# Using airborne LiDAR to map forest microclimate temperature buffering or amplification

Eva Gril, Marianne Laslier, Emilie Gallet-Moron, Sylvie Durrieu, Fabien Spicher, Vincent Le Roux, Boris Brasseur, Stef Haesen, Koenraad van Meerbeek, Guillaume Decocq, et al.

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## To cite this version:

Eva Gril, Marianne Laslier, Emilie Gallet-Moron, Sylvie Durrieu, Fabien Spicher, et al. Using airborne LiDAR to map forest microclimate temperature buffering or amplification. Remote Sensing of Environment, 2023, 298 (1), pp.113820. 10.1016/j.rse.2023.113820. hal-04556088v2

HAL Id: hal-04556088

https://hal.science/hal-04556088v2

Submitted on 19 Jan 2025

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1 Using airborne LiDAR to map forest

2 microclimate temperature buffering or

3 amplification

4 Eva Gril (a*), Marianne Laslier (a), Emilie Gallet-Moron (a), Sylvie Durrieu (b), Fabien Spicher

5 (a), Vincent Le Roux (a), Boris Brasseur (a), Stef Haesen (c), Koenraad Van Meerbeek (c),

6 Guillaume Decocq (a), Ronan Marrec (a), Jonathan Lenoir (a)

7 (a) UMR CNRS 7058 "Ecologie et Dynamique des Systèmes Anthropisés" (EDYSAN),

8 Université de Picardie Jules Verne, 1 rue des Louvels, 80000 Amiens, France

9 (b) UMR Territoires, Environnement, Télédétection et Information Spatiale (TETIS), INRAE,

10 AgroParisTech, CIRAD, CNRS, Univ Montpellier, 500 Rue Jean-François Breton 34196

11 Montpellier, France

12 (c) Department of Earth and Environmental Sciences, KU Leuven, Leuven, Belgium

13 *Corresponding author: gril.eva@free.fr; eva.gril@u-picardie.fr

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## Highlights

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* Airborne LiDAR is ideal to predict forest thermal environment at a high resolution

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* We used Maximum height, Plant Area & Vertical Complexity Index as structure metrics

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* Forest structure explained 91 % of microclimate variability at a 10-m resolution

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* We mapped the buffering or amplification effect of forest on microclimate temperature

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* Ecologists, conservationists and forest managers would highly benefit from such maps

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## Abstract

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Mapping the microclimate effect of forest canopies on understory temperature requires spatially explicit predictors at very fine spatial resolutions. Light Detection And Ranging (LiDAR) offers promising prospects in that regard, as it allows capturing the vertical dimension of vegetation structure at a very high resolution over large areas.

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To explore the potential of airborne LiDAR-derived metrics to predict understory temperature, we focused on the forest of Blois (France), a 2740-ha lowland managed forest dominated by oak (*Quercus petraea*). We installed HOBO sensors measuring microclimate air temperature at one-metre height in 53 stands of contrasting vegetation structure, from open to very dense and from young regeneration to mature stages. Using a nearby weather station as the macroclimate temperature reference, we calculated the slope (log scale)

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coefficient of the linear regression between microclimate and macroclimate, as a simple
parameter describing the microclimatic buffering (log(slope) < 0) or amplification (log(slope)
> 0) capacity of the habitat.

An airborne LiDAR flight was conducted during summer 2021, matching the timing of our
temperature measurements. From the resulting 3D point cloud, three complementary
metrics of forest structure were derived: the maximum height, the Plant Area Index and the
Vertical Complexity Index. They were calculated for circular buffers of different radii (1 m to
100 m) centred on each HOBO sensor.

