Canopy temperatures of selected tree species growing in the forest and outside the forest using aerial thermal infrared (3.6–4.9 µm) data

Zakrzewska, Agata; Kopeć, Dominik; Krajewski, Karol; Charyton, Jakub

doi:10.1080/22797254.2022.2062055

Cited by 9 publications

(7 citation statements)

References 56 publications

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“…Our results illustrated that despite extreme conditions, trees had substantially lower surface temperatures of up to 6 • C in the median compared to the air temperature, which is in agreement with some existing findings during different conditions (e.g., sufficient water supply) that show that vegetation can have surface temperatures of up to 6.7 • C below air temperature in the afternoon [74]. Lower tree surface temperatures than air temperatures were also reported by a number of other studies [15,21,33,75] under normal conditions, but other findings also showed that parts of tree surfaces were above air temperatures, while others were considerably below [19], and several researchers also reported tree surfaces above air temperatures in summer [18,20,44]. Different reasons for this discrepancy exist, such as thermal camera characteristics, atmospheric effects, weather patterns, conversion of radiative temperatures to surface temperatures, tree and site conditions, various thermal regulation of leaves, and the selection of the reference air temperature stations [76].…”

Section: Cooling Potential Under Extreme Heat and Droughtsupporting

confidence: 91%

“…To provide a good overview of tree species differences in surface temperatures similar to existing studies [18,21,22], we calculated boxplots based on tree species-specific pixel values. We preferred boxplots because pixel-based analyses tend to have outliers and extreme values that strongly influence the calculation of mean and standard deviation.…”

Section: Discussionmentioning

confidence: 99%

“…High-resolution approaches using UAV multispectral and thermal data in Changchun, China, reported very large differences in the cooling capacity of tree species up to 10 • C in a parklike environment of a university campus [13]. Research using airborne thermal sensors investigating five tree species in a national park in Poland confirmed species-specific temperature differences, whereby the largest differences were reported at noon compared to morning surveys [21]. A high-resolution approach with 0.4 × 0.4 m thermal data obtained using a gyrocopter platform in an urban floodplain forest site in Leipzig, Germany, resulted in median tree species differences of around 1 • C during dry-to-moderate conditions [22].…”

Section: Introductionmentioning

confidence: 92%

“…The detector signal was converted to object temperature following Stefan-Boltzmann law, which uses camera internal temperature measurements and a number of additional variables, such as emissivity, atmospheric transmittance, background radiation, and atmospheric temperatures [40,41]. We set emissivity to a constant value of 1, following existing remote sensing studies [18,21,22], due to several reasons, such as the negligible effect of emissivity corrections on temperatures if research focuses on vegetation surfaces [18,21], the close proximity of tree species emissivities to one [42], the convergence of vegetation emissivities toward one with increasing sensor distance to the vegetation [43,44], and missing emissivity information for all species used in this study [22]. Existing research also showed that the distribution of emissivity-corrected and uncorrected data was almost identical, and errors within vegetation were around 0.1 to 0.6 K [45], so impacts on species comparisons were expected to be low.…”

Section: Processing Of Remote Sensing Imagerymentioning

confidence: 99%

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Cooling Potential of Urban Tree Species during Extreme Heat and Drought: A Thermal Remote Sensing Assessment

Zandler,

Samimi

2024

Remote Sensing

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The cooling potential of tree species in Central European cities is insufficiently studied during extreme heat and drought, although a stronger surge in heatwaves compared to the global average is observed in this region. Remote sensing-based thermal surveys are an important tool to shed light on the mitigation effects of green infrastructure, but approaches covering extreme events are scarce. In this study, we present a simple, low-cost thermal airborne methodology that covers the current daily heat record in 2022, after the second warmest and third driest spring-to-summer period since 1949, in the medium-sized German city of Forchheim. We found that in spite of record-breaking heat and drought conditions, trees still had a considerable cooling potential with surface temperatures of 2 °C to 6 °C below air temperatures. Tree species were characterized by substantial median differences in tree surface temperatures up to 3.64 °C. Conifers and drought-sensitive broadleaf species showed the highest temperatures during the extreme event, while riparian species with potentially good water provision showed the highest cooling potential. In addition to tree species, imperviousness and tree NDVI were important variables for urban tree surface temperature, showing positive (imperviousness) and negative (NDVI) correlations with tree surface temperatures. Our study provides a methodological remote sensing example for the spontaneous and rapid coverage of extreme events, documenting the benefits of tree species in the urban context.

