AndreaBozzo/ceres-open-data-index huggingface dataset:
Developing online training modules to support use of Google Earth Engine in Teaching, Research and Outreach

Google Earth Engine (GEE) is a cloud-based platform that combines a catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities. During GY19, NYView developed a series of free-to-access Google Earth Engine training videos that transformed and expanded prior written GEE training materials into online video modules. These modules have comprehensive coverage (from beginner to advanced) and demonstrate applications to support teaching, research, and outreach. 

Organization: New YorkView
Introduction to Precision Agriculture

This video playlist was developed primarily for a middle school / 4-H audience, and includes 3 videos. The videos, narrated by Dan Swafford, provide an introduction to some basic agricultural concepts, and include:  An Introduction to Agriculture - Understanding Basic Concepts (26 mins); Precision Agriculture- Introduction to Pesticides and Herbicides (17 min); and Precision Agriculture - GNSS (16 minutes).  These videos were developed as a companion to the workbook:  An Introduction to Precision Agriculture:  An Educator's Guide to Agricultural Earth Observation (P. 138).  However, they can also be used as stand-alone resources.  

Organization: VirginiaView
Remote Sensing with ArcGIS Pro

This video playlist contains 28 short videos. These video tutorials can be viewed as a companion to the ebook "Remote Sensing with ArcGIS Pro" or they can be viewed as stand-alone tutorials. Each of the 28 short videos target a specific topic.  Tutorials 1-6 provide an introduction to ArcGIS Pro.  The remaining videos in the playlist introduce remote sensing concepts. These tutorials can be used by any self-paced learner. 

Organization: VirginiaView
sUAS Tutorials: Flight Planning - Video Playlist

This series of 7 short video tutorials (ranging from 7 mins. to 15 mins in length) provide a general introduction to flight planning for small unmanned aircraft systems (sUAS).  Tutorial topics include:  sUAS Flight Planning, Digital Scouting, Flight Planning for Fixed Wing Aircraft, Flight Planning for Multi-rotor Aircraft, 3D Model and Linear Flight Plans, Weather Briefings (part 1 and Part 2). 

Organization: VirginiaView
Iowa BMP Project – ACCESSORY DATA AND DOCUMENTS PAGE

This page contains links to the following documents:

Supplemental Data for the the Handbook zip – This zip contains a template file geodatabase, a template mxd, template excel documents foe watershed assignment and watershed summary statistics, and a folder containing the python scripts for automating various processes described in the handbook.

Tutorial 1- Digitizing Conservation Practices Tutorial zip– Tutorial 1 is a basic guide for creating the six conservation practices in the baseline conservation inventory data set and associated files.  This zip includes a pdf of the tutorial, an mxd, a file geodatabase containing the necessary data and a solutions folder with the completed file geodatabase. 

Tutorial 2 – Historical Occurrence Evaluation Tutorial zip– Tutorial 2 provides instructions on how to perform historical evaluations as well as updates using the baseline conservation inventory dataset. This zip includes a pdf of the tutorial, an mxd, a file geodatabase containing the necessary data and a solutions folder with the completed file geodatabase. 

Tutorial 3 – Identifying and Mapping Tile Drainage Tutorial.pdf – Tutorial 3 explains the methods used to identify and map tile drainage using high resolution imagery. Accessory files in a zip, include a pdf of the tutorial, an mxd, a file geodatabase containing the necessary data, a tile mapping reference  folder (containing an additional file geodatabase, DEMs (digital elevation models), shapefile, and tif) and a solutions folder with the completed file geodatabase and mxd. 

Organization: IowaView
Iowa Geographic Map Server

This site provides free access to Iowa geographic map data through online map viewers and web map services. The site was developed by the Iowa State University Geographic Information Systems Support and Research Facility in cooperation with the Iowa Department of Natural Resources, the USDA Natural Resources Conservation Service, and the Massachusetts Institute of Technology. 

