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Spatial feature manipulation in remote sensing refers to the process of altering or modifying the spatial characteristics of a particular feature in an image or data set. This can be done for a variety of reasons, such as to improve the accuracy or clarity of the image, to enhance the interpretability of the data, or to extract specific information from the image. One common method of spatial feature manipulation is resampling, which involves changing the resolution or spatial extent of an image. This can be done to match the resolution of other data sets, to reduce the size of the image for faster processing, or to increase the resolution for greater detail. Another technique is spatial filtering, which involves applying mathematical operations to the image data to remove noise or highlight specific features. This can be done using convolution filters, which apply a pre-defined mathematical function to the image data, or using image enhancement techniques, such as contrast stretching

Denman Glacier Losing Some of Its Footing

- May 19, 2020

Denman Glacier Losing Some of Its Footing Using a combination of satellite sensors, scientists recently found that Denman Glacier has been retreating both above and below the water line. That one glacier in East Antarctica holds as much ice as half of West Antarctica, so scientists are concerned about its stability. From 1996 to 2018, the grounding line along the western flank of Denman Glacier retreated 5.4 kilometers (3.4 miles), according to a new study by scientists from NASA's Jet Propulsion Laboratory and the University of California, Irvine (UCI). The grounding line is the point at which a glacier last touches the seafloor before it begins to float. Behind the grounding line, the ice is attached to the bedrock; beyond it, glacial ice floats on the ocean as an ice tongue or shelf. The retreat of the grounding line at Denman means more of the glacier's underside is now in contact with water that could warm and melt it from below. If the grounding line continues to retreat,

Difference between Kriging and IDW

- October 25, 2020

Difference between Kriging and IDW. What does kriging mean? least squares estimate. Kriging is a type of regression that gives a least squares estimate of data (Remy et. ... Unlike linear regression or inverse distance weighted interpolation, kriging interpolation is based primarily on empirical observations, the observed sample data points, rather than on a pre-assumed model. What is GIS IDW? Inverse Distance Weighted (IDW) is a method of interpolation that estimates cell values by averaging the values of sample data points in the neighborhood of each processing cell. ... Specifying a lower power will give more influence to the points that are farther away, resulting in a smoother surface. Which interpolation method is best? The most used and promising techniques are universal Kriging and linear regression models in combination with Kriging (residual Kriging) or IDW. E.g.: Air temperature data – Kriging is most likely to produce the best estimation of a continuous surface, followed by

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Applied Geography