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

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.

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A New Model to Downscale Urban and Rural Surface and Air Temperatures Evaluated in Shanghai, China

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-17-0255.1 ◽

2018 ◽

Vol 57 (10) ◽

pp. 2267-2283 ◽

Cited By ~ 3

Author(s):

Dongwei Liu ◽

C. S. B. Grimmond ◽

Jianguo Tan ◽

Xiangyu Ao ◽

Jie Peng ◽

...

Keyword(s):

Land Cover ◽

Surface Temperature ◽

Spatial Resolution ◽

Air Temperature ◽

Surface Air Temperature ◽

Wave Radiation ◽

Climate Services ◽

Near Surface ◽

Surface Temperatures ◽

Air Temperatures

AbstractA simple model, the Surface Temperature and Near-Surface Air Temperature (at 2 m) Model (TsT2m), is developed to downscale numerical model output (such as from ECMWF) to obtain higher-temporal- and higher-spatial-resolution surface and near-surface air temperature. It is evaluated in Shanghai, China. Surface temperature (Ts) and near-surface air temperature (Ta) submodels account for variations in land cover and their different thermal properties, resulting in spatial variations of surface and air temperature. The net all-wave radiation parameterization (NARP) scheme is used to compute net wave radiation for the surface temperature submodel, the objective hysteresis model (OHM) is used to calculate the net storage heat fluxes, and the surface temperature is obtained by the force-restore method. The near-surface air temperature submodel considers the horizontal and vertical energy changes for a column of well-mixed air above the surface. Modeled surface temperatures reproduce the general pattern of MODIS images well, while providing more detailed patterns of the surface urban heat island. However, the simulated surface temperatures capture the warmer urban land cover and are 10.3°C warmer on average than those derived from the coarser MODIS data. For other land-cover types, values are more similar. Downscaled, higher-temporal- and higher-spatial-resolution air temperatures are compared to observations at 110 automatic weather stations across Shanghai. After downscaling with TsT2m, the average forecast accuracy of near-surface air temperature is improved by about 20%. The scheme developed has considerable potential for prediction and mitigation of urban climate conditions, particularly for weather and climate services related to heat stress.

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Derivation of Nighttime Urban Air Temperatures Using a Satellite Thermal Image

Journal of Applied Meteorology and Climatology ◽

10.1175/2008jamc2001.1 ◽

2009 ◽

Vol 48 (4) ◽

pp. 863-872 ◽

Cited By ~ 38

Author(s):

W. Y. Fung ◽

K. S. Lam ◽

Janet Nichol ◽

Man Sing Wong

Keyword(s):

Remote Sensing ◽

Hong Kong ◽

Surface Temperature ◽

Air Temperature ◽

Thermal Emission ◽

Suburban Areas ◽

Air Temperatures ◽

Urban Heat ◽

Remote Sensing Techniques

Abstract The aim of this study is to characterize the urban heat island (UHI) intensity in Hong Kong. The first objective is to explore the UHI intensity in Hong Kong by using the mobile transverse and remote sensing techniques. The second objective is to produce a satellite-derived air temperature image by integrating satellite remote sensing with a mobile survey, the methodology involved in making simultaneous ground measurements when the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite made an overpass. The average UHI intensity of Hong Kong was about 2°–3.5°C, although a very high value of 12°C UHI was observed on a calm winter night by ASTER. The satellite-derived surface temperature was compared with the in situ measurements. The bias was found to be only about 1.1°C. A good correlation was also found between the in situ surface and air temperature pair of readings at nighttime on 31 January 2007. The linear regression lines of temperatures in urban and suburban areas were then used to convert the satellite-derived surface temperatures into air temperatures. The satellite-derived air temperatures showed a good correlation with temperatures observed by 12 fixed stations. It is possible to derive the nighttime air temperature from the satellite surface temperature on calm and clear winter nights.

