| Title |
A Global Database of Gas Fluxes from Soils after Rewetting or
Thawing, Version 1.0.
|
| Description |
This database contains information compiled from published studies
on gas flux from soil following rewetting or thawing. The resulting
database includes 222 field and laboratory observations focused on
rewetting of dry soils, and 116 field laboratory observations
focused on thawing of frozen soils studies conducted from 1956 to
2010. Fluxes of carbon dioxide, methane, nitrous oxide, nitrogen
oxide, and ammonia (CO2, CH4, N2O, NO and NH3) were compiled from
the literature and the flux rates were normalized for ease of
comparison. Field observations of gas flux following rewetting of
dry soils include events caused by natural rainfall, simulated
rainfall in natural ecosystems, and irrigation in agricultural
lands. Similarly, thawing of frozen soils include field observations
of natural thawing, simulated freezing-thawing events (i.e., thawing
of simulated frozen soil by snow removal), and thawing of seasonal
ice in temperate and high latitude regions (Kim et al., 2012).
Reported parameters include experiment type, location, site type,
vegetation, climate, soil properties, rainfall, soil moisture, soil
gas flux after wetting and thawing, peak soil gas flux properties,
and the corresponding study references. There is one comma-delimited
data file.
|
| Theme/Category |
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| Period Coverage |
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| Spatial Coverage |
|
| Keywords |
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| Landing Page |
|
| Access URL |
https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1078
|
| Creator |
|
| Publisher |
|
| Released Date |
2012-04-16 |
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
|
| Distribution Format |
|
| Publication |
Kim, D.G., Vargas, R., Bond-Lamberty, B. and Turetsky, M.R., 2012.
Effects of soil rewetting and thawing on soil gas fluxes: a review
of current literature and suggestions for future research.
Biogeosciences, 9(7), p.2459.
|
| Software to view the data |
|
| Title |
CMS: Soil CO2 Efflux and Properties, Site Vegetation Measurements,
Mexico, 2011-2012. ORNL DAAC, Oak Ridge, Tennessee, USA.
|
| Description |
This data set provides the results of (1) monthly measurements of
soil CO2 efflux, volumetric water content, and temperature, and (2)
seasonal measurements of soil (porosity, bulk density, nitrogen (N)
and carbon (C) content) and vegetation (leaf area index (LAI),
litter and fine root biomass) properties in a water-limited
ecosystem in Baja California, Mexico. Measurements and samples were
collected from August 2011 to August 2012.
|
| Theme/Category |
|
| Period Coverage |
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| Spatial Coverage |
|
| Keywords |
|
| Landing Page |
|
| Access URL |
https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1298
|
| Creator |
|
| Publisher |
|
| Released Date |
2015-11-30 |
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
|
| Distribution Format |
|
| Publication |
Leon, E., Vargas, R., Bullock, S., Lopez, E., Panosso, A.R. and La
Scala Jr, N., 2014. Hot spots, hot moments, and spatio-temporal
controls on soil CO2 efflux in a water-limited ecosystem. Soil
Biology and Biochemistry, 77, pp.12-21.
|
| Software to view the data |
|
| Title |
CMS: Evapotranspiration and Meteorology, Water-Limited Shrublands,
Mexico, 2008-2010. ORNL DAAC, Oak Ridge, Tennessee, USA.
|
| Description |
This data set provides daily average observations for
evapotranspiration (measured and gap-filled), precipitation, net
radiation, soil water content, air temperature, vapor pressure
deficit, and normalized vegetation index (NDVI) from two
water-limited shrubland sites for years 2008-2010. Both sites are
located in the northwest part of Mexico and are part of the MexFlux
network.
|
| Theme/Category |
|
| Period Coverage |
|
| Spatial Coverage |
|
| Keywords |
|
| Landing Page |
|
| Access URL |
https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1309
|
| Creator |
|
| Publisher |
|
| Released Date |
2016-03-21 |
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
|
| Distribution Format |
|
| Publication |
Villarreal, S., Vargas, R., Yepez, E.A., Acosta, J.S., Castro, A.,
Escoto‐Rodriguez, M., Lopez, E., Martínez‐Osuna, J., Rodriguez,
J.C., Smith, S.V. and Vivoni, E.R., 2016. Contrasting precipitation
seasonality influences evapotranspiration dynamics in water‐limited
shrublands. Journal of Geophysical Research: Biogeosciences, 121(2),
pp.494-508.
|
| Software to view the data |
|
| Title |
CMS: MODIS GPP, fPAR, and SST, and ENSO Index, Baja California,
Mexico, 2000-2013. ORNL DAAC, Oak Ridge, Tennessee, USA.
|
| Description |
This data set provides data for MODIS-derived (1) gross primary
productivity (GPP) for the years 2000-2010, (2) fraction of
photosynthetically active radiation (fPAR) for the years 2003-2013,
(3) sea surface temperature (SST) for the years 2003-2013, and (4)
the NOAA-source Multivariate ENSO Index (MEI) data for the years
2003-2013 (as a measure of the El Nino/Southern Oscillation). The
study areas were three transects on the Baja California Peninsula,
Mexico, and the adjacent Pacific Ocean. The terrestrial transects,
in order from North to South, West to East included Punta Colonet
(three sites-PC1, PC2, PC3), Punta Abreojos (two sites-PA1, PA2),
and Magdalena Bay (three sites-MB1, MB2, MB3).
|
| Theme/Category |
|
| Period Coverage |
|
| Spatial Coverage |
|
| Keywords |
|
| Landing Page |
|
| Access URL |
https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1310
|
| Creator |
|
| Publisher |
|
| Released Date |
2016-03-21 |
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
|
| Distribution Format |
|
| Publication |
|
| Software to view the data |
|
| Title |
Soil Organic Carbon Stock Estimates with Uncertainty across Latin
America. ORNL DAAC, Oak Ridge, Tennessee, USA.
|
| Description |
This dataset provides 5 x 5 km gridded estimates of soil organic
carbon (SOC) across Latin America that were derived from existing
point soil characterization data and compiled environmental
prediction factors for SOC. This dataset is representative for the
period between 1980 to 2000s corresponding with the highest density
of observations available in the WoSIS system and the covariates
used as prediction factors for soil organic carbon across Latin
America. SOC stocks (kg/m2) were estimated for the SOC and bulk
density point measurements and a spatially explicit measure of the
SOC estimation error was also calculated. A modeling ensemble, using
a linear combination of five statistical methods (regression
Kriging, random forest, kernel weighted nearest neighbors, partial
least squared regression and support vector machines) was applied to
the SOC stock data at (1) country-specific and (2) regional scales
to develop gridded SOC estimates (kg/m2) for all of Latin America.
Uncertainty estimates are provided for the two model predictions
based on independent model residuals and their full conditional
response to the SOC prediction factors.
|
| Theme/Category |
|
| Period Coverage |
|
| Spatial Coverage |
|
| Keywords |
|
| Landing Page |
|
| Access URL |
https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1615
|
| Creator |
|
| Publisher |
|
| Released Date |
2019-03-07 |
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
|
| Distribution Format |
|
| Publication |
|
| Software to view the data |
|
| Title |
CO2_Efflux_Eden |
| Description |
Dataset of soil CO2 efflux from a seasonally dry tropical forest.
Dataset includes information from January 2006 to April 2008. Soil
CO2 efflux was measured using the "gradient method" where
soil CO2 concentrations were measured in conjunction with soil
temperature and soil moisture. The study site was hit by a hurricane
on October 2005, thus soil CO2 efflux rates are high during the
beginning of 2006. For more details about the study site and
methodology please see:
|
| Theme/Category |
|
| Period Coverage |
|
| Spatial Coverage |
|
| Keywords |
|
| Landing Page |
|
| Access URL |
https://figshare.com/articles/CO2_Efflux_Eden/8309567/1
|
| Creator |
|
| Publisher |
|
| Released Date |
2019-06-26 |
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
|
| Distribution Format |
|
| Publication |
Vargas, R., 2012. How a hurricane disturbance influences extreme CO2
fluxes and variance in a tropical forest. Environmental Research
Letters, 7(3), p.035704.
|
| Software to view the data |
|
| Title |
Annual soil moisture predictions across conterminous United States
using remote sensing and terrain analysis across 1 km grids
(1991-2016)
|
| Description |
We provide 26 annual soil moisture predictions across conterminous
United States for the years 1991-2016. These predictions are
provided in raster files with a geographical (lat, long) projection
system and a spatial resolution of 1 x 1 km grids (folder:
soil_moisture_annual_grids_1991_2016). These raster files were
populated with soil moisture data based on multiple kernel based
machine learning models for coupling hydrologically meaningful
terrain parameters (the explanatory variables) with soil moisture
microwave records (the response variable) from the European Space
Agency Climate Change Initiative. We provide a raster stack with the
annual training data from satellite soil moisture estimates (file:
annual_means_of _ESA_CCI_soil_moiture_1991_2016.tif) and the
explanatory variables (terrain) calculated on SAGA GIS (System of
Automated Geoscientific Analysis) using digital terrain analysis
(folder: explanatory_variables_dem). The explained variance for all
models-years was >70% (10-fold cross-validation). The 1 km soil
moisture grids (compared to the original satellite soil moisture
estimates) had higher correlations with field soil moisture
observations from the North American Soil Moisture Database (n=668
locations with available data between 1991-2013; 0-5 cm depth) than
soil moisture microwave records.
|
| Theme/Category |
|
| Period Coverage |
|
| Spatial Coverage |
|
| Keywords |
|
| Landing Page |
|
| Access URL |
https://www.hydroshare.org/resource/b8f6eae9d89241cf8b5904033460af61/
|
| Creator |
|
| Publisher |
|
| Released Date |
2019-06-20 |
| Updated Date |
2020-03-23 |
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
|
| Distribution Format |
|
| Publication |
Guevara, M. and Vargas, R., 2019. Downscaling satellite soil
moisture using geomorphometry and machine learning. PloS one, 14(9).
|
| Software to view the data |
|
| Title |
Ecosystem Functional Type Distribution Map for Mexico, 2001-2014
|
| Description |
This dataset provides a map of the distribution of ecosystem
functional types (EFTs) at 0.05 degree resolution across Mexico for
2001 to 2014. EFTs are groupings of ecosystems based on their
similar ecosystem functioning that are used to represent the spatial
patterns and temporal variability of key ecosystem functional traits
without prior knowledge of vegetation type or canopy architecture.
Sixty-four EFTs were derived from the metrics of a 2001-2014
time-series of satellite images of the Enhanced Vegetation Index
(EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS)
product MOD13C2. EFT diversity was calculated as the modal (most
repeated) EFT for each pixel.
|
| Theme/Category |
|
| Period Coverage |
|
| Spatial Coverage |
|
| Keywords |
|
| Landing Page |
|
| Access URL |
https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1693
|
| Creator |
|
| Publisher |
|
| Released Date |
2019-08-15 |
| Updated Date |
2019-09-10 |
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
|
| Distribution Format |
|
| Publication |
Villarreal, S., Guevara, M., Alcaraz‐Segura, D. and Vargas, R.,
2019. Optimizing an environmental observatory network design using
publicly available data. Journal of Geophysical Research:
Biogeosciences, 124(7), pp.1812-1826.
|
| Software to view the data |
|
| Title |
Global Gridded 1-km Annual Soil Respiration and Uncertainty Derived
from SRDB V3
|
| Description |
This dataset provides six global gridded products at 1-km resolution
of predicted annual soil respiration (Rs) and associated
uncertainty, maps of the lower and upper quartiles of the prediction
distributions, and two derived annual heterotrophic respiration (Rh)
maps. A machine learning approach was used to derive the predicted
Rs and uncertainty data using a quantile regression forest (QRF)
algorithm trained with observations from the global Soil Respiration
Database (SRDB) version 3 spanning from 1961 to 2011. The two Rh
maps were derived from the predicted Rs with two different empirical
equations. These products were produced to support carbon cycle
research at local- to global-scales, and highlight the immense
spatial variability of soil respiration and our ability to predict
it across the globe.
|
| Theme/Category |
|
| Period Coverage |
|
| Spatial Coverage |
|
| Keywords |
|
| Landing Page |
|
| Access URL |
https://doi.org/10.3334/ORNLDAAC/1736
|
| Creator |
|
| Publisher |
|
| Released Date |
|
| Updated Date |
2020-01-09 |
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
|
| Distribution Format |
|
| Publication |
Warner, D.L., Bond‐Lamberty, B., Jian, J., Stell, E. and Vargas, R.,
2019. Spatial predictions and associated uncertainty of annual soil
respiration at the global scale. Global Biogeochemical Cycles.
|
| Software to view the data |
|
| Title |
Oklahoma Soil Moisture Predictions |
| Description |
Monthly soil moisture predictions over a region of interest centered
on Oklahoma and surrounded areas from January 2000 to September
2012. Data were acquired from the European Space Agency Climate
Change Initiative soil moisture product version 4.5, 0.25-degrees
spatial resolution. The modeled product aims to fill soil moisture
spatial gaps from the original product over the region of Interest.
Soil moisture values were calculated based on three methods, e.g.
Ordinary Kriging, Regression Kriging and Generalized Linear Model.
Reference monthly soil moisture layers were generated based on daily
soil moisture estimates over each 0.25-degrees pixel in the region
of interest. Three different sampling approaches were considered to
model soil moisture estimates, using 100% of available data from the
original satellite data, 75% and 50% of available soil moisture
estimates respectively. Data were randomly removed to simulate
different scenarios of gap presence in the original ESA CCI product.
Soil Moisture values were validated by means of 10-fold cross
validation and ground-truth validation with records from the North
American Soil Moisture Data Base
|
| Theme/Category |
|
| Period Coverage |
|
| Spatial Coverage |
|
| Keywords |
|
| Landing Page |
|
| Access URL |
https://www.hydroshare.org/resource/f0091cf90bcc4487bf401ca19783d1eb/
|
| Creator |
|
| Publisher |
|
| Released Date |
2020-02-07 |
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
|
| Distribution Format |
|
| Publication |
Llamas, R.M., Guevara, M., Rorabaugh, D., Taufer, M. and Vargas, R.,
2020. Spatial Gap-Filling of ESA CCI Satellite-Derived Soil Moisture
Based on Geostatistical Techniques and Multiple Regression. Remote
Sensing, 12(4), p.665.
|
| Software to view the data |
|
| Title |
Soil respiration dataset from a temperate mix forest (James San
Jacinto Mountain Reserve)
|
| Description |
Dataset of soil CO2 efflux from a temperate mix forest. Includes
information from January 2006 to December 2008. Soil CO2 efflux was
measured using the "gradient method" where soil CO2
concentrations were measured in conjunction with soil temperature
and soil moisture. The study was conducted at the University of
California James San Jacinto Mountain Reserve, a UC Natural Reserve
System field station. The Reserve is a mixed conifer-oak forest at
1640m.a.s.l. located in the San Jacinto Mountains, CA, USA
(33048'30''N, 116046'40''W).
|
| Theme/Category |
|
| Period Coverage |
|
| Spatial Coverage |
|
| Keywords |
|
| Landing Page |
|
| Access URL |
https://doi.org/10.6084/m9.figshare.11739459
|
| Creator |
|
| Publisher |
|
| Released Date |
2020-05-03 |
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
|
| Distribution Format |
|
| Publication |
Vargas, R., Detto, M., Baldocchi, D.D. and Allen, M.F., 2010.
Multiscale analysis of temporal variability of soil CO2 production
as influenced by weather and vegetation. Global Change Biology,
16(5), pp.1589-1605.
|
| Software to view the data |
|
| Title |
Global Gridded Dataset of Crop-specific Green and Blue Water
Requirements
|
| Description |
Global gridded (5 arcminute) dataset of monthly green and blue crop
water requirements for 5 major crops and annual green and blue crop
water requirements for 23 crops and 3 crop groups for the average
year 2000 (i.e. averaged among 1998–2002 yearly simulations) and the
year 2016.
|
| Theme/Category |
Environmental |
| Period Coverage |
2000, 2016 |
| Spatial Coverage |
Global |
| Keywords |
|
| Landing Page |
https://springernature.figshare.com/collections/Global_Gridded_Dataset_of_Crop-specific_Green_and_Blue_Water_Requirements/4893084
|
| Access URL |
|
| Creator |
|
| Publisher |
|
| Released Date |
|
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
|
| Distribution Format |
Net-CDF |
| Publication |
https://doi.org/10.1038/s41597-020-00612-0 |
| Software to view the data |
|
| Project |
| Title |
WATNEEDS |
| Description |
Project to build global process-based crop model (WATNEEDS)
that solves a daily vertical soil water balance in order to
generate global gridded estimates of green and blue crop water
requirements.
|
| Principal Investigator |
Dr. Kyle F. Davis |
|
| Title |
Mexican ATI Request-Response Dataset |
| Description |
This dataset records individual access to information (ATI) requests
and responses for Mexico's federal (national) ATI system. The
text of each individual request (along with OCR'd text of each
request attachment) is included along with a variety of indicators
of each government response. LDA-derived topics have been estimated
and assigned to reach request text. Additional request information
is then included for a unique request identifier, the date and time
of each request, the requester's location (country, state,
municipality and postal code), and the requester's designated
federal Mexican government agency or unit. Additional response data
include the government agency or unit's official response
decision, the timing (date and time) of that decision, and the
elapsed time between request and response.
|
| Theme/Category |
Social Science |
| Period Coverage |
06/2003-06/2020 |
| Spatial Coverage |
Mexico, with unique identifiers for requester municipality, state,
country, and postal code
|
| Keywords |
Mexico, Access to Information, Accountability, Transparency |
| Landing Page |
|
| Access URL |
|
| Creator |
|
| Publisher |
|
| Released Date |
|
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Non-public (Dataset is not available to members of the public)
|
| Version |
2 |
| Distribution Format |
CSV |
| Publication |
Berliner, D., B.E. Bagozzi, and B. Palmer-Rubin. 2018. "What
Information Do Citizens Want? Evidence from One Million Public
Information Requests in Mexico.'' World Development. 109:
222-235.
|
| Software to view the data |
|
| Project |
| Title |
Access to Information in Mexico |
| Description |
This project leverages text-as-data tools to better understand
the nature and drivers of information requests in Mexico.
Extensions to this analysis also consider the nature and
drivers of the Mexican government's responsiveness to
these requests, as well as the influences of politics and the
media on these processes.
|
| Principal Investigator |
Dr. Benjamin E. Bagozzi
|
|
| Title |
Mexico ATI-HRA Dataset |
| Description |
This dataset records counts of human rights abuse-related ATI
requests for each Mexican municipality-month, 06/2003-06/2018. Human
rights-abuse related ATI requests were generated via a supervised
machine learning approach at the individual request level, before
being aggregated to municipality-month counts. Separate supervised
machine coded ATI-based human abuse counts are then recorded for
state perpetrated human rights abuse, non-state perpetrated human
rights abuse, unknown perpetrator human rights abuse, human rights
abuse incidents, and human rights abuses by any perpetrator. For the
municipality-month dataset, the counts of human rights abuse-related
requests are paired with additional municipality-month variables
recording total homicide rates, counts of human rights abuse events
as derived from the ICEWS event dataset, counts of human rights
abuse events as derived from the GED dataset, distance to the US
border, economic marginalization, population, internet access, and
total ATI requests, as well as unique identifiers for each
municipality and time-point.
|
| Theme/Category |
Social Science |
| Period Coverage |
06/2003-06/2018 |
| Spatial Coverage |
Mexico, with unique identifiers for municipality and state |
| Keywords |
Mexico, Access to Information, Accountability, Human Rights Abuse
|
| Landing Page |
|
| Access URL |
|
| Creator |
|
| Publisher |
|
| Released Date |
|
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
1 |
| Distribution Format |
CSV |
| Publication |
Ellington, S.A.V., B.E. Bagozzi, D. Berliner, B. Palmer-Rubin, and
A. Erlich. 2021. "Measuring Human Rights Abuse from Access to
Information Requests" Working Paper.
|
| Software to view the data |
|
| Project |
| Title |
Measuring Human Rights Abuse from Access to Information
Requests
|
| Description |
This project uses supervised machine learning to measure fine
grained citizen concerns over human rights abuses in Mexico
via access to information request texts. The resultant data
are internally and externally validated in several manners.
The current dataset then aggregates these supervised-coded
requests to the municipality-month level, and merges these
data with several additional covariates.
|
| Principal Investigator |
Dr. Benjamin Bagozzi
|
| Funding Agency |
GUR-Bagozzi |
|
| Title |
Do or Die Corpus and Metadata |
| Description |
Issues 1-10 of Do or Die, a set of British radical environmentalist
magazine ('zine) associated with the United Kingdom's
Earth First! movement. The 10-issue 'zine was published
semi-annually from 1992 to 2003. Original PDFs of each 'zine
were collected from online sources. Multiple formats and versions of
each 'zine are included, including PDF images, OCR'd pages
of each PDF 'zine (in .rtf and .txt format) as well as scraped
webpage versions of some individual stories, where applicable.
