 Hi, my name is Todd Bacastow. Welcome to Geospatial Intelligence and Geospatial Revolution. This is lesson three, lecture one. By the end of this lesson, you should be able to describe the need for geospatial intelligence requirements process, define the high-level geodata collection strategies and sources, describe the advantages and disadvantages of each of these, and match a strategy and source to a task. Geodata is collected by a variety of methods, systems, platforms, and sensors to include humans. In general, intelligence sources are those means to observe and record information related to something's condition, situation, or activity. Geoance primary data source traditionally has been imagery. While imagery still dominates, and it's the most important source, there are other emerging sources that are being recognized within the community. If you recall, the principal purpose of geoint is to supply the decision maker with timely geospatial insights that allow for informed and knowledgeable decisions. In order to fulfill this purpose, we prioritize the intelligence requirements of the decision maker. These requirements define the mission, function, and structure of geoint. They also drive data collection, analysis, and budget. The intelligence cycle depicted here starts with requirements. The requirements are sorted and prioritized and then used to drive the requirement activities of the members of the broader intelligence community. Once information has been collected, it is initially evaluated and processed and reported to the consumers. The cycle is then repeated until the intelligence requirement is fully satisfied. Requirements are often difficult to define. They focus in the long term, while decision makers really don't know what they want until they're in a situation. Collection management is a formal process of converting intelligence requirements into collection requirements, establishing priorities, tasking or coordinating with appropriate collection resources, monitoring results, and retasking. The collection process includes determining what should be collected. There are four criteria for this. Is the intelligence requirement necessary? Is it feasible to collect the information? Is the requirement timely? And do we have enough information to properly collect the information? A collection strategy is a high-level plan to satisfy information requirements. The key goals of a collection strategy, in addition to meeting the requirements, are to detect deception and minimize the effects of deception and provide redundancy. Detecting deception requires analysis of information from a variety of sources so that information from one source can be verified or confirmed by others. Multiple collection sources enable collection managers to cross-cue between different sources. Cross-cuing is assisting in the completion of the collection task by having another sensor look at the target. In this case, the target is a place, a thing, or person at which the sensor is aimed. For example, one sensor may look at a target and find trees and foliage. Another sensor may reveal that the object that we think are green leaves are actually painted plastic and the tree is camouflaging something. Once camouflaged and determined, another sensor that will not be confused by the camouflage may investigate the object beneath the painted plastic tree. Collection may also require redundancy so that loss or failure of one source can be compensated by another. A strategy is a plan to achieve goals. It provides direction and scope for an effort. Collection strategy is used to note a high-level way to pursue joint data collection goals. Collection strategy is important because the resources available to achieve goals are often limited. We have artificially divided the continuous strategies into two categories. These categories are persistent collection and discontinuous collection. Persistent collection is a strategy that emphasizes the ability to use collecting systems that linger for a period of time over an area. These detect, locate, characterize, identify, track, and possibly provide near real-time data. Persistent collection facilitates the collection and prediction of human behavior, for example, pattern of life. The goal is to achieve near perfect knowledge by increasing the rate and regularity of data collected and thereby understanding about the target. This enables a faster decision cycle with more frequent and detailed data. The collection strategy is that a target will be unable to move, hide, disperse, deceive, or break contact. The intent is to improve decision making. In different domains, persistent collection is called persistent surveillance, intelligence surveillance and reconnaissance or ISR, persistent stare, or pervasive knowledge. Unmanned aerial vehicles or UAVs are frequently associated with persistent collection. UAVs are also known as drones. UAVs are being used in traditional and emerging industries such as agriculture, land development, surveying and mapping, forestry, marketing, environmental and marine research, oil and gas, exploration, homeland security, and law enforcement. Discontinuous collection is not a full record of activity during a period of time. It may also be called non-persistent collection. Discontinuous collection is not a permanent stare at the target. A permanent stare might not be possible. We might not have the assets available, might not be technically feasible. We might have limited resources. While losing information in the practice of discontinuous collection of data has long dominated the discipline. Satellite remote sensing systems are an example of discontinuous source strategy. Here sensors are mounted on aircraft or satellites orbiting the earth. Spaceborne remote sensing provides repetitive coverage of an area of interest. Discontinuous collection occurs when any sensor sporadically provides data. Consider your cell phone as a sensor. A cell phone can be a discontinuous sensor. There are numerous other examples of discontinuous joint data. For example, highway toll devices that record your point of entry and report of exit from a toll road. Twitter is another example. Tweets can be geotagged to record information about the location of the device that created the tweet. Images taken with a phone or GPS enabled camera also record location. These are all examples of discontinuous data. This concludes the first lecture of lesson three. The next lecture we will discuss joint data sources.