, PlanetScope, and PhenoCam imagery
journal, December 2021

  • Moon, Minkyu; Richardson, Andrew D.; Friedl, Mark A.
  • Remote Sensing of Environment, Vol. 266
  • DOI: 10.1016/j.rse.2021.112716

Monitoring tree-crown scale autumn leaf phenology in a temperate forest with an integration of PlanetScope and drone remote sensing observations
journal, January 2021

The Phenology of Tropical Forests: Adaptive Significance and Consequences for Primary Consumers
journal, November 1993

Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate
journal, July 2010

First operational BRDF, albedo nadir reflectance products from MODIS
journal, November 2002

Deep learning to represent subgrid processes in climate models
journal, September 2018

  • Rasp, Stephan; Pritchard, Michael S.; Gentine, Pierre
  • Proceedings of the National Academy of Sciences, Vol. 115, Issue 39
  • DOI: 10.1073/pnas.1810286115

Environmental and biological controls on seasonal patterns of isoprene above a rain forest in central Amazonia
journal, June 2018

New global observations of the terrestrial carbon cycle from GOSAT: Patterns of plant fluorescence with gross primary productivity: CHLOROPHYLL FLUORESCENCE FROM SPACE
journal, September 2011

  • Frankenberg, Christian; Fisher, Joshua B.; Worden, John
  • Geophysical Research Letters, Vol. 38, Issue 17
  • DOI: 10.1029/2011GL048738

Ash Presence and Abundance Derived from Composite Landsat and Sentinel-2 Time Series and Lidar Surface Models in Minnesota, USA
journal, April 2020

  • Host, Trevor K.; Russell, Matthew B.; Windmuller-Campione, Marcella A.
  • Remote Sensing, Vol. 12, Issue 8
  • DOI: 10.3390/rs12081341

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data
journal, July 2020

Leaf development and demography explain photosynthetic seasonality in Amazon evergreen forests
journal, February 2016

Endmember Variability in Hyperspectral Analysis: Addressing Spectral Variability During Spectral Unmixing
journal, January 2014

Resource acquisition and reproductive strategies of tropical forest in response to the El Niño–Southern Oscillation
journal, March 2018

Mapping forest degradation in the Eastern Amazon from SPOT 4 through spectral mixture models
journal, November 2003

GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery
journal, January 2021

A review of assessing the accuracy of classifications of remotely sensed data
journal, July 1991

Long-term increases in tropical flowering activity across growth forms in response to rising CO 2 and climate change
journal, December 2017

  • Pau, Stephanie; Okamoto, Daniel K.; Calderón, Osvaldo
  • Global Change Biology, Vol. 24, Issue 5
  • DOI: 10.1111/gcb.14004

Pantropical variability in tree crown allometry
journal, December 2020

  • Loubota Panzou, Grace Jopaul; Fayolle, Adeline; Jucker, Tommaso
  • Global Ecology and Biogeography, Vol. 30, Issue 2
  • DOI: 10.1111/geb.13231

Increased control of vegetation on global terrestrial energy fluxes
journal, March 2020

  • Forzieri, Giovanni; Miralles, Diego G.; Ciais, Philippe
  • Nature Climate Change, Vol. 10, Issue 4
  • DOI: 10.1038/s41558-020-0717-0

Earlier leaf-out warms air in the north
journal, February 2020

Deep Half-Siamese Networks for Hyperspectral Unmixing
journal, November 2021

  • Han, Zhu; Hong, Danfeng; Gao, Lianru
  • IEEE Geoscience and Remote Sensing Letters, Vol. 18, Issue 11
  • DOI: 10.1109/LGRS.2020.3011941

Spectral analysis of amazon canopy phenology during the dry season using a tower hyperspectral camera and modis observations
journal, September 2017

  • de Moura, Yhasmin Mendes; Galvão, Lênio Soares; Hilker, Thomas
  • ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 131
  • DOI: 10.1016/j.isprsjprs.2017.07.006

Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers
journal, June 2019

  • Ryu, Jae Yong; Kim, Hyun Uk; Lee, Sang Yup
  • Proceedings of the National Academy of Sciences, Vol. 116, Issue 28
  • DOI: 10.1073/pnas.1821905116

Cryptic phenology in plants: Case studies, implications, and recommendations
journal, August 2019

  • Albert, Loren P.; Restrepo‐Coupe, Natalia; Smith, Marielle N.
  • Global Change Biology, Vol. 25, Issue 11
  • DOI: 10.1111/gcb.14759

Combinational Build-Up Index (CBI) for Effective Impervious Surface Mapping in Urban Areas
journal, May 2016

  • Sun, Genyun; Chen, Xiaolin; Jia, Xiuping
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 9, Issue 5
  • DOI: 10.1109/JSTARS.2015.2478914

Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest
journal, December 2017

  • Wu, Jin; Kobayashi, Hideki; Stark, Scott C.
  • New Phytologist, Vol. 217, Issue 4
  • DOI: 10.1111/nph.14939

A cross‐scale approach to unravel the molecular basis of plant phenology in temperate and tropical climates
journal, December 2021

  • Satake, Akiko; Nagahama, Ai; Sasaki, Eriko
  • New Phytologist, Vol. 233, Issue 6
  • DOI: 10.1111/nph.17897

Reviews and syntheses: Australian vegetation phenology: new insights from satellite remote sensing and digital repeat photography
journal, January 2016

Automatic cloud and cloud shadow detection in tropical areas for PlanetScope satellite images
journal, October 2021

Leaf Demography and Phenology in Amazonian rain Forest: a Census of 40 000 Leaves of 23 tree Species
journal, January 2004

  • Reich, Peter B.; Uhl, Christopher; Walters, Michael B.
  • Ecological Monographs, Vol. 74, Issue 1
  • DOI: 10.1890/02-4047

Contrasted allometries between stem diameter, crown area, and tree height in five tropical biogeographic areas
journal, June 2016

A Cubesat enabled Spatio-Temporal Enhancement Method (CESTEM) utilizing Planet, Landsat and MODIS data
journal, May 2018

The least-squares mixing models to generate fraction images derived from remote sensing multispectral data
journal, January 1991

  • Shimabukuro, Y. E.; Smith, J. A.
  • IEEE Transactions on Geoscience and Remote Sensing, Vol. 29, Issue 1
  • DOI: 10.1109/36.103288

Daily Retrieval of NDVI and LAI at 3 m Resolution via the Fusion of CubeSat, Landsat, and MODIS Data
journal, June 2018

  • Houborg, Rasmus; McCabe, Matthew
  • Remote Sensing, Vol. 10, Issue 6
  • DOI: 10.3390/rs10060890

Seasonal and interannual variability of climate and vegetation indices across the Amazon
journal, August 2010

  • Brando, P. M.; Goetz, S. J.; Baccini, A.
  • Proceedings of the National Academy of Sciences, Vol. 107, Issue 33
  • DOI: 10.1073/pnas.0908741107

Amazon forests maintain consistent canopy structure and greenness during the dry season
journal, February 2014

  • Morton, Douglas C.; Nagol, Jyoteshwar; Carabajal, Claudia C.
  • Nature, Vol. 506, Issue 7487
  • DOI: 10.1038/nature13006

Phenology of short vegetation cycles in a Kenyan rangeland from PlanetScope and Sentinel-2
journal, October 2020

A Phytosociological Analysis of Species‐Rich Tropical Forest on Barro Colorado Island, Panama
journal, June 1975

  • Knight, Dennis H.
  • Ecological Monographs, Vol. 45, Issue 3
  • DOI: 10.2307/1942424

Spruce budworm tree host species distribution and abundance mapping using multi-temporal Sentinel-1 and Sentinel-2 satellite imagery
journal, February 2021

  • Bhattarai, Rajeev; Rahimzadeh-Bajgiran, Parinaz; Weiskittel, Aaron
  • ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 172
  • DOI: 10.1016/j.isprsjprs.2020.11.023

