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.
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
Monitoring landscape dynamics in central U.S. grasslands with harmonized Landsat-8 and Sentinel-2 time series data
Optimizing a remote sensing production efficiency model for macro-scale GPP and yield estimation in agroecosystems
Exploring relationships of spring green-up to moisture and temperature across Wyoming, U.S.A
Multi-year data from satellite- and ground-based sensors show details and scale matter in assessing climate’s effects on wetland surface water, amphibians, and landscape conditions
Drought and land-cover conditions in the Great Plains
Priority questions in multidisciplinary drought research
Challenges in complementing data from ground-based sensors with satellite-derived products to measure ecological changes in relation to climate – lessons from temperate wetland-upland landscapes
Effect of NOAA satellite orbital drift on AVHRR-derived phenological metrics
Building the vegetation drought response index for Canada (VegDRI-Canada) to monitor agricultural drought: first results
Assessing the evolution of soil moisture and vegetation conditions during the 2012 United States flash drought
Exploring drought controls on spring phenology
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
Non-USGS Publications**
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
Methods - QuickDRI
Methods - VegDRI
Spring Arriving at EROS
EROS Phenocam - Live
Methods for Deriving Metrics
Challenges in Deriving Phenological Metrics
Deriving Phenological Metrics from NDVI
Data Smoothing - Reducing the "Noise" in NDVI
NDVI from Other Sensors
NDVI from AVHRR
Remote Sensing Phenology
NDVI, the Foundation for Remote Sensing Phenology
Participated in these Eyes on Earth podcast episodes.
Science and Products
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Filter Total Items: 63
Monitoring landscape dynamics in central U.S. grasslands with harmonized Landsat-8 and Sentinel-2 time series data
Remotely monitoring changes in central U.S. grasslands is challenging because these landscapes tend to respond quickly to disturbances and changes in weather. Such dynamic responses influence nutrient cycling, greenhouse gas contributions, habitat availability for wildlife, and other ecosystem processes and services. Traditionally, coarse-resolution satellite data acquired at daily intervals haveAuthorsQiang Zhou, Jennifer Rover, Jesslyn F. Brown, Bruce B. Worstell, Danny Howard, Zhuoting Wu, Alisa L. Gallant, Bradley Rundquist, Morgan BurkeOptimizing a remote sensing production efficiency model for macro-scale GPP and yield estimation in agroecosystems
Earth observation data are increasingly used to provide consistent eco-physiological information over large areas through time. Production efficiency models (PEMs) estimate Gross Primary Production (GPP) as a function of the fraction of photosynthetically active radiation absorbed by the canopy, which is derived from Earth observation. GPP can be summed over the growing season and adjusted by a crAuthorsMichael Marshall, Kevin Tu, Jesslyn F. BrownExploring relationships of spring green-up to moisture and temperature across Wyoming, U.S.A
Vegetation green-up signals the timing of available nutritious plants and shrubs providing high-quality forage for ungulates. In this study, we characterized spatial and temporal patterns of spring phenology and explored how they were related to preceding temperature and moisture conditions. We tested correlations between late winter weather and indicators of the onset and the length of the springAuthorsJesslyn F. Brown, Lei Ji, Alisa L. Gallant, Matthew KauffmanMulti-year data from satellite- and ground-based sensors show details and scale matter in assessing climate’s effects on wetland surface water, amphibians, and landscape conditions
Long-term, interdisciplinary studies of relations between climate and ecological conditions on wetland-upland landscapes have been lacking, especially studies integrated across scales meaningful for adaptive resource management. We collected data in situ at individual wetlands, and via satellite for surrounding 4-km2 landscape blocks, to assess relations between annual weather dynamics, snow duratAuthorsWalter Sadinski, Alisa L. Gallant, Mark Roth, Jesslyn F. Brown, Gabriel Senay, Wayne L. Brininger, Perry M. Jones, Jason M. StokerDrought and land-cover conditions in the Great Plains
Land–atmosphere interactions play a critical role in the Earth system, and a better understanding of these interactions could improve weather and climate models. The interaction among drought, vegetation productivity, and land cover is of particular significance. In a semiarid environment, such as the U.S. Great Plains, droughts can have a large influence on the productivity of agriculture and graAuthorsHeather J. Tollerud, Jesslyn F. Brown, Thomas Loveland, Rezaul Mahmood, Norman B. BlissPriority questions in multidisciplinary drought research
Addressing timely and relevant questions across a multitude of spatio-temporal scales, state-of-the-art interdisciplinary drought research will likely increase in importance under projected climate change. Given the complexity of the various direct and indirect causes and consequences of a drier world, scientific tasks need to be coordinated efficiently. Drought-related research endeavors rangingAuthorsMiroslav Trnka, Michael Hayes, František Jurečka, Lenka Bartošová, Martha Anderson, Rudolf Brázdil, Jesslyn F. Brown, Jesus J. Camarero, Pavel Cudlín, Petr Dobrovolný, Josef Eitzinger, Song Feng, Taryn Finnessey, Gregor Gregorič, Petr Havlik, Christopher Hain, Ian Holman, David Johnson, Kurt Christian Kersebaum, Fredrik Charpentier Ljungqvist, Jürg Luterbacher, Fabio Micale, Claudia Hartl-Meier, Martin Možný, Pavol Nejedlik, Jørgen Eivind Olesen, Margarita Ruiz-Ramos, Reimund P. Rötter, Gabriel Senay, Sergio M. Vicente-Serrano, Mark Svoboda, Andreja Susnik, Tsegaye Tadesse, Adam Vizina, Brian D. Wardlow, Zdeněk Žalud, Ulf BüntgenChallenges in complementing data from ground-based sensors with satellite-derived products to measure ecological changes in relation to climate – lessons from temperate wetland-upland landscapes
Assessing climate-related ecological changes across spatiotemporal scales meaningful to resource managers is challenging because no one method reliably produces essential data at both fine and broad scales. We recently confronted such challenges while integrating data from ground- and satellite-based sensors for an assessment of four wetland-rich study areas in the U.S. Midwest. We examined relatiAuthorsAlisa L. Gallant, Walter J. Sadinski, Jesslyn F. Brown, Gabriel B. Senay, Mark F. RothEffect of NOAA satellite orbital drift on AVHRR-derived phenological metrics
The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center routinely produces and distributes a remote sensing phenology (RSP) dataset derived from the Advanced Very High Resolution Radiometer (AVHRR) 1-km data compiled from a series of National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-11, −14, −16, −17, −18, and −19). Each NOAA satellite experienAuthorsLei Ji, Jesslyn F. BrownBuilding 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 that manifests in different forms, monitoring can be iAuthorsTsegaye Tadesse, Catherine Champagne, Brian D. Wardlow, Trevor A. Hadwen, Jesslyn F. Brown, Getachew B. Demisse, Yared A. Bayissa, Andrew M. DavidsonAssessing 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) and Vegetation Drought Response Index (VegDRI) and soAuthorsJason A. Otkin, Martha C. Anderson, Christopher Hain, Mark Svoboda, David Johnson, Richard Mueller, Tsegaye Tadesse, Brian D. Wardlow, Jesslyn F. BrownExploring 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 aspen green-up in the spring, snow moisture as measAuthorsJesslyn F. Brown, Gretchen MeierThe 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 remote sensing input data – to estimate cheatgrass beginnAuthorsStephen P. Boyte, Bruce K. Wylie, Donald J. Major, Jesslyn F. BrownNon-USGS Publications**
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
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Filter Total Items: 26
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.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...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...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...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...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... - Data
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Participated in these Eyes on Earth podcast episodes.
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