Environmental Earth Sciences volume 79, Article number: 424
Wheat is a staple crop that is critical for feeding a hungry and growing planet, but its nutritive value has declined as global temperatures have warmed. The price offered to producers depends not only on yield but also grain protein content (GPC), which are often negatively related at the field scale but can positively covary depending in part on management strategies, emphasizing the need to understand their variability within individual fields. We measured yield and GPC in a winter wheat field in Sun River, Montana, USA, and tested the ability of normalized difference vegetation index (NDVI) measurements from an unoccupied aerial vehicle (UAV) on spatial scales of ~10 cm and from Landsat on spatial scales of 30 m to predict them. Landsat observations were poorly related to yield and GPC measurements. A multiple linear model using information from four (three) UAV flyovers was selected as the most parsimonious and predicted 26% (40%) of the variability in wheat yield (GPC). We sought to understand the optimal spatial scale for interpreting UAV observations given that the ~ 10 cm pixels yielded more than 12 million measurements at far finer resolution than the 12 m scale of the harvester. The variance in NDVI observations was “averaged out” at larger pixel sizes but only ~ 20% of the total variance was averaged out at the spatial scale of the harvester on some measurement dates. Spatial averaging to the scale of the harvester also made little difference in the total information content of NDVI fit using Beta distributions as quantified using the Kullback-Leibler divergence. Radially-averaged power spectra of UAV-measured NDVI revealed relatively steep power-law relationships with exponentially less variance at finer spatial scales. Results suggest that larger pixels can reasonably capture the information content of within-field NDVI, but the 30 m Landsat scale is too coarse to describe some of the key features of the field, which are consistent with topography, historic management practices, and edaphic variability. Future research should seek to determine an ‘optimum’ spatial scale for NDVI observations that minimizes effort (and therefore cost) while maintaining the ability of producers to make management decisions that positively impact wheat yield and GPC.
The Spatial Variability of NDVI Within a Wheat Field: Information Content and Implications for Yield and Grain Protein Monitoring
3.4.1 How is Texas Changing? Handout Lesson
Change is everywhere. We see change best when we can compare things—like before and after images. Landsat helps us do this. Landsat satellites have been imaging our planet over four decades, allowing us to see how the Earth’s land surface has changed. This poster provides examples of change over time as observed by Landsat satellites.
The data shows the location of cities in Afghanistan.
Maps with wind speed, wind rose and wind power density potential in India. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). GIS data is available as JSON and CSV. The second link provides poster size (.pdf) and midsize maps (.png).
India - Wind Speed and Wind Power Potential Maps
NASA’s Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was launched in September, 2018. The satellite carries a single instrument, ATLAS (Advanced Topographic Laser Altimeter System), a green wavelength, photon-counting lidar, enabling global measurement and monitoring of elevation with a primary focus on the cryosphere. Although bathymetric mapping was not one of the design goals for ATLAS, pre-launch work by our research team showed the potential to map bathymetry with ICESat-2, using data from MABEL (Multiple Altimeter Beam Experimental Lidar), NASA’s high-altitude airborne ATLAS emulator, and adapting the laser-radar equation for ATLAS specific parameters. However, many of the sensor variables were only approximations, which limited a full assessment of the bathymetric mapping capabilities of ICESat-2 during pre-launch studies. Following the successful launch, preliminary analyses of the geolocated photon returns have been conducted for a number of coastal sites, revealing several salient examples of seafloor detection in water depths of up to ~40 m. The geolocated seafloor photon returns cannot be taken as bathymetric measurements, however, since the algorithm used to generate them is not designed to account for the refraction that occurs at the air–water interface or the corresponding change in the speed of light in the water column. This paper presents the first early on-orbit validation of ICESat-2 bathymetry and quantification of the bathymetric mapping performance of ATLAS using data acquired over St. Thomas, U.S. Virgin Islands. A refraction correction, developed and tested in this work, is applied, after which the ICESat-2 bathymetry is compared against high-accuracy airborne topo-bathymetric lidar reference data collected by the U.S. Geological Survey (USGS) and the National Oceanic and Atmospheric Administration (NOAA). The results show agreement to within 0.43—0.60 m root mean square error (RMSE) over 1 m grid resolution for these early on-orbit data. Refraction-corrected bottom return photons are then inspected for four coastal locations around the globe in relation to Visible Infrared Imaging Radiometer Suite (VIIRS) Kd(490) data to empirically determine the maximum depth mapping capability of ATLAS as a function of water clarity. It is demonstrated that ATLAS has a maximum depth mapping capability of nearly 1 Secchi in depth for water depths up to 38 m and Kd(490) in the range of 0.05–0.12 m−1. Collectively, these results indicate the great potential for bathymetric mapping with ICESat-2, offering a promising new tool to assist in filling the global void in nearshore bathymetry.
Validation of ICESat-2 ATLAS Bathymetry and Analysis of ATLAS’s Bathymetric Mapping Performance
The Netherlands - Wind Speed and Wind Power Potential Maps - GIS
2021 Earth Observation Day Online Rivers Game
Yearly fact sheets that highlight the efforts and success of CaliforniaView
Data on Nigeria Settlement Location and Distribution downloaded from the The Bill & Melinda Gates Foundation Vaccination Tracking System (VTS). To learn more about the VTS project, please visit http://vts.eocng.org/Home/About?campaignId=0
Maps with wind speed, wind rose and wind power density potential in The Republic of Korea. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). GIS data is available as JSON and CSV. The second link provides poster size (.pdf) and midsize maps (.png).
Republic of Korea - Wind Speed and Wind Power Potential Maps
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.
The purpose of the study is to map agricultural drainage systems (ADS) at the watershed scale using remote sensing and GIS techniques and examine the effect of ADS. For achieving the purpose of the study, this study selected the Red River Valley (RRV) of the North in which agriculture is a primary industry at the region. Excessive nutrients, sediment, and pesticide from this agricultural area flow into the Red River throughout subsurface drainages. The ADS aims to remove excessed water from agricultural fields, and this ADS is divided into two systems - uncontrolled drainage system (UCDS) and controlled drainage system (CDS). While UCDS allows water flows to the stream or river through using pipes without controlling water table, CDS regulates water table by an equipped structure that controls the volume of water flows in the agricultural fields. For mapping artificially drained tiles between UCDS and CDS fields in the RRV, this study used DEM to digitize linear, map slope, and to calculate surface area. This study digitized linear maps with eight UCDS and twenty CDS fields and the map contains a digitization of tile drainage locations, drainage system patterns, ADS types, and artificially drained surface areas. In the analysis of the two different ADS systems – i.e., UCDS and CDS, this study obtained the indexes by using NDVI, NDWI, and MSAVI2 provided by PlanetScope imagery. In testing the group difference between UCDS and CDS in the three different indexes is examined by computing Analysis of Variance Analysis (ANOVA). Also, this study postulated CDS is more effective to the healthiness of crops than UCDS does in the ADS system. The results of ANOVA indicated that there is no difference in the spectral indexes analyzed by ADS type (i.e., NDVI, NDWI, and MSAVI2). This result implies that the healthiness of crops is not affected ADS type, at least for the year studied here. The causes of this result, as a limitation, is derived from missing information, which is that agricultural research should consider region-specific crop calendars that involve significant idiosyncratic information, such as crop types and cycles, and regional climates. Nonetheless, natural factors in the RRV in 2019 – e.g., weather – are, of course, things outside of the control of researchers and agricultural producers.