Available maps include:

Statewide historical aerial imagery – 1930s, 1950s, 1960s, 1970s, and 1980s

NAIP summer imagery – 1990s, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2013, 2014, 2015, 2017, 2019

Spring color-infrared imagery – 2002, 2007-2010, 2016-2018 

Elevation maps

Historical maps

Land use/land cover maps
 

Organization: IowaView
Detection of Shelterbelt Density Change Using Historic APFO and NAIP Aerial Imagery

Grand Forks County, North Dakota, boasts the highest concentration of shelterbelts in the World. As trees age and reach their lifespan limits, renovations should have taken place with new trees being planted. However, in recent years, the rate of tree removal is thought to exceed the rate of replanting, which can result in a net loss of shelterbelts. Through manual digitization and geographic object-based image analysis (GEOBIA), we mapped shelterbelt densities in the Grand Forks County using historical and contemporary aerial photography, and estimated actual changes in density over 54 years. Our results showed a doubling in shelterbelt densities from 1962 to 2014, with an increase of 6402 m2/km2 over the 52 years (or 123 m2/km2/year). From 2014 to 2016, we measured 1,040,178 m2 of shelterbelt areas removed from the county, creating a density loss of −157 m2/km2/year. The total change over two years was relatively small compared with that seen over the previous 52 years. However, the fact that the rate of shelterbelt planting has slowed, and more removal is occurring, should be of concern for an increased risk of wind erosion, similar to that experienced in Midwestern U.S. during the 1930s. The reduction of shelterbelt density is likely related to changes in farming practices and a decline in the Conservation Reserve Program, resulting from the increased returns of growing other row crops. To encourage shelterbelt planting as a conservation practice, additional guidelines and financial support should be considered to balance the tradeoff between soil erosion and agricultural intensification

Organization: North DakotaView
Airborne Lidar Sampling Strategies to Enhance Forest Aboveground Biomass Estimation from Landsat Imagery

Accurately estimating aboveground biomass (AGB) is important in many applications, including monitoring carbon stocks, investigating deforestation and forest degradation, and designing sustainable forest management strategies. Although lidar provides critical three-dimensional forest structure information for estimating AGB, acquiring comprehensive lidar coverage is often cost prohibitive. This research focused on developing a lidar sampling framework to support AGB estimation from Landsat images. Two sampling strategies, systematic and classification-based, were tested and compared. The proposed strategies were implemented over a temperate forest study site in northern New York State and the processes were then validated at a similar site located in central New York State. Our results demonstrated that while the inclusion of lidar data using systematic or classification-based sampling supports AGB estimation, the systematic sampling selection method was highly dependent on site conditions and had higher accuracy variability. Of the 12 systematic sampling plans, R2 values ranged from 0.14 to 0.41 and plot root mean square error (RMSE) ranged from 84.2 to 93.9 Mg ha−1. The classification-based sampling outperformed 75% of the systematic sampling strategies at the primary site with R2 of 0.26 and RMSE of 70.1 Mg ha−1. The classification-based lidar sampling strategy was relatively easy to apply and was readily transferable to a new study site. Adopting this method at the validation site, the classification-based sampling also worked effectively, with an R2 of 0.40 and an RMSE of 108.2 Mg ha−1 compared to the full lidar coverage model with an R2 of 0.58 and an RMSE of 96.0 Mg ha−1. This study evaluated different lidar sample selection methods to identify an efficient and effective approach to reduce the volume and cost of lidar acquisitions. The forest type classification-based sampling method described in this study could facilitate cost-effective lidar data collection in future studies.

Organization: New YorkView
Differential responses of native and managed prairie pastures to environmental variability and management practices

Future weather and climates, especially rainfall, are expected to have larger variability in the Southern Plains of the United States. However, the degree and timing of environmental variability that affect productivity of pastures managed differently have not been well studied. We examined the impacts of environmental variability on grassland productivity using 17 years of gross primary productivity (GPP) data for co-located native and managed prairie pastures in Oklahoma. We also considered the interactive effects of management factors and environmental variability into the regression models and identified the critical temporal windows of environmental variables (CWE) that influence annual variability in GPP. Managed pasture (MP) showed greater variability of GPP than did native pasture (NP), particularly with reduced GPP in drought years. The resilience of native prairies under unfavorable climate extremes was evident by lower GPP anomalies in NP than MP during the 2011–2012 drought. Although both pastures experienced the same degree of environmental variability, the CWE affecting GPP was significantly different between NP and MP due to the modulating impact of management practices on the responses of GPP. Not only the range but also the timings of the CWE were different between NP and MP as MP was more responsive to the spring temperature and fall rainfall. Our findings warrant the incorporation of MP as a different commodity from NP when accounting for the ecosystem responses to environmental variability in global climate models.

Organization: OklahomaView
Iowa BMP Mapping Project Website

The goal of the Iowa BMP (Best Management Practices) Mapping Project is to provide a complete baseline set of BMPs dating from the 2007-2010 timeframe for use in watershed modeling, historic occurrence, and future practice tracking.  The BMPs mapped are: Terraces, Water and Sediment Control Basins (WASCOB), Grassed Waterways, Pond Dams, Contour Strip Cropping and Contour Buffer Strips.  We can't guarantee that mapped practices meet NRCS standards or that they are actually the indicated practice since no ground reference analysis is being performed. Data being utilized to digitize the BMPs include LiDAR derived products such as DEM, Hillshade and Slope grids; CIR aerial photography from the 2007-2010 timeframe, NAIP aerial photography and historic aerial photography. 

Organization: IowaView
Inspecting the Food–Water Nexus in the Ogallala Aquifer Region Using Satellite Remote Sensing Time Series

Agricultural production in the Great Plains provides a significant amount of food for the United States while contributing greatly to farm income in the region. However, recurrent droughts and expansion of crop production are increasing irrigation demand, leading to extensive pumping and attendant depletion of the Ogallala aquifer. In order to optimize water use, increase the sustainability of agricultural production, and identify best management practices, identification of food–water conflict hotspots in the Ogallala Aquifer Region (OAR) is necessary. We used satellite remote sensing time series of agricultural production (net primary production, NPP) and total water storage (TWS) to identify hotspots of food–water conflicts within the OAR and possible reasons behind these conflicts. Mean annual NPP (2001–2018) maps clearly showed intrusion of high NPP, aided by irrigation, into regions of historically low NPP (due to precipitation and temperature). Intrusion is particularly acute in the northern portion of OAR, where mean annual TWS (2002–2020) is high. The Oklahoma panhandle and Texas showed large decreasing TWS trends, which indicate the negative effects of current water demand for crop production on TWS. Nebraska demonstrated an increasing TWS trend even with a significant increase of NPP. A regional analysis of NPP and TWS can convey important information on current and potential conflicts in the food–water nexus and facilitate sustainable solutions. Methods developed in this study are relevant to other water-constrained agricultural production regions

Organization: OklahomaView
Grassland Wildfires in the Southern Great Plains: Monitoring Ecological Impacts and Recovery

Devastating wildfires in Texas, Oklahoma, and Kansas in 2016 and 2017 resulted in significant social, economic, and environmental losses, with the agricultural sector among those severely affected. Several satellite-based indices were evaluated as potential monitoring tools for post-wildfire ecological recovery and management of grasslands. All indices evaluated provided useful information and indicated rapid vegetation recovery from wildfire. The Leaf Water Stress Index (LSWI) and Gross Primary Productivity (GPP) showed a distinct response to the wildfire events, and differentiated between burned and unburned areas throughout the post-wildfire growing seasons better than the Normalized Difference Vegetative Index (NDVI) and Enhanced Vegetative Index (EVI). In particular, the LSWI may provide a useful tool for mapping the footprint of wildfire, with potential utility for organizations that provide post-fire recovery resources. The GPP, which estimates the biomass productivity of vegetation, can provide information to livestock operators to guide the re-stocking of cattle in the aftermath of wildfire. In sum, satellite-based proxies can provide timely information both to characterize a wildfire’s footprint and to guide post-fire grazing management in a manner that balances short term needs for forage with long-term productivity and ecological function.