Additional data from these texts in terms of UK environmental group
names listed within each issue's contact section, quantities
derived from topic models of these texts, and network information is
also included.
|
| Theme/Category |
Social Science |
| Period Coverage |
1992-2003 |
| Spatial Coverage |
United Kingdom |
| Keywords |
Environmentalism, Text-as-Data, Social Movement, Direct Action
|
| Landing Page |
|
| Access URL |
https://doi.org/10.7910/DVN/PNK7AB
|
| Creator |
|
| Publisher |
|
| Released Date |
|
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
1 |
| Distribution Format |
PDF, TXT, RTF, CSV |
| Publication |
Almquist, Zack W. and Benjamin E. Bagozzi. 2019. "Using
Radical Environmentalist Texts to Uncover Network Structure and
Network Features" Sociological Methods & Research. 49(4):
905-960.
|
| Software to view the data |
|
| Project |
| Title |
Radical Environmentalist Project |
| Description |
This project seeks to better understand radical
environmentalist movements through text-as-data and network
methods.
|
| Principal Investigator |
Dr. Benjamin Bagozzi
|
|
| Title |
Topical Attention Bias Measures of US State Department Human Rights
Reports
|
| Description |
A corpus of 6,298 State Department Country Reports on Human Rights
Practices (1977-2012) was topic modeled to recover a plausible set
of country-year topics pertaining to the US State Department's
monitoring of countries' human rights practices. Recovered
topics are conditional on nation-states actual human rights
performance and additional covariates, and include topics such as
killings and disappearances, freedoms of expression and movement,
and labor rights, among others. The posterior probability of topical
assignment for each estimated topic is recorded for each
country-year, 1977-2012. These quantities are argued to capture time
varying (topical) attention bias in US State Department human rights
reporting in relation to individual countries.
|
| Theme/Category |
Social Science |
| Period Coverage |
1977-2012 |
| Spatial Coverage |
Global (at the country-level) |
| Keywords |
State Department, Human Rights, Monitoring, Repression, Topic
Modeling
|
| Landing Page |
|
| Access URL |
https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/W1QQTP
|
| Creator |
|
| Publisher |
|
| Released Date |
|
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
1 |
| Distribution Format |
CSV |
| Publication |
Bagozzi, B.E. and D. Berliner. 2018. "The Politics of Scrutiny
in Human Rights Monitoring: Evidence from Structural Topic Models of
US State Department Human Rights Reports." Political Science
Research and Methods. 6(4): 661-677.
|
| Software to view the data |
|
| Project |
| Title |
Topical Attention Bias in Human Rights Reporting |
| Description |
This project seeks to better understand US State Department
human rights monitoring with text-as-data tools. Patterns of
human rights reporting are considered over time, and in
relation to a variety of US-specific and cross-national
covariates.
|
| Principal Investigator |
Dr. Benjamin Bagozzi
|
|
| Title |
UNFCCC Climate Speech Data |
| Description |
This text-as-data dataset includes nation-states' high-level
segment speeches from the 16th to 26th United Nations Framework
Convention on Climate Change (UNFCCC) Conference of the Parties
(COPs), 10-years of speeches in total. Transcripts of all available
speeches were collected in PDF form from UN websites and then
converted to machine readable text and machine translated to
English. All resultant texts are stored as individual .txt files and
in combined .csv files. For network modeling, co-occurrences related
to each speech's co-mentions of nation-states are also
extracted and recorded as .csv files. Subsets of these speeches have
also been topic modeled.
|
| Theme/Category |
Social Science |
| Period Coverage |
2010-2020 |
| Spatial Coverage |
Global (at the country-level) |
| Keywords |
Climate Change, International Relations, International Cooperation,
UNFCCC
|
| Landing Page |
|
| Access URL |
|
| Creator |
|
| Publisher |
|
| Released Date |
|
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Non-public (Dataset is not available to members of the public)
|
| Version |
2 |
| Distribution Format |
CSV, PDF, TXT |
| Publication |
Bagozzi, B. E. 2015. "The Multifaceted Nature of Global
Climate Change Negotiations." Review of International
Organizations. 10(4): 439-464.
|
| Software to view the data |
|
| Project |
| Title |
UNFCCC Speeches and Networks |
| Description |
This project seeks to use text-as-data and network methods to
understand patterns of country-level negotiation over climate
change, both at the UNFCCC and more generally. Core dimensions
include understanding the scope and dimensionality of
negotiating themes, negotiation networks, and how these
features change over time.
|
| Principal Investigator |
Dr. Benjamin Bagozzi
|
|
| Title |
DEOS Real-Time and Historical Environmental Data |
| Description |
DEOS operates and maintains over 80 environmental monitoring
platforms and brings in data for the following variables: air
temperature, dew point, relative humidity, wind speed, wind
direction, wind gust, heat index, wind chill, atmospheric pressure,
solar radiation, rainfall, water levels, and snow depth. (some
variables are season observations only)
|
| Theme/Category |
Environmental |
| Period Coverage |
2009 to present (some stations are earlier) |
| Spatial Coverage |
Delaware, Chester County PA |
| Keywords |
environment, monitoring, climate, atmosphere, temperature, rain,
wind
|
| Landing Page |
http://deos.udel.edu |
| Access URL |
|
| Creator |
|
| Publisher |
|
| Released Date |
|
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
|
| Distribution Format |
|
| Publication |
|
| Software to view the data |
|
| Project |
| Title |
Delaware Environmental Observing System |
| Description |
For over 15 years, DEOS has been providing real-time and
archived daily and monthly environmental conditions for
Delaware and the surrounding region. Today, DEOS operates and
maintains over 80 environmental monitoring platforms and
brings in data from over 200 additional environmental
monitoring platforms throughout our region.
|
| Principal Investigator |
Dr. Kevin Brinson |
|
| Title |
Discourses of Fame and Celebrity |
| Description |
a systematic sample of roughly 10k N-grams (~100k words) pulled from
digital newspaper databases, along with coded identification terms,
that track how fame and celebrity were described over time and space
within the U.S., 1760-1900.
|
| Theme/Category |
Language/Discourse (though geospatial data is included) |
| Period Coverage |
1760-1900 |
| Spatial Coverage |
modern U.S. |
| Keywords |
Fame; Celebrity; Discourse; Text Mining; History; Periodicals |
| Landing Page |
N/A |
| Access URL |
|
| Creator |
|
| Publisher |
|
| Released Date |
|
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Non-public (Dataset is not available to members of the public)
|
| Version |
|
| Distribution Format |
.xlsx |
| Publication |
|
| Software to view the data |
|
| Project |
| Title |
Library of Congress Chronicling America; Readex/Am Antiquarian
Society America's Historical Newspapers Series I
|
| Description |
An effort to track the evolution of discourses of fame and
celebrity, supporting a monograph in-process on the history of
Halls of Fame.
|
| Principal Investigator |
Dr. Kenneth Cohen |
|
| Title |
CORSIKA Simulations of Cosmic-Ray Air Showers |
| Description |
We have various subsets of CORSIKA 7 simulations of cosmic-ray air
showers, covering a range of energies and arrival direction. As we
are continuing to add simulations, ask for details.
|
| Theme/Category |
Astroparticle Physics |
| Period Coverage |
none |
| Spatial Coverage |
Locations of the IceCube Neutrino Observatory (South Pole) and the
Pierre Auger Observatory (Argentina)
|
| Keywords |
|
| Landing Page |
|
| Access URL |
|
| Creator |
|
| Publisher |
|
| Released Date |
|
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Can be made available upon individual requests. |
| Version |
|
| Distribution Format |
proprietary CORSIKA format |
| Publication |
|
| Software to view the data |
|
| Project |
| Title |
Various projects: IceCube, Pierre Auger Observatory, and
others
|
| Description |
We use CORSIKA to simulate expected cosmic-ray signals in
specialzed observatories.
|
| Principal Investigator |
Dr. Frank Schroeder
|
|
| Title |
Fall landbird migration stopover distribution maps for the
northeastern U.S.
|
| Description |
"The national network of weather surveillance radars (NEXRAD)
detects birds in flight and has proven to be a useful remote-sensing
tool for ornithological study. These maps were derived from data
collected during Fall 2008 to 2014 by 16 NEXRAD and four terminal
Doppler weather surveillance radars (TDWR) in the northeastern U.S.
to map and study the spatial distribution of landbirds shortly after
they leave daytime stopover sites to embark on nocturnal migratory
flights. NEXRAD observations of Vertically-Integrated Reflectivity
(VIR) - an estimate of the total amount of reflected cross-sectional
area of birds per hectare (i.e., bird density) from 0 to 1750 m
above the ground in units of cm2 ha-1 - were summarized across time
and aggregated to a spatial resolution of 1 km x 1 km. Observed data
(i.e., reflectivity data as measured by radars) have been filtered
to exclude areas of partial radar beam blockage, ground clutter
(i.e., non-biological reflectivity from objects on the ground) and
where radars were unable to detect birds for more than 25% of
sampling nights. This dataset represents input and output variables
of Boosted Generalized Additive Models (BGAM) developed to predict
bird stopover use throughout the Northeast and Mid-Atlantic U.S.
Predicted bird densities increased with increasing hardwood cover at
multiple scales and with vegetation productivity. Contrastingly,
predicted bird densities decreased with increasing agricultural,
emergent marsh and coniferous land cover, but did not change with
fraction of urban cover. Bird stopover density increased closer to
bright areas and the Atlantic coast. Moreover, interactive effects
indicated that migrants were extra concentrated in wooded areas that
were both brightly lit and near the Atlantic coast. Large areas of
predicted regionally important stopover sites were located along the
coastlines of Maine, Long Island Sound, New Jersey, the lower
Delmarva Peninsula, within the Adirondack Mountains, Catskill
Mountains, and eastern Virginia. Migrant densities peaked along the
Adirondack Mountains early in September, and along the Atlantic
coast in late September with the passage of Neotropical migrants.
Stopover densities peaked in the most northern extent of Maine and
New England states in late October with the arrival of temperate
migrants. The maps and ecological understanding produced can help
inform conservation planning to protect and enhance stopover sites
for migratory landbirds in the future. Known Issues and
Uncertainties We caution against relying too heavily on our
region-wide predictions to assess the relative importance of sites
outside of radar-sampled areas. The predicted bird densities within
radar-sampled areas agreed quite well with radar-observed densities
(explained Deviances of BGAM models predicting mean bird density
were around 0.7). However, the accuracy of predicted bird densities
elsewhere remains unvalidated. Additionally, the results are not
highly precise; they are depicted at a resolution of 1 km by 1 km.
