Remote Sensing & Image Interpretation Flashcards

What is remote sensing primarily used for in photogrammetry?

Answer: Capturing spatial information from a distance

Remote sensing is the process of acquiring information about an object or phenomenon without making physical contact with it, typically using sensors on aircraft or satellites. In photogrammetry, it specifically involves capturing aerial or satellite images to derive spatial data, such as measurements, maps, and 3D models. It allows for broad-scale data collection from a distance.

Which type of satellite sensor detects reflected sunlight?

Answer: Passive sensor

Passive sensors detect and measure natural radiation that is reflected or emitted from the Earth's surface, such as sunlight or thermal energy. They do not generate their own energy source, relying instead on ambient energy, making them dependent on external illumination like the sun. Examples include traditional cameras and many satellite imagers.

What is the term for the smallest object that can be detected by a sensor?

Answer: Spatial resolution

Spatial resolution refers to the size of the smallest feature on the ground that can be distinctly detected and represented by a sensor's pixel. A higher spatial resolution means smaller pixels, allowing for the detection of finer details and smaller objects on the Earth's surface. It dictates the level of detail visible in an image.

Which color band is most effective for detecting healthy vegetation?

Answer: Near-infrared band

Healthy vegetation strongly reflects near-infrared (NIR) light due to the cellular structure of its leaves, while absorbing most visible red light for photosynthesis. This distinct spectral signature makes the near-infrared band highly effective for distinguishing healthy vegetation from other land cover types and assessing plant vigor, biomass, and stress levels.

Which type of remote sensing uses emitted microwave energy to collect data?

Answer: Active radar sensors

Active radar sensors, such as Synthetic Aperture Radar (SAR), emit their own microwave energy pulses and then measure the reflected signal. This allows them to collect data independently of natural illumination and through atmospheric conditions like clouds or smoke. This capability makes them valuable for all-weather, day-and-night remote sensing applications.

What is a key advantage of active remote sensing systems like LiDAR?

Answer: They function in any lighting conditions

A key advantage of active remote sensing systems, such as LiDAR or radar, is their ability to emit their own energy (e.g., laser pulses or microwaves) and measure the reflected signal. This independence from external light sources allows them to collect data effectively during both day and night, and often through atmospheric conditions like clouds or haze, ensuring consistent data acquisition.

Which factor determines how frequently a satellite can revisit the same location?

Answer: Temporal resolution

Temporal resolution refers to how frequently a remote sensing system can acquire imagery of the same geographic area. It is determined by the satellite's orbital characteristics and sensor capabilities. This factor is crucial for monitoring dynamic phenomena like vegetation growth, disaster response, or land-use change over time, as it dictates the frequency of data updates.

Which term describes the interpretation of remotely sensed images to classify land cover?

Answer: Image classification

Image classification is the process of categorizing pixels in a remotely sensed image into different land cover or land use classes (e.g., forest, water, urban, agriculture). This is achieved by analyzing the spectral characteristics of pixels and assigning them to predefined categories, creating thematic maps that represent the distribution of various features on the Earth's surface.

Why is multi-spectral imaging valuable in remote sensing?

Answer: It captures images in multiple wavelength bands

Multi-spectral imaging is valuable because it captures reflected or emitted energy across several discrete wavelength bands of the electromagnetic spectrum, beyond just visible light. This allows for the differentiation of features that might look similar in visible light but have distinct spectral signatures in other bands. This provides richer information for detailed analysis, classification, and monitoring of various phenomena.