Evaluation of a rule-based compositing technique for Landsat-5 TM and Landsat-7 ETM+ images

International Journal of Applied Earth Observation and Geoinformation

Volumn 47, Issue , 2016, Pages 1-14

  Luck W ^a   van Niekerk, A ^b  

^a Forest Sense cc   (South Africa)

^b STELLENBOSCH UNIVERSITY   (South Africa)

Author keywords

Image compositing; Landsat; Spectral rule based classification

Indexed keywords

IMAGE ANALYSIS; IMAGE CLASSIFICATION; IMAGERY; LAND COVER; LANDSAT THEMATIC MAPPER; NDVI; OPTIMIZATION; SPECTRAL ANALYSIS;

EID: 84988735799     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2015.11.019     Document Type: Article

References (46)

1. Baraldi, A., Boschetti, L., Humber, M.L., Probability sampling protocol for thematic and spatial quality assessment of classification maps generated from spaceborne/airborne very high resolution images. Geosc. Remote Sens. IEEE Trans. 52:1 (2014), 701–760.
2. Baraldi, A., Durieux, L., Simonetti, D., Conchedda, G., Holecz, F., Blonda, P., Automatic Spectral rule-based preliminary classification of radiometrically calibrated SPOT-4/-5/IRS, AVHRR/MSG, AATSR, IKONOS/QuickBird/OrbView/GeoEye, and DMC/SPOT-1/-2 imagery—part II: classification accuracy assessment. Geosci. Remote Sens. IEEE Trans. 48:3 (2010), 1326–1354.
3. Baraldi, A., Durieux, L., Simonetti, D., Conchedda, G., Holecz, F., Blonda, P., Automatic spectral-rule-based preliminary classification of radiometrically calibrated SPOT-4/-5/IRS, AVHRR/MSG, AATSR, IKONOS/QuickBird/OrbView/GeoEye, and DMC/SPOT-1/-2 imagery—part I: system design and implementation. Geosci. Remote Sens. IEEE Trans. 48:3 (2010), 1299–1325.
4. Baraldi, A., Durieux, L., Simonetti, D., Conchedda, G., Holecz, F., Blonda, P., Corrections to automatic spectral rule-based preliminary classification of radiometrically calibrated SPOT-4/-5/IRS, AVHRR/MSG, AATSR, IKONOS/QuickBird/OrbView/GeoEye, and DMC/SPOT-1/-2 imagery [Mar 10 1299–1325 and 1326–1354]. Geosci. Remote Sens. IEEE Trans., 48(3), 2010, 1635.
5. Baraldi, A., Gironda, M., Simonetti, D., Operational two-stage stratified topographic correction of spaceborne multispectral imagery employing an automatic spectral-rule-based decision-tree preliminary classifier. Geosci. Remote Sens. IEEE Trans. 48:1 (2010), 112–146.
6. Baraldi, A., Puzzolo, V., Blonda, P., Bruzzone, L., Tarantino, C., Automatic spectral rule-based preliminary mapping of calibrated Landsat TM and ETM+ images. Geosci. Remote Sens. IEEE Trans. 44:9 (2006), 2563–2586.
7. Barbosa, P.M., Pereira, J.M.C., Grégoire, J., Compositing criteria for burned area assessment using multitemporal low resolution satellite data. Remote Sensi. Environ. 65:1 (1998), 38–49.
8. Breaker, L.C., Armstrong, E.M., Endris, C.A., Establishing an objective basis for image compositing in satellite oceanography. Remote Sens. Environ. 114:2 (2010), 345–362.
9. Brems, E., Lissens, G., Veroustraete, F., MC-FUME: a new method for compositing individual reflective channels. Geosci. Remote Sens. IEEE Trans. 38:1 (2000), 553–569.
10. Chuvieco, E., Ventura, G., Martín, M.P., Gómez, I., Assessment of multitemporal compositing techniques of MODIS and AVHRR images for burned land mapping. Remote Sens. Environ. 94:4 (2005), 450–462.
11. Cihlar, J., Latifovic, R., Chen, J., Trishchenko, A., Du, Y., Fedosejevs, G., Guindon, B., Systematic corrections of AVHRR image composites for temporal studies. Remote Sens. Environ. 89:2 (2004), 217–233.
12. Danaher, T., Xiaoliang, Wu, Campbell, N., Bi-directional reflectance distribution function approaches to radiometric calibration of Landsat ETM+ imagery. Geoscience and Remote Sensing Symposium, 2001. IGARSS ‘01. IEEE 2001 International, 2001, 2654.
