Abstract
Large volumes of data from satellite sensors with high time-resolution exist today, e.g. Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS), calling for efficient data processing methods. TIMESAT is a free software package for processing satellite time-series data in order to investigate problems related to global change and monitoring of vegetation resources. The assumptions behind TIMESAT are that the sensor data represent the seasonal vegetation signal in a meaningful way, and that the underlying vegetation variation is smooth. A number of processing steps are taken to transform the noisy signals into smooth seasonal curves, including fitting asymmetric Gaussian or double logistic functions, or smoothing the data using a modified Savitzky-Golay filter. TIMESAT can adapt to the upper envelope of the data, accounting for negatively biased noise, and can take missing data and quality flags into account. The software enables the extraction of seasonality parameters, like the beginning and end of the growing season, its length, integrated values, etc. TIMESAT has been used in a large number of applied studies for phenology parameter extraction, data smoothing, and general data quality improvement. To enable efficient analysis of future Earth Observation data sets, developments of TIMESAT are directed towards processing of high-spatial resolution data from e.g. Landsat and Sentinel-2, and use of spatio-temporal data processing methods.
Keywords
- Normalize Difference Vegetation Index
- Advance Very High Resolution Radiometer
- Advance Very High Resolution Radiometer
- Enhance Vegetation Index
- Bidirectional Reflectance Distribution Function
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
Alcantara C, Kuemmerle T, Prishchepov AV, Radeloff VC (2012) Mapping abandoned agriculture with multi-temporal MODIS satellite data. Remote Sens Environ 124:334–347
Barichivich J, Briffa KR, Osborn TJ, Melvin TM, Caesar J (2012) Thermal growing season and timing of biospheric carbon uptake across the Northern Hemisphere. Glob Biogeochem Cycles 26:GB4015
Barichivich J, Briffa KR, Myneni RB, Osborn TJ, Melvin TM, Ciais P, Piao S, Tucker C (2013) Large-scale variations in the vegetation growing season and annual cycle of atmospheric CO2 at high northern latitudes from 1950 to 2011. Glob Chang Biol 19:3167–3183
Beck PSA, Jönsson P, Hogda KA, Karlsen SR, Eklundh L, Skidmore AK (2007) A ground-validated NDVI dataset for monitoring vegetation dynamics and mapping phenology in Fennoscandia and the Kola peninsula. Int J Remote Sens 28:4311–4330
Bolin D, Lindström J, Eklundh L, Lindgren F (2009) Fast estimation of spatially dependent temporal vegetation trends using Gaussian Markov random fields. Comput Stat Data Anal 53:2885–2896
Boyd DS, Almond S, Dash J, Curran PJ, Hill RA (2011) Phenology of vegetation in Southern England from Envisat MERIS terrestrial chlorophyll index (MTCI) data. Int J Remote Sens 32:8421–8447
Bradley BA, Jacob RW, Hermance JF, Mustard JF (2007) A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data. Remote Sens Environ 106:137–145
Buma B, Pugh ET, Wessman CA (2013) Effect of the current major insect outbreaks on decadal phenological and LAI trends in southern Rocky Mountain forests. Int J Remote Sens 34:7249–7274
Campbell PKE, Middleton EM, Corp LA, Kim MS (2008) Contribution of chlorophyll fluorescence to the apparent vegetation reflectance. Sci Total Environ 404:433–439
Campos AN, Di Bella CM (2012) Multi-temporal analysis of remotely sensed information using wavelets. J Geogr Inf Syst 4:383–391
Chen W, Foy N, Olthof I, Latifovic R, Zhang Y, Li J, Fraser R, Chen Z, McLennan D, Poitevin J, Zorn P, Quirouette J, Stewart HM (2013) Evaluating and reducing errors in seasonal profiles of AVHRR vegetation indices over a Canadian northern national park using a cloudiness index. Int J Remote Sens 34:4320–44343
