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INVESTIGATING WORLD HUNGER

GIJC2025

Kuala Lumpur

Thin Lei Win, Lighthouse Reports thin@lighthousereports.com

Margot Gibbs, Lighthouse Reports, margot@lighthousereports.com

Kaamil Ahmed, The Guardian kaamil.ahmed@theguardian.com

Ryan McNeill, Reuters, ryan.mcneill@thomsonreuters.com

Deborah Nelson, University of Maryland dnelson4@umd.edu

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GIJN TIPSHEET: Guide to Investigating Food Insecurity

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PIONEERING COLLABORATIVE JOURNALISM

Investigating Hunger

Thin Lei Win

Nov 22, 2025

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Objectives

Burst prevailing myths around hunger, malnutrition, and food production

Look at hunger through a food systems lens

Provide examples of food systems investigations

Zooming in on corporate concentration

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The Myth: Hunger & Global Food Production

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Source: Our World in Data (Wheat, Rice)

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The Myth: Hunger & Global Food Production

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Source: Our World in Data (Cereal, Meat)

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The Myth: Hunger & Global Food Production

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Source: Our World in Data

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The Myth: Hunger & Global Food Production

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Source: FAOSTAT

However, if you look at the number of people who are chronically undernourished - which is a UN jargon for hunger - you can see that we were on the right trajectory for a while, until about 2012, before hunger numbers stagnating and then they were creeping up again, even before COVID-19 hit.
What about severe food insecurity, which means when there’s not enough food and you’re regularly going without eating? Well, that number has been on an upward trajectory for the past decade, again, even before COVID.
What I’m trying to say here is that agricultural productivity isn’t a good indicator of hunger.
I like to say that hunger, in this day and age, is a political failure and a political decision. It is also often intentional, by people in power who make food inaccessible and unaffordable. Because food is available. It’s just either too expensive (or) not accessible to those who need it.

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The Myth: Hunger, malnutrition, & poor nations

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Source: FAOSTAT

There are also other myths around hunger: that it is the most important indicator of health and food security in a country, that it’s a poor country’s problem, that malnutrition is about not eating enough, and that technological solutions will solve hunger.
Here we have three graphs charting hunger and malnutrition for four countries: Cambodia, Kenya, Germany & USA. So two developing and two developed/industrialised nations.
Hunger in the textbook definition is very low and remains very low in the US and Germany (less than 2.5%), according to the UN figures which are based on national statistics. But these are national-level figures so they don’t reflect the pockets of hunger in these countries.
If you look at Cambodia, it has seen a significant downward trend in hunger.
Kenya was also doing well until about a decade ago when things shifted and hunger crept up again, before rising sharply after COVID-19.
But that’s not the full picture. We should look at nutrition. So what about in terms of overweight and obesity?
It shows that adult obesity figures are increasing (or) not going down in ALL countries, while the % of children going overweight have not seen significant downward trends either.
So whether hunger levels are going down (or) increasing (or) staying the same, you are seeing other forms of malnutrition in countries of varying economic development.

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The Myth: Hunger, malnutrition, & poor nations

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Source: FAOSTAT

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The Biggest Challenges Facing Food Systems Today

Healthy

Green

Fair

Production

Trade/�Processing

Sale/ Consumption

This is why we need to look through the Food Systems lens.
What are food systems? There are lots of definitions but the easiest way to think is that they include all the actors and activities related to producing, processing, transporting, distributing and consuming food. So it’s not just about agriculture. It’s not just about consumers. It’s not just about trade. It’s not just about food policy. It’s all of the above.
And the reason why we are increasingly talking about food systems is because these actions and actors are all interconnected and decisions in one area affect the others.
At a personal level, I also really like using this lens but far too often, a lot of burden and shame and blame is put on individuals for not living the right way or eating the right way or buying the right things. But it’s impossible to do that if the system is opaque, skewed, and stacked against us.
And that’s where our current food systems are: they are environmentally destructive, they’re making us sick, they’re not providing food to the people who needs it, and they are deeply inequitable.
Which is also exactly why they are RIPE for good investigations.
The way I see it, three biggest challenges facing food systems. How do we make our food systems HEALTHIER, GREENER, and FAIRER?
We then break them down into three generic but key components: production, trade/processing, and sale/consumption. This is about trying to understand at what stage of the food value chain are things going wrong, and where do the changes need to take place?

