Mapping Forest Disturbance with Landsat

BY CAITLIN DEMPSEY MORAIS

The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) takes advantage of the30 year Landsat archive to inventory recent disturbances and forest-cover change. Using mid-summer, cloud free Landsat data from the Global Land Survey (GLS) project, LEDAPS first corrects the images to remove atmospheric effects from surface reflectance (source code for LEDAPS) before applying change detection techniques to map out disturbance, regrowth, and permanent forest conversion across the continental United States.

One of the resulting products is a map of the continental United States and Canada showing forest disturbance rates from 1990-2000. Areas on the map that are green indicate the least amount of disturbance during that time period which pink to red areas indicated the highest amount of disturbance.

Data produced from NASA’s project is contribute to the North American Carbon Program (NACP), a component of the USGCRP Carbon Cycle Science Program. The NACP is a coalition of researchers seeking to better understand the carbon cycle such as carbon sources and sinks and changes in carbon stocks.

Disturbance data can be downloaded from North American Forest Dynamics (NAFD) product archive at ORNL DAAC.

LANDSAT-DERIVED FOREST DISTURBANCE RATE (STAND REPLACING), 1990-2000, AGGREGATED TO 500M GRID. SOURCE: NASA.

Reference


Goward, S.N., C. Huang, J.G. Masek, W.B. Cohen, G.G. Moisen and K. Schleeweis. 2012. NACP North American Forest Dynamics Project: Forest Disturbance and Regrowth Data. Available on-line [http://daac.ornl.gov] from ORNL DAAC, Oak Ridge, Tennessee, U.S.A.http://dx.doi.org/10.3334/ORNLDAAC/1077

Source: GIS Lounge - Maps and GIS

Cloud Computing Used to Analyze Landsat Imagery and Detect Deforestation

BY ZACHARY ROMANO


Cloud computing allows individuals, firms, and institutions to manage and process large amounts of data faster than ever before. Landsat, NASA’s longest running initiative for the acquisition of Earth imagery, has generated nearly 50 trillion pixels of data by capturing one image per season, of every place on Earth, for the past 43 years. Now, “the cloud” has allowed researchers like Matthew Hansen and Sam Goward to make use of this abundant imagery data.

Every time a disturbance occurs to a forest, the growth cycle restarts and this can be seen in satellite images of Earth. The challenge, however, comes when working with lower­ resolution imagery, as it requires at least a 30 meter resolution to track small­scale changes to a forest via imagery. Hansen and Goward were bound to this low­ resolution data for quite some time. If resources allowed, these researchers would want to develop a live forest tracking system, that alerted a locale when forest destruction reached a high level, to the point of identifying the exact cause of the deforestation. Eventually, the team obtained Landsat imagery but due to the high cost, they could only obtain what they could afford.

SINCE JANUARY 1, 2000, MORE THAN 4.3 MILLION SCENES HAVE BEEN CAPTURED BY LANDSAT SATELLITES AND MADE AVAILABLE TO THE PUBLIC. GRAPH BY JOSHUA STEVENS, USING DATA COLLECTED FROM THE U.S. GEOLOGICAL SURVEY ACQUISITIONS ARCHIVE.

At a conference in 2008, the University of Maryland team met Rebecca Moore, a Google developer, at a conference and realized the value in Google’s high­-powered computing ability for their 700,000 Landsat scenes (more: New Detailed Maps Show Changes in Earth’s Forests). The team worked with Google to process these images to analyze them and track whether a pixel was forested or not, and aggregated this information to better understand forest growth cycle trends. Many methods were utilized but the system has the ability to measure the levels of RGB color density in each image pixel. By doing so, this allows the research team to hone in on those small­scale changes by tracking the variation in this color density over time. In total, the analysis process required 10,000 central processing units and took 1 million hours ­ a process that would have taken 15 years on a single computer.

