Helical graphs to visualize the NDVI temporal variation of forest vegetation in an open source space

Global change caused by human activity (e.g., land fragmentation, deforestation, pollution, anthropization of natural landscapes) has several effects on the biomes of the Earth, leading to alterations in the functioning of ecological systems. In this context, remote sensing represents an important tool to assess ecosystem changes, as it allows to collect a huge amount of data at different temporal and spatial resolutions concerning various com-partments of the Earth system (land, ocean, atmosphere, and cryosphere). This information can be used to es-timate precipitation patterns, global temperatures, snow cover and aerosol concentrations. The aim of this work is to exploit this wide availability of data to display the ecosystem changes using a new visualization method: the helical graphs. The helical graphs represent the change of a variable over time, reporting on the y-axis its moving averages and on the x-axis its rates of change.These new charts were tested on the NDVI index retrieved from Google Earth Engine (https://earthengine.goo gle.com/) to visualize trends on selected biomes of the Earth (tropical and boreal forests). The results show that the helical graphs are a useful tool to highlight trends that might not be easily detected in a time series. In conclusion, the helical graphs can have a lot of application in ecology, especially exploiting the wide amount of data available thanks to the remote sensing.

Automated summary

Global change caused by human activity has diverse effects on Earth's biomes and alters the functioning of ecological systems. Remote sensing is a vital tool to assess these changes by collecting extensive data on different components of the Earth system at various temporal and spatial resolutions. This study aims to utilize a novel visualization method, helical graphs, to display ecosystem changes using the abundant data obtained through remote sensing. The results demonstrate that helical graphs are valuable in highlighting trends that may not be easily detected in traditional time series analysis. Overall, helical graphs have significant applications in ecology, leveraging the wealth of data available from remote sensing.