Abstract
The moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) product plays an important role in eco-environmental monitoring of natural disasters. However, its validation has long been an important scientific problem that has yet to be resolved. In this study, the downscaling model of NDVI was established based on the fractal Iterated Function System (IFS), and the MOD13 Q1 product was then validated based on the model. Xiamen was selected as the core study area, and utilizing the 30 m resolution Landsat 8 operational land imager (OLI) images as the validation data, the validation of MOD13 Q1 was implemented. The results showed the following. (1) The overall quality of the MOD13 Q1 product is good. While in the NDVI range of 0.2 to 0.6, the MOD13Q1 has an overestimation and the difference recognition of the NDVI is low, which should be paid attention to in practical applications; (2) The experiment proved that the fractal IFS was an effective methodology to establish downscaling models for RS land surface parameters such as NDVI. The inherent physical meaning and dynamic process expression advantages of this method make it have great application potential, which needs further digging.
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References
Huesca M, Litago J, Palaciosorueta A et al (2009) Assessment of forest fire seasonality using MODIS fire potential: a time series approach[J]. Agric For Meteorol 149(11):1946–1955
Leon JR, Van Leeuwen WJ, Casady GM et al (2012) Using MODIS-NDVI for the modeling of post-wildfire vegetation response as a function of environmental conditions and pre-fire restoration treatments[J]. Remote Sens 4(3):598–621
Zhang B, Zhang L, Xie D et al (2015) Application of synthetic NDVI time series blended from landsat and MODIS data for grassland biomass estimation[J]. Remote Sens 8(1):10
Nestola E, Calfapietra C, Emmerton CA et al (2016) Monitoring grassland seasonal carbon dynamics, by integrating MODIS NDVI, proximal optical sampling, and Eddy covariance measurements[J]. Remote Sens 8(3):260
Du L, Tian Q, Yu T et al (2013) A comprehensive drought monitoring method integrating MODIS and TRMM data[J]. Int J Appl Earth Obs Geoinf 23(1):245–253
Kim Y (2013) Drought and elevation effects on MODIS vegetation indices in northern Arizona ecosystems[J]. Int J Remote Sens 34(14):4889–4899
Veron SR, Paruelo JM (2010) Desertification alters the response of vegetation to changes in precipitation[J]. J Appl Ecol 47(6):1233–1241
Gao X, Huete AR, Didan K et al (2003) Multisensor comparisons and validation of MODIS vegetation indices at the semiarid Jornada experimental range[J]. IEEE Trans Geosci Remote Sens 41(10):2368–2381
Fensholt R, Sandholt I, Stisen S et al (2006) Evaluating MODIS, MERIS, and VEGETATION vegetation indices using in situ measurements in a semiarid environment[J]. IEEE Trans Geosci Remote Sens 44(7):1774–1786
Geng L, Ma M, Yu W et al (2014) Validation of the MODIS NDVI products in different land-use types using in situ measurements in the Heihe River Basin[J]. IEEE Geosci Remote Sens Lett 11(9):1649–1653
Liang S (2009) Quantitative Remote sensing[M]. (trans: Fan WJ). Science Press, Beijing 180–183. (in Chinese)
Gao F, Masek J, Schwaller M et al (2006) On the blending of the landsat and MODIS surface reflectance: predicting daily landsat surface reflectance[J]. IEEE Trans Geosci Remote Sens 44(8):2207–2218
Zhu XL, Chen JM, Gao F et al (2010) An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions[J]. Remote Sens Environ 114(11):2610–2623
Huang B, Zhang HK, Song HH et al (2013) Unified fusion of remote sensing imagery: generating simultaneously high-resolution synthetic spatial-temporal spectral earth observations[J]. Remote Sens Lett 4:561–569
