Derivation of phenometrics from high resolution RapidEye imagery of semi-arid grasslands in South Africa

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1 André Parplies Student Research Colloquium Derivation of phenometrics from high resolution RapidEye imagery of semi-arid grasslands in South Africa

2 Introduction Introduction Study area Methodology Results Discussion André Parplies Student Research Colloquium

3 Introduction Motivation: Degradation of rangelands: ecological and socioeconomic impact (e.g. loss of biodiversity, threat to local livelihoods) Comparison of different rangeland management & tenure systems (communal vs. commercial farms; different herding structures) Support development of dynamic rangeland model (simulation of spatio-temporal vegetation patterns) André Parplies Student Research Colloquium

4 Motivation Objectives: Study and test a phenometrical approach to retrieve seasonal information at local farm scale using high spatial resolution satellite imagery Investigate the detectability of spatial patterns of derived key phenometrics André Parplies Student Research Colloquium

5 Remote Sensing Most recent studies on phenometrics are based on (freely available) coarse spatial resolution imagery MODIS/AVHRR (e.g. Neeti et al. 2012; Barraza et al. 2013; Forkel et al. 2013; Li et al. 2013) RapidEye s higher 5m pixel resolution is similar to field measurements (i.e. for this study local farm and sub-farm level) (Brüser et al. 2014) Source: André Parplies Student Research Colloquium

6 Phenometrics Definition: Vegetation phenology Seasonal parameters Seasonal metrics: start/end of growing season, length, middle, peak, vegetation productivity Based on Vegetation Indices Source: Redraw of Jönsson & Eklundh (2004) taken from Wessels et al. (2011). André Parplies Student Research Colloquium

7 Study area André Parplies Student Research Colloquium

8 Study area Thaba Nchu study area: Province: Free State, South Africa Grassland biome Annual precipitation: 537 mm/yr (on avg. 90% in summer period) Max. mean temp.: 17 C (July) to 33 C (Jan) (Woyessa et al. 2006, Swemmer et al. 2007) André Parplies Student Research Colloquium

9 Methodology André Parplies Student Research Colloquium

10 Remote Sensing Data acquisition Gap filling: simple linear interpolation approach André Parplies Student Research Colloquium

11 Methodology Image pre-processing: CATENA: Fully automatic generic processing chain provided by DLR (Generating GCP s, image matching to DEM, ATCOR) Source: (accesed 2014/04/27) André Parplies Student Research Colloquium

12 Methodology NDVI time series Choice for NDVI instead of other VI s: None is absolutely free of any criticism/limitations (White et al., 2009) Different VI s do not lead to significantly different results in trend analysis (Sonnenschein et al., 2011) Source: Illustration by Robert SImmon André Parplies Student Research Colloquium

13 Methodology Seasonal metrics Based on VI (here: NDVI) TIMESAT software Advantages: Freely available Works with any NDVI raster image (after certain pre-processing) Implemented noise reduction techniques (tested by Hird & McDermid, 2009) Automatic computation of phenometrical data per pixel (e.g. start/end of growing season, etc.) Source: Redraw of Jönsson & Eklundh (2004) taken from Wessels et al. (2011). André Parplies Student Research Colloquium

14 Methodology Noise reduction: Savitzky-Golay filter using a moving window approach Size of window determines degree of smoothing (large window=large smoothing effect) Large window size Small window size Source: Taken from the TIMESAT software manual of Jönsson & Eklundh (2011). André Parplies Student Research Colloquium

15 Methodology André Parplies Student Research Colloquium

16 Results André Parplies Student Research Colloquium

17 Results André Parplies Student Research Colloquium

18 Results André Parplies Student Research Colloquium

19 Discussion At this state no reference data/validation from field or same year from similar studies Possible validation: Rainfall data Field observations MODIS WAMIS web service Source: wamis.meraka.org.za/time-series-viewer (accessed 2014/04/27). André Parplies Student Research Colloquium

20 Discussion 1. Available RapidEye imagery: Temporal resolution (1 per month enough)? Image distribution/evenness: Time span ranging from 3 till 57 days! André Parplies Student Research Colloquium

21 Conclusion Succesfully derived phenometrics with reasonable results using TIMESAT Showed the detectability of spatial patterns on a local farm scale Certain limitations regarding temporal resolution Referencing with available ground observations André Parplies Student Research Colloquium

22 Further work André Parplies Student Research Colloquium

23 The End Thanx for your patience! André Parplies Student Research Colloquium

24 References Barraza, V., et al., Monitoring and modelling land surface dynamics in Bermejo River Basin, Argentina: time series analysis of MODIS NDVI data. International Journal of Remote Sensing, 34 (15), Brüser, K., et al., Discrimination and characterization of management systems in semi-arid rangelands of South Africa using RapidEye time series. International Journal of Remote Sensing, 35 (5), Eklundh, L. and Jönsson, P., TIMESAT 3.1 Software Manual. Lund University, Sweden. Forkel, M., et al., Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology. Remote Sensing, 5 (5), Hird, J.N. and McDermid, G.J., Noise reduction of NDVI time series: An empirical comparison of selected techniques. Remote Sensing of Environment, 113 (1), Jönsson, P. and Eklundh, L., Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Transactions on Geoscience and Remote Sensing, 40 (8), Jönsson, P. and Eklundh, L., TIMESAT a program for analyzing time-series of satellite sensor data. Computers & Geosciences [online], 30 (8), Available from:./././literature/gis_remotesensing/time_series_analysis&change_detection/timesat_ekhlundh/ 2003_Jonsson&Eklund_Timesat_progam for analyzing time series of RS data.pdf [Accessed 17 Feb 2014]. Li, Z., et al., Monitoring and modeling spatial and temporal patterns of grassland dynamics using time-series MODIS NDVI with climate and stocking data. Remote Sensing of Environment, 138, Neeti, N., et al., Mapping seasonal trends in vegetation using AVHRR-NDVI time series in the Yucatán Peninsula, Mexico. Remote Sensing Letters, 3 (5), Wessels, K., et al., Remotely sensed vegetation phenology for describing and predicting the biomes of South Africa. Applied Vegetation Science, 14 (1), White, M.A., et al., Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for Global Change Biology, 15 (10), André Parplies Student Research Colloquium