Investigating the phenology changes of three plant species in different ecosystems using radar and optical data

Document Type : Full length article


1 M.Sc. Student in Remote Sensing and GIS, University of Tehran, Tehran, Iran

2 Associate Professor Department of Remote Sensing and GIS, University of Tehran, Tehran, Iran


Extended Abstract
The Earth's ecosystems play an important role in regional and global climate. Most natural vegetation covers change through the year- as they are influenced by the seasons. In vegetation studies, different types of remote sensing images such as optics and synthetic aperture radar (SAR) have been used in different scales from leaf area to global scale. These images provide data that is difficult to access through other methods such as field surveys. Remote sensing sensors capture images from the Earth surfaces with an adequate spatial and temporal resolution for the environmental studies. In remote sensing approaches, the study of the phenological cycle (the study of plant life cycles and the way is affected by weather) is mainly based on changes in reflectance values in different spectral bands of optic sensors or vegetation indices (VI), such as normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and soil-adjusted vegetation index (SAVI). Spectral indices have been widely used to monitor the seasonal cycle of vegetation photosynthesis over the past decades. Many of these studies have reported promising results. SAR systems can capture images in all weather conditions and overcome the limitation of optic sensors in cloudy weather. Increased access to SAR images broadens the image application in vegetation studies. SAR sensors operate at a microwave range of electromagnetic spectrum and are able to penetrate more in vegetation canopy. In this study, the efficiency of Landsat 8, Sentinel-2, and Sentinel-1 images in monitoring vegetation phonological cycle have been verified. For that, three regions with different vegetation types including mangrove forests, woodland, and Shadegan date palms in Iran have been studied.
All available Landsat 8, Sentinel-2, and Sentinel-1images in 2017 have been acquired. The Landsat 8 and Sentinel-2 images have been pre-processed. NDVI, EVI, and SAVI have been calculated from corresponding optical bands. Field survey was not possible at the study areas, therefore, sample points have been chosen by the help of high-resolution Google Erath images. For that, archive Google Earth images with the acquisition date close to the acquisition date of images have been used to confirm the presence the vegetation covers at the specific location. The NDVI, EVI, and SAVI values have been extracted in the location of sample points. The Sentinel-1 images have been processed; speckle effect has been minimized and radiometric terrain corrections have been implemented by means of digital elevation model (DEM). Median filter with 5*5 window size has been applied. Median Filter has been chosen, because it is not affected by very high or low DN values and it is one of the efficient filter in minimizing speckle effect in SAR images. Then, digital number (DN) values have been converted into backscattering coefficient in dB. Backscattering responses in VV (vertical-vertical) and VH (vertical-horizontal) polarimetric bands have been extracted at the same sample location. The extracted NDVI, EVI, SAVI, VH, and VV values on different days of the year (DOY) have been separately analyzed for each study site.
Results and discussion
In woodlands, EVI and SAVI indices in comparison to NDVI are more compatible with natural phenological cycles. However, optical images were not available for the whole year, therefore the changes of optical indices cannot be surveyed completely over the year. The changes of backscattering values follow the natural trend of vegetation cover, however, optical indices match better with the natural cycle. The growth cycle of woodlands is affected by temperature and rainfall variation; therefore, it will change in different years.
The changes in spectral curves in date palms show that spectral indices present the initial steps of the growth cycles better than the final steps. Few optical images were available because there was cloud cover in this area. Spectral indices do not follow the last stages of natural phenological cycles. In this stage, backscattering values increase due to the increased volume scattering of the trees, therefore radar images are more efficient in presenting the last part of phenological cycles of date palms in comparison to optical images. Spectral indices are sensitive to the greenness of the leaves; in this stage, no substantial changes in vegetation greenness occur, therefor the spectral indices do not change accordingly.
Mangrove forests have specific phenological cycles and are affected more by environmental conditions. Both spectral indices and backscattering values follow the natural trend of this kind of vegetation.
VH backscattering values are more compatible with spectral indices in comparison to VV backscattering values. Spectral indices and VH backscattering values follow the natural seasonal changes of vegetation especially in deciduous vegetation such as woodlands. This matches with previous studies. The highest values of backscattering are observed in the time that vegetation cover reaches the highest amount of biomass. EVI and SAVI trends are more similar to the backscattering values trend in comparison to NDVI values. This study only considers images captured during one year (2017). Vegetation cover is influenced by seasonal, gradual, and sudden changes, therefore monitoring of vegetation in a longer period and shorter revisit time will provide complete monitoring of vegetation growth cycles.