We found that the 5-m radius combining the three metrics into a single multivariate model
explained the greatest proportion of variance in the microclimate effect of each stand (R² =
0.91). We mapped the buffering or amplification effect of vegetation structure on
understory temperatures over the entire forest of Blois at a 10-m resolution. 91.4 % of the
surface of the forest was significantly buffered relative to macroclimate temperature, while
2.7 % was amplified, especially in road verges, clear-cut and regeneration areas. Based on
our simple linear model, we were able to derive understory air temperature maps for any
temporal resolution (i.e., hourly, daily, or seasonal). The results highlight the great capacity
of airborne LiDAR to retrieve forest structure parameters and generate high-resolution maps
of the thermal environment.

Applications for mapping the buffering or amplification of microclimate temperature are
plentiful, especially in the context of climate change. They include improving the
understanding of physiological processes such as thermoregulation or phenology, modelling
the thermal connectivity of the landscape, improving species redistribution models, spotting
microrefugia for conservation, informing forest management for tree regeneration or
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wildfire control, or even prioritising cooling recreational areas for humans to escape

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heatwaves in urban forests.

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## Graphical abstract

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From three LiDAR metrics describing **forest structure**:

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And **temperature measurements** from one weather station + 53 microclimate sensors...

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... To a map of the **buffering** or **amplifying** effect of forest cover on temperature!

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## Key-Words

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amplification; buffering; forest structure; forest management; airborne LiDAR; ALS; canopy

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height; leaf area index; microclimate; open areas; plant area index; regeneration; resolution;

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slope and equilibrium; temperature extremes; understory; urban forest; vegetation layers;

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vertical complexity index; weather station

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72 **1. Introduction**

73 To investigate current and future impacts of temperature on species' fitness, survival or
74 distribution, ecologists often rely on available gridded macroclimate data, with a coarse
75 spatial resolution of 1 km² or more. This contrasts with an organism-relevant scale, where
76 depending on their body size and physiology, species respond to variations in microclimatic
77 temperatures at a resolution of centimetres to a few metres (Bramer et al., 2018). Another
78 major issue is that macroclimate data is interpolated from weather stations, which are
79 always located in open areas, exposed to sunshine and dominant winds and away from the
80 shade of trees for the sake of standardisation (De Frenne and Verheyen, 2016). Below forest
canopies, the difference between macroclimate and the local microclimate temperature is
81 especially significant (Geiger et al., 2003). In the understory, species usually experience a
82 buffered microclimate, with higher minimum and lower maximum air temperatures, as
83 vegetation moderates extreme temperatures through mechanisms such as transpiration and
84 shading (De Frenne et al., 2021). However, temperatures can instead be amplified near the
85 ground in open or sparse vegetation (Gril et al., 2023; Pincebourde and Suppo, 2016; von Arx
86 et al., 2013). This is especially true when airflow is reduced by trees, causing a reduction in
87 thermal mixing alongside typical absorption of radiation, which may not be fully
88 compensated by transpiration when vegetation is sparse, e.g. within an intraforest clearing,
89 meadows, or under very low or sparse canopies, where lower minima and higher maxima
90 are usually recorded for temperature compared to free-air weather station records.
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92 These buffering or amplification effects can be highly heterogeneous, depending on local
93 parameters such as vegetation structure. In managed forests under regular forestry, this
94 results in diverse conditions of microclimate across small areas (Chen et al., 1999). To
95 effectively quantify this fine-scale thermal heterogeneity, we need spatially explicit models
96 of temperature at fine spatial resolutions and across large spatial extents (Milling et al.,
97 2018; Zellweger et al., 2019b).

98 Recent efforts have been made to obtain and gather global microclimatic data, with the goal
99 to downscale temperature grids and get closer to conditions experienced by most terrestrial
100 organisms (Lembrechts et al., 2020). Field-acquired dendrometric data from forest
101 inventories are good predictors of the microclimate effect in forests (Zellweger et al., 2019a),
102 but they are measured pointwise, and are thus inadequate for continuous mapping over a
103 whole landscape. Using remote sensing data to upscale field measurements could help in
104 tackling this challenge. Well-chosen remote sensing-based predictors may also have an
105 equal, or even higher capacity than traditional field-measured variables to explain the
106 variability in microclimate air temperature (Greiser et al., 2018; Kašpar et al., 2021). For
107 instance, predictors acquired by satellites such as digital elevation models or the Normalised
108 Difference Vegetation Index (NDVI) have wide coverage and have been used to generate
109 maps of understory temperature over Europe at a 25-m resolution (Haesen et al., 2021).

