Multi-resolution time series imagery for forest disturbance and regrowth monitoring in Queensland, Australia

Remote Sensing of Environment

Volumn 158, Issue , 2015, Pages 156-168

  Schmidt, Michael ^a,b   Lucas, Richard ^c   Bunting, Peter ^d   Verbesselt, Jan ^e   Armston, John ^a,b  

^a DEPARTMENT OF ENVIRONMENT AND RESOURCE MANAGEMENT   (Australia)

^b UNIVERSITY OF QUEENSLAND   (Australia)

^c UNIVERSITY OF NEW SOUTH WALES   (Australia)

^d ABERYSTWYTH UNIVERSITY   (United Kingdom)

^e WAGENINGEN UNIVERSITY   (Netherlands)

Author keywords

BFAST; Clearing; Data fusion; Forest change; Landsat TM ETM+; MODIS; Regrowth; STARFM; Time series

Indexed keywords

ADAPTIVE OPTICS; DATA FUSION; FORESTRY; IMAGE RECONSTRUCTION; RADIOMETERS; REMOTE SENSING; SATELLITE IMAGERY; TIME SERIES;

BFAST; CLEARING; FOREST CHANGE; LANDSAT TM/ETM; MODIS; REGROWTH; STARFM;

SENSOR DATA FUSION;

DATA SET; DISTURBANCE; FOREST DYNAMICS; IMAGERY; LANDSAT THEMATIC MAPPER; MODIS; REGROWTH; SPATIAL RESOLUTION; TIME SERIES;

DATA PROCESSING; FOREST MANAGEMENT; SATELLITES;

AUSTRALIA; QUEENSLAND;

EID: 84913553639     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2014.11.015     Document Type: Article

References (37)