Mapping And Quantifying The Spatial Characteristics Of Agricultural Drainage Systems In Red River Valley Of The North
Data repository for measurements from 5 automated solar stations in Nepal. Data will be uploaded in batches, on a monthly basis, and will transmit daily reports on 1 minute average values for solar radiation levels, temperature, air pressure, liquid precipitation and wind speed. For more information and additional outputs, please visit: http://esmap.org/re_mapping_nepal For download access to GIS layers, please visit the Global Solar Atlas: http://globalsolaratlas.info/ Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP). For more information: Nepal-Solar Radiation Measurement Data, 2018,
Nepal - Solar Radiation Measurement Data
Photogrammetric Engineering & Remote Sensing, Volume 85, Number 4
Zambia - Wind Mapping Candidate Site Identification Report
In Summer 2016, an OregonView-sponsored research team, led by OSU graduate student, Kory Kellum, tested procedures for using Landsat 8 (L8) TIRS bands and in situ data for generating multi-temporal water surface temperature data products for the Iron Gate Reservoir, impounded by the Iron Gate Dam on the Klamath River. The water surface temperature map for Iron Gate Reservoir on July 20, 2016 is provided as a download from the OregonView website.
Water surface temperature map for Iron Gate Reservoir
Form 990 Return of Organization Exempt From Income Tax of AmericaView provided to the IRS.
The dataset contains location of Power Stations in Kenya. It was provided by Kenya Power and Lighting Company (KPLC).
Rhonda Jordan Antoine; Patrick Balla; Laurencia Njagi; Sudeshna Banerjee.
Small unmanned aircraft systems (sUAS) are being increasingly utilized to support an array of applications including agriculture, natural resource management, facility/infrastructure inspections, and package delivery. Currently, precision agriculture is the fastest growing market for sUAS. One of the major barriers to the adoption of transformative innovation is education. Currently, educators lack classroom ready resources that can be utilized to introduce precision agriculture. This 120 page publication was developed to directly address this need. This publication will support the needs of agricultural educators across Virginia, by providing an overview of precision agricultural principles, coupled with activities and learning lessons. All of the developed curriculum modules incorporate various STEM related concepts. The information, resources, and activities in this workbook are provided as an 'educational buffet'. These materials do not need to be consumed all at once. Educators can sample, take, modify, and deliver just the topics / activities / resources in this workbook that are needed.
An Introduction to Precision Agriculture: An Educator's Guide to Agricultural Earth Observation
Coastal Mapping and Charting Workshop Power Point Presentation
Yearly fact sheets that highlight the efforts and successes of ArkansasView.
Republic of Korea - Wind Speed and Wind Power Potential Maps - Posters
Taiwan - Wind Speed and Wind Power Potential Maps - GIS
The MTF survey is a global baseline survey on household access to electricity and clean cooking, which goes beyond the binary approach to look at access as a spectrum of service levels experienced by households. Resources included are raw data, codebook, questionnaires, sampling strategy document, and country diagnostic report. Formats include zip file (i.e. raw data sets in dta format), excel spreadsheet, pdf, and docx.
Kenya - Multi-Tier Framework (MTF) Survey
Alisha Pinto; Marina Brutinel; Bryan Bonsuk Koo; Dana Rysankova; Elisa Portale
2017 EOD Landsat Poster Spanish Version (front)
Philippines - Wind Speed and Wind Power Potential Maps - Posters
This dataset contains the input parameters for Model for Electricity Technology Assessment (META) tool for US, Romania, Jamaica and India (base year 2010). Furthermore, the META projected costs for 2015 and 2020 are presented.
META tool: Inputs Generation Technology, Projected Costs
Lebanon has traditionally been a major potato producer with 451,860 tons produced in 2014. Generally, potatoes make up 30% of the total Lebanese agricultural exports where approximately 60% of the potato production is exported to the Arab region, the UK and Brazil. The purpose of this study is to promote precision agriculture techniques in Lebanon that will help local farmers in the central Bekaa Valley with land management decisions. The European Space Agency’s satellite missions Sentinel-2A, launched June 23rd 2015, and the Sentinel-2B, recently launched on March 7th 2017, are multispectral high resolution imaging systems that provide global coverage every 5 days. The Sentinel program is a land monitoring program that includes an aim to improve agricultural practices. The imagery is 13 band data in the visible, near infrared and short wave infrared parts of the electromagnetic spectrum and ranges from 10-20 m including three 60 m bands pixel resolution. Sentinel is freely available data that has the potential to empower farmers with information to respond quickly to maximize crop health. Due to the political and security conflicts in the region, utilizing satellite imagery for Lebanon is more reasonable and realistic than operating Unmanned Aircraft Systems (UAS) for high resolution remote sensing. During the 2017 growing season, local farmers provided detailed information in designated fields on their farming practices, crop health, and pest threats. In parallel, Sentinel-2 imagery was processed to study crop health using the following vegetation indices: Normalized Difference Vegetation Index, Green Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index and Modified Soil Adjusted Vegetation Index 2. Most Lebanese farmers inherit their land from their parents over generations, and as a result most still use traditional farming techniques for irrigation, where decisions are based on prior generations’ practices. However, with the changes in climate conditions within the region, these practices are no longer as efficient as they used to be. Normalized Difference Water Indices are calculated from satellite bands in the near-infrared and short-wave infrared to provide a better understanding about the water stress status of crops within the field. Preliminary results demonstrate that Sentinel-2 data can provide detailed and timely data for farmers to effectively manage fields. Despite the fact that most Lebanese farmers rely on traditional farming methods, providing them with crop health information on their mobile phones and allowing them to test its efficiency has the potential to be a catalyst to help them improve their farming practices.
Utilizing Sentinel-2 Satellite Imagery for Precision Agriculture over Potato Fields in Lebanon
3.1.3 Which is Which? I Have, Who Has Game
More than Just a Pretty Picture: How Landsat Images are Made
In anticipation of the National Aeronautics and Space Administration (NASA) ICESat-2 mission, which will employ the Advanced Topographic Laser Altimeter System (ATLAS), a 532 nm photon-counting Light Detection and Ranging (lidar), we demonstrate a spaceborne data-fusion approach that has the potential to significantly shrink the global nearshore data gap often referred to as the "white ribbon". Bathymetry algorithms relying on multispectral imagery are conventionally limited by the availability of in situ reference depths, particularly in remote or difficult-to-map areas. Therefore, a completely spaceborne approach could greatly extend the usefulness of such algorithms. The approach is tested with data from NASA's airborne ICESat-2 ATLAS simulator, Multiple Altimeter Beam Experimental Lidar (MABEL), and passive optical imagery from Landsat-8 using an existing spectral-ratio algorithm. The output bathymetric data set agrees with high-resolution Fugro LADS MK II bathymetric data to within an RMS difference of 1.1 m. The spatiotemporal variability of areas where this spaceborne data-fusion approach will potentially be useful is evaluated, based on worldwide coastal water clarity as interpreted from Visible Infrared Imaging Radiometer Suite (VIIRS) Kd(490) data.
