ارزیابی روش‌های هموارسازی برای بازسازی سری‌زمانی نمایه‌ گیاهی Greenness و برآورد فنولوژی کلزا از داده‌های ماهواره لندست8 مطالعه موردی: منطقه فرخشهر

نوع مقاله : مقاله کامل

نویسندگان

1 دانشجوی دکتری گروه جغرافیای طبیعی، دانشگاه اصفهان، اصفهان، ایران

2 دانشیار گروه جغرافیای طبیعی، دانشگاه اصفهان، اصفهان، ایران

3 دانشیار گروه مهندسی نقشه‏برداری، دانشگاه اصفهان، اصفهان، ایران

چکیده

سری‏های زمانی شاخص‏های گیاهی سنجش از دور امکان بازیابی فنولوژی گیاهان را در سطح زمین فراهم کرده است. ابرها، رطوبت، و هواویزها باعث ایجاد نوفه در سیگنال‏های دریافتی سنجنده‏های ماهواره‏‏ای می‏شوند و در نتیجه کیفیت سری‏های زمانی کاهش می‏یابد. برای رفع این مشکل و بازسازی سری‏های زمانی، چندین تابع هموارسازی داده‏ها برای حذف نوفه استفاده می‏شود که، به دلیل اختلاف نظر درمورد عملکرد آن‏ها، مقایسة بین آن‏ها لازم است. اهداف این تحقیق ارزیابی عملکرد توابع مختلف هموارسازی در نرم‏افزار TIMESAT و تأثیرات آن‏ها در بازسازی سری‏های زمانی و برآورد پارامترهای فنولوژیکی آغاز فصل رشد (SOS) و پایان فصل رشد (EOS) با استفاده از داده‏های نمایه Greenness (سبزینگی) ماهوارة لندست 8 است. پالایشگر ساویتزکی- گولی (S-G)، تابع نامتقارن گوسی (AG)، و لجستیک دوگانه (DL) برای برازش داده‏های Greenness   استفاده شد و عملکرد آن‏ها با استفاده از اندازه‏گیری خطای مجذور میانگین مربع (RMSE) و ضریب همبستگی پیرسون (r) ارزیابی شد. نتایج نشان داد که روش هموارسازی S-G در بازسازی سری‏های زمانی از دقت بیشتری (935/0 = r) برخوردار است. در برآورد پارامترهای فنولوژی، تابع هموارساز DL در برآورد آغاز فصل و تابع هموارساز AG در برآورد پایان فصل به‏ترتیب با 8 و 14 روز اختلاف با داده‏های مشاهداتی بهترین عملکرد را داشتند. این مطالعه نشان داد که روش‏های هموارسازی نرم‏افزار TIMESAT عملکرد مناسبی دارند

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Evaluation of smoothing methods for GREENNESS time series reconstruction and phenological estimation from Landsat 8 satellite data

نویسندگان [English]

  • Akbar Mirahmadi 1
  • Hojjatolah Yazdanpanah 2
  • Mehdi Momeni Shahraki 3
1 Associate Professor of climatology, University of Isfahan, Isfahan, Iran
2 Associate Professor of climatology, University of Isfahan, Isfahan, Iran
3 Associate Professor of Remote Sensing Engineering, University of Isfahan, Isfahan, Iran
چکیده [English]

Extended abstract
Introduction
Vegetation indices (VI) time-series have been used for land surface phenology retrieval but these time series are affected by clouds and aerosols, which add noise to the signal sensor. In this sense, several smoothing functions are used to remove noise introduced by undetected clouds and poor atmospheric conditions, but a comparison between methods is still necessary due to disagreements about its performance in the literature. The application of a smoothing function is a necessary previous step to describe land surface phenology in different ecosystems. Satellite-derived phenological parameters do not specifically provide information on the phenology of a single plant, their species or pheno-phases (e.g., bud opening, leaf emergence, leaf opening and flowering). Remote sensing Vegetation Indices are usually able to estimate a few phenological parameters such as start of season (SOS), end of season (EOS). The aims of this research were to evaluate the consistency of different smoothing functions from TIMESAT software and agricultural regions using the Greenness-Landsat time-series. To overcome the problems associated with remaining noise, various methods have been developed to estimate phenology and production metrics based on the VI time series. Some of them are wavelet decomposition, double logistic (DL) function, the asymmetric Gaussian (AG) function fitting, Savitzky–Golay (SG) filters, the Weighted Least Square (WLS). Some studies have compared these smoothing approaches, but most of them focus on coarse spatial resolution satellite image time series, such as the Moderate Resolution Imaging Spectroradiometer (MODIS). Due to the variety of results and the lack of consensus on smoothing methods, quality evaluation of smoothing operations should be done for each plant index and crop. Thus, the objectives of this paper are to evaluate and analyze the performance of various smoothing functions in TIMESAT software and their effects on estimating the phenological parameters of start of season (SOS) and end of season (EOS) of rapeseed.
 
Methodology
In this study, we used two kinds of data: 1- phenological data of rapeseed that was obtained from Field observation, and 2- GREENNESS index data extracted from Landsat 8 satellite images in the Agricultural Years (2016-2017, 2017-2018, 2018-2019). Geometric and radiometric corrections were applied to satellite images. The DN value was also converted to TOA to calculate Vegetation Indices. An adaptive Savitzky–Golay (SG) filter, Asymmetric Gaussian (AG), and Double Logistic (DL) functions to fitting Greenness data were used and their performances were assessed using the measures root mean square error (RMSE), Pearson correlation coefficient(r). Besides, differences in the estimation of the SOS and EOS were obtained. In all methods, the adaptation to upper envelope with the raw GREENNESS time series was used to reduce bias. In the Savitsky-Goli method, in addition to adapting upper envelope, the window size parameter (r) was also used.
 
Results and discussion
Statistical evaluation of smoothed time series
Statistical analysis of the output of smoothing functions showed that the time series produced by the AG model compared to the raw time series of the GREENNESS index had the lowest root mean square error (RMSE = 0.415) and the highest correlation (r = 0.935) belong to S-G model. The advantage of DL and AG models is that the difference between the mean correlation coefficient for all performances and the correlation coefficient for the best execution is small and it can be inferred that the software parameter settings have little effect on the outputs of these models. After plotting the smoothed time series curves, the results showed that the use of smoothing models effectively eliminated noise and disturbed the raw time series of the GREENNESS index, and reconstructed smoother and softer time series. The results also showed that time series that have a higher correlation coefficient show more details and changes within the inter-season, such as the recession stage(dormancy). Overall, it can be concluded that for reconstructing GREENNESS time series data, Pearson correlation coefficient (r) is more accurate than root mean square error (RMSE) and S-G model is more accurate than the other two models.
 
Conclusions
In this study, we showed to what extent the time series of the three smoothing methods SG, AG and DL in the reconstruction of the raw time series of the GREENNESS from the Landsat 8 and estimating the phenological parameters of the start and end of the season are accurate. The results of this study showed that the adaptive S-G model is more robust for reconstructing raw time series than AG and DL functions, and this is due to the sensitivity of this model to small changes in the GREENNESS time series. The AG and DL functions tend to eliminate noise at the peaks and bottoms of the time series. The results also showed that the time series with the highest correlation coefficient (r) are more suitable for reconstructing the raw time series of the GREENNESS index compared to the time series that produced the smallest RMSE. In SOS estimation, the S-G model performs worse than the AG and DL functions. Compared to the observational data, all smoothing methods used in this study estimate EOS late and SOS early. The results also showed that both AG and DL functions have time lag in SOS estimation compared to S-G model and time precedence in EOS estimation compared to S-G model. The efficiency of any smoothing method depends on the choice of parameters. For example, the use of adaptation upper envelope generally improves the results. AG and DL fitting function methods are the preferred option for smoothing low-quality data (eg high noise and high data loss) due to less sensitivity to regulatory parameters. The AG and DL fitting functions are limited when giving inter-seasonal details of the time series curve. Numerous factors such as vegetation index selection, satellite sensor data and vegetation type are affected in evaluating time series and estimating phenological parameters. However, the results of this study are valid for the data and the location under study, and the results may vary with other data or under other circumstances.
This study showed that the statistical criterion of Pearson correlation coefficient (r) is superior to the root mean square error (RMSE) and the S-G model is superior to the AG and DL models for reconstruction of time series. The DL function and AG function show the best performance for estimating SOS and EOS phenological parameters, respectively.