110 The use of detailed environmental descriptors may be relevant – if not necessary – for land
111 use planning, biodiversity conservation, better informed species distribution models, or for
112 informing forestry management (Lenoir et al., 2017; Haesen et al., 2023; Randin et al., 2020;
113 Zellweger et al., 2019b). Airborne thermal imaging brings interesting perspectives in that
114 regard (Hoffrén and García, 2023), but it can only record canopy temperature, not
115 understory temperatures near the ground – where most of the biodiversity lives in
116 temperate forests (Gilliam, 2007). While the vertical structure of the vegetation is not well
117 captured by satellite or airborne multispectral imagery, airborne Light Detection And
118 Ranging (LiDAR) allows the user to access the whole vertical structure hidden below the tree
crown, including the understory shrub layer in forests, an important microclimate driver
119 often neglected in microclimate modelling (Kovács et al., 2017; Stickley and Fraterrigo,
120 2021). LiDAR is the perfect tool to describe the fine-scale 3-dimensional (3D) structure of a
121 forest and has been used for this purpose for decades (Davis et al., 2019; Lim et al., 2003;
122 Nelson et al., 1984; Moudrý et al., 2022; Zellweger et al., 2019b). LiDAR is thus most
123 appropriate to map microclimate processes under tree crowns (Frey et al., 2016; George et
124 al., 2015; Greiser et al., 2018; Jucker et al., 2018).
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126 From airborne LiDAR point clouds, we can quantify three components of vegetation
127 structure, especially relevant in forest ecosystems: height, plant area index and vertical
128 complexity. Tall, structurally complex forests with a large amount of vegetation are expected
129 to be the most thermally buffered relative to macroclimate (Frey et al., 2016; Jucker et al.,
130 2018). After creating a canopy height model from top vegetation points extracted from the
131 3D point cloud, maximum height can easily be mapped (Jucker et al., 2018; van Leeuwen and
132 Nieuwenhuis, 2010).

133 Leaf Area Density profiles – or, more accurately, Plant Area Density profiles, given that it is
difficult to separate leaves from other plant material – can be assessed from LiDAR point
134 clouds (Bouvier et al., 2015). From these profiles, Plant Area Index (PAI) can be computed
135 (Lenoir et al., 2022). PAI represents the cumulative area of biotic tissue per unit ground area
136 and is related to Leaf Area Index (LAI), a key parameter for modelling exchanges (water,
137 carbon and radiation and heat energy) between the biosphere and the atmosphere (Yang et
138 al., 2019) which is acknowledged to have strong influence on understorey microclimate (De
139 Frenne et al., 2016).
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141 Vegetation structural complexity is the third important morphological component of an
142 ecosystem (Valbuena et al., 2020), and can be summarised by the Vertical Complexity Index
143 (VCI), which quantifies the "evenness" of plant material distribution, i.e., whether vegetation
144 is clustered into layers – VCI close to 0 – or distributed evenly – VCI close to 1 – along the
145 vertical profile (van Ewijk et al., 2011).

146 Here, we propose a method to map microclimate temperature in forest ecosystems, based
147 on simple linear models and three integrative predictors of forest structure derived from
148 airborne LiDAR. We focus on a managed forest with little to no variation in topography and
149 with a homogenous composition in terms of the dominant tree species, but very diverse
150 stand structures.