Active-Passive Spaceborne Data Fusion for Mapping Nearshore Bathymetry
This educational poster was created as part of the 2017 IowaView Earth as Art exhibit to give exhibit visitors information about how the images were made using Landsat imagery. The poster briefly explains how we see light and how then explains how the Landsat satellite can collect a much broader range of energy.
Additional material for Questionnaire on solar devices.pdf
Solar-Measurements_Zambia-Fig Tree_IFC_QC
This seminar is an applied study of deep learning methods for extracting information from geospatial data, such as aerial imagery, multispectral imagery, digital terrain data, and other digital cartographic representations. We first provide an introduction and conceptualization of artificial neural networks (ANNs). Next, we explore appropriate loss and assessment metrics for different use cases followed by the tensor data model, which is central to applying deep learning methods. Convolutional neural networks (CNNs) are then conceptualized with scene classification use cases. Lastly, we explore semantic segmentation, object detection, and instance segmentation. The primary focus of this course is semantic segmenation for pixel-level classification. The associated GitHub repo provides a series of applied examples. We hope to continue to add examples as methods and technologies further develop. These examples make use of a vareity of datasets (e.g., SAT-6, topoDL, Inria, LandCover.ai, vfillDL, and wvlcDL). Please see the repo for links to the data and associated papers. All examples have associated videos that walk through the process, which are also linked to the repo. A variety of deep learning architectures are explored including UNet, UNet++, DeepLabv3+, and Mask R-CNN. Currenlty, two examples use ArcGIS Pro and require no coding. The remaining five examples require coding and make use of PyTorch, Python, and R within the RStudio IDE. It is assumed that you have prior knowledge of coding in the Python and R enviroinments. If you do not have experience coding, please take a look at our Open-Source GIScience and Open-Source Spatial Analytics (R) courses, which explore coding in Python and R, respectively. After completing this seminar you will be able to: 1. explain how ANNs work including weights, bias, activation, and optimization. 2. describe and explain different loss and assessment metrics and determine appropriate use cases. 3. use the tensor data model to represent data as input for deep learning. 4. explain how CNNs work including convolutional operations/layers, kernel size, stride, padding, max pooling, activation, and batch normalization. 5. use PyTorch, Python, and R to prepare data, produce and assess scene classification models, and infer to new data. 6. explain common semantic segmentation architectures and how these methods allow for pixel-level classification and how they are different from traditional CNNs. 7. use PyTorch, Python, and R (or ArcGIS Pro) to prepare data, produce and assess semantic segmentation models, and infer to new data. 8. explain how object and instance segmentation are different from traditional CNNs and semantic segmentation and how they can be used to generate bounding boxes and feature masks for each instance of a class. 9. use ArcGIS Pro to perform object detection (to obtain bounding boxes) and instance segmentation (to obtain pixel-level instance masks).
Geospatial Deep Learning Seminar Online Course
The MTF survey is a global baseline survey on household access to electricity and clean cooking, which goes beyond the binary approach to look at access as a spectrum of service levels experienced by households. Resources included are raw data, codebook, questionnaires, sampling strategy document, and country diagnostic report. Formats include zip file (which includes raw data sets of dta format), excel spreadsheet, pdf, and docx.
Liberia - Multi-Tier Framework (MTF) Survey
Bangladesh - Solar Wind Measurements Implementation Plan
Summary of previous results (some presented at JALBTCX) • NASA MABEL (high- altitude airborne emulator for ATLAS) capable of mapping bathymetry • Agrees with reference bathymetry to within 0.7 m RMS • Visible Infrared Imaging Radiometer Suite (VIIRS) Kd490 useful in assessing utility of ATLAS for bathymetric mapping worldwide Objectives of current (post-launch) research 1. Identify examples of ICESat-2 ATLAS bathymetry 2. Empirically assess bathymetric mapping accuracy through comparison against reference data 3. Assess ATLAS’s maximum depth penetration capability as a function of Kd and Secchi depth through analysis at multiple locations around the globe
Empirical Analysis of ICESat‐2 ATLAS's Bathymetric Mapping Capability
International Journal of Remote Sensing Volume 40, 2019 - Issue 1
Throughout 2021, the Unoccupied Aircraft System (UAS) Team at the University of Vermont Spatial Analysis Lab carried out a variety of missions and projects, ranging from individual flight operations to multiple missions for ongoing partnerships and grants. Additional projects included research, training sessions, and professional workshops as well.
Operating power plants in Afghanistan as in 2016. Digitization based on Afghan Energy Information Center (AEIC) maps.
Afghanistan - Existing and Planned Power Plants
Europe and Central Asia - Wind Speed and Wind Power Potential Maps
South Asia - Wind Speed and Wind Power Potential Maps
2016 EOD Landsat Change over Time Poster
Cloud-based image processing platforms like the Google Earth Engine (GEE) bring unprecedented possibilities for education, research, and outreach. This workshop will focus on an interactive exploration of GEE capabilities, the repository of all of publicly available aerial and satellite data, and user upload of imagery for analysis. The workshop will begin with a presentation of examples of GEE projects with a focus on education, undergraduate research, and outreach followed by hands-activities.
Fundamentals of Image Analysis in Google Earth Engine
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.
Airborne Lidar Sampling Strategies to Enhance Forest Aboveground Biomass Estimation from Landsat Imagery
Snow cover is an important variable in both climatological and hydrological studies because of its relationship to environmental energy and mass flux. However, variability in snow cover can confound satellite-based efforts to monitor vegetation phenology. This research explores the utility of the PhenoCam network cameras to estimate Fractional Snow Cover (FSC) in grassland. The goal is to operationalize FSC estimates from PhenoCams to inform and improve satellite-based determination of phenological metrics. The study site is the Oakville Prairie Biological Field Station, located near Grand Forks, North Dakota. I developed a mostly semi-automated process to estimate FSC from PhenoCam images through Python coding. Compared with previous research, which make use of RGB images only, the use of the monochrome RGB + NIR (near-infrared) channel reduced pixel misclassification and increased accuracy. The results have an average RMSE of 7.67 compared to visual estimates. This is a promising outcome, although not every PhenoCam system has NIR capability.
Automated Fractional Snow Cover Monitoring From Near-Surface Remote Sensing In Grassland
Yearly fact sheets that highlight the efforts and success of KentuckyView.
