People, livelihoods, and infrastructure in Myanmar suffer from devastating monsoonal flooding on a frequent basis. Quick and effective management of flood risk relies on planning and preparedness to ensure the availability of supplies, shelters and emergency response personnel. The mandated government agency Department of Disaster Management (DDM) as well as local and international organizations play … CONTINUE READING
Tag: Remote Sensing Page 1 of 3
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
Last week, I was doing some testing using the Python API interface for sentinel-1 toolbox with Snappy and the SNAP desktop to see the time taken by each of them. The workflow for processing the data is described here. The results were astonishing. Each and every steps were significantly slower, especially the co-registration process … CONTINUE READING
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
NASA’s Earth Observing System Data and Information System (EOSDIS) is the data distribution system facilitated with NASA’s Distributed Active Archive Centers (DAACs). Alaska Satellite Facility (ASF) is one of the DAAC providing various data sources to the public for free. We will use the ASF website (as of 28th October 2019) to download the 12.5 … 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
In this tutorial, we are going to perform the mosaicing of two adjacent Sentinel-1 scenes using Snappy, the Python interface for SNAP.
If you haven’t, follow along this tutorial to see how you can setup the development environment for SNAP in your machine. To overview, the basic steps that we cover in this tutorial are:… CONTINUE READING
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