Preparing Landsat Image Time Series (LITS) for Monitoring Changes in Vegetation Phenology in Queensland, Australia

Remote Sensing - Tập 4 Số 6 - Trang 1856-1886
Santosh Bhandari1,2, Stuart Phinn1, Tony Gill3
1Biophysical Remote Sensing Group, Centre for Spatial Environmental Research, School of Geography, Planning and Environmental Management, The University of Queensland, Brisbane, QLD 4072, Australia
2Indufor Asia Pacific Ltd, 55 Shortland St, PO Box 105039, Auckland , 1143, New Zealand
3Remote Sensing Centre, Remote Sensing Unit, NSW Office of Environment and Heritage, P.O. Box 717, Dubbo, NSW 2830, Australia

Tóm tắt

Time series of images are required to extract and separate information on vegetation change due to phenological cycles, inter-annual climatic variability, and long-term trends. While images from the Landsat Thematic Mapper (TM) sensor have the spatial and spectral characteristics suited for mapping a range of vegetation structural and compositional properties, its 16-day revisit period combined with cloud cover problems and seasonally limited latitudinal range, limit the availability of images at intervals and durations suitable for time series analysis of vegetation in many parts of the world. Landsat Image Time Series (LITS) is defined here as a sequence of Landsat TM images with observations from every 16 days for a five-year period, commencing on July 2003, for a Eucalyptus woodland area in Queensland, Australia. Synthetic Landsat TM images were created using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm for all dates when images were either unavailable or too cloudy. This was done using cloud-free scenes and a MODIS Nadir BRDF Adjusted Reflectance (NBAR) product. The ability of the LITS to measure attributes of vegetation phenology was examined by: (1) assessing the accuracy of predicted image-derived Foliage Projective Cover (FPC) estimates using ground-measured values; and (2) comparing the LITS-generated normalized difference vegetation index (NDVI) and MODIS NDVI (MOD13Q1) time series. The predicted image-derived FPC products (value ranges from 0 to 100%) had an RMSE of 5.6. Comparison between vegetation phenology parameters estimated from LITS-generated NDVI and MODIS NDVI showed no significant difference in trend and less than 16 days (equal to the composite period of the MODIS data used) difference in key seasonal parameters, including start and end of season in most of the cases. In comparison to similar published work, this paper tested the STARFM algorithm in a new (broadleaf) forest environment and also demonstrated that the approach can be used to form a time series of Landsat TM images to study vegetation phenology over a number of years.

Từ khóa


Tài liệu tham khảo

Cohen, 2004, Landsat’s role in ecological applications of remote sensing, Bioscience, 54, 535, 10.1641/0006-3568(2004)054[0535:LRIEAO]2.0.CO;2

Santillan, 2011, Integrated Landsat image analysis and hydrologic modeling to detect impacts of 25-year land-cover change on surface runoff in a Philippine watershed, Remote Sens, 3, 1067, 10.3390/rs3061067

Matejicek, 2010, 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, 2, 1463, 10.3390/rs2061463

Nagendra, 2010, Assessing plant diversity in a dry tropical forest: comparing the utility of Landsat and IKONOS satellite images, Remote Sens, 2, 478, 10.3390/rs2020478

Coppin, 2004, Digital change detection methods in ecosystem monitoring: A review, Int. J. Remote Sens, 25, 1565, 10.1080/0143116031000101675

Lu, 2004, Change detection techniques, Int. J. Remote Sens, 25, 2365, 10.1080/0143116031000139863

Kennedy, 2007, Trajectory based change detection for automated characterization of forest disturbance dynamics, Remote Sens. Environ, 110, 370, 10.1016/j.rse.2007.03.010

Verbesselt, 2010, Detecting trend and seasonal changes in satellite image time series, Remote Sens. Environ, 114, 106, 10.1016/j.rse.2009.08.014

Moreno, 2008, Seasonal variations of leaf area index of agricultural fields retrieved from Landsat data, Remote Sens. Environ, 112, 810, 10.1016/j.rse.2007.06.018

