TY - GEN
T1 - Dashboard of Robust Landscape Drivers of LST in Diverse Landscape Characters
AU - Eneche, Patrick Samson Udama
AU - Pfeffer, Karin
AU - Atun, Funda
AU - Zeng, Yijian
PY - 2024/2/13
Y1 - 2024/2/13
N2 - This dashboard reveals (49) robust landscape metrics (LMs) that have been confirmed (by at least two separate studies) to influence urban land surface temperatures (ULSTs) in diverse landscape characters. It a summary of our findings in the study on Robust Drivers of Urban Land Surface Temperature Dynamics Across Diverse Landscape Characters: An Augmented Systematic Literature Review. Hence, the dashboard is to aid in communicating our findings in a simplified, interactive, and engaging manner. It is also expected to enhance understanding of the drivers of LST dynamics in diverse landscape characters, by both scientific and non-scientific (urban) stakeholders. Also, it is proposed as a tool that can be used alongside other existing scientific criteria (e.g., most applied, theoretical significance, ease of interpretation and applicability) and stakeholders' indigenous knowledge for selecting LMs to be included in the modelling of ULST dynamics. Meanwhile, with the help of filters, the Dashboard readily indicates the methodology applied in the studies wherein robust LMs were found and their sources as well – which can be immediately accessed.
AB - This dashboard reveals (49) robust landscape metrics (LMs) that have been confirmed (by at least two separate studies) to influence urban land surface temperatures (ULSTs) in diverse landscape characters. It a summary of our findings in the study on Robust Drivers of Urban Land Surface Temperature Dynamics Across Diverse Landscape Characters: An Augmented Systematic Literature Review. Hence, the dashboard is to aid in communicating our findings in a simplified, interactive, and engaging manner. It is also expected to enhance understanding of the drivers of LST dynamics in diverse landscape characters, by both scientific and non-scientific (urban) stakeholders. Also, it is proposed as a tool that can be used alongside other existing scientific criteria (e.g., most applied, theoretical significance, ease of interpretation and applicability) and stakeholders' indigenous knowledge for selecting LMs to be included in the modelling of ULST dynamics. Meanwhile, with the help of filters, the Dashboard readily indicates the methodology applied in the studies wherein robust LMs were found and their sources as well – which can be immediately accessed.
U2 - 10.17026/PT/XO0VIX
DO - 10.17026/PT/XO0VIX
M3 - Other contribution
PB - DATA Archiving and Networked Services (DANS)
ER -