Organization: OklahomaView
Integrating eddy fluxes and remote sensing products in a rotational grazing native tallgrass prairie pasture

Eddy covariance (EC) systems provide integrated fluxes within their footprint areas. Spatial heterogeneity of up-scaled areas and spatio-temporal mismatches between EC footprint and remote sensing pixels jeopardize the performance of most satellite-based models. To examine the impact of spatial resolution of satellite products on up-scaling of fluxes, we compared the relationships between measured eddy fluxes and enhanced vegetation index (EVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) at 500 and 250 m spatial resolutions, Visible Infrared Imaging Radiometer Suite (VIIRS) at 500 m spatial resolution, and Landsat at 30 m spatial resolution but integrated at the paddock-scale. The experiment was conducted over a grazed native tallgrass prairie pasture, which was divided into nine paddocks for rotational grazing. The EVI data from all satellites showed consistency in detecting vegetation phenology. Seasonality of EC-measured fluxes corresponded well with remotely-sensed vegetation phenology. Approximately 80% of contribution to eddy fluxes came from within 80 m upwind distance of the 2.7 m tall EC tower. As a result, the major contributing area for the measured fluxes was mostly limited to the paddock containing the EC tower. Different timings and duration of grazing caused some heterogeneity among paddocks within the pasture. The EVI of different spatial scales showed strong relationships with CO2 fluxes. However, Landsat-derived EVI integrated for the paddock containing the EC tower showed substantially stronger relationships with CO2 fluxes than did MODIS and VIIRS-derived EVI integrated for multiple paddocks, most likely due to similar spatial resolutions of remote sensing and EC observations. Results illustrate that satellite products of fine-scale spatial resolution that are comparable to EC footprints can help improve the performance of satellite-based models for modeling or up-scaling of eddy fluxes, especially in heterogeneous ecosystems.

Organization: OklahomaView
Exploring GeoSpatial Intelligence (GEOINT) Opportunities within K - 12 School Settings

Geospatial Intelligence (GEOINT) uses geospatial science and technologies, human geography, as well as fundamental knowledge of statistics, mathematics and physics to solve real world issues. GEOINT offers viable solutions to insure human safety and security in diverse environments, and is both a conceptual and applied discipline that provides the “where,” “when,” “what,” “how,” and “so what” in problem solving.

GEOINT is being applied in most career fields, and even in our daily lives—whether it’s using GPS to get to your destination or geotagging where you are on Twitter. Everyone is using GEOINT is some fashion, but they don’t realize it’s called GEOINT or the many career possibilities that uses these skills.

Engaging K-12 students and teachers in GEOINT is not as hard as it may seem—it’s all about finding the right tools to do so. The United States Geospatial Intelligence Foundation (USGIF) and other organizations have resources, materials, and lessons to help geographers engage in GEOINT outreach in educational settings. These fun and educational materials integrate with school curriculum and testing requirements, while giving students the knowledge and skills required for GEOINT careers and being more “location aware.” Panelists will present examples of successful curriculum development, outreach and dissemination, and share examples of teaching resources.

Organization: Pennsylvania View
Unmanned Aircraft Systems on Campus

Unmanned Aircraft Systems (UAS), or drones, are a revolutionary technology that are making it easier than ever to collect information from above the Earth’s surface. For institutions of higher education, UAS technology is offering exciting new opportunities for carrying out research, inspecting campus infrastructure, and improving safety on campus. UAS also pose a number of challenges, particularly on issues relating to federal regulations, privacy, and responsible use. In this webinar, Jarlath O’Neil-Dunne, director of the University of Vermont’s (UVM) Spatial Analysis Laboratory and founder of the UVM UAS Team, provides participants with a comprehensive understanding of UAS technology and discusses how UAS are used in action from disaster response and public safety efforts to traffic monitoring, infrastructure, and environmental research. This webinar is appropriate for those working in senior campus administration, campus safety and security, emergency management, law enforcement, threat assessment, residential life, student conduct and affairs, legal counsel, or any other personnel working to establish policies and procedures for UAS technology on campus.