Lunetta, 2006, Land-cover change detection using multi-temporal MODIS NDVI data, Remote Sens. Environ, 105, 142, 10.1016/j.rse.2006.06.018

Maignan, 2008, Interannual vegetation phenology estimates from global AVHRR measurements—Comparison with in situ data and applications, Remote Sens. Environ, 112, 496, 10.1016/j.rse.2007.05.011

Potter, 2005, Recent history of large-scale ecosystem disturbances in North America derived from the AVHRR satellite record, Ecosystems, 8, 808, 10.1007/s10021-005-0041-6

Hayes, 2007, Spatial, spectral and temporal patterns of tropical forest cover change as observed with multiple scales of optical satellite data, Remote Sens. Environ, 106, 1, 10.1016/j.rse.2006.07.002

Woodcock, 2008, Free access to Landsat imagery, Science, 320, 1011, 10.1126/science.320.5879.1011a

Ju, 2008, The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally, Remote Sens. Environ, 112, 1196, 10.1016/j.rse.2007.08.011

Huang, 2009, Development of time series stacks of Landsat images for reconstructing forest disturbance history, Int. J. Dig. Earth, 2, 195, 10.1080/17538940902801614

Gao, 2006, On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance, IEEE Trans. Geosci. Remote Sens, 44, 2207, 10.1109/TGRS.2006.872081

Roy, 2008, Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data, Remote Sens. Environ, 112, 3112, 10.1016/j.rse.2008.03.009

Zhu, 2010, An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions, Remote Sens. Environ, 114, 2610, 10.1016/j.rse.2010.05.032

Wolfe, 2002, Achieving sub-pixel geolocation accuracy in support of MODIS land science, Remote Sens. Environ, 83, 31, 10.1016/S0034-4257(02)00085-8

Justice, 2002, An overview of MODIS Land data processing and product status, Remote Sens. Environ, 83, 3, 10.1016/S0034-4257(02)00084-6

Pohl, 1998, Multisensor image fusion in remote sensing: Concepts, methods and applications, Int. J. Remote Sens, 19, 823, 10.1080/014311698215748

Arai, 2011, A multi-resolution multi-temporal technique for detecting and mapping deforestation in the Brazilian Amazon rainforest, Remote Sens, 3, 1943, 10.3390/rs3091943

Hilker, 2009, 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, 113, 1988, 10.1016/j.rse.2009.05.011

Liang, 2011, Validating satellite phenology through intensive ground observation and landscape scaling in a mixed seasonal forest, Remote Sens. Environ, 115, 143, 10.1016/j.rse.2010.08.013

DEWR (2005). Present Major Vegetation Subgroups - NVIS Stage 1, Australian Government Department of the Environment and Water Resources. Version 3.1.

Sun, 1997, Review of vegetation classification and mapping systems undertaken by major forested land management agencies in Australia, Aust. J. Bot, 45, 929, 10.1071/BT96121

Specht, 1981, The balance between the foliage projective covers of overstorey and understorey strata in Australian vegetation, Aust. J. Ecol, 6, 193, 10.1111/j.1442-9993.1981.tb01290.x

Leeper, G.W. (1970). The Australian Environment, CSIRO and Melbourne University Press.

Roy, 2006, The global impact of clouds on the production of MODIS bidirectional reflectance model-based composites for terrestrial monitoring, IEEE Geosci. Remote Sens. Lett, 3, 452, 10.1109/LGRS.2006.875433

Huang, 2010, An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks, Remote Sens. Environ, 114, 183, 10.1016/j.rse.2009.08.017

Green, 2003, On-orbit radiometric and spectral calibration characteristics of EO-1 hyperion derived with and underflight of AVIRIS and in situ measurements at Salar de Arizaro, Argentina, IEEE Trans. Geosci. Remote Sens, 41, 1194, 10.1109/TGRS.2003.813204

Jensen, J.R. (2005). Introductory Digital Image Processing: A Remote Sensing Perspective, Pearson Education Inc.. [3rd ed.].

