5.12.1. Spatial Resolution

A digital image consists of an array of pixels in rows and columns, and each pixel contains information about a small area on the land surface. Spatial resolution is the size of the smallest dimension on the Earth’s surface over which an independent measurement can be made by the sensor. It is the minimum separation between the two objects that a sensor is able to record distinctly.

Spatial resolution refers to the smallest portion of the Earth's surface that can be distinguished in a satellite image. It is defined by the pixel size in the image, meaning that each individual pixel represents a specific area on the ground. If two objects are closer together than the spatial resolution, they will appear merged together in the image. Therefore, spatial resolution plays a crucial role in determining how much detail can be observed in satellite images.

Think of spatial resolution like a camera. If you're using a camera with a low megapixel count, the individual images may be blurry, making it hard to distinguish small details. Conversely, a high-resolution camera captures sharp images where you can see fine details clearly, just like a satellite with higher spatial resolution shows more detail in its images.

It is usually described by the instantaneous field of view (IFOV) of the sensor. The IFOV of the ground seen from the detector of a sensor is also called a pixel. The IFOV is dependent on the altitude of the satellite; higher the altitude, larger is the IFOV.

The term Instantaneous Field of View (IFOV) defines the area on the ground that is captured by a sensor at any given moment. Each pixel in a satellite image corresponds to this area. Importantly, the altitude of the satellite impacts the IFOV; as the satellite flies higher, it can see a larger area. However, this broader view reduces the level of detail that can be discerned. Thus, a balance between altitude and detail must be considered when selecting satellites for specific observations.

Consider looking at a map from a distance versus up close. When you look from afar (high altitude), you see a wide area but may not make out details such as street names or parks. However, squinting to look closely at a small section (a lower altitude) will reveal a lot more detail about street names, individual buildings, etc. Similarly, satellites at different altitudes provide varying levels of detail.

High spatial resolution images have < 5 m, while low spatial resolution images are > 500 m pixel size. An object of smaller dimension than a pixel but with good contrast with respect to its background can also be detected.

Spatial resolution can be categorized as high or low based on pixel size. High-resolution images typically have pixel sizes less than 5 meters, allowing for detailed observation of features like individual trees or structures. Low-resolution images have pixel sizes greater than 500 meters, which results in less clarity, showing only broad patterns without detail. The ability to detect smaller objects is also influenced by how distinct they are compared to their background.

Imagine a puzzle. If the individual pieces are very small (high resolution), you can easily see different colors and designs on each piece. If the pieces are large (low resolution), you can only tell broad shapes and colors, losing the details. Similarly, satellite images with high spatial resolution reveal intricate details about the land, while those with low spatial resolution provide just an overview.

Remote sensing systems with spatial resolution more than 500 m are generally considered as low resolution systems, e.g., MODIS and AVHRR sensors. The moderate resolution systems have the spatial resolution between 100–500 m, for example, IRS WiFS (188m), band 6, i.e., thermal infrared band of the Landsat TM (120m), and bands 1-7 of MODIS having resolution 250-500 m. High resolution systems have spatial resolution approximately 5-100 m, such as Landsat ETM+ (30 m), IRS LISS-III (23 m MSS and 6 m Panchromatic) and AWiFS (56-70 m). Very high resolution systems provide less than 5 m spatial resolution, such as GeoEye (0.45 m for Panchromatic and 1.65 m for MSS), IKONOS (0.8-1 m Panchromatic), and QuickBird (2.4-2.8 m).

Remote sensing systems are categorized by their spatial resolution: low (more than 500 m), moderate (100-500 m), high (5-100 m), and very high (<5 m). This classification reflects how detailed the images are. For instance, systems like MODIS serve broad applications across environment monitoring but lack fine detail, whereas systems like GeoEye and QuickBird are designed for detailed urban surveys or infrastructure projects. Each type has its ideal uses based on resolution.

Consider different types of cameras. A basic point-and-shoot camera represents low spatial resolution, capturing images that appear fuzzy for small subjects. A DSLR camera, known for its high resolution, allows for sharp images of tiny details like individual leaves on a tree. In the context of remote sensing, higher resolution systems are like the high-end cameras, offering detailed images of the Earth's features.

--