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Pedogenic Threshold in Acidity Explains Context-Dependent Tree Species Effects on Soil Carbon

Frontiers in Forests and Global Change ◽

10.3389/ffgc.2021.679813 ◽

2021 ◽

Vol 4 ◽

Author(s):

Ellen Desie ◽

Bart Muys ◽

Boris Jansen ◽

Lars Vesterdal ◽

Karen Vancampenhout

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Forest Soils ◽

Tree Species ◽

Litter Quality ◽

Species Selection ◽

Species Effects ◽

Tree Species Effects ◽

Soc Stocks ◽

The Way

Despite the general agreement that maximizing carbon storage and its persistence in forest soils are top priorities in the context of climate change mitigation, our knowledge on how to steer soil organic carbon (SOC) through forest management remains limited. For some soils, tree species selection based on litter quality has been shown a powerful measure to boost SOC stocks and stability, whereas on other locations similar efforts result in insignificant or even opposite effects. A better understanding of which mechanisms underpin such context-dependency is needed in order to focus and prioritize management efforts for carbon sequestration. Here we discuss the key role of acid buffering mechanisms in belowground ecosystem functioning and how threshold behavior in soil pH mediates tree species effects on carbon cycling. For most forests around the world, the threshold between the exchange buffer and the aluminum buffer around a pH-H2O of 4.5 is of particular relevance. When a shift between these buffer domains occurs, it triggers changes in multiple compartments in the soil, ultimately altering the way carbon is incorporated and transformed. Moreover, the impact of such a shift can be amplified by feedback loops between tree species, soil biota and cation exchange capacity (CEC). Hence, taking into account non-linearities related to acidity will allow more accurate predictions on the size and direction of the effect of litter quality changes on the way soil organic carbon is stored in forest soils. Consequently, this will allow developing more efficient, context-explicit management strategies to optimize SOC stocks and their stability.

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Sea surface temperature variations in the south-eastern BalticSea in 1960–2015

Baltica ◽

10.5200/baltica.2017.30.09 ◽

2018 ◽

Vol 30 (2) ◽

pp. 75-85 ◽

Cited By ~ 1

Author(s):

Viktorija Rukšėnienė ◽

Inga Dailidienė ◽

Loreta Kelpšaitė-Rimkienė ◽

Tarmo Soomere

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Air Temperature ◽

Weather Conditions ◽

Sea Surface ◽

South Eastern ◽

Air Temperatures ◽

Joint Application ◽

Open Sea ◽

And Cluster Analysis

This study focuses on time scales and spatial variations of interrelations between average weather conditions and sea surface temperature (SST), and long-term changes in the SST in south-eastern Baltic Sea. The analysis relies on SST samples measured in situ four times a year in up to 17 open sea monitoring stations in Lithuanian waters in 1960–2015. A joint application of non-metric multi-dimensional scaling and cluster analysis reveals four distinct SST regimes and associated sub-regions in the study area. The increase in SST has occurred during both winter and summer seasons in 1960–2015 whereas the switch from relatively warm summer to colder autumn temperatures has been shifted by 4–6 weeks over this time in all sub-regions. The annual average air temperature and SST have increased by 0.03°C yr–1 and 0.02°C yr–1, respectively, from 1960 till 2015. These data are compared with air temperatures measured in coastal meteorological stations and averaged over time intervals from 1 to 9 weeks. Statistically significant positive correlation exists between the SST and the average air temperature. This correlation is strongest for the averaging interval of 35 days.

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Impact of Observational Environment Change on Air Temperature Based on High-Spatial-Resolution Satellite Remote Sensing Data

Acta Optica Sinica ◽

10.3788/aos202040.1028001 ◽

2020 ◽

Vol 40 (10) ◽

pp. 1028001

Author(s):

陈世涵 Chen Shihan ◽

李玲 Li Ling ◽

蒋弘凡 Jiang Hongfan ◽

居伟杰 Ju Weijie ◽

张曼玉 Zhang Manyu ◽

...

Keyword(s):

Remote Sensing ◽

Spatial Resolution ◽

Air Temperature ◽

Satellite Remote Sensing ◽

High Spatial Resolution ◽

Remote Sensing Data ◽

Environment Change ◽

Sensing Data

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Earth Observations-Based Evapotranspiration in Northeastern Thailand

Remote Sensing ◽

10.3390/rs11020138 ◽

2019 ◽

Vol 11 (2) ◽

pp. 138 ◽

Cited By ~ 4

Author(s):

