ELIZABETH I. MCCAFFREY



SELECTED WORKS


OUR ENERGY NEXUS
OUR CHANGING TIDES
DISPLACEMENT FLOWERS
GREENERY ATLAS
CLIMATE RINGS


AFFILIATED CONTRIBUTIONS
SENSEABLE CITY LAB | MIT
2022
 GREENERY ATLAS



DESCRIPTION
 This project takes an experimental and abstract approach to visualizing global greenery, using algorithmic 'growth' as both method and metaphor. The process investigates how algorithms, data, and design can intersect to generate organic visual forms. The aim of the project is to explore the expressive potential of computational processes in creating new, unconventional modes of visual representation

Rising levels of CO2 in Earth’s atmosphere cause an increase in plant photosynthesis, known as the carbon fertilization effect. Between 1982 and 2020 atmosphere CO2 levels have risen 17% yielding an increase in plant photosynthesis of 12%. This serves as one of the reasons we can observe an increase in the global vegetation index between 2001 and 2022.

However, this initial positive effect for plant growth is deceptive. With the growing levels of CO2 in the atmosphere and in the vegetation, the plants contain less nitrogen, an essential nutrient for growth. This nutrient deficiency shows the potential negative impacts of the carbon fertiliation effects. In addition, the Earth’s rising temperatures greatly affect the efficiency of plant’s CO2  absorption and can reduce photosynthesis efficiency. These cases demonstrate the importance of reducing CO2  emissions and global warming effects in order to preserve global plant life.

source: Columbia Climate School


ABOUT THE DATA The utilized data set represents the Normalized Difference Vegetation Index [NDVI] of each given month of a year. The data is publically accessible through NASA NEO, collected via “Moderate Resolution Imaging Spectoradiometer [MODIS] aboard NASA’s Terra satellite.”

The graphic mapping represents the conversion of each NDVI data point into colored pixels and plotted at their correct geographical position.




CREATION
The vegeation visualization is experimental in nature and based upon a custom generative growth algorithm. The growth utilizes the pixel data, originally from the NDVI maps, growing unique patterns. Each month of the year is restricted to its encolsed circular area also driving the generative patterns. The colors are based off of the NDVI index classifying the vegetation, ranging from -0.1 [arid] to 0.9 [dense].




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Last Updated 07.2025