Fine Characterization of Leafing Phenology in the Brazilian Atlantic Forest by Optical and Microwave Remote Sensing

<p>Tropical forests provide important ecosystem functions in the global biosphere, but they remain among the most poorly understood elements of land surface models, especially with regard to their seasonal dynamics. For instance, in seasonally dry forests, the pattern of the annual green-up in their canopies closely follows annual patterns of rainfall. The same, however, does not occur in wet forest canopies which are dominated by evergreen trees. In the latter, water is not scarce enough to limit leaf photosynthetic function. Canopy leafing phenology in these forests is therefore poorly characterized by optical remote sensing methods which are not sensitive to small variations in water content in canopy constituents and sensitive to cloud cover. Meanwhile, microwave radar is sensitive to vegetation moisture and so I ask whether radar remote sensing data can complement optical data in characterizing canopy seasonality by tracking the cyclical pattern in the water content of plant tissues and thus improve vegetation classifications of native tropical forest canopies. I expect that over evergreen tropical forests a vegetation index based on optical remote sensing data would diverge in time from a vegetation index based on microwave data because of the lag in the green-up behavior behind rainfall resulting from the decoupling between leaf photosynthesis and water accumulation in the canopy. In contrast, I expect a smaller lag between green-up and water accumulation in seasonal tropical forests. To test this, I gathered six years of Sentinel-1 microwave backscatter imagery (2016-2021) and three years (2019-2021) of Sentinel-2 surface reflectance imagery over 1-ha sampling plots from two protected areas in Brazil (REBIO Sooretama, Espírito Santo and PE Rio Doce, Minas Gerais). These parks represent two vegetation types common to the Atlantic Forest of Brazil: evergreen broadleaf and seasonal semi-deciduous forest. I also gathered a historical record of IMERG rainfall data representing this study period as well as an early period (2002-2007) in order to assess the evolution in the meteorological constraints affecting the region. To characterize seasonality in each of the forest types, I modeled annual variation in canopy green-up and water accumulation as both first-order and third-order harmonic functions in Google Earth Engine, assuming one season per year (unimodal annual variation). Temporally congruent sampling from distinct slices of the electromagnetic spectrum also enabled a calculation in the mismatch (time lag) in the seasonal green-up/water accumulation (start of season date) variation of each forest type. I also modeled seasonality in a more traditional sense, as the seasonal variation in intensity (amplitude) of the green-up and water accumulation to compare the two forest types. I found unexpected and large asynchrony (lag) of up to four months between the seasonal green-up (based on the optical index) and water accumulation (based on the microwave index) in both seasonal semi-deciduous and evergreen broadleaf forests. Also, depending on the modeling method used, the green-up/water accumulation lag in the evergreen broadleaf forest was actually smaller that the semi-deciduous forest by nearly a month. This suggests that introducing lag information in seasonality may not enhance the differences between forest canopy seasonality patterns already indicated by optical amplitude. Alternatively, it may reveal greater sensitivity of evergreen broadleaf forests than semi-deciduous forests to abnormal meteorological conditions such as El Niño, since I found support for significant declines in total rainfall over the study region in the last 20 years.</p>

Name thesis:degree_name

Master of Science (M.S.)

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Biology

Year dc:date.available

2023

Author dc:creator

• Bell, James B

Contributors dc:contributor

• Ana Carnaval
• Fabian Michelangeli
• Kyle McDonald

• Tropical Forests
• Sentinel-1
• Sentinel-2
• Seasonality
• Atlantic Forest
• Radar Vegetation Index
• Biogeochemistry
• Forest Biology
• Integrative Biology
• Systems Biology
• Terrestrial and Aquatic Ecology

Repository record dc:identifier

https://academicworks.cuny.edu/cc_etds_theses/1117

OAI identifier oai:identifier

oai:academicworks.cuny.edu:cc_etds_theses-2187