Land cover change and its impact on food security is a topic that has major implications for development in population-dense Southeast Asia. The main drivers of forest loss include the expansion of agriculture and plantation estates, growth of urban centers,extraction of natural resources, and water infrastructure development. The design and implementation of appropriate land use … CONTINUE READING
Category: Google Earth Engine Page 1 of 3
Land cover maps are a critical component to make informed policy, development, planning, and resource management decisions. However, technical, capacity and institutional challenges inhibit the creation of consistent and relevant land cover maps for use in developing regions. Many developing regions lack coordinated capacity, infrastructure, and technologies to produce a robust land cover monitoring system … CONTINUE READING
Land cover maps play an integral role in environmental management. However, countries and institutes encounter many challenges with producing timely, efficient, and temporally harmonized updates to their land cover maps. To address these issues we present a modular Regional Land Cover Monitoring System (RLCMS) architecture that is easily customized to create land cover products using … CONTINUE READING
After the Geo for Good Summit 2019, all my focus has been geared toward integrating Machine Learning Models using Tensorflow with the Satellite data. One of the issues for exporting the training and testing data especially with a large number of features or huge areas for the neighborhood pixels if you are using the Fully … CONTINUE READING
Connected Pixel Count is one of the ways where the concept of the Minimum Mapping Unit (MMU) can be applied. Basically, the connected pixel count gives the image with every pixel containing the information on the number of the connected neighbors including the pixel in context. The neighbors can be 4- or 8-connected neighbors, and … CONTINUE READING
There are a couple of ways to calculate the area of the image in the Google Earth Engine. The full implementation of both method can be accessed using this link.
- Pixel Count Method
We can calculate the area of the image by counting the total number of unmasked pixels in that image. Then, multiply
With the recent release of TensorFlow 2.0, showcased at GEO for Good 2019, there is increased interest in employing an array of neural net approaches to solve various Remote Sensing research questions. A dedicated group from NASA SERVIR has spent the last 5 days exploring the Google Earth Engine, Google Colaboratory, and Google … CONTINUE READING
Thresholding the primitives to generate the Land Cover Maps: a UI application in Google Earth Engine
This simple UI interface is built on the Google Earth Engine. It uses the primitives as percentage layers to assemble them to derive land cover maps. Here we are using the Random Forest algorithm to generate the land cover maps.
You can use the sliders to change the threshold input for the tree node structure … CONTINUE READING
Let’s use Google Earth Engine and satellite data to visualize the time-series of the Amazon Fires 2019. In this post, I will be using FIRMS (Fire Information for Resource Management System). FIRMS disseminates Near-Real-Time active fire from the NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) and NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) satellite. This data … CONTINUE READING
Use the following code or follow the link… CONTINUE READING
The full code for classification after obtaining sample points is here. The full code for classification with sample points generated on the fly is here.
Note: This is a very simple method for generating Landcover maps meant for showing the basic workflow for generating Landcover maps. You can use more sophisticated algorithms like … CONTINUE READING
In this exercise, we will try to sample a composite image of the Landsat with the landcover feature collection that we have. The full code can be found here.
The Feature Collection has the following integer assigned for ‘landcover’ column.
0 -> urban
1 -> vegetation
2 -> water
- We will use Landsat.simpleComposite method
