Application of synthetic NDVI time series blended from landsat and MODIS data for grassland biomass estimation

Remote Sensing

Volumn 8, Issue 1, 2016, Pages

  Zhang, Binghua ^a,b,c   Zhang, Li ^a,b   Xie, Dong ^a,d   Yin, Xiaoli ^a   Liu, Chunjing ^a,e   Liu, Guang ^a  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b Hainan Key Laboratory of Earth Observation   (China)

^c UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^d GEORGE WASHINGTON UNIVERSITY   (United States)

^e SHANDONG AGRICULTURAL UNIVERSITY   (China)

Author keywords

Biomass; Data fusion; Landsat; MODIS; STARFM; Support vector machine (SVM)

Indexed keywords

EID: 84957883899     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs8010010     Document Type: Article

References (63)

1. He, C.; Zhang, Q.; Li, Y.; Li, X.; Shi, P. Zoning grassland protection area using remote sensing and cellular automata modeling-A case study in Xilingol steppe grassland in northern China. J. Arid Environ. 2005, 63, 814-826.
2. Jobbagy, E.G.; Sala, O.E. Controls of grass and shrub aboveground production in the Patagonian steppe. Ecol. Appl. 2000, 10, 541-549.
3. Nordberg, M.L.; Evertson, J. Monitoring change in mountainous dry-heath vegetation at a regional scale using multitemporal Landsat TM data. Ambio 2003, 32, 502-509.
4. Huete, A.; Didan, K.; Miura, T.; Rodriguez, E.P.; Gao, X.; Ferreira, L.G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 2002, 83, 195-213.
5. Price, J.C. How unique are spectral signatures. Remote Sens. Environ. 1994, 49, 181-186.
6. Asner, G.P. Cloud cover in Landsat observations of the Brazilian Amazon. Int. J. Remote Sens. 2001, 22, 3855-3862.
7. Jorgensen, P.V. Determination of cloud coverage over Denmark using Landsat MSS/TM and NOAA-AVHRR. Int. J. Remote Sens. 2000, 21, 3363-3368.
8. Ju, J.C.; Roy, D.P. The availability of cloud-free Landsat ETM plus data over the conterminous United States and globally. Remote Sens. Environ. 2008, 112, 1196-1211.
9. Gur, E.; Zalevsky, Z. Resolution-enhanced remote sensing via multi spectral and spatial data fusion. Int. J. Image Data Fusion 2011, 2, 149-165.
10. Carper, W.J.; Lillesand, T.M.; Kiefer, R.W. The use of intensity-hue-saturation transformations for merging SPOT panchromatic and multispectral image data. Photogramm. Eng. Remote. Sens. 1990, 56, 459-467.
11. Shettigara, V.K. A generalized component substitution technique for spatial enhancement of multispectral images using a higher resolution data set. Photogramm. Eng. Remote. Sens. 1992, 58, 561-567.
12. Yocky, D.A. Multiresolution wavelet decomposition image merger of Landsat thematic mapper and SPOT panchromatic data. Photogramm. Eng. Remote. Sens. 1996, 62, 1067-1074.
13. Fu, D.J.; Chen, B.Z.; Wang, J.; Zhu, X.L.; Hilker, T. An improved image fusion approach based on enhanced spatial and temporal the adaptive reflectance fusion model. Remote Sens. 2013, 5, 6346-6360.
14. Gao, F.; Masek, J.; Schwaller, M.; Hall, F. On the blending of the Landsat and MODIS surface reflectance:Predicting daily Landsat surface reflectance. IEEE Trans. Geosci. Remote Sens. 2006, 44, 2207-2218.
15. Zhu, X.L.; Chen, J.; Gao, F.; Chen, X.H.; Masek, J.G. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sens. Environ. 2010, 114, 2610-2623.