Backscattering values in the cross-polarized VH (in comparison to the VV band) band show more sensitivity to vegetation changes over the year and are therefore more suitable for monitoring the annual growth cycle of plants. Among the optics indices, EVI and SAVI have shown more acceptable results since their variations are more consistent with the natural phenological cycle. In an aquatic ecosystem where mangrove forest grows, SAR responses show promising results as they can better represent the phenological cycle in comparison to spectral reflectance or vegetation indices. The results of this study show that backscattering responses at C-band follow the natural vegetation’s phenological cycle and can be used in vegetation monitoring in these three ecosystems. The results of this study can be further used to identify vegetation phenological stages in similar ecosystems. Additional studies are necessary to generalize these results to other areas.


Main Subjects

  1. ثابتی، ح. (1355). جنگلها، درختان و درختچه‏های ایران، تهران: سازمان تحقیقات کشاورزی و منابع طبیعی.
  2. حسامی، س. م. و دوازده‏امامی، س. (1395). بررسی فنولوژی گونة بلوط ایرانی (Quercus brantii Lindl) در سه رویشگاه مختلف در استان فارس، مجلة تحقیقات جنگل‏های زاگرس، دورة 3، شمارة 1، صص 33-46.
  3. زارع‏زاده مهریزی، ط.؛ خوشبخت، ک.؛ مهدوی دامغانی، ع. و کامبوزیا، ج. (1390). مطالعة اثرات کاهش جریانات جزر و مدی بر ساختار رویشی جنگل‏های حرا مطالعة موردی: پارک ملی- ساحلی نای‏بند، علوم محیطی، دورة 8، شمارة 4، صص 43-58.
  4. کوهپایه، ن.؛ ناصرزاده، م. و حجازی‏زاده بیگم، ز. (1397). طبقه‏بندی و ارتباط‏سنجی الگوهای فشار با مراحل فنولوژی خرما (مناطق سراوان و آبادان)، جغرافیای طبیعی، 12 (43): 89-105.
  5. یوسفی، ب. (1392). جمع‏آوری، شناسایی، و ارزیابی مورفولوژیک و فنولوژیک بیدهای استان کردستان، تحقیقات جنگل و صنوبر ایران، 21(1): 184-202.
  6. Amalisana, B. and Hernina, R. (2017). Land cover analysis by using pixel-based and object-based image classification method in Bogor. In IOP Conference Series: Earth and Environmental Science (Vol. 98, No. 1, p. 012005).
  7. Bazzi, H.; Baghdadi, N.; El Hajj, M.l Zribi, M.; Minh, D. H. T.; Ndikumana, E.; ... and Belhouchette, H. (2019). Mapping paddy rice using Sentinel-1 SAR time series in Camargue, France. Remote Sensing, 11(7): ‏
  8. Bequette, B. W. (2010). Continuous glucose monitoring: real-time algorithms for calibration, filtering, and alarms. Journal of diabetes science and technology, 4(2): 404-418.
  9. Buitenwerf, R.; Rose, L. and Higgins, S. I. (2015). Three decades of multi-dimensional change in global leaf phenology. Nature Climate Change, 5(4): 364-368.‏
  10. Burrows, M. T.; Schoeman, D. S.; Buckley, L. B.; Moore, P.; Poloczanska, E. S.; Brander, K. M.; ... and Richardson, A. J. (2011). The pace of shifting climate in marine and terrestrial ecosystems. Science, 334(6056): 652-655.
  11. Cai, Y.; Lin, H. and Zhang, M. (2019). Mapping paddy rice by the object-based random forest method using time series Sentinel-1/Sentinel-2 data. Advances in Space Research, 64(11): 2233-2244.
  12. Cai, Y.; Li, X.; Zhang, M. and Lin, H. (2020). Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data. International Journal of Applied Earth Observation and Geoinformation, 92: ‏
  13. Chambers, J. Q.; Asner, G. P.; Morton, D. C.; Anderson, L. O.; Saatchi, S. S.; Espírito-Santo, F. D.; ... and Souza Jr, C. (2007). Regional ecosystem structure and function: ecological insights from remote sensing of tropical forests. Trends in Ecology & Evolution, 22(8): 414-423.‏
  14. Chen, X.; Vierling, L.; Deering, D. and Conley, A. (2005). Monitoring boreal forest leaf area index across a Siberian burn chronosequence: a MODIS validation study. International Journal of Remote Sensing, 26(24): 5433-5451.‏
  15. Christian, B.; Joshi, N.; Saini, M.; Mehta, N.; Goroshi, S.; Nidamanuri, R. R. and Krishnayya, N. S. R. (2015). Seasonal variations in phenology and productivity of a tropical dry deciduous forest from MODIS and Hyperion. Agricultural and Forest Meteorology, 214-215: 91-
  16. Clark, R. N.; Kokaly, R. F.; Swayze, G. A.; Livo, K. E.; Hoefen, T. M.; Pearson, N. C.; …and Klein, A. J. (2017). USGS Spectral Library Version 7: Data Series 1035. 61.
  17. Cohen, W. B.; Yang, Z. G. and Kennedy, R. )2010(. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync - Tools for calibration and validation. Remote Sensing of Environment, 114: 2911-
  18. Drusch, M.; Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Bargellini, P. (2012). Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment, 120: 25-
  19. Duke, N. C.; Meynecke, J. O.; Dittmann, S.; Ellison, A. M.; Anger, K.; Berger, U.; ... and Dahdouh-Guebas, F. (2007). A world without mangroves?. Science, 317(5834): 41-42.
  20. Ellison, J.C. and Simmonds, S. (2003). Structure and Productivity of inland mangrove stands at Lake MacLeod, Western Journal of the Royal Society of Western Australia, 86: 25-30.
  21. ESA (2017). Sentinels Scientific Data Hub. Retrieved from. Dhus/#/home.
  22. Field, C. B.; Gamon, J. A. and Peñuelas, J. (1995). Remote sensing of terrestrial photosynthesis. In Ecophysiology of photosynthesis (pp. 511-527). Springer, Berlin, Heidelberg.‏
  23. Fisher, J. I.; Mustard, J. F. and Vadeboncoeur, M. A. (2006). Green leaf phenology at Landsat resolution: Scaling from the field to the satellite. Remote sensing of environment, 100(2): 265-279.‏
  24. Flores-Anderson, A. I.; Herndon, K. E.; Thapa, R. B. and Cherrington, E. (2019). Sampling Designs for SAR-Assisted Forest Biomass Surveys. THE SAR HANDBOOK Comprehensive Methodologies for Forest Monitoring and Biomass Estimation, 1-
  25. Frankie, G.W.; Baker, H.G. and Opler, P.A., (1974). Comparative phenological studies of trees in tropical wet and dry forests in the lowlands of Costa Rica. Ecol., 881-919.
  26. Frison, P. L.; Fruneau, B.; Kmiha, S.; Soudani, K.; Dufrene, E.; Le Toan, T.; ... and Rudant, J. P. (2018). Potential of Sentinel-1 data for monitoring temperate mixed forest phenology. Remote Sensing, 10(12): ‏
  27. Hansen, M. C.; Potapov, P. V.; Goetz, S. J.; Turubanova, S.; Tyukavina, A.; Krylov, A. and Egorov, A. (2016). Mapping tree height distributions in Sub-Saharan Africa using Landsat 7 and 8 data. Remote Sensing of Environment, 185: 221-
  28. Helman, (2018).Land surface phenology: What do we really 'see' from space?. Sci Total Environ. 618: 665-673.
  29. Hesami, M. and Davazdahemami, (2016).Phenology of Persian Oak (Quercus brantii Lindl.) in Three Different Sites in Fars Province, Iran. 3. 3 (1) :33-46. URL: (in Persian).
  30. Hu, L.; Xu, N.; Liang, J.; Li, Z.; Chen, L.; and Zhao, F. (2020). Advancing the Mapping of Mangrove Forests at National-Scale Using Sentinel-1 and Sentinel-2 Time-Series Data with Google Earth Engine: A Case Study in China. Remote Sensing, Vol. 12.
  31. Huang, N.; Wang, L.; Song, X.-P.; Black, T. A.; Jassal, R. S.; Myneni, R. B.; …and Ji, D. (2020). Spatial and temporal variations in global soil respiration and their relationships with climate and land cover. Science Advances, 6(41):
  32. Huete, A. R. (1988); A soil adjusted vegetation index (SAVI), Remote Sensing of Environment. 25: 295 309.
  33. Huete, A. R.; Liu, H. Q.; Batchily, K. and van Leeuwen, W. J. D. (1997). A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environment, 59: 440-
  34. Huete, A.; Didan, K.; Miura, T.; Rodriguez, E. P.; Gao, X. and Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1-2): 195-
  35. Jensen, J. R. (1996). Introductory Digital Image Processing: A Remote Sensing Perspective. New Jersey: Prentice Hall, Inc.
