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 Sensing

Volumn 5, Issue 9, 2013, Pages 4255-4279

  Tian, Feng ^a   Wang, Yunjia ^a   Fensholt, Rasmus ^b   Wang, Kun ^c   Zhang, Li ^c   Huang, Yi ^a  

^a CHINA UNIVERSITY OF MINING AND TECHNOLOGY   (China)

^b UNIVERSITY OF COPENHAGEN   (Denmark)

^c INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

Author keywords

Coal mining; Landsat; MODIS; STARFM; Synthetic NDVI; Trend analysis

Indexed keywords

COAL MINING; LANDSAT; MODIS; STARFM; SYNTHETIC NDVI; TREND ANALYSIS;

COAL DEPOSITS; COAL MINES; ECOSYSTEMS; INFRARED DEVICES; RADIOMETERS; REFLECTION; SATELLITE IMAGERY; VEGETATION;

TIME SERIES;

EID: 84884688597     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs5094255     Document Type: Article

References (60)

1. Comprehensive Management Planning Framework for Loess Plateau Area (2010-2030). Available online: http://www.sdpc.gov.cn/zcfb/zcfbtz/2010tz/W020110117531609590135.pdf (accessed on 29 August 2013).
2. Laflen, J.M.; Tian, J.; Huang, C. Soil Erosion and Dryland Farming; CRC Press: Boca Raton, FL, USA, 2000; pp. 1-54.
3. Lei, S.; Bian, Z.; Zhang, R.; Li, L. Study on the change rule of groundwater level and its impacts on vegetation at arid mining area. J. Coal Sci. Eng. 2007, 13, 179-182.
4. Zhang, J.Y.; Guo, L.W.; Gong, J.L.; Zhu, L.Q.; Gong, X.M. Analysis on impacting the soil environment of the underground coal mining. Prog. Min. Sci. Saf. Tech. 2007, 8, 1966-1969.
5. Zhang, X. Research on Temporal and Spatial Variation for Land Cover in Mining Disturbed Zone (In Chinese). M.Sc., Taiyuan University of Technology, Taiyuan, China, May 2010.
6. Al-Hamdan, M.; Cruise, J.; Rickman, D.; Quattrochi, D. Effects of spatial and spectral resolutions on fractal dimensions in forested landscapes. Remote Sens. 2010, 2, 611-640.
7. Röder, A.; Udelhoven, T.; Hill, J.; del Barrio, G.; Tsiourlis, G. Trend analysis of Landsat-TM and -ETM+ imagery to monitor grazing impact in a rangeland ecosystem in Northern Greece. Remote Sens. Environ. 2008, 112, 2863-2875.
8. Stellmes, M.; Udelhoven, T.; Röder, A.; Sonnenschein, R.; Hill, J. Dryland observation at local and regional scale-Comparison of Landsat TM/ETM+ and NOAA AVHRR time series. Remote Sens. Environ. 2010, 114, 2111-2125.
9. Lasanta, T.; Vicente-Serrano, S.M. Complex land cover change processes in semiarid Mediterranean regions: An approach using Landsat images in northeast Spain. Remote Sens. Environ. 2012, 124, 1-14.
10. Parent, M.B.; Verbyla, D. The browning of Alaska's boreal forest. Remote Sens. 2010, 2, 2729-2747.
11. Matejicek, L.; Kopackova, V. Changes in Croplands as a result of large scale mining and the associated impact on food security studied using time-series landsat images. Remote Sens. 2010, 2, 1463-1480.
12. Trejo, I.; Dirzo, R. Deforestation of seasonally dry tropical forest: a national and local analysis in Mexico. Biol. Conserv. 2000, 94, 133-142.
13. Stockli, R.; Vidale, P.L. European plant phenology and climate as seen in a 20-year AVHRR land-surface parameter dataset. Int. J. Remote Sens. 2004, 25, 3303-3330.
14. Slayback, D.A.; Pinzon, J.E.; Los, S.O.; Tucker, C.J. Northern hemisphere photosynthetic trends 1982-1999. Global Change Biol. 2003, 9, 1-15.
15. Julien, Y.; Sobrino, J.A. Global land surface phenology trends from GIMMS database. Int. J. Remote Sens. 2009, 30, 3495-3513.
16. Maignan, F.; Breon, F.M.; Bacour, C.; Demarty, J.; Poirson, A. Interannual vegetation phenology estimates from global AVHRR measurements-Comparison with in situ data and applications. Remote Sens. Environ. 2008, 112, 496-505.
