1. Assessing the Phenological Suitability of Global Landsat Data Sets for Forest Change Analysis www.landcover.org The Global Land Cover Facility What does forest phenology look like from satellite? How does forest phenology vary from place to place? How to retrieve the phenology time series? A Global Collection of Forest Phenology Profiles for the Remote Sensing Community Why do you need forest phenology information, if you are not studying phenology in particular? How to interpret and use the phenology profile? Summary: Vegetation phenology is a major consideration in acquiring satellite images for forest change analysis, especially in regions having deciduous forests. When trees have little or no leaves during the leaf-off season, spectrally deciduous forests are difficult to separate from non-forested surfaces, including disturbed forests. Therefore, use of images acquired during or near the leaf-off season in forest change analysis can result in substantial errors in the derived change products. In this study, we evaluate the phenological suitability of two global Landsat data sets centered around 1990 and 2000 for forest change analysis. These two data sets, together with a third one centered around 2005, which is being assembled, will be used to produce an Earth Science Data Record of global forest cover change. A series of forest phenology around Washington DC in year 2000. Mostly this is deciduous forest A series of forest phenology in Northern California, WRS-2 tile 45/33. Mostly this is evergreen forest The optical property of forest canopy is such that dense forests are the ‘dark objects’ in remote sensing. The darker the forest canopy, the easier it is to distinguish forest from other land cover types. This property is very useful for automatic image analysis of any land cover application. For this purpose we generated a global archive of forest phenology profiles for each Landsat footprints in the 1990s and 2000s. 1. The phenology time series is shown as the average NDVI value of the forest canopy in a calendar year 2. The NDVI value is provided by the AVHRR GIMMS data set The GIMMS (Global Inventory Modeling and Mapping Studies) data set is a normalized difference vegetation index (NDVI) product available for a 25 year period spanning from 1981 to 2006. The data set is derived from imagery obtained from the Advanced Very High Resolution Radiometer (AVHRR) instrument onboard the NOAA satellite series 7, 9, 11, 14, 16 and 17. This is an NDVI dataset that has been corrected for calibration, view geometry, volcanic aerosols, and other effects not related to vegetation change. 3. The forested land is defined as >30% vegetation canopy field value in the MODIS MOD44B. Two forest types are labeled: Deciduous and Evergreen. Shown below are the forest mask used for the sites of California, Colorado, and DC at similar latitudes: 4. Phenology time series is calculated preferably on the deciduous forest, on the dates of GeoCover imagery set. Again for the sites of California, Colorado, and DC at similar latitudes: The growing season and satellite data collection window The three sites of Washington DC, Colorado, and Northern California were deliberately chosen at the same latitudes. Different growing seasons show up at very different geographical conditions. For the evergreen forest of northern California on the west coast, satellite data can be collected all year long for land cover and change mapping. For the deciduous forest of Washington DC on the east coast, decent data can be acquired from April to October. And for the deciduous forest in Colorado, the ideal observation window is during May to September. For high resolution satellite imagery, researchers usually choose the cloud- free dates for mapping. Less considered is the phenology situation in the choice of imagery. With our database, researchers can select leaf-on Landsat scenes just like they select cloud-free scenes from USGS. Our study shows that, the GeoCover global Landsat image set offered by NASA is excellent in terms of phenology. Most (>90%) of the imagery were collected in the leaf-on seasons. This makes the GeoCover imagery set ideal for land cover mapping and change detection. The phenology profiles will be freely downloadable at www.landcover.org This project activity is sponsored by NASA MEaSUREs and LCLUC programs. A series of forest phenology in Colorado, WRS-2 tile 34/33. Deciduous Forest phenology shown here: January March May July September November Bright Green: Deciduous Forest Dark Green: Evergreen Forest Siena: Non-forest Blue: Water Beijing, China Delhi, India Canberra, Australia Moscow, Russia Brasilia, Brazil Hanoi, Vietnam Kuan Song, Chengquan Huang, John R. G. Townshend, Paul E. Davis, Saurabh Channan, Matthew Smith Department of Geography & Global Land Cover Facility, University of Maryland