Google Earth is a digital globe which displays the Earth’s surface primarily based on mosaics of very high resolution aerial images (after zooming in far enough). Google Earth thus serves as a common entry point to remote sensing and, moreover, opened up the remote sensing technology to the broad public. The desktop application Google Earth Pro offers several features and tools which are useful for visual image interpretation and remote sensing research in general. Among others, these include the availability of various geodata layers (e.g., borders, roads, 3D buildings, etc.) and the possibilities to display historical images, to import both vector and raster geodata, and to digitize objects.
The Earth’s surface is a mosaic of various land cover and land use types. Remote sensing based satellite imagery provide synoptic views in space and time and are an ideal source for mapping this diversity. Mapping with remote sensing requires a basic understanding of the difference between land cover and land use.
The terms land cover and land use are often used interchangeably, however, they are distinct from each other. Land cover describes the biophysical composition of the Earth’s surface. Land use describes the anthropogenic use of the Earth’s surface. The differentiation of both terms is crucial in remote sensing as satellites provide imagery of land cover, whereas information on land use is mostly based on additional human interpretation.
The analysis of Earth observation data allows us to draw conclusions about conditions and processes of the Earth’s surface. We often make use of satellite and aerial images taken from spaceborne and, respectively, airborne platforms. Different image properties, such as object features and context, can be used to interpret what is on the ground (e.g., land cover and land use). With the help of images from different dates, we can observe and analyze changes of the Earth’s surface.
The quantity and quality of both satellite and aerial images have enhanced over time. With regard to very high spatial resolution images that are well suited for detailed visual image interpretation, satellite images with a spatial resolution of less than 1 m are available for most parts of the Earth. In urban agglomerations, aerial images often even exceed 10 cm spatial resolution.
Visual image interpretation implies the human’s ability to analyze the content of images, e.g., land cover and land use from remote sensing imagery. Visual image interpretation encompasses two steps, first the perception of objects according to their external attributes and, second, the actual interpretation of their meaning. The following table provides a guideline for visually interpreting images. The complexity increases as the table progresses starting with the basic elements such as contrast and color and ending with the interpretation of the spatial context:
|Attribute||Description (example)||Interpretation (example)|
|Contrast, color, brightness||Transition from light to dark blue||Variations in water depth|
|Geometry (shape, size)||Sinuous ribbon-like object||River|
|Texture (structure of a surface)||Rough surface with vertical line patterns||Maize cultivation|
|Spatial context (functional interrelationship)||Rail tracks that intersect a building||Railway station|
The objective of this assignment is to characterize the land cover and land use change in Berlin using Google Earth Pro.
In Google Earth Pro, choose four locations in Berlin that you’re interested in. Find two time steps for each location that show a change process. Hint: Close to the former Berlin wall and between 2000 and now a lot has changed.
Document your answers to the above questions using screenshots, bullet points and a table in PowerPoint, Word or another software package. Please use the following layout suggestion:
Upload your documentation as a PDF file on Moodle.
General submission notes: Submission deadline for the weekly assignment is always the following Monday at 10am. Please use the naming convention indicating session number and family name of all students in the respective team, e.g. ‘s01_surname1_surname2_surname3_surname4.pdf’. Each team member has to upload the assignment individually. Provide single file submissions, in case you have to submit multiple files, create a *.zip archive.
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