Image differencing involves subtracting the pixel values of two images taken at different times to highlight changes in the scene.

Change detection techniques are used for a variety of purposes, including identifying areas of land cover change, monitoring environmental changes, and detecting deforestation.

NDVI is a widely used change detection technique that measures the difference between near-infrared and red bands to identify changes in vegetation cover.

Image ratioing involves dividing the pixel values of two images to highlight changes in the scene.

Principal Component Analysis (PCA) is a change detection technique that transforms multiple spectral bands into a smaller number of principal components, which can be used to identify changes in the scene.

Post-classification comparison involves classifying images taken at different times and then comparing the resulting thematic maps to identify changes. This method provides accurate change information but can be computationally intensive.

Change Vector Analysis (CVA) is a change detection technique that calculates the magnitude and direction of change between two images. It is particularly useful for detecting changes in urban areas, where the spectral characteristics of land cover types can change significantly over time.

Image differencing is sensitive to noise and other image artifacts, which can lead to false change detections.

LandTrendr is a change detection technique specifically designed for monitoring deforestation and forest degradation. It uses time series analysis to identify trends in forest cover change.

VID involves subtracting the pixel values of two vegetation indices, such as NDVI, to highlight changes in vegetation cover.

NDWI is a change detection technique specifically designed for detecting changes in water bodies. It uses the difference between near-infrared and shortwave infrared bands to highlight changes in water extent.

Using multi-temporal data for change detection reduces the impact of noise and atmospheric effects, allows for the analysis of long-term changes, and provides more accurate change information.

LULCC Analysis is a change detection technique specifically designed to detect changes in land use and land cover. It involves classifying images taken at different times and then comparing the resulting thematic maps to identify changes.

Post-classification comparison can be computationally intensive, especially when dealing with large images or multiple time periods.

NDISI is a change detection technique specifically designed for detecting changes in impervious surfaces. It uses the difference between shortwave infrared and near-infrared bands to highlight changes in impervious surface extent.