  36. Jiang, Y.; Zhang, L.; Yan, M.; Qi, J.; Fu, T.; Fan, S. and Chen, B. (2021). High-Resolution Mangrove Forests Classification with Machine Learning Using Worldview and UAV Hyperspectral Data. Remote Sensing, Vol. 13.
  37. Jiao, X.; McNairn, H.; Shang, J. and Liu, J. (2010, July). The sensitivity of multi-frequency (X, C and L-band) radar backscatter signatures to bio-physical variables (LAI) over corn and soybean fields. In ISPRS TC VII Symposium—100 Years ISPRS (pp. 317-325).
  38. Jin, H., Eklundh, L.,. (2014). A physically based vegetation index for improved monitoring of plant phenology. Remote Sens. Environ. 152, 512–
  39. Kang, J.; Hou, X.; Niu, Z.; Gao, S. and Jia, K. (2014). Decision tree classification based on fitted phenology parameters from remotely sensed vegetation data. Transactions of the Chinese Society of Agricultural Engineering, 30(9): 148-156.
  40. Khouly, A.A. and Khedr, A. (2007). Zonation pattern of Avicennia marina and Rhizophora mucronata along the Red Sea Coast, Egypt. World applied sciences Journal, 2(4): 283-288.
  41. Knipling, E. B. (1970). Physical and Physiological Basis for the Reflectance of Visible and Near-Infrared Radiation from 1:155-159.
  42. Knyazikhin, Y.; Schull, M. A.; Stenberg, P.; Mottus, M.; Rautiainen, M.; Yang, Y.; … Myneni, R. B. (2013). Hyperspectral remote sensing of foliar nitrogen content. Proceedings of the National Academy of Sciences, 110(3): E185–E192.
  43. Koohpaye, N.; Naserzade, M. and Hejazizade Bigom, Z. (2019). Classification and measurement of pressure patterns with date phenological stages (Saravan and Abadan regions). Physical Geography Quarterly, 12(43), 89-104. (in Persian).
  44. Li, J., Pei, Y., Zhao, S., Xiao, R., Sang, X. and Zhang, C. (2020). A Review of Remote Sensing for Environmental Monitoring in China. Remote Sensing, 12(7):
  45. Liao, C.; Wang, J.; Dong, T.; Shang, J., Liu, J. and Song, Y. (2019). Using spatio-temporal fusion of Landsat-8 and MODIS data to derive phenology, biomass and yield estimates for corn and soybean. Science of the total environment, 650: 1707-1721.
  46. Louis, J.; Debaecker, V.; Pflug, B.; Main-Knorn, M.; Bieniarz, J.; Mueller-Wilm, U.; ... and Gascon, F. (2016). Sentinel-2 sen2cor: L2a processor for users. In Proceedings Living Planet Symposium (pp. 1-8). Spacebooks Online.
  47. Lovelock, C. E.; Feller, I. C.; Ellis, J.; Schwarz, A. M.; Hancock, N.; Nichols, P. and Sorrell, B. (2007). Mangrove growth in New Zealand estuaries: the role of nutrient enrichment at sites with contrasting rates of sedimentation. Oecologia, 153(3), 633-641.‏
  48. Lu, X., Cheng, X., Li, X., Chen, J., Sun, M., Ji, M., ... & Tang, J. (2018). Seasonal patterns of canopy photosynthesis captured by remotely sensed sun-induced fluorescence and vegetation indexes in mid-to-high latitude forests: A cross-platform comparison. Science of the total environment, 644, 439-451.
  49. Macelloni, G.; Paloscia, S.; Pampaloni, P.; Marliani, F. and Gai, M. (2001). The relationship between the backscattering coefficient and the biomass of narrow and broad leaf crops. IEEE Transactions on Geoscience and Remote Sensing, 39(4): 873-884.‏
  50. Miao, N.; Jiao, P.; Tao, W.; Li, M.; Li, Z.; Hu, B. and Moermond, T. C. (2020). Structural dynamics of Populus euphratica forests in different stages in the upper reaches of the Tarim River in China. Scientific Reports, 10(1):
  51. Montesano, P. M.; Nelson, R.; Sun, G.; Margolis, H.; Kerber, A. and Ranson, K. J. )2009(. MODIS tree cover validation for the circumpolar taiga-tundra transition zone. Remote Sensing of Environment, 113(10): 2130-
  52. Moran, M. S.; Vidal, A.; Troufleau, D.; Qi, J.; Clarke, T. R.; Pinter Jr, P. J.; ... and Neale, C. M. U. (1997). Combining multifrequency microwave and optical data for crop management. Remote Sensing of Environment, 61(1): 96-109.