Idaho and Lebanon rely on potatoes as an economically important crop. NDVI (Normalized Difference Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and MSAVI2 (Modified Soil Adjusted Vegetation Index 2) indices were calculated from PlanetScope satellite imagery for the 2017 growing season cloud free days. Variations in vegetation health were tracked over time and correlated to yield data provided by growers in Idaho. Based on ordinary least squares regression an Idaho yield forecast model was developed. Vegetation response during the growth stage at which potato tubers were filling out was significant in predicting yield for both the Norkotah and Russet potato variety. This corresponded to a week with high recorded temperatures that impacted the health status of the crops. The yield forecasting model was validated with a cross validation approach and then applied to potato fields in Lebanon. The Idaho model successfully displayed yield variation in crops for Lebanon. Spectral indices along with field topography allow the prediction of yield based on the crop type and variety.
From pixel to yield: forecasting potato productivity in Lebanon and Idaho
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.
Is high spatial resolution DEM data necessary for mapping palustrine wetlands?
Flash droughts can lead to significant agricultural and ecosystem impacts via rapid land surface desiccation. While gridded weather and climate datasets, land surface models, or widely spaced in situ observations are typically used to quantify flash drought development, coarse spatial data limits the ability to determine fine-scale spatial evolution of flash drought at landscape and ecosystem scales. In this study, a novel approach is introduced to objectively identify flash drought using the land surface water index (LSWI) derived from satellite observations. LSWI is a water-related vegetation index that represents the total water content in vegetation by using the near-infrared and shortwave infrared bands. LSWI was derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra surface reflectance (MOD09A1) with a 500 m spatial resolution and an 8-day temporal resolution. When applied to two well-established case studies, LSWI anomalies were able to capture the temporal and spatial evolution of flash drought over Oklahoma during the years of 2011 and 2012. In addition, rapid changes of LSWI during flash drought were comparable across space and time to the reanalysis-based Standardized Evaporative Stress Ratio (SESR), while negative anomalies of LSWI following flash drought corresponded with drought impacts via the United States Drought Monitor. It was found that LSWI was able to identify flash drought with a finer spatial resolution (500 m) and revealed spatial propagation of flash drought events that would not otherwise be seen with coarser meteorological data (e.g., ∼32 km at the lowest latitude for the North American Regional Reanalysis data). Furthermore, LSWI greatly enhanced the ability to detect rapid changes in surface conditions driven by flash drought and was able to provide early warning for drought development when compared with the USDM across Oklahoma. As such, the temporal and spatial evolution of flash drought depicted by LSWI and the presented methodology improves our ability to identify flash drought at high spatial resolution using satellite remote sensing and detect rapid changes in surface conditions. In light of these results, the novel LSWI approach demonstrates that satellite remote sensing applications using an objective technique are advantageous for flash drought detection in near real-time and at fine spatial scales.
Flash Drought Identification from Satellite-Based Land Surface Water Index
The existing (2015) and planned transmission network for Afghanistan including lines above 110 kV. Digitization based on the Afghan Energy Information Center (AEIC) maps available in March 2015.
Data repository for measurements from 8 wind masts in Zambia. Data transmits daily reports for wind speed, wind direction, air pressure, relative humidity and temperature. Please refer to the country project page for additional outputs and reports, including installation reports: https://www.esmap.org/re-mapping/zambia. For access to maps and GIS layers, please visit the Global Wind Atlas: http://globalwindatlas.info/ Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP).
KansasView also developed an AGOL web mapping application to provide users the ability to explore and visualize the National Science Foundation’s National Earth Observation Network (NEON) Airborne Observation Platform (AOP) remote sensing data collected at the KU Field Station (KUFS) along with Sentinel2, Landsat 8, and aerial image services (mid right). KansasView previously supported processing of tiled NEON AOP data to create data mosaics that were then made available through the online tool.
Kansas University Field Station NEON Data Viewer
30" x 20" poster about Landsat Collections
Data on healthcare facility locations in Kenya. The dataset was provided by the Government of Kenya.
Maps with worldwide wind speed and wind power density potential. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). The link provides poster size (.pdf) and midsize maps (.png).
Wind speed raster file over the territory of Afghanistan. Aggregated annual average values. Spatial resolution 5km.
Afghanistan - Annual Average Wind Speed Data
This exercise developed by Dr. Tom Mueller of California University of Pennsylvania addresses two National Geographic Standards: Standard 1: How to use maps and other geographic representations, geospatial technologies, and spatial thinking to understand and communicate information and Standard 14: How human actions modify the physical environment
Cropland and ArcGIS Online Exercise: Pixels and Crop Changes
Yearly fact sheets that highlight the efforts and success of OhioView.
Ground measurement data from 3 solar meteorological stations in Senegal. Data contains 1 minute average values for solar radiation, air temperature, relative humidity, barometric pressure, precipitation, wind speed at 3m, wind speed calculated for 10m, wind direction and cleaning events. For download access to GIS layers, please visit the Global Solar Atlas: http://globalsolaratlas.info/
Senegal - Solar Radiation Measurement Data
Unmanned aerial vehicles (UAV) are increasingly used for spatiotemporal monitoring of invasive plants in coastal wetlands. Early identification of invasive species is necessary in planning, restoring, and managing wetlands. This study assessed the effectiveness of UAV technology to identify invasive Phragmites australis in the Old Woman Creek (OWC) estuary using machine learning (ML) algorithms: Neural network (NN), support vector machine (SVM), and k-nearest neighbor (kNN). The ML algorithms were compared with the parametric maximum likelihood classifier (MLC) using pixel- and object-based methods. Pixel-based NN was identified as the best classifier with an overall accuracy of 94.80% and the lowest error of omission of 1.59%, the outcome desirable for effective eradication of Phragmites. The results were reached combining Sequoia multispectral imagery (green, red, red edge, and near-infrared bands) combined with the canopy height model (CHM) acquired in the mid-growing season and normalized difference vegetation index (NDVI) acquired later in the season. The sensitivity analysis, using various vegetation indices, image texture, CHM, and principal components (PC), demonstrated the impact of various feature layers on the classifiers. The study emphasizes the necessity of a suitable sampling and cross-validation methods, as well as the importance of optimum classification parameters.
Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers
Latin America and Caribbean - Wind Speed and Wind Power Potential Maps
India - Wind Speed and Wind Power Potential Maps - GIS
Interest in mapping bathymetry from multispectral satellite imagery has grown steadily since the 1970s. Notwithstanding the significant advancements made during this time, one limitation is that the group of algorithms collectively referred to as Satellite Derived Bathymetry (SDB) generally require “seed depths,” hindering their use for areas lacking reliable reference data. Recently, NASA’s ICESat-2 ATLAS, a satellite-based, photon counting green lidar, has been shown capable of bathymetric mapping. Although ICESat-2 bathymetry is constrained to discrete tracklines, synergistic fusion of ICESat-2 and multispectral, passive imagery can generate contiguous bathymetry over large spatial extents. Despite these significant recent results and the possibilities they open for filling shallow nearshore data voids worldwide, significant challenges remain, including: 1) ICESat 2/multispectral imagery fusion techniques are still in the early research stage and not efficient enough for widespread operational use for the average user, and 2) detailed, empirical accuracy assessments of fusion-based bathymetric products generated through operationally-efficient workflows have not been conducted. This research addresses these gaps. A highly-efficient workflow is developed and tested for generating bathymetric DEMs for large areas through largely-automated retrieval and fusion of ICESat-2 photon clouds and multispectral imagery, with the outputs disseminated via a webGIS. Empirical accuracy assessments are performed in two locations, selected to encompass vastly differing environmental conditions. One is a freshwater, inland water site where airborne imagery was used, while the other is a tropical, coastal saltwater site, covered by Landsat 8 and Sentinel-2 data. The workflow is shown to be operationally efficient and capable of producing bathymetry of sufficient accuracy for many coastal science applications, with RMSEs for the two sites being 0.95 m for Lake Tahoe (σ = 0.89 m, μ = 0.34 m) and 0.80 m (Sentinel-2; σ = 0.77 m, μ = -0.19 m) and 1.42 m (Landsat 8; σ = 1.19 m, μ = 0.77 m) for St. Croix.
