Remote Sensing and GIS are two different terms however, people seem to have used them interchangeably. Remote Sensing is a field which deals with acquiring, interpreting, and analyzing remotely sensed imagery particularly Landsat, MODIS, Sentinel, or even Lidar data whereas GIS is a tool that helps in this process. Before learning the importance of GIS in Remote Sensing, let’s learn what is GIS and Remote Sensing?
GIS (Geographic Information Science)
GIS is a set of computer tools that allows to work with data that are tied to a location on the earth. GIS is more than just a mapping system because it does some sophisticated spatial analysis, network analysis, geocoding and geo-referencing, and many more. A GIS is a database system that uses both spatial and attribute data to answer questions about where things are and how they are related. It has many functions, including creating data, making maps, and analyzing relationships.
“A geographic information system (GIS) is a framework for gathering, managing, and analyzing data. Rooted in the science of geography, GIS integrates many types of data. It analyzes spatial location and organizes layers of information into visualizations using maps and 3D scenes. With this unique capability, GIS reveals deeper insights into data, such as patterns, relationships, and situations—helping users make smarter decisions.”ESRI. What is GIS?
Now-a-days, people use GIS works for different applications including land use planning, environmental management, sociological analysis, business marketing, weather prediction, city planning, wastewater panning, urban planning, navigation tools, and many more. Check frequently asked GIS questions here.
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“Remote sensing is the science of obtaining information about objects or areas from a distance, typically from aircraft or satellites”.NOAA. What is remote sensing? National Ocean Service website.
“Remote sensing is the process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance (typically from satellite or aircraft). Special cameras collect remotely sensed images, which help researchers “sense” things about the Earth”.USGS. What is remote sensing and what is it used for? Mapping, Remote Sensing, and Geospatial Data
The images captured by sensors mounted on satellites or aircraft are received from the ground station on earth. These images (in the case of Landsat) are acquired and archived in the USGS EROS Center at Sioux Falls, SD. The Landsat images are processed and uploaded to USGS Earth Explorer for public use. Remote sensing has a wide range of applications in many different fields which includes coastal applications, ocean applications, hazard assessment, natural resource management, land cover mapping, weather predictions, large forest fires mapping, and more. Learn more about Remote Sensing application in NOAA and USGS webpages.
Importance of GIS in Remote Sensing
As discussed above, Remote Sensing deals with the remotely sensed images; and these images are the fundamental data sources for GIS. Remote Sensing images, henceforth called as raster images, contains plethora of information that may be in the bands form or pixel information. GIS tool has a unique functionality to analyze these raster and extract readable/presentable information—simply by using Raster Analysis tool. This information can be shared or be used for further analysis in different areas such land cover mapping, weather predictions, large forest fires mapping, coastal applications, ocean applications, hazard assessment, natural resource management, and what-not.
From simple image interpretation to modeling the earth data and predicting the natural hazards using the remote sensing, GIS has proven to be very handy, convenient, and effective. Various organizations including the USGS, NOAA, and NASA uses GIS tool to monitor the changes, understand the trends, and perform forecasting. GIS has capability to provide results in the form of maps, data, webservices, and even apps.
These days, various programming and scripting have become popular in automating the huge pile of datasets. The scripting uses machine learning algorithms to automate similar steps thousands of time and provide robust and near-real-time results. The scripting capability in GIS has and will continue to contribute in producing accurate, robust, and impactful results in easier and convenient manner.