کلیدواژه‌ها [English]

  • start of season
  • end of season
  • Tasseled Cap Transform
  • smoothing function
  • TIMESAT
  1. آقابابایی، م.؛ ابراهیمی، ع. و طهماسبی، پ. (1397). مقایسة شاخص‏های گیاهی و تبدیل تسلدکپ در برآورد میزان کربن آلی خاک با استفاده از تصاویر سنجندة لندست 8 -OLI در مراتع نیمه‏استپی، سنجش‏ از دور و سامانة اطلاعات جغرافیایی در منابع طبیعی، 9(3): 85-99.
  2. پورغلام آمیجی، م.؛ انصاری قوجقار، م.؛ بذرافشان، ج.؛ لیاقت، ع. و عراقی‏نژاد، ش. (1399). مقایسة عملکرد مدل‏های سری زمانی SARIMA و Holt-Winters با روش‏های هوش مصنوعی در پیش‏ بینی طوفان‏های گردوغبار (مطالعة موردی: استان سیستان و بلوچستان)، پژوهش‏های جغرافیای طبیعی، 52(4): 567-587.
  3. حیدری بنی، م.؛ یزدان‏پناه، ح. و محنت‏کش، ع. (1397). بررسی اثرات تغییر اقلیم بر عملکرد و مراحل فنولوژیکی کلزا (مطالعۀ موردی: استان چهارمحال و بختیاری)، پژوهش‏های جغرافیای طبیعی، 50(2): 373-389.
  4. رایگانی، ب.؛ ارزانی، ح.؛ حیدری علمدارلو، ا. و مقدمی، م. م. (1398). کاربرد سنجش از دور به‏منظور ارزیابی تغییر اقلیم بر تولید و فنولوژی گیاهان (منطقة مورد مطالعه: استان تهران)، مرتع، 13(3): 450-460.
  5. رایگانی، ب. (1398). شناسایی کانون‏های بالقوة تولید گرد و غبار با استفاده از داده‏های سنجش از دور (مطالعة موردی: استان البرز)، مخاطرات محیط طبیعی، 8(20): 1-20.
  6. ریگی، م.؛ پیری ‏صحراگرد، ح.؛ دهمرده قلعه‏نو، م. و شهرکی، ا. (1397). ارزیابی تغییرات کاربری اراضی با استفاده از داده‏های سنجش از دور (مطالعة موردی: حوضة آبخیز نوک‏آباد، شهرستان خاش)، جغرافیا، 16(59): 191-204.
  7. زنگنه، م.؛ صفایی، م. ج. و سمیعی، م. (1398). کنکاشی بر رویکرد توانمندسازی جهت سامان‏دهی سکونتگاه‏های غیررسمی (نمونة موردی: شهر تربت حیدریه)، جغرافیا، 17(62): 191-205.
  8. فاطمی م ، رضائی ع؛ 1393 ،مبانی سنجش از دور، انتشارات آزاده، تهران، چاپ چهارم، ص296.
  9. قائمی، م.؛ ثنایی‏نژاد، س. ح.؛ آستارایی، ع. و میرحسینی، پ. (1389). بررسی و مقایسة شاخص‏های مختلف گیاهی با استفاده از تصاویر ماهواره‏ای ETM برای مطالعات پوشش گیاهی دشت نیشابور، خراسان رضوی، نشریة پژوهش‏های زراعی ایران، 8(1): 128-137.
  10. قمقامی، م.؛ قهرمان، ن.؛ قربانی، خ. و ایران‏نژاد، پ. (1396). کاربرد تصاویر ماهواره‏ای چندزمانه در بهبود دقت مدل‏های پیش‏یابی فنولوژی ذرت، تحقیقات آب و خاک ایران، 48(1): 11-24.
  11. Atkinson, P. M.; Jeganathan, C.; Dash, J. and Atzberger, C. (2012). Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology, Remote sensing of environment, 123, 400-417.‏
  12. Baig, M. H. A.; Zhang, L.; Shuai, T. and Tong, Q. (2014). Derivation of a tasseled cap transformation based on Landsat 8 at-satellite reflectance, Remote Sensing Letters, 5(5): 423-431.
  13. Beck, P. S.; Atzberger, C.; Høgda, K. A.; Johansen, B. and Skidmore, A. K. (2006). Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI, Remote sensing of Environment, 100(3): 321-334.‏
  14. Bradley, B. A.; Jacob, R. W.; Hermance, J. F. and Mustard, J. F. (2007). A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data, Remote sensing of environment, 106(2): 137-145.‏
  15. Cai, Z.; Jönsson, P.; Jin, H. and Eklundh, L. (2017). Performance of smoothing methods for reconstructing NDVI time-series and estimating vegetation phenology from MODIS data, Remote Sensing, 9(12): 1-17.‏
