Next Article in Journal
Editorial for Special Issue “Remote Sensing of Precipitation: Part II”
Previous Article in Journal
Surface Tradeoffs and Elevational Shifts at the Largest Italian Glacier: A Thirty-Years Time Series of Remotely-Sensed Images
Open AccessArticle

Detecting Tree Species Effects on Forest Canopy Temperatures with Thermal Remote Sensing: The Role of Spatial Resolution

1
German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Puschstraße 4, 04103 Leipzig, Germany
2
Systematic Botany and Functional Biodiversity, Institute for Biology, Leipzig University, Johannisallee 21, 04103 Leipzig, Germany
3
Geoinformatics and Remote Sensing, Institute for Geography, Leipzig University, Johannisallee 19a, 04103 Leipzig, Germany
4
Remote Sensing Centre for Earth System Research, Leipzig University, Talstraße 35, 04103 Leipzig, Germany
5
Max-Planck Institute for Biogeochemistry, 07745 Jena, Germany
6
Institute of Geoinformation and Surveying, Hochschule Anhalt, 06846 Dessau-Roßlau, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(1), 135; https://doi.org/10.3390/rs13010135
Received: 30 November 2020 / Revised: 23 December 2020 / Accepted: 30 December 2020 / Published: 3 January 2021
Canopy temperatures are important for understanding tree physiology, ecology, and their cooling potential, which provides a valuable ecosystem service, especially in urban environments. Linkages between tree species composition in forest stands and air temperatures remain challenging to quantify, as the establishment and maintenance of onsite sensor networks is time-consuming and costly. Remotely-sensed land surface temperature (LST) observations can potentially acquire spatially distributed crown temperature data more efficiently. We analyzed how tree species modify canopy air temperature at an urban floodplain forest (Leipzig, Germany) site equipped with a detailed onsite sensor network, and explored whether mono-temporal thermal remote sensing observations (August, 2016) at different spatial scales could be used to model air temperatures at the tree crown level. Based on the sensor-network data, we found interspecific differences in summer air temperature to vary temporally and spatially, with mean differences between coldest and warmest tree species of 1 °C, and reaching maxima of up to 4 °C for the upper and lower canopy region. The detectability of species-specific differences in canopy surface temperature was found to be similarly feasible when comparing high-resolution airborne LST data to the airborne LST data aggregated to 30 m pixel size. To realize a spatial resolution of 30 m with regularly acquired data, we found the downscaling of Landsat 8 thermal data to be a valid alternative to airborne data, although detected between-species differences in surface temperature were less expressed. For the modeling of canopy air temperatures, all LST data up to the 30 m level were similarly appropriate. We thus conclude that satellite-derived LST products could be recommended for operational use to detect and monitor tree species effects on temperature regulation at the crown scale. View Full-Text
Keywords: Landsat 8; downscaling; random forest; canopy temperature; land surface temperature; air temperature; broadleaf tree species; microclimate regulation Landsat 8; downscaling; random forest; canopy temperature; land surface temperature; air temperature; broadleaf tree species; microclimate regulation
Show Figures

Graphical abstract

MDPI and ACS Style

Richter, R.; Hutengs, C.; Wirth, C.; Bannehr, L.; Vohland, M. Detecting Tree Species Effects on Forest Canopy Temperatures with Thermal Remote Sensing: The Role of Spatial Resolution. Remote Sens. 2021, 13, 135. https://doi.org/10.3390/rs13010135

AMA Style

Richter R, Hutengs C, Wirth C, Bannehr L, Vohland M. Detecting Tree Species Effects on Forest Canopy Temperatures with Thermal Remote Sensing: The Role of Spatial Resolution. Remote Sensing. 2021; 13(1):135. https://doi.org/10.3390/rs13010135

Chicago/Turabian Style

Richter, Ronny; Hutengs, Christopher; Wirth, Christian; Bannehr, Lutz; Vohland, Michael. 2021. "Detecting Tree Species Effects on Forest Canopy Temperatures with Thermal Remote Sensing: The Role of Spatial Resolution" Remote Sens. 13, no. 1: 135. https://doi.org/10.3390/rs13010135

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop