TY - GEN
T1 - A UAS Platform for Assessing Spectral, Structural, and Thermal Patterns of Arctic Tundra Vegetation
AU - Meng, Ran
AU - Yang, Dedi
AU - McMahon, Andrew
AU - Hantson, Wouter
AU - Hayes, Dan
AU - Breen, Amy
AU - Serbin, Shawn
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - As climate continues to warm, detailed geospatial spectral, structural, and thermal information, related to water, carbon, and energy cycling, are required for modeling the future state of the Arctic biome. To address these needs we have developed a cost-effective, multi-sensor UAS-based remote sensing platform for acquiring high resolution spectral, structural, and thermal measurements of Arctic vegetation. We successfully deployed this remote sensing platform in three sites along an elevation gradient near Nome, Alaska in summer 2017. Corresponding workflows have been further developed for assessing spectral, structural, and thermal patterns of arctic tundra biomes from the collected datasets. Our results demonstrate that the UAS platform can successfully (1) map heterogenous vegetation composition and (2) assess corresponding spectral, structural, and thermal patterns of Arctic tundra biomes. This study not only presents a novel and innovative approach for collecting high resolution spectral, structural and thermal information for characterizing Arctic tundra biome patterns, but also a basis for informing the modeling of climate feedbacks between the biosphere and the atmosphere in response to ongoing global change.
AB - As climate continues to warm, detailed geospatial spectral, structural, and thermal information, related to water, carbon, and energy cycling, are required for modeling the future state of the Arctic biome. To address these needs we have developed a cost-effective, multi-sensor UAS-based remote sensing platform for acquiring high resolution spectral, structural, and thermal measurements of Arctic vegetation. We successfully deployed this remote sensing platform in three sites along an elevation gradient near Nome, Alaska in summer 2017. Corresponding workflows have been further developed for assessing spectral, structural, and thermal patterns of arctic tundra biomes from the collected datasets. Our results demonstrate that the UAS platform can successfully (1) map heterogenous vegetation composition and (2) assess corresponding spectral, structural, and thermal patterns of Arctic tundra biomes. This study not only presents a novel and innovative approach for collecting high resolution spectral, structural and thermal information for characterizing Arctic tundra biome patterns, but also a basis for informing the modeling of climate feedbacks between the biosphere and the atmosphere in response to ongoing global change.
KW - object-oriented classification
KW - plant traits
KW - spectroscopy
KW - thermal infrared
KW - vegetation composition
UR - http://www.scopus.com/inward/record.url?scp=85077691697&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2019.8897953
DO - 10.1109/IGARSS.2019.8897953
M3 - Conference contribution
AN - SCOPUS:85077691697
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 9113
EP - 9116
BT - 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Y2 - 28 July 2019 through 2 August 2019
ER -