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The Biggest Challenges Facing Food Systems Today

Production

Healthy

Green

Fair

Trade/�Processing

Sale/ Consumption

Overconsumption of UPFs & red meats
Homogenous, non-diverse diets
Exposure to toxic chemicals by ingestion

Overuse of antibiotics, chemicals (pesticides & fertilisers) & plastics
Focus on staples & a handful of crops
Poor sanitation & handling
Use of artificial chemicals in processing & preservation
Policies (laws/guidelines on food supply chains & environments, subsidies)
New pandemic risks

WTO rules/FTAs that favour Big Food & restrict govt action on unhealthy foods & make healthy foods costly
Pesticide trade
Export-driven agri favours cash crops & monocropping

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The Biggest Challenges Facing Food Systems Today

Production

Healthy

Green

Fair

Trade/�Processing

Sale/ Consumption

Transport: Air miles, shipping
Cold storage (retail & in transit)
Importing emissions
Environmental costs of food processing

Packaging
Food waste at retail & household level
Animal-heavy diets

Food Systems account for 1/3rd of total manmade GHG emissions
Use of equipment & transport: CO₂
Livestock, rice, waste treatment: CH₄
N fertiliser & animal waste: N₂O
Beyond GHG: deforestation, land & water usage, air, water, & soil pollution
“An Unhappy Marriage with an addiction problem”
Policies (‘polluter pays’, subsidies, R&D on alt-proteins, climate adaptation, land use)

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The Biggest Challenges Facing Food Systems Today

Production

Healthy

Green

Fair

Trade/�Processing

Sale/ Consumption

Consolidation & corporate concentration
Colonisation of farming practices (seeds, data, inputs, etc)
Inequitable access to resources (finance, tech, land, support services)
Exploitative labour practices
Lack of representation of smallholders
Policies (subsidies, privileged access)

Supply chain profits North vs South
Unfair trade deals & trade policies (tariffs)
Double dependence
Corporate concentration (ABCDs)
Financial speculation

Inequitable access to nutritious foods (food deserts, SNAP, UPFs)
Power imbalances
Grocery/retail profiteering
Availability vs. Accessibility vs. Affordability

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The Biggest Challenges Facing Food Systems Today

Production

Healthy

Green

Fair

Trade/�Processing

Sale/ Consumption

Overconsumption of UPFs & red meats
Homogenous, non-diverse diets
Exposure to toxic chemicals by ingestion

Transport: Air miles, shipping
Cold storage (retail & in transit)
Importing emissions
Environmental costs of food processing

Packaging
Food waste at retail & household level
Animal-heavy diets

Food Systems account for 1/3rd of total manmade GHG emissions
Use of equipment & transport: CO₂
Livestock, rice, waste treatment: CH₄
N fertiliser & animal waste: N₂O
Beyond GHG: deforestation, land & water usage, air, water, & soil pollution
“An Unhappy Marriage with an addiction problem”
Policies (‘polluter pays’, subsidies, R&D on alt-proteins, climate adaptation, land use)

Consolidation & corporate concentration
Colonisation of farming practices (seeds, data, inputs, etc)
Inequitable access to resources (finance, tech, land, support services)
Exploitative labour practices
Lack of representation of smallholders
Policies (subsidies, privileged access)

Supply chain profits North vs South
Unfair trade deals & trade policies (tariffs)
Double dependence
Corporate concentration (ABCDs)
Financial speculation

Inequitable access to nutritious foods (food deserts, SNAP, UPFs)
Power imbalances
Grocery/retail profiteering
Availability vs. Accessibility vs. Affordability

Overuse of antibiotics, chemicals (pesticides & fertilisers) & plastics
Focus on staples & a handful of crops
Poor sanitation & handling
Use of artificial chemicals in processing & preservation
Policies (laws/guidelines on food supply chains & environments, subsidies)
New pandemic risks

WTO rules/FTAs that favour Big Food & restrict govt action on unhealthy foods & make healthy foods costly
Pesticide trade
Export-driven agri favours cash crops & monocropping

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Examples of food systems investigations

Healthy

Green

Fair

Sources: Civil Eats, Public Eye, ProPublica, Grist, Lighthouse Reports, Lighthouse Reports, Reuters

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Examples of food systems investigations

The Findings

In the first week of March, commodity-linked “exchange traded funds” received $4.5 billion in investment.
By April, two top agricultural ETFs had attracted net investor investment of $1.2 billion – compared to just $197 million for the whole of 2021.
FOIA documents showed the industry successfully lobbied the EU regulator to weaken regulations.
First in a series on how pension funds fueled and hedge funds profited from the global food crisis.

