Editing Landsat program (section)

== Uses of Landsat imagery ==
[[File:Happy Launch Anniversary, Landsat 8 (12463694884).jpg|thumb|One year after launch, [[Landsat 8]] imagery had over one million file downloads by data users.]]
Landsat data provides information that allows scientists to predict the distribution of species, as well as detecting both naturally occurring and human-generated changes over a greater scale than traditional data from field work. The different spectral bands used on satellites in the Landsat program provide many applications, ranging from ecology to geopolitical matters. Land cover determination is a common use of Landsat imagery around the world.<ref>{{cite journal |last1=Cohen |first1=Warren B. |last2=Goward |first2=Samuel N. |title=Landsat's Role in Ecological Applications of Remote Sensing |journal=BioScience |date=2004 |volume=54 |issue=6 |pages=535–545 |doi=10.1641/0006-3568(2004)054[0535:LRIEAO]2.0.CO;2 |s2cid=86219373 |url=https://andrewsforest.oregonstate.edu/sites/default/files/lter/pubs/pdf/pub3748.pdf}}</ref>

Landsat imagery provides one of the longest uninterrupted time series available from any single remote sensing program, spanning from 1972 to present.<ref name="Hemati Hasanlou Mahdianpari Mohammadimanesh p=2869">{{cite journal | last1=Hemati | first1=MohammadAli | last2=Hasanlou | first2=Mahdi | last3=Mahdianpari | first3=Masoud | last4=Mohammadimanesh | first4=Fariba | title=A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth | journal=Remote Sensing | publisher=MDPI AG | volume=13 | issue=15 | date=22 July 2021 | issn=2072-4292 | doi=10.3390/rs13152869 | page=2869| bibcode=2021RemS...13.2869H | doi-access=free }}</ref> Looking to the future, the successful launch of [[Landsat 9|Landsat-9]] in 2021 shows that this time series will be continued forward.<ref name="Landsat9">{{cite web | title=Landsat 9 Reaches Orbit, Makes Ground Contact to Continue Legacy | website=USGS.gov | url=https://www.usgs.gov/center-news/landsat-9-reaches-orbit-makes-ground-contact-continue-legacy?qt-news_science_products=1#qt-news_science_products | access-date=14 October 2021}}</ref>

[[File:Garden City Kansas irrigation-Landsat7-segment2.jpg|thumb|A false-color image of [[Irrigation|irrigated]] fields near [[Garden City, Kansas]], taken by the [[Landsat 7]] satellite]]
In 2015, the Landsat Advisory Group of the National Geospatial Advisory Committee reported that the top 16 applications of Landsat imagery produced savings of approximately 350 million to over 436 million dollars each year for federal and state governments, NGO's, and the private sector. That estimate did not include further savings from other uses beyond the top sixteen categories.<ref name="USGS.gov">{{cite web | title=Landsat Seen as Stunning Return on Public Investment | website=USGS.gov | url=https://www.usgs.gov/news/landsat-seen-stunning-return-public-investment | access-date=14 October 2021}}</ref> The top 16 categories for Landsat imagery use, listed in order of estimated annual savings for users, are: 
# U.S. Department of Agriculture risk management
# U.S. Government mapping
# Agricultural water use monitoring
# Global security monitoring
# Support for fire management
# Detection of forest fragmentation
# Detection of forest change
# World agriculture supply and demand estimates
# Vineyard management and water conservation
# Flood mitigation mapping
# Agricultural commodities mapping
# Waterfowl habitat mapping and monitoring
# Coastal change analysis
# Forest health monitoring
# National Geospatial-Intelligence Agency global shoreline mapping
# Wildfire risk assessment <ref name="USGS.gov"/>

Further uses of Landsat imagery include, but are not limited to: fisheries, forestry, shrinking inland water bodies, fire damage, glacier retreat, urban development, and discovery of new species. A few specific examples are explained below.

=== Natural resources management ===
[[File:NASA's Landsat Satellite Looks for a Cloud-Free View (8778994889).jpg|thumb|Landsat image of the [[Aral Sea]] in 2013]]
[[File:Fire and the Future of Yellowstone - NASA Earth Observatory.jpg|thumb|Landsat images of burned land in [[Yellowstone National Park]] in 1989 and 2011]]
[[File:World of Change Columbia Glacier, Alaska (7215814492).png|thumb|[[Landsat 5|Landsat-5]] false color images of the [[Columbia Glacier (Alaska)|Columbia Glacier, Alaska]] in 1986 and 2011]]
[[File:Vancouver Landsat.jpg|thumb|Landsat false color image highlighting developed areas in pink in [[Vancouver]], British Columbia, Canada]]

==== Fisheries ====
In 1975, one potential application for the new satellite-generated imagery was to find high yield [[fishery]] areas. Through the Landsat Menhaden and Thread Investigation, some satellite data of the eastern portion of the [[Mississippi]] sound and another area off the coast of the [[Louisiana]] coast data was run through classification [[algorithm]]s to rate the areas as high and low probability fishing zones, these algorithms yielded a classification that was proven with [[in situ]] measurements – to be over 80% accurate and found that water color, as seen from space, and turbidity significantly correlate with the distribution of [[menhaden]] – while surface temperature and salinity do not appear to be significant factors. Water color – measured with the multispectral scanners four spectral bands, was used to infer [[Chlorophyll]], [[turbidity]], and possibly fish distribution.<ref>{{cite web|url=http://spo.nmfs.noaa.gov/mfr391/mfr3913.pdf|title=Finding Fish With Satellites|last=Kemmerer|first=Andrew|date=March 2017 |publisher=NOAA}} {{PD-notice}}</ref>

