This document discusses remote sensing of phenology, which is the study of periodic biological events and their relationship to climate and environment. Satellite imagery can be used to observe seasonal landscape dynamics over large areas by analyzing time-series vegetation index data. Key phenological metrics like start of season, end of season, and duration of growing season can be derived from this data. This provides insights into how plant life cycles respond to climate change at regional scales.

1. Remote Sensing & Phenology SWES 573 Spring 2004

2. Introduction Phenology (greek word phaino ; to show or to appear) is the study of periodic biological evebts as influenced by the environment. It includes the timing of biological events, particularly in response to climatic changes to the environment. Sprouting and flowering of plants; color changes of leaves in autumn, insect hatches, hibernation, bird migrations Certain biological events, such as the time of the start of the growing season, have a key role in changing land surface/atmosphere boundary conditions such as surface roughness, albedo, humidity, etc. The phenology of ecosystems and its connection to climate is a key to understanding ongoing global change. Phenology has historically been studied as direct observations of the timing of leaf opening, flowering, leaf fall and such events. Seasonality is a related term, referring to similar non-biological events Spring break-up of ice; snow melt; dry & wet seasons

3. Has been a quiet scientific objective, now becoming more important. Only people in biology, ecology, and meteorology using datasets on this. Ground truth observation of biological fluctuating phenomena (how? - outmoded?) Today: modeling of phenologic events; comparisons with meteorologic parameters, and correlation attempts with global remote sensing data sets - important for global change and climate change analyses. A major value of for phenologic data is their validation value for sesonality models. These models have gained prominence in global climatic change models to predict biosphere responses to climatic parameter changes.

4. Introduction Phenology is an interdisciplinary environmental science It is integrative (climate, moisture) Global change science is stimulating, challenging, and transforming the discipline of phenology. What are the and consequences of variation in timing of life cycle events

5. Applications Biodiversity (resource availability) Agriculture, Range, Forestry, and Fisheries reproduction, productivity, pests, diseases adjust management strategies Human health (allergies, diseases, water quality, etc..) Transportation (leaf fall, bird migrations) Tourism & recreation

6. Remote Sensing (1) The use of satellite imagery provides a unique vantage point for observing seasonal dynamics of the landscape. Key to understanding large area seasonal phenomena Repeat observations provide a mechanism to move from plant-specific to regional scale studies of phenology Moderate resolution sensors preferred over high spatial resolution sensors due to frequency of coverage and radiometric rectification issues.

7. Remote Sensing (2) R.S. tracks integrated greenness of largely heterogeneous 1-km pixels, rather than single plants or dominant plant types. By analyzing the time-series vegetation index, a set of algorithms derive phenology metrics such as onset, end, and duration of growing season. The output of these metrics then may be analyzed to produce products, such as temporal trends in integrated NDVI values.

8. Remotely-sensed measures Greenness (vegetation indices) LAI/ FPAR products Albedo Land surface temperature Fires Photosynthesis, NPP Evapotranspiration

9. The USGS EROS Data Center has developed a data set of seasonal metrics derived from multitemporal Advanced Very High Resolution Radiometer (AVHRR) satellite sensor Normalized Difference Vegetation Index (NDVI) observations. (http://edc2.usgs.gov/phenological/)

11. Figure 1 is an animated loop of the NDVI observed over Colorado from 1990 through 1996. http://geochange.er.usgs.gov/sw/impacts/biology/Phenological-CO/

12. Land characterization from AVHRR Trends in the seasonal characteristics become more apparent when analysis is stratified according to land cover type. The post-classification refinement is performed with the aid of digital elevation, ecoregions data and a collection of other land cover/vegetation reference data (Brown and others 1993). The interpretation is based on extensive use of computer-assisted image processing tools (Brown and others, in press); however, the classification process is not completely automated and resembles a traditional manual image interpretation technique. One hundred fifty-nine seasonal land cover regions resulted from the analysis of the conterminous United States. The seasonal land cover regions were then cross-referenced to several different classification schemes, including an adaptation of the USGS/Anderson Level II scheme (Anderson and others 1976) (see Figure 4).

14. Figure shows smoothed time-series NDVI data for representative pixels of several land cover types in Colorado observed on a biweekly basis from 1990 to 1996. Between-year variability of the cropland pixel is apparent, with the low values in 1991 and 1994. The shrubland pixel also shows significant variability, with low peak values in 1990 and 1991. The grassland pixel shows relative consistency in the amplitude of the NDVI values, but exhibits differences in the length of the growing season. The length of the growing season in 1992 appears to be significantly longer than that illustrated in 1994. The coniferous forest curve exhibits a marked consistency, both in amplitude and in length of growing season. While the coniferous forest is green throughout the year, background effects (such as snow cover) affect the seasonal NDVI profile.