Within an area of this size, suitability for migrants may vary
dramatically depending on the types of land use and development
present. Large, intact, productive hardwood forests that provide an
abundance of insects and fruits appear to be particularly valuable
to migrants. Thus, the region-wide map should be viewed as a coarse
and preliminary guide for conservation purposes. NOTE - The data
represented were converted to raster for display purposes. The
download includes the original shapefile and layer files. In order
to view the attributes of a cell using the identify tool, select the
record for “Attributes for Migratory Landbird Stopover Habitat Data”
(dataset must be turned on). Attribute definitions can be found in
the Attachments tab on Data Basin or in the metadata available in
the download package."
|
| Theme/Category |
Environmental |
| Period Coverage |
Autumn 2008 - 2014 |
| Spatial Coverage |
12 northeastern US states from Virgina to Maine |
| Keywords |
migration, bird, stopover, ecology, distribution, wildlife,
conservation
|
| Landing Page |
https://nalcc.databasin.org/galleries/f5cc97e920ec49dfb76bc039a53c3e0a#expand=159202
|
| Access URL |
https://nalcc.databasin.org/galleries/f5cc97e920ec49dfb76bc039a53c3e0a#expand=159202
|
| Creator |
|
| Publisher |
|
| Released Date |
2018-01-30 |
| Updated Date |
2018-04-09 |
| Publishing Frequency |
1 |
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
1 |
| Distribution Format |
ArcGIS shapfile |
| Publication |
McLaren, J. D., J. J. Buler, T. Schreckengost, J. A. Smolinsky, M.
Boone, E. E. van Loon, D. K. Dawson, E. L. Walters. 2018. Artificial
light confounds broad-scale habitat use by migrating birds. Ecology
Letters 21:356-364.
http://onlinelibrary.wiley.com/doi/10.1111/ele.12902/full
|
| Software to view the data |
|
| Project |
| Title |
Validation of NEXRAD data and models of bird migration
stopover sites in the Northeast U.S.
|
| Description |
This project, led by Jeffrey Buler of the University of
Delaware with a number of colleagues, fills a key gap in our
understanding of stopover habitat for migratory landbirds. It
provides a large-scale perspective on important sites for
migrants across multiple states in the Eastern U.S. Using
innovative analyses of weather radar data to detect migrating
birds, coupled with field surveys, the reports make
predictions about the importance of sites for migrants across
the Northeast and Mid-Atlantic U.S. “Importance” is defined
here by the relative number of birds that stop over throughout
the migratory season and how consistently birds depart sites
from day to day. Areas with the highest and most consistent
bird densities are considered to be of greatest stopover
importance.
|
| Principal Investigator |
Dr. Jeff Buler
|
| Funding Agency |
US Fish and Wildlife Service (#F13AC00402) |
|
| Title |
Cooperative Congressional Election Study (CCES) 2018 |
| Description |
The Center for Political Communication (CPC) purchased two modules
on the CCES, a nationally-representative survey of U.S. residents.
The CCES data include "common content" questions asked of
respondents across all modules; the CPC-specific items were asked of
two modules of 1,000 respondents each.
|
| Theme/Category |
Political |
| Period Coverage |
Fall 2018 |
| Spatial Coverage |
National U.S. |
| Keywords |
|
| Landing Page |
https://cces.gov.harvard.edu
|
| Access URL |
|
| Creator |
|
| Publisher |
|
| Released Date |
|
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Restricted public (Dataset is available under certain use
restrictions)
|
| Version |
|
| Distribution Format |
SPSS, Stata, or csv |
| Publication |
|
| Software to view the data |
|
| Project |
| Title |
Center for Political Communication |
| Description |
The CPC is a nonpartisan, interdisciplinary center connecting
academics, students, and the community to relevant issues in
political communication. Since its creation, the CPC
consistently touches on issues that are part of, or about to
be part of, the national conversation. Whether it is racial
injustice, climate change, polarization, or the future of our
democracy, the CPC provides context through courses, research,
and programs.
|
| Principal Investigator |
Dr. Phil Jones
|
|
| Title |
Delaware Environmental Observing System (DEOS): Station Data |
| Description |
Environmental data for the last 24 hours for all real-time data
networks, including the DEOS, Delaware Department of Transportation
(DelDOT), Delaware National Estuarine Research Reserve (DNERR) Water
Quality, National Data Buoy Center (NDBC), National Weather Service
(NWS), and United States Geological Survey (USGS) stream and tide
gauge networks.
|
| Theme/Category |
Environmental |
| Period Coverage |
Last 24 hours |
| Spatial Coverage |
New Castle, Delaware |
| Keywords |
environment, monitoring, climate, atmosphere, temperature, rain,
wind
|
| Landing Page |
http://www.deos.udel.edu/data/
|
| Access URL |
http://www.deos.udel.edu/station/index.php?station=DAGF
|
| Creator |
Center for Environmental Monitoring & Analysis (CEMA) |
| Publisher |
Center for Environmental Monitoring & Analysis (CEMA) |
| Released Date |
|
| Updated Date |
|
| Publishing Frequency |
Daily |
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
|
| Distribution Format |
PNG, JPEG, SVG, PDF, CSV, XLS |
| Publication |
|
| Software to view the data |
|
| Project |
| Title |
Delaware Environmental Observing System |
| Description |
For over 15 years, DEOS has been providing real-time and
archived daily and monthly environmental conditions for
Delaware and the surrounding region. Today, DEOS operates and
maintains over 80 environmental monitoring platforms and
brings in data from over 200 additional environmental
monitoring platforms throughout our region.
|
| Principal Investigator |
Dr. Kevin Brinson
|
|
| Title |
Delaware Environmental Observing System (DEOS): Latest Conditions
|
| Description |
A map and table of the most recent conditions at each of the
networks for Meteorological, Hydrological, Wave, and Water Quality
parameters.
|
| Theme/Category |
Environmental |
| Period Coverage |
Past 3 hours |
| Spatial Coverage |
New Castle, Delaware |
| Keywords |
Air Temperature, PAR, Visibility, 1-Hr Precipitation, 24-Hr
Precipitation, Volumetric Water Content (2 in.), Soil Temperature (2
in.), Dew Point, Relative Humidity, Wind Speed, Wind Direction, Wind
Gust, Pressure, Wind Chill, Heat Index, Solar Radiation,
|
| Landing Page |
http://www.deos.udel.edu/data/
|
| Access URL |
http://www.deos.udel.edu/data/network_summary.php
|
| Creator |
Center for Environmental Monitoring & Analysis (CEMA) |
| Publisher |
Center for Environmental Monitoring & Analysis (CEMA) |
| Released Date |
|
| Updated Date |
|
| Publishing Frequency |
Hourly |
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
|
| Distribution Format |
|
| Publication |
|
| Software to view the data |
|
| Project |
| Title |
Delaware Environmental Observing System |
| Description |
For over 15 years, DEOS has been providing real-time and
archived daily and monthly environmental conditions for
Delaware and the surrounding region. Today, DEOS operates and
maintains over 80 environmental monitoring platforms and
brings in data from over 200 additional environmental
monitoring platforms throughout our region.
|
| Principal Investigator |
Dr. Kevin Brinson
|
|
| Title |
Delaware Environmental Observing System (DEOS): Daily Summary |
| Description |
Daily meteorological summary data for the DEOS and DelDOT networks.
|
| Theme/Category |
Environmental |
| Period Coverage |
2004 to present |
| Spatial Coverage |
New Castle County, Delaware |
| Keywords |
Air Temperature, PAR, Visibility, 1-Hr Precipitation, 24-Hr
Precipitation, Volumetric Water Content (2 in.), Soil Temperature (2
in.), Dew Point, Relative Humidity, Wind Speed, Wind Direction, Wind
Gust, Pressure, Wind Chill, Heat Index, Solar Radiation
|
| Landing Page |
http://www.deos.udel.edu/data/
|
| Access URL |
http://www.deos.udel.edu/data/daily_retrieval.php
|
| Creator |
Center for Environmental Monitoring & Analysis (CEMA) |
| Publisher |
Center for Environmental Monitoring & Analysis (CEMA) |
| Released Date |
|
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
|
| Distribution Format |
|
| Publication |
|
| Software to view the data |
|
| Project |
| Title |
Delaware Environmental Observing System |
| Description |
For over 15 years, DEOS has been providing real-time and
archived daily and monthly environmental conditions for
Delaware and the surrounding region. Today, DEOS operates and
maintains over 80 environmental monitoring platforms and
brings in data from over 200 additional environmental
monitoring platforms throughout our region.
|
| Principal Investigator |
Dr. Kevin Brinson
|
|
| Title |
Delaware Environmental Observing System (DEOS): Monthly Summary
|
| Description |
Monthly data summaries for networks archived for historical use by
DEOS.
|
| Theme/Category |
Environmental |
| Period Coverage |
2004 to present |
| Spatial Coverage |
New Castle County, Delaware |
| Keywords |
Air Temperature, PAR, Visibility, 1-Hr Precipitation, 24-Hr
Precipitation, Volumetric Water Content (2 in.), Soil Temperature (2
in.), Dew Point, Relative Humidity, Wind Speed, Wind Direction, Wind
Gust, Pressure, Wind Chill, Heat Index, Solar Radiation
|
| Landing Page |
http://www.deos.udel.edu/data/
|
| Access URL |
http://www.deos.udel.edu/data/monthly_retrieval.php
|
| Creator |
Center for Environmental Monitoring & Analysis (CEMA) |
| Publisher |
Center for Environmental Monitoring & Analysis (CEMA) |
| Released Date |
|
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
|
| Distribution Format |
|
| Publication |
|
| Software to view the data |
|
| Project |
| Title |
Delaware Environmental Observing System |
| Description |
For over 15 years, DEOS has been providing real-time and
archived daily and monthly environmental conditions for
Delaware and the surrounding region. Today, DEOS operates and
maintains over 80 environmental monitoring platforms and
brings in data from over 200 additional environmental
monitoring platforms throughout our region.
|
| Principal Investigator |
Dr. Kevin Brinson
|
|
| Title |
Delaware Environmental Observing System (DEOS): AgWeather Summary
|
| Description |
Monthly AgWeather data for a subset of DEOS weather stations. These
summaries agriculturally important data like reference
evapotranspiration, growing degree days, and soil moisture and soil
temperature values where these are available.
