Estimating County-level Urban Intensity using Google Earth Engine: A case of California, American Association of Geographers Annual Meeting.Google Maps has transformed the way we see our world. Feel free to shoot me an email if you would like to chat about ideas working with this tool! References There are plenty of resources available online discussing the various capabilities of using GEE. We will observe a lot of interesting patterns of urban agglomeration, network and clusters of cities, etc.Īll in all, I find GEE is a really useful tool to facilitate planetary-scale raster data processing, which could be helpful for land use and environmental planning, as well as serving as an interactive mapping tool to explore the Earth’s surface. Toggle between DMSP and VIIRS layers by clicking checkboxes on the top right. We can play around with it in the demonstration notebook. In this map, we overlap the mean values of the whole nighttime light datasets on top of the global OpenStreetMap. The final step is to map the global nighttime light data onto an interactive map. We can save the two thumbnails as GeoTIFFs into Google Drive or download them from a generated link. Comparing the two thumbnails using DMSP/OLS and VIIRS, we see the latter has a higher resolution and less overcast. The images below depict 1) the mean DMSP/OLS values of Alameda County throughout the years between 19 (left) and 2) the mean VIIRS values between 20 (right). # Display a thumbnail of global nighttime light. # Import the Image function from the IPython.display module. I select data from the whole date range between January 1st, 1992 and January 1st, 2014: We can use ee.ImageCollection to import the CCNL dataset ( ‘b1’ is the band with corrected nighttime light intensity values). It is an annual time-series dataset of the global nighttime light intensity from 1992 to 2013. CCNL DMSP/OLS DatasetĬCNL stands for consistent and corrected nighttime lights, which is a reprocessed version of the Defense Meteorological Program (DMSP) Operational Line-Scan System (OLS) Version 4 data. The Earth Engine Snippet for the CCNL dataset, for example, is “BNU/FGS/CCNL/v1” as is shown in the following screenshot. VIIRS Stray Light Corrected Nighttime Day/Night Band Composites Version 1 The two datasets I use for this project are:ĬCNL: Consistent And Corrected Nighttime Light Dataset from DMSP-OLS (1992-2013) An “Earth Engine Snippet” is a piece of unique identifier for users to search and access geospatial data such as image collection and feature collection, hosted on the GEE platform. The Earth Engine Data Catalog has a variety of public raster datasets, including daytime and nighttime satellite imagery, which we can import directly to our projects by referencing an "Earth Engine Snippet". You will need to proceed through several steps to authenticate and initialize the EE Python client library, which are detailed both in my Colab notebook and the GEE documentation. Before using the API, you will need to sign up for Earth Engine Access, which grants access to the Code Editor and API. The API allows for flexibility to integrate Earth Engine (EE) with the project workflow. There are two ways to access GEE: either through the Earth Engine Code Editor or via an API in Python or Javascript. Google Earth Engine (GEE) is a general purpose tool capable of extracting time-series remote sensing data from the GEE Data Catalog.In this blog post, I walk through the process of using the GEE to obtain remote sensing data, filter it by time and geographic region, and finally visualize the data on static and interactive maps.įull demonstration code and examples can be found at this Google Colab notebook. For example, nighttime light intensity extracted from satellite images can identify areas with high concentrations of human settlements and activities, especially in locations where traditional data are scarce. Remote sensing imagery such as satellite imagery has the potential to reveal land use patterns and human activities at a planetary scale. Mapping Time-Series Satellite Images with Google Earth Engine API
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