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Title: Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems

Abstract

Tree–grass ecosystems are widely distributed. However, their phenology has not yet been fully characterized. The technique of repeated digital photographs for plant phenology monitoring (hereafter referred as PhenoCam) provide opportunities for long-term monitoring of plant phenology, and extracting phenological transition dates (PTDs, e.g., start of the growing season). Here, we aim to evaluate the utility of near-infrared-enabled PhenoCam for monitoring the phenology of structure (i.e., greenness) and physiology (i.e., gross primary productivity—GPP) at four tree–grass Mediterranean sites. We computed four vegetation indexes (VIs) from PhenoCams: (1) green chromatic coordinates (GCC), (2) normalized difference vegetation index (CamNDVI), (3) near-infrared reflectance of vegetation index (CamNIRv), and (4) ratio vegetation index (CamRVI). GPP is derived from eddy covariance flux tower measurement. Then, we extracted PTDs and their uncertainty from different VIs and GPP. The consistency between structural (VIs) and physiological (GPP) phenology was then evaluated. CamNIRv is best at representing the PTDs of GPP during the Green-up period, while CamNDVI is best during the Dry-down period. Moreover, CamNIRv outperforms the other VIs in tracking growing season length of GPP. In summary, the results show it is promising to track structural and physiology phenology of seasonally dry Mediterranean ecosystem using near-infrared-enabled PhenoCam. We suggestmore » using multiple VIs to better represent the variation of GPP.« less

Authors:
 [1];  [1];  [2];  [1];  [1];  [3];  [4];  [2];  [2];  [1];  [5];  [6];  [7];  [1];  [8];  [9]; ORCiD logo [10];  [1]
  1. Max-Planck-Inst. for Biogeochemistry, Jena (Germany). Dept. for Biogeochemical Integration
  2. Environmental Protection Agency of Aosta Valley, Aosta (Italy)
  3. Fundación Centro de Estudios Ambientales del Mediterráneo (CEAM), Paterna (Spain)
  4. National Inst. for Agriculture and Food Research and Technology (INIA), Madrid (Spain). Dept. of Environment
  5. Max-Planck-Inst. for Biogeochemistry, Jena (Germany). Dept. for Biogeochemical Integration; Spanish National Research Council (CSIC), Madrid (Spain). Environmental Remote Sensing and Spectroscopy Lab. (SpecLab)
  6. Spanish National Research Council (CSIC), Madrid (Spain). Environmental Remote Sensing and Spectroscopy Lab. (SpecLab)
  7. Univ. of Extremadura, Plasencia (Spain). Inst. for Dehesa Research
  8. Max-Planck-Inst. for Biogeochemistry, Jena (Germany). Dept. for Biogeochemical Integration; Michael-Stifel-Center Jena for Data-Driven and Simulation Science, Jena (Germany)
  9. Harvard Univ., Cambridge, MA (United States). Dept. of Organismic and Evolutionary Biology; Northern Arizona Univ., Flagstaff, AZ (United States). School of Informatics, Computing and Cyber Systems; Northern Arizona Univ., Flagstaff, AZ (United States). Center for Ecosystem Science and Society
  10. Friedrich Schiller Univ., Jena (Germany). Inst. of Ecology and Evolution, Plant Biodiversity Group; German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig (Germany)
Publication Date:
Research Org.:
Princeton Univ., NJ (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1499900
Grant/Contract Number:  
SC0016011
Resource Type:
Accepted Manuscript
Journal Name:
Remote Sensing
Additional Journal Information:
Journal Volume: 10; Journal Issue: 8; Journal ID: ISSN 2072-4292
Publisher:
MDPI
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION

Citation Formats

Luo, Yunpeng, El-Madany, Tarek S., Filippa, Gianluca, Ma, Xuanlong, Ahrens, Bernhard, Carrara, Arnaud, Gonzalez-Cascon, Rosario, Cremonese, Edoardo, Galvagno, Marta, Hammer, Tiana W., Pacheco-Labrador, Javier, Martín, M. Pilar, Moreno, Gerardo, Perez-Priego, Oscar, Reichstein, Markus, Richardson, Andrew D., Römermann, Christine, and Migliavacca, Mirco. Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems. United States: N. p., 2018. Web. doi:10.3390/rs10081293.
Luo, Yunpeng, El-Madany, Tarek S., Filippa, Gianluca, Ma, Xuanlong, Ahrens, Bernhard, Carrara, Arnaud, Gonzalez-Cascon, Rosario, Cremonese, Edoardo, Galvagno, Marta, Hammer, Tiana W., Pacheco-Labrador, Javier, Martín, M. Pilar, Moreno, Gerardo, Perez-Priego, Oscar, Reichstein, Markus, Richardson, Andrew D., Römermann, Christine, & Migliavacca, Mirco. Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems. United States. doi:10.3390/rs10081293.
Luo, Yunpeng, El-Madany, Tarek S., Filippa, Gianluca, Ma, Xuanlong, Ahrens, Bernhard, Carrara, Arnaud, Gonzalez-Cascon, Rosario, Cremonese, Edoardo, Galvagno, Marta, Hammer, Tiana W., Pacheco-Labrador, Javier, Martín, M. Pilar, Moreno, Gerardo, Perez-Priego, Oscar, Reichstein, Markus, Richardson, Andrew D., Römermann, Christine, and Migliavacca, Mirco. Wed . "Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems". United States. doi:10.3390/rs10081293. https://www.osti.gov/servlets/purl/1499900.
@article{osti_1499900,
title = {Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems},
author = {Luo, Yunpeng and El-Madany, Tarek S. and Filippa, Gianluca and Ma, Xuanlong and Ahrens, Bernhard and Carrara, Arnaud and Gonzalez-Cascon, Rosario and Cremonese, Edoardo and Galvagno, Marta and Hammer, Tiana W. and Pacheco-Labrador, Javier and Martín, M. Pilar and Moreno, Gerardo and Perez-Priego, Oscar and Reichstein, Markus and Richardson, Andrew D. and Römermann, Christine and Migliavacca, Mirco},
abstractNote = {Tree–grass ecosystems are widely distributed. However, their phenology has not yet been fully characterized. The technique of repeated digital photographs for plant phenology monitoring (hereafter referred as PhenoCam) provide opportunities for long-term monitoring of plant phenology, and extracting phenological transition dates (PTDs, e.g., start of the growing season). Here, we aim to evaluate the utility of near-infrared-enabled PhenoCam for monitoring the phenology of structure (i.e., greenness) and physiology (i.e., gross primary productivity—GPP) at four tree–grass Mediterranean sites. We computed four vegetation indexes (VIs) from PhenoCams: (1) green chromatic coordinates (GCC), (2) normalized difference vegetation index (CamNDVI), (3) near-infrared reflectance of vegetation index (CamNIRv), and (4) ratio vegetation index (CamRVI). GPP is derived from eddy covariance flux tower measurement. Then, we extracted PTDs and their uncertainty from different VIs and GPP. The consistency between structural (VIs) and physiological (GPP) phenology was then evaluated. CamNIRv is best at representing the PTDs of GPP during the Green-up period, while CamNDVI is best during the Dry-down period. Moreover, CamNIRv outperforms the other VIs in tracking growing season length of GPP. In summary, the results show it is promising to track structural and physiology phenology of seasonally dry Mediterranean ecosystem using near-infrared-enabled PhenoCam. We suggest using multiple VIs to better represent the variation of GPP.},
doi = {10.3390/rs10081293},
journal = {Remote Sensing},
number = 8,
volume = 10,
place = {United States},
year = {2018},
month = {8}
}

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