Chaolei Zheng ◽

Li Jia ◽

Guangcheng Hu ◽

Jing Lu

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Microwave Remote Sensing ◽

Spatiotemporal Pattern ◽

Monsoon Climate ◽

Thermal Remote Sensing ◽

Clear Sky

Thailand is characterized by typical tropical monsoon climate, and is suffering serious water related problems, including seasonal drought and flooding. These issues are highly related to the hydrological processes, e.g., precipitation and evapotranspiration (ET), which are helpful to understand and cope with these problems. It is critical to study the spatiotemporal pattern of ET in Thailand to support the local water resource management. In the current study, daily ET was estimated over Thailand by ETMonitor, a process-based model, with mainly satellite earth observation datasets as input. One major advantage of the ETMonitor algorithm is that it introduces the impact of soil moisture on ET by assimilating the surface soil moisture from microwave remote sensing, and it reduces the dependence on land surface temperature, as the thermal remote sensing is highly sensitive to cloud, which limits the ability to achieve spatial and temporal continuity of daily ET. The ETMonitor algorithm was further improved in current study to take advantage of thermal remote sensing. In the improved scheme, the evaporation fraction was first obtained by land surface temperature—vegetation index triangle method, which was used to estimate ET in the clear days. The soil moisture stress index (SMSI) was defined to express the constrain of soil moisture on ET, and clear sky SMSI was retrieved according to the estimated clear sky ET. Clear sky SMSI was then interpolated to cloudy days to obtain the SMSI for all sky conditions. Finally, time-series ET at daily resolution was achieved using the interpolated spatio-temporal continuous SMSI. Good agreements were found between the estimated daily ET and flux tower observations with root mean square error ranging between 1.08 and 1.58 mm d−1, which showed better accuracy than the ET product from MODerate resolution Imaging Spectroradiometer (MODIS), especially for the forest sites. Chi and Mun river basins, located in Northeast Thailand, were selected to analyze the spatial pattern of ET. The results indicate that the ET had large fluctuation in seasonal variation, which is predominantly impacted by the monsoon climate.

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Deep Learning Approaches for the Mapping of Tree Species Diversity in a Tropical Wetland Using Airborne LiDAR and High-Spatial-Resolution Remote Sensing Images

Forests ◽

10.3390/f10111047 ◽

2019 ◽

Vol 10 (11) ◽

pp. 1047 ◽

Cited By ~ 5

Author(s):

Ying Sun ◽

Jianfeng Huang ◽

Zurui Ao ◽

Dazhao Lao ◽

Qinchuan Xin

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Species Diversity ◽

Spatial Resolution ◽

Tree Species ◽

High Spatial Resolution ◽

Diversity Index ◽

Tree Species Diversity ◽

Individual Tree ◽

Rgb Images

The monitoring of tree species diversity is important for forest or wetland ecosystem service maintenance or resource management. Remote sensing is an efficient alternative to traditional field work to map tree species diversity over large areas. Previous studies have used light detection and ranging (LiDAR) and imaging spectroscopy (hyperspectral or multispectral remote sensing) for species richness prediction. The recent development of very high spatial resolution (VHR) RGB images has enabled detailed characterization of canopies and forest structures. In this study, we developed a three-step workflow for mapping tree species diversity, the aim of which was to increase knowledge of tree species diversity assessment using deep learning in a tropical wetland (Haizhu Wetland) in South China based on VHR-RGB images and LiDAR points. Firstly, individual trees were detected based on a canopy height model (CHM, derived from LiDAR points) by the local-maxima-based method in the FUSION software (Version 3.70, Seattle, USA). Then, tree species at the individual tree level were identified via a patch-based image input method, which cropped the RGB images into small patches (the individually detected trees) based on the tree apexes detected. Three different deep learning methods (i.e., AlexNet, VGG16, and ResNet50) were modified to classify the tree species, as they can make good use of the spatial context information. Finally, four diversity indices, namely, the Margalef richness index, the Shannon–Wiener diversity index, the Simpson diversity index, and the Pielou evenness index, were calculated from the fixed subset with a size of 30 × 30 m for assessment. In the classification phase, VGG16 had the best performance, with an overall accuracy of 73.25% for 18 tree species. Based on the classification results, mapping of tree species diversity showed reasonable agreement with field survey data (R2Margalef = 0.4562, root-mean-square error RMSEMargalef = 0.5629; R2Shannon–Wiener = 0.7948, RMSEShannon–Wiener = 0.7202; R2Simpson = 0.7907, RMSESimpson = 0.1038; and R2Pielou = 0.5875, RMSEPielou = 0.3053). While challenges remain for individual tree detection and species classification, the deep-learning-based solution shows potential for mapping tree species diversity.

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