Book Image

Learning Geospatial Analysis with Python

By : Joel Lawhead
Book Image

Learning Geospatial Analysis with Python

By: Joel Lawhead

Overview of this book

Geospatial Analysis is used in almost every field you can think of from medicine, to defense, to farming. This book will guide you gently into this exciting and complex field. It walks you through the building blocks of geospatial analysis and how to apply them to influence decision making using the latest Python software. Learning Geospatial Analysis with Python, 2nd Edition uses the expressive and powerful Python 3 programming language to guide you through geographic information systems, remote sensing, topography, and more, while providing a framework for you to approach geospatial analysis effectively, but on your own terms. We start by giving you a little background on the field, and a survey of the techniques and technology used. We then split the field into its component specialty areas: GIS, remote sensing, elevation data, advanced modeling, and real-time data. This book will teach you everything you need to know about, Geospatial Analysis from using a particular software package or API to using generic algorithms that can be applied. This book focuses on pure Python whenever possible to minimize compiling platform-dependent binaries, so that you don’t become bogged down in just getting ready to do analysis. This book will round out your technical library through handy recipes that will give you a good understanding of a field that supplements many a modern day human endeavors.
Table of Contents (17 chapters)
Learning Geospatial Analysis with Python Second Edition
About the Author
About the Reviewers

Remote sensing

Remote sensing is the collection of information about an object without making physical contact with that object. In the context of geospatial analysis, the object is usually the Earth. Remote sensing also includes the processing of 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 this cost of geospatial analysis, but it took the field in entirely new directions. In addition to powerful reference data for GIS systems, remote sensing has made possible the automated and semi-automated generation of GIS data 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:

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 70,000 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. The satellite initiatives were not entirely a secret. They were driven by scientific motives as part of the International Geophysical Year (IGY). Advancement 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 for the sole reason of launching a satellite before the Americans was 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 radio frequency physics.

The Sputnik program's biggest impact was on the American space program. America's chief adversary had gained a tremendous advantage in the race to space. The United States ultimately responded with the Apollo moon landings. However, before this, the U.S. launched a program that would remain a national secret until 1995. The classified CORONA program resulted in the first pictures from space. The U.S. and Soviet Union had signed an agreement to end spy plane flights but satellites were conspicuously absent from the negotiations. The following map shows the CORONA process. Dashed lines are the satellite flight paths, the longer white tubes are the satellites, the smaller 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:

The first CORONA satellite was a four-year effort with many setbacks. However, the program ultimately succeeded. The difficulty of 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 the film canister parachuted to Earth, a U.S. military plane would catch the package in midair. If the plane missed the canister, it would float for a brief duration in the water before sinking into the ocean to protect the sensitive information. The U.S. 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 could no longer be of service. Additionally, the film recovery was on a set schedule making the system unsuitable to monitor 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 U.S. 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 were 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 to 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, Radio Corporation of America (RCA). The RBV immediately had problems, which included disabling the satellite's altitude guidance system. The second attempt at a satellite was the highly experimental Multispectral Scanner (MSS). The MSS performed flawlessly and produced superior results than 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 the Earth every 16 days. The following image from the U.S. National Aeronautics and Space Administration (NASA) illustrates this flight and collection pattern that is a series of overlapping swaths as the sensor orbits the Earth capturing tiles of data each time the sensor images a location on the Earth:

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. The multispectral capability and digital format meant that the aerial view provided by Landsat wasn't just another photograph from the sky. It was beaming down 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 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 and 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 image from NASA is a sample of a Landsat 7 product:

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 U. S. Geological Survey (USGS).