|
| Theme/Category |
Environmental |
| Period Coverage |
2004 to present |
| Spatial Coverage |
New Castle County, Delaware |
| Keywords |
Air Temperature, PAR, Visibility, 1-Hr Precipitation, 24-Hr
Precipitation, Volumetric Water Content (2 in.), Soil Temperature (2
in.), Dew Point, Relative Humidity, Wind Speed, Wind Direction, Wind
Gust, Pressure, Wind Chill, Heat Index, Solar Radiation
|
| Landing Page |
http://www.deos.udel.edu/data/
|
| Access URL |
http://www.deos.udel.edu/data/agirrigation_retrieval.php
|
| Creator |
Center for Environmental Monitoring & Analysis (CEMA) |
| Publisher |
Center for Environmental Monitoring & Analysis (CEMA) |
| Released Date |
|
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
|
| Distribution Format |
|
| Publication |
|
| Software to view the data |
|
| Project |
| Title |
Delaware Environmental Observing System |
| Description |
For over 15 years, DEOS has been providing real-time and
archived daily and monthly environmental conditions for
Delaware and the surrounding region. Today, DEOS operates and
maintains over 80 environmental monitoring platforms and
brings in data from over 200 additional environmental
monitoring platforms throughout our region.
|
| Principal Investigator |
Dr. Kevin Brinson
|
|
| Title |
Delaware Environmental Observing System (DEOS): Satellite Data
|
| Description |
The University of Delaware Satellite Receiving Station (UDSRS)
provides satellite data for both geostationary and polar orbiting
satellite missions. Visible, infrared, water vapor, and other
imagery layers are available through the UDSRS website.
|
| Theme/Category |
Environmental |
| Period Coverage |
|
| Spatial Coverage |
Conus, Mid-altantic |
| Keywords |
X-Band, MODIS, L-band, NOAA, MetOP, and Fung-yen satelltes |
| Landing Page |
http://www.deos.udel.edu/data/
|
| Access URL |
http://udsrs.udel.edu/ |
| Creator |
Center for Environmental Monitoring & Analysis (CEMA) |
| Publisher |
Center for Environmental Monitoring & Analysis (CEMA) |
| Released Date |
|
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
|
| Distribution Format |
|
| Publication |
|
| Software to view the data |
|
| Project |
| Title |
Delaware Environmental Observing System |
| Description |
For over 15 years, DEOS has been providing real-time and
archived daily and monthly environmental conditions for
Delaware and the surrounding region. Today, DEOS operates and
maintains over 80 environmental monitoring platforms and
brings in data from over 200 additional environmental
monitoring platforms throughout our region.
|
| Principal Investigator |
Dr. Kevin Brinson
|
|
| Title |
Multiple cropping alone does not improve year-round food security
among smallholders in rural India
|
| Description |
We specifically examine if planting multiple food crops within a
year is associated with dietary diversity and food security. We
collected information on demographic and economic variables, farming
activities and livelihood choices, from 200 unique households for
three seasons (monsoon/wet, winter, summer) during 2016-2018
(n=600). Based on both a 24-hour and a 30-day recall, we calculated
several indicators, including the Household Dietary Diversity Score
(HDDS), the Minimum Dietary Diversity for Women (MDD-W), and
Household Food Insecurity Access Scale (HFIAS). At least 43% of the
sample population experiences moderate to severe food insecurity in
all seasons. Cereals (mainly rice) remain the most important food
item irrespective of the season, with negligible consumption of
other nutrient-rich food such as tubers, fish, eggs, and meats.
Around 81% of women in all seasons do not consume a minimally
diverse diet. Multiple cropping is associated with higher food
security only during monsoon, while selling monsoon crops is
associated with winter food security. Households practicing multiple
cropping consume more pulses (a plant-based protein source) compared
to single-cropping or non-farming households (p<0.05)
|
| Theme/Category |
Geospatial |
| Period Coverage |
|
| Spatial Coverage |
India - 40 villages within five districts in the state of Madhya
Pradesh – Balaghat, Chhindwara, Dindori, Mandla and Seoni
|
| Keywords |
food security, dietary diversity, smallholder agriculture, India
|
| Landing Page |
https://datadryad.org/dataset/doi:10.5061/dryad.tdz08kq07#usage
|
| Access URL |
https://datadryad.org/downloads/file_stream/731884
|
| Creator |
|
| Publisher |
|
| Released Date |
2021-06-06 |
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
|
| Distribution Format |
CSV |
| Publication |
10.1088/1748-9326/ac05ee |
| Software to view the data |
|
| Project |
| Title |
Multiple cropping alone does not improve year-round food
security among smallholders in rural India
|
| Description |
Achieving and maintaining food and nutrition security is an
important Sustainable Development Goal (SDG), especially in
countries with largely vulnerable population with high
occurrence of hunger and malnutrition. By studying a
small-scale agricultural system in India, we aim to understand
the current state of dietary diversity and food insecurity
among the farmer communities. The study landscape has
witnessed a steady rise in multiple cropping along with
irrigation over the last two decades. Whether this multiple
cropping can be expected to improve year-round food security
is not well understood. We specifically examine if planting
multiple food crops within a year is associated with dietary
diversity and food security. We collected information on
demographic and economic variables, farming activities and
livelihood choices, from 200 unique households for three
seasons (monsoon/wet, winter, summer) during 2016-2018
(n=600). Based on both a 24-hour and a 30-day recall, we
calculated several indicators, including the Household Dietary
Diversity Score (HDDS), the Minimum Dietary Diversity for
Women (MDD-W), and Household Food Insecurity Access Scale
(HFIAS). At least 43% of the sample population experiences
moderate to severe food insecurity in all seasons. Cereals
(mainly rice) remain the most important food item irrespective
of the season, with negligible consumption of other
nutrient-rich food such as tubers, fish, eggs, and meats.
Around 81% of women in all seasons do not consume a minimally
diverse diet. Multiple cropping is associated with higher food
security only during monsoon, while selling monsoon crops is
associated with winter food security. Households practicing
multiple cropping consume more pulses (a plant-based protein
source) compared to single-cropping or non-farming households
(p<0.05). We find that multiple cropping cannot be used as
a cure-all strategy. Rather a combination of income and
nutrition strategies, including more diverse home garden,
diverse income portfolio, and access to clean cooking fuel, is
required to achieve year-round dietary diversity or food
security.
|
| Principal Investigator |
Dr. Pinki Mondal
|
| Funding Agency |
NASA Land-Cover Land-Use Change Program (Grant No. 522363)
|
|
| Title |
High-resolution inundation dataset for coastal India and Bangladesh
|
| Description |
This collection of gridded data layers provides the extent of
inundation in May 2020 resulting from the cyclone Amphan in 39
coastal districts in India and Bangladesh. These geospatial data
layers are derived from Sentinel-1 dual-polarization C-band
Synthetic Aperture Radar (SAR) data for pre-Amphan (May 5-18, 2020)
and post-Amphan (May 22-30, 2020) periods. We accessed ready-to-use
SAR data on Google Earth Engine (GEE). These input data were
preprocessed using Ground Range Detected (GRD) border-noise removal,
thermal noise removal, radiometric calibration, and terrain
correction, to derive backscatter coefficients (σ°) in decibels
(dB). We used VH polarisation instead of VV, since the latter is
known to be affected by windy conditions as compared to VH.
|
| Theme/Category |
Geospatial |
| Period Coverage |
May 5-18, 2020, May 22-30, 2020 |
| Spatial Coverage |
India - Odisha and West Bengal. Bangladesh- lower and upper region
|
| Keywords |
Amphan, cyclone, mangrove, rapidassessment, Sentinel, Sundarban
|
| Landing Page |
https://zenodo.org/records/4390084
|
| Access URL |
https://doi.org/10.5281/zenodo.4390084
|
| Creator |
|
| Publisher |
|
| Released Date |
2020-12-23 |
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
|
| Distribution Format |
TIF |
| Publication |
https://zenodo.org/records/4390084 |
| Software to view the data |
|
| Project |
| Title |
Radar and optical remote sensing for near real-time
assessments of cyclone impacts on coastal ecosystems
|
| Description |
Rapid impact assessment of cyclones on coastal ecosystems is
critical for timely rescue and rehabilitation operations in
highly human-dominated landscapes. Such assessments should
also include damage assessments of vegetation for restoration
planning in impacted natural landscapes. Our objective is to
develop a remote sensing-based approach combining satellite
data derived from optical (Sentinel-2), radar (Sentinel-1),
and LiDAR (Global Ecosystem Dynamics Investigation) platforms
for rapid assessment of post-cyclone inundation in
non-forested areas and vegetation damage in a primarily
forested ecosystem. We apply this multi-scalar approach for
assessing damages caused by the cyclone Amphan that hit
coastal India and Bangladesh in May 2020, severely flooding
several districts in the two countries, and causing
destruction to the Sundarban mangrove forests. Our analysis
shows that at least 6821 sq. km. land across the 39 study
districts was inundated even after 10 days after the cyclone.
We further calculated the change in forest greenness as the
difference in normalized difference vegetation index (NDVI)
pre- and post-cyclone. Our findings indicate a <0.2 unit
decline in NDVI in 3.45 sq. km. of the forest. Rapid
assessment of post-cyclone damage in mangroves is challenging
due to limited navigability of waterways, but critical for
planning of mitigation and recovery measures. We demonstrate
the utility of Otsu method, an automated statistical approach
of the Google Earth Engine platform to identify inundated
areas within days after a cyclone. Our radar-based inundation
analysis advances current practices because it requires
minimal user inputs, and is effective in the presence of high
cloud cover. Such rapid assessment, when complemented with
detailed information on species and vegetation composition,
can inform appropriate restoration efforts in severely
impacted regions and help decision makers efficiently manage
resources for recovery and aid relief. We provide the datasets
from this study on an open platform to aid in future research
and planning endeavors.
|
| Principal Investigator |
Dr. Pinki Mondal
|
| Funding Agency |
NASA EPSCoR grant (DE-80NSSC20M0220), University of Delaware
Research Foundation
|
|
| Title |
High-resolution remotely sensed datasets for saltwater intrusion
across the Delmarva Peninsula
|
| Description |
"Saltwater intrusion (SWI) on coastal farmlands can change the
soil properties (physical and chemical), rendering it unusable for
agricultural purposes. Globally, over a quarter of arable land is
negatively impacted by soil salinization, including more than 50% of
irrigated land. These salt-impacted lands account for more than 30%
of food production worldwide. However, the visible impacts of SWI on
coastal ecosystems are challenging to map due to the fine spatial
resolution of the salt patches. Here we provide the first mapping of
the early visual evidences of SWI impacts on the Delmarva (Delaware,
Maryland, Virginia) Peninsula region's farmlands by quantifying
and mapping the proportions of the farmlands where the spectral
signature of a white salt patch was detected. We focus our effort on
fourteen counties on the Delmarva Peninsula. We utilized very
high-resolution (1-m) aerial imagery from the National Agriculture
Imagery Program (NAIP) and seasonal information derived from the
moderate resolution (30-m) Landsat satellite imagery collection.
