Google Earth Engine tasks

The main aim of the task is to analyze the vast amount of remotely sensed datasets and to automate the process. To complete the task, you will need to have access to Google Earth Engine ([login to view URL]), and you need to have R installed. For each of the following five tasks, follow the steps, document the workflow. Keep in mind that for each of the tasks along with the report, you will have to submit the code as well (in case of EarthEngine include the link to the shared code, and in the case of R, attach the file).

1. Create a processing chain for Landsat 8 imagery in GEE.

• Get the Landsat-8 Surface Reflectance collection, filter it by time and location.

• Apply a cloud masking function on a collection*

• Apply a function to calculate NDVI over a collection and calculate the maximum of the collection.

2. Binary classification (water/no water, forest no forest)

• Get Sentinel-2 data, filter it a with date, location, and metadata (select images with cloud cover is less than 10%) and make a median composite.

• Collect some training data for water/no water areas (you can choose another binary class such as forest/no forest, this depends on the area of interest selected and the landscapes in that area) and apply a classifier (e.g., Random Forest) for delineation of the areas.

3. Use the MODIS collection to inspect time series over different biomes.

• Get MODIS NDVI collection (make sure to select the collection with 16-day frequency), apply filters and plot times series for different biomes. Describe the patterns in the series that you extracted.

4. In this section you will use BFAST on the NDVI time series that you extracted in EE (from 5 areas of interest) to detect changes within the time series. Use the time series from the previous task, read it in R and apply the BFAST function. Copy the resulting R BFAST graph in the report and describe the detected components and change types

detected within the time series.

5. Get Land Surface Temperature and NDVI data from MODIS* and calculate the correlation between these two datasets.

Kemahiran: Geographical Information System (GIS), Geospatial, Penderiaan Jauh

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Tentang Majikan:
( 0 ulasan ) Bonn, Germany

ID Projek: #19029833

8 pekerja bebas membida secara purata $318 untuk pekerjaan ini


4+ years of professional experience as GIS Developer. Worked on ArcGIS, QGis plugins, python, R, Google Maps and many more. Contact me. Thanks

$600 USD dalam 10 hari
(6 Ulasan)

Greetings, We have read the project description and you will be glad to know that it is in our domain. Allow us to assist you in the project. Warmest Regards

$555 USD dalam 10 hari
(12 Ulasan)

Hello, I can do this for you in R. Do you want to receive the code in an R script file? Regards, Ruben

$222 USD dalam 7 hari
(2 Ulasan)

Hello Dear, Greetings of the day! If you are looking for awesome civil engineer, AutoCAD drawings and structural designing expert, you have just found the right one! I'm a registered civil engineer with an experienc Lagi

$277 USD dalam 7 hari
(0 Ulasan)

Hi sir I am a geomatician, i master the tools of geographical information systems. I well be happy to work with you. Thank you.

$244 USD dalam 10 hari
(0 Ulasan)

This project is exactly what I learned how to do during my time in college. I have worked through many projects that required determining NDVI and delineating certain types of vegetation based on their reflectance. If Lagi

$200 USD dalam 6 hari
(0 Ulasan)

Hello I have a post graduate degree in Geography and GIS and Remote Sensing and more than 11th years of experience in GIS professional exposure. GIS & RS type Work:- 1. Watershed Mapping 2. LULC 3. Georeferenc Lagi

$246 USD dalam 8 hari
(0 Ulasan)

Yo Soy ingeniero en telecomunicaciones trabajado en Autocad OK Google earth y gis ...............................

$200 USD dalam 8 hari
(0 Ulasan)