View allAll Photos Tagged RemoteSensing,
Date: 2019-01-01
Global Land Cover (Copernicus Services) + Custom Script
Author: Monja Šebela
Contains modified Copernicus Sentinel data [2021], processed by Sentinel Hub
Reference: APAAME_20221110_SAlK-0704
Photographer: Sufyan Al Karaimeh
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
On the horizon, then, at the furthest edge of the possible, it is a matter of producing the space of the human species-the collective (generic) work of the species-on the model of what used to be called "art" ; indeed, it is still so called, but art no longer has any meaning at the level of an "object" isolated by and for the individual.
Henri Lefebvre, Openings and Conclusions. from On Installation and Site Specificity (introduction) Erika Suderburg
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 biofuels, produced from six crops (maize, soybean, sugarcane, rapeseed, sunflower, and oil palm). The area-ranked threat scores are based on values of maximum potential gallons of gasoline equivalent multiplied by the fraction of agricultural expansion by 2030.
Reference: APAAME_20221121_DS-0329
Photographer: Dana Salameen
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
Reference: APAAME_20221110_SAlK-0707
Photographer: Sufyan Al Karaimeh
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
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
A river cuts through the forest before the rainy season starts, Papua, Indonesia.
Photo by Manuel Boissière for CIRAD and 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-0175
Photographer: Dana Salameen
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
Reference: APAAME_20221121_RHB-0355
Photographer: Robert Bewley
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
Bob Dolph (left) and Tommy Gregg using infrared film on the mountain pine beetle project. La Grande Ranger District, Wallowa-Whitman National Forest, Oregon.
Photo by: Peter W. Orr
Date: August 1970
Credit: USDA Forest Service, Region 6, State and Private Forestry, Forest Health Protection.
Source: Division of Timber Management, Insect and Disease Control Branch Collection; Regional Office, Portland, Oregon.
Image: ID-839
Image provided by USDA Forest Service, Pacific Northwest Region, State and Private Forestry, Forest Health Protection: www.fs.usda.gov/main/r6/forest-grasslandhealth
Reference: APAAME_20221110_BT-0075
Photographer: Bashar Tabbah
MAP&LENS
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
Aerial shot of a cityscape 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_20221110_SAlK-0690
Photographer: Sufyan Al Karaimeh
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
Reference: APAAME_20221110_FB-0072
Photographer: Firas Bqa'in
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
Aerial view of a cityscape in Papua, Indonesia.
Photo by Agus Andrianto/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
Landscape of rural area in outskirt of Yaounde, Cameroon.
Photo by Mokhamad Edliadi/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
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 utility-scale solar power. The area-ranked threat scores are based on a combined metric of solar resources in watts per square meter (W/m2), land suitability, and economic feasibility for solar power development.
Reference: APAAME_20221110_SAlK-0706
Photographer: Sufyan Al Karaimeh
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
Houses pocket along the coast as the aquamarine Arafura sea blends seamlessly with the absinthe upland of Papua, Indonesia.
Photo by Manuel Boissière for CIRAD and 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
Tommy Gregg working with infrared transparencies on the mountain pine beetle project. La Grande Ranger District, Wallowa-Whitman National Forest, Oregon.
Photo by: Peter W. Orr
Date: August 1970
Credit: USDA Forest Service, Region 6, State and Private Forestry, Forest Health Protection.
Source: Division of Timber Management, Insect and Disease Control Branch Collection; Regional Office, Portland, Oregon.
Image: ID-835
Image provided by USDA Forest Service, Pacific Northwest Region, State and Private Forestry, Forest Health Protection: www.fs.usda.gov/main/r6/forest-grasslandhealth
Global Pastures in 2000 map the proportion of each 5 minute (10 km) grid cell land area that is under pasture. Dark shaded areas denote higher proportion of are under pasture. Data from Moderate Imaging Spectroradiometer (MODIS) land cover product and Satellite Pour l'Observation de la Terre (SPOT) VEGETATION's Global Land Cover 2000 product were combined with UN Food and Agriculture (FAO) agricultural statistics to generate the data set.
Reference: APAAME_20221110_BT-0075
Photographer: Bashar Tabbah
MAP&LENS
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
Two images from the SEVIRI instrument aboard Meteosat Second Generation that show recent flooding in West Africa. One image is from mid-October 2012 and the other from mid-October 2008. The Senegal, Niger and Benue rivers all show much larger areas of water in 2012 than they did in 2008. These floods have caused problems all across West Africa, with Nigeria hit particularly hard - Reliefweb states that over 400 have died and more than 1 million forced to move since early July.
The images are composites of the Red, Near-InfraRed and ShortWave-InfraRed bands of the SEVIRI sensor, they are color corrected to more closely resemble the natural color of the landscape. The SEVIRI produces one 'full disk' image every 15 minutes, a unique capability that allows tracking of tropical storms and other severe weather events.
The images were received and processed by the Earth Observation Group at the University of Copenhagen. Please email questions or comments to Simon Proud
Original data Copyright EUMETSAT 2012
Reference: APAAME_20221110_SAlK-0702
Photographer: Sufyan Al Karaimeh
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
Reference: APAAME_20221115_FBal-88
Photographer: Fadi Bala'wi
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
Reference: APAAME_20221121_DS-0232
Photographer: Dana Salameen
Credit: APAAME
Copyright: Creative Commons Attribution-Noncommercial-NoDerivative Works
This is a supervised classification of a patch of western Victoria, southwest of Ballarat. The original satellite image was recorded in November 2004.
The idea is to generate statistical models of the characteristic spectra of different vegetation classes, by demarcating 'training regions' on the original Landsat image, and then getting the software to calculate the spectral band means and covariances for those patches. Then, using the statistical descriptions of each vegetation type, the software looks at every pixel in the full scene and decides which of the classes the pixel is most likely to belong to (the maximum likelihood algorithm). This is a small portion of the resulting classified scene.
Pine plantations are colour coded dark blue, dry grazing is pinkish buff or brick orange, eucalypt forest is dark green, wheat is bright blue-green, pasture is bright green, ripe crops and mown hay are yellow, other crops are sage green. The classification was performed in ENVI. Assayed accuracy of the classification was 86%.
By doing this for images taken spaced several years apart, you can count the pixels indicating each land use class in each year, to quantify how much and in what direction agricultural land use has been changing over the years.