What

In March 2022, world food prices reached highest ever levels following Russia’s invasion of Ukraine
Import-dependent developing countries were facing a “double dependency”: (i) relying on imports to feed their people, (ii) relying on a handful of exporters for a high percentage of those imports
Concerns that over a quarter of a billion people could fall into extreme poverty

How

Tracked money flows into some of the biggest publicly traded agricultural funds dealing in wheat
Analysed data to understand the level of speculation
FOIA-ed for documents to understand lax laws
Interviewed experts, whistleblowers, industry veterans, aid workers, and ordinary people

Fair

Source: Lighthouse Reports

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Examples of food systems investigations

What

Tip off on a conference on greener solutions to pesticides being sabotaged led us to the US-based PR firm v-Fluence
It worked on Paraquat, a popular toxic herbicide linked to acute and chronic health impacts, and banned in the EU

How

Freedom of information requests on correspondence between the US civil servants and v-Fluence
Money trails analysis and searches on public spending records for contracts by USAID to v-Fluence
Court records
Good old-fashioned source building

The Findings

v-Fluence, established by a former Monsanto executive, received funding from US government to promote GMOs in Asia and Africa
This includes building a private social network monitoring hundreds of scientists, environmentalists, campaigners and writers critical of agrochemicals and GMOs
Currently being sued in the U.S. for allegedly suppressing information for over 20 years on the health risks associated with Paraquat
Worked with another PR firm aimed at undermining the EU’s Farm to Fork policy
In Kenya, which has the highest number of members to network outside of the US and Canada, farmers unaware of the risks continue using Paraquat while v-Fluence’s senior counsel promoted pesticides and GMO for food security

Healthy

Green

Fair

Source: Lighthouse Reports

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Examples of food systems investigations

The Findings

Copa-Cogeca secretary-general himself acknowledged their claim to represent all 22 million European farmers was more of an aspiration.
Yet the group uses this claim to access privileges and block climate and environmental reform.
Spain, Poland, and Romania pay a combined €1.4-million a year in public money for certain national unions to be part of Copa-Cogeca.
However, these unions are not at all representative of a majority of farmers in the countries.
In fact, members tend to be made up of large-scale, industrial, export-oriented farms and are close to the agrochemical industry.
Smallholder farmers or unions feel neglected and sidelined.

What

Copa-Cogeca, Europe’s biggest and most powerful farm lobby
Made up of farmers’ associations in member countries
Self-proclaimed voice of European farmers and agri-cooperatives
Enjoys special privileges and access to the corridors of power, and dominated EU agricultural policy for over half a century
Opposes environmental reforms including efforts to slash pesticide use and restore ecosystems

How

Interviews, interviews, interviews, and more interviews
Our own survey of membership figures of Copa-Cogeca affiliates

Green

Fair

Source: Lighthouse Reports

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Examples of food systems investigations

What

How the triple whammy of a pandemic, climate change, and an inflationary surge is affecting hunger levels in middle-income countries
Not all food systems stories need to be investigations

How

Old-fashioned beat reporting

The Findings

Eight countries across Asia, Europe, Africa, and South America
But has striking commonalities

Soaring inflation and stagnant incomes
Healthy food out of reach for most consumers
Heavy reliance on global markets - soaring import bills
Wild weather either already reducing yields or threatening to do so
Environmentally harmful farming practices worsening climate change
Decades of policies focused on efficiency and exports
Rising hunger

Healthy

Green

Fair

Source: The New Humanitarian

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Why corporate consolidation is a problem

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Source: Farm Action Concentration Data, IPES-Food (2023), ETC Group (Updated, 2025)

Global Seed Market

Bayer: 23%, Corteva: 17%, ChemChina: 7%, BASF: 4% = 51%

Global Agrichemical Market

ChemChina: 24.6%, Bayer: 16%, BASF: 11.3%, Corteva: 10.4% = 62%

Global Agricultural Machinery Market

Deere & Co., CNH, AGCO, Kubota = 40%

Global Trade in Agricultural Commodities

ABCDs account for 60%-70%

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Why were egg prices so high in Kenya?

