Measuring phenological variability from satellite imagery

Abstract

Vegetation phenological phenomena are closely related to seasonal dynamics of the lower atmosphere and are therefore important elements in global models and vegetation monitoring. Normalized difference vegetation index (NDVI) data derived from the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer (AVHRR) satellite sensor offer a means of efficiently and objectively evaluating phenological characteristics over large areas. Twelve metrics linked to key phenological events were computed based on time-series NDVI data collected from 1989 to 1992 over the conterminous United States. These measures include the onset of greenness, time of peak NDVI, maximum NDVI, rate of greenup, rate of senescence, and integrated NDVI. Measures of central tendency and variability of the measures were computed and analyzed for various land cover types. Results from the analysis showed strong coincidence between the satellite-derived metrics and predicted phenological characteristics. In particular, the metrics identified interannual variability of spring wheat in North Dakota, characterized the phenology of four types of grasslands, and established the phenological consistency of deciduous and coniferous forests. These results have implications for largearea land cover mapping and monitoring. The utility of remotely sensed data as input to vegetation mapping is demonstrated by showing the distinct phenology of several land cover types. More stable information contained in ancillary data should be incorporated into the mapping process, particularly in areas with high phenological variability. In a regional or global monitoring system, an increase in variability in a region may serve as a signal to perform more detailed land cover analysis with higher resolution imagery.

Using digital time-lapse cameras to monitor species-specific understorey and overstorey phenology in support of wildlife habitat assessment

“…While originally applied to carbon-related studies of vegetation, previous research indicates that changes in the timing of plant developmental phases may signal important inter-annual climatic variations (Reed et al 1994;White and Nemani 2003;Xiao and Moody 2004), and may therefore be relevant to grizzly bear feeding patterns. Time series of phenology data can be developed using single or multiple observers at single locations, or through phenological networks, where multiple observers record observations of the same species at different locations (Bertin 2008).…”

Spring phenology of European beech (Fagus sylvatica L.) in a submountain beech stand with different stocking in 1995-2004

“…It is known that phenological events reflect biological characteristics (endogenous factors) of the species in relation to the course of climatic factors. According to Reed et al (1994) and Lechowicz (1995) the exogenous factors influencing phenology of plants include photoperiod duration, soil moisture and temperature, air temperature, solar illumination, snow cover, etc. However, the details of the physiological control of spring phenophases such as budburst are not still clear.…”

Long-Term Detection of Global Vegetation Phenology from Satellite Instruments

“…The commonly used methods are the threshold-based technique which is divided into absolute VI threshold (e.g., Lloyd, 1990;Fischer, 1994;Myneni et al, 1997;Zhou et al, 2001) and relative threshold (e.g., White et al, 1997;Jonsson and Eklundh, 2002;Delbart et al, 2005;Karlsen et al, 2006;Dash et al, 2010), moving average (Reed et al, 1994), spectral analysis (Jakubauskas et al, 2001;Moody and Johnson, 2001), and inflection point estimation in the time series of vegetation indices (Moulin et al 1997;Zhang et al 2003;Tan et al, 2011). Various approaches in detecting phenological timing, particularly the greenup onset, are compared using the same dataset (de Beurs and Henebry, 2010;White et al, 2009).…”

“…The satellite-derived vegetation indices, commonly termed normalized difference vegetation index (NDVI), provides an indication of the canopy ''greenness" of vegetation communities, which is a composite property of leaf chlorophyll content, leaf area, canopy cover and structure. Therefore, the time series of NDVI data derived from the Advanced Very High Resolution Radiometer (AVHRR) have been used extensively for monitoring vegetation phenology (Lloyd, 1990;Reed et al, 1994;White et al, 1997;Zhang et al, 2007 vegetation indices at spatial resolutions of 250 m, 500 m, and 1 km globally, with substantially improved geometric and radiometric properties (Huete et al, 2002).…”

“…These products include: (1) the MODIS Land Cover Dynamics Product (MCD12Q2) derived from MODIS NBAR (nadir bidirectional reflectance distribution function adjusted reflectance) EVI (enhanced vegetation index) (500m-1000m), which is the only global product that is produced on an operational basis from 2001 to present Ganguly et al, 2010); (2) the MODIS-based product generated at NASA-GSFC (Goddard Space Flight Center) in support of the North American Carbon Program, which was produced using MODIS data at a spatial resolution of 250m-500m (Morisette et al, 2009;Tan et al, 2011); (3) the MODIS phenology product being generated for the contiguous United States (CONUS) by the US Forest Service (Hargrove et al, 2009); (4) the USGS long-term 1-km AVHRR phenology product for CONUS (1989-present;Reed et al, 1994); (5) the NOAA 4-km GVIx phenology over North America from 1982(Zhang et al, 2007; (6) the global 4.6 km product for 2005 from the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) (Dash et al, 2010); and (6) the global product based on FPAR (Fraction of Photosynthetically Active Radiation) developed by the European Space Agency (Verstraete et al, 2008).…”