16. Hilker, T.; Wulder, M.A.; Coops, N.C.; Seitz, N.; White, J.C.; Gao, F.; Masek, J.G.; Stenhouse, G. Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model. Remote Sens. Environ. 2009, 113, 1988-1999.
17. Walker, J.J.; de Beurs, K.M.; Wynne, R.H.; Gao, F. Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology. Remote Sens. Environ. 2012, 117, 381-393.
18. Tian, F.; Wang, Y.J.; Fensholt, R.; Wang, K.; Zhang, L.; Huang, Y. Mapping and evaluation of NDVI trends from synthetic time series obtained by blending Landsat and MODIS data around a coalfield on the Loess Plateau. Remote Sens. 2013, 5, 4255-4279.
19. Schmidt, M.; Udelhoven, T.; Gill, T.; Roder, A. Long term data fusion for a dense time series analysis with MODIS and Landsat imagery in an Australian savanna. J. Appl. Remote Sens. 2012, 6, 063512.
20. Watts, J.D.; Powell, S.L.; Lawrence, R.L.; Hilker, T. Improved classification of conservation tillage adoption using high temporal and synthetic satellite imagery. Remote Sens. Environ. 2011, 115, 66-75.
21. Singh, D. Generation and evaluation of gross primary productivity using Landsat data through blending with MODIS data. Int. J. Appl. Earth Obs. 2011, 13, 59-69.
22. Singh, D. Evaluation of long-term NDVI time series derived from Landsat data through blending with MODIS data. Atmosfera 2012, 25, 43-63.
23. Bhandari, S.; Phinn, S.; Gill, T. Preparing Landsat image time series (LITS) for monitoring changes in vegetation phenology in Queensland, Australia. Remote Sens. 2012, 4, 1856-1886.
24. Senf, C.; Leitao, P.J.; Pflugmacher, D.; van der Linden, S.; Hostert, P. Mapping land cover in complex Mediterranean landscapes using Landsat: Improved classification accuracies from integrating multi-seasonal and synthetic imagery. Remote Sens. Environ. 2015, 156, 527-536.
25. Huang, B.; Song, H.H. Spatiotemporal reflectance fusion via sparse representation. IEEE Trans. Geosci. Remote Sens. 2012, 50, 3707-3716.
26. Hilker, T.; Wulder, M.A.; Coops, N.C.; Linke, J.; McDermid, G.; Masek, J.G.; Gao, F.; White, J.C. A new data fusion model for high spatial and temporal-resolution mapping of forest disturbance based on Landsat and MODIS. Remote Sens. Environ. 2009, 113, 1613-1627.
27. Gevaert, C.M.; García-Haro, F.J. A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion. Remote Sens. Environ. 2015, 156, 34-44.
28. Shen, H.F.; Wu, P.H.; Liu, Y.L.; Ai, T.H.; Wang, Y.; Liu, X.P. A spatial and temporal reflectance fusion model considering sensor observation differences. Int. J. Remote Sens. 2013, 34, 4367-4383.
29. Meng, J.H.; Du, X.; Wu, B.F. Generation of high spatial and temporal resolution NDVI and its application in crop biomass estimation. Int. J. Digit. Earth 2013, 6, 203-218.
30. Huang, B.; Zhang, H.K. Spatio-temporal reflectance fusion via unmixing: Accounting for both phenological and land-cover changes. Int. J. Remote Sens. 2014, 35, 6213-6233.
31. Zhang, F.; Zhu, X.L.; Liu, D.S. Blending MODIS and Landsat images for urban flood mapping. Int. J. Remote Sens. 2014, 35, 3237-3253.
32. Emelyanova, I.V.; McVicar, T.R.; van Niel, T.G.; Li, L.T.; van Dijk, A.I.J.M. Assessing the accuracy of blending Landsat-MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics:A framework for algorithm selection. Remote Sens. Environ. 2013, 133, 193-209.
