View allAll Photos Tagged RemoteSensing,
The Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD), 1998-2019, V4.GL.03 consists of annual concentrations (micrograms per cubic meter) of all composition ground-level fine particulate matter (PM2.5). This data set combines AOD retrievals from multiple satellite algorithms including NASA MODerate resolution Imaging Spectroradiometer Collection 6.1 (MODIS C6.1), Multi-angle Imaging SpectroRadiometer Version 23 (MISRv23), MODIS Multi-Angle Implementation of Atmospheric Correction Collection 6 (MAIAC C6), and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) Deep Blue Version 4. The GEOS-Chem chemical transport model is used to relate this total column measure of aerosol to near-surface PM2.5 concentration. Geographically Weighted Regression (GWR) is used with global ground-based measurements from the World Health Organization (WHO) database to predict and adjust for the residual PM2.5 bias per grid cell in the initial satellite-derived values. This map represents concentration of all composition ground-level fine particulate matter for the year 2008.
Reference: APAAME_20221103_RHB-0039
Photographer: Robert Bewley
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
Reference: APAAME_20221123_RHB-0273
Photographer: Robert Bewley
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
Reference: APAAME_20221123_RHB-0269
Photographer: Robert Bewley
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
Reference: APAAME_20221103_FB-0323
Photographer: Firas Bqa'in
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
Reference: APAAME_20221123_RHB-0284
Photographer: Robert Bewley
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
Reference: APAAME_20221123_FB-0398
Photographer: Firas Bqa'in
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
Reference: APAAME_20221123_RHB-0275
Photographer: Robert Bewley
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
Reference: APAAME_20221103_RHB-0051
Photographer: Robert Bewley
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
The Global Development Potential Indices are part of the Land Use Land Cover collection. The data set contains 13 sector-level Development Potential Indices (DPIs) for sectors related to renewable energy (concentrated solar power, photovoltaic solar, wind, hydropower), fossil fuels (coal, conventional and unconventional oil and gas), mining (metallic, non-metallic), and agriculture (crop, biofuels expansion). Each DPI is a 1-km spatially-explicit, global land suitability map that has been validated using locations of planned development as well as examined for uncertainty and sensitivity. This map displays the DPI for conventional oil, grouped into six 6 classes ranging from very low to very high.
In 1962, the Appalachian Laboratory was founded in the mountains of western Maryland at the headwaters of the Chesapeake Bay watershed. Faculty there study the effects of land-use change on the freshwater and terrestrial ecosystems of the region, how they function in the Chesapeake Bay watershed, and how human activity may influence their health and sustainability.
Pictured: At the cutting edge of the linkage of remote sensing, ecology and earth science, Dr. Andrew Elmore is well known for his research on water resources and climate variability and his innovated work with satellite imagery.
Attribution: University of Maryland Center for Environmental Science/Amy Pelsinsky
We are in the remote island of Heard in the southern Indian Ocean looking at this great stratovolcano (Big Ben massif) which has been intermittently active since 1910. Copernicus Sentinel2
Satellite imagery of both the Rock House Fire in Jeff Davis and Presidio counties as well as the Roper Fire in Brewster County two days after both fires started.
The Global Development Potential Indices are part of the Land Use Land Cover collection. The data set contains 13 sector-level Development Potential Indices (DPIs) for sectors related to renewable energy (concentrated solar power, photovoltaic solar, wind, hydropower), fossil fuels (coal, conventional and unconventional oil and gas), mining (metallic, non-metallic), and agriculture (crop, biofuels expansion). Each DPI is a 1-km spatially-explicit, global land suitability map that has been validated using locations of planned development as well as examined for uncertainty and sensitivity. This map displays the DPI for unconventional gas, grouped into six 6 classes ranging from very low to very high.
Reference: APAAME_20221115_FBal-163
Photographer: Fadi Bala'wi
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
Reference: APAAME_20221115_FBal-156
Photographer: Fadi Bala'wi
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
Reference: APAAME_20221103_RHB-0021
Photographer: Robert Bewley
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
The Global Development Potential Indices are part of the Land Use Land Cover collection. The data set contains 13 sector-level Development Potential Indices (DPIs) for sectors related to renewable energy (concentrated solar power, photovoltaic solar, wind, hydropower), fossil fuels (coal, conventional and unconventional oil and gas), mining (metallic, non-metallic), and agriculture (crop, biofuels expansion). Each DPI is a 1-km spatially-explicit, global land suitability map that has been validated using locations of planned development as well as examined for uncertainty and sensitivity. This map displays the DPI for metallic mining, grouped into six 6 classes ranging from very low to very high.
Reference: APAAME_20221123_RHB-0274
Photographer: Robert Bewley
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
Characterization of Unpaved Road Conditions Through the Use of Remote Sensing: Road Condition Data Collection and Analysis
Michigan Tech Research Institute of Ann Arbor September 2013 Recent Projects Poster Presentations
The GPS is used to calculatethe X,Y,Z location of data collected by the lidar unit. NEON will provide free lidar data for 30 years over all of its core sites.
Reference: APAAME_20221123_RHB-0277
Photographer: Robert Bewley
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
Running through the rainforest, the Crepori ↖️ in the Pará state of north-central #Brazil brings its turbid waters to the Tapajós ↗️ which is a major tributary of the #Amazon River
The Global Development Potential Indices are part of the Land Use Land Cover collection. The data set contains 13 sector-level Development Potential Indices (DPIs) for sectors related to renewable energy (concentrated solar power, photovoltaic solar, wind, hydropower), fossil fuels (coal, conventional and unconventional oil and gas), mining (metallic, non-metallic), and agriculture (crop, biofuels expansion). Each DPI is a 1-km spatially-explicit, global land suitability map that has been validated using locations of planned development as well as examined for uncertainty and sensitivity. This map displays the DPI for non-metallic mining, grouped into six 6 classes ranging from very low to very high.
Chlorophyll-a concentration values indicate the statistically significant percent change in chlorophyll-a concentrations in near coastal waters (10-100 km) from 1998-2007, derived from SeaWiFS level-3 annual composites. The Change in Chlorophyll-a Concentration, v1 (1998-2007) data set is part of the Indicators of Coastal Water Quality collection. See more information at dx.doi.org/10.7927/H48W3B88.
India Village-Level Geospatial Socio-Economic Data Set: 1991, 2001 is part of the India Data Collection. This map represents female literates as a percent of the total population for the years 1991, and 2001 in the state of Gujarat.
Reference: APAAME_20221123_RHB-0279
Photographer: Robert Bewley
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
The Country Trends in Major Air Pollutants data set is part of the Air Quality for Health-Related Applications collection. This map represents country changes in Carbon Monoxide (CO) in parts per million (ppm), from the average CO for the years 2003, 2004, and 2005 to the average CO for the years 2016, 2017, and 2018.