Jessica J Walker
Jessica started with the USGS as a Mendenhall Postdoctoral Researcher in 2014.
Jessica's broad research interests center on the analysis of landscape change using remote sensing data. Past projects have included tracking the vegetative trajectories of areas recovering from fire events, both in the semi-arid, high-elevation forests of Arizona and the boreal forests of Alaska. Additional recent work includes examining surface inundation patterns via Landsat and MODIS imagery as part of the PLACE project (Patterns in the Landscape - Analyses of Cause and Effect).
Education and Certifications
2012 - Ph.D. Geospatial and Environmental Analysis, Virginia Tech
2000 - M.A. Geography, University of Arizona, Tucson
1991 - B.A. Applied Math, Williams College
Science and Products
Investigation of Lidar Data Processing and Analysis in the Cloud
Lower technical and financial barriers have led to a proliferation of lidar point-cloud datasets acquired to support diverse USGS projects. The objective of this effort was to implement an open-source, cloud-based solution through USGS Cloud Hosting Solutions (CHS) that would address the needs of the growing USGS lidar community. We proposed to allow users to upload point-cloud datasets...
Patterns in the Landscape – Analyses of Cause and Effect
Understanding the rates and causes of land-use/land-cover (LULC) change helps answer questions about what, where, how, and why the Earth’s surface is changing. Land-surface change results from human activities or natural processes like floods, droughts, and wildfires, and many of these change processes are observable in satellite imagery. The growing historical catalog of satellite images allows...
Filter Total Items: 13
Data on winter surface water dynamics in California’s Central Valley using satellite remote sensing Data on winter surface water dynamics in California’s Central Valley using satellite remote sensing
These data support the following publication: "Assessing causes and consequences of winter surface water dynamics in California’s Central Valley using satellite remote sensing". The file contains information associated with surface water inundation, antecedent soil moisture, and atmospheric river storm characteristics for 2003-2023 in the three HUC-4 hydrologic sub-basins that make up...
MODIS Fire Radiative Power data compiled from active fires in Alaska, 2003-2022 MODIS Fire Radiative Power data compiled from active fires in Alaska, 2003-2022
This data release supports the publication: "Linking Fire Radiative Power to land cover, fire history, and environmental setting in Alaska, 2003 - 2022". The *.csv file contains data associated with fire radiative power (FRP) observations derived from 1-km MODIS data over Alaska from 2003 to 2022. The file allows users to replicate the statistical analysis of FRP values with vegetation...
Classification of crop types in central California from 2005 - 2020 Classification of crop types in central California from 2005 - 2020
This dataset is support materials for the publication "Crop type classification, trends, and patterns of central California agricultural fields from 2005 – 2020". This data release is comprised of two child datasets. The first dataset, 'Labeled_CropType_Points', is a shapefile that consists of randomly selected point locations in which crop types were verified using high resolution...
Wetlands in the state of Arizona Wetlands in the state of Arizona
We created a single map of surface water presence by intersecting water classes from available land cover products (National Wetland Inventory, Gap Analysis Program, National Land Cover Database, and Dynamic Surface Water Extent) across the U.S. state of Arizona. We derived classified samples for four wetland classes from the harmonized map: water, herbaceous wetlands, wooded wetlands...
DSWE_GEE v1.0.0 DSWE_GEE v1.0.0
Code for implementation of the Dynamic Surface Water Extent algorithm in Google Earth Engine. Multiple scripts allow the creation of single-scene or composited Dynamic Surface Water Extent (DSWE) images from Landsat and MODIS data. All code is written for use in the JavaScript API.
County-level maps of cropland surface water inundation measured from Landsat and MODIS County-level maps of cropland surface water inundation measured from Landsat and MODIS
This dataset represents a summary of potential cropland inundation for the state of California applying high-frequency surface water map composites derived from two satellite remote sensing platforms (Landsat and Moderate Resolution Imaging Spectroradiometer [MODIS]) with high-quality cropland maps generated by the California Department of Water Resources (DWR). Using Google Earth Engine...