CHANGES IN THE LANDSCAPE CAN BE DETECTED AS SMALL AS THE SIZE OF A BASEBALL DIAMOND. THESE TWO SATELLITE IMAGES SHOW PRE (LEFT) AND POST (RIGHT) CLEARING OF A FOREST IN NORTHERN ALABAMA. (NASA EARTH OBSERVATORY IMAGE BY JOSHUA STEVENS, USING LANDSAT DATA FROM THE U.S. GEOLOGICAL SURVEY)

One of the first case studies for this method looked at the Democratic Republic of Congo and found significant deforestation between 2000­ to 2010. This amounted to 5,5­72 teragrams of carbon lost due to slash and burn for agriculture and the need for wood as a fuel source. For nation’s like DCR, which lack any form of a forest or tree inventory, there is incredible value to those making land use and resource planning decisions. These images offer policymakers the most succinct understanding of deforestation. As this method gets more refined, the University of Maryland team hopes to expand the application of this tree inventory to other areas like tracking human health, protected nature areas, and modeling biodiversity.

References
Big Data Helps Scientists Dig Deeper by Holly Riebeek, Earth Observatory, NASA. March 26, 2015.

Source: GIS Lounge - Maps and GIS

Five Applications of Satellite Data

BY SBL

Remote sensing data provides much essential and critical information for monitoring many applications such as image fusion, change detection, and land cover classification. Remote sensing is an important technique to obtain information relating to the Rarth’s resources and environment.

What popularized satellite data are the easily accessed online mapping applications like Google Earth and Bing Maps. From being simply able to find “where is my house” these applications have helped the GIS community in project planning, monitoring disasters and natural calamities, and guiding civil defense people.

Remotely sensed satellite images and data are comprised of spectral, spatial and temporal resolution. Spectral statistics is the substance of remotely sensed image classification. The main aspect which influences the accuracy of ground object is spatial resolution. Temporal resolution will help in generation of land cover maps for environmental planning, land use change detection and transportation planning. Data assimilation and analysis of urban areas using medium resolution remote sensing imagery is mainly concentrated on documentation of built up areas or for judgement between residential, commercial and industrial zones.

There are hundreds of applications for satellite imagery and remotely sensed data. From the pioneering Landsat and SPOT imagery and when nations used to use information derived from the satellite imagery for spying on each other under the guise of scientific experiments, industry has grown in leap and bounds and today every sphere of life, government decision making, civil defense operations, police, you name the sphere of life, every one of which is influenced by satellite imagery in particular and Geographic Information Systems (GIS) in general.

SBL has been active in the field of satellite imagery processing and has got in-house expertise to handle any kind of sensor and product demands. Our projects have helped clients world over to help in having a better say in sustainability management and environmental assessment and management. To illustrate the benefits, here are five uses of satellite imagery and data.

1. Optimizing solar panel energy output with irradiance values.
Sustainable living has lot of interest in solar energy and it interest is growing rapidly across the world. Finding out location for placement of solar panels and If you were to choose a single position anywhere on Earth to install a solar panel, then we have to use Global Horizontal Irradiance (GHI) map. GHI measures the rate of total incoming solar energy at the Earth’s surface in watts per square kilometer. Epochs of satellite data (derived from GOES and Meteosat) has created this data with a standard error of only 5%.

GLOBAL MAP OF GLOBAL HORIZONTAL IRRADIANCE (GHI). MAP: SOLARGIS.INFO

2. Generating Earth’s surface elevation with the Shuttle Radar Topography Mission
The SRTM digital elevation data, produced by NASA originally, is a major breakthrough in digital mapping of the world, and provides a major advance in the accessibility of high quality elevation data for large portions of the tropics and other areas of the developing world. From the Global Land Cover Facility:


The Shuttle Radar Topography Mission (SRTM) obtained elevation data on a near-global scale to generate the most complete high-resolution digital topographic database of Earth. SRTM consisted of a specially modified radar system that flew onboard the Space Shuttle Endeavour during an 11-day mission in February of 2000. SRTM is an international project spearheaded by the National Geospatial-Intelligence Agency (NGA), NASA, the Italian Space Agency (ASI) and the German Aerospace Center (DLR).

MAP OF CHILE GENERATED WITH TOPOGRAPHY FROM SRTM. MAP: RAVL, 2008, WIKIMEDIA COMMONS. 