1. Armston J.D., Denham R.J., Danaher T.J., Scarth P.F., Moffiet T.N. Prediction and validation of foliage projective cover from Landsat-5 TM and Landsat-7 ETM+Imagery. Journal of Applied Remote Sensing 2009, 3(033540). 10.1117/1.3216031.
2. Asner G.P., Knapp D.E., Broadbent E.N., Oliveira P.J.C., Keller M., Silva J.N. Selective logging in the Brazilian Amazon. Science 2005, 310(5747):480-482. 10.1126/science.1118051.
3. Bastin G., Scarth P., Chewings V., Sparrow A., Denham R., Schmidt M., et al. Separating grazing and rainfall effects at regional scale using remote sensing imagery: A dynamic reference-cover method. Remote Sensing of Environment 2012, 121:443-457.
4. Baumann M., Kuemmerle T., Elbakidze M., Ozdogan M., Radeloff V.C., Keuler N.S., et al. Patterns and drivers of post-socialist farmland abandonment in Western Ukraine. Land Use Policy 2011, 28(3):552-562.
5. Bhandari S., Phinn S.R., Gill T. Preparing Landsat image time series (LITS) for monitoring changes in vegetation phenology in Queensland, Australia. Remote Sensing 2012, 2012(4):1856-1886. 10.3390/rs4061856.
6. Carroll C., Waters D., Vardy S., Silburn M., Attard S., Thorburn P., et al. A paddock to reef monitoring and modelling framework for the Great Barrier Reef: Paddock and catchment component. Marine Pollution Bulletin 2012, 65:136-149.
7. Danaher T., Scarth P., Armston J., Collett L., Kitchen J., Gillingham S. Remote sensing of tree-grass systems - The Eastern Australian Woodlands. Ecosystem function in Savannas: Measurement and modeling at landscape to global scales 2010, 175-193. Taylor and Francis. M.J. Hill, N.P. Hanan (Eds.).
8. Emelyanova I.V., McVicar T.M., van Neil T.G., van Dijk A. Assessing the accuracy of blending Landsat-MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: A framework for algorithm selection. Remote Sensing of Environment 2013, 133:193-209. 10.1016/j.rse.2013.02.007.
9. Flood N., Danaher T., Gill T., Gillingham S. An operational scheme for deriving standardised surface reflectance from Landsat TM/ETM+and SPOT HRG imagery for Eastern Australia. Remote Sensing 2013, 5(1):83-109. 10.3390/rs5010083.
10. Fu D., Chne B., Wang J., Zhu X., Hilker T. An improved image fusion approach based on enhanced spatial and temporal the adaptive reflectance fusion model. Remote Sensing 2013, 5:6346-6360. 10.3390/rs5126346.
11. Gao F., Masek J., Schwaller M., Hall F. On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance. IEEE Transactions on Geoscience and Remote Sensing 2006, 44(8):2207-2218.
12. Goodwin N.R., Collett L.J. Development of an automated method for mapping fire history captured in Landsat TM and ETM+time series across Queensland, Australia. Remote Sensing of Environment 2014, 148:206-221.
13. Goulevitch B. Ten years of using earth observation data in support of Queensland's vegetation management framework. Evidence from earth observation satellites 2012, Brill, Leiden, The Netherlands. R. Purdy, D. Leung (Eds.).
14. Hilker T., Wulder M.A., Coops N.C., Linke J., McDermid G., Masek J.G., et al. A new data fusion model for high resolution mapping of forest disturbance based on Landsat and MODIS. Remote Sensing of Environment 2009, 113(8):1613-1627.
15. Hilker T., Wulder M.A., Coops N.C., Seitz N., White J.C., Gao F., et al. Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model. Remote Sensing of Environment 2009, 113(9):1988-1999.
16. Jönsson P., Eklundh L. TIMESAT - A program for analyzing time series of satellite sensor data. Computers and Geosciences 2004, 30(8):833-845.
17. Kauth R., Thomas G. The tasselled cap - A graphical description of the spectral-temporal development of agricultural crops as seen by Landsat. Proceedings of the Symposium on Machine Processing of Remotely Sensed Data 1976, Purdue University of West Lafayette, Indiana, (4B-41 to 4B-51).
18. Kennedy R.E., Townsend P.A., Gross J.E., Cohen W.B., Bolstad P., Wang Y.Q., et al. Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects. Remote Sensing of Environment 2009, 113(7):1382-1396.
19. Lee A.C., Lucas R.M. A LiDAR-derived canopy density model for tree stem and crown mapping in Australian forests. Remote Sensing of Environment 2007, 111(4):493-518.
20. Lucas R.M., Bunting P.J., Paterson M., Chisholm L. Classification of Australian forest communities using aerial photography, CASI and HyMap data. Remote Sensing of Environment 2008, 112(5):2088-2103.
21. Pringle M.J., Schmidt M., Muir J.S. Geostatistical interpolation of SLC-off Landsat ETM+images. ISPRS Journal of Photogrammetry and Remote Sensing 2009, 64(6):654-664.
22. Roy D.P., Ju J., Lewis P., Schaaf C., Gao F., Hansen M., et al. Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data. Remote Sensing of Environment 2008, 112(6):3112-3130.
23. Running S.W., Nemani R.R. Relating seasonal patterns of the AVHRR vegetation index to simulated photosynthesis and transpiration of forests in different climates. Remote Sensing of Environment 1988, 24(2):347-367.
24. Scarth P., Roeder A., Schmidt M. Tracking grazing pressure and climate interaction - The role of Landsat fractional cover in time series analysis. 15th Australian Remote Sensing and Photogrammetry Conference 2010, Alice Springs. B. Sparrow, G. Bhalia (Eds.).
25. Schmidt M., Scarth P. Spectral mixture analysis for ground-cover mapping. Innovations in remote sensing and photogrammetry 2009, 349-359. Springer, Heidelberg, Dortrecht, London, New York. S. Jones, K. Reinke (Eds.).
26. 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. Journal of Applied Remote Sensing 2012, 6(063512). 10.1117/1.JRS.6.063512.
27. Schöttker B., Phinn S., Schmidt M. How does the global MODIS FPAR product relate to regionally developed land cover and vegetation products in a semi-arid Australian Savanna?. Journal of Applied Remote Sensing 2010, 4(43538). 10.1117/1.3463721.
28. Tickle P.K., Lee A., Lucas R.M., Austin J., Witte C. Quantifying Australian forest floristics and structure using small footprint LiDAR and large scale aerial photography. Forest Ecology and Management 2006, 223(1-3):379-394.
29. Tucker C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 1979, 8(2):127-150.
30. 2 emissions from forest loss. Nature Geoscience 2009, 2(11):737-738.
31. Verbesselt J., Hyndman R., Newnham G., Culvenor D. Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment 2010, 114(1):106-115.
32. Verstraete M.M., Pinty B. Designing optimal spectral indexes for remote sensing applications. IEEE Transactions on Geoscience and Remote Sensing 1996, 34(5):1254-1265.
33. 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 Sensing of Environment 2012, 117:381-393.
34. 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 Sensing of Environment 2011, 115(1):66-75.
35. 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 Sensing of Environment 2012, 122:2-10. 10.1016/j.rse.2012.01.010.
36. 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 Sensing of Environment 2010, 114(11):2610-2623.
37. Zhu Z., Woodcock C.E. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment 2012, 118:83-94.