Clark ML, Aide TM, Grau HR, Riner G (2010) A scalable approach to mapping annual land cover at 250 m using MODIS time series data: a case study in the Dry chaco ecoregion of South America. Remote Sens Environ 114:2816–2832
Cleveland RB, Cleveland WS, McRae JE, Terpenning I (1990) STL: a seasonal-trend decomposition procedure based on loess. J Off Stat 6:3–73
Cong N, Wang T, Nan H, Ma Y, Wang X, Myneni RB, Piao S (2013) Changes in satellite-derived spring vegetation green-up date and its linkage to climate in China from 1982 to 2010: a multimethod analysis. Glob Chang Biol 19:881–891
Dash J, Curran PJ (2007) Evaluation of the MERIS terrestrial chlorophyll index (MTCI). Adv Space Res 39:100–1104
Defries RS, Townshend JRG (1994) NDVI-derived land cover classifications at a global scale. Int J Remote Sens 15:3567–3586
Eklundh L, Jönsson P (2012) TIMESAT 3.1 software manual. Lund University, Lund
Eklundh L, Jönsson P (2013) A new spatio-temporal smoother for extracting vegetation seasonality with TIMESAT. The 35th international symposium on remote sensing of environment, 22–26 Apr 2013, Beijing, China
Eklundh L, Olsson L (2003) Vegetation index trends for the African Sahel 1982–1999. Geophys Res Lett 30:1430–1433
Eklundh L, Johansson T, Solberg S (2009) Mapping insect defoliation in Scots pine with MODIS time-series data. Remote Sens Environ 113:1566–1573
Eklundh L, Jin H, Schubert P, Guzinski R, Heliasz M (2011) An optical sensor network for vegetation phenology monitoring and satellite data calibration. Sensors 11:7678–7709
Eklundh L, Sjöström M, Ardö J, Jönsson P (2012) High resolution mapping of vegetation dynamics from Sentinel-2. In: Proceedings of the first Sentinel-2 preparatory symposium, 23–27 Apr 2012, Frascati, Italy
Fensholt R, Proud SR (2012) Evaluation of earth observation based global long term vegetation trends – comparing GIMMS and MODIS global NDVI time series. Remote Sens Environ 119:131–147
Fisher JI, Mustard JF, Vadeboncoeur MA (2006) Green leaf phenology at Landsat resolution: scaling from the field to the satellite. Remote Sens Environ 100:265–279
Gamon JA, Serrano L, Surfus JS (1997) The photochemical reflectance index: an optical indicator of photosynthetic radiation-use efficiency across species, functional types, and nutrient levels. Oecologia 112:492–501
Han Q, Luo G, Li C (2013) Remote sensing-based quantification of spatial variation in canopy phenology of four dominant tree species in Europe. J Appl Remote Sens 7:073485
Hermance JF, Jacob RW, Bradley BA, Mustard JF (2007) Extracting phenological signals from multiyear AVHRR NDVI time series: framework for applying high-order annual splines with roughness damping. IEEE Trans Geosci Remote Sens 45:3264–3276
Heumann BW, Seaquist JW, Eklundh L, Jönsson P (2007) AVHRR derived phenological change in the Sahel and Soudan, Africa, 1982–2005. Remote Sens Environ 108:385–392
Hickler T, Eklundh L, Seaquist J, Smith B, Ardö J, Olsson L, Sykes M, Sjöström M (2005) Precipitation controls Sahel greening trend. Geophys Res Lett 32:L21415
Hird JN, McDermid GJ (2009) Noise reduction of NDVI time series: an empirical comparison of selected techniques. Remote Sens Environ 113:248–258
Holben BN (1986) Characteristics of maximum-value composite images from temporal AVHRR data. Int J Remote Sens 7:1417–1443
Huang C, Goward SN, Masek JG, Thomas N, Zhu Z, Vogelmann JE (2010) An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sens Environ 114:183–198
Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83:195–213
Hufkens K, Friedl M, Sonnentag O, Braswell BH, Milliman T, Richardson AD (2012) Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology. Remote Sens Environ 117:307–3321
Jamali S, Seaquist J, Eklundh L, Ardö J (2014) Automated mapping of vegetation trends with polynomials using NDVI imagery over the Sahel. Remote Sens Environ 141:79–89
Jamali S, Eklundh L, Jönsson P, Seaquist J, Ardö J (2015) Detecting changes in vegetation trends using time series segmentation. Remote Sens Environ 156:182–195
James ME, Kalluri SNV (1994) The pathfinder AVHRR land data set: an improved coarse resolution data set for terrestrial monitoring. Int J Remote Sens 15:3347–3363
Jiang N, Zhu W, Zheng Z, Chen G, Fan D (2013) A comparative analysis between GIMSS NDVIg and NDVI3g for monitoring vegetation activity change in the Northern Hemisphere during 1982–2008. Remote Sens 5:4031–4044
Jin H, Eklundh L (2014) A physically based vegetation index for improved monitoring of plant phenology. Remote Sens Environ 152:512–525
Jönsson P, Eklundh L (2002) Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Trans Geosci Remote Sens 40:1824–1832
Jönsson P, Eklundh L (2004) TIMESAT – a program for analysing time-series of satellite sensor data. Comput Geosci 30:833–845
Jönsson AM, Eklundh L, Hellström M, Bärring L, Jönsson P (2010) Annual changes in MODIS vegetation indices of Swedish coniferous forests in relation to snow dynamics and tree phenology. Remote Sens Environ 114:2719–2730
Justice CO, Townshend JRG, Holben BN, Tucker CJ (1985) Analysis of the phenology of global vegetation using meteorological satellite data. Int J Remote Sens 6:1271–1318
Keenan TF, Gray J, Friedl MA, Toomey M, Bohrer G, Hollinger DY, Munger JW, O’Keefe J, Schmid HP, Wing IS, Yang B, Richardson AD (2014) Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nat Clim Chang 4:598–604
Kennedy RE, Yang Z, Cohen WB (2010) Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr – temporal segmentation algorithms. Remote Sens Environ 114:2897–2910
Le Page Y, Oom D, Silva JMN, Jönsson P, Pereira JMC (2010) Seasonality of vegetation fires as modified by human action: observing the deviation from eco-climatic fire regimes. Glob Ecol Biogeogr 19:575–588
Leinenkugel P, Kuenzer C, Oppelt N, Dech S (2013) Characterisation of land surface phenology and land cover based on moderate resolution satellite data in cloud prone areas – a novel product for the Mekong basin. Remote Sens Environ 136:180–198
Lobell DB, Ortiz-Monasterio JI, Sibley AM, Sohu VS (2013) Satellite detection of earlier wheat sowing in India and implications for yield trends. Agric Syst 115:137–143
Lu X, Liu R, Liu J, Liang S (2007) Removal of noise by wavelet method to generate high quality temporal data of terrestrial MODIS products. Photogramm Eng Remote Sens 73:1129–1139
Menenti M, Azzali S, Verhoef W, van Swol R (1993) Mapping agroecological zones and time lag in vegetation growth by means of Fourier analysis of time series of NDVI images. Adv Space Res 13:233–237
O’Connor B, Dwyer E, Cawkwell F, Eklundh L (2012) Spatio-temporal patterns in vegetation start of season across the island of Ireland using the MERIS Global Vegetation Index. ISPRS J Photogramm Remote Sens 68:79–94
Olofsson P, Eklundh L (2007) Estimation of absorbed PAR across Scandinavia from satellite measurements. Part II: modeling and evaluating the fractional absorption. Remote Sens Environ 110:240–251
Olofsson P, Eklundh L, Lagergren F, Jönsson P, Lindroth A (2007) Estimating net primary production for Scandinavian forests using data from Terra/MODIS. Adv Space Res 39:125–130
Olofsson P, Lagergren F, Lindroth A, Lindström J, Klemedtsson L, Kutsch W, Eklundh L (2008) Towards operational remote sensing of forest carbon balance across northern Europe. Biogeosciences 5:817–832
Olsson L, Eklundh L (1994) Fourier transformation for analysis of temporal sequences of satellite imagery. Int J Remote Sens 15:3735–3741
Olsson L, Eklundh L, Ardö J (2005) A recent greening of the Sahel – trends, patterns and potential causes. J Arid Environ 63:556–566
Olsson PO, Jönsson AM, Eklundh L (2012) A new invasive insect in Sweden – physokermes inopinatus – tracing forest damage with satellite based remote sensing. For Ecol Manag 285:29–37
Reed BC, Brown JF, VanderZee D, Loveland TR, Merchant JW, Ohlen DO (1994) Measuring phenological variability from satellite imagery. J Veg Sci 5:703–714
Richardson AD, Jenkins JP, Braswell BH, Hollinger DY, Ollinger SV, Smith ML (2007) Use of digital webcam images to track spring green-up in a deciduous broadleaf forest. Oecologia 152:323–334
Running SW, Loveland TR, Pierce LL (1994) A vegetation classification logic based on remote sensing for use in global biogeochemical models. Ambio 23:77–81
Sakamoto T, Yokozawa M, Toritani H, Shibayama M, Ishitsuka N, Ohno H (2005) A crop phenology detection method using time-series MODIS data. Remote Sens Environ 96:366–374
Sakamoto T, Wardlow BD, Gitelson AA, Verma SB, Suyker AE, Arkebauer TJ (2010) A two-step filtering approach for detecting maize and soybean phenology with time-series MODIS data. Remote Sens Environ 114:2146–2159
Schubert P, Eklundh L, Lund M, Nilsson M (2010) Estimating northern peatland CO2 exchange from MODIS time series data. Remote Sens Environ 114:1178–1189
Schubert P, Lagergren F, Aurela M, Christensen T, Grelle A, Heliasz M, Klemedtsson L, Lindroth A, Pilegaard K, Vesala T, Eklundh L (2012) Modeling GPP in the Nordic forest landscape with MODIS time series data – comparison with the MODIS GPP product. Remote Sens Environ 126:136–147
Seaquist JW, Hickler T, Eklundh L, Ardö J, Heumann B (2009) Disentangling the effects of climate and people on Sahel vegetation dynamics. Biogeosciences 6:469–477
Sjöström M, Ardö J, Eklundh L, El-Tahir BA, El-Khidir HAM, Hellström M, Pilesjö P, Seaquist J (2009) Evaluation of satellite based indices for gross primary production estimates in a sparse savanna in the Sudan. Biogeosciences 6:129–138
Sjöström M, Ardö J, Arneth A, Cappelaere B, Eklundh L, de Grandcourt A, Kutsch WL, Merbold L, Nouvellon Y, Scholes B, Seaquist J, Veenendaal EM (2011) Exploring the potential of MODIS EVI for modeling gross primary production across African ecosystems. Remote Sens Environ 115:1081–1089
Stisen S, Sandholt I, Norgaard A, Fensholt R, Eklundh L (2007) Estimation of diurnal air temperature using MSG SEVIRI data in West Africa. Remote Sens Environ 110:262–274
Tang XG, Wang X, Wang ZM, Liu DW, Jia MM, Dong ZY, Xie J, Ding Z, Wang HR, Liu XP (2013) Influence of vegetation phenology on modelling carbon fluxes in temperate deciduous forest by exclusive use of MODIS time-series data. Int J Remote Sens 34:8373–8392
Tottrup C, Schultz Rasmussen M, Eklundh L, Jönsson P (2007) Mapping fractional forest cover across the highlands of mainland Southeast Asia using MODIS data and regression tree modelling. Int J Remote Sens 28:23–46
Townshend JRG, Justice CO (1986) Analysis of the dynamics of African vegetation using the normalized difference vegetation index. Int J Remote Sens 7:1435–1445
Tucker CJ, Pinzon JE, Brown ME, Slayback D, Pak EW, Mahoney R, Vermote E, El Saleous N (2005) An extended AVHRR 8-km NDVI data set compatible with MODIS and SPOT vegetation NDVI data. Int J Remote Sens 26:4485–5598
van Dijk A, Callis SL, Sakamoto CM, Decker WL (1987) Smoothing vegetation index profiles: an alternative method for reducing radiometric disturbance in NOAA/AVHRR data. Photogramm Eng Remote Sens 53:1059–1067
