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 Learning Geospatial Analysis with Python

You're reading from   Learning Geospatial Analysis with Python Unleash the power of Python 3 with practical techniques for learning GIS and remote sensing

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Product type Paperback
Published in Nov 2023
Publisher Packt
ISBN-13 9781837639175
Length 432 pages
Edition 4th Edition
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Author (1):
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Joel Lawhead Joel Lawhead
Author Profile Icon Joel Lawhead
Joel Lawhead
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Table of Contents (18) Chapters Close

Preface 1. Part 1:The History and the Present of the Industry
2. Chapter 1: Learning about Geospatial Analysis with Python FREE CHAPTER 3. Chapter 2: Learning about Geospatial Data 4. Chapter 3: The Geospatial Technology Landscape 5. Part 2:Geospatial Analysis Concepts
6. Chapter 4: Geospatial Python Toolbox 7. Chapter 5: Python and Geospatial Algorithms 8. Chapter 6: Creating and Editing GIS Data 9. Chapter 7: Python and Remote Sensing 10. Chapter 8: Python and Elevation Data 11. Part 3:Practical Geospatial Processing Techniques
12. Chapter 9: Advanced Geospatial Modeling 13. Chapter 10: Working with Real-Time Data 14. Chapter 11: Putting It All Together 15. Assessments 16. Index 17. Other Books You May Enjoy

Remote sensing

Remote sensing is where you collect information about an object without making physical contact with that object. In the context of geospatial analysis, that object is usually the Earth. Remote sensing also includes processing the collected information. The potential of geographic information systems is limited only by the available geographic data. The cost of land surveying, even using a modern GPS to populate a GIS, has always been resource-intensive.

The advent of remote sensing not only dramatically reduced the cost of geospatial analysis but took the field in entirely new directions. In addition to powerful reference data for GIS systems, remote sensing has made generating automated and semi-automated GIS data possible by extracting features from images and geographic data. The eccentric French photographer Gaspard-Félix Tournachon, also known as Nadar, took the first aerial photograph in 1858, from a hot air balloon over Paris:

Figure 1.6 – An aerial photo of Paris from a hot air balloon taken in 1858 by Nadar. It is considered to be the first aerial photo and the dawn of geospatial remote sensing

Figure 1.6 – An aerial photo of Paris from a hot air balloon taken in 1858 by Nadar. It is considered to be the first aerial photo and the dawn of geospatial remote sensing

The value of a true bird’s-eye view of the world was immediately apparent. As early as 1920, books on aerial photo interpretation began to appear.

When the United States entered the Cold War with the Soviet Union after World War II, aerial photography to monitor military capability became prolific with the invention of the American U-2 spy plane. The U-2 spy plane could fly at 75,000 feet, putting it out of the range of existing anti-aircraft weapons designed to reach only 50,000 feet. The American U-2 flights over Russia ended when the Soviets finally shot down a U-2 and captured the pilot.

However, aerial photography had little impact on modern geospatial analysis. Planes could only capture small footprints of an area. Photographs were tacked to walls or examined on light tables but not in the context of other information. Though extremely useful, aerial photo interpretation was simply another visual perspective.

The game changer came on October 4, 1957, when the Soviet Union launched the Sputnik 1 satellite. The Soviets had scrapped a much more complex and sophisticated satellite prototype because of manufacturing difficulties. Once corrected, this prototype would later become Sputnik 3. Instead, they opted for a simple metal sphere with four antennae and a simple radio transmitter. Other countries, including the United States, were also working on satellites. These satellite initiatives were not entirely a secret. They were driven by scientific motives as part of the International Geophysical Year (IGY).

Advancements in rocket technology made artificial satellites a natural evolution for Earth science. However, in nearly every case, each country’s defense agency was also heavily involved. Similar to the Soviets, other countries were struggling with complex satellite designs packed with scientific instruments. The Soviets’ decision to switch to the simplest possible device was for the sole reason of launching a satellite before the Americans were effective. Sputnik was visible in the sky as it passed over, and its radio pulse could be heard by amateur radio operators. Despite Sputnik’s simplicity, it provided valuable scientific information that could be derived from its orbital mechanics and radiofrequency physics.

The Sputnik program’s biggest impact was on the American space program. America’s chief adversary had gained a tremendous advantage in the space race. The United States ultimately responded with the Apollo moon landings. However, before this, the US launched a program that would remain a national secret until 1995. The classified CORONA program resulted in the first pictures from space. The US and Soviet Union had signed an agreement to end spy plane flights, but satellites were conspicuously absent from the negotiations.

