Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Chandi Witharana"'
Publikováno v:
IEEE Access, Vol 12, Pp 43062-43077 (2024)
Climate change pressure on the Arctic permafrost is rising alarmingly, creating a decisive need to produce Pan-Arctic scale permafrost landform and thaw disturbance information from remote sensing (RS) data. Very high spatial resolution (VHSR) satell
Externí odkaz:
https://doaj.org/article/168757335bdd431282769a3a77399523
Autor:
Harshana Wedagedara, Chandi Witharana, Robert Fahey, Diego Cerrai, Jason Parent, Amal S. Perera
Publikováno v:
Applied Sciences, Vol 14, Iss 12, p 4991 (2024)
Trees in proximity to power lines can cause significant damage to utility infrastructure during storms, leading to substantial economic and societal costs. This study investigated the effectiveness of non-parametric machine learning algorithms in mod
Externí odkaz:
https://doaj.org/article/e34298a05e6a4f6bada3083ffac8397f
Autor:
Wenwen Li, Chia-Yu Hsu, Sizhe Wang, Yezhou Yang, Hyunho Lee, Anna Liljedahl, Chandi Witharana, Yili Yang, Brendan M. Rogers, Samantha T. Arundel, Matthew B. Jones, Kenton McHenry, Patricia Solis
Publikováno v:
Remote Sensing, Vol 16, Iss 5, p 797 (2024)
This paper assesses trending AI foundation models, especially emerging computer vision foundation models and their performance in natural landscape feature segmentation. While the term foundation model has quickly garnered interest from the geospatia
Externí odkaz:
https://doaj.org/article/cbc58a9c457941a3ae5a2b4e1d4fde77
Autor:
Hana L. Sellers, Sergio A. Vargas Zesati, Sarah C. Elmendorf, Alexandra Locher, Steven F. Oberbauer, Craig E. Tweedie, Chandi Witharana, Robert D. Hollister
Publikováno v:
Remote Sensing, Vol 15, Iss 8, p 1972 (2023)
Plot-level photography is an attractive time-saving alternative to field measurements for vegetation monitoring. However, widespread adoption of this technique relies on efficient workflows for post-processing images and the accuracy of the resulting
Externí odkaz:
https://doaj.org/article/d902a074a8e64eb2881a648ab76bc833
Publikováno v:
Science of Remote Sensing, Vol 4, Iss , Pp 100023- (2021)
We evaluated the performance of a variety of time series models, that include the harmonic (HR) model, autoregressive (AR) model, linear Gaussian state-space (LGSS) model, cubic spline (SP) model, double logistic (DL) model, and asymmetric Gaussian (
Externí odkaz:
https://doaj.org/article/366ef9a8ba0246599b4f43e5cdd4b219
Autor:
Chandi Witharana, Mahendra R. Udawalpola, Anna K. Liljedahl, Melissa K. Ward Jones, Benjamin M. Jones, Amit Hasan, Durga Joshi, Elias Manos
Publikováno v:
Remote Sensing, Vol 14, Iss 17, p 4132 (2022)
Retrogressive thaw slumps (RTS) are considered one of the most dynamic permafrost disturbance features in the Arctic. Sub-meter resolution multispectral imagery acquired by very high spatial resolution (VHSR) commercial satellite sensors offer unique
Externí odkaz:
https://doaj.org/article/e677cc35a9ed43499a6f782fbdc11b8c
Publikováno v:
Remote Sensing, Vol 14, Iss 11, p 2719 (2022)
Rapid global warming is catalyzing widespread permafrost degradation in the Arctic, leading to destructive land-surface subsidence that destabilizes and deforms the ground. Consequently, human-built infrastructure constructed upon permafrost is curre
Externí odkaz:
https://doaj.org/article/e24a4f98f9b04576ba5465e0ebb8a3ce
Publikováno v:
GIScience & Remote Sensing, Vol 55, Iss 2, Pp 183-204 (2018)
Increasing availability and advancements of aerial Light Detection and Ranging (LiDAR) data have radically been shifting the way archeological surveys are performed. Unlike optical remote sensing imagery, LiDAR pulses travel through small gaps in den
Externí odkaz:
https://doaj.org/article/47999e59a8874174a3b9de8d7516ee68
Publikováno v:
Remote Sensing, Vol 13, Iss 22, p 4630 (2021)
Advanced deep learning methods combined with regional, open access, airborne Light Detection and Ranging (LiDAR) data have great potential to study the spatial extent of historic land use features preserved under the forest canopy throughout New Engl
Externí odkaz:
https://doaj.org/article/847966d477074eab88e929d3419c6d03
Autor:
Benjamin M. Jones, Ken D. Tape, Jason A. Clark, Allen C. Bondurant, Melissa K. Ward Jones, Benjamin V. Gaglioti, Clayton D. Elder, Chandi Witharana, Charles E. Miller
Publikováno v:
Remote Sensing, Vol 13, Iss 23, p 4863 (2021)
Beavers have established themselves as a key component of low arctic ecosystems over the past several decades. Beavers are widely recognized as ecosystem engineers, but their effects on permafrost-dominated landscapes in the Arctic remain unclear. In
Externí odkaz:
https://doaj.org/article/b30b113b90714a07842b03eec92250ea