33. Gao, T.; Xu, B.; Yang, X.C.; Jin, Y.X.; Ma, H.L.; Li, J.Y.; Yu, H.D. Using MODIS time series data to estimate aboveground biomass and its spatio-temporal variation in Inner Mongolia's grassland between 2001 and 2011. Int. J. Remote Sens. 2013, 34, 7796-7810.
34. Tomppo, E.; Nilsson, M.; Rosengren, M.; Aalto, P.; Kennedy, P. Simultaneous use of Landsat-TM and IRS-1C WIFS data in estimating large area tree stem volume and aboveground biomass. Remote Sens. Environ. 2002, 82, 156-171.
35. Xie, Y.C.; Sha, Z.Y.; Yu, M.; Bai, Y.F.; Zhang, L. A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia, China. Ecol. Model 2009, 220, 1810-1818.
36. Jin, Y.Q.; Liu, C. Biomass retrieval from high-dimensional active/passive remote sensing data by using artificial neural networks. Int. J. Remote Sens. 1997, 18, 971-979.
37. Uno, Y.; Prasher, S.O.; Lacroix, R.; Goel, P.K.; Karimi, Y.; Viau, A.; Patel, R.M. Artificial neural networks to predict corn yield from compact airborne spectrographic imager data. Comput. Electron. Agric. 2005, 47, 149-161.
38. Yool, S.R. Land cover classification in rugged areas using simulated moderate-resolution remote sensor data and an artificial neural network. Int. J. Remote Sens. 1998, 19, 85-96.
39. Ban, Y.F. Synergy of multitemporal ERS-1 SAR and Landsat TM data for classification of agricultural crops. Can. J. Remote Sens. 2003, 29, 518-526.
40. Erbek, F.S.; Ozkan, C.; Taberner, M. Comparison of maximum likelihood classification method with supervised artificial neural network algorithms for land use activities. Int. J. Remote Sens. 2004, 25, 1733-1748.
41. Mathur, P.; Govil, R. Detecting temporal changes in satellite imagery using ANN. In Proceedings of the RAST 2005 2nd International Conference on Recent Advances in Space Technologie, Istanbul, Turkey, 9-11 June 2005; pp. 645-647.
42. Balabin, R.M.; Lomakina, E.I. Support vector machine regression (SVR/lS-SVM)-An alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data. Analyst 2011, 136, 1703-1712.
43. Chen, G.; Hay, G.J.; Zhou, Y.L. Estimation of forest height, biomass and volume using support vector regression and segmentation from Lidar transects and Quickbird imagery. In Proceedings of the 2010 18th International Conference on Geoinformatic, Beijing, China, 18-20 June 2010; pp. 1-4.
44. Gleason, C.J.; Im, J. Forest biomass estimation from airborne LiDAR data using machine learning approaches. Remote Sens. Environ. 2012, 125, 80-91.
45. Jachowski, N.R.A.; Quak, M.S.Y.; Friess, D.A.; Duangnamon, D.; Webb, E.L.; Ziegler, A.D. Mangrove biomass estimation in southwest Thailand using machine learning. Appl. Geogr. 2013, 45, 311-321.
46. Tu, Y. Based on Meteorological Data of Drought Disaster Forecast Study in Pastoral Area of Xilingol League. Master's Thesis, Inner Mongolia Normal University, Hohhot, China, 2013. (In Chinese)
47. Jin, Y.X.; Yang, X.C.; Qiu, J.J.; Li, J.Y.; Gao, T.; Wu, Q.; Zhao, F.; Ma, H.L.; Yu, H.D.; Xu, B. Remote sensing-based biomass estimation and its spatio-temporal variations in temperate grassland, northern china. Remote Sens. 2014, 6, 1496-1513.