The following figure shows the CORONA process. The dashed lines are the satellite flight paths, the long white tubes are the satellites, the small white cones are the film canisters, and the black blobs are the control stations that triggered the ejection of the film so that a plane could catch it in the sky:

Figure 1.7 – An illustration of the early CORONA spy satellite that ejected film canisters that were caught in mid-air by a plane

Figure 1.7 – An illustration of the early CORONA spy satellite that ejected film canisters that were caught in mid-air by a plane

The first CORONA satellite was a four-year effort with many setbacks. However, the program ultimately succeeded. The difficulty with satellite imaging, even today, is retrieving the images from space. The CORONA satellites used canisters of black and white film that were ejected from the vehicle once exposed. As a film canister parachuted to Earth, a US military plane would catch the package in midair. If the plane missed the canister, it would float for a brief period in the water before sinking into the ocean to protect the sensitive information.

The US continued to develop the CORONA satellites until they matched the resolution and photographic quality of the U-2 spy plane photos. The primary disadvantages of the CORONA instruments were reusability and timeliness. Once out of film, a satellite would no longer be of service. Additionally, the film’s recovery was on a set schedule, making the system unsuitable for monitoring real-time situations. The overall success of the CORONA program, however, paved the way for the next wave of satellites, which ushered in the modern era of remote sensing.

Due to the CORONA program’s secret status, its impact on remote sensing was indirect. Photographs of the Earth taken on manned US space missions inspired the idea of a civilian-operated remote-sensing satellite. The benefits of such a satellite were clear, but the idea was still controversial. Government officials questioned whether a satellite was as cost-efficient as aerial photography. The military was worried that the public satellite could endanger the secrecy of the CORONA program. Other officials worried about the political consequences of imaging other countries without permission. However, the Department of the Interior (DOI) finally won permission for NASA to create a satellite to monitor Earth’s surface resources.

On July 23, 1972, NASA launched the Earth Resources Technology Satellite (ERTS). The ERTS was quickly renamed Landsat 1. The platform contained two sensors. The first was the Return Beam Vidicon (RBV) sensor, which was essentially a video camera. It was built by the radio and television giant known as the Radio Corporation of America (RCA). The RBV immediately had problems, which included disabling the satellite’s altitude guidance system. The second sensor was the highly experimental Multispectral Scanner (MSS). The MSS performed flawlessly and produced superior results compared to the RBV. The MSS captured four separate images at four different wavelengths of the light reflected from the Earth’s surface.

This sensor had several revolutionary capabilities. The first and most important capability was the first global imaging of the planet scanning every spot on Earth every 16 days. It also recorded light beyond the visible spectrum. While it did capture green and red light visible to the human eye, it also scanned near-infrared light at two different wavelengths not visible to the human eye. The images were stored and transmitted digitally to three different ground stations in Maryland, California, and Alaska. Its multispectral capabilities and digital format meant that the aerial view provided by Landsat wasn’t just another photograph from the sky. It was beaming down the data. This data could be processed by computers to output derivative information about the Earth in the same way a GIS provided derivative information about the Earth by analyzing one geographic feature in the context of another. NASA promoted the use of Landsat worldwide and made the data available at very affordable prices to anyone who asked.

This global imaging capability led to many scientific breakthroughs, including the discovery of previously unknown geography, which occurred as late as 1976. For example, using Landsat imagery, the government of Canada located a tiny uncharted island inhabited by polar bears. They named the new landmass Landsat Island.

Landsat 1 was followed by six other missions that were turned over to the National Oceanic and Atmospheric Administration (NOAA) as the responsible agency. Landsat 6 failed to achieve orbit due to a ruptured manifold, which disabled its maneuvering engines. During some of these missions, the satellites were managed by the Earth Observation Satellite (EOSAT) company, now called Space Imaging, but returned to government management by the Landsat 7 mission. The following figure from NASA is a sample of a Landsat 7 product over Cape Cod, Massachusetts, USA:

Figure 1.8 – An example of a Landsat 7 satellite image over Cape Cod, Massachusetts, USA

Figure 1.8 – An example of a Landsat 7 satellite image over Cape Cod, Massachusetts, USA

The Landsat Data Continuity Mission (LDCM) was launched on February 13, 2013, and began collecting images on April 27, 2013, as part of its calibration cycle to become Landsat 8. The LDCM is a joint mission between NASA and the US Geological Survey (USGS).

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Learning Geospatial Analysis with Python - Fourth Edition
Published in: Nov 2023
Publisher: Packt
ISBN-13: 9781837639175
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