In this blog, we have written about the revolution occurring in the remote sensing world, centered on inexpensive and crowdsourced remote sensing. As described in this TED talk from Planet Labs’ Will Marshall, Planet Labs has launched small satellites of the dimensions 10 x 10 x 30 cm, weighing 4 kg, which can take images at 10 times higher resolution than conventional large satellites. Early in 2014, the International Space Station launched 28 of these small satellites. They plan to launch more than 100 that will image the Earth from a single orbital plane as the planet rotates beneath it. Will refers to this system as a “line scanner for the planet.”
While our book and this blog discuss geotechnologies from a technical point of view, we also highlight the societal implications of these innovations. Planet Labs’ work fits in well with these themes, because they are not only technically innovative, but their goal is to democratize remote sensing data. They are asking: “If you had access to imagery for the whole planet on a daily basis, what would you do with it?” Every point on the planet will be imaged every day with their platform.
And while the partnerships and avenues of dissemination data are still being worked out, this and similar efforts in the remote sensing world will surely impact data availability, crowdsourcing, copyright, privacy, decision-making, and other topics important to science, education, and society, in the months and years ahead.
Recently, while at the Applied Geography Conference in Atlanta, I decided to test the spatial accuracy of my smartphone’s GPS in a challenging environment–a rooftop running track. Although located on a roof, the track was surrounded by buildings far taller, and in downtown Atlanta, a location with many other buildings impeding signals from GPS, wi-fi hotspots, and cell phone towers. A further challenge to the GPS positional accuracy was that each lap on the track was only 0.10 miles (0.16 km), and therefore, I would not travel very far across the Earth’s surface.
After an hour of walking, and collecting the track on my smartphone with a fitness app (Runkeeper), I uploaded my track as a GPX file and created a web map of it in ArcGIS Online. As I expected, the track’s position was compromised by the tall buildings–I only had a view of about half the sky during my time on the roof. As you can measure for yourself on the map linked above, the track lines formed a band about 15 meters wide, but interestingly, were more spatially precise along the eastern side of the track, where the signal was better, as you can see in my video that I recorded at the same time.
Also, as I have encountered numerous times in the past, a line about 100 meters long stretches to the north. Rest assured that I did not leap off the building, but rather, the first point that the GPS app laid down as I opened the doors to walk outside was about a block away. Then, as I remained outside, the points became more accurate. When you collect data, the more time you spend on the point you are collecting, typically the more accurate that point is spatially.
Another interesting aspect of this study is that if the basemap is changed to satellite imagery, it appears that the track overlaps the tall building to the west. Try it, using the map link above. However, a closer investigation reveals that this is a result of the orthocorrection that was performed on the imagery; the buildings do not appear from “straight overhead”, but rather, they “fall away” to the east. Turn this into another teachable moment: Images, like maps, are not perfect, but they are very useful. We can learn to manage error and imperfection through critical thinking and through the use of geotechnologies. This is a central topic of our book and of this blog.
To dig deeper into issues of GPS track accuracy, see my related post on errors and teachable moments in collecting data, and on comparing the accuracy of GPS receivers and smartphones and mapping field collected data in ArcGIS Online here and here.
Despite these challenges, overall, I was quite pleased with my track’s spatial accuracy, even more so considering that I had the phone in my pocket most of the time I was walking.
As we state in our book, The GIS Guide to Public Domain Data, oftentimes, technological advancement and adoption proceeds at a faster pace than regulations accompanying it. A perfect example is what is probably the hottest technology in remote sensing right now, and that is UAVs, or Unmanned Aerial Vehicles. The Internet is becoming rapidly filled with stories and videos of footage from UAVs deployed by aerial survey companies, but even more commonly, operated by the general public. For example, this storymap contains footage of UAV imagery flown over a rocket launch, a cruise ship, and more.
While I as a geographer are fascinated by these images and videos, I am at the same time sensitive to the myriad of privacy and safety issues raised by the operation of UAVs. We are beginning to see laws passed to regulate the operation of UAVs on certain lands, such as the recent policy directive against flying these in national parks in the USA.
Jonathan Jarvis, director of the National Park Service, said that “We embrace many activities in national parks because they enhance visitor experiences with the iconic natural, historic and cultural landscapes in our care. However, we have serious concerns about the negative impact that flying unmanned aircraft is having in parks, so we are prohibiting their use until we can determine the most appropriate policy that will protect park resources and provide all visitors with a rich experience.” Some parks had already initiated bans after noise and nuisance complaints from park visitors, an incident in which park wildlife were harassed, and park visitor safety concerns. For example, earlier this year, visitors at Grand Canyon National Park gathered for a quiet sunset were interrupted by a loud unmanned aircraft flying back and forth and eventually crashing in the canyon. Volunteers at Zion National Park witnessed an unmanned aircraft disturb a herd of bighorn sheep, reportedly separating adults from young animals.
The policy memorandum directs park superintendents to take a number of steps to exclude unmanned aircraft from national parks. The steps include drafting a written justification for the action, ensuring compliance with applicable laws, and providing public notice of the action. The memorandum does not affect the primary jurisdiction of the Federal Aviation Administration over the National Airspace System.
