Tutorials/Post - ICT, GIS, Remote Sensing, Earth System, Humanitarian, Disaster Management, Travel

Category: geomatics Page 1 of 4

Creating NDVI based thresholded thematic map from MODIS

In this tutorial, we will use the MODIS based V6 Terra Vegetation Indices 16-Day Global 250m product. This MODIS NDVI and EVI products are computed from atmospherically corrected bi-directional surface reflectances that have been masked for water, clouds, heavy aerosols, and cloud shadows. We will use this product to create NDVI based threshold classified thematic … CONTINUE READING

Predictive Analytics for Identifying Land Cover Change Hotspots in the Mekong Region

Understanding land cover change dynamics and potential pathways of change is of critical importance for sustainable resource management, to promote food security and resilience on a range of spatial scales. Data scarcity is a key concern, however, with the availability of free Earth Observation (EO) data, such challenges can be suitably addressed. In this research, … CONTINUE READING

Operational Flood Risk Index Mapping for Disaster Risk Reduction using Earth Observations and Cloud Computing Technologies: a Case Study on Myanmar

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

Linking Earth Observations for Assessing the Food Security Situation in Vietnam: a Landscape Approach

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

Convert the GPS waypoints to Shapefile

In this document, I will convert the GPS way-points (with photo-link, name, latitude, longitude) into shapefile using arcpy and python modules. Find the example gpx file from this link. Use the following example code and modify according to your need. The photos are in the same folder as the gpx file.… CONTINUE READING

Ditch Snappy to use Graph Processor Tool (GPT) to process your Sentinel-1 Data

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 Mapping in Data Scarce Environments: Challenges and Opportunities. Another paper on Land Cover

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

Downloading 12.5 m ALOS PALSAR High-Resolution DEM

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

Creating Mosaic of Sentinel-1 using Snappy

In this tutorial, we are going to perform the mosaicing of two adjacent Sentinel-1 scenes using Snappy, the Python interface for SNAP.
world-veiew-of-sentinel-scene
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

River-Channel

Earth from Space

Processed and exported from Google Earth Engine.

 … CONTINUE READING

Animation of the Amazon Fires 2019 using Google Earth Engine

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

Processing Sentinel-1 SAR images using Snappy – SNAP Python Interface

The back-end on SNAP has been written in Java. But good news to Python enthusiast, they provide Python interface to Java API. It’s through their module called Snappy. In my previous tutorial, I showed you how you can install snappy in your machine and get geared up for the development work. And before we … CONTINUE READING

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