3. Extracting mineral deposits with remote sensing based spectral analysis
During the pre feasibility and feasibility stages of the mineral exploration it is very essential to know the mineral potentiality of the area under consideration. In such scenario satellite remote sensing based lithological mapping, geological structural mapping, geomorphological mapping etc and its integration in a GIS platform will enable geo scientist to map the mineral potential zones. This will be further enhanced with the help of spectral analysis of satellite image bands to identify and map hydro thermal alteration zones which a indicators of mineral availability. This will enable exploration geologist to confine his geo physical, geo chemical and test drilling activities to high potential zones.

GEOLOGY MAP FROM SBL.

4. Providing a basemap for graphical reference and assisting planners and engineers

The amount of details that orthoimagery produces using high resolution satellite imagery is of immense value and provides an extreme amount of detail of the focus and surrounding areas. Maps are designed to communicate highly structured message about the world. As maps are location-based, aerial imagery supports people to orient themselves.

AERIAL IMAGERY FROM SBL.

5. Disaster mitigation planning and recovery
The result of a natural calamity can be calamitous and at times difficult to assess. But a disaster risk assessment is essential for rescue workers. This has to be prepared and executed quickly and with accuracy. Object-based image classification using change detection (pre- and post-event) is a quick way to get damage assessments. Other similar applications using satellite imagery in disaster assessments include measuring shadows from buildings and digital surface models.

References
Shuttle Radar Topography Mission (SRTM) – Global Land cover Facility (GLCF)

Solar Radiation Maps: Global Horizontal Irradiation (GHI) – SolarGIS.info

About the Author


Anil Narendran Pillai – (Vice President – Geomatics @ SBL) Mr. Pillai heads the GSS (Geospatial Services) domain at SBL. He has worked in the digital mapping, remote sensing, and GIS industries for over 23 years. He has 23+ years experience managing and coordinating GIS projects and 12 years senior management experience. He has extensive experience in all aspects of aerial and satellite imaging technology and applications. He has utilized remotely sensed satellite and airborne imagery for a variety of environmental applications including site location analysis, forestry, telecommunications and utility corridor mapping. He has a strong background in management of GIS and Photogrammetry imaging projects to support Government and private industry needs.His Passion lies in Need Analysis and Documentation, Topographical Mapping (ArcGIS), Spatial Data Management, Integrity and Security, GIS Data transformations and projections from multiple sources, Image Processing Software user testing and documentation, Project Coordination and Tech. Support, Inter-agency communication and support, 3D Data Generation and Management,Project Management, Digital Photogrammetry, Satellite Image Processing, Pre-Sales Presentations.

See more about SBL Geospatial services http://www.sblcorp.com/geospatial-services

Source: GIS Lounge - Maps and GIS

Forests on diet – the map of global forest extension

By Stefan Mühlbauer



A new high resolution map of global forest extension covering the time span 2000 – 2012 was recently presented by Department of Geographical Sciences, University of Maryland, US. The time series is based on 654.178 LANDSAT images resulting in a global wide map displaying forest change at a never seen spatial detail of down to 30m. Thus, the map entails globally ‘consistent but locally relevant information’, according to a geographer of University of Maryland. Indeed, the map is useful for extracting information on local forest change, while potentially every corner of the globe may be entered. The huge amount of data processing was possible only through cloud computing.

Methodologically, forests were considered as all vegetation taller than 5m and are expressed as a percentage per output grid cell as ‘2000 Percent Tree Cover’. ‘Forest Loss’ is defined as a stand-replacement disturbance, or a change from a forest to non-forest state. ‘Forest Gain’ is defined as the inverse of loss, or a non-forest to forest change entirely within the study period.

