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 our understanding of changes in terrestrial vegetation related to climate and other driving forces and advancing the use of remote sensing imagery in applications.
Biography
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 for 30 years. Since finishing her graduate program at the University of Nebraska—Lincoln in 1990, she has worked in applied geographic research utilizing remote sensing approaches. Jess’s main interests involve improving our understanding of changes in terrestrial vegetation related to climate and other driving forces 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. 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 in the 1990s. From 2001 to 2017, she led multiple projects mainly focused on developing new monitoring tools 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 a new role leading the Land Change Monitoring Assessment and Projection (LCMAP) science team. LCMAP is a relatively new USGS initiative developing 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.
Science and Products
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...
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...
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.
Building the vegetation drought response index for Canada (VegDRI-Canada) to monitor agricultural drought: first results
Drought is a natural climatic phenomenon that occurs throughout the world and impacts many sectors of society. To help decision-makers reduce the impacts of drought, it is important to improve monitoring tools that provide relevant and timely information in support of drought mitigation decisions. Given that drought is a complex natural hazard...
Tadesse, Tsegaye; Champagne, Catherine; Wardlow, Brian D.; Hadwen, Trevor A.; Brown, Jesslyn F.; Demisse, Getachew B.; Bayissa, Yared A.; Davidson, Andrew M. Assessing the evolution of soil moisture and vegetation conditions during the 2012 United States flash drought
This study examines the evolution of several model-based and satellite-derived drought metrics sensitive to soil moisture and vegetation conditions during the extreme flash drought event that impacted major agricultural areas across the central U.S. during 2012. Standardized anomalies from the remote sensing based Evaporative Stress Index (ESI)...
Otkin, Jason A.; Anderson, Martha C.; Hain, Christopher; Svoboda, Mark; Johnson, David; Mueller, Richard; Tadesse, Tsegaye; Wardlow, Brian D.; Brown, Jesslyn F. Application-ready expedited MODIS data for operational land surface monitoring of vegetation condition
Monitoring systems benefit from high temporal frequency image data collected from the Moderate Resolution Imaging Spectroradiometer (MODIS) system. Because of near-daily global coverage, MODIS data are beneficial to applications that require timely information about vegetation condition related to drought, flooding, or fire danger. Rapid satellite...
Brown, Jesslyn F.; Howard, Daniel M.; Wylie, Bruce K.; Friesz, Aaron M.; Ji, Lei; Gacke, Carolyn Exploring drought controls on spring phenology
The timing of spring phenology can be influenced by several drivers. Many studies have shown the effect of temperature on spring vegetation growth, but the role of moisture is complex and not as well researched. We explored drivers for aspen spring phenology in the mountains of the western U.S. While temperature exerted control over the timing of...
Hayes, M.; Trnka, M.; Brown, Jesslyn F.; Meier, Gretchen The integration of geophysical and enhanced Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index data into a rule-based, piecewise regression-tree model to estimate cheatgrass beginning of spring growth
Cheatgrass exhibits spatial and temporal phenological variability across the Great Basin as described by ecological models formed using remote sensing and other spatial data-sets. We developed a rule-based, piecewise regression-tree model trained on 99 points that used three data-sets – latitude, elevation, and start of season time based on...
Boyte, Stephen P.; Wylie, Bruce K.; Major, Donald J.; Brown, Jesslyn F. Assessing the vegetation condition impacts of the 2011 drought across the U.S. southern Great Plains using the vegetation drought response index (VegDRI)
The vegetation drought response index (VegDRI), which combines traditional climate- and satellite-based approaches for assessing vegetation conditions, offers new insights into assessing the impacts of drought from local to regional scales. In 2011, the U.S. southern Great Plains, which includes Texas, Oklahoma, and New Mexico, was plagued by...
Tadesse, Tsegaye; Wardlow, Brian D.; Brown, Jesslyn F.; Svoboda, Mark; Hayes, Michael; Fuchs, Brian; Gutzmer, Denise Phenology and climate relationships in aspen (Populus tremuloides Michx.) forest and woodland communities of southwestern Colorado
Trembling aspen (Populus tremuloides Michx.) occurs over wide geographical, latitudinal, elevational, and environmental gradients, making it a favorable candidate for a study of phenology and climate relationships. Aspen forests and woodlands provide numerous ecosystem services, such as high primary productivity and biodiversity, retention and...
Meier, Gretchen A.; Brown, Jesslyn F.; Evelsizer, Ross J.; Vogelmann, James E.
Remote sensing of land surface phenology
Remote sensing of land-surface phenology is an important method for studying the patterns of plant and animal growth cycles. Phenological events are sensitive to climate variation; therefore phenology data provide important baseline information documenting trends in ecology and detecting the impacts of climate change on multiple scales. The USGS...
Meier, G.A.; Brown, Jesslyn F. Merging remote sensing data and national agricultural statistics to model change in irrigated agriculture
Over 22 million hectares (ha) of U.S. croplands are irrigated. Irrigation is an intensified agricultural land use that increases crop yields and the practice affects water and energy cycles at, above, and below the land surface. Until recently, there has been a scarcity of geospatially detailed information about irrigation that is comprehensive,...
Brown, Jesslyn F.; Pervez, Md Shahriar Variability and trends in irrigated and non-irrigated croplands in the central U.S
Over 23 million hectares (233 thousand km2) of U.S. croplands are irrigated and there was an overall net expansion of 522 thousand hectares nationally from 2002 to 2007. Most of this expansion occurred across the High Plains Aquifer (HPA) in the central Great Plains. Until recently, there has been a lack of geospatially-detailed irrigation data...
Brown, Jesslyn F.; Pervez, Md Shahriar The Vegetation Drought Response Index (VegDRI): An integration of satellite, climate, and biophysical data
Wardlow, Brian D.; Anderson, Martha C.; Verdin, James P.; Wardlow, Brian D.; Tadesse, Tsegaye; Brown, Jesslyn F.; Callahan, Karin; Swain, Sharmistha; Hunt, Eric Merging climate and multi-sensor time-series data in real-time drought monitoring across the U.S.A.
Droughts occur repeatedly in the United States resulting in billions of dollars of damage. Monitoring and reporting on drought conditions is a necessary function of government agencies at multiple levels. A team of Federal and university partners developed a drought decision- support tool with higher spatial resolution relative to traditional...
Brown, Jesslyn F.; Miura, T.; Wardlow, B.; Gu, Yingxin
New Warning System Identifies Flash Drought Quickly
Agricultural crops can wither in a flash when the days turn hot, the air dries, the rain stops and moisture evaporates quickly from the soil. A new early warning system can help alert managers and others as drought begins to happen.