13. Dennison, P.E., Roberts, D.A., Peterson, S.H., Spectral shape-based temporal compositing algorithms for MODIS surface reflectance data. Remote Sens. Environ. 109:4 (2007), 510–522.
14. Fontana, F.M.A., Trishchenko, A.P., Khlopenkov, K.V., Luo, Y., Wunderle, S., Impact of orthorectification and spatial sampling on maximum NDVI composite data in mountain regions. Remote Sens. Environ. 113:12 (2009), 2701–2712.
15. Gutman, G., Byrnes, R., Masek, J., Covington, S., Justice, C., Franks, S., Headley, R., Towards monitoring land-cover and land-use changes at a global scale: the Global Land Survey. Photogramm. Eng. Remote Sens. 75:1 (2008), 6–10.
16. Gutman, G.G., Ignatov, A.M., Olson, S., Towards better quality of AVHRR composite images over land: reduction of cloud contamination. Remote Sens. Environ. 50:2 (1994), 134–148.
17. Hagolle, O., Lobo, A., Maisongrande, P., Cabot, F., Duchemin, B., De Pereyra, A., Quality assessment and improvement of temporally composited products of remotely sensed imagery by combination of VEGETATION 1 and 2 images. Remote Sens. Environ. 94:2 (2005), 172–186.
18. Hu, B., Lucht, W., Strahler, A.H., Barker Schaaf, C., Smith, M., Surface albedos and angle-corrected NDVI from AVHRR observations of South America. Remote Sens. Environ. 71:2 (2000), 119–132.
19. Huang, C., Wylie, B., Yang, L., Homer, C., Zylstra, G. 2002, Derivation of a Tasseled Cap Transformation based on Landsat 7 At-Satellite Reflectance. Available: http://landcover.usgs.gov/pdf/tasseled.pdf 2010.
20. Hüttich, C., Herold, M., Wegmann, M., Cord, A., Strohbach, B., Schmullius, C., Dech, S., Assessing effects of temporal compositing and varying observation periods for large-area land-cover mapping in semi-arid ecosystems: implications for global monitoring. Remote Sens. Environ. 115:10 (2011), 2445–2459.
21. Irish, R. 2012. Landsat 7 Science data user's handbook; Report 430-15-01-003-0, National Aeronautics and Space Administration. Available: http://landsathandbook.gsfc.nasa.gov/pdfs/Landsat7_Handbook.pdf [2012].
22. Ju, J., Roy, D.P., Vermote, E., Masek, J., Kovalskyy, V., Continental-scale validation of MODIS-based and LEDAPS Landsat ETM+ atmospheric correction methods. Remote Sens. Environ. 122:0 (2012), 175–184.
23. Karnieli, A., Kaufman, Y.J., Remer, L., Wald, A., AFRI—aerosol free vegetation index. Remote Sens. Environ. 77:1 (2001), 10–21.
24. Kauth, R.J., Thomas, G.S., The tasseled cap—a graphic description of the spectral-temporal development of agricultural crops as seen in Landdat. Symposium on Machine Processing of Remotely Sensed Data, June 29–July 1, 1976, pp. 41.
25. Kross, A., Fernandes, R., Seaquist, J., Beaubien, E., The effect of the temporal resolution of NDVI data on season onset dates and trends across Canadian broadleaf forests. Remote Sens. Environ. 115:6 (2011), 1564–1575.
26. Liang, S., Quantitative Remote Sensing of Land Surfaces. 2004, John Wiley & Sons, New Jersey United States.
27. Advanced Remote Sensing, Terrestrial Information Extraction and Applications. Liang, S., Li, X., Wang, J., (eds.), 2012, Academic Press, China.
28. Luo, Y., Trishchenko, A.P., Khlopenkov, K.V., Developing clear-sky, cloud and cloud shadow mask for producing clear-sky composites at 250-meter spatial resolution for the seven MODIS land bands over Canada and North America. Remote Sens. Environ. 112:12 (2008), 4167–4185.
29. Mather, P., Computer Processing of Remotely-Sensed Images: An Introduction. 2004, John Wiley & Sons, Chichester, England.
30. Maxwell, S.K., Sylvester, K.M., Identification of ever-cropped land (1984- using Landsat annual maximum NDVI image composites: Southwestern Kansas case study. Remote Sens. Environ. 121:0 (2010), 186–195.
