A preprocessing of remote sensing data is a crucial step in every analytical workflow, and can possibly be the most time consuming one.
\nAnyway, we want to focus on converting the just-downloaded Landsat 8 and Sentinel 2 products into GeoTIFF files and the visualization in R and QGIS. The pre-processing of Sentinel 1 is a bit more complex. For this purpose, we derive the intensity images on the basis of GRD scenes.<\/p>\n
In this chapter, the following content awaits you: The basic understanding of the various operators and data types in R is required. If you want to refresh this knowledge, you can now go to the R Crash Course<\/a>!<\/p>\n <\/p>\n <\/p>\n","protected":false},"excerpt":{"rendered":" A preprocessing of remote sensing data is a crucial step in every analytical workflow, and can possibly be the most time consuming one. Anyway, we want to focus on converting the just-downloaded Landsat 8 and Sentinel 2 products into GeoTIFF files and the visualization in R and QGIS. The pre-processing of Sentinel 1 is a … Continue reading “Preprocess”<\/span><\/a><\/p>\n","protected":false},"author":3237,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1553","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/blogs.fu-berlin.de\/reseda\/wp-json\/wp\/v2\/pages\/1553","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.fu-berlin.de\/reseda\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/blogs.fu-berlin.de\/reseda\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.fu-berlin.de\/reseda\/wp-json\/wp\/v2\/users\/3237"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.fu-berlin.de\/reseda\/wp-json\/wp\/v2\/comments?post=1553"}],"version-history":[{"count":32,"href":"https:\/\/blogs.fu-berlin.de\/reseda\/wp-json\/wp\/v2\/pages\/1553\/revisions"}],"predecessor-version":[{"id":2943,"href":"https:\/\/blogs.fu-berlin.de\/reseda\/wp-json\/wp\/v2\/pages\/1553\/revisions\/2943"}],"wp:attachment":[{"href":"https:\/\/blogs.fu-berlin.de\/reseda\/wp-json\/wp\/v2\/media?parent=1553"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\nPreprocess Landsat 8<\/strong><\/a>
\n– unzip downloaded image data products
\n– stack all bands of interest into a rasterstack and save it
\n– create pyramid layers for rasterstacks
\n– automate everything for BIG DATA analysis using R
\nPreprocess Sentinel 2<\/strong><\/a>
\n– convert image data from SAFE to tif format
\n– resample image data to uniform geometrical resolution
\n– automate everything for BIG DATA analysis using SNAP and GPT\/ R
\nPreprocess Sentinel 1<\/strong><\/a>
\n– generate intensity images in different polarizations
\n– expand feature space of S1 intensity images via texture metrics (GLCM)
\nVisualization<\/strong><\/a>
\n– use R and QGIS for visualization of Landsat 8 data<\/p>\n<\/div>\n
\n
\nPREPROCESS OPTICAL DATA<\/em><\/strong><\/span><\/a><\/div>\n