Detecting gold deposits with satellites
PLUS: new platform on port disruptions, removing cloud cover from satellites, and identifying weather-related crop losses.
Hey guys, welcome to this week’s edition of the Spatial Edge — a weekly round-up of geospatial news that you can digest in less than 5 minutes. As many people have told me, they come for the geospatial dad jokes and they stay for the updates…
In today’s newsletter:
Mapping Gold Deposits: Remote sensing reveals gold-rich zones in Egypt.
Cloud Removal in Satellite Imagery: VPint2 simplifies cloud removal.
Crop Loss Detection: Satellite data identifies weather-related crop losses.
Food Security Data: The World Bank provides detailed food security data.
Tracking Port Disruptions: The IMF provides info on port and supply-chain disruptions.
Research you should know about
1. Identifying gold deposits using geospatial data
A new paper in Scientific Reports looks at how satellite data (in addition to spectrometry and magnetic data), can be used to identify areas rich in gold deposits. The goal was to map potential mineral zones, both at the surface and underground.
The paper looks specifically at Abu Marawat in Egypt. For the study, they used a few different types of datasets:
Satellite imagery: ASTER satellite images and ALOS PALSAR digital elevation models
Airborne Spectrometry and Magnetic Data: Collected back in 1983, the data measured radiation and magnetic changes below the surface. It helped identify elements like potassium and uranium.
Geological maps and field samples were used to validate the accuracy of the data
These datasets were combined to create a map showing areas with the highest potential for minerals like gold. The final map highlighted eight promising areas for mineral deposits, including the known Abu Marawat gold mine, which confirmed the accuracy of these techniques. Since Abu Marawat was correctly identified, the authors think it’s pretty likely the seven new areas also hold valuable minerals.
I find that pretty cool.
2. Training-free cloud removal model for Sentinel-2 images
A study in the ISPRS Journal of Photogrammetry and Remote Sensing introduces Value Propagation Interpolation (VPint) 2, which provides a new way of addressing cloud cover in Sentinel-2 images.
Let’s face it, cloud contamination is the absolute worst when it comes to optical imagery. Hence, I’m always interested in exploring new ways to both remove clouds and accurately predict the missing data.
VPint2 offers a pretty simple approach that doesn’t need training. It uses clear parts of older satellite images to impute cloudy areas of new images.
They upgraded VPint with two important improvements:
Identity Priority: This ensures that when filling in cloudy areas, the algorithm uses data from the same type of land feature (e.g. a forest or field) to keep boundaries between different land types clear and accurate.
Elastic Band Resistance: This stops the algorithm from spreading unrealistic values across the image, which can happen due to sensor errors or sudden environmental changes.
To test it out, the researchers created a dataset called SEN2-MSI-T with images from different types of land areas. They compared VPint2 to three other cloud removal methods:
Mosaicking,
A machine learning-based method, and
A method that fills cloudy areas with data from nearby pixels.
They found that VPint2 outperformed the other methods in most scenarios, improving accuracy by 2.4% to 34.3% depending on the situation.
It’s definitely something I’m looking forward to trying out.
3. Using satellite and weather data to pinpoint the cause of crop loss
A recent study explores how extreme weather events like drought, frost, and heatwaves affect cereal crop yields in South Australia. The researchers developed a new method using Sentinel-2 data, weather data (i.e. precipitation and temperature), and crop information to track crop growth and identify damage.
They developed a Crop Damage Index (CDI), which uses satellite data to track how crops are growing compared to what’s considered a normal and healthy growth pattern. By monitoring differences between actual and expected growth, the CDI helps detect when crops are being affected by things like drought, frost, or other extreme weather events.
The index uses data from the two-band Enhanced Vegetation Index (EVI2) and aligns it with thermal time (heat accumulation) instead of regular calendar dates, which makes it easier to pinpoint when crops are stressed. By doing this, they can predict potential yield loss and also link these losses to specific weather events.
All in all, this can be pretty helpful because CDI provides a precise, in-season way of monitoring crop health and predicting yield losses. In other words, it can be used as an early warning signal for crop stress.
