Jesslyn Brown
Jesslyn Brown is a research geographer with the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center in Sioux Falls, South Dakota, USA. Jess's main interests involve improving the understanding of changes in terrestrial vegetation, drought early warning, and transitions in land cover and land use by advancing the use of remote sensing imagery in applications.
Jesslyn Brown is a research geographer with the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center in Sioux Falls, South Dakota, USA, where she has worked over 30 years. Her work in applied geographic research has contributed to improving understanding of terrestrial vegetation patterns and advancing the use of remotely sensed imagery for applications including drought early warning, tracking vegetation phenology (i.e., seasonal dynamics), and mapping land cover and land use. In the 1990s, Jess was a member of the Global Land Cover Characteristics team that created the first map of global land cover at a 1-km resolution. From 2001 to 2017, she led multiple projects mainly focused on developing new monitoring tools, including VegDRI and QuickDRI, to improve agricultural drought monitoring capabilities in the U.S. in a strong collaboration with the University of Nebraska-Lincoln’s National Drought Mitigation Center. During that time, she also led efforts to investigate recent land use change specifically focused on irrigated agriculture across the country. In 2017, she began leading the Land Change Monitoring Assessment and Projection (LCMAP) science team. LCMAP was a USGS initiative to develop an end-to-end capability to use the deep Landsat record to continuously track and characterize changes in land cover state and condition and translate the information into assessments of current and historical processes of cover and change. In 2023, the LCMAP project was integrated with the National Land Cover Database team. Under Jess’s leadership, this team developed and produced a new annual land cover database for the conterminous U.S. covering 39 years from 1985 to 2023 utilizing the long Landsat record, deep learning methods, and cloud computing.
Science and Products
Filter Total Items: 38
Methods - QuickDRI
Similar in methodology to VegDRI, QuickDRI is a hybrid drought index that incorporates multiple sources of input data informative of drought or rapidly drying conditions. The QuickDRI methodology includes many steps shown in Figure 1.
Methods - VegDRI
The VegDRI is a hybrid drought index that incorporates multiple sources of input data informative of drought stress. The VegDRI methodology includes many steps shown in Figure 1 and our publications can be consulted for further technical details.
EROS Phenocam - Live
In September 2014, the USGS Earth Resources Observation and Science (EROS) Center established a near-ground automated digital camera and joined over 100 other core sites in the PhenoCam Network. Following the protocols of the network, the USGS-EROS camera regularly captures digital imagery and data used to better understand vegetation cycles. As part of a scientific network of automated cameras...
Spring Arriving at EROS
Temperatures are slowly getting warmer and it looks like our long winter is coming to an end. Volunteers at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center will soon mobilize to monitor and observe the timing of nature's calendar (events like bud burst, flowering, and leaf drop) in conjunction with the USA National Phenology Network. This seasonal activity...
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 such...
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 derive...
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 problem...
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 AVHRR, but which cover a smaller area. Furthermore, Landsat's orbit repeats every 16 days, compared to...
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 late...
Science and Products
Filter Total Items: 38
Methods - QuickDRI
Similar in methodology to VegDRI, QuickDRI is a hybrid drought index that incorporates multiple sources of input data informative of drought or rapidly drying conditions. The QuickDRI methodology includes many steps shown in Figure 1.
Methods - VegDRI
The VegDRI is a hybrid drought index that incorporates multiple sources of input data informative of drought stress. The VegDRI methodology includes many steps shown in Figure 1 and our publications can be consulted for further technical details.
EROS Phenocam - Live
In September 2014, the USGS Earth Resources Observation and Science (EROS) Center established a near-ground automated digital camera and joined over 100 other core sites in the PhenoCam Network. Following the protocols of the network, the USGS-EROS camera regularly captures digital imagery and data used to better understand vegetation cycles. As part of a scientific network of automated cameras...
Spring Arriving at EROS
Temperatures are slowly getting warmer and it looks like our long winter is coming to an end. Volunteers at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center will soon mobilize to monitor and observe the timing of nature's calendar (events like bud burst, flowering, and leaf drop) in conjunction with the USA National Phenology Network. This seasonal activity...
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 such...
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 derive...
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 problem...
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 AVHRR, but which cover a smaller area. Furthermore, Landsat's orbit repeats every 16 days, compared to...
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 late...