  53. Moran, M. S., Alonso, L., Moreno, J. F., Mateo, M. P. C., De La Cruz, D. F., & Montoro, A. (2011). A RADARSAT-2 quad-polarized time series for monitoring crop and soil conditions in Barrax, Spain. IEEE Transactions on Geoscience and Remote Sensing, 50(4), 1057-1070.‏
  54. Murphy, P. G. and Lugo, A. E. (1986). Ecology of tropical dry forest. Annual review of ecology and systematics, 17(1): 67-88.‏
  55. Naidoo, G. (2010). Ecophysiological differences between fringe and dwarf Avicennia marina mangroves. Trees, 24: 667-673.
  56. Niphadkar, M.; Nagendra, H.; Tarantino, C.; Adamo, M. and Glenn, N. F. (2017). Comparing Pixel and Object-Based Approaches to Map an Understorey Invasive Shrub in Tropical Mixed Forests. 8(May), 1-
  57. Pastor-Guzman, J.; Dash, J. and Atkinson, P. M. (2018). Remote sensing of mangrove forest phenology and its environmental drivers. Remote sensing of environment, 205: 71-84.‏
  58. Piao, S.; Wang, X.; Park, T.; Chen, C.; Lian, X. U.; He, Y.; ... and Myneni, R. B. (2020). Characteristics, drivers and feedbacks of global greening. Nature Reviews Earth & Environment, 1(1); 14-27.
  59. Potere, D. (2008). Horizontal positional accuracy of Google Earth's high-resolution imagery archive. Sensors, 8: 7973-
  60. Potter, C. S.; Klooster, S. A. and Brooks, V. (1999). Interannual variability in terrestrial net primary production: exploration of trends and controls on regional to global scales. Ecosystems, 2: 36-
  61. Proisy, C.; Mougin, E.; Dufrêne, E.; Dantec, V.L. (2000). Monitoring seasonal changes of a mixed temperate forest using ERS SAR observations. IEEE Trans. Geosci. Remote Sens. 38, 540–552.
  62. Qiao, K.; Zhu, W.; Xie, Z. and Li, P. (2019). Estimating the seasonal dynamics of the leaf area index using piecewise LAI-VI relationships based on phenophases. Remote Sensing, 11(6):
  63. Reich, P. B. and Borchert, R. (1984). Water stress and tree phenology in a tropical dry forest in the lowlands of Costa Rica. The Journal of Ecology, 61-74.
  64. Richards, J.A. (2009). Remote sensing with imaging radar. New York, Springer.
  65. Rocha, A. V. and Shaver, G. R. (2009). Advantages of a two band EVI calculated from solar and photosynthetically active radiation fluxes. Agricultural and Forest Meteorology, 149(9): 1560-
  66. Rüetschi, M.; Schaepman, M. E. and Small, D. (2018). Using multitemporal sentinel-1 c-band backscatter to monitor phenology and classify deciduous and coniferous forests in northern switzerland. Remote Sensing, 10(1):
  67. Saadat, M.; Hasanlou, M. and Homayouni, S. (2019). Rice Crop Mapping Using SENTINEL-1 Time Series Images (case Study: Mazandaran, Iran). The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 42: 897-904.
  68. Sabeti, H. (1355). Forests, trees and shrubs of Iran. Agricultural and Natural Resources Research Organization, p 810. (in Persian).
  69. Savoy, P. and Mackay, D. S. (2015). Modeling the seasonal dynamics of leaf area index based on environmental constraints to canopy development. Agricultural and Forest Meteorology, 200: 46-56.
  70. Schlund, M. and Erasmi, S. (2020). Remote Sensing of Environment Sentinel-1 time series data for monitoring the phenology of winter wheat. Remote Sensing of Environment, 246(March), 111814.
  71. Song, C. and Woodcock, C. E. (2003). Monitoring forest succession with multitemporal Landsat images: Factors of uncertainty. IEEE Transactions on Geoscience and Remote Sensing, 41(11): 2557-2567.