Huang B, Zhang HK (2014) Spatio-temporal reflectance fusion via unmixing: accounting for both phenological and land-cover changes[J]. Int J Remote Sens 35(16):6213–6233
Wang Q, Shi W, Wang L (2014) Allocating classes for soft-then-hard subpixel mapping algorithms in units of class[J]. IEEE Trans Geosci Remote Sens 52(5):2940–2959
Wang Q, Shi W, Atkinson PM et al (2015) Downscaling MODIS images with area-to-point regression kriging[J]. Remote Sens Environ 166:191–204
Wang Q, Atkinson PM, Shi W (2015) Indicator cokriging-based subpixel mapping without prior spatial structure information[J]. IEEE Trans Geosci Remote Sens 53(1):309–323
Wang Q, Atkinson PM, Shi W (2015) Fast sub-pixel mapping algorithms for sub-pixel resolution change detection[J]. IEEE Trans Geosci Remote Sens 53(4):1692–1706
Shi W, Wang Q (2015) Soft-then-hard sub-pixel mapping with multiple shifted images[J]. Int J Remote Sens 36(5):1329–1348
Zhang RH, Tian J, Li ZL et al (2008) Spatial scaling and information fractal dimension of surface parameters used in quantitative remote sensing[J]. Int J Remote Sens 29:5145–5159
Zhang RH, Tian J, Li ZL et al (2010) Principles and methods for the validation of quantitative remote sensingproducts[J]. Sci China Earth Sci 53:741–751
Luan HJ, Tian QJ, Gu XF et al (2013) Establishing continuous scaling of NDVI based on fractal theory and GEOEYE-1 image[J]. J Infrared Millimeter Waves 32(6): 538–544, 549. (in Chinese)
Luan HJ, Tian QJ, Yu T et al (2015) Establishing continuous spatial scaling model of NDVI on fractal theory and five-index estimation system[J]. J Remote Sens 19(1):116–125 (in Chinese)
Wu L, Qin Q, Liu X et al (2016) Spatial up-scaling correction for leaf area index based on the fractal theory[J]. Remote Sens 8(3):197
Kim G, Barros AP (2002) Downscaling of remotely sensed soil moisture with a modified fractal interpolation method using contraction mapping and ancillary data[J]. Remote Sens Environ 83:400–413
Chen Y, Chen L (2005) Fractal geometry, 2nd edn [M]. Earthquake Press, Beijing, pp 95–98, 49-51. (in Chinese)
Xie HP, Sun HQ (1997) The study on bivariate fractal interpolation functions and creation of fractal interpolated surfaces[J]. Fractals 5(4):625–634
Wen JG, Liu Q, Liu QH et al (2009) Scale effect and scale correction of land-surface albedo in rugged terrain[J]. Int J Remote Sens 30(20):5397–5420
Luan H, Chen R, Zhang X et al (2017) Bidirectional reflectance distribution function estimation of typical ground objects in Xiamen and its availability in validation of normalized-difference vegetation index[C]. Int Congr Image Signal Process IEEE 625–631. (Datong)
Acknowledgements
This work was supported by the National Natural Science Foundation of China “Coupling of NDVI’s up-scaling and downscaling fusing with ground objects classification” (No. 41601350), the Natural Science Foundation of Fujian Province, China “Research on NDVI’s scaling fusing with ground objects classification” (No. 2017J05069), and the “Scientific Research Climbing Plan” Project from Xiamen University of Technology “Spatial Distribution Estimation and Dynamic Monitoring of Soil Organic Matter Based on Multi-source and Heterogeneous Data” (No. XPDKT19010).
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Luan, H., Zhang, M., Wan, Y., He, Y., Nie, Q., Zhang, X. (2020). Establishing the Downscaling Model of NDVI Based on the Iterated Function System. In: Wang, L., Wu, Y., Gong, J. (eds) Proceedings of the 6th China High Resolution Earth Observation Conference (CHREOC 2019). CHREOC 2019. Lecture Notes in Electrical Engineering, vol 657. Springer, Singapore. https://doi.org/10.1007/978-981-15-3947-3_45
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DOI: https://doi.org/10.1007/978-981-15-3947-3_45
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