Filter Total Items: 19
Assessing causes and consequences of winter surface water dynamics in California’s Central Valley using satellite remote sensing Assessing causes and consequences of winter surface water dynamics in California’s Central Valley using satellite remote sensing
California's Central Valley is increasingly vulnerable to winter floods. A comprehensive spatial baseline of flood extents is critical for inundation analyses that can enhance future flood predictions, but cloud cover has prevented the regular observation of surface water extents with optical satellite imagery. In this study, we leveraged the daily resolution of Moderate Resolution...
Authors
Christine M. Albano, Christopher E. Soulard, Blake A. Minor, Jessica J. Walker, Britt Windsor Smith, Eric K. Waller, Michael D. Bartles, Tom Corringham, Anthony T. O'Geen, Melissa M. Rohde, Anne Wein
Linking fire radiative power to land cover, fire history, and environmental setting in Alaska, 2003–2022 Linking fire radiative power to land cover, fire history, and environmental setting in Alaska, 2003–2022
Background Fire radiative power (FRP) shows promise as a diagnostic and predictive indicator of fire behavior and post-fire effects in Alaska, USA. Aims To investigate relationships between FRP, vegetation functional groups, and environmental settings in Alaska (2003–2022) under various fire history conditions. Methods We tested for distinctness of MODIS FRP distributions associated with...
Authors
Jessica J. Walker, Rachel A. Loehman, Britt Windsor Smith, Christopher E. Soulard
Phenology in higher education Phenology in higher education
Phenological data collection and analysis are well-suited to higher education settings, providing valuable opportunities for hands-on data collection, manipulation, and interpretation. Few subjects are more conducive or accessible for engaging diverse learners in meaningful and impactful science at such large scales and minimal cost. In this chapter, we provide a range of examples of how
Authors
Theresa Crimmins, Brittany S. Barker, Darby D. Bergl, Samantha Brewer, Kirsten de Beurs, Sarah Jones, Tammy Long, Emily Mohl, Emma Oschrin, Andrew D. Richardson, Tiffany A. Schriever, Jessica J. Walker, Tanisha M. Williams
The feasibility of using national-scale datasets for classifying wetlands in Arizona with machine learning The feasibility of using national-scale datasets for classifying wetlands in Arizona with machine learning
The advent of machine learning techniques has led to a proliferation of landscape classification products. These approaches can fill gaps in wetland inventories across the United States (U.S.) provided that large reference datasets are available to develop accurate models. In this study, we tested the feasibility of expediting the classification process by sourcing requisite training and...
Authors
Christopher E. Soulard, Jessica J. Walker, Britt Windsor Smith, Jason R. Kreitler
Crop type classification, trends, and patterns of central California agricultural fields from 2005 to 2020 Crop type classification, trends, and patterns of central California agricultural fields from 2005 to 2020
California produces many key agricultural products in the United States. Current geospatial agricultural datasets are limited in mapping accuracy, spatial context, or observation period. This study uses machine learning and high-resolution imagery to produce a time series of crop maps to assess crop type trends and patterns across central California from 2005 to 2020. National...
Authors
Britt Windsor Smith, Christopher E. Soulard, Jessica J. Walker
Using Landsat and MODIS satellite collections to examine extent, timing, and potential impacts of surface water inundation in California croplands☆ Using Landsat and MODIS satellite collections to examine extent, timing, and potential impacts of surface water inundation in California croplands☆
The state of California, United States of America produces many crop products that are both utilized domestically and exported throughout the world. With nearly 39,000 km2 of croplands, monitoring unintentional and intentional surface water inundation is important for water resource management and flood hazard readiness. We examine inundation dynamics in California croplands from 2003 to...
Authors
Britt Windsor Smith, Christopher E. Soulard, Jessica J. Walker, Anne Wein
DSWE_GEE v2.1.0 DSWE_GEE v2.1.0
Code for implementation of the Dynamic Surface Water Extent (DSWE) algorithm in Google Earth Engine. Multiple scripts allow for the generation of single-scene or composited (monthly) DSWE images from Landsat and MODIS imagery. All code is written for use in the Google Earth Engine JavaScript API.
DSWE_GEE v2.0.0 DSWE_GEE v2.0.0
Code for implementation of the Dynamic Surface Water Extent algorithm in Google Earth Engine. Multiple scripts allow the creation of single-scene or composited Dynamic Surface Water Extent (DSWE) images from Landsat (updated for Collection 2 imagery) and MODIS data. All code is written for use in Google Earth Engine in the JavaScript API.