151 Our goal was to quantify the buffering or amplification effect of the forest matrix on
152 understory air temperature, and to find the spatial scale at which these opposing processes
153 operate. Instead of mapping microclimate temperature directly on the relevant time step
154 (e.g. hourly, weekly or monthly), we used a novel approach to map a summary metric
155 representing the impact on the microclimate generated by vegetation structure (i.e., the
156 buffering or amplification capacity, Fig. 1; Gril et al., 2023). From this metric, any point-in-
157 time or summary statistic of microclimate temperature can easily be calculated. We
158 investigated the best scale of effect of our three LiDAR predictors, and mapped the fine-scale
159 thermal environment of the forest at this optimal resolution.

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161 2. Material and methods

162 2.1. Study area and sampling design

163 The forest of Blois is a 2,749-ha block of forest, with little modification of its geographical
delimitation since the fifteenth century, being part of the royal, then state's domain. It is a
164 lowland temperate forest dominated by *Quercus petraea* (sessile oak), with *Carpinus betulus*
165 (hornbeam), *Ilex aquifolium* (holly) and *Fagus sylvatica* (beech) in the understory. The forest
166 is managed by the French national forest office (ONF) as a regular "high forest". It is
167 subjected to a degraded oceanic climate (Joly et al., 2010), with a mean annual temperature
168 of 11.3°C (absolute minimum - 12°C, absolute maximum 40.1°C) and annual precipitation of
169 744 mm over the 1996-2004 period (ONF data). The elevation ranges from 94 to 141 m, with
170 limited topographic variation. Soils are poor and acidic. To study a gradient of forest
171 structure, we established 53 plots throughout the forest (Fig. 2) in diverse stages of forest
172 management: seed trees, thicket, saplings, poles, young, adult, or mature stands.
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175 2.2. Data acquisition

176 2.2.1. Microclimate and macroclimate temperature measurements

177 We measured hourly microclimate temperatures with commonly used sensors (Bramer et
178 al., 2018), Onset® HOBO® Pendant data loggers UA-001-64, which have a manufacturer-
179 reported accuracy of ± 0.53°C from 0° to 50°C, and resolution of 0.14°C at 25°C. One sensor
180 was placed at the centre of each plot, and localized with a Trimble Geo 7X Differential Global
181 Positioning System, with a precision of 50 centimetres in open areas but lower (up to about 182 2 metres) under a dense canopy. Sensors were set up at 1-m height above the ground 183 surface on the northside of a tree trunk (George et al., 2015). To prevent overheating due to 184 the exposure to direct solar radiation, sensors were hung by a hook inside homemade white 185 shields shaped like caps to ensure that there is sufficient shade and airflow at the sensor 186 level (Gril et al., 2023; Zellweger et al., 2019a), made out of a 15-cm long, 10-cm wide PVC 187 tube (See Fig. S0). Hourly macroclimate temperatures were recorded from a weather station 188 located in a grassland outside but close to the forest (Fig. 2). The macroclimate sensor was 189 shielded and placed at 1.5-m high, a common height for standardised weather stations, 190 typically within 1.2 to 2 m above ground. Because of the deciduous canopy and because it is 191 the most crucial period for plants and many organisms, we focused on the leaf-on period, 192 from the beginning of June 2021 to the end of September 2021, representing a total of 193 155,018 hourly microclimate temperature measurements across our 53 plots.

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195 2.2.2. Airborne LiDAR data

196 High-density LiDAR data were acquired by the private company ALTOA 197 (http://www.altoa.fr/) on the 17th of June 2021, which is also why we focused this study on 198 the leaf-on period. The LiDAR sensor used was a RIEGLVQ780-I mounted on a Partenavia P68 199 Observer Islander. Flight and sensor specifications are described in Table 1. Pre-processing 200 steps such as point cloud filtering, classification (vegetation, soil, other) and point 201 normalisation according to soil surface elevation were also performed by ALTOA. We 202 removed points located in artificial surfaces such as buildings according to the national layer
203 BD TOPO® (provided by the National Institute of Geographic and Forest Information, IGN

204 2021, v. 3_0), plus a 1-m buffer around these polygons.