van Leeuwen WJD, Davison JE, Casady GM, Marsh SE (2010) Phenological characterization of desert sky island vegetation communities with remotely sensed and climate time series data. Remote Sens 2:388–415
van Leeuwen WJD, Hartfield K, Miranda M, Meza FJ (2013) Trends and ENSO/AAO driven variability in NDVI derived productivity and phenology alongside the Andes mountains. Remote Sens 5:1177–1203
Veraverbeke S, Lhermitte S, Verstraeten WW, Goossens R (2010) The temporal dimension of differenced Normalized Burn Ratio (dNBR) fire/burn severity studies: the case of the large 2007 Peloponnese wildfires in Greece. Remote Sens Environ 114:2548–2563
Verbesselt J, Jönsson P, Lhermitte S, van Aardt J, Coppin P (2006) Evaluating satellite and climate data-derived indices as fire risk indicators in savannah ecosystems. IEEE Trans Geosci Remote Sens 44:1622–1632
Verbesselt J, Hyndman R, Newnham G, Culvenor D (2010) Detecting trend and seasonal changes in satellite image time series. Remote Sens Environ 114:106–115
Viovy N, Arino O, Belward AS (1992) The Best Index Slope Extraction (BISE): a method for reducing noise in NDVI time-series. Int J Remote Sens 13:1585–1590
Weiss M, Hurk B, Haarsma R, Hazeleger W (2012) Impact of vegetation variability on potential predictability and skill of EC-Earth simulations. Clim Dyn 39:2733–2746
Wessels K, Steenkamp K, von Maltitz G, Archibald S (2011) Remotely sensed vegetation phenology for describing and predicting the biomes of South Africa. Appl Veg Sci 14:49–66
White MA, De Beurs KM, Didan K, Inouye DW, Richardson AD, Jensen OP, O’keefe J, Zhang G, Nemani RR, Van Leeuwen WJD, Brown JF, De WITA, Schaepman M, Lin X, Dettinger M, Bailey AS, Kimball J, Schwartz MD, Baldocchi DD, Lee JT, Lauenroth WK (2009) Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006. Glob Chang Biol 15:2335–2359
Wulder MA, Masek JG, Cohen WB, Loveland TR, Woodcock CE (2012) Opening the archive: how free data has enabled the science and monitoring promise of Landsat. Remote Sens Environ 122:2–10
Yuan H, Dai Y, Xiao Z, Ji D, Shangguan W (2011) Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling. Remote Sens Environ 115:1171–1187
Zeng H, Jia G, Forbes BC (2013) Shifts in Arctic phenology in response to climate and anthropogenic factors as detected from multiple satellite time series. Environ Res Lett 8:035036
Zhang X, Friedl MA, Schaaf CB, Strahler AH, Hodges JCF, Gao F, Reed BC, Huete A (2003) Monitoring vegetation phenology using MODIS. Remote Sens Environ 84:471–475
Zhang MQ, Guo HQ, Xie X, Zhang TT, Ouyang ZT, Zhao B (2013a) Identification of land-cover characteristics using MODIS time series data: an application in the Yangtze river estuary. PLoS One 8:e70079
Zhang Y, Moran MS, Nearing MA, Ponce Campos GE, Huete AR, Buda AR, Bosch DD, Gunter SA, Kitchen SG, McNab WH, Morgan JA, McClaran MP, Montoya DS, Peters DPC, Starks PJ (2013b) Extreme precipitation patterns and reductions of terrestrial ecosystem production across biomes. J Geophys Res Biogeosci 118:2169–8961
Zhao J, Wang Y, Hashimoto H, Melton FS, Hiatt SH, Zhang H, Nemani RR (2013) The variation of land surface phenology from 1982 to 2006 along the Appalachian trail. IEEE Trans Geosci Remote Sens 51:2087–2095
Zhu Z, Woodcock CE, Olofsson P (2012) Continuous monitoring of forest disturbance using all available Landsat imagery. Remote Sens Environ 122:75–91
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Eklundh, L., Jönsson, P. (2015). TIMESAT: A Software Package for Time-Series Processing and Assessment of Vegetation Dynamics. In: Kuenzer, C., Dech, S., Wagner, W. (eds) Remote Sensing Time Series. Remote Sensing and Digital Image Processing, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-319-15967-6_7
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