The policy memorandum is a temporary measure, and it seems like a wise move. Jarvis said the next step will be to propose a Servicewide regulation regarding unmanned aircraft. That process can take considerable time, depending on the complexity of the rule, and includes public notice of the proposed regulation and opportunity for public comment. The National Park Service may use unmanned aircraft for administrative purposes such as search and rescue, fire operations and scientific study. These uses must also be approved by the associate director for Visitor and Resource Protection.
Near the Esri office in Colorado a month ago, I witnessed my first UAV flight where I did not know who was operating the vehicle. I’m sure we will look back in years to come and realize that we in 2014 were at the dawn of a technology that will no doubt transform GIS and our everyday lives. I anticipate sensors soon capable of capturing imagery in a wide variety of wavelengths, as well as atmospheric and other types of sensors that will further hasten the era of big data. I am hopeful that we will chart a prudent course through the advent of UAVs, taking advantage of the innumerable benefits that UAVs can offer the GIS industry and also society as a whole.
The World Resources Institute (WRI) has recently announced the launch of Global Forest Watch (GFW), a dynamic forest monitoring system that provides aims to provide ‘timely and reliable’ information about the state of the world’s forests. Using a combination of satellite imagery, open access data and crowd sourced information, GFW builds on earlier projects such as the Forest Frontiers Initiative and the Forest Atlases, one of the case studies we discussed in The GIS Guide to Public Domain Data, which promoted the sustainable management of forest resources.
One of the big issues for monitoring forest reserves has been, given the often inaccessible locations, by the time harmful and illegal logging was reported it was invariably too late to stop the deforestation. GFW aims to provide near real-time information on forest clearing activities so local authorities, governments, global business and the general public have access to the latest, and hopefully most accurate, status of forest reserves. The listed data sources include:
- Forest change ( many derived from MODIS data)
- Forest cover
- Forest Use
The GFW web site provides access to a global map based on the University of Maryland Tree Cover Loss and Gain data.
The GFW site also provides a time-lapse run through of the last twelve years change in tree cover.
Although the predominance of forest cover loss (pink) as opposed to gain (blue) in many areas tells a depressingly familiar tale, providing public access to the latest information like this should help shine a light on illegal logging activities.
In an article entitled “The Watchers”, David Samuels discusses a company seeking to deploy small satellites into orbit 500 miles (805 km) above the Earth. This company, Skybox, founded by ex-Stanford University students, seeks to shake up the commercial space imaging industry by doing two things: (1) Deploying smaller, less expensive satellites than what the commercial space imaging industry is currently using, the size of a dormitory room refrigerator, and (2) Using crowdsourcing for data classification. They seek to have ordinary citizens classify the incoming data, as well as do some classification themselves, even from images that the company has collected but does not sell. This could be the number of cars in every WalMart parking lot in the USA, the size of slag heaps outside the world’s largest gold mines in South Africa, and the rate at which the wattage along key stretches of the Ganges River is growing. These bits of information, they reason, are clues about the economic health of countries, industries, and individual businesses. Therefore, this information will be so valuable to investors, environmentalists, activists, and journalists, to name a few, that they will be willing to pay for the information. The company is working with the government of Russia for a launch vehicle and hopes to launch its first satellite this month, SkySat-1.
This story connects well with issues we raise in the book The GIS Guide to Public Domain Data, including data quality and resolution, military vs. civilian uses of data, crowdsourcing, and privacy. The resolution of the images returned from Skybox’s satellites will be comparable–less than 1 meter–to those from large commercial satellite imaging companies such as Digital Globe. However, the cost of constructing them should be considerably less and the size of the satellite itself considerably smaller. Skybox has added numerous advisers with connections in the defense industry “to avoid any military-industrial squelching of its technology before launch.” Relying on crowdsourcing to classify images is not a new concept, but what is new here is the scale at which it could be employed, and that it is embedded in the company’s business model. How standards will be established to assure data quality to potential purchasers of the derived information will be very interesting indeed. Lastly, the idea of inexpensive, high resolution, easy-to-deploy satellites imaging the planet has enormous privacy implications for those of us on the ground, whether from Skybox or for others who are sure to follow.
The Web-enabled Landsat Data (WELD) project generates 30-meter composites of Landsat 7 Enhanced Thematic Mapper Plus (ETM+) terrain corrected (Level 1T) mosaics at weekly, monthly, seasonal and annual periods for the conterminous United States (CONUS) and Alaska. These mosaics provide consistent data that can be used to derive land cover as well as geophysical and biophysical products for regional assessment of surface dynamics and to study Earth system functioning.
A collaboration between the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and academic partner South Dakota State University Geographic Information Science Center of Excellence, this is an excellent resource for all who seek to compare land use through time and through seasonal variation using Landsat data in the continental USA and in Alaska. The WELD documentation site describes the WELD products on the site, known issues, and future plans.
WELD products are available as custom GeoTiff subsets via a new interactive web ordering system and as tiled HDF products via FTP. I found the site fairly intuitive, simple, and straightforward to use. Its products are directly importable into GIS software and hence it provides much more than visualizations, but rather, products useful to the GIS analyst. The “good news, bad news” is that the GIS data user is confronted with an array of Landsat sites from which they may obtain data. Each has its own interface and formats, but the situation is still far better than 10 years ago when nearly all of it was either for fee or difficult to obtain. Because it is not well linked to other sites, the WELD site is difficult to “stumble across” unless the data user is familiar with the acronym. However, it is well worth a visit as it is one of the most intuitive and resource-rich.