==== Forestry ====
An ecological study used 16 [[Orthophoto|ortho-rectified]] Landsat images to generate a land cover map of [[Mozambique]]'s [[mangrove]] forest. The main objective was to measure the mangrove cover and above ground [[biomass]] on this zone that until now could only be estimated, the cover was found with 93% accuracy to be 2909 square kilometers (27% lower than previous estimates). Additionally, the study helped confirm that geological setting has a greater influence on biomass distribution than latitude alone - the mangrove area is spread across 16° of latitude but it the biomass volume of it was affected more strongly by geographic conditions.<ref>{{cite journal|title=Landscape-scale extent, height, biomass, and carbon estimation of Mozambique's mangrove forest with Landsat ETM+ and Shuttle Radar Topography Mission elevation data|journal=Journal of Geophysical Research: Biogeosciences|volume=113|last=Fatoyinbo|first=Temilola|date=March 2017|pages=n/a|doi=10.1029/2007JG000551|doi-access=free}}</ref>

=== Climate change and environmental disasters ===
==== Shrinking of the Aral Sea ====
The shrinking of the [[Aral Sea]] has been described as "One of the planet's worst environmental disasters". Landsat imagery has been used as a record to quantify the amount of water loss and the changes to the shoreline. Satellite visual images have a greater impact on people than just words, and this shows the importance of Landsat imagery and satellite images in general.<ref>{{cite magazine |url=https://www.wired.com/2012/07/landsat-40-significant-images/|title=Landsat's Most Historically Significant Images of Earth From Space|last=Mason|first=Betsy|date=March 2017|magazine=Wired}}</ref>

==== Fires in Yellowstone National Park ====
The [[Yellowstone fires of 1988]] were the worst in the recorded history of the national park. They lasted from 14 June to 11 September 1988, when rain and snow helped halt the spread of the fires. The area affected by the fire was estimated to be 3,213 square kilometers – 36% of the park. Landsat imagery was used for the area estimation, and it also helped determine the reasons why the fire spread so quickly. Historic drought and a significant number of lightning strikes were some of the factors that created conditions for the massive fire, but anthropogenic actions amplified the disaster. On images generated previous to the fire, there is an evident difference between lands that display preservation practices and the lands that display clear cut activities for timber production. These two type of lands reacted differently to the stress of fires, and it is believed that that was an important factor on the behavior of the wildfire. Landsat imagery, and satellite imagery in general, have contributed to understanding fire science; fire danger, wildfire behavior and the effects of wildfire on certain areas. It has helped understanding of how different features and vegetation fuel fires, change temperature, and affect the spreading speed.<ref name="Zhao Meng Huang Zhao p=898">{{cite journal | last1=Zhao | first1=Feng | last2=Meng | first2=Ran | last3=Huang | first3=Chengquan | last4=Zhao | first4=Maosheng | last5=Zhao | first5=Feng | last6=Gong | first6=Peng | last7=Yu | first7=Le | last8=Zhu | first8=Zhiliang | title=Long-Term Post-Disturbance Forest Recovery in the Greater Yellowstone Ecosystem Analyzed Using Landsat Time Series Stack | journal=Remote Sensing | publisher=MDPI AG | volume=8 | issue=11 | date=29 October 2016 | issn=2072-4292 | doi=10.3390/rs8110898 | page=898| bibcode=2016RemS....8..898Z | doi-access=free | hdl=1903/31531 | hdl-access=free }}</ref><ref name="USGS_fires">{{cite web | title=EarthView–Fire and Rebirth: Landsat Tells Yellowstone's Story | website=USGS.gov | url=https://www.usgs.gov/news/earthview-fire-and-rebirth-landsat-tells-yellowstones-story | access-date=15 October 2021}}</ref>

==== Glacier retreat ====
The serial nature of Landsat missions and the fact that is the longest-running satellite program gives it a unique perspective to generate information of Earth. Glacier retreat in a big scale can be traced back to previous Landsat missions, and this information can be used to generate climate change knowledge. The [[Columbia Glacier (Alaska)|Columbia glacier]] retreat for example, can be observed in false-composite images since [[Landsat 4]] in 1986.<ref name="Landslides Monitoring">{{citation|editor-last=Ray, Ram|title=Landslides - Investigation and Monitoring|publisher=IntechOpen|publication-date=19 November 2020|isbn=978-1-78985-824-2}}</ref>

====Urban development====
Landsat imagery gives a time-lapse like series of images of development. Human development specifically, can be measured by the size a city grows over time. Further than just population estimates and energy consumption, Landsat imagery gives an insight of the type of urban development, and study aspects of social and political change through visible change. In Beijing for example, a series of ring roads started to develop in 1980s following the economic reform of 1970, and the change in development rate and construction rate was accelerated in these time periods.<ref name="Landslides Monitoring"/>

===Ecology===
==== Discovery of new species ====
In 2005, Landsat imagery assisted in the discovery of new species. Conservation scientist Julian Bayliss wanted to find areas that could potentially become conservation forests using Landsat generated satellite images. Bayliss saw a patch in Mozambique that until then had no detailed information. On a reconnaissance trip, he found great diversity of wildlife as well as three new species of butterflies and a new snake species. Following his discovery, he continued to study this forest and was able to map and determine the forest extent.<ref>{{cite web|url=https://landsat.gsfc.nasa.gov/landsat-imagery-leads-to-discovery-of-new-species/|archive-url=https://web.archive.org/web/20170430083925/https://landsat.gsfc.nasa.gov/landsat-imagery-leads-to-discovery-of-new-species/|archive-date=2017-04-30|title=Landsat Imagery Leads to Discovery of New Species - Landsat Science|publisher=NASA}} {{PD-notice}}</ref>