Over the past 40 years, satellite data has shown that vegetation cover has increased in the planet, referred to as ‘greening’. This greening phenomenon has been attributed to (1) changing climate (warmer and wetter conditions), (2) increase in CO2 (i.e. the fertilization effect), (3) farming and other landscape changes and (4) increased deposition of nitrogen (a limiting factor for plant growth). Greening has been reported over afforested and intensely farmed areas, but also in biomes with low human footprint such as the Arctic. From an Earth Systems perspective, this greening could potentially help in mitigating global warming by absorbing more CO2 from the atmosphere. Some 20% or more of human carbon dioxide emissions are absorbed by plants each year. However, greener canopies also have lower albedo than bare ground and thus absorb more sunlight and can lead to local warming.
NDVI is an acronym that stands for the Normalized Difference Vegetation Index and is a parameter used to indicate the ‘greenness’ of a surface. When sunlight hits objects, some wavelengths are absorbed, while others are reflected. Chlorophyll (plant pigment) strongly absorbs visible red for use in photosynthesis, and it also strongly reflects near infrared. NDVI is based on the relationship between visible red and near-infrared wavelengths, with the following formula: NDVI = NIR – Red / NIR + Red. Values range from -1.0 to 1.0. Negative values to 0 mean minimal green leaves, while values close to 1 indicate the highest possible density of green leaves. For this project, we will use the NDVI dataset from Moderate Reso
• Create difference maps of remotely sensed Normalized Difference Vegetation Index (NDVI) data to visualize global greening in the last decade.
• Look at global patterns of NDVI and identify areas of highest productivity.
• Pick a region, map the changes in said region or country, and formulate a hypothesis on what is causing this area to green up.
– I will provide decadal datasets of NDVI and you will create difference maps between data from 2010-2020 minus data from 2000-2010. After describing the global patterns and regions of high and low productivity, you will pick a region that you are interested to study in detail. You will use Panoply to zoom in on said region and create decadal and difference maps. What are the factors that drive the greening in your chosen region? What are the regional implications?
You should briefly introduce the motivation for the project and the major results/findings. 1.Why is this an important topic?
2.What is the “problem” you are addressing?
3.What is the approach or method you are using to address this problem?
4.What is 1 key result?
5.What is a second result?
6.What is the big takeaway message?
In this case, you will describe how the global NDVI is changing and why this is important to global earth system processes. You will describe how we detect vegetation index through satellite sensors. You will briefly introduce the region that you selected and think about the factors that cause or limits greening. You should also include one concise statement of the objective and/or the questions you are trying to answer in this project.
Results and Interpretations:
Here you will provide Figures with titles and descriptive captions, and your interpretation of these figures. You will present a handful of analyses on NDVI global and regional patterns and explain the potential drivers and implications
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