Automated mapping of tropical deforestation and forest degradation: CLASlite
journal, January 2009

  • Asner, Gregory P.
  • Journal of Applied Remote Sensing, Vol. 3, Issue 1
  • DOI: 10.1117/1.3223675

DAEN: Deep Autoencoder Networks for Hyperspectral Unmixing
journal, July 2019

  • Su, Yuanchao; Li, Jun; Plaza, Antonio
  • IEEE Transactions on Geoscience and Remote Sensing, Vol. 57, Issue 7
  • DOI: 10.1109/TGRS.2018.2890633

Do dynamic global vegetation models capture the seasonality of carbon fluxes in the Amazon basin? A data-model intercomparison
journal, August 2016

  • Restrepo-Coupe, Natalia; Levine, Naomi M.; Christoffersen, Bradley O.
  • Global Change Biology, Vol. 23, Issue 1
  • DOI: 10.1111/gcb.13442

Rainforest-initiated wet season onset over the southern Amazon
journal, July 2017

  • Wright, Jonathon S.; Fu, Rong; Worden, John R.
  • Proceedings of the National Academy of Sciences, Vol. 114, Issue 32
  • DOI: 10.1073/pnas.1621516114

Estimating forest canopy density of tropical mixed deciduous vegetation using Landsat data: a comparison of three classification approaches
journal, November 2011

An enhanced bloom index for quantifying floral phenology using multi-scale remote sensing observations
journal, October 2019

Age-dependent leaf physiology and consequences for crown-scale carbon uptake during the dry season in an Amazon evergreen forest
journal, March 2018

  • Albert, Loren P.; Wu, Jin; Prohaska, Neill
  • New Phytologist, Vol. 219, Issue 3
  • DOI: 10.1111/nph.15056

Both near-surface and satellite remote sensing confirm drought legacy effect on tropical forest leaf phenology after 2015/2016 ENSO drought
journal, February 2020

  • Gonçalves, Nathan Borges; Lopes, Aline Pontes; Dalagnol, Ricardo
  • Remote Sensing of Environment, Vol. 237
  • DOI: 10.1016/j.rse.2019.111489

Amazon rainforests green-up with sunlight in dry season
journal, January 2006

  • Huete, Alfredo R.; Didan, Kamel; Shimabukuro, Yosio E.
  • Geophysical Research Letters, Vol. 33, Issue 6
  • DOI: 10.1029/2005GL025583

Climate drivers of the Amazon forest greening
journal, July 2017

Multiple site tower flux and remote sensing comparisons of tropical forest dynamics in Monsoon Asia
journal, May 2008

Benchmark map of forest carbon stocks in tropical regions across three continents
journal, May 2011

  • Saatchi, S. S.; Harris, N. L.; Brown, S.
  • Proceedings of the National Academy of Sciences, Vol. 108, Issue 24
  • DOI: 10.1073/pnas.1019576108

Leaf phenology as one important driver of seasonal changes in isoprene emissions in central Amazonia
journal, January 2018

  • Alves, Eliane G.; Tóta, Julio; Turnipseed, Andrew
  • Biogeosciences, Vol. 15, Issue 13
  • DOI: 10.5194/bg-15-4019-2018

Joint control of terrestrial gross primary productivity by plant phenology and physiology
journal, February 2015

  • Xia, Jianyang; Niu, Shuli; Ciais, Philippe
  • Proceedings of the National Academy of Sciences, Vol. 112, Issue 9
  • DOI: 10.1073/pnas.1413090112

Using phenological cameras to track the green up in a cerrado savanna and its on-the-ground validation
journal, January 2014

Novel algorithms for remote estimation of vegetation fraction
journal, April 2002

Discrimination of tropical forest types, dominant species, and mapping of functional guilds by hyperspectral and simulated multispectral Sentinel-2 data
journal, April 2016

  • Vaglio Laurin, Gaia; Puletti, Nicola; Hawthorne, William
  • Remote Sensing of Environment, Vol. 176
  • DOI: 10.1016/j.rse.2016.01.017