Using a Random Forest algorithm with 100 trees and over 94,240
reference points for training and testing, we developed
high-resolution geospatial datasets for the study area for two
time-steps: 2011-2013 and 2016-2017. The nine coastal Maryland
counties witnessed an average of 79% increase in the salt patches on
farmlands. The average increase across the state of Delaware is 81%.
Virginia experienced an average of 243% increase in these salt
patches. While the expansion rate is alarming, the absolute area
with these salt deposits remained rather small even in 2017: about
122 ha in Virginia; 339 ha in Delaware; and 445 ha in Maryland.
Visible white salt patches remained a small fraction of total
farmlands in each of these counties, ranging between 0.01% and 0.18%
in 2011-2013, and between 0.01% and 0.39% in 2016-2017. This
collection of gridded data layers provides the spatial distribution
of salt patches along with seven other land cover classes for 14
counties in the Delmarva (Delaware, Maryland and Virginia) Peninsula
in the United States of America (USA). We developed high-resolution
datasets for the study area for two time-steps: 2011-2013 and
2016-2017. The geospatial datasets are classified images for each
time-step and have eight land cover categories as shown below:"
|
| Theme/Category |
Geospatial |
| Period Coverage |
2011-2013, 2016-2017 |
| Spatial Coverage |
14 Counties on the Delmarva (Delaware, Maryland, Virginia) Peninsula
|
| Keywords |
saltwater intrusion, Delmarva peninsula, agricultural losses |
| Landing Page |
https://zenodo.org/records/6685695
|
| Access URL |
https://zenodo.org/api/records/6685695/files-archive
|
| Creator |
|
| Publisher |
|
| Released Date |
2022-12-30 |
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
|
| Distribution Format |
.tif |
| Publication |
https://doi.org/10.1038/s41893-023-01186-6 |
| Software to view the data |
|
| Project |
| Title |
The spread and cost of saltwater intrusion in the US
Mid-Atlantic
|
| Description |
Saltwater intrusion on coastal farmlands can render productive
land unsuitable for agricultural activities. While the visible
extent of salt-impacted land provides a useful saltwater
intrusion proxy, it is challenging to identify in early
stages. Moreover, associated ecological and economic impacts
are often underestimated as reduced crop yields in farmlands
surrounding salt patches are difficult to quantify. Here we
develop a high-resolution (1 m) dataset showing salt patches
on farm fringes and quantify the extent of salt-impacted lands
across the Delmarva Peninsula, United States. Our method is
transferable to other regions across and beyond the
mid-Atlantic with similar saltwater intrusion issues, such as
Georgia and the Carolinas. Our results show that between 2011
and 2017, visible salt patches almost doubled and 8,096 ha of
farmlands converted to marsh—another saltwater intrusion
consequence. Field-based electrical conductivity measurements
show elevated salinity values hundreds of metres from visible
salt patches, indicating the broader extent of at-risk
farmlands. More farmland areas were within 200 m of a visible
salt patch in 2017 compared to 2011, a rise ranging between
68% in Delaware and 93% in Maryland. On the basis of assumed
100% profit loss in at-risk farmlands within a 200 m buffer
around salt patches in 2016–2017, the range of economic losses
was estimated between US$39.4 million and US$107.5 million
annually, under 100% soy or corn counterfactuals,
respectively.
|
| Principal Investigator |
Dr. Pinki Mondal
|
| Funding Agency |
National Science Foundation EPSCoR Grant No. 1757353 / State
of Delaware, National Aeronautics and Space Administration
EPSCoR Grant No. DE-80NSSC20M0220, and the Delaware Space
Grant College and Fellowship Program (NASA Grant
80NSSC20M0045). Partial support: National Fish and Wildlife
Foundation, the State of Maryland, and Harry R. Hughes Center
for Agro-Ecology
|
|
| Title |
High Resolution Greenspace Land Cover in Philadelphia, Pennsylvania
|
| Description |
"This dataset provides a high resolution (1-m) land cover map
for Philadelphia, Pennsylvania in the United States of America
during the summer of 2017. This dataset was created to differentiate
two types of green space in Philadelphia: tree and grass cover. The
dataset includes four numerically coded land cover classes. This
classification is derived from National Agriculture Imagery Program
(NAIP) 1-m aerial imagery captured in the State of Pennsylvania
during June of 2017. To improve classification accuracy, NAIP data
was stacked with Sentinel-2 level 1C 10-m and 20-m data using the
.addBands() function in Google Earth Engine. Two files are available
for download: (1) Philadelphia_classification_points.zip: Contains a
shapefile of the 8,961 reference points used to train and test the
classifier. (2) Philadelphia_Landcover_2017.zip: Contains a GEOTIFF
of the classified image over Philadelphia, Pennsylvania for the
summer of 2017."
|
| Theme/Category |
Geospatial |
| Period Coverage |
summer 2017 |
| Spatial Coverage |
city of Philadelphia, PA |
| Keywords |
remote sensing, landcover, classification, greenspace, Philadelphia
|
| Landing Page |
https://zenodo.org/records/7604104
|
| Access URL |
https://zenodo.org/api/records/7604104/files-archive
|
| Creator |
|
| Publisher |
|
| Released Date |
2023-02-06 |
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
|
| Distribution Format |
.shp, geotiff |
| Publication |
https://doi.org/10.1038/s41598-023-39579-4 |
| Software to view the data |
|
| Project |
| Title |
Social media analysis reveals environmental injustices in
Philadelphia urban parks
|
| Description |
The United Nations Sustainable Development Goal (SDG) target
11.7 calls for access to safe and inclusive green spaces for
all communities. Yet, historical residential segregation in
the USA has resulted in poor quality urban parks near
neighborhoods with primarily disadvantaged socioeconomic
status groups, and an extensive park system that addresses the
needs of primarily White middle-class residents. Here we
center the voices of historically marginalized urban residents
by using Natural Language Processing and Geographic
Information Science to analyze a large dataset (n = 143,913)
of Google Map reviews from 2011 to 2022 across 285 parks in
the City of Philadelphia, USA. We find that parks in
neighborhoods with a high number of residents from
historically disadvantaged demographic groups are likely to
receive lower scores on Google Maps. Physical characteristics
of these parks based on aerial and satellite images and
ancillary data corroborate the public perception of park
quality. Topic modeling of park reviews reveal that the
diverse environmental justice needs of historically
marginalized communities must be met to reduce the uneven park
quality—a goal in line with achieving SDG 11 by 2030.
|
| Principal Investigator |
Dr. Pinki Mondal
|
| Funding Agency |
NASA Delaware Space Grant College and Fellowship Program
(80NSSC20M0045)
|
|
| Title |
High Resolution Phragmites Australis Classification in Delaware
Estuaries
|
| Description |
"This dataset provides a high resolution (1-m) land cover map
for Estuarine wetlands in the State of Delaware in the United States
of America during the summer of 2017. This dataset was created to
identify populations of the invasive marsh species Phragmites
australis. This classification is s derived from National
Agriculture Imagery Program (NAIP) 1-m aerial imagery captured in
the State of Delaware during June of 2017. A zipped file containing
a GEOTIFF titled ""Phrag_DE_5PC"" with the
classified image for 2017 is available for download. "
|
| Theme/Category |
Geospatial |
| Period Coverage |
June 2017 |
| Spatial Coverage |
Delaware |
| Keywords |
phragmites, classification, landcover, wetlands, marsh, estuary,
delaware, naip, national agriculture imagery program, usda, remote
sensing, gis
|
| Landing Page |
https://zenodo.org/records/7644496
|
| Access URL |
https://zenodo.org/api/records/7644496/files-archive
|
| Creator |
|
| Publisher |
|
| Released Date |
2023-02-20 |
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
|
| Distribution Format |
.tif |
| Publication |
https://doi.org/10.1007/s10661-023-11071-6 |
| Software to view the data |
|
| Project |
| Title |
Mapping of Phragmites in estuarine wetlands using
high-resolution aerial imagery
|
| Description |
Phragmites australis is a widespread invasive plant species in
the USA that greatly impacts estuarine wetlands by creating
dense patches and outcompeting other plants. The invasion of
Phragmites into wetland ecosystems is known to decrease
biodiversity, destroy the habitat of threatened and endangered
bird species, and alter biogeochemistry. While the impact of
Phragmites is known, the spatial extent of this species is
challenging to document due to its fragmented occurrence.
Using high-resolution imagery from the National Agriculture
Imagery Program (NAIP) from 2017, we evaluated a geospatial
method of mapping the spatial extent of Phragmites across the
state of DE. Normalized difference vegetation index (NDVI) and
principal component analysis (PCA) bands are generated from
the NAIP data and used as inputs in a random forest classifier
to achieve a high overall accuracy for the Phragmites
classification of around 95%. The classified gridded dataset
has a spatial resolution of 1 m and documents the spatial
distribution of Phragmites throughout the state’s estuarine
wetlands (around 11%). Such detailed classification could aid
in monitoring the spread of this invasive species over space
and time and would inform the decision-making process for
landscape managers.
|
| Principal Investigator |
Dr. Pinki Mondal
|
| Funding Agency |
NSF EPSCoR grant no. 1757353 and the State of Delaware, NASA
EPSCoR grant (DE-80NSSC20M0220), NASA Delaware Space Grant
College and Fellowship Program (80NSSC20M0045)
|
|
| Title |
Fractional Abundance Datasets for Salt Patch and Marshland Across
the Delmarva Peninsula, v2
|
| Description |
Coastal agricultural lands in the eastern USA are increasingly
plagued by escalating soil salinity, rendering them unsuitable for
profitable farming. Increasing soil salinization can lead to both
land cover modification and conversion. Two notable instances of
such transformations include the conversion of farmland to marshland
or to barren salt patches devoid of vegetation. However, quantifying
these land cover changes across large geographic regions poses a
significant challenge due to their varying spatial granularity. To
tackle this issue, we first developed a machine-learning based
method using Sentinel-2 imagery for 2022 where we used a non-linear
spectral unmixing approach utilizing a Random Forest (RF) algorithm
(Sarupria et al., 2025). The RF models were constructed using 100
trees and 27,437 reference data points, resulting in two sets of ten
models: one for salt patches and another for marshland. Validation
metrics for sub-pixel fractional abundances revealed a moderate
R-squared value of 0.50 for the salt model ensemble and a high
R-squared value of 0.90 for the marsh model ensemble. Building upon
this methodology, we then generated annual gridded datasets of
fractional abundance for salt patch and marshland across the
Delmarva Peninsula (14 coastal counties in Delaware, Maryland and
Virginia, USA) for 2019-2023. In these datasets, we only report mean
fractional abundance values ranging from 0.4 to 1 for salt patches
and 0.25 to 1 for marshland, along with the standard deviation
associated with each value.