NASA (2000). Landdsat 7 Science Data User’s Hand Book, Landat Project Science Office.

Helder, 2008, Updated radiometric calibration for the Landsat-5 Thematic Mapper reflective bands, IEEE Trans. Geosci. Remote Sens, 46, 3309, 10.1109/TGRS.2008.920966

Vermote, 1997, Second simulation of the satellite signal in the solar spectrum, 6S: An overview, IEEE Trans. Geosci. Remote Sens, 35, 675, 10.1109/36.581987

Ziemke, 2006, Tropospheric ozone determined from aura OMI and MLS: Evaluation of measurements and comparison with the Global Modeling Initiative’s Chemical Transport Model, J. Geophys. Res.-Atmos, 111, D19303, 10.1029/2006JD007089

Jeffrey, 2001, Using spatial interpolation to construct a comprehensive archive of Australian climate data, Environ. Model. Softw, 16, 309, 10.1016/S1364-8152(01)00008-1

Kaufman, 1988, Algorithm for automatic atmospheric corrections to visible and near-IR satellite imagery, Int. J. Remote Sens, 9, 1357, 10.1080/01431168808954942

Gillingham, 2012, Limitations of the dense dark vegetation method for aerosol retrieval under Australian conditions, Remote Sens. Lett, 3, 67, 10.1080/01431161.2010.533298

Guanter, 2008, Coupled retrieval of aerosol optical thickness, columnar water vapor and surface reflectance maps from ENVISAT/MERIS data over land, Remote Sens. Environ, 112, 2898, 10.1016/j.rse.2008.02.001

Davies, 2010, Improvements in aerosol optical depth estimation using multiangle CHRIS/PROBA images, IEEE Trans. Geosci. Remote Sens, 48, 18, 10.1109/TGRS.2009.2027024

Radhi, 2009, Optical, physical and chemical characteristics of Australian Desert dust aerosols: Results from a field experiment, Atmos. Chem. Phys. Discuss, 9, 25085

Bhandari, 2011, 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, 32, 7513, 10.1080/01431161.2010.524675

Schaaf, 2002, First operational BRDF, albedo nadir reflectance products from MODIS, Remote Sens. Environ, 83, 135, 10.1016/S0034-4257(02)00091-3

Rueda, C.A., Greenberg, J.A., and Ustin, S.L. (2005). StarSpan: A Tool for Fast Selective Pixel Extraction from Remotely Sensed Data, Center for Spatial Technologies and Remote Sensing (CSTARS), University of California at Davis.

Armston, 2009, Prediction and validation of foliage projective cover from Landsat-5 TM and Landsat-7 ETM+ imagery, J. Appl. Remote Sens, 3, 033540, 10.1117/1.3216031

Huete, 2002, Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sens. Environ, 83, 195, 10.1016/S0034-4257(02)00096-2

Cleveland, 1990, STL: A seasonal-trend decomposition procedures based on Loess, J. Off. Stat, 6, 3

Jonsson, 2004, TIMESAT—A program for analyzing time-series of satellite sensor data, Comput. Geosci, 30, 833, 10.1016/j.cageo.2004.05.006

Mitchell, 2010, Recent increase in aerosol loading over the Australian arid zone, Atmos. Chem. Phys, 10, 1689, 10.5194/acp-10-1689-2010

Bhandari, S (2011). Monitoring Forest Dynamics Using Time Series of Satellite Data in Queensland, Australia. Ph.D. Thesis, The University of Queensland: Brisbane, QLD, Australia.

Colditz, 2008, TiSeG: a flexible software tool for time-series generation of MODIS data utilizing the quality assessment science data set, IEEE Trans. Geosci. Remote Sens, 46, 3296, 10.1109/TGRS.2008.921412

Tan, 2006, 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, 98, 10.1016/j.rse.2006.06.008

Hilker, 2009, A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS, Remote Sens. Environ, 113, 1613, 10.1016/j.rse.2009.03.007