17. Bai, Z.G.; Dent, D.L.; Olsson, L.; Schaepman, M.E. Proxy global assessment of land degradation. Soil Use Manag. 2008, 24, 223-234.
18. Fensholt, R.; Rasmussen, K.; Kaspersen, P.; Huber, S.; Horion, S.; Swinnen, E. Assessing land degradation/recovery in the African Sahel from long-term earth observation based primary productivity and precipitation relationships. Remote Sens. 2013, 5, 664-686.
19. Mao, J.; Shi, X.; Thornton, P.; Hoffman, F.; Zhu, Z.; Myneni, R. Global latitudinal-asymmetric vegetation growth trends and their driving mechanisms: 1982-2009. Remote Sens. 2013, 5, 1484-1497.
20. de Beurs, K.M.; Henebry, G.M. Trend Analysis of the Pathfinder AVHRR Land (PAL) NDVI-Data for the Deserts of Central Asia. IEEE Geosci. Remote Sens. Lett. 2004, 1, 282-286.
21. 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.
22. Bechtel, B.; Zakšek, K.; Hoshyaripour, G. Downscaling land surface temperature in an urban area: A case study for Hamburg, Germany. Remote Sens. 2012, 4, 3184-3200.
23. Gao, F.; Kustas, W.; Anderson, M. A data mining approach for sharpening thermal satellite imagery over land. Remote Sens. 2012, 4, 3287-3319.
24. Wu, P.; Shen, H.; Ai, T.; Liu, Y. Land-surface temperature retrieval at high spatial and temporal resolutions based on multi-sensor fusion. Int. J. Digit. Earth 2013, doi:10.1080/17538947.2013.783131.
25. Zurita-Milla, R.; Clevers, J.G.P.W.; Schaepman, M.E. Unmixing-based Landsat TM and MERIS FR data fusion. IEEE Geosci. Remote Sens. Lett. 2008, 5, 453-457.
26. Wu, M.Q.; Niu, Z.; Wang, C.Y.; Wu, C.Y.; Wang, L. Use of MODIS and Landsat time series data to generate high-resolution temporal synthetic Landsat data using a spatial and temporal reflectance fusion model. J. Appl. Remote Sens. 2012, 6. 063507-1-063507-13
27. Acerbi-Junior, F.W.; Clevers, J.; Schaepman, M.E. The assessment of multi-sensor image fusion using wavelet transforms for mapping the Brazilian Savanna. Int. J. Appl. Earth Obs. 2006, 8, 278-288.
28. 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.
29. Wulder, M.A.; Masek, J.G.; Cohen, W.B.; Loveland, T.R.; Woodcock, C.E. Opening the archive: How free data has enabled the science and monitoring promise of Landsat. Remote Sens. Environ. 2012, 122, 2-10.
30. 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.
31. Singh, D. Evaluation of long-term NDVI time series derived from Landsat data through blending with MODIS data. Atmosfera 2012, 25, 43-63.
32. 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.
33. 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.
34. Roy, D.P.; Ju, J.; Lewis, P.; Schaaf, C.; Gao, F.; Hansen, M.; Lindquist, E. Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data. Remote Sens. Environ. 2008, 112, 3112-3130.
35. Schmidt, M.; Udelhoven, T.; Gill, T.; Röder, 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, 063512-1-063512-18.
36. Meng, J.; Du, X.; Wu, B. Generation of high spatial and temporal resolution NDVI and its application in crop biomass estimation. Int. J. Digit. Earth 2011, 6, 203-218.
37. Shen, H.; Wu, P.; Liu, Y.; Ai, T.; Wang, Y.; Liu, X. A spatial and temporal reflectance fusion model considering sensor observation differences. Int. J. Remote Sens. 2013, 34, 4367-4383.
38. Zhu, X.; Chen, J.; Gao, F.; Chen, X.; Masek, J.G. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sens. Environ. 2010, 114, 2610-2623.
39. Jönsson, P.; Eklundh, L. TIMESAT-A program for analyzing time-series of satellite sensor data. Comput. Geosci. 2004, 30, 833-845.
