As part of the comprehensive course Natural Climate and Human Impacts on Climate, given by Professor Dr. Wolfgang Buermann of the University of Augsburg, this series of entries are a compilation of a seminar on the fundamental mechanisms of planetary climate, methods for reconstructing past climates, current consequences of global climate change, and other relevant topics.12
How do we observe global changes in the vegetation cover of the planet?
This question is covered by a relatively new area of climate research that seeks to understand how plants respond to rising temperatures, shifting weather patterns, and changes in atmospheric composition caused by global climate change.
Since 1981, technology has given us the ability to observe global vegetation, when the Advanced Very High Resolution Radiometer (AVHRR) was installed on the NOAA-N spacecraft. This sensor could take pictures of the Earth beyond the visible spectrum; it was highly sensitive to near infrared, but could not sense the blue range. More advanced sensors, such as MODIS, added the blue range to the picture.
The collected data is analyzed using indices. The two most popular indices are the Normalized Difference Vegetation Index (NDVI) and the Leaf Area Index (LAI). The NDVI shows the ratio of the difference between the amount of light reflected and absorbed in the near-infrared and visible spectra. Healthy green foliage absorbs most of the visible spectrum and reflects more than half of the infrared spectrum. The higher the NDVI index, the greener the surface. LAI measures vegetation density by comparing total one-sided leaf surface area to ground area covered (m²/m²). Monitoring helps track vegetation trends, estimate evapotranspiration, and forecast agricultural yields.
Several important metrics are commonly used to assess vegetation dynamics and ecosystem responses. These include SOS (Start of the Growing Season), which marks the beginning of active plant growth, and EOS (End of the Growing Season), which indicates the decline of vegetation activity. LOS (Length of the Growing Season) represents the duration between SOS and EOS and provides insight into seasonal shifts influenced by climate change.
Primary productivity, measured as gross primary productivity (GPP) and net primary productivity (NPP), reflects the overall carbon uptake by plants. GPP refers to the total amount of carbon fixed through photosynthesis, and NPP represents the portion remaining after respiration, serving as an indicator of biomass accumulation and ecosystem productivity.
What do we observe in vegetation dynamics?
Although data obtained earlier from the AVHRR sensor may be less ideal for long-term analysis due to internal limitations, calibration in combination with data from more modern sensors clearly shows a distinct global greening trend since at least the 1980s. Piao et al.’s study3 indicates that, from the 1980s to the 2010s, leaf area increased by 5.4 million km², equivalent to the area of the Amazon rainforest.
However, trends vary when scaled. In certain regions, reverse dynamics, or browning, may be observed. It is also possible to identify greening hotspots. Seasonal shifts are also observed. The growing season is starting earlier and ending later, effectively extending its duration. Peak greenness is occurring earlier and becoming more pronounced. Seasonal patterns of vegetation greenness are shifting. High-latitude regions are showing reduced seasonality, with patterns similar to those of regions farther south in the past. The start and end of the growing season are moving northward faster than peak greenness. These changes have implications for the agricultural sector and ecosystems, which will be explored in the following sections.
Greening Hotspots
China and India have emerged as key contributors to global greening, albeit through different pathways. In China, 42% of the greening is attributed to forests, while 32% is attributed to croplands. This greening has been driven by large-scale afforestation and reforestation programs, which have increased forest cover, reduced land degradation, and enhanced carbon sequestration. However, these efforts have also placed additional pressure on water resources. Meanwhile, agricultural productivity rose by 43% from 2000 to 2016, supported by multiple cropping, irrigation systems, and intensive fertilizer use. In contrast, 82% of India’s greening is cropland-based, with only 4% stemming from forests. This trend is largely the result of agricultural intensification, which led to a 26% increase in cereal production over the same period through expanded cultivation areas and intensified farming practices, similar to China’s approach.
In the case of Arctic regions experiencing greening, agricultural fields and afforested territories are no longer the main contributors. In these regions, vegetation covers the land due to natural growth, primarily through shrubbery. Gaspard et al. refer to this phenomenon as “shrubification.” In their study, the methodology included working with indices, overlaying vegetation maps, and mapping the types of cover and surface deposits in the studied region using ecological models. This allowed the authors to determine the role of plant communities and zonal dynamics in the observed phenomenon.
Recent greening trends in the Arctic and boreal regions reveal significant changes in land cover. Currently, approximately 13.6% of the region has experienced land cover change, and if this pace continues, the entire area could transform within 200 years. In boreal forests, the net loss of evergreen cover coincides with an increasingly active fire regime. In Arctic areas, the gradual expansion of shrubs is more difficult to detect, yet it contributes to the overall greening. Additionally, there has been southern herbaceous growth, primarily driven by extensive agriculture in regions such as Alberta and British Columbia. However, this gain is largely seasonal and linked to agricultural cycles. It should not be interpreted as a long-term ecological shift, such as the more persistent vegetation changes occurring in the Arctic.
Please, stay tuned to know more about what drives vegetation dynamics and what are its ramifications.
- This is the first entry on the series ↩︎
- Header image taken from: https://mpimet.mpg.de/en/research/independent-research-group/climate-vegetation-dynamics ↩︎
- Piao, S., Wang, X., Park, T. et al. Characteristics, drivers and feedbacks of global greening. Nat Rev Earth Environ 1, 14–27 (2020). https://doi.org/10.1038/s43017-019-0001-x ↩︎
- Statistically significant trends (P ≤ 0.1, Mann–Kendall test) are color-coded. Gray areas depict vegetated land with statistically insignificant trends. White areas depict barren land, permanent ice-covered areas, permanent wetlands, and built-up areas. Blue areas represent water. The inset shows the frequency distribution of statistically significant trends. The red circles highlight the greening areas, which mostly overlap with croplands except for circle number 4. Similar patterns are seen at P ≤ 0.05, and the seven greening clusters are visible at P ≤ 0.01. ↩︎
After finishing my B.E. in Sustainable Innovation Engineering in Mexico, I wanted to deepen my understanding of the connections between climate, ecosystems, and society. Growing up in a highly industrialized city, I witnessed the rise of pollution and biodiversity loss firsthand, which inspired my commitment to finding meaningful solutions. That’s why I chose the Master’s in Global Change Ecology. I’m especially interested in the role of climate diplomacy and how international cooperation can lead to real, impactful change.
Since early childhood, I have been enchanted by the world around me, resonating with it only through understanding how everything interacts and where it all originates. As a city child, I was fascinated by the interplay between the natural and the urban, which eventually led me to earn a Bachelor's degree in Landscape Architecture.
Climate change became an inseparable part of how I perceive the world many years ago and has naturally taken center stage in my interests in landscape design and urban planning.