An Efficient Workflow and Accuracy Assessment for ICESat-2 and Multispectral Imagery Fusion for Bathymetric Mapping
UtahView developed an interactive tour about the history of the Landsat Program using Google Earth Web.
Google Earth Web Tours - A History of the Landsat Program
This video playlist contains 17 short videos. These video tutorials can be viewed as a companion to the ebook "Working with Lidar Using ArcGIS Pro" or they can be viewed as stand-alone tutorials. Each of the 17 short videos target a specific topic, ranging from acquiring lidar data to Classifying Points Using Geoprocessing Tools, to Creating Surface Constraints. Note that these tutorials begin with Chapter 7. Chapters 1-6 are covered in Remote Sensing with ArcGIS Pro video tutorials.
Working with Lidar Using ArcGIS Pro - Video Tutorial Playlist
Zambia - Wind Mapping Tower Commissioning Report
OhioLink / Graduate College of Bowling Green State University thesis
The study aimed at assessing multi-scale correlation between vegetation characteristics and topographic parameters in Chittagong Hill Tracts (CHT), Bangladesh. Traditional shifting cultivation and its associated activities are considered unsuitable practices in CHT, therefore, the alternative land use systems e.g. agroforestry, tree farming, and afforestation are more suitable and sustainable in the region. We assessed the topographic controls on vegetation indices (NDII, NDVI, and EVI and their corresponding characteristics) to identify the potential sites for alternative land use practices. The result shows that NDII, EVI, and NDVI values vary 48%, 27%, and 23%, respectively, relative to their respective mean values. The soil moisture decreases with higher slope and elevation having the coefficient (R2) values of − 0.17 and − 0.19, respectively because the underlying soil cannot retain rainwater as run-off are comparatively faster in higher elevation and slope. Elevation and slope exert influence on all vegetation characteristics under consideration. Soil moisture is negatively paired with NDVI (R2 value of − 0.04) indicating, interestingly, that soil moisture content does not influence vegetation water content. Aspect has no significant correlation to any of the vegetation indices (R2 is ~ 0.02 in all cases) and, hence, while selecting sites for alternative agricultural practices, the topographic parameter of aspect can be ignored. NDII and EVI are significantly correlated more with elevation than slope. Thus, the potential site for the biomass or vegetation which retains moisture content should be in an area that is more susceptible to elevation followed by slope. In contrast, NDVI is significantly correlated with the soil moisture, slope, and altitude. Therefore, to get a generalized vigor of vegetation of higher green leaf content, primary production, chlorophyll content, and green leaf biomass, one must consider a combination of elevation and slope, as well as soil moisture, in CHT while selecting the potential sites for alternative agricultural practice.
Assessing topographic controls on vegetation characteristics in Chittagong Hill Tracts (CHT) from remotely sensed data
Human encroachment into wildlands has resulted in a rapid increase in wildland–urban interface (WUI) expansion, exposing more buildings and population to wildfire risks. More frequent mapping of structures and WUIs at a finer spatial resolution is needed for WUI characterization and hazard assessment. However, most approaches rely on high-resolution commercial satellite data with a particular focus on urban areas. We developed a deep learning framework tailored for building footprint detection in the transitional wildland–urban areas. We leveraged meter scale aerial imageries publicly available from the National Agriculture Imagery Program (NAIP) every 2 years. Our approach integrated Mobile-UNet and generative adversarial network. The deep learning models trained over three counties in California performed well in detecting building footprints across diverse landscapes, with an F1 score of 0.62, 0.67, and 0.75 in the interface WUI, intermix WUI, and rural regions, respectively. The bi-annual mapping captured both housing expansion and wildfire-caused building damages. The 30 m WUI maps generated from these finer footprints showed more granularity than the existing census tract-based maps and captured the transition of WUI dynamics well. More frequent updates of building footprint and improved WUI mapping will improve our understanding of WUI dynamics and provide guidance for adaptive strategies on community planning and wildfire hazard reduction.
Aerial Imagery-Based Building Footprint Detection with an Integrated Deep Learning Framework: Applications for Fine Scale Wildland–Urban Interface Mapping
Taiwan - Wind Speed and Wind Power Potential Maps - Posters
Maps with wind speed, wind rose and wind power density potential in The Netherlands. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). GIS data is available as JSON and CSV. The second link provides poster size (.pdf) and midsize maps (.png).
The Netherlands - Wind Speed and Wind Power Potential Maps
MTF Energy Access Country Diagnostics Report_Honduras
Bangladesh - Solar Wind Measurements Installation Report
5.2.2 NASA and USGS Change Imagery Resources
Accuracy Assessment Exercise 3: Stratified Random Sampling
2021 EOD Poster Insert - Spanish Version
Yearly fact sheets that highlight the efforts and success of MichiganView.
São Tomé and Príncipe - Multi-Tier Framework (MTF) Survey
Marina Brutinel; Yuhan Wang; Bryan Bonsuk Koo; Elisa Portale; Dana Rysankova
Iowa Best Management Practices Mapping Handbook
Coastal regions are undergoing rapid change, due to natural and anthropogenic forcings. A current constraint in understanding and modeling these changes is the lack of multi-temporal bathymetric data, or recursive observations. Often, it is difficult to obtain the repeat observations needed to quantify bathymetric change over time or events. However, the recent availability of ICESat-2 bathymetric lidar creates the option to map coastal bathymetry from solely space-based measurements via satellite-derived bathymetry with multispectral imagery (IS-2/SDB). This compositional space-based bathymetric mapping technique can assess temporal change along the coasts without other remote sensing or in situ data. However, questions exist as to the accuracy of the technique relative to both quantitative uncertainties and the ability to resolve the spatial patterns of erosion and deposition in the nearshore environment, indicative of geomorphologic change. This paper addresses the concept using data from the Florida panhandle (Northern Gulf of Mexico) collected by Sentinel-2 and ICESat-2 at two epochs to assess the feasibility of using IS-2/SDB for bathymetric change detection at scientifically relevant scales, spatial resolutions and accuracies. The comparison of the satellite-only result is compared to airborne data collected at similar epochs to reveal both quantitatively and qualitatively the utility of this technique.