Assessing winter wheat foliage disease severity using aerial imagery acquired from small Unmanned Aerial Vehicle (UAV)
journal, September 2020

Land surface phenology from MODIS: Characterization of the Collection 5 global land cover dynamics product
journal, August 2010

  • Ganguly, Sangram; Friedl, Mark A.; Tan, Bin
  • Remote Sensing of Environment, Vol. 114, Issue 8
  • DOI: 10.1016/j.rse.2010.04.005

Linking plant phenology to conservation biology
journal, March 2016

  • Morellato, Leonor Patrícia Cerdeira; Alberton, Bruna; Alvarado, Swanni T.
  • Biological Conservation, Vol. 195
  • DOI: 10.1016/j.biocon.2015.12.033

Seasonal trends of Amazonian rainforest phenology, net primary productivity, and carbon allocation: Seasonal Trends of Amazonian Forests
journal, May 2016

  • Girardin, Cécile A. J.; Malhi, Yadvinder; Doughty, Christopher E.
  • Global Biogeochemical Cycles, Vol. 30, Issue 5
  • DOI: 10.1002/2015GB005270

Carbon exchange in an Amazon forest: from hours to years
journal, January 2018

A Classification-Based Model for Multi-Objective Hyperspectral Sparse Unmixing
journal, December 2019

  • Xu, Xia; Shi, Zhenwei; Pan, Bin
  • IEEE Transactions on Geoscience and Remote Sensing, Vol. 57, Issue 12
  • DOI: 10.1109/TGRS.2019.2928021

A Comparison Between Global and Context-Adaptive Pansharpening of Multispectral Images
journal, April 2009

  • Aiazzi, B.; Baronti, S.; Lotti, F.
  • IEEE Geoscience and Remote Sensing Letters, Vol. 6, Issue 2
  • DOI: 10.1109/LGRS.2008.2012003

uDAS: An Untied Denoising Autoencoder With Sparsity for Spectral Unmixing
journal, March 2019

Deep learning and process understanding for data-driven Earth system science
journal, February 2019

Dry-season greening of Amazon forests
journal, March 2016

  • Saleska, Scott R.; Wu, Jin; Guan, Kaiyu
  • Nature, Vol. 531, Issue 7594
  • DOI: 10.1038/nature16457

Scaling Phenocam GCC, NDVI, and EVI2 with Harmonized Landsat-Sentinel using Gaussian Processes
journal, April 2021

Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology
journal, October 2010

  • Motohka, Takeshi; Nasahara, Kenlo Nishida; Oguma, Hiroyuki
  • Remote Sensing, Vol. 2, Issue 10
  • DOI: 10.3390/rs2102369

A Quantitative and Comparative Assessment of Unmixing-Based Feature Extraction Techniques for Hyperspectral Image Classification
journal, April 2012

  • Dopido, Inmaculada; Villa, Alberto; Plaza, Antonio
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 5, Issue 2
  • DOI: 10.1109/JSTARS.2011.2176721

Tree Species Diversity, Richness, and Similarity in Intact and Degraded Forest in the Tropical Rainforest of the Congo Basin: Case of the Forest of Likouala in the Republic of Congo
journal, January 2016

  • Ifo, Suspense Averti; Moutsambote, Jean-Marie; Koubouana, Félix
  • International Journal of Forestry Research, Vol. 2016
  • DOI: 10.1155/2016/7593681

Leaf flush drives dry season green-up of the Central Amazon
journal, September 2016

Hierarchical mapping of annual global land cover 2001 to present: The MODIS Collection 6 Land Cover product
journal, March 2019

  • Sulla-Menashe, Damien; Gray, Josh M.; Abercrombie, S. Parker
  • Remote Sensing of Environment, Vol. 222
  • DOI: 10.1016/j.rse.2018.12.013

Mapping Tree Species Deciduousness of Tropical Dry Forests Combining Reflectance, Spectral Unmixing, and Texture Data from High-Resolution Imagery
journal, November 2020