15. Figures below show images depicting four of the seasonal characteristics, including the time of the start of the growing season, time of maximum NDVI, duration of growing season, and time-integrated NDVI over a five year period from 1991 to 1995. Since the calculation of the seasonal characteristics requires data that both precede and follow the NDVI data for a single year, 1990 and 1996 are not shown.

16. The time of the onset of the growing season in Colorado for 1991-1995 is depicted in Figure 6. Features that regularly stand out are the late onset of growing season for the coniferous forests, especially in 1995 and the croplands in northeastern Colorado. Shrublands and grasslands have an earlier onset of greenness. The bar chart in Figure 10 illustrates the interannual variability in the time of the start of the growing season over the five years, but does not exhibit any noticeable trend of earlier or later starts to the growing season.

17. Key phenological variables Start of the growing season End of the growing season Growing season length The integrated phenology of the pixel is usually addressed in terms of a more general statement such as SOS, EOS, rather than first-leaf, etc.. Phenology of a mosaic of vegetation types

18. Phenological Metrics Deriving metrics that describe the phenology (seasonality) of vegetation growth is key to understanding when changes in the land surface/atmosphere boundary layer take place. Time-series NDVI data track the greenup and senescence cycle of vegetation well (Figure). The USGS has developed an algorithm to extract key phenological phenomena from this time-series curve. Our approach is to utilize a delayed moving average (DMA) as a comparison to the smoothed NDVI time-series. NDVI data values are compared to the average of the previous (user-defined) n NDVI observations to identify departures from an established trend (Reed and others, 1994). This departure is labeled as the start of the growing season (SOS). The end of the growing season (EOS) is calculated in a similar manner, but with the moving average running in the opposite direction. Duration of growing season is the difference between the time of EOS and SOS. The peak of the growing season is simply the time of the maximum NDVI. Several other metrics can then be derived, including the rate of greenup (slope from SOS to peak), rate of senescence (slope from peak to EOS), and total integrated NDVI (area under the curve). The figure below summarizes graphically these metrics.

19. The full set of metrics and their phenological interpretation are shown above. Note that the phenological interpretation is not an absolute value of photosynthesis, but the phenological metrics are surrogates for such values. Deceleration of photosynthesis Rate of senescence Acceleration of photosynthesis Rate of greenup Photosynthetic activity in growing season Seasonally integrated NDVI Maximum level of photosynthetic activity Maximum NDVI Level of photosynthetic activity at EOS NDVI at end of growing season Level of photosynthetic activity at SOS NDVI at start of growing season Time of maximum photosynthesis Time of Maximum Greenness - Julian Day Duration of photosynthetic activity Duration of Growing Season Cessation of measurable photosynthesis Time of End of Season (EOS) Julian Day Beginning of measurable photosynthesis Time of Start of Season (SOS) Julian Day Phenological Interpretation Phenological Metric

20. Metric Data gzip.file 131 Mb readme Cessation of measurable photosynthesis (1989-2001) End of Season Date gzip.file 70 Mb readme Level of photosynthetic activity at SOS (1989-2001) Start of Season NDVI gzip.file 129 Mb readme Beginning of measurable photosynthesis (1989-2001) Start of Season Date gzip.file 83 Mb readme Simulated Net primary production (1989-2001) Seasonal Integrated NDVI Image File More Info Description Metric Name

21. 3 types of phenology approaches with R.S. Threshold-based Phenology E.g., NDVI>0.1 - SOS achieved Seasonal midpoint NDVI (SMN) - uses midpoint between minimum and maximum NDVI 10% distance between max and min.--SOS Inflection point Phenology 2 inflection points, width, peak Greenup, maturity, senescence, and dormancy (Zhang et al., 2003) Curve derivative Phenology

22. Sensors & Data Smoothing Composited data are inherently affected by a number of phenomena including cloud contamination, atmospheric perturbations, variable illumination and viewing geometry (sun angle and sensor view angle) In the case of NDVI data, these factors usually reduce the values and thus, compositing could be accomplished using the maximum value over a specified time period: usually a week, ten days, or two weeks. The maximum value compositing has been shown to increases data quality, however, bidirectional (mainly view angle) have been found over partially vegetated surfaces.

23. Sensors & Data Smoothing In the MODIS era, new problems arise In general, as better sensors and algorithms are developed, new flaws that could not be seen in previous datasets, appear (more flaws but better form in the data) Atmosphere corrected data allows angular variations to become pronounced. The reflectance product may over-correct atmosphere influences resulting in higher NDVI values. Clouds may cause both false increases in the EVI and false decreases in both NDVI & EVI. Over-corrected, inland water bodies may become “green”, particularly with NDVI. Role of BRDF, role of modeled data.