Organization: VermontView
Visitors to urban greenspace have higher sentiment and lower negativity on Twitter

With more people living in cities, we are witnessing a decline in exposure to nature. A growing body of research has demonstrated an association between nature contact and improved mood.
Here, we used Twitter and the Hedonometer, a world analysis tool, to investigate how sentiment, or the estimated happiness of the words people write, varied before, during, and after visits to San Francisco's urban park system. We found that sentiment was substantially higher during park visits and remained elevated for several hours following the visit.
Leveraging differences in vegetative cover across park types, we explored how different types of outdoor public spaces may contribute to subjective well-being. Tweets during visits to Regional Parks, which are greener and have greater vegetative cover, exhibited larger increases in sentiment than tweets during visits to Civic Plazas and Squares.
Finally, we analysed word frequencies to explore several mechanisms theorized to link nature exposure with mental and cognitive benefits. Negation words such as ‘no’, ‘not’, and ‘don't’ decreased in frequency during visits to urban parks.
These results can be used by urban planners and public health officials to better target nature contact recommendations for growing urban populations.

Organization: VermontView
Is high spatial resolution DEM data necessary for mapping palustrine wetlands?

Digital elevation models (DEMs) have been found to be an effective data source for automated mapping of wetlands. However, it is unclear whether high spatial resolution DEMs, which tend to be more expensive to acquire and process, are necessary for mapping wetlands such as those in the US National Wetland Inventory (NWI). Therefore, we compared predictions of the probability of palustrine wetland occurrence with a random forests (RF) algorithm using DEMs generated from light detection and ranging (LiDAR) at 1 m, 3 m, and 10 m raster cell sizes; and photogrammetrically-derived DEMs at 3 m and 10 m. For each classification, a wide range of terrain derivatives were generated and used as the input data for the classification. Comparisons between the wetland predictions were made using the receiver operating characteristic (ROC) area under the curve (AUC) measure, the Kappa statistic, overall accuracy, class user’s and producer’s accuracy, and the out of bag (OOB) error rate. For two different study sites, irrespective of the source of the digital terrain data, palustrine wetland occurrence was predicted with AUC values greater than 0.95, overall accuracies greater than 88%, Kappa greater than 0.77, and wetland user’s and producer’s accuracies above 0.85 when using a large training data set derived from the NWI or a small separate data set of non-NWI data derived from field samples. We therefore conclude that the source (LiDAR vs photogrammetric) and spatial scale (1 m, 3 m, or 10 m) of the DEM data does not have a large impact on the accuracy of the prediction of wetlands such as those in the NWI. However, for small wetlands, or more generally for wetlands unlike those in the NWI, finer scale data (e.g. 1 m) derived from LiDAR may be preferable.

Organization: West Virginia View
Large-Area, High Spatial Resolution Land Cover Mapping Using Random Forests, GEOBIA, and NAIP Orthophotography: Findings and Recommendations

Despite the need for quality land cover information, large-area, high spatial resolution land cover mapping has proven to be a difficult task for a variety of reasons including large data volumes, complexity of developing training and validation datasets, data availability, and heterogeneity in data and landscape conditions. We investigate the use of geographic object-based image analysis (GEOBIA), random forest (RF) machine learning, and National Agriculture Imagery Program (NAIP) orthophotography for mapping general land cover across the entire state of West Virginia, USA, an area of roughly 62,000 km2. We obtained an overall accuracy of 96.7% and a Kappa statistic of 0.886 using a combination of NAIP orthophotography and ancillary data. Despite the high overall classification accuracy, some classes were difficult to differentiate, as highlight by the low user’s and producer’s accuracies for the barren, impervious, and mixed developed classes. In contrast, forest, low vegetation, and water were generally mapped with accuracy. The inclusion of ancillary data and first- and second-order textural measures generally improved classification accuracy whereas band indices and object geometric measures were less valuable. Including super-object attributes improved the classification slightly; however, this increased the computational time and complexity. From the findings of this research and previous studies, recommendations are provided for mapping large spatial extents.

Organization: West Virginia View