Monthly Archives: June 2016

Mapping 3 Decades of Global Surface Water Occurrence with Landsat

Recently, I posted an analysis of the Orbital Insight’s Global Water Reserves product, in which they use deep learning to automatically detect global surface water on a weekly to bi-weekly basis using Landsat images. In this post, I want to draw attention to work done by the European Commission’s Joint Research Centre (JRC) in which they used Google Earth Engine‘s extensive Landsat archive to derive global surface water occurrence map, along with probability and seasonality measures. They have used Landsat 5, 7, and 8 for this study.

This work by JRC is of a much more scientific nature than Orbital Insight’s global water mapping, giving the capability of study and analysis of river dynamics and morphology also. The study also reports some validation statistics.

See this amazing talk video on the study from the Google Earth Engine User Summit, Oct., 2015. The slide deck is available here.

Other research groups are also working on similar solutions; see, for example, this news report about Amy Hudson at the University of Maryland trying to use GEE in a similar manner to analyse global surface water dynamics using Landsat.

Sentinel Delivers Postcards from Space

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Sentinel Hub Postcard for Islamabad, Pakistan, May 2016.

The Sentinel Hub webpage has an interesting postcards app, which lets you create a quick postcard from the Sentinel-2 imagery archive. The image can be downloaded or shared directly from the webpage. The image can be displayed in true color, enhanced color, NDVI, along with some other satellite-measured parameters.

Orbital Insight’s Global Water Reserves: Automatic Detection of Water in Landsat Imagery using Deep Learning

A few months ago, an article in MIT Technology Review showed how Orbital Insight utilized deep learning to automatically monitor and analyze water levels over the whole world on a weekly basis utilising publicly available Landsat 7 / 8 imagery.

It is interesting to note that on a basic level, detecting water in Landsat images is not a too complex problem, as water is known to have a very weak reflectance in NIR, and very often just using Band 4 in Landsat 7 can give a clear indication and differentiation of water from other land surface features.

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Spectral reflection curve of water, soil and vegetation, overlaid with the spectral bands of Landsat 7. Source: http://www.seos-project.eu/modules/remotesensing/remotesensing-c01-p05.html

The amazing thing that Orbital Insight has done is to largely automate this whole processing, and build a process chain to utilizing the huge Landsat archive and do this on a running weekly to bi-weekly basis. I’m sure there is a certain degree of accuracy in this, which I hope will be reported somewhere soon (maybe it has been already, but I have not come across it). Cloud shadows and mountain shadows can give significant errors in detecting water in Landsat imagery. Orbital Insight is analyzing huge chunks of images, turning it practically into a big data problem, and the task of automatically adjusting the algorithm for multiple images in time and spread all over the world is a big achievement because of varying local conditions.

Learn more about Orbital Insight’s Global Water Reserves product here.

Measuring 3D Forest Structure through Radar Remote Sensing

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Polarimetric L-band F-SAR image of the study site in southeastern Bavaria, Germany. The image is shown in false color: forest areas appear green, while surfaces with low vegetation are shown in blue / red. Image credit: DLR

Radar remote sensing can enable us to see and construct a full 3D view of forest structure and trees. In a joint research study conducted last year, NASA and DLR proved this concept in airborne flights over a test region in southeastern Bavaria, Germany, where both agencies flew their own airborne radar sensors over a period of a few days. NASA flew its well-known L-band UAVSAR sensor, while DLR flew its F-SAR system. The F-SAR system is unique as it does coincident radar imaging at L-, C-, and X- bands. Radar remote sensing analysts know well that lower frequencies like L-band can penetrate right down to the forest floor, C-band frequencies penetrate the canopy to some extent, while X-band frequencies are reflected from the top of the tree canopy. Utilizing these three frequencies simultaneously for forest imaging allows full 3D mapping of the forest, from the upper section of the forest crown, canopy, branches, down to the under-canopy vegetation and forest floor.

See the DLR official press release for more info.

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Example vertical profile of radar backscatter from F-SAR. Backscatter is scaled in shades of green. Solid green lines represent liar-measured heights of forest floor and crown. Image credit: DLR

Many other research groups are also pursuing similar goals to measure forests in 3D using SAR remote sensing. One such technique which can be applied to both airborne and spaceborne SAR sensors is POLinSAR (Polarimetric Interferometric SAR).

The Finnish Geodetic Institute is leading a research effort to measure 3D forest structure using a multiple active sensors, including SAR imagery from Sentinel-1, TerraSAR-X / TanDEM-X, and ALOS-2 PALSAR, along with optical satellite stereo imagery, and Airborne Laser Scanning (ALS). Learn more about their research here and here.