31. McDonald, E.R., Wu, X., Caccetta, P., Campbell, N. 2001, Illumination Correction of Landsat TM Data in South East NSW, www.environment.gov.au/land/publications/pubs/illumination.pdf, Australia.
32. Olthof, I., Pouliot, D., Fernandes, R., Latifovic, R., Landsat-7 ETM+ radiometric normalization comparison for northern mapping applications. Remote Sens. Environ. 95:3 (2005), 388–398.
33. Potapov, P.V., Turubanova, S.A., Hansen, M.C., Adusei, B., Broich, M., Altstatt, A., Mane, L., Justice, C.O., Quantifying forest cover loss in Democratic Republic of the Congo, 2000–2010, with Landsat ETM + data. Remote Sens. Environ. 122:0 (2012), 106–116.
34. Potapov, P., Turubanova, S., Hansen, M.C., Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia. Remote Sens. Environ. 115:2 (2011), 548–561.
35. Pouliot, D., Latifovic, R., Fernandes, R., Olthof, I., Evaluation of compositing period and AVHRR and MERIS combination for improvement of spring phenology detection in deciduous forests. Remote Sens. Environ. 115:1 (2011), 158–166.
36. Richter, R., Atmospheric Correction Methods for Optical Remote Sensing Imagery of Land in Advances in Environmental Remote Sensing. Weng, Q., (eds.) Sensors, Algorithms, and Applications, 2011, CRC Press, Florida, United States, 161–172.
37. Richter, R., Kellenberger, T., Kaufmann, H., Comparison of topographic correction methods. Remote Sens. 1:3 (2009), 184–196.
38. Roy, D.P., Ju, J., Kline, K., Scaramuzza, P.L., Kovalskyy, V., Hansen, M., Loveland, T.R., Vermote, E., Zhang, C., Web-enabled landsat data (WELD): Landsat ETM+ composited mosaics of the conterminous United States. Remote Sens. Environ. 114:1 (2010), 35–49.
39. Roy, D.P., Ju, J., Mbow, C., Frost, P., Loveland, T., Accessing free Landsat data via the Internet: Africa's challenge. Remote Sens. Lett. 1:2 (2010), 111–117.
40. Roy, D.P., Ju, J., Kommadreddy, I., Hansen, M., Vermote, E., Zhang, C., Kommadreddy, A., Web Enabled Landsat Data (WELD) Products—Algorithm Theoretical Basis Document. 1.0th ed., 2011, South Dacota State University Since January 2008, the U.S. Geological Survey has been providing free terrain-corrected and radiometrically calibrated Landsat data via the Internet. This revolutionary data policy provides the opportunity to use all the data in the U.S. Landsat archive a.
41. Roy, D.P., Ju, J., Kommadreddy, I., Hansen, M., Vermote, E., Zhang, C., Kommadreddy, A., Web Enabled Landsat Data (WELD) Products. 1.0th ed., 2011, University of South Dacota http://globalmonitoring.sdstate.edu/projects/weld/WELD_ATBD.pdf.
42. Shuai, Y., Masek, J.G., Gao, F., Schaaf, C.B., An algorithm for the retrieval of 30-m snow-free albedo from Landsat surface reflectance and MODIS BRDF. Remote Sens. Environ. 115:9 (2011), 2204–2216.
43. Tan, B., Woodcock, C.E., Hu, J., Zhang, P., Ozdogan, M., Huang, D., Yang, W., Knyazikhin, Y., Myneni, R.B., The impact of gridding artifacts on the local spatial properties of MODIS data: Implications for validation, compositing, and band-to-band registration across resolutions. Remote Sens. Environ. 105:2 (2006), 98–114.
44. van Leeuwen, W.J.D., Huete, A.R., Laing, T.W., MODIS vegetation index compositing approach: a prototype with AVHRR data. Remote Sens. Environ. 69:3 (1999), 264–280.
45. Xiaoliang, W., Danaher, T., Wallace, J., Campbell, N., A BRDF-corrected Landsat 7 mosaic of the Australian continent. Geoscience and Remote Sensing Symposium, 2001. IGARSS ‘01. IEEE 2001 International, 2001, pp. 3274.
46. Zhang, Y., Guindon, B., Cihlar, J., An image transform to characterize and compensate for spatial variations in thin cloud contamination of Landsat images. Remote Sens. Environ. 82:2–3 (2002), 173–187.