Jesslyn Brown
Science and Products
Methods for Deriving Metrics
Phenological metrics can be derived from satellite data in several ways. Some researchers use complex mathematical models. Others employ threshold-based approaches that use either relative or pre-defined (global) reference values at which vegetative activity is assumed to begin. For example, seasonal midpoint NDVI (SMN) is a threshold-based approach that uses relative reference values to...
Challenges in Deriving Phenological Metrics
Regardless of the method used, it is difficult to create algorithms sufficiently robust to derive phenological metrics from certain types of real time-series NDVI curves (as opposed to modeled or simulated data). For example, in desert shrublands (see below), time series NDVI shows little seasonal amplitude. Ideally, algorithms should be able to identify these regions and assign them a value...
Deriving Phenological Metrics from NDVI
Plotting time-series NDVI data produces a temporal curve that summarizes the various stages that green vegetation undergoes during a complete growing season. Such curves can be analyzed to extract key phenological variables, or metrics, about a particular season, such as the start of the growing season (SOS), peak of the season (POS), and end of the season (EOS). These characteristics may not...
Data Smoothing - Reducing the "Noise" in NDVI
The reflected light waves that satellite sensors detect coming from vegetation on the Earth's surface can be altered or blocked by a variety of phenomena, including aerosols and clouds in the atmosphere as well as changing illumination patterns and the angle at which the satellite views the ground at any given time. These phenomena introduce "noise" into raw satellite data. To address this...
NDVI from Other Sensors
Readily available, no-charge data gathered by Landsat 7's Enhanced Thematic Mapper Plus (ETM+) sensor can also be used to generate NDVI image products (see Table 1). With a resolution of 30 m, ETM+ data can be transformed into NDVI images that have greater spatial detail than those derived from...
NDVI from AVHRR
The sensor responsible for the longest running series of NDVI products used for large-area phenology studies is carried aboard National Oceanic and Atmospheric Administration (NOAA) polar-orbiting weather satellites (see Table 1). This sensor, known as the Advanced Very High Resolution Radiometer (AVHRR), has a daily repeat cycle and, despite its name, a 1-km resolution (an AVHRR image pixel...
Remote Sensing Phenology
Phenology is the study of plant and animal life cycles in relation to the seasons. EROS maintains a set of nine annual phenological metrics for the conterminous United States, all curated from satellite data. Taken together, the metrics represent a powerful tool for documenting life cycle trends and the impacts of climate change on ecosystems.
NDVI, the Foundation for Remote Sensing Phenology
Remote sensing phenology studies use data gathered by satellite sensors that measure wavelengths of light absorbed and reflected by green plants. Certain pigments in plant leaves strongly absorb wavelengths of visible (red) light. The leaves themselves strongly reflect wavelengths of near-infrared light, which is invisible to human eyes. As a plant canopy changes from early spring growth to...
Land Cover and Change Monitoring
Land Change Monitoring, Assessment, and Projection (LCMAP) is a U.S. Geological Survey (USGS) science initiative being implemented at the USGS Earth Resources Observation and Science (EROS) Center, that centers on structured, operational, ongoing, and timely collection and delivery of accurate and relevant data, information, and knowledge on land use, cover, and condition.
Remote Sensing Phenology
Phenology is the study of plant and animal life cycles in relation to the seasons. EROS maintains a set of nine annual phenological metrics for the conterminous United States, all curated from satellite data. Taken together, the metrics represent a powerful tool for documenting life cycle trends and the impacts of climate change on ecosystems.