24. Sensors & Data Smoothing While maximum value compositing increases data quality, further processing-- in effect a smoothing-- of the temporal NDVI signal must be performed to facilitate some time-series analyses. The smoothing algorithm must serve as a rough interpolation between observations and, in order to remove the effects of the remaining NDVI-reducing phenomena, upwardly bias the results.

25. Temporal VI smoothing methods Best Index Slope Extraction (BISE, Viovy et al., 1992) Compound mean and median filters Splines Weighted least-squares approach (Swets et al., 1999) Eliminates some of the timeshifts caused by over generalization of the signal by giving more weight to locally (in time domain) high values. Smoothing algorithm should retain key temporal features without over generalization, eliminate spurious downward spikes in the VI, and retain sustained temporary declines in VI that are representative of declines in vegetation condition.

26. Example of compound median smoother The “smoother” iteratively processes the time-series by applying median filters of various widths, then applies a "re- roughing" by reintroducing the original NDVI time-series into the process. The upward bias, or NDVI "peak-catching", is applied by reintroducing unsmoothed NDVI values that are greater than the smoothed values. Further work is being conducted that investigates other smoothing approaches, such as Fourier analysis similar to that used in the FASIR adjustments (Sellers and others 1994) and other statistical approaches. In the Figure, a three-year time series of NDVI is illustrated in the solid line, while the result of the compound median smoother are shown as a dashed line. The smoother eliminates cloud contamination (illustrated by the extremely low NDVI value in Year 2), as well as NDVI reducing perturbations (illustrated during the greenup during Year 1).

27. Seasonal characteristics Once the database is smoothed of temporal discontinuities, methods can be applied to extract a suite of seasonal characteristics from the time-series data set. Some of the more important seasonal characteristics that are needed are the time of the start of the growing season, the time of maximum photosynthetic activity, and the duration of the growing season. Reed and colleagues (1994) developed a methodology to derive a set of 12 seasonal characteristics from the smoothed NDVI time series that summarizes characteristics of ecosystem dynamics. The methodology involves applying a moving average filter to the time series, which essentially creates a new time series with a time lag. The moving average time-series (MATS) then can serve as a predicted NDVI based on the previous n (user-defined) observations. When the actual (smoothed) values are greater than the value predicted by the MATS, then a trend change (start of growing season) is occurring. The end of the growing season can be found similarly and the duration can be calculated as the difference between the two. Other seasonal characteristics such as the time of maximum NDVI and the time-integrated NDVI (using the value of NDVI at start of season as a baseline) are also important surrogate measure of ecosystem characteristics.

29. Smoothing The input data to extract phenological metrics is time-series advanced very high resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) data. NDVI data may be affected by a number of phenomena that contaminate the signal, including clouds, atmospheric perturbations, and variable illumination and viewing geometry. Each of these phenomena reduce the NDVI. To reduce the contamination of the NDVI signal, we develop a weighted least-squares linear regression approach to temporally smooth the data (Swets, 1999). This approach uses a moving temporal window to calculate a regression line. The window is moved one period at a time, resulting in a family of regression lines associated with each point; this family of lines is then averaged at each point and interpolated between points to provide a continuous temporal NDVI signal. Also, since the factors that cause contamination usually reduce the NDVI values, we apply a weighting factor that favors peak points over sloping or valley points. A final operation assures that all peak NDVI values are retained. The resulting relationship between the smoothed curve and the original data is statistically based. The smoothed data may be used to improve applications involving the analysis of time-series NDVI data, such as land cover classification, seasonal vegetation characterization, and vegetation monitoring.

30. Smoothing Swets, D.L., B.C. Reed, J.R. Rowland, S.E. Marko, 1999. A weighted least-squares approach to temporal smoothing of NDVI. In 1999 ASPRS Annual Conference, From Image to Information, Portland, Oregon, May 17-21, 1999, Proceedings: Bethesda, Maryland, American Society for Photogrammetry and Remote Sensing, CD-ROM, 1 disc.

31. Problems Ground-based observations of phenology are subject to inaccuracies due to reliance of observation skills of the observers & scale. Satellite data is confounded by a number of factors, but it is an objective measure of environmental dynamics One of the difficulties with RS-phenology is creating an algorithm to handle the wide variety of real-time -series curves, rather than a model curves. Some biomes have no distinct seasonal signal (evergreen, desert shrub), then there are multiple growing curves.