  16. Chen, J.; Jönsson, P.; Tamura, M.; Gu, Z.; Matsushita, B. and Eklundh, L. (2004). A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter, Remote sensing of Environment, 91(3-4): 332-344.‏
  17. Chen, W.; Foy, N.; Olthof, I.; Latifovic, R.; Zhang, Y.; Li, J. ... and Stewart, H. M. (2013). Evaluating and reducing errors in seasonal profiles of AVHRR vegetation indices over a Canadian northern national park using a cloudiness index, International Journal of Remote Sensing, 34(12): 4320-4343.‏
  18. Cheng, J., and Liang, S. (2014), Estimating the broadband longwave emissivity of global bare soil from the MODIS shortwave albedo product, J. Geophys. Res. Atmos., 119, 614– 634,
  19. Cong, N.; Wang, T.; Nan, H.; Ma, Y.; Wang, X.; Myneni, R. B. and Piao, S. (2013). Changes in satellite‐derived spring vegetation green‐up date and its linkage to climate in China from 1982 to 2010: a multimethod analysis, Global change biology, 19(3): 881-891.‏
  20. Crist, E. P. and Cicone, R. C. (1984). Application of the tasseled cap concept to simulated thematic mapper data, Photogrammetric engineering and Remote sensing, 50(3): 343-352.‏
  21. Dymond, C. C.; Mladenoff, D. J. and Radeloff, V. C. (2002). Phenological differences in Tasseled Cap indices improve deciduous forest classification, Remote sensing of environment, 80(3): 460-472.‏
  22. Eklundha, L. and Jönsson, P. (2017). TIMESAT 3.3 with seasonal trend decomposition and parallel processing Software Manual, Lund and Malmo University, Sweden. Retrieved from http://www.nateko.lu.se/TIMESAT/ 2017- 05-29.
  23. Gao, F.; Morisette, J. T.; Wolfe, R. E.; Ederer, G.; Pedelty, J.; Masuoka, E. ... and Nightingale, J. (2008). An algorithm to produce temporally and spatially continuous MODIS-LAI time series, IEEE Geoscience and Remote Sensing Letters, 5(1): 60-64.‏
  24. Geng, L.; Ma, M.; Wang, X.; Yu, W.; Jia, S. and Wang, H. (2014). Comparison of eight techniques for reconstructing multi-satellite sensor time-series NDVI data sets in the Heihe river basin, China, Remote Sensing, 6(3): 2024-2049.‏
  25. Gómez, C.; Wulder, M. A.; White, J. C.; Montes, F. and Delgado, J. A. (2012). Characterizing 25 years of change in the area, distribution, and carbon stock of Mediterranean pines in Central Spain, International Journal of Remote Sensing, 33(17): 5546-5573.‏
  26. Goward, S. N.; Markham, B.; Dye, D. G.; Dulaney, W. and Yang, J. (1991). Normalized difference vegetation index measurements from the Advanced Very High Resolution Radiometer, Remote sensing of environment, 35(2-3): 257-277.‏
  27. Guo, L.; An, N. and Wang, K. (2016). Reconciling the discrepancy in ground‐and satellite‐observed trends in the spring phenology of winter wheat in China from 1993 to 2008, Journal of Geophysical Research: Atmospheres, 121(3): 1027-1042.
  28. Han, H.; Bai, J.; Ma, G. and Yan, J. (2020). Vegetation Phenological Changes in Multiple Landforms and Responses to Climate Change, ISPRS International Journal of Geo-Information, 9(2) 111.
  29. Hanes, J. M.; Liang, L. and Morisette, J. T. (2013). Land surface phenology, In Biophysical applications of satellite remote sensing, Springer, Berlin, Heidelberg.‏
  30. Hird, J. N. and McDermid, G. J. (2009). Noise reduction of NDVI time series: An empirical comparison of selected techniques, Remote Sensing of Environment, 113(1): 248-258.‏