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Section: Cooling Potential Under Extreme Heat and Droughtsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 92%

Section: Processing Of Remote Sensing Imagerymentioning

confidence: 99%

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Cooling Potential of Urban Tree Species during Extreme Heat and Drought: A Thermal Remote Sensing Assessment

Zandler,

Samimi

2024

Remote Sensing

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“…Imagen analysis with remote sensing (RS) techniques can significantly improve the deficiencies of ground visits by obtaining accurate maps of large areas. A significant number of RS sensors are utilized, alone or in combination, to estimate and map different tree parameters in agroforestry environments to assist in making appropriate management decisions in a non-destructive manner [13]: RGB (Red-Green-Blue) [14,15], multispectral [16][17][18][19], hyperspectral [20][21][22][23], thermal [24][25][26][27], or even LiDAR (Light Detection and Ranging) and RADAR (Radio Detection and Ranging) sensors [28][29][30][31]. Specifically, in olive grove studies, different combinations of RS platforms and technologies have been widely used.…”

Section: Introductionmentioning

confidence: 99%

Detection of Planting Systems in Olive Groves Based on Open-Source, High-Resolution Images and Convolutional Neural Networks

et al. 2022

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This paper aims to evaluate whether an automatic analysis with deep learning convolutional neural networks techniques offer the ability to efficiently identify olive groves with different intensification patterns by using very high-resolution aerial orthophotographs. First, a sub-image crop classification was carried out. To standardize the size and increase the number of samples of the data training (DT), the crop images were divided into mini-crops (sub-images) using segmentation techniques, which used a different threshold and stride size to consider the mini-crop as suitable for the analysis. The four scenarios evaluated discriminated the sub-images efficiently (accuracies higher than 0.8), obtaining the largest sub-images (H = 120, W = 120) for the highest average accuracy (0.957). The super-intensive olive plantings were the easiest to classify for most of the sub-image sizes. Nevertheless, although traditional olive groves were discriminated accurately, too, the most difficult task was to distinguish between the intensive plantings and the traditional ones. A second phase of the proposed system was to predict the crop at farm-level based on the most frequent class detected in the sub-images of each crop. The results obtained at farm level were slightly lower than at the sub-images level, reaching the highest accuracy (0.826) with an intermediate size image (H = 80, W = 80). Thus, the convolutional neural networks proposed made it possible to automate the classification and discriminate accurately among traditional, intensive, and super-intensive planting systems.

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Mapping Tree Water Deficit with UAV Thermal Imaging and Meteorological Data

Krause,

Sanders

2023

Remote Sens Earth Syst Sci

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The mapping of forest stands and individual trees affected by drought stress is a crucial step in targeted forest management, aimed at fostering resilient and diverse forests. Unoccupied aerial vehicle (UAV)-based thermal sensing is a promising method for obtaining high-resolution thermal data. However, the reliability of typical low-cost sensors adapted for UAVs is compromised due to various factors, such as internal sensor dynamics and environmental variables, including solar radiation intensity, relative humidity, object emissivity and wind. Additionally, accurately assessing drought stress in trees is a complex task that usually requires laborious and cost-intensive methods, particularly in field settings. In this study, we investigated the feasibility of using the thermal band of the Micasense Altum multispectral sensor, while also assessing the potential for modelling tree water deficit (TWD) through point dendrometers and UAV-derived canopy temperature. Our indoor tests indicated that using a limited number of pixels (< 3) could result in temperature errors exceeding 1 K. However, enlarging the spot-size substantially reduced the mean difference to 0.02 K, validated against leaf temperature sensors. Interestingly, drought-treated (unwatered) leaves exhibited a higher root mean squared error (RMSE) (RMSE = 0.66 K and 0.73 K) than watered leaves (RMSE = 0.55 K and 0.53 K), likely due to lower emissivity of the dry leaves. Comparing field acquisition methods, the mean standard deviation (SD) for tree crown temperature obtained from typical gridded flights was 0.25 K with a maximum SD of 0.59 K (n = 12). In contrast, a close-range hovering method produced a mean SD of 0.09 K and a maximum SD of 0.1 K (n = 8). Modelling the TWD from meteorological and point dendrometer data for the 2021 growth season (n = 2928) yielded an R2 = 0.667 using a generalised additive model (GAM) with vapor pressure deficit (VPD), wind speed, and solar radiation as input features. A point dendrometer lag of one hour was also implemented. When predicting individual tree TWD with UAV-derived tree canopy temperature, relative humidity, and air temperature, an RMSE of 4.92 (μm) and R2 of 0.87 were achieved using a GAM. Implementing leaf-to-air pressure deficit (LVPD) as an input feature resulted in an RMSE of 6.87 (μm) and an R2 of 0.71. This novel single-shot approach demonstrates a promising method to acquire thermal data for the purpose of mapping TWD of beech trees on an individual basis. Further testing and development are imperative, and additional data from drought periods, point dendrometers, and high-resolution meteorological sources are required.

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Canopy temperatures of selected tree species growing in the forest and outside the forest using aerial thermal infrared (3.6–4.9 µm) data

Cited by 9 publications

References 56 publications

Cooling Potential of Urban Tree Species during Extreme Heat and Drought: A Thermal Remote Sensing Assessment

Cooling Potential of Urban Tree Species during Extreme Heat and Drought: A Thermal Remote Sensing Assessment

Detection of Planting Systems in Olive Groves Based on Open-Source, High-Resolution Images and Convolutional Neural Networks

Mapping Tree Water Deficit with UAV Thermal Imaging and Meteorological Data

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