32. References Reed, B.C., and E. Bartels, 1999. Phenological Characterization (abs.) in Proceedings of Pecora 14 Conference, Denver, Colorado, December 1999. Schwartz, M.D. and B.C. Reed, 1999. Surface phenology and satellite sensor-derived onset of greenness: an initial comparison. International Journal of Remote Sensing, Vol. 20, No. 17, pp. 3451-3457. Swets, D.L., B.C. Reed, J.R. Rowland, S.E. Marko, 1999. A weighted least-squares approach to temporal smoothing of NDVI. In 1999 ASPRS Annual Conference, From Image to Information, Portland, Oregon, May 17-21, 1999, Proceedings: Bethesda, Maryland, American Society for Photogrammetry and Remote Sensing, CD-ROM, 1 disc. Reed, B.C., 1998. Derivation of phenological metrics. Proceedings of the Northern Great Plains Regional Workshop on Climate Change and Climate Variability. pp. 47-56. Yang, L., B.K. Wylie, L.L. Tieszen, B.C. Reed, 1998. An analysis of relationships among climate forcing and time-integrated NDVI of grasslands over the U.S. Northern and Central Great Plains. Remote Sensing of Environment, 65: 25-37. Reed, B.C., 1997. Applications of the U.S. Geological Survey's global land cover product. Acta Astronautica, Vol. 41, Nos. 4-10, pp. 671-680. Reed, B.C. and K. Sayler, 1997. A method for deriving phenological metrics from satellite data, Colorado 1991-1995. Impact of Climate Change and Land Use in the Southwestern United States, an electronic workshop. ( http://geochange.er.usgs.gov/sw/impacts/biology/Phenological-CO/) Reed, B.C. and Yang, L. 1997. Seasonal Vegetation Characteristics of the United States. Geocarto International 12(2): 65-71. Reed, B.C., J.F. Brown, D. VanderZee, T.L. Loveland, J.W. Merchant, D.O. Ohlen, 1994. "Measuring phenological variability from satellite imagery," Journal of Vegetation Science 5: 703-714.

33. North side of SF Peaks Between SF Peaks and South Rim, GC North Rim, GC South Rim, GC

35. Stress index= (NIR-MIR)/(NIR+MIR)

39. Droughts Droughts are normal recurring climatic phenomena that vary in space, time, and intensity. They may affect people and the landscape at local scales for short periods or cover broad regions and have impacts that are felt for years. The spatial and temporal variability and multiple impacts of droughts provide challenges for mapping and monitoring on all scales. A team of researchers from the USGS EROS Data Center, the National Drought Mitigation Center (University of Nebraska) and the High Plains Regional Climate Center are developing methods for regional-scale mapping and monitoring of drought conditions for the conterminous U.S. The goal is to deliver timely geo-referenced information about areas where the vegetation is impacted by drought.

40. We are integrating information provided by satellite-derived phenology metrics (top) and climate-based drought indicators (bottom) to produce a timely and spatially detailed drought monitoring product.

41. Phenology and Drought Monitoring Projects Phenology: Bradley Reed [email_address] http://edc2.usgs.gov/phenological Drought Monitoring: Jesslyn Brown [email_address] http://edc2.usgs.gov/phenological/drought/ Phenology is the study of the timing of biological events, particularly in response to climatic changes to the environment. Certain biological events, such as the time of the start of the growing season, have a key role in changing land surface/atmosphere boundary conditions such as surface roughness, albedo, humidity, etc. The phenology of ecosystems and its connection to climate is a key to understanding ongoing global change. The use of satellite imagery provides a unique vantage point for observing seasonal dynamics of the landscape. The USGS EROS Data Center has developed a data set of seasonal metrics derived from multitemporal Advanced Very High Resolution Radiometer (AVHRR) satellite sensor Normalized Difference Vegetation Index (NDVI) observations for the conterminous U.S. By analyzing the time-series vegetation index (Fig. 1), a set of algorithms derive phenology metrics such as onset, end, and duration of growing season. The output of these metrics then may be analyzed to produce products, such as temporal trends in integrated NDVI values (Fig. 2) Droughts are normal recurring climatic phenomena that vary in space, time, and intensity. They may affect people and the landscape at local scales for short periods or cover broad regions and have impacts that are felt for years. The spatial and temporal variability and multiple impacts of droughts provide challenges for mapping and monitoring on all scales. A team of researchers from the USGS EROS Data Center, the National Drought Mitigation Center (University of Nebraska) and the High Plains Regional Climate Center are developing methods for regional-scale mapping and monitoring of drought conditions for the conterminous U.S. The goal is to deliver timely geo-referenced information about areas where the vegetation is impacted by drought. We are integrating information provided by satellite-derived phenology metrics (Fig. 3) and climate-based drought indicators (Fig. 4) to produce a timely and spatially detailed drought monitoring product. Research and methods for Drought Monitoring are developed in tandem with Phenological Characterization. Figure 1 Figure 2 Figure 3 Figure 4