View allAll Photos Tagged RemoteSensing,

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.

Reference: APAAME_20221123_FB-0399

Photographer: Firas Bqa'in

Credit: APAAME

Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works

Remote Sensing Center, WREA, Vientiane, Laos (March 22, 2010)

Reference: APAAME_20221103_BT-0030

Photographer: Bashar Tabbah

MAP&LENS

Credit: APAAME

Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works

Reference: APAAME_20221103_FB-0380

Photographer: Firas Bqa'in

Credit: APAAME

Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works

OLYMPUS DIGITAL CAMERA

Copernicus Sentinel2 2022-08-03

Reference: APAAME_20221115_FBal-152

Photographer: Fadi Bala'wi

Credit: APAAME

Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works

Farms east of Cairo Egypt are islands of vegetation in otherwise desert areas.

 

Imagery from European Space Agency Sentinel-2A satellite. Downloaded and processed by Jordan Lui 2017

Reference: APAAME_20221121_FB-0817

Photographer: Firas Bqa'in

Credit: APAAME

Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works

Reference: APAAME_20221123_FB-0397

Photographer: Firas Bqa'in

Credit: APAAME

Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works

Reference: APAAME_20221123_FB-0248

Photographer: Firas Bqa'in

Credit: APAAME

Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works

Flying from Philly⇒Seattle

Copernicus Sentinel2 2022-08-03 Iraq

Passive Remote sensing Passive remote sensing is a class of Remote Sensing that make use of Passive Remote Sensors. The sensors are used to detect natural radiations that are emitted by the object or by its surrounding areas. The most common source of energy that is measured by Passive Remote Sensors is “Reflected Sunlight”.

Somewhere in China, CopernicusEU Sentinel2 2022-02-20

Reference: APAAME_20221123_RHB-0280

Photographer: Robert Bewley

Credit: APAAME

Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works

Active Remote Sensing, Active Remote Sensing is a class of remote Sensing that makes use of Active Remote Sensors. These sensors provide their own source of illumination and they emit radiations that are directed towards the target body that is to be investigated. Active Remote sensors emit energy in order to scan the objects and areas and they then detect and measure the radiations that are reflected or are backscattered from the target body.

Reference: APAAME_20221115_FBal-162

Photographer: Fadi Bala'wi

Credit: APAAME

Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works

Reference: APAAME_20221123_FB-0079

Photographer: Firas Bqa'in

Credit: APAAME

Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works

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