The new forest map reveals that between 2000 and 2012 2.3 millions km² of forest havevanished. To present the gain and loss more clearly I am going to state the raw numbers:

2000 – 2012 global forest dynamics

• Gains: 800.000 km²
• Losses: 2.300.000 km²
• Loss and Re-gain: 200.000km²

The greatest amount of loss still occurred in the tropics that count for 32% of all losses. While in Brazil due to political efforts the rate of loss reduced slightely (though after 2012 the restrictions for deforestation were loosened up again), the deforestation rate in Indonesia doubled after 2003 from 10.000 km² to more than 20.000 km² forest cut per year. Considerable are also the losses in the Canadian and Russian boreal forests.

Forest monitoring belongs to one of the highly significant topics of today. Initiatives such as UN’s REDD+ highlight the need for information upon forest change and biomass. Forests impact the climate (CO2 household), biodiversity of plants and animals, but also the humans in a positive manner. A researcher of the mapping team found out that tree cover correlates with human health as people living close to forests eat a healthier diet than people in other environments do (FAO article1, FAO article2).

In an increased situation of urbanisation, loss of biodiversity and enhanced consumption of resources the protection of forests as ecological regulators is of great importance. As political desicions for stopping deforestations unfortuantely need hard facts those forest and biomass monitoring programs in my opinion are strongly necessary in order not to experience forests being on diet themselves!

The new global map of deforestation reveals 2.3 million square kilometers lost between 2000 and 2012. Red shows losses, blue gains, purple loss and gain.

Indonesia lost forests the fastest of any nation between 2000 and 2012. Red shows losses, blue gains, purple gain and losses.Credit: Image courtesy Matt Hansen, University of Maryland

Forest losses in tropical South America between 2000 and 2012. Particularly at the southern edge of the Amazonian Basin, in Bolivia, Paraguay the loss of forest are considerable. Red shows losses, blue gains, purple losses and gains.

A map of change in North American forests between 2000 and 2012. Red is loss and pink represents areas of loss and gain.

Credit: Image courtesy Matt Hansen, University of Maryland

Losses in the Canadian boreal forests in a more detailed view. Red shows losses, bllue gains, purple losses and gains

.

Forest change in Europe: A wind storm in 2009 leveled a forested area in the south-west of France. Portugal exhibits a strong dynamic of forest loss and gain. Red shows losses, blue gains, purple losses and gains.

Source: Geoawesomeness

River Width GIS Data Created from 1,756 Landsat Images

BY CAITLIN DEMPSEY MORAIS




Researchers used 1,756 Landsat images to develop a GIS database of river widths for the entire North American continent. Previously, river widths were estimated based on topographic maps and calculating the river discharge measured at certain points along the watershed. Using software developed in Exelis VIS IDL, hydrologists from the University of North Carolina were able to extract river width calculations using remote sensed data.

Called North American River Width Data Set (NARWidth), the GIS data is available for downloading in shapefile format with by tile or as a bulk download. In order to avoid seasonal variations in river widths, images for each river were selected during a time of that river’s average flow.

The resulting dataset contains attribute data about the river’s width, the number of channels/braids, whether the segment is a river or a reservoir as well as lat/long coordinates. The resulting map of the river width data shows that the widest rivers are found along the Yukon in Alaska, the Mackenzie in Canada’s Northwest Territories, the Hudson in New York, the St. Lawrence along the border of New England and Quebec, and the Mississippi.

The availability of a more accurate river width database will allow for the analyses of flood hazards, studies of ecological diversity, and estimates of the volume of greenhouse gases released by rivers and reservoirs due to bacterial activity.

More: A Satellite View of River Width

MAP OF RIVER WIDTHS FOR NORTH AMERICA. THE DARKER THE BLUE, THE WIDER THE RIVER. SOURCE: RIVER WIDTH MAP JOSHUA STEVENS, NASA, USING DATA FROM ALLEN, G. H., & PAVELSKY, T.M. (2015).

References

Allen, G. H., Pavelsky T.M., (2015), Patterns of river width and surface area newly revealed by the satellite-derived North American River Width data set. Geophysical Research Letters. doi: 10.1002/2014GL062764

Pavelsky, T.M. and L. C. Smith , RivWidth: A Software Tool for the Calculation of River Widths from Remotely Sensed Imagery, IEEE Geoscience and Remote Sensing Letters, v. 5 no. 1, January 2008.

Source