Assessing the Ability to Quantify Bathymetric Change over Time Using Solely Satellite-Based Measurements
This StoryMap explains introductory remote sensing concepts and the influence of environmental factors and human land use on the historic and current Kansas landscape. It also takes you on a tour of Kansas, as seen on the ground and from above. It also shows you the new Kansas Ecological Systems Map, showing the 49 mapped vegetation and land cover types.
Ground measurement data from 3 solar meteorological stations in Zambia. Data contains 1 minute average values for solar radiation, air temperature, relative humidity, barometric pressure, precipitation, wind speed and wind direction, cleaned and soiled radiance sensor (soiling measurement) and cleaning events.
Zambia - Solar Radiation Measurement Data
Yearly fact sheets that highlight the efforts and success of KansasView.
Nepal Presentation Final Results, Solar Measurement Campaign_April-26-WB-ESMAP.pdf
The application of remote sensing techniques in mapping, classifying and monitoring land cover, land use and vegetation are popular among the researchers and scientists for several decades. It became more productive and economical in recent years with the advancement of information technology in a sophisticated and revolutionary manner. Currently, remote sensing is a widely used effective technique that provides spatial and temporal information about vegetation and invasive species in wetlands. The first objective of this study was to assess the effectiveness of the data obtained via Unmanned Ariel Vehicles (UAV) to identify invasive Phragmites australis in the Old Woman Creek (OWC) in Ohio. Secondly, the study aimed to determine the most suitable algorithm to distinguish between Phragmites australis and other vegetation types using pixel based and object based classification methods and a combination of feature layers derived from the UAV images. Pixel based classification found to be performing better than object based classification. Pixel based Neural Network (NN) was identified as the best classifier to map Phragmites in OWC with the least error of omission of 1.59% and the overall accuracy of 94.80% based on the Sequoia image acquired in August that was stacked with Canopy Height Model (CHM) from August and NDVI, which was derived using UAV data acquired in October (NDVIOct). The study emphasizes the necessity of a suitable sampling method and the use of optimum parameters of non-parametric classifiers. The study provides future directions for data acquisition to map Phrgamites at early and mid-summer to find data to eradicate Phragmites effectively in OWC estuary.
Mapping Invasive Phragmites australis in the Old Woman Creek Using Remote Sensing
Manual and scripts in R for conducting “agnostic” image classification and accuracy assessment.
Program - Solar Resource Measurement in Nepal - April 26-27, 2021.pdf
Global Horizontal Irradiation (GHI) raster file over the territory of Afghanistan. Aggregated annual average values. Spatial resolution 5km.
Afghanistan - Global Horizontal Irradiation
Changes in land use have resulted in range shifts of many wildlife species, including those entering novel environments, resulting in the critical need to understand their spatial ecology to inform ecosystem effects and management decisions. Dispersing elk (Cervus elaphus) were colonizing areas of suitable habitat in the Northern Great Plains, USA, resulting in crop depredation complaints in these areas. Although state resource managers had little information on these elk herds, limited evidence suggested temporal movements into Canada. We collected and analyzed essential information on home range and habitat selection for 3 elk herds residing in North Dakota. We captured 5 adult female elk in each study area, affixed global positioning system collars, and monitored them for 1 year (2016–2017). We estimated diel period, seasonal, and hunting season home ranges using Brownian Bridge Movement Models for each individual. We analyzed habitat selection using multinomial logit models to test for differences in use of land classes, and for departures from proportionate use based on random sampling; our predictor variables included individual elk, diel period, and season. Home ranges differed between the 3 herds, seasons, and diel period; gun and winter season home ranges were both larger than in summer, as was night when compared with day. Female elk generally restricted themselves to cover during the day and entered open areas at night and during winter months. Our results also suggest that elk in our study areas tended to seek more cover, and in the case of our Turtle Mountain study area, some cross into Canada during gun season. Our study provides a better understanding of the spatial ecology of elk in the Northern Great Plains while highlighting the need for enhanced international cooperative management efforts.
Seasonal home ranges and habitat selection of three elk (Cervus elaphus) herds in North Dakota
Remote Sensing Applications: Society and Environment Volume 11
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W11, 1–7, 2020
Accuracy Assessment Exercise 1: Descriptive Statistics
The Netherlands - Wind Speed and Wind Power Potential Maps - Posters
Learn everything about why and how to do an accuracy assessment on remotely sensed data through online videos and associated exercises. This video series was developed by Dr. Russ Congalton with New HampshireView.
Accuracy Assessment of Remotely Sensed Data Video Series
The 2021 poster turns to the theme of “Water Today and for the Future.” This poster contains the creative game experience to help bring understanding to how water resources in America are used and managed for future use. You can download and print the poster and you can play the game online.
Learn foundations of GIS concepts and geospatial technology to view, understand, query, visualize, and interpret geospatial data to reveal patterns and relationships for effective problem-solving and better decision making.
3.4.2 How is Texas Changing? Scramble Game
The GIS Biomass Atlas of Pakistan is the final output from the biomass resource mapping component of the activity “ Renewable Energy Resource Mapping and Geospatial Planning – Pakistan” [Project ID: P146140]. You can find more information about the project here: https://www.esmap.org/re_mapping_pakistan To visualize the geospatial data generated in this study, please access: https://irena.masdar.ac.ae/GIS/?map=2636 Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP). For more information: Pakistan Biomass GIS Atlas, 2016, https://energydata.info/dataset/pakistan-biomass-gis-atlas"
An Earth Observations education resource for grades 4 -12 in support of TEKS covering Earth Science and Environmental Science Standards Watching over Texas from Space has been growing over the past six years, as TexasView has expanded its outreach effort through teacher-training workshops. It is modeled after an online publication from the Canada Centre from Remote Sensing “Watching Over Our Planet from Space” The initial lessons were developed specifically to address the Eighth-grade requirement for Texas’ Earth and Space Science course that states “the student is expected to interpret topographic maps and satellite views to identify land and erosional features and predict how these features may be reshaped by weathering”. Teachers from other grade levels always attend these workshops and found the lessons applicable for diverse requirements. Lessons that provide imagery for teachers to use at multiple grade levels have been integrated with lectures and games, to enable teachers to easily find and use imagery in the classroom and in laboratory activities to address knowledge and skills defined within the Texas Essential Knowledge and Skills (TEKS) standards. The imagery used in the lessons is predominantly over the State of Texas, but the topics that are covered also fit very well with national-level standards defined in the Next Generation Science Standards (NGSS).
Watching over Texas from Space Lesson Book
GY19 UtahView MiniGrant - Google Earth Web Tours - A History of the Landsat Program
This empirical research examines the normalized difference vegetation index (NDVI) and the percent impervious surface area (%ISA) as indicators of urban heat island (UHI) effects, using the relationships between land surface temperature (LST), %ISA, and NDVI. Landsat 8 Operational Land Imager and Thermal Infrared Sensor data were used to estimate the LST in Connecticut at different times. A map of the percent impervious surface was generated using the Impervious Surface Analysis Tool developed by the Center for Land Use Education and Research and distributed through the National Oceanic and Atmospheric Administration. Strong linear relationships between LST and %ISA exist, as stated in previous studies, whereas the relationship between LST and NDVI is evidently affected by the seasons. As the patterns of LST in urban areas are influenced by the UHI, the results demonstrate that %ISA is a more reliable indicator of UHI effects than NDVI. Thus, %ISA could be a promising alternative for use in the quantitative analysis of LST when studying UHI effects.