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<table><caption>206 Table 1: Characteristics of the LiDAR sensor and flight</caption><tr><td>Flight Altitude</td><td>900 m</td></tr><tr><td>Wavelength</td><td>NIR</td></tr><tr><td>Pulse frequency</td><td>2000 kHz</td></tr><tr><td>Mean total point density</td><td>72.6 pts/m²</td></tr><tr><td>Mean ground points density</td><td>9.7 pts/m²</td></tr><tr><td>Overlap between flight lines</td><td>60 %</td></tr><tr><td>Scan angle</td><td>+/- 30°</td></tr><tr><td>Precision (Z)</td><td>4.5 cm</td></tr><tr><td>Geolocation error (XY)</td><td>&lt; 0.1 m</td></tr><tr><td>Diameter of the spot on the ground</td><td>16.7 cm</td></tr></table>

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208 2.3. Data processing

209 2.3.1. Computing the buffering or amplification capacity

210 Recent studies have introduced a new conceptual model linking microclimate temperature
211 to macroclimate temperature (De Frenne et al., 2021; Gril et al., 2023). The model, validated
212 in a wide range of temperate forest environments, uses a simple linear relationship and
213 requires only two parameters: the slope and the equilibrium. The slope of the linear
214 relationship between microclimate and macroclimate describes the buffering (< 1) or
215 amplification (> 1) capacity of the habitat (Fig. 1) and was found to be dependent on the
216 surrounding vegetation. With a log-transformation of the slope, a value of 0 means a neutral
217 effect of forest on temperature, while negative and positive values indicate buffering and
218 amplification, respectively. We used a log-transformation for two reasons: first, it is easier to
219 understand the meaning of the microclimate effect if the variable is centred on zero, where
220 zero means a neutral effect. And second, this allows for log-log relationships in the
221 subsequent modelling, in case a variable has a non-linear effect. The slope parameter or its
222 log-transformation can be modelled with the usual drivers of microclimate, including forest
223 structure metrics (Gril et al., 2023).

224 As for the equilibrium, it is the temperature at which microclimate equals macroclimate, i.e.
225 the point where the identity line (Y = X) crosses the regression line of the linear relationship
226 between microclimate and macroclimate (Fig. 1, Appendix 1). Unlike the y-intercept of a
227 regression, it has a biophysical meaning. During sunny days, large incoming radiative fluxes
228 exacerbate the difference between microclimate and macroclimate temperatures in forests.
229 On the opposite, in case of wet, cloudy days with low radiative fluxes, a mild temperature is
230 expected inside and outside the forest, leading to this situation of "equilibrium". Therefore,
231 the equilibrium can be approximated by the mean macroclimate temperature. Within a
232 given season and small region, like the forest of Blois, it can be considered constant and
233 independent from forest structure (Gril et al., 2023). We verified this independence by
234 calculating the correlation coefficients and R² of linear models relating equilibrium
235 temperatures to forest structure metrics (Appendix 2).

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With both the slope and the equilibrium, the intercept can be deduced (see Appendix 1 for a demonstration), and therefore we have the complete linear relationship between microclimate and macroclimate.

<figure><img src="image_7.png" /></figure>

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**Fig. 1.** The concept of the slope and equilibrium approach: two simple parameters summarising the linear relationship between microclimate and macroclimate. The slope of the linear regression describes how microclimate temperature is either buffered (e.g., under well-closed forest cover) or amplified (e.g., above the grass in an open meadow), relative to macroclimate temperature, as measured by a standardised weather station. The slope will be lower than one if temperature is buffered, and higher than one if temperature is amplified in the habitat of interest, and therefore the log-transformation of the slope parameter will respectively be lower or higher than zero. The equilibrium is the temperature for which the regression line crosses the identity line, where microclimate equals macroclimate. It is considered constant in a given site and season. For instance, if the forest
and grassland depicted here are close to one another and have the same macroclimate, then
the equilibrium temperature should be considered relatively similar in both habitats.