|
| Theme/Category |
Geospatial |
| Period Coverage |
2019-2023 |
| Spatial Coverage |
14 counties on Delmarva peninsula (Delaware, Maryland, Virginia)
|
| Keywords |
coastal agriculture, saltwater intrusion, spectral unmixing,
sentinel-2, random forest,
|
| Landing Page |
https://zenodo.org/records/15866496
|
| Access URL |
https://zenodo.org/api/records/15866496/files-archive
|
| Creator |
|
| Publisher |
|
| Released Date |
2025-07-17 |
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
V2 |
| Distribution Format |
.tif |
| Publication |
https://doi.org/10.1016/j.rse.2025.114642 |
| Software to view the data |
|
| Project |
| Title |
Non-linear spectral unmixing for monitoring rapidly salinizing
coastal landscapes
|
| Description |
Coastal farmlands in the eastern United States of America
(USA) are increasingly suffering from rising soil salinity,
rendering them unsuitable for economically productive
agriculture. Saltwater intrusion (SWI) into the groundwater
reservoir or soil salinization can result in land cover
modification (e.g. reduced plant growth) or land cover
conversion. Two primary examples of such land cover conversion
are farmland to marsh or farmland to salt patches with no
vegetation growth. However, due to varying spatial granularity
of these conversions, it is challenging to quantify these land
covers over a large geographic scale. To address this
challenge, we evaluated a non-linear spectral unmixing
approach with a Random Forest (RF) algorithm to quantify
fractional abundance of salt patch and marshes. Using
Sentinel-2 imagery from 2022, we generated gridded datasets
for salt patches and marshes across the Delmarva Peninsula,
and the associated uncertainty. Moreover, we developed two new
spectral indices to enhance the spectral unmixing accuracy:
the Normalized Difference Salt Patch Index (NDSPI) and the
Modified Salt Patch Index (MSPI). We constructed two sets of
ten RF models: one for salt patches and the other for marshes,
achieving high (>99 %) training and testing accuracies for
classification. The consistently high accuracy and low error
values across different model runs demonstrate the
method's reliability for classifying spectrally similar
land cover classes in the mid-Atlantic region and beyond.
Validation metrics for sub-pixel fractional abundances in the
salt model revealed a moderate R-squared value of 0.50, and a
high R-squared value of 0.90 for the marsh model. Our method
complements labor-intensive field-based salinity measurements
by offering a reproducible method that can be repeated
annually and scaled up to cover large geographic regions.
|
| Principal Investigator |
Dr. Pinki Mondal
|
| Funding Agency |
NASA EPSCoR Grant DE-80NSSC20M0220, EPA (Assistance Agreement
no. CB96358101), USDA NRCS (Assistance Agreement no.
NR193A750007C005) and the National Fish and Wildlife
Foundation’s Chesapeake Bay Stewardship Fund (grant
0603.20.071142), Additionally: State of Maryland, Harry R.
Hughes Center for Agro-Ecology
|
|
| Title |
High-resolution remotely sensed datasets for saltwater intrusion
across the Delmarva Peninsula
|
| Description |
"Saltwater intrusion (SWI) on coastal farmlands can change the
soil properties (physical and chemical), rendering it unusable for
agricultural purposes. Globally, over a quarter of arable land is
negatively impacted by soil salinization, including more than 50% of
irrigated land. These salt-impacted lands account for more than 30%
of food production worldwide. However, the visible impacts of SWI on
coastal ecosystems are challenging to map due to the fine spatial
resolution of the salt patches. Here we provide the first mapping of
the early visual evidences of SWI impacts on the Delmarva (Delaware,
Maryland, Virginia) Peninsula region's farmlands by quantifying
and mapping the proportions of the farmlands where the spectral
signature of a white salt patch was detected. We focus our effort on
fourteen counties on the Delmarva Peninsula. We utilized very
high-resolution (1-m) aerial imagery from the National Agriculture
Imagery Program (NAIP) and seasonal information derived from the
moderate resolution (30-m) Landsat satellite imagery collection.
Using a Random Forest algorithm with 100 trees and over 94,240
reference points for training and testing, we developed
high-resolution geospatial datasets for the study area for two
time-steps: 2011-2013 and 2016-2017. The nine coastal Maryland
counties witnessed an average of 79% increase in the salt patches on
farmlands. The average increase across the state of Delaware is 81%.
Virginia experienced an average of 243% increase in these salt
patches. While the expansion rate is alarming, the absolute area
with these salt deposits remained rather small even in 2017: about
122 ha in Virginia; 339 ha in Delaware; and 445 ha in Maryland.
Visible white salt patches remained a small fraction of total
farmlands in each of these counties, ranging between 0.01% and 0.18%
in 2011-2013, and between 0.01% and 0.39% in 2016-2017. This
collection of gridded data layers provides the spatial distribution
of salt patches along with seven other land cover classes for 14
counties in the Delmarva (Delaware, Maryland and Virginia) Peninsula
in the United States of America (USA). We developed high-resolution
datasets for the study area for two time-steps: 2011-2013 and
2016-2017. The geospatial datasets are classified images for each
time-step and have eight land cover categories as shown below:"
|
| Theme/Category |
Geospatial |
| Period Coverage |
2011-2013, 2016-2017 |
| Spatial Coverage |
14 Counties on the Delmarva (Delaware, Maryland, Virginia) Peninsula
|
| Keywords |
saltwater intrusion, Delmarva peninsula, agricultural losses |
| Landing Page |
https://zenodo.org/records/6685695
|
| Access URL |
https://zenodo.org/api/records/6685695/files-archive
|
| Creator |
|
| Publisher |
|
| Released Date |
2022-12-30 |
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
|
| Distribution Format |
.tif |
| Publication |
https://doi.org/10.1038/s41893-023-01186-6 |
| Software to view the data |
|
| Project |
| Title |
The spread and cost of saltwater intrusion in the US
Mid-Atlantic
|
| Description |
Saltwater intrusion on coastal farmlands can render productive
land unsuitable for agricultural activities. While the visible
extent of salt-impacted land provides a useful saltwater
intrusion proxy, it is challenging to identify in early
stages. Moreover, associated ecological and economic impacts
are often underestimated as reduced crop yields in farmlands
surrounding salt patches are difficult to quantify. Here we
develop a high-resolution (1 m) dataset showing salt patches
on farm fringes and quantify the extent of salt-impacted lands
across the Delmarva Peninsula, United States. Our method is
transferable to other regions across and beyond the
mid-Atlantic with similar saltwater intrusion issues, such as
Georgia and the Carolinas. Our results show that between 2011
and 2017, visible salt patches almost doubled and 8,096 ha of
farmlands converted to marsh—another saltwater intrusion
consequence. Field-based electrical conductivity measurements
show elevated salinity values hundreds of metres from visible
salt patches, indicating the broader extent of at-risk
farmlands. More farmland areas were within 200 m of a visible
salt patch in 2017 compared to 2011, a rise ranging between
68% in Delaware and 93% in Maryland. On the basis of assumed
100% profit loss in at-risk farmlands within a 200 m buffer
around salt patches in 2016–2017, the range of economic losses
was estimated between US$39.4 million and US$107.5 million
annually, under 100% soy or corn counterfactuals,
respectively.
|
| Principal Investigator |
Dr. Pinki Mondal
|
| Funding Agency |
National Science Foundation EPSCoR Grant No. 1757353 / State
of Delaware, National Aeronautics and Space Administration
EPSCoR Grant No. DE-80NSSC20M0220, and the Delaware Space
Grant College and Fellowship Program (NASA Grant
80NSSC20M0045). Partial support: National Fish and Wildlife
Foundation, the State of Maryland, and Harry R. Hughes Center
for Agro-Ecology
|
|
| Title |
High Resolution Greenspace Land Cover in Philadelphia, Pennsylvania
|
| Description |
"This dataset provides a high resolution (1-m) land cover map
for Philadelphia, Pennsylvania in the United States of America
during the summer of 2017. This dataset was created to differentiate
two types of green space in Philadelphia: tree and grass cover. The
dataset includes four numerically coded land cover classes. This
classification is derived from National Agriculture Imagery Program
(NAIP) 1-m aerial imagery captured in the State of Pennsylvania
during June of 2017. To improve classification accuracy, NAIP data
was stacked with Sentinel-2 level 1C 10-m and 20-m data using the
.addBands() function in Google Earth Engine. Two files are available
for download: (1) Philadelphia_classification_points.zip: Contains a
shapefile of the 8,961 reference points used to train and test the
classifier. (2) Philadelphia_Landcover_2017.zip: Contains a GEOTIFF
of the classified image over Philadelphia, Pennsylvania for the
summer of 2017."
|
| Theme/Category |
Geospatial |
| Period Coverage |
summer 2017 |
| Spatial Coverage |
city of Philadelphia, PA |
| Keywords |
remote sensing, landcover, classification, greenspace, Philadelphia
|
| Landing Page |
https://zenodo.org/records/7604104
|
| Access URL |
https://zenodo.org/api/records/7604104/files-archive
|
| Creator |
|
| Publisher |
|
| Released Date |
2023-02-06 |
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
|
| Distribution Format |
.shp, geotiff |
| Publication |
https://doi.org/10.1038/s41598-023-39579-4 |
| Software to view the data |
|
| Project |
| Title |
Social media analysis reveals environmental injustices in
Philadelphia urban parks
|
| Description |
The United Nations Sustainable Development Goal (SDG) target
11.7 calls for access to safe and inclusive green spaces for
all communities. Yet, historical residential segregation in
the USA has resulted in poor quality urban parks near
neighborhoods with primarily disadvantaged socioeconomic
status groups, and an extensive park system that addresses the
needs of primarily White middle-class residents. Here we
center the voices of historically marginalized urban residents
by using Natural Language Processing and Geographic
Information Science to analyze a large dataset (n = 143,913)
of Google Map reviews from 2011 to 2022 across 285 parks in
the City of Philadelphia, USA. We find that parks in
neighborhoods with a high number of residents from
historically disadvantaged demographic groups are likely to
receive lower scores on Google Maps. Physical characteristics
of these parks based on aerial and satellite images and
ancillary data corroborate the public perception of park
quality. Topic modeling of park reviews reveal that the
diverse environmental justice needs of historically
marginalized communities must be met to reduce the uneven park
quality—a goal in line with achieving SDG 11 by 2030.
|
| Principal Investigator |
Dr. Pinki Mondal
|
| Funding Agency |
NASA Delaware Space Grant College and Fellowship Program
(80NSSC20M0045)
|
|
| Title |
High Resolution Phragmites Australis Classification in Delaware
Estuaries
|
| Description |
"This dataset provides a high resolution (1-m) land cover map
for Estuarine wetlands in the State of Delaware in the United States
of America during the summer of 2017. This dataset was created to
identify populations of the invasive marsh species Phragmites
australis. This classification is s derived from National
Agriculture Imagery Program (NAIP) 1-m aerial imagery captured in
the State of Delaware during June of 2017. A zipped file containing
a GEOTIFF titled ""Phrag_DE_5PC"" with the
classified image for 2017 is available for download. "
|
| Theme/Category |
Geospatial |
| Period Coverage |
June 2017 |
| Spatial Coverage |
Delaware |
| Keywords |
phragmites, classification, landcover, wetlands, marsh, estuary,
delaware, naip, national agriculture imagery program, usda, remote
sensing, gis
|
| Landing Page |
https://zenodo.org/records/7644496
|
| Access URL |
https://zenodo.org/api/records/7644496/files-archive
|
| Creator |
|
| Publisher |
|
| Released Date |
2023-02-20 |
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
|
| Distribution Format |
.tif |
| Publication |
https://doi.org/10.1007/s10661-023-11071-6 |
| Software to view the data |
|
| Project |
| Title |
Mapping of Phragmites in estuarine wetlands using
high-resolution aerial imagery
|
| Description |
Phragmites australis is a widespread invasive plant species in
the USA that greatly impacts estuarine wetlands by creating
dense patches and outcompeting other plants. The invasion of
Phragmites into wetland ecosystems is known to decrease
biodiversity, destroy the habitat of threatened and endangered
bird species, and alter biogeochemistry. While the impact of
Phragmites is known, the spatial extent of this species is
challenging to document due to its fragmented occurrence.