Using Landsat 8 data to compare percent impervious surface area and normalized difference vegetation index as indicators of urban heat island effects in Connecticut, USA
30" x 20" poster about Landsat products (Level-1 data products, Level-2 science products, and Level-3 science products)
Yearly fact sheets that highlight the efforts and success of North DakotaView.
Maps with wind speed, wind rose and wind power density potential in The United States of America. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). GIS data is available as JSON and CSV. The second link provides poster size (.pdf) and midsize maps (.png).
Unites States of America - Wind Speed and Wind Power Potential Maps
The dataset contains primary substations in Kenya. The dataset was provided by Kenya Power and Lighting Company (KPLC).
3.4.3 How is Texas Changing? Change Pair Quiz
AmeriGEOSS Community Platform DataHub. (BETA)
Participants in this course will learn about remote sensing of wildfires from instructors at the University of Alaska Fairbanks, located in one of the world’s most active wildfire zones. Students will learn about wildfire behavior, and get hands-on experience with tools and resources used by professionals to create geospatial maps that support firefighters on the ground. Upon completion, students will be able to: Access web resources that provide near real-time updates on active wildfires, Navigate databases of remote sensing imagery and data, Analyze geospatial data to detect fire hot spots, map burn areas, and assess severity, Process image and GIS data in open source tools like QGIS and Google Earth Engine.
Remote Sensing of Wildfire Online Course
Honduras - Multi-Tier Framework (MTF) Survey
Lucia Luzi, Abdul-Farouk Bemba Nabourema, Bryan Bonsuk Koo, Dana Rysankova, Elisa Portale
5.1 Answers Keys to all Activities and Quizzes
Republic of Korea - Wind Speed and Wind Power Potential Maps - GIS
2017 EOD Landsat Poster Spanish Version (back)
This study integrated ecosystem services (ES) and 3D geo-visualization with urban design practices to inform future development in Pocatello, Idaho. Pocatello is a medium-sized city in southeastern Idaho that faces a range of challenges for sustainable future growth. It is important to understand what different urban designs can contribute to local ES. The objectives for this study were: building a 3D model for Pocatello Old Town, integrating key attributes in the model for planning and geodesign, visualizing in 3D using VR, and WebGL, and further, creating and analyzing the changes on local ES via future scenarios.The results showed that planting more treesand replacing impervious parking lots with green parking lots could reduce land surface temperature and improve air quality by removing particulate matter. With this approach, the decision makers in the City of Pocatello can analyze future scenarios both visually and statistically.
Modeling and Visualizing Ecosystem Services for 3D Urban Planning
3.1.5 Which is Which? Different Perspectives Matching Quiz
Solar-Measurements_Zambia-Fig Tree_IFC_Header
Workshop at ASPRS Annual Conference (Denver Jan 2019)
Maps with wind speed, wind rose and wind power density potential in Taiwan. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). GIS data is available as JSON and CSV. The second link provides poster size (.pdf) and midsize maps (.png).
Taiwan - Wind Speed and Wind Power Potential Maps
Maps with wind speed, wind rose and wind power density potential in Pakistan. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). GIS data is available as JSON and CSV. The second link provides poster size (.pdf) and midsize maps (.png).
Pakistan - Wind Speed and Wind Power Potential Maps
Remote Sensing Applications: Society and Environment
Learn about 12 individuals that contributed to the development of the Landsat Program.
The Pioneers of the Landsat Program Poster
Nigeria - Jigawa State Settlement Dataset
Philippines - Wind Speed and Wind Power Potential Maps - GIS
TexasView 2022 Student Research Fact Sheet
United States of America - Wind Speed and Wind Power Potential Maps - Posters
MTF Energy Access Country Diagnostics Report_LIBERIA
Maps with wind speed and wind power density potential for South Asia. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). The link provides poster size (.pdf) and midsize maps (.png).
Accuracy Assessment Exercise 2: Random Sampling
The capacity of terrestrial ecosystems to sequester carbon dioxide (CO2) from the atmosphere is expected to be altered by climate change and CO2 fertilization, but this projection is limited by our understanding of how the soil system interacts with plants. Understanding the soil–vegetation interactions is essential to assess the magnitude and response of terrestrial ecosystems to the changing climate. Here, we used soil profile and satellite data to explore the role that soil properties play in regulating water and carbon use by plants. Data obtained for 19 terrestrial ecosystem sites in a warm temperate and humid climate were used to investigate the relationship between remotely sensed data and soil physical and chemical properties. Classification and regression tree results showed that in situ soil carbon isotope (δ13C), and soil order were significant predictors (r2 = 0.39, mean absolute error (MAE) = 0 of 0.175 gC/KgH2O) of remotely sensed water use efficiency (WUE) based on the Moderate Resolution Imaging Spectroradiometer (MODIS). Soil extractable calcium (Ca), and land cover type were significant predictors of remotely sensed carbon use efficiency (CUE) based on MODIS and Landsat data-(r2 = 0.64–0.78, MAE = 0.04–0.06). We used gross primary productivity (GPP) derived from solar-induced fluorescence (SIF) data, based on the Orbiting Carbon Observatory-2 (OCO-2), to calculate WUE and CUE (referred to as WUESIF and CUESIF, respectively) for our study sites. The regression tree analysis revealed that soil organic matter and soil extractable magnesium (Mg), δ13C, and soil silt content were the important predictors of both WUESIF (r2 = 0.19, MAE = 0.64 gC/KgH2O) and CUESIF (r2 = 0.45, MAE = 0.1), respectively. Our results revealed the importance of soil extractable Ca, soil carbon (S13C is a facet of soil carbon content), and soil organic matter predicting CUE and WUE. Insights gained from this study highlighted the importance of biotic and abiotic factors regulating plant and soil interactions. These types of data are timely and critical for accurate predictions of how terrestrial ecosystems respond to climate change.
Linking Remotely Sensed Carbon and Water Use Efficiencies with In Situ Soil Properties
This web app allows users to search a map of Iowa for conservation practice data by HUC 12 watershed and then download it as a pdf or geodatabase.
Iowa BMP Mapping Project Data Download Website
WyomingView interns (University of Wyoming students) describe the value of remotely sensed data for mapping and monitoring crop and rangelands.
Data Acquisition • Acquiring data stored on Google’s servers for use in Google Earth Engine.