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We used the method described by Gril et al. (2023) to calculate the slope and equilibrium.

We fitted a separate linear model for each of the 53 sensors, linking hourly temperature
records during the leaf-on period from a given HOBO sensor to the matching hourly
temperatures measured by the nearby weather station, representing macroclimate. We
visually checked linearity, and computed the R squared of the regressions. In order to verify
that the relationship between microclimate and macroclimate was indeed linear in this
study, we compared the fit of linear models to the fit of non-linear generalized additive
models (GAMs). The slope and equilibrium parameters were extracted from each linear
regression, using the following equation for the latter: equilibrium = intercept / (1 - slope).

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Finally, we used the log of the slope as our response variable in subsequent analyses.

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All analyses were performed in R version 4.1.2 (2021). We used the {tidverse} and
{lubridate} packages (Grolemund and Wickham, 2011; Wickham et al., 2019) to handle data
preparation and visualisation, and {mgcv} to fit GAMs.

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2.3.2. Forest structure predictors based on LiDAR data

On each of the 53 plots, we used the {lidR} package (Roussel et al., 2020) to retrieve
structural metrics in different buffer sizes around the location of our microclimate sensors,
ranging from a 1-m to a 100-m radius. Forest structure metrics may indeed differ in the
optimal resolution used to infer them from LiDAR data (Atkins et al., 2023). We calculated

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274 three metrics from these 3D point clouds: maximum height, Plant Area index (PAI) and
275 Vertical Complexity Index (VCI). Note that the {lidR} package has predefined functions to
276 compute the maximum height and the VCI, whereas users have to sum Plant Area Density
277 (PAD) values along the vertical profile (using the LAD function) post hoc to acquire PAI. These
278 three metrics give complementary information on the structure of the forest: the height,
279 density, and distribution of vegetation along the vertical profile.

280 The maximum height was calculated as the highest vegetation point in a given buffer area,
281 and was log-transformed to account for non-linear effects (Jucker et al., 2018). Hereafter we
282 refer to this predictor variable as log(Hmax). Canopy height is well captured by airborne
283 LiDAR, although the combination of a thick understory layer and low point density may lead
284 to an underestimation of the height due to the understory mistakenly being considered as
285 ground. Here, this issue is minimised since we had a very high point density (70 pts/m² on
286 average) so that the mean density of points reaching the ground surface was approximately
287 10 points per square metre, even though the flight was during leaf-on conditions (Table 1).

288 The PAI is an estimation of the total plant area (leaves and woody structures) per unit
289 ground surface area participating in light occlusion, and is based on the Beer-Lambert Law
290 considering that light pulses will be attenuated through canopy cover (Vincent et al., 2015;
291 Lenoir et al., 2022). It represents the integral, or area under the curve of the Plant Area
292 Density (PAD) profiles, and is calculated as the sum of PAD in each vertical "vegetation slice".
293 Here, 1-m slices were used starting from 1.5 m height above the ground up to a maximum of
294 40.5 m for the highest slice that contained the maximum canopy height observed across the
295 state forest of Blois.
Finally, the VCI quantifies the vertical layering of the vegetation (van Ewijk et al., 2011). It is a fixed normalisation of the Shannon Index, bounded between 0 and 1, with values close to 0 indicating a very uneven distribution of vegetation points (i.e., most vegetation points are located at the same height, within a single layer), while values close to 1 indicate a very even distribution of vegetation points throughout the profile (i.e., most height bins have an equal amount of vegetation, suggesting many overlapping layers).

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<figure><img src="image_8.png" /></figure>

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<caption>**Fig. 2.** Canopy height (at 50-cm resolution) over our study area, the forest of Blois (France). The dots depict the location of our study plots, with temperature sensors measuring air temperature. We show three contrasting examples of LiDAR point clouds, with their</caption>
307 associated plant area density (PAD) profile for a 20-m radius around the central plot. The
308 colour scale on LiDAR profiles corresponds to canopy height as well.

