Active microwave remote sensing of the Amazon forest region

Active microwave remote sensing of the Amazon forest region

Petchiappan, Ashwini (TU Delft Civil Engineering and Geosciences)

Steele-Dunne, Susan (mentor)
Coenders-Gerrits, Miriam (graduation committee)
Vardon, Phil (graduation committee)
Vreugdenhil, Mariette (graduation committee)

Delft University of Technology

2019-09-17

The Amazon rainforest is among the most vital ecosystems on earth, holding about a quarter of the global terrestrial carbon sink. Since 2005, three 100-year return period droughts have occurred, the likes of which have the potential to turn the forest from a carbon sink to a source – meaning disastrous consequences for the planet. Monitoring of the Amazon is hence, indispensable. This study explored avenues for monitoring the canopy water dynamics in the region through ASCAT backscatter and dynamic vegetation parameters. These dynamic vegetation parameters are slope and curvature - the first and second derivatives of a second-order Taylor polynomial describing the incidence angle dependence of ASCAT backscatter data. A 10-year length of data from 2007-16 was used to find spatial and temporal patterns in the backscatter, slope, and curvature over Amazonia, and related them to climatic variables such as radiation and precipitation to find the driving force behind the parameters. The findings suggest that the spatial patterns of the ASCAT parameters match the distribution of major land cover types in the region, with significant differences between the major cover types. The evergreen forests have high mean backscatter and low mean curvature compared to other cover types, and weak seasonal variations. The savannas, on the other hand show much stronger amplitudes of seasonal changes. The wetlands, as well, have strong seasonality with especially high ranges in curvature. They also show a change in the backscatter-incidence angle relationship during flooding seasons, thus demonstrating potential for forest flood detection and monitoring. Consistent diurnal differences were observed especially in the backscatter of all regions. These diurnal differences are shown to be an interaction between vegetation phenological activity and precipitation seasons. In the dry season, the morning values are generally higher as the vegetation transpires water through the day. Water stress and a consequent decrease in backscatter are also detected during the Amazon droughts of 2010 and 2015. The spatial distribution of these negative backscatter anomalies matched that of precipitation deficits during the droughts. The anomalies were also seen to be strongest during the peak drought months. These findings indicate that ASCAT backscatter can detect water stress and droughts in the Amazon vegetation. Seasonal cycles in backscatter and dynamic vegetation parameters are visible in all regions. While backscatter follows the moisture availability in the canopy, the slope and curvature are related to variables of moisture demand (such as radiation and humidity) through a strong influence of vegetation phenology. In the radiation-limited Amazon vegetation, the slope peaks with the period of photosynthetic activity following the radiation maximum, while the curvature peak covers the leaf-flushing season. The ASCAT parameters show a relation to the vegetation water dynamics in all major cover types in the Amazon. There is, thus, a solid prospect for the use of ASCAT backscatter and vegetation parameters for long-term monitoring of the Amazon with respect to canopy water dynamics in a variety of land cover types, as well as events such as droughts.

Amazonia
active microwave remote sensing
vegetation
amazon forest
ASCAT
drought
Drought monitoring
Backscatter
vegetation water content
Seasonality
diurnal
Transpiration
slope
curvature
dynamic vegetation parameters
canopy water dynamics
scatterometers
radiation
water stress

http://resolver.tudelft.nl/uuid:59a345ae-a135-444f-b180-12dc1e192fa0

2020-11-01

Student theses

master thesis

© 2019 Ashwini Petchiappan