GEE_1: Google Earth Engine Tutorial Pt. I
LAWRENCE — Fall break is barely behind us, but a group of University of Kansas students has just finished an innovative eight-week course in using drones to develop aerial maps. Over the past two months, they’ve visited sites in KU's West District and at the Baker Wetlands, taking still images and videos over those areas. “The drone mapping course has been excellent in providing a hands-on experience with the drones,” said Siddharth Shankar, graduate student from Lucknow, India. “The course has focused not just on drones and how to fly them but also has made us aware of the FAA rules and regulations about drone flying and safety precautions. “My research has been in glaciology, with the study of icebergs in Greenland. The drone mapping course has provided new insights into incorporating it with my research in the near future.” The course, offered annually during the fall semester, is designed to teach students about the rapidly growing technology of small unmanned aerial systems, referred to as drones, and its wide-ranging applications — which include search-and-rescue, real estate and environmental monitoring. Students in the course come from a variety of disciplines including geography & atmospheric science, geology, ecology & evolutionary biology and civil engineering. Enthusiasm for the course has been very high, and it has filled rapidly each time it has been offered.
KU Students Wrap Up Course in Aerial Mapping Using Drones
The 2017 Earth Observation Day poster features a mosaic of Landsat 8 satellite images showing the contiguous United States of America on the front and a board game-style exploration of the history and contributions of the Landsat program on the back.
Nigeria - Katsina State Settlement Dataset
Data repository for measurements from a solar measurement station in Bangladesh. Data will be uploaded in batches, on a monthly basis, and will transmit daily reports on 1 minute average values for solar radiation levels, temperature, air pressure and wind speed. For download access to GIS layers, please visit the Global Solar Atlas: http://globalsolaratlas.info/
Bangladesh - Solar Radiation Measurement Data
United States of America - Wind Speed and Wind Power Potential Maps - GIS
Yearly factsheets that highlight the efforts and success of MinnesotaView.
This database contains parameters gathered after an assessment of the technological potential for development of floating solar photovoltaic (PV) projects on existing hydropower dams and other reservoirs, starting with a pilot in FY18 focused on West Africa and India gathered from World Bank hydro projects. This database has been expanded to include the International Hydropower Association (IHA) dataset for India: https://energydata.info/dataset/india-hydropower-database It also includes data gathered from the FAO's public Aquastat database for Africa: http://www.fao.org/aquastat/en/databases/dams This dataset also contains data gathered from the public Global Reservoir and Dam Database (GRanD) for Africa.The database will be included in the Global Solar Atlas (http://globalsolaratlas.info/) where they will be directly linked to the solar resource through the Floating Solar module. The purpose of the resulting Floating Solar Atlas is to enable potential developers or stakeholders to quickly navigate to the site of interest and obtain information about the potential for development of a floating solar PV project. Following the FY19 updates, the database will continue to expand to other regions until a global dataset has been completed.The current coverage spans the continent of Africa and India only, with upcoming updates to include other continents.
Yearly fact sheets that highlight the efforts and success of VermontView.
Yearly fact sheets that highlight the efforts and success of MontanaView.
India - Wind Speed and Wind Power Potential Maps - Posters
Through the cooperation of the LouisianaView consortium members and co-sponsored with the local USGS liaison, this annual workshop is offered free to everyone interested in up-to-date information on data availability for the geospatial emergency responder. This is a 4-day virtual workshop hosts speakers from multiple Federal, State and Private Response Teams, each presenting their data, websites, links, and contacts while also fielding questions live from those in attendance, proving again and again what a cohesive and informed network of geospatial responders can mean to the inhabitants and economic base within Louisiana, the Gulf of Mexico region and the Caribbean.
Hurricane Season Geospatial Data Mining Workshop
3.4.4 How is Texas Changing? Poster Activity
Maps with wind speed, wind rose and wind power density potential in The Philippines. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). GIS data is available as JSON and CSV. The second link provides poster size (.pdf) and midsize maps (.png).
Philippines - Wind Speed and Wind Power Potential Maps
Maps with wind speed and wind power density potential in Latin America and Caribbean. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). The link provides poster size (.pdf) and midsize maps (.png).
Idaho State University MS in Geosciences thesis
TexasView 2023 Student Research Fact Sheet
Pakistan - Wind Speed and Wind Power Potential Maps - GIS
Yearly fact sheets highlighting the efforts and successes of TexasView.
Yearly fact sheets about the accomplishments of NevadaView activities.
Data repository for measurements from a wind measurement station with a lidar in Bangladesh. Data will be uploaded in batches, on a monthly basis, and will transmit daily reports on 1 minute average values. Please refer to the country project page for additional outputs and reports, including the installation reports: http://esmap.org/re-mapping/bangladesh. For access to maps and GIS layers, please visit the Global Wind Atlas: https://globalwindatlas.info/
Pakistan - Wind Speed and Wind Power Potential Maps - Posters
Yearly fact sheets that highlight the efforts and success of New YorkView.
Accuracy assessment is one of the most important components of both applied and research-oriented remote sensing projects. For mapped classes that have sharp and easily identified boundaries, a broad array of accuracy assessment methods has been developed. However, accuracy assessment is in many cases complicated by classes that have fuzzy, indeterminate, or gradational boundaries, a condition which is common in real landscapes; for example, the boundaries of wetlands, many soil map units, and tree crowns. In such circumstances, the conventional approach of treating all reference pixels as equally important, whether located on the map close to the boundary of a class, or in the class center, can lead to misleading results. We therefore propose an accuracy assessment approach that relies on center-weighting map segment area to calculate a variety of common classification metrics including overall accuracy, class user’s and producer’s accuracy, precision, recall, specificity, and the F1 score. This method offers an augmentation of traditional assessment methods, can be used for both binary and multiclass assessment, allows for the calculation of count- and area-based measures, and permits the user to define the impact of distance from map segment edges based on a distance weighting exponent and a saturation threshold distance, after which the weighting ceases to grow. The method is demonstrated using synthetic and real examples, highlighting its use when the accuracy of maps with inherently uncertain class boundaries is evaluated.
Thematic Classification Accuracy Assessment with Inherently Uncertain Boundaries: An Argument for Center-Weighted Accuracy Assessment Metrics
Data repository for measurements from 5 automated solar stations in Vietnam. Data will be uploaded in batches, on a monthly basis, and will transmit daily reports on 1 minute average values for solar radiation levels, temperature, air pressure and wind speed. For more information and additional outputs, please visit: http://esmap.org/re_mapping_vietnam For download access to GIS layers, please visit the Global Solar Atlas: http://globalsolaratlas.info/ 5 stations: VNCEH (Central Highlands): Tier 1 HelioScale omega / VNDAN (Da Nang): Tier 2 HelioScale phi / VNHAN (Hanoi): Tier 1 HelioScale omega / VNSOB (Song Binh): Tier 2 HelioScale phi / VNTRA (Tri-An): Tier 1 HelioScale omega Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP). For more information: Vietnam-Solar Radiation Measurement Data, 2017,
Vietnam - Solar Radiation Measurement Data
This document provides an overview of the Iowa BMP project and three associated projects. It provides background on the project, information about the data structure and necessary input data, descriptions and images of the conservation practices, management considerations, and additional documents needed for project tracking. This project was made possible because of strong collaboration among different funding partners, including AmericaView and state government agencies as well as private industry.