Between Sept 2021 and Jan 2023, Kenyans pay 36% to 85% more than Ugandans for a tray of 30 eggs.
Why?
Four suppliers control over half the market for commercial feed.
This means Kenyan poultry and dairy farmers pay up to 40% more for animal feed.
Sub-sector for feed ingredients: again, four vertically integrated firms dominate this market, meaning small feed producers have to pay more. Some ended up leaving the market.
This left farmers with fewer choices, lowered farm income, and increased prices of eggs.

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Source: Shamba Centre for Food & Climate, Animal Feed Market Inquiry Report

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Why we need to use a systems lens

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Source: Jennifer Clapp (IPES-Food)

Military Conflicts

Aid Cuts

Higher/more volatile food and agricultural input prices
More opportunities for corporate profiteering
Import-dependent countries worse off
Debt burden in the Global South will grow
Higher ecological costs, lower climate budgets
Deepening hunger & inequality

Trade Wars & Economic Chaos

Weak Global Institutions

Climate, Biodiversity & Pollution Crises

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PIONEERING COLLABORATIVE JOURNALISM

Investigating Hunger Profiteers

Margot Gibbs

Nov 22, 2025

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Why do we interrogate food prices?

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This is Almas Caviar. It costs up to €40,000 a kilo. It’s that expensive because it’s very rare (it single region harvested from an extremely rare albino sturgeon) and some of the richest (and possibly stupidest) people in the world want to buy it.

I’m going to talk about Investigating how affordability of food is not only dictated by scarcity. Often, food prices are dictated by matters that are not to do with so called “fundamentals”, but by other factors - such as corporate profiteering or financial speculation.

This has important repercussions. Fundamentals are more like acts of god, not in the region of things which are controllable by governments. The same can’t be said for the profiteering and financial speculation. They are within the control of regulators. And that makes them important subjects for investigation. �
Another reason to look at this is because of the differential impact of food prices: eating Food is not a choice we humans can forego if we want to stay healthy. What that means is that food is always going to make up more of poorer households’ spending - and food price inflation is always going to hit the poorest, hardest. This is true at the international level (In Nigerira, people spend on average ome 70% of income on food; British people, some 11%). But this is also the case at the national level. �
Finally: The climate crisis is going to drive more crises. Some will lead to fundamental changes that might drive prices. But they can also be used as excuses for raising prices.

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OBJECTIVES

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Understand why to look beyond scarcity: food crises vs. food price crises.

Investigative Technique: financial statements for unpicking industry narratives

Technique: how to investigate financial market speculation can drive prices

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Corporate profiteering

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Corporate profiteering

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Pepsico: what’s driving profits - volume or price?

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Profit + loss account: what passing on costs look like

Pre-Ukraine/Cov-19

Cost of sales = $4

Burger = $5

Gross profit = $1

Gross margin = 20%

Markup = 25%

Passing on cost

Cost of sales = $5

Burger = $6

Gross profit = $1

Gross margin = 17%

Markup = 20%

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Maintaining margins

Cost of sales = $5

Burger = $6.25

Gross profit = $1.25

Gross margin = 20%

Markup = 25%

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What do we see in the accounts?

Profit + loss* account of financial statements

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*also known as

Income Statement

Consolidated Statement of Income

Earnings Statement

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Increasing margin/markup

Data for Tyson (here)

All you need is a profit and loss statement
Markups: how much profit for every unit of spend.

= Gross profit ÷ cost of sales

= (Sales revenue - cost of sales)÷ cost of sales�If it increases or stays the same: they are not just passing on costs

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Profit + loss account: what passing on costs look like

Tyson (here)

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Corporate profiteering - source data

Profit and loss accounts give you the data you need.

Published by public companies (and larger private companies in some countries)

Company Website: investor relations

Annual report

Income statement

Published by large private companies in some countries

Opencorporates for links to company registries
Look to parent companies (e.g Singapore)

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Corporate profiteering - reading list

US food companies: Your food is more expensive – are US corporate profits to blame? [Guardian, 2024]
Fertilizer companies: A corporate cartel fertilizes food inflation [IATP/Grain, 2023]
ABCD/grain companies: How monopoly power tripled the profits of global agricultural commodity traders in the last three years [Somo, 2024]
Popular format: More Perfect Union tiktok [November 2021]

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Hunger Profiteers - investigating food speculators

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Hunger profiteers

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Who’s driving prices?

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ETF data (money flows + what ETFs bought):

Bloomberg (compiled from ETF sources)
Press office of other aggregators (Refinitiv Eikon, Morningstar)

Wheat closing price, trading volumes:

Many aggregators of data published by the exchanges (e.g CME for US, Euronext for France)

e.g Marketwatch here

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Who’s buying? Commitments of traders

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Commitment of Traders - who is buying/selling:

Chicago - COT data here

Paris - Commitment of Traders reports here

Other countries:

Brazil here (e.g soy)
India here (e.g pulses)

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Other methodologies

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Index of “excessive speculation” using ratio between trading volume and open interest [here]. Information on volumes and total open interest available from trading exchanges and regulators

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Speculation profiteers -- hedge funds

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SogGen index

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Speculation profiteers -- investment banks

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Global food and fuel price crises have helped to drive an “obscene” £4bn increase in trading revenues for the world’s top five investment banks, new research has found.

Of the five investment banks, the biggest winner is Goldman Sachs, according to Lighthouse Reports. The bank has seen an increase of £1.9bn for its fixed income and commodity and currency (FICC) division.

Sources:

Annual reports on FICC income
Industry analysis from Coalition Greenwich

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Speculation -- pension fund drivers

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Sources:

Annual reports (notional value of OTC, futures investments)
Press offices of pension funds

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Reading list

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UNCTAD 2023: Food Commodities, Corporate Profiteering and Crises: Revisiting the International Regulatory Agenda

IPES Food, May 2022: How the failure to reform food systems has allowed the war in Ukraine to spark a third global food price crisis in 15 years, and what can be done to prevent the next one

IDOS, 2023: Food price inflation, its causes and speculation risks

Terre Solidaire/FoodWatch 2024: Inflation: When speculators profit from the food crisis

Foreign Policy, 2011, How Goldman Sachs Created the Food Crisis

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Can we let go of the myth?

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Weaponising food in conflict

Kaamil Ahmed

The Guardian

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How are conflicts stopping people getting food?

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“We must never accept hunger as a weapon of war” - Antonio Guterres, July 2025

In reality, we regularly see hunger weaponised during conflicts
Aid movements restricted while other things can be moved around
But it’s not all about aid - exports, agricultural inputs and land all matter

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The Black Sea Blockade

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Russia’s blockade of the Black Sea and Ukrainian grain exports created world-wide panic.
A lot of that was unwarranted but some countries were already vulnerable
Egypt, Lebanon and Turkey all got around 80% of their wheat imports from Ukraine and Russia. All had high inflation rates already.
There were also concerns for East Africa, which also imported high levels of wheat, especially Sudan.

The Black Sea blockade: mapping the impact of war in Ukraine on the world’s food supply – interactive | Global development | The Guardian

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Gaza: What gets in?

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Food scarcity was from very early on a concern in Gaza
We had reports from the IPC but regular contact with people in Gaza was important but difficult
Constant monitoring and conversation with the displaced gave us the insights we needed

Price levels
Black market activity
Looting
Poor quality aid
Exposure to danger at aid pickups - GHF, air drops
The intersection of environmental damage and food production

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The Gaza Humanitarian Foundation

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Blockade of Gaza culminated in creation of the Gaza Humanitarian Foundation
Aid blocked and funnelled through a militarised system with few points
We reported by speaking to those dependent on that aid and looking into the data
11 minutes - the average time between announcements that a GHF point was open and closed
Palestinians told us there was danger everywhere - on the route and at the pickup point itself

Eleven-minute race for food: how aid points in Gaza became ‘death traps’ – a visual story | Global development | The Guardian

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Ryan McNeill and Deb Nelson

GIJC2025

The Starving World

Famines are not supposed to happen anymore. There’s an elaborate international system, established 20 years ago, to prevent them — to detect food crises early and head them off before they snowball into mass starvation. It evolved after the worst famine of the century — in Somalia. Yet the last 10 years saw a surge in countries with populations teetering on the edge of famine. Reuters assembled a team to investigate why this was happening. The system’s official hunger monitor is called the Integrated Food Security Phase Classification, or IPC. It has a 5-phase classification system for food crises (none / stressed / crisis / emergency / famine) that is used by the UN, NGOs and donor countries to determine where to direct assistance. Each phase from stressed onward is supposed to trigger a swift response, aided and abetted by the government of the country in need, to prevent the crisis from reaching the next phase. What we found is that almost every facet of what I just described above is broken. It’s a system that isn’t well designed to assess or respond to what has become the leading driver of today’s food crises. Conflict. For starters: The evidence, the data that the IPC requires to detect and assess crises — how many people are in jeopardy and to what degree — is near impossible to get out of conflict zones.