  72. Shimada, Masanobu; Takuya Itoh; Takeshi Motooka; Manabu Watanabe; Tomohiro Shiraishi; Rajesh Thapa; and Richard Lucas (2014). New Global Forest/Non-Forest Maps from ALOS PALSAR Data (2007-2010). Remote Sensing of Environment, 155: 13-
  73. Stendardi, L.; Karlsen, S. R.; Niedrist, G.; Gerdol, R.; Zebisch, M.; Rossi, M. and Notarnicola, C. (2019). Exploiting time series of Sentinel-1 and Sentinel-2 imagery to detect meadow phenology in mountain regions. Remote Sensing, 11(5):
  74. Thevs, Niels; Stefan Zerbe; Jan Peper; and Michael Succow(2008). Vegetation and Vegetation Dynamics in the Tarim River Floodplain of Continental-Arid Xinjiang, NW China. Phytocoenologia, 38(1-2): 65-
  75. Tian, H.- Huang, N.- Niu, Z., Qin, Y.- Pei, J. and Wang, J. (2019). Mapping winter crops in China with multi-source satellite imagery and phenology-based algorithm. Remote sensing, 11(7):
  76. Torres, R.; Snoeij, P.; Geudtner, D.; Bibby, D.; Davidson, M.; Attema, E.; ... and Rostan, F. (2012). GMES Sentinel-1 mission. Remote Sensing of Environment, 120: 9-24.
  77. Treshkin, S. Y. (2012). The Tugai Forests of Floodplain of the Amudarya River: Ecology, Dynamics and Their. Springer, 95.
  78. Vavlas, N. C.; Waine, T. W.; Meersmans, J.; Burgess, P. J.; Fontanelli, G. and Richter, G. M. (2020). Deriving Wheat Crop Productivity Indicators Using Sentinel-1 Time Series. Remote Sensing, 12(15):
  79. Venter, O.; Brodeur, N. N.; Nemiroff, L.; Belland, B.; Dolinsek, I. J. and Grant, J. W. (2006). Threats to endangered species in Canada. Bioscience, 56(11): 903-910.
  80. Verbesselt, J., Hyndman, R., Newnham, G., & Culvenor, D. (2010). Remote Sensing of Environment Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114(1), 106–115.
  81. Wang, J.; Xiao, X.; Bajgain, R.; Starks, P.; Steiner, J.; Doughty, R. B. and Chang, Q. (2019). Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images. ISPRS Journal of Photogrammetry and Remote Sensing, 154: 189-201.
  82. Wang, C., Chen, J., Wu, J., Tang, Y., Shi, P., Black, T.A., Zhu, K. (2017). A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems. Remote Sens. Environ. 196, 1–12. ‏
  83. Yang, W.; Kobayashi, H.; Wang, C.; Shen, M.; Chen, J.; Matsushita, B.; ... and Kondoh, A. (2019). A semi-analytical snow-free vegetation index for improving estimation of plant phenology in tundra and grassland ecosystems. Remote Sensing of Environment, 228: 31-44.
  84. Yousefi, B. (2013). Collection, identification and morphological - phonological evaluation of Willows accessions at Kurdistan province of Iran. Iranian Journal of Forest and Poplar Research, 21(1): 184- (in Persian).
  85. Zare Zadeh Mehrizi, T.; Khoshbakht, K.; Mahdavi Damghani, A. and Kambouzia, J. (2011). Studying Effects of Reduction in Tidal Flooding on the Structure of Mangrove Forests, A Case Study From Nayband Coastal National Park. Environmental Sciences, 8(4): 43- Retrieved from. (in Persian).
  86. Zhang, M.; Lin, H.; Wang, G.; Sun, H. and Fu, J. (2018). Mapping paddy rice using a convolutional neural network (CNN) with Landsat 8 datasets in the Dongting Lake Area, China. Remote Sensing, 10(11): ‏
  87. Zheng, G.; Chen, J. M.; Tian, Q. J.; Ju, W. M. and Xia, X. Q. (2007). Combining remote sensing imagery and forest age inventory for biomass mapping. Journal of Environmental Management, 85(3): 616-623.
Volume 54, Issue 1
June 2022
Pages 111-133
  • Receive Date: 28 December 2021
  • Revise Date: 03 March 2022
  • Accept Date: 27 April 2022
  • First Publish Date: 27 April 2022