DSWE_GEE v1.0.0 DSWE_GEE v1.0.0
Code for implementation of the Dynamic Surface Water Extent algorithm in Google Earth Engine. Multiple scripts allow the creation of single-scene or composited Dynamic Surface Water Extent (DSWE) images from Landsat and MODIS data. All code is written for use in the JavaScript API.
Science and Products
Investigation of Lidar Data Processing and Analysis in the Cloud
Lower technical and financial barriers have led to a proliferation of lidar point-cloud datasets acquired to support diverse USGS projects. The objective of this effort was to implement an open-source, cloud-based solution through USGS Cloud Hosting Solutions (CHS) that would address the needs of the growing USGS lidar community. We proposed to allow users to upload point-cloud datasets...
Patterns in the Landscape – Analyses of Cause and Effect
Understanding the rates and causes of land-use/land-cover (LULC) change helps answer questions about what, where, how, and why the Earth’s surface is changing. Land-surface change results from human activities or natural processes like floods, droughts, and wildfires, and many of these change processes are observable in satellite imagery. The growing historical catalog of satellite images allows...
Filter Total Items: 13
Data on winter surface water dynamics in California’s Central Valley using satellite remote sensing Data on winter surface water dynamics in California’s Central Valley using satellite remote sensing
These data support the following publication: "Assessing causes and consequences of winter surface water dynamics in California’s Central Valley using satellite remote sensing". The file contains information associated with surface water inundation, antecedent soil moisture, and atmospheric river storm characteristics for 2003-2023 in the three HUC-4 hydrologic sub-basins that make up...
MODIS Fire Radiative Power data compiled from active fires in Alaska, 2003-2022 MODIS Fire Radiative Power data compiled from active fires in Alaska, 2003-2022
This data release supports the publication: "Linking Fire Radiative Power to land cover, fire history, and environmental setting in Alaska, 2003 - 2022". The *.csv file contains data associated with fire radiative power (FRP) observations derived from 1-km MODIS data over Alaska from 2003 to 2022. The file allows users to replicate the statistical analysis of FRP values with vegetation...
Classification of crop types in central California from 2005 - 2020 Classification of crop types in central California from 2005 - 2020
This dataset is support materials for the publication "Crop type classification, trends, and patterns of central California agricultural fields from 2005 – 2020". This data release is comprised of two child datasets. The first dataset, 'Labeled_CropType_Points', is a shapefile that consists of randomly selected point locations in which crop types were verified using high resolution...
Wetlands in the state of Arizona Wetlands in the state of Arizona
We created a single map of surface water presence by intersecting water classes from available land cover products (National Wetland Inventory, Gap Analysis Program, National Land Cover Database, and Dynamic Surface Water Extent) across the U.S. state of Arizona. We derived classified samples for four wetland classes from the harmonized map: water, herbaceous wetlands, wooded wetlands...
DSWE_GEE v1.0.0 DSWE_GEE v1.0.0
Code for implementation of the Dynamic Surface Water Extent algorithm in Google Earth Engine. Multiple scripts allow the creation of single-scene or composited Dynamic Surface Water Extent (DSWE) images from Landsat and MODIS data. All code is written for use in the JavaScript API.
County-level maps of cropland surface water inundation measured from Landsat and MODIS County-level maps of cropland surface water inundation measured from Landsat and MODIS
This dataset represents a summary of potential cropland inundation for the state of California applying high-frequency surface water map composites derived from two satellite remote sensing platforms (Landsat and Moderate Resolution Imaging Spectroradiometer [MODIS]) with high-quality cropland maps generated by the California Department of Water Resources (DWR). Using Google Earth Engine...
Filter Total Items: 19
Assessing causes and consequences of winter surface water dynamics in California’s Central Valley using satellite remote sensing Assessing causes and consequences of winter surface water dynamics in California’s Central Valley using satellite remote sensing
California's Central Valley is increasingly vulnerable to winter floods. A comprehensive spatial baseline of flood extents is critical for inundation analyses that can enhance future flood predictions, but cloud cover has prevented the regular observation of surface water extents with optical satellite imagery. In this study, we leveraged the daily resolution of Moderate Resolution...