  31. Jonsson, P. and Eklundh, L. (2002). Seasonality extraction by function fitting to time-series of satellite sensor data, IEEE transactions on Geoscience and Remote Sensing, 40(8): 1824-1832.‏
  32. Jönsson, P. and Eklundh, L. (2004). TIMESAT—a program for analyzing time-series of satellite sensor data, Computers & geosciences, 30(8): 833-845.‏
  33. Kandasamy, S. and Fernandes, R. (2015). An approach for evaluating the impact of gaps and measurement errors on satellite land surface phenology algorithms: Application to 20 years NOAA AVHRR data over Canada, Remote Sensing of Environment, 164: 114-129.‏
  34. Karkauskaite, P.; Tagesson, T. and Fensholt, R. (2017). Evaluation of the plant phenology index (PPI), NDVI and EVI for start-of-season trend analysis of the Northern Hemisphere boreal zone, Remote Sensing, 9(5): 1:21‏
  35. Kowalski, K.; Senf, C.; Hostert, P. and Pflugmacher, D. (2020). Characterizing spring phenology of temperate broadleaf forests using Landsat and Sentinel-2 time series, International Journal of Applied Earth Observation and Geoinformation, 92: :118.
  36. Lara, B. and Gandini, M. (2016). Assessing the performance of smoothing functions to estimate land surface phenology on temperate grassland, International Journal of Remote Sensing, 37(8): 1801-1813.‏
  37. Ma, X.; Huete, A.; Yu, Q.; Coupe, N. R.; Davies, K.; Broich, M. ... and Eamus, D. (2013). Spatial patterns and temporal dynamics in savanna vegetation phenology across the North Australian Tropical Transect, Remote sensing of Environment, 139: 97-115.‏
  38. Melaas, E. K.; Friedl, M. A. and Zhu, Z. (2013). Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM/ETM+ data, Remote Sensing of Environment, 132: 176-185.‏
  39. Richardson, A. D.; Anderson, R. S.; Arain, M. A.; Barr, A. G.; Bohrer, G.; Chen, G. ... and Xue, Y. (2012). Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis, Global Change Biology, 18(2): 566-584.‏
  40. Sakamoto, T.; Yokozawa, M.; Toritani, H.; Shibayama, M.; Ishitsuka, N. and Ohno, H. (2005). A crop phenology detection method using time-series MODIS data, Remote sensing of environment, 96(3-4): 366-374.‏
  41. Samarawickrama, U.; Piyaratne, D. and Ranagalage, M. (2017). Relationship between NDVI with Tasseled cap Indices: A Remote Sensing based Analysis, IJIRT, 3(12): 13-19.
  42. Shao, Y.; Lunetta, R. S.; Wheeler, B.; Iiames, J. S. and Campbell, J. B. (2016). An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data, Remote Sensing of Environment, 174: 258-265.‏
  43. St Peter, J.; Hogland, J.; Hebblewhite, M.; Hurley, M. A.; Hupp, N. and Proffitt, K. (2018). Linking phenological indices from digital cameras in Idaho and Montana to MODIS NDVI, Remote Sensing, 10(10) 1601:1612.‏
  44. Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation, Remote sensing of Environment, 8(2): 127-150.‏
  45. White, M. A.; de Beurs, K. M.; Didan, K.; Inouye, D. W.; Richardson, A. D.; Jensen, O. P. ... and Lauenroth, W. K. (2009). Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006, Global Change Biology, 15(10): 2335-2359.‏