  • Huechacona-Ruiz, Astrid Helena; Dupuy, Juan Manuel; Schwartz, Naomi B.
  • Forests, Vol. 11, Issue 11
  • DOI: 10.3390/f11111234

Colour and shape analysis techniques for weed detection in cereal fields
journal, February 2000

Convolutional Autoencoder for Spectral–Spatial Hyperspectral Unmixing
journal, January 2021

  • Palsson, Burkni; Ulfarsson, Magnus O.; Sveinsson, Johannes R.
  • IEEE Transactions on Geoscience and Remote Sensing, Vol. 59, Issue 1
  • DOI: 10.1109/TGRS.2020.2992743

Climate Change and Phenological Mismatch in Trophic Interactions Among Plants, Insects, and Vertebrates
journal, November 2018

Plant hydraulics, stomatal control, and the response of a tropical forest to water stress over multiple temporal scales
journal, April 2022

  • Detto, Matteo; Pacala, Stephen W.
  • Global Change Biology, Vol. 28, Issue 14
  • DOI: 10.1111/gcb.16179

The tropical forest carbon cycle and climate change
journal, July 2018

Mapping canopy defoliation by herbivorous insects at the individual tree level using bi-temporal airborne imaging spectroscopy and LiDAR measurements
journal, September 2018

A Fast Learning Algorithm for Deep Belief Nets
journal, July 2006

A spatially adaptive spectral mixture analysis for mapping subpixel urban impervious surface distribution
journal, June 2013

Satellite prediction of forest flowering phenology
journal, March 2021

  • Dixon, Dan J.; Callow, J. Nikolaus; Duncan, John M. A.
  • Remote Sensing of Environment, Vol. 255
  • DOI: 10.1016/j.rse.2020.112197

Spatial patterns and temporal dynamics in savanna vegetation phenology across the North Australian Tropical Transect
journal, December 2013

Combining spectral and spatial information to map canopy damage from selective logging and forest fires
journal, October 2005

  • Souza, Carlos M.; Roberts, Dar A.; Cochrane, Mark A.
  • Remote Sensing of Environment, Vol. 98, Issue 2-3
  • DOI: 10.1016/j.rse.2005.07.013

Mapping Crop Phenology in Near Real-Time Using Satellite Remote Sensing: Challenges and Opportunities
journal, March 2021

Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence
journal, May 2019

  • Magney, Troy S.; Bowling, David R.; Logan, Barry A.
  • Proceedings of the National Academy of Sciences
  • DOI: 10.1073/pnas.1900278116

Quantifying Leaf Phenology of Individual Trees and Species in a Tropical Forest Using Unmanned Aerial Vehicle (UAV) Images
journal, June 2019

  • Park, John Y.; Muller-Landau, Helene C.; Lichstein, Jeremy W.
  • Remote Sensing, Vol. 11, Issue 13
  • DOI: 10.3390/rs11131534

Canopy near-infrared reflectance and terrestrial photosynthesis
journal, March 2017

  • Badgley, Grayson; Field, Christopher B.; Berry, Joseph A.
  • Science Advances, Vol. 3, Issue 3
  • DOI: 10.1126/sciadv.1602244

Remote-Sensing-Based Water Balance for Monitoring of Evapotranspiration and Water Stress of a Mediterranean Oak–Grass Savanna
journal, May 2020

  • Carpintero, Elisabet; Andreu, Ana; Gómez-Giráldez, Pedro J.
  • Water, Vol. 12, Issue 5
  • DOI: 10.3390/w12051418

Overview of the radiometric and biophysical performance of the MODIS vegetation indices
journal, November 2002

Endmember variability in Spectral Mixture Analysis: A review
journal, July 2011

  • Somers, Ben; Asner, Gregory P.; Tits, Laurent
  • Remote Sensing of Environment, Vol. 115, Issue 7
  • DOI: 10.1016/j.rse.2011.03.003

Random Forests
journal, January 2001

    Sort by:
    [ × clear filter / sort ]

    Similar Records in DOE PAGES and OSTI.GOV collections: