UN MODELO DE ACTIVACIÓN INTERACTIVA

Concluding Remarks

The Earth’s climate has changed a lot due to anthropogenic influences, such as burning of fossil fuels, land cover/land use change, etc. Vegetation is an important component in determining the future of Earth’s climate, and understanding the dynamics of vegetation requires both field plot experiments as well as satellite remote sensing. Satellite remote sensing is especially indispensible for continuous monitoring of vegetation dynamics over large areas. This dissertation investigated the dynamics of vegetation over two critical regions: the arctic-boreal region in northern high latitudes and Amazonian rainforests in South America, in the context of climate change, using satellite remote sensing.

The pole-ward amplification of global warming resulted in vegetation green-up over northern high latitudes (Myneni et al., 1997). Previous research showed the arctic- boreal vegetation green-up patterns were different between North America and Eurasia (Zhou et al. 2001; Bogaert et al., 2002), and have attributed the reason to various factors: temperature induced drought (Angert et al., 2005; Barber et al., 2000; Buerman et al., 2007; Piao et al., 2011), increase in winter snow depth (Bulygina et al., 2011), and disturbances (e.g. fires, insects; Goetz et al., 2005; Goetz et al., 2007; Lantz et al., 2010; Soja et al., 2007). Since temperature increase is the main driving factor of arctic-boreal vegetation green-up, this dissertation investigated the relationship between growing season mean temperature and growing season average vegetation photosynthetic activity

for the arctic-boreal vegetation over North America and Eurasia separately over the 30- year period since 1982, and found that while the relationship between temperature increase and vegetation green-up was stable over Eurasia, the amount of vegetation green-up per unit amount of warming has decreased in high latitudes of North America since the beginning of 21st century. This dissertation included precipitation variable into the analysis and translated the temporal increases of temperature and precipitation to northward movements of these two climatic variables, suggesting a possible explanation for the divergent arctic-boreal vegetation changes between North America and Eurasia: the unmatched northward movement velocities of temperature and precipitation patterns in North America, which have resulted in unfavorable climate conditions to maintain the stable relationship between temperature increase and vegetation green-up.

Amazonian forests are important for the global climate system. Our understanding about the intra-annual variation of Amazonian forests used to be clear: increase of

photosynthesis, leaf area, and evapotranspiration rates during dry season (e.g. Huete et al., 2006; Myneni et al. 2007; Brando et al., 2010; Samanta et al., 2012; Restrepo-Coupe et al., 2013). The understanding was based on both field observations and satellite remote sensing, and of course, satellite remote sensing provided more complete assessment of Amazonian forests. However, recently, one study (Morton et al., 2014) challenged this conclusion about the seasonality of Amazonian forests and claimed that Amazonian forests were constant and the previous conclusions based on remote sensing were an artifact of variation of sun-sensor geometry. This dissertation re-confirmed the

Index product, the retrieval algorithm of which explicitly accounts for the variation of sun-sensor geometry; MAIAC EVI data, which have normalized the sun-sensor geometry to 45° solar zenith angle and nadir view; and MISR sensor, which has different sampling strategies than MODIS. Analysis of these data clarified the seasonality of Amazonian rainforests, and sunlight is the proximate cue for leaf production. Sun-sensor geometry influences the satellite observations of Amazonian rainforests, but after accounting for the variation of sun-sensor geometry, satellite remote sensing data show Amazonian rainforests have sunlight mediated seasonality in canopy structure and photosynthetic activity.

Amazonian forests’ greenness not only has intra-annual variation, but also inter- annual variation, which may be caused by many factors, including water stress. Satellite remote sensing provides more comprehensive assessment of such variation than field observations. Assessment of inter-annual variation of Amazonian forests has to be done carefully due to clouds and high aerosol loadings (Samanta et al., 2010). The standard MODIS vegetation index product, the most suitable remote sensing data for this task because of its high spectral resolution for clouds detection and aerosol identification, shows continuous decline of Amazonian forests’ greenness after 2006 even when there were no droughts (Atkinson et al., 2011). The Multi-Angle Implementation of

Atmospheric Correction for MODIS (MAIAC; Lyapustin et al., 2012) product have more advanced atmospheric correction and cloud detection algorithm than standard MODIS product. This dissertation assessed Amazonian forests’ response to droughts using the MAIAC vegetation index data. MAIAC data show no continuous browning of Amazon

forests, and the recent droughts decreased the photosynthetic activities of Amazonian forests. In the 2005 drought, while as a whole, Amazon forests did not show greening or browning, the forests at the center of the drought area decreased photosynthetic activities. In the 2010 drought, wide spread decrease of forests’ photosynthetic activity was

observed. In 2004 and 2007, when there was lower water abundance during wet season compared to normal years, the photosynthetic activity of Amazonian rainforests also decreased. This implies that the likely more frequent future water deficits in the Amazon basin, irrespective of whether they occur in dry season or in wet season, can decrease the photosynthetic activity of Amazonian forests, and provide a positive feedback to global warming.

This dissertation showed that satellite remote sensing of vegetation dynamics was important for our understanding about how the Earth’s climate system would evolve in future, so monitoring of vegetation dynamics from various satellite remote sensing platforms should continue into future.

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