4. How marine heatwaves accelerate ocean deoxygenation
A new study from Nature Communications examines marine heatwaves are increasingly causing ‘extreme low-oxygen and heatwave events’ in the ocean. This essentially worsens the ocean’s ongoing deoxygenation and causes massive issues for marine ecosystems.
The authors use:
In situ observations: Temperature and oxygen measurements from sources like the World Ocean Database
Climate model simulations: They used simulations using the Community Earth System Model (CESM) to track trends in marine heatwaves and low-oxygen events from 1960 to 2014.
They found a sharp increase in the combined occurrence of heatwaves and low-oxygen events:
Before the 21st century, only about 10% of the global ocean experienced these events occasionally.
By the early 2000s, that number had risen to 24.5%.
On average, oceans now see more than 20 additional days per year, with the biggest jumps in the North Pacific and North Atlantic.
Certain areas with a lot of marine life, like the North Sea and East China Sea, are particularly affected. This rising threat to marine ecosystems could massively impact biodiversity and fisheries, which is obviously not good news for either the environment or food security.
P.S. The CESM code used for the simulations is available here.
Geospatial datasets
1. Datasets from the GEE Community Catalog
The awesome GEE Community Catalog, which provides datasets that can be used on GEE, has released a bunch of new datasets including:
The Global Mangrove Canopy Height Map (12m resolution)
Global 30m Wetland Map (2000-2022)
2. FEWS NET food security data
The World Bank has put together a harmonised food security dataset from FEWS NET, the Famine Early Warning Systems Network.
FEWS NET creates detailed food security classifications at the sub-national level, so you get a sense of which areas are facing challenges in terms of food security.
3. Olympic medals dataset
I haven’t seen too many geospatial analyses on the Olympics just yet. So to try and get this cracking, here’s an easy-to-use dataset on Olympic medals, which includes a list of countries and their medal counts from both the Summer and Winter Olympic Games.
Other useful bits
The IMF has launched an online platform to monitor trade disruptions from satellite imagery. It includes a disaster alert system, demonstrates how port disruptions impact international supply chains, and features a climate scenario analysis to identify 1,400 ports that are subject to climate risks.
Cesium has launched ‘Cesium Moon Terrain’, an incredibly cool 3D model of the Moon's surface. They put this together using data from NASA's Lunar Reconnaissance Orbiter.
The United Kingdom Humanitarian Innovation Hub seeking applications from organisations in East Africa for geospatial data and technical support to aid humanitarian efforts. The initiative aims to improve humanitarian responses through improved data-driven decision-making.
Sentinel-2 images show the ongoing melting of ice caps in the Svalbard Archipelago.
Using satellites to detect illegal gas trading. While doom-scrolling on X, I saw this interesting analysis from Synmax which can monitor ‘dark shipping’ using satellites.
Jobs
The University of Illinois is after an Assistant/Associate Professor of Geography & GIS.
My team (the Data Division) at the Asian Development Bank is looking for a couple of interns. There are two internship spots:
The University of Leicester is looking for a Geographic Information Science (GIS) and Remote Sensing Data Analyst for their Knowledge Transfer Partnership with Bluesky International Ltd.
Ordnance Survey is looking to hire their Head of Geospatial Data, who will be accountable for their geospatial data governance and data strategy, among other things.
GIM - Smart Geo Insights is looking for a Data Scientist to help with Earth Observation AI developments.
Tuatara is looking for a Remote Sensing Specialist, who will be working on spatial data for detection and classification problems on satellite, aerial and drone imagery.
OpenGeoHub is looking for a Data Scientist: Geovisualization and dashboard building for multivariate EO data.
Spatial Vision is looking for a GIS Developer/Spatial Software Engineer based in Australia, who will be under their Enterprise Spatial Solutions team.
The Climate and Environmental Remote Sensing Unit (CLIMERS) of the Department of Geodesy and Geoinformation of TU Wien is looking for an Earth observation scientist.
That’s it for this week.
I’m always keen to hear from you, so please let me know if you have:
new geospatial datasets
newly published papers
geospatial job opportunities
and I’ll do my best to showcase them here.
Yohan
So how i can download this app ?