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The Starving World

Famines are not supposed to happen anymore. There’s an elaborate international system, established 20 years ago, to prevent them — to detect food crises early and head them off before they snowball into mass starvation. It evolved after the worst famine of the century — in Somalia. Yet the last 10 years saw a surge in countries with populations teetering on the edge of famine. Reuters assembled a team to investigate why this was happening.

Famines are not supposed to happen anymore. There’s an elaborate international system, established 20 years ago, to prevent them — to detect food crises early and head them off before they snowball into mass starvation. It evolved after the worst famine of the century — in Somalia. Yet the last 10 years saw a surge in countries with populations teetering on the edge of famine. Reuters assembled a team to investigate why this was happening. The system’s official hunger monitor is called the Integrated Food Security Phase Classification, or IPC. It has a 5-phase classification system for food crises (none / stressed / crisis / emergency / famine) that is used by the UN, NGOs and donor countries to determine where to direct assistance. Each phase from stressed onward is supposed to trigger a swift response, aided and abetted by the government of the country in need, to prevent the crisis from reaching the next phase. What we found is that almost every facet of what I just described above is broken. It’s a system that isn’t well designed to assess or respond to what has become the leading driver of today’s food crises. Conflict. For starters: The evidence, the data that the IPC requires to detect and assess crises — how many people are in jeopardy and to what degree — is near impossible to get out of conflict zones.

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The system’s official hunger monitor is called the Integrated Food Security Phase Classification (IPC).

It has a 5-phase classification system for food crises that is used by the UN, NGOs and donor nations to determine where to direct assistance. Each phase from stressed onward is supposed to trigger a swift response, aided and abetted by the government of the country in need, to prevent the crisis from reaching the next phase.

What we found is that almost every facet of what I just described above is broken. It’s a system that isn’t well designed to assess or respond to what has become the leading driver of today’s food crises: conflict.

For starters: The evidence, the data that the IPC requires to detect and assess crises — how many people are in jeopardy and to what degree — is near impossible to get out of conflict zones.

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The IPC had struggled to get the health data that its classification system relies on due to government resistance and the remote camp’s location in a conflict zone. Facing the same obstacles, we used satellites to document a significant increase in the size of cemeteries around the camp. The IPC hadn’t taken that relatively simple extra step to determine the severity of the situation (though they said they’d try using satellite data going forward).

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The IPC was using an antiquated system that relied on paper records and jpegs of data — and that was better suited to hunger crises caused by drought and failed crops than conflicts. There was brief UN recognition of the need to revamp the approach to monitoring and response several years ago, with creation of a special commission by the UN secretary general. But nothing came of it.

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Conflict: A leading driver of the world’s worst food crises

Many are in places inaccessible to reporters

due to violence, political obstruction and newsroom resources….

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Stories are spatial

It’s not enough to just wave our hands and say something is happening.

The best stories show — with precision — who was affected where and when.

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Satellite imagery is DATA

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What food insecurities measures can we track with satellites?

Vegetation health
Weather and climate
Water
Conflict
Mortality

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Many images to data

When Matt Waite, now a professor at the University of Nebraska, first used satellite imagery at the St. Pete Times, it was a struggle to get even one image, process it and analyze it. Now we are swimming in satellite data. And to me, the lowest hanging fruit when it comes to turning satellite images to data is taking lots of images over time and creating a new dataset to analyze change.

Now to do this kind of work you have to make an investment. You need to learn how GIS software works. If you aren’t working with satellite imagery in GIS software, you are running with scissors.

In Zamzam, there was very little hard information about the human toll. So we built a time series of satellite images with high-enough resolution to see individual graves. Then we built a spatial dataset, where each grave was a data point with a date. From there, we could analyze the data to see how many graves were being added over time.

We identified more than 1,200 new graves dug at seven cemeteries between June 2023 and November 2024.

An average of 1.5 new mounds appeared daily in December 2023. By August 2024, when the IPC finally determined famine had taken hold in Zamzam, the rate had risen to four per day, Reuters found.

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Remote sensing

When a multispectral satellite passes over an area, its sensors are collecting data across the visible and non-visible spectrum.
Think of satellite data like a spreadsheet: Each column is a band (red, green, blue, near-infrared, etc.) and each row is a pixel.
Each pixel has a value for each band, representing the intensity of light reflected in that band.
When you see an image in natural color, it is merely the visual reprenstation of the red, green and blue reflectance numbers.