  46. You, X.; Meng, J.; Zhang, M. and Dong, T. (2013). Remote sensing-based detection of crop phenology for agricultural zones in China using a new threshold method, Remote Sensing, 5(7): 3190-3211.‏
  47. Zhang, X.; Schaaf, C. B.; Friedl, M. A.; Strahler, A. H.; Gao, F. and Hodges, J. C. (2002). MODIS tasseled cap transformation and its utility, In IEEE International Geoscience and Remote Sensing Symposium, Vol. 2, PP. 1063-1065.
  48. Zhu, L. and Meng, J. (2015). Determining the relative importance of climatic drivers on spring phenology in grassland ecosystems of semi-arid areas, International journal of biometeorology, 59(2): 237-248.
  49. Aghababaei, M.; Ebrahimi, A.; Tahmasebi, P. (2018). Comparison of vegetation indices and Tassled cap Transformation in estimating soil organic carbon content using Landsat 8 -OLI sensor images in semi-steppe rangelands, Remote Sensing and Geographic Information System in Natural Resources, 9(3): 85-99.
  50. Ghamghami, M.; Ghahreman, N.; Ghorbani, K. and Irannejad, P. (2017). Application of Multi-Time Satellite Images in Improving the Accuracy of Corn Phenology Prediction Models, Iranian Soil and Water Research, 48 (1): 11-24.
  51. Heydari Beni, M.; Yazdanpanah, H. and Mehnatkesh, A. (2018). Investigating the effects of climate change on canola yield and phenological stages (Case study: Chaharmahal and Bakhtiari province), Natural Geography Research, 50 (2): 373-389.
  52. Pourghlam Amiji, M.; Ansari Qujqar, M.; Bazrafshan, J.; Liaqat, A. and Iraqi Nejad, Sh. (2020). Comparison of the performance of SARIMA and Holt-Winters time series models with artificial intelligence methods in predicting dust storms (Case study: Sistan and Baluchestan province), Natural Geography Research, 52 (4): 567-587.
  53. Qaemi, M.; Sanaei Nejad, S. H.; Astarai, A. and Mir Hosseini, P. (2010). Study and comparison of different vegetation indices using ETM satellite images for vegetation studies in Neishabour plain, Khorasan Razavi, Iranian Journal of Crop Research, 8 (1): 128-137.
  54. Raiegani, B.; Arzani, H.; Heidari Alamdarloo, A. and Moghaddami, M. M. (2019). Application of remote sensing to evaluate climate change on plant production and phenology (study area: Tehran province), Range, 13(3): 450-460.
  55. Raiegani, B. (2019). Identification of potential centers of dust production using remote sensing data (Case study: Alborz province), Natural hazards, 8 (20): 1-20.
  56. Rigi, M.; Piri Sahragard, H., Dehmardeh Qaleh, M. and Shahraki, A. (2018). Evaluation of Land Use Changes Using Remote Sensing Data (Case Study: Nokabad Watershed, Khash County), Geography, 16(59): 191-204.
  57. Zanganeh, M.; Safaei, M. J. and Samiei, M. (2019). Research on the empowerment approach for organizing informal settlements (Case study: Torbat-e Heydariyeh), Geography, 17(62): 191-205.
دوره 54، شماره 1
این شماره با همکاری و مشارکت «انجمن ایرانی ژئومورفولوژی» منتشر شده است، بدینوسیله از مشارکت این انجمن در «داوری مقالات» ، «معرفی داوران» و «دبیران تخصصی » و «شرکت در جلسات و نشست های مرتبط» تشکر می گردد.
اردیبهشت 1401
صفحه 55-70
  • تاریخ دریافت: 06 دی 1400
  • تاریخ بازنگری: 08 بهمن 1400
  • تاریخ پذیرش: 06 اردیبهشت 1400