View allAll Photos Tagged RemoteSensing,
Reference: APAAME_20221123_RHB-0290
Photographer: Robert Bewley
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
Reference: APAAME_20221103_FB-0223
Photographer: Firas Bqa'in
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
The Global Summer Land Surface Temperature (LST) Grids, 2013, part of the Satellite-Derived Environmental Indicators collection, estimate daytime (1:30 p.m.) maximum temperature and nighttime (1:30 a.m.) minimum temperature in degrees Celsius at a spatial resolution of ~1km during summer months of the northern and southern hemispheres for the year 2013. The LST grids are produced using the Aqua Level-3 Moderate Resolution Imaging Spectroradiometer (MODIS) Version 5 global daytime and nighttime LST 8-day composite data product (MYD11A2). See more information at dx.doi.org/10.7927/H408638T.
Using AI and Drones to Aid Precision Regenerative Agriculture
Supervisor: Dr. Wayne Forsythe
Theme: Physical Geography, Remote Sensing, Agriculture
Location: City of Toronto
This poster shows a supervised classification of the soil of a natural color image of marginal urban greenspace. In particular, this project classifies the parts per million measurement of nutrients in soil. In this case, it was to help the Garden Guild at The Church of St Peter and St Simon-the-Apostle, near Bloor and Parliament, in the City of Toronto.
PCI Geomatica is used to train and run the supervised classification, though QGIS also has open source plugins for the same operation. In any event, these types of classification maps can theoretically aid autonomous regenerative agriculture efforts worldwide. The goal is to create a deep learning algorithm, from a large sample of completed classifications, that can create soil classification maps from natural colour images, so that farmers all around the world can have a dependable soil analysis tool without the need to know how to train and run their own supervised classifications.
Reference: APAAME_20221123_RHB-0306
Photographer: Robert Bewley
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
The Global Summer Land Surface Temperature (LST) Grids, 2013, part of the Satellite-Derived Environmental Indicators collection, estimate daytime (1:30 p.m.) maximum temperature and nighttime (1:30 a.m.) minimum temperature in degrees Celsius at a spatial resolution of ~1km during summer months of the northern and southern hemispheres for the year 2013. The LST grids are produced using the Aqua Level-3 Moderate Resolution Imaging Spectroradiometer (MODIS) Version 5 global daytime and nighttime LST 8-day composite data product (MYD11A2). See more information at dx.doi.org/10.7927/H408638T.
Lake Sentarum in West Kalimantan, Indonesia.
Photo by Yayan Indriatmoko/CIFOR
If you use one of our photos, please credit it accordingly and let us know. You can reach us through our Flickr account or at: cifor-mediainfo@cgiar.org and m.edliadi@cgiar.org
Reference: APAAME_20221121_DS-0331
Photographer: Dana Salameen
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
Reference: APAAME_20221123_RHB-0288
Photographer: Robert Bewley
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
False-colour image of a patch of central Queensland near Charleville.
Bands displayed:
R: 5
G: 4
B: 2
With this choice of band display, fresh grass is usually bright green, trees dark green, and bare soil and dead, dry grass various shades of pink, magenta and buff. Blackish purple areas can be recent bushfire or recently ploughed dark soil.
If you compare two images from different dates using these bands, you can see a change from dark green to bright pink if someone has cleared a patch of woodland or forest. There was a lot of clearing going on in 2003, but in later years it has all-but stopped, due to stricter legislative control over clearance of native tree cover.
Landsat 5's orbit is 705 kilometres above the earth's surface, and each pixel in its Thematic Mapper imagery corresponds to a 30 x 30 metre square on the earth's surface.
Second version of the Bahamas image I published in 2019. This one is taken in 2022 by CopernicusEU Sentinel2 satellite
Image of LIDAR (Light Detection and Ranging) data, collected by the NEON airborne observation platform over the Soaproot Saddle site in California.
Reference: APAAME_20221123_RHB-0302
Photographer: Robert Bewley
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
Copernicus Sentinel2 2022-04-17 Bear River Bay and Compass Minerals, close to Ogden, Utah, United States
Reference: APAAME_20221123_RHB-0295
Photographer: Robert Bewley
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
The Global Fire Emissions Indicators, Grids: 1997-2015 contain a time-series of rasters from 1997 to 2015 for total area burned (hectares) and total carbon content (tons). The data are produced by combining 500m MODIS (Moderate-Resolution Imaging Spectroradiometer) burn area maps with active fire data from ATSR (Along-Track Scanning Radiometer) and TRMM (Tropical Rainfall Measuring Mission) VIRS (Visible and Infrared Scanner). The annual total area burned is for all fire types (Agricultural, Boreal, Tropical Deforestation, Peat, Savanna, and Temperate forests) and represents the total area (hectares) in each 0.25 degree x 0.25 degree grid cell.
Reference: APAAME_20221123_RHB-0287
Photographer: Robert Bewley
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
The Global Human Modification of Terrestrial Systems, part of the Land Use Land Cover collection, provides a cumulative measure of the human modification of terrestrial lands across the globe at a 1-km resolution. It is a continuous 0-1 metric that reflects the proportion of a landscape modified, based on modeling the physical extents of 13 anthropogenic stressors and their estimated impacts using spatially-explicit global data sets with a median year of 2016. This map displays the Cumulative Degree of Human Modification (HMc) categorized as No Presence of Stressors (0.00), Low (0.01 ? HMc ? 0.10), Moderate (0.10 < HMc ? 0.40), High (0.40 < HMc ? 0.70), and Very High (0.70 < HMc ? 1.00).
Reference: APAAME_20221123_RHB-0291
Photographer: Robert Bewley
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
UMass Boston Professor of Remote Sensing Crystal Schaaf, UMass Lowell Professor Supriya Chakrabarti, and Boston University Professor Alan Strahler make up a National Science Foundation-funded team building two new LiDARs, technology that measures distance by illuminating a target with a laser and analyzing the reflected light. One of the LiDARs (called a DWEL, or Dual-Wavelength Echidna LiDAR) is for the U.S. and the other is for Australia. Both scan forests and give researchers a better idea of how much carbon is being stored.
To provide additional data, the UMass Boston students used two custom-built devices, officially called Canopy Biomass Lidars, or CBLs, to create 360-degrees views of Australia’s forest ecosystem.
Reference: APAAME_20221123_FB-0401
Photographer: Firas Bqa'in
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
The Development Threat Index is part of the Land Use Land Cover collection. The cumulative development threat index is a terrestrial global, future development threat map based on combining these resources: agricultural expansion, urban expansion, conventional oil and gas, unconventional oil and gas, coal, mining, biofuels, solar, and wind. Each threat ranked potential development from 0–100 with 100 indicating the highest potential for future development of the resource and were produced at a 50 square kilometer (km2) grid cell resolution, excluding all cells overlapping Antarctica and those with >50% considered marine. This map displays the projected future development threat of coal. The area-ranked threat scores are based on coal basin reserve estimates in million short tons utilizing country- and state-level coal reserve data.