This project aims to show how Google Earth Engine can help the emergency responders and the decision-makers. By detecting the affected area by natural disaster using remote sensing indices. Then let the user download the results and use them in further analysis with a local database, use the below link to see the app:
https://masawdah.users.earthengine.app/view/emergency-management
Three different indices were used, namely Normal Burn Area Index (BAI), Normalized Difference Water Index (NDWI), and Normalized Difference Vegetation Index (NDVI). These indices were used for the following disasters forest fire, flood, and deforestation, respectively.The user can test the app using any case study that he/she knows or use the case studies mentioned in the following sections, with guidance from the user manual.
Moving the questions to the data is more efficient than moving the data to the questions (Jim gray). The previous sentence describes the power of the Google Earth Engine very well because it provides a massive amount of images from different sources to analyze them on the cloud without downloading them. The fantastic part is that you do not want to do the geoprocessing tasks on your machine. Instead, you send a request to the Google Engine server about the geoprocessing tool or algorithm you want to perform, and the results will be sent in no time.
With just one click, the google engine will automatically check new daily images and automatically run your algorithm, including the new images in the analysis and sending new results. Google engine makes it possible and easy to build a monitoring system for different environment purposes, such as vegetation, by analyzing a huge amount of daily images using a very modern and fast server and providing updated results actively and efficiently.
The code used Google Earth Engine (GEE) through JavaScript API. It is an open-source library, and the user can access the app's code, modify it, and add more functionality. Here you will find the guidance to download the code.
It was used to detect the burnt area for wildfires in Emperador, Chile, in February 2017. In this case, Burn Area Index (BAI) was used to highlight the burnt area, especially in the vast areas, using the following formula:
BAI = 1 / ((0.1+(Red))^2*(0.06-NIR)^2)
Which Red is a red band and NIR is near infrared band. The value range of the BAI is 0 to 100. The difference between post-BAI and pre-BAI was used to estimate the burn severity. The high value of the difference will represent severe damage, low and negative values representing the less damaged area.
This index's primary use is monitoring different water bodies. Therefore, it helps to detect the flooded area. NDWI was used to detect the affected area by Russian river flooding in California in February 2019.
Normalized Difference Water Index (NDWI) calculated using the following formula:
NDWI = (NIR – SWIR) / (NIR + SWIR)
Which NIR is a red band and SWIR is a shortwave infrared. The value range of the NDWI is -1 to 1.
The rainforest is considered the much-effected area by deforestation and losing trees, so it is very important to build a system to monitor the vegetation and detect the much-effected areas by deforestation. Google engine can monitor the vegetation using a time series of images and highlight the affected area. This app shows the capability of GEE for monitoring vegetation. By analyzing the difference of NDVI between two images acquired at different dates.
NDVI = (NIR – Red) / (NIR + Red)
Which NIR is a near infrared band and Red is a red band. NDVI values range from -1 to 1.
A user manual was designed to assist the users. This guide describes the apps' functionality, describing the tools available on it, and how the user can download the results on his machine. Click Here to see the manual.