Using high-resolution imagery from the National Agriculture
Imagery Program (NAIP) from 2017, we evaluated a geospatial
method of mapping the spatial extent of Phragmites across the
state of DE. Normalized difference vegetation index (NDVI) and
principal component analysis (PCA) bands are generated from
the NAIP data and used as inputs in a random forest classifier
to achieve a high overall accuracy for the Phragmites
classification of around 95%. The classified gridded dataset
has a spatial resolution of 1 m and documents the spatial
distribution of Phragmites throughout the state’s estuarine
wetlands (around 11%). Such detailed classification could aid
in monitoring the spread of this invasive species over space
and time and would inform the decision-making process for
landscape managers.
|
| Principal Investigator |
Dr. Pinki Mondal
|
| Funding Agency |
NSF EPSCoR grant no. 1757353 and the State of Delaware, NASA
EPSCoR grant (DE-80NSSC20M0220), NASA Delaware Space Grant
College and Fellowship Program (80NSSC20M0045)
|
|
| Title |
Fractional Abundance Datasets for Salt Patch and Marshland Across
the Delmarva Peninsula, v2
|
| Description |
Coastal agricultural lands in the eastern USA are increasingly
plagued by escalating soil salinity, rendering them unsuitable for
profitable farming. Increasing soil salinization can lead to both
land cover modification and conversion. Two notable instances of
such transformations include the conversion of farmland to marshland
or to barren salt patches devoid of vegetation. However, quantifying
these land cover changes across large geographic regions poses a
significant challenge due to their varying spatial granularity. To
tackle this issue, we first developed a machine-learning based
method using Sentinel-2 imagery for 2022 where we used a non-linear
spectral unmixing approach utilizing a Random Forest (RF) algorithm
(Sarupria et al., 2025). The RF models were constructed using 100
trees and 27,437 reference data points, resulting in two sets of ten
models: one for salt patches and another for marshland. Validation
metrics for sub-pixel fractional abundances revealed a moderate
R-squared value of 0.50 for the salt model ensemble and a high
R-squared value of 0.90 for the marsh model ensemble. Building upon
this methodology, we then generated annual gridded datasets of
fractional abundance for salt patch and marshland across the
Delmarva Peninsula (14 coastal counties in Delaware, Maryland and
Virginia, USA) for 2019-2023. In these datasets, we only report mean
fractional abundance values ranging from 0.4 to 1 for salt patches
and 0.25 to 1 for marshland, along with the standard deviation
associated with each value.
|
| Theme/Category |
Geospatial |
| Period Coverage |
2019-2023 |
| Spatial Coverage |
14 counties on Delmarva peninsula (Delaware, Maryland, Virginia)
|
| Keywords |
coastal agriculture, saltwater intrusion, spectral unmixing,
sentinel-2, random forest,
|
| Landing Page |
https://zenodo.org/records/15866496
|
| Access URL |
https://zenodo.org/api/records/15866496/files-archive
|
| Creator |
|
| Publisher |
|
| Released Date |
2025-07-17 |
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
V2 |
| Distribution Format |
.tif |
| Publication |
https://doi.org/10.1016/j.rse.2025.114642 |
| Software to view the data |
|
| Project |
| Title |
Non-linear spectral unmixing for monitoring rapidly salinizing
coastal landscapes
|
| Description |
Coastal farmlands in the eastern United States of America
(USA) are increasingly suffering from rising soil salinity,
rendering them unsuitable for economically productive
agriculture. Saltwater intrusion (SWI) into the groundwater
reservoir or soil salinization can result in land cover
modification (e.g. reduced plant growth) or land cover
conversion. Two primary examples of such land cover conversion
are farmland to marsh or farmland to salt patches with no
vegetation growth. However, due to varying spatial granularity
of these conversions, it is challenging to quantify these land
covers over a large geographic scale. To address this
challenge, we evaluated a non-linear spectral unmixing
approach with a Random Forest (RF) algorithm to quantify
fractional abundance of salt patch and marshes. Using
Sentinel-2 imagery from 2022, we generated gridded datasets
for salt patches and marshes across the Delmarva Peninsula,
and the associated uncertainty. Moreover, we developed two new
spectral indices to enhance the spectral unmixing accuracy:
the Normalized Difference Salt Patch Index (NDSPI) and the
Modified Salt Patch Index (MSPI). We constructed two sets of
ten RF models: one for salt patches and the other for marshes,
achieving high (>99 %) training and testing accuracies for
classification. The consistently high accuracy and low error
values across different model runs demonstrate the
method's reliability for classifying spectrally similar
land cover classes in the mid-Atlantic region and beyond.
Validation metrics for sub-pixel fractional abundances in the
salt model revealed a moderate R-squared value of 0.50, and a
high R-squared value of 0.90 for the marsh model. Our method
complements labor-intensive field-based salinity measurements
by offering a reproducible method that can be repeated
annually and scaled up to cover large geographic regions.
|
| Principal Investigator |
Dr. Pinki Mondal
|
| Funding Agency |
NASA EPSCoR Grant DE-80NSSC20M0220, EPA (Assistance Agreement
no. CB96358101), USDA NRCS (Assistance Agreement no.
NR193A750007C005) and the National Fish and Wildlife
Foundation’s Chesapeake Bay Stewardship Fund (grant
0603.20.071142), Additionally: State of Maryland, Harry R.
Hughes Center for Agro-Ecology
|
|
| Title |
Multi-Temporal Land Cover Dataset for the Delmarva Peninsula (2000,
2002, 2005, 2009, 2016) at 30m Resolution
|
| Description |
"Coastal regions of the eastern United States are increasingly
vulnerable to saltwater intrusion and rising soil salinity, which
can significantly alter land cover patterns. Monitoring these
changes over time requires consistent, high-quality datasets. This
work presents a multi-temporal, classified land cover dataset for
the Delmarva Peninsula (14 coastal counties in Delaware, Maryland,
and Virginia, USA) for five target years: 2000, 2002, 2005, 2009,
and 2016. The datasets were produced using a Random Forest
classifier trained on Continuous Change Detection and Classification
(CCDC)-derived synthetic Landsat surface reflectance, and target
variables dervied from a high-resolution (1m) NAIP–Landsat derived
dataset. The classification distinguishes seven land cover classes:
Bare Soil, Built, Farmland, Forest, Marsh, Other Vegetation, and
Water. Overall testing accuracy reached 90.6% with a five-fold
cross-validation accuracy of 90.2% ± 0.13%, demonstrating robust
model performance. These datasets can support further studies on
land use change, coastal resilience, and salinity-linked landscape
dynamics. This dataset contains five integer-coded raster files, one
for each study year. Each raster is a single-band GeoTIFF with 30m
spatial resolution."
|
| Theme/Category |
Geospatial |
| Period Coverage |
2000, 2002, 2005, 2009, 2016 |
| Spatial Coverage |
14 counties of the Delmarva peninsula |
| Keywords |
Land cover classification, Remote sensing, Random Forest, Landsat
time series, Coastal landscape dynamics
|
| Landing Page |
https://zenodo.org/records/16783114
|
| Access URL |
https://zenodo.org/api/records/16783114/files-archive
|
| Creator |
|
| Publisher |
|
| Released Date |
2025-08-08 |
| Updated Date |
|
| Publishing Frequency |
|
| Access Rights |
Public (Dataset is or could be made publicly available to all
without restrictions)
|
| Version |
1 |
| Distribution Format |
GeoTiff |
| Publication |
|
| Software to view the data |
|
| Project |
| Title |
Multi-temporal land cover dataset for the Delmarva Peninsula
(2000, 2002, 2005, 2009, 2016) at 30m Resolution
|
| Description |
"*Subject to change* Coastal regions of the eastern
United States are increasingly vulnerable to saltwater
intrusion and rising soil salinity, which can significantly
alter land cover patterns. Monitoring these changes over time
requires consistent, high-quality datasets. This work presents
a multi-temporal, classified land cover dataset for the
Delmarva Peninsula (14 coastal counties in Delaware, Maryland,
and Virginia, USA) for five target years: 2000, 2002, 2005,
2009, and 2016. The datasets were produced using a Random
Forest classifier trained on Continuous Change Detection and
Classification (CCDC)-derived synthetic Landsat surface
reflectance, and target variables dervied from a
high-resolution (1m) NAIP–Landsat derived dataset. The
classification distinguishes seven land cover classes: Bare
Soil, Built, Farmland, Forest, Marsh, Other Vegetation, and
Water. Overall testing accuracy reached 90.6% with a five-fold
cross-validation accuracy of 90.2% ± 0.13%, demonstrating
robust model performance. These datasets can support further
studies on land use change, coastal resilience, and
salinity-linked landscape dynamics. This dataset contains five
integer-coded raster files, one for each study year. Each
raster is a single-band GeoTIFF with 30m spatial
resolution."
|
| Principal Investigator |
Dr. Pinki Mondal
|
| Funding Agency |
NASA EPSCoR Grant DE-80NSSC20M0220, NSF EPsCOR grants:
2418394, 2418395, 2418396. Additionally: State of Maryland,
Harry R. Hughes Center for Agro-Ecology.
|
|