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Underneath the hood

aerosols	blue	green	red	red_edge_1	red_edge_2	red_edge_3	nir	red_edge_4	water_vapor	swir_1	swir_2
0.1164	0.1521	0.2104	0.2764	0.3027	0.3034	0.3151	0.3289	0.3206	0.3482	0.3333	0.3181
0.1208	0.1624	0.2233	0.2940	0.3204	0.3182	0.3297	0.3480	0.3324	0.3680	0.3465	0.3353
0.1216	0.1696	0.2374	0.3116	0.3456	0.3423	0.3538	0.3660	0.3372	0.3942	0.3729	0.3574
0.1190	0.1571	0.2173	0.2826	0.3096	0.3150	0.3259	0.3342	0.3318	0.3622	0.3443	0.3289
0.1156	0.1501	0.2097	0.2764	0.3012	0.3030	0.3110	0.3192	0.3238	0.3507	0.3339	0.3108
0.1146	0.1485	0.2066	0.2743	0.3005	0.3056	0.3139	0.3222	0.3199	0.3543	0.3395	0.3170
0.1153	0.1499	0.2071	0.2724	0.3115	0.3156	0.3206	0.3081	0.3289	0.3679	0.3467	0.3278
0.1130	0.1442	0.1990	0.2643	0.2961	0.3054	0.3142	0.3236	0.3272	0.3582	0.3387	0.3164
0.1068	0.1429	0.1971	0.2649	0.2937	0.3016	0.3151	0.3235	0.3177	0.3627	0.3406	0.3204
0.1007	0.1240	0.1756	0.2401	0.2651	0.2740	0.2855	0.2852	0.3081	0.3495	0.3363	0.2966

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Underneath the hood

By analyzing these values, we can derive insights about the Earth’s surface, such as vegetation health, soil moisture or burned areas through spectral indices.
One of the oldest spectral indices is the Normalized Difference Vegetation Index (NDVI), which uses the red and near-infrared bands to assess vegetation health.
The formula is: NDVI = (NIR - Red) / (NIR + Red)
It turns all those numbers we saw in the last slide into a single number.

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Example: Tracking vegetation health in Sudan

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Example: Tracking vegetation health in Sudan

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December 2023

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December 2024

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Change in NDVI

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What got worse?

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Time series

But like any data analysis, we don’t want to stop with just a before and after.
Now there’s resources such as Google Earth Engine (not the Google Earth you use to look at high-res images), we can do things such as:

Create a time series
Create the median NDVI for the previous 10 Decembers and see whether this year is an anomaly.
Do calculations for broad areas (i.e. what’s the average NDVI for all pixels within 10 km of a point?

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Awesome spectral indices

Here’s a random sample of spectral indices pulled from the Awesome Spectral Indices database. I highly encourage you to check out the site.

long_name	application_domain	formula
Normalized Difference Snow Ice Index	snow	(G - N)/(G + N)
New Built-Up Index	urban	((S1 - N)/(10.0 * (T + S1) ** 0.5)) - (((N - R) * (1.0 + L))/(N - R + L)) - (G - S1)/(G + S1)
Transformed Triangular Vegetation Index	vegetation	0.5 * ((865.0 - 740.0) * (RE3 - RE2) - (N2 - RE2) * (783.0 - 740))
Chlorophyll Carotenoid Index	vegetation	(G1 - R)/(G1 + R)
Normalized Burn Ratio Thermal 1	burn	(N - (S2 * T / 10000.0)) / (N + (S2 * T / 10000.0))
Redness Index	vegetation	(R - G)/(R + G)
Transformed Red Range Vegetation Index	vegetation	((RE2 - R) / (RE2 + R)) / (((N - R) / (N + R)) + 1.0)

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Final thoughts

Satellites are an underutilized source of data for tracking food insecurity.
By treating satellite imagery as data, we can derive quantitative insights about critical factors affecting food security
Learning to work with GIS software and remote sensing data is essential for modern data journalists.
The potential for satellite data to inform and enhance data journalism is vast, especially in areas where traditional data sources are lacking. Or reporters cannot go.
There are tons of new sensors useful for food insecurity: thermal, radar, RF (used to measure soil moisture) and more.

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GIJN TIPSHEET: Guide to Investigating Food Insecurity