40. Zhao, H.; Wang, Y.J. Research on the factors affecting the classification accuracy of ETM remote sensing image land cover/use (In Chinese). Remote Sens. Tech. Appl. 2012, 27, 600-608.
41. Fensholt, R.; Proud, S.R. Evaluation of earth observation based global long term vegetation trends-Comparing GIMMS and MODIS global NDVI time series. Remote Sens. Environ. 2012, 119, 131-147.
42. Xiao, X.M.; Braswell, B.; Zhang, Q.Y.; Boles, S.; Frolking, S.; Moore, B. Sensitivity of vegetation indices to atmospheric aerosols: Continental-scale observations in Northern Asia. Remote Sens. Environ. 2003, 84, 385-392.
43. Fensholt, R.; Nielsen, T.T.; Stisen, S. Evaluation of AVHRR PAL and GIMMS 10-day composite NDVI time series products using SPOT-4 vegetation data for the African continent. Int. J. Remote Sens. 2006, 27, 2719-2733.
44. 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.
45. Kaufman, Y.J.; Wald, A.E.; Remer, L.A.; Gao, B.-C.; Li, R.-R.; Flynn, L. The MODIS 2.1-μm channel-correlation with visible reflectance for use in remote sensing of aerosol. IEEE Trans. Geosci. Remote Sens. 1997, 35, 1286-1298.
46. Sen, P.K. Estimates of the regression coefficient based on Kendall's Tau. J. Am. Stat. Assoc. 1968, 63, 1379-1389.
47. Theil, H. A rank-invariant method of linear and polynomial regression analysis. I, II and III. Nederl. Akad. Wetensch. Proc. 1950, 53, 386-392, 521-525, 1397-1412.
48. Rousseeuw, P.J.; Leroy, A.M. Robust Regression and Outlier Detection; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2003.
49. Hirsch, R.M.; Slack, J.R. A nonparametric trend test for seasonal data with serial dependence. Water Resour. Res. 1984, 20, 727-732.
50. Vanbelle, G.; Hughes, J.P. Nonparametric-tests for trend in water-quality. Water Resour. Res. 1984, 20, 127-136.
51. de Beurs, K.M.; Henebry, G.M. A statistical framework for the analysis of long image time series. Int. J. Remote Sens. 2005, 26, 1551-1573.
52. Hirsch, R.M.; Slack, J.R.; Smith, R.A. Techniques of Trend Analysis for Monthly Water Quality Data. Water Resour. Res. 1982, 18, 107-121.
53. de Jong, R.; de Bruin, S.; de Wit, A.; Schaepman, M.E.; Dent, D.L. Analysis of monotonic greening and browning trends from global NDVI time-series. Remote Sens. Environ. 2011, 115, 692-702.
54. Fensholt, R.; Langanke, T.; Rasmussen, K.; Reenberg, A.; Prince, S.D.; Tucker, C.; Scholes, R.J.; Le, Q.B.; Bondeau, A.; Eastman, R.; et al. Greenness in semi-arid areas across the globe 1981-2007-An Earth Observing Satellite based analysis of trends and drivers. Remote Sens. Environ. 2012, 121, 144-158.
55. Yin, H.; Udelhoven, T.; Fensholt, R.; Pflugmacher, D.; Hostert, P. How Normalized Difference Vegetation Index (NDVI) trendsfrom Advanced Very High Resolution Radiometer (AVHRR) and Système Probatoire d'Observation de la Terre VEGETATION (SPOT VGT) time series differ in agricultural areas: An inner mongolian case study. Remote Sens. 2012, 4, 3364-3389.
56. Mann, H.B. Nonparametric tests against trend. Econometrica 1945, 13, 245-259.
57. Kendall, M.G. Rank Correlation Methods, 4th ed.; Charles Griffin: London, UK, 1975.
58. Wang, X.L.L.; Swail, V.R. Changes of extreme wave heights in Northern Hemisphere oceans and related atmospheric circulation regimes. J. Climate 2001, 14, 2204-2221.
59. Mirkin, B. Eleven ways to look at the chi-squared coefficient for contingency tables. Am. Stat. 2001, 55, 111-120.
60. Bhandari, S.; Phinn, S.; Gill, T. Assessing viewing and illumination geometry effects on the MODIS vegetation index (MOD13Q1) time series: implications for monitoring phenology and disturbances in forest communities in Queensland, Australia. Int. J. Remote Sens. 2011, 32, 7513-7538.