48. Guan, L.L.; Liu, L.Y.; Peng, D.L.; Hu, Y.; Jiao, Q.J.; Liu, L.L. Monitoring the distribution of C3 and C4 grasses in a temperate grassland in northern China using moderate resolution imaging spectroradiometer normalized difference vegetation index trajectories. J. Appl. Remote Sens. 2012, 6.
49. Masek, J.G.; Vermote, E.F.; Saleous, N.E.; Wolfe, R.; Hall, F.G.; Huemmrich, K.F.; Gao, F.; Kutler, J.; Lim, T.K. A Landsat surface reflectance dataset for North America, 1990-2000. IEEE Geosci. Remote Sens. Lett. 2006, 3, 68-72.
50. Bai, Y.F.; Han, X.G.; Wu, J.G.; Chen, Z.Z.; Li, L.H. Ecosystem stability and compensatory effects in the Inner Mongolia grassland. Nature 2004, 431, 181-184.
51. Vermonte, E.F.; Kotchenova, S.Y.; Ray, J.P. MODIS Surface Reflectance User's Guide, Version 1.4. Available online: http://modis-sr.ltdri.org/guide/MOD09_UserGuide_v1_3.pdf (accessed on 16 November 2015).
52. Makela, H.; Pekkarinen, A. Estimation of forest stand volumes by Landsat TM imagery and stand-level field-inventory data. For. Ecol. Manag. 2004, 196, 245-255.
53. Katz, R.W.; Glantz, M.H. Anatomy of a rainfall index. Mon. Weather Rev. 1986, 114, 764-771.
54. Jarihani, A.A.; McVicar, T.R.; van Niel, T.G.; Emelyanova, I.V.; Callow, J.N.; Johansen, K. Blending Landsat and MODIS data to generate multispectral indices: A comparison of "Index-then-blend"and "Blend-then-index"approaches. Remote Sens. 2014, 6, 9213-9238.
55. Hsu, C.W.; Chang, C.C.; Lin, C.J. A Practical Guide to Support Vector Classification. Available online:https://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf (accessed on 19 November 2015).
56. Rembold, F.; Atzberger, C.; Savin, I.; Rojas, O. Using low resolution satellite imagery for yield prediction and yield anomaly detection. Remote Sens. 2013, 5, 1704-1733.
57. Kawamura, K.; Akiyama, T.; Yokota, H.; Tsutsumi, M.; Yasuda, T.; Watanabe, O.; Wang, S.P. Quantifying grazing intensities using geographic information systems and satellite remote sensing in the Xilingol steppe region, Inner Mongolia, China. Agric. Ecosyst. Environ. 2005, 107, 83-93.
58. Chun, F.; Li, C.L.; Bao, Y.H. The wavelet analysis of average temperature and precipitation in Xilinhot during 57 years. J. Inner Mong. Normal Univ. (Nat. Sci. Ed.) 2013, 42, 47-52. (In Chinese)
59. Farmer's Daily. Available online: http://szb.farmer.com.cn/nmrb/html/2011-04/13/nw.D110000nmrb_20110413_1-03.htm?div=-1 (accessed on 4 September 2015).
60. Hang, Y.L.; Bao, G.; Bao, Y.H.; Burenjirigala; Altantuya, D. Spatiotemporal changes of vegetation coverage in Xilingol grassland and its responses to climate change during 2000-2010. Acta Agres. Sin. 2014, 22, 1194-1204. (In Chinese)
61. Yu, H.D.; Yang, X.C.; Xu, B.; Jin, Y.X.; Gao, T.; Li, J.Y. Changes of grassland vegetation growth in Xilingol league over 10 years and analysis on the influence factors. J. Geo-Inf. Sci. 2013, 15, 270-279. (In Chinese)
62. Xilinhaote News. Available online: http://xilinhaote.nmgnews.com.cn/system/2009/07/20/010253966.shtml (accesed on 4 September 2015).
63. Liu, C.L.; Fan, R.H.; Wu, J.J.; Yan, F. Temporal lag of grassland vegetation growth response to precipitation in Xilinguolemeng. Arid Land Geogr. 2009, 32, 512-518. (In Chinese)