Authors
Christine M. Albano, Christopher E. Soulard, Blake A. Minor, Jessica J. Walker, Britt Windsor Smith, Eric K. Waller, Michael D. Bartles, Tom Corringham, Anthony T. O'Geen, Melissa M. Rohde, Anne Wein
Linking fire radiative power to land cover, fire history, and environmental setting in Alaska, 2003–2022 Linking fire radiative power to land cover, fire history, and environmental setting in Alaska, 2003–2022
Background Fire radiative power (FRP) shows promise as a diagnostic and predictive indicator of fire behavior and post-fire effects in Alaska, USA. Aims To investigate relationships between FRP, vegetation functional groups, and environmental settings in Alaska (2003–2022) under various fire history conditions. Methods We tested for distinctness of MODIS FRP distributions associated with...
Authors
Jessica J. Walker, Rachel A. Loehman, Britt Windsor Smith, Christopher E. Soulard
Phenology in higher education Phenology in higher education
Phenological data collection and analysis are well-suited to higher education settings, providing valuable opportunities for hands-on data collection, manipulation, and interpretation. Few subjects are more conducive or accessible for engaging diverse learners in meaningful and impactful science at such large scales and minimal cost. In this chapter, we provide a range of examples of how
Authors
Theresa Crimmins, Brittany S. Barker, Darby D. Bergl, Samantha Brewer, Kirsten de Beurs, Sarah Jones, Tammy Long, Emily Mohl, Emma Oschrin, Andrew D. Richardson, Tiffany A. Schriever, Jessica J. Walker, Tanisha M. Williams
The feasibility of using national-scale datasets for classifying wetlands in Arizona with machine learning The feasibility of using national-scale datasets for classifying wetlands in Arizona with machine learning
The advent of machine learning techniques has led to a proliferation of landscape classification products. These approaches can fill gaps in wetland inventories across the United States (U.S.) provided that large reference datasets are available to develop accurate models. In this study, we tested the feasibility of expediting the classification process by sourcing requisite training and...
Authors
Christopher E. Soulard, Jessica J. Walker, Britt Windsor Smith, Jason R. Kreitler
Crop type classification, trends, and patterns of central California agricultural fields from 2005 to 2020 Crop type classification, trends, and patterns of central California agricultural fields from 2005 to 2020
California produces many key agricultural products in the United States. Current geospatial agricultural datasets are limited in mapping accuracy, spatial context, or observation period. This study uses machine learning and high-resolution imagery to produce a time series of crop maps to assess crop type trends and patterns across central California from 2005 to 2020. National...
Authors
Britt Windsor Smith, Christopher E. Soulard, Jessica J. Walker
Using Landsat and MODIS satellite collections to examine extent, timing, and potential impacts of surface water inundation in California croplands☆ Using Landsat and MODIS satellite collections to examine extent, timing, and potential impacts of surface water inundation in California croplands☆
The state of California, United States of America produces many crop products that are both utilized domestically and exported throughout the world. With nearly 39,000 km2 of croplands, monitoring unintentional and intentional surface water inundation is important for water resource management and flood hazard readiness. We examine inundation dynamics in California croplands from 2003 to...
Authors
Britt Windsor Smith, Christopher E. Soulard, Jessica J. Walker, Anne Wein
DSWE_GEE v2.1.0 DSWE_GEE v2.1.0
Code for implementation of the Dynamic Surface Water Extent (DSWE) algorithm in Google Earth Engine. Multiple scripts allow for the generation of single-scene or composited (monthly) DSWE images from Landsat and MODIS imagery. All code is written for use in the Google Earth Engine JavaScript API.
DSWE_GEE v2.0.0 DSWE_GEE v2.0.0
Code for implementation of the Dynamic Surface Water Extent algorithm in Google Earth Engine. Multiple scripts allow the creation of single-scene or composited Dynamic Surface Water Extent (DSWE) images from Landsat (updated for Collection 2 imagery) and MODIS data. All code is written for use in Google Earth Engine in the JavaScript API.
DSWE_GEE v1.0.0 DSWE_GEE v1.0.0
Code for implementation of the Dynamic Surface Water Extent algorithm in Google Earth Engine. Multiple scripts allow the creation of single-scene or composited Dynamic Surface Water Extent (DSWE) images from Landsat and MODIS data. All code is written for use in the JavaScript API.