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310 2.4. Modelling the buffering or amplification impact of forest on
311 microclimate

312 Using linear models, we related the log of the slope (extracted for each of our 53 focal
313 sensors) to our three LiDAR-derived metrics. We compared the explanatory power of the
314 three LiDAR-derived metrics, separately and jointly in each buffer size, by looking at the
315 model r squared (R²) (Kašpar et al., 2021).

316 For the multiple regression models, we checked for multicollinearity issues among our three
317 predictors using the Variance Inflation Factor (VIF), which remained below the advisable
318 threshold of 5 (Sheather, 2009) for each of the three variables. Hence, we kept all three
319 LiDAR-derived variables as predictors in our multiple regression models, since the estimated
320 coefficient values are still interpretable and trustworthy. Then, we calculated an
321 independent R², root-mean-square error (RMSE) and mean absolute error (MAE) of the best
322 model using a leave-one-out cross-validation, by rerunning the model iteratively with only 52
323 plots for calibration and the remaining plot for validation. Finally, we checked for a potential
324 spatial autocorrelation signal in the residuals of the best model.

325 To print model coefficients, we used *tab_model* from the *sjPlot* package (Lüdecke, 2021),
326 and to plot model predictions, *ggpredict* from the *ggeffects* package (Lüdecke, 2018). We
327 used the *caret* package for the leave-one-out procedure (Kuhn, 2008). The correlogram of
328 model residuals was built with the *ncf* package (Bjornstad and Cai, 2022).
## 2.5. Spatial predictions

### 2.5.1. Mapping the temperature buffering or amplification effect

### 2.5.2. Predicting and mapping point-in-time or mean microclimate temperature

To predict forest microclimate, we first calculated the three LiDAR-derived metrics over the whole forest of Blois, setting the spatial resolution of each raster map to the size of the buffer for which our model performed the best. Then, we used the estimated coefficients of our linear model to predict microclimate buffering or amplification (the log of the slope) in each pixel of the forest. Additionally, we mapped the standard error of predicted means over the entire forest in order to spot areas of higher uncertainty of our model, using the *predict* function as well. The microclimate of individual pixels was considered coupled to macroclimate temperature if the log of the slope was equal to 0 +/- the maximum error of the model, buffered if lower and amplified if higher. We used the R package {terra} and its dependencies to handle and represent spatial data (Hijmans et al., 2023).

Since the equilibrium parameter can be considered stable in a given macroclimate and season, we used the median of all 53 calculated equilibria over the leaf-on season (17.47°C) as the single equilibrium value for all predictions (Fig. S1, S2). For a given moment in time, the slope parameter (back-transformed, i.e., exp(log(slope))) is thus the only input variable that varies in space in the following equation: $T_{micro} = slope \times T_{macro} + equilibrium \times (1 - slope)$. From the magnitude of this buffering or amplification effect, we predicted all hourly
microclimate temperature over the whole period on each of our 53 plots, from hourly
macroclimate temperature recorded by the weather station. We tested the quality of these
predictions of understory air temperature against the actual and matching hourly
measurements made by the 53 sensors, by plotting predicted versus observed hourly
temperatures. We computed the R², RMSE and MAE value of this observed versus predicted
relationship.

Once the slope values are mapped over the entire forest, we can derive a map of subcanopy
air temperature at any time (e.g., a given hour of a given day) if we know the corresponding
macroclimate air temperature for that same time period (George et al., 2015). For illustrative
purposes, we focused on the warmest day (14 August 2021) at the hour of maximum (15:00)
and minimum (05:00) temperature that were recorded by the neighbouring weather station.
We also mapped the mean of two daily statistics (daily minimum and maximum
temperatures) over the whole June-September period. We assumed that no significant
change in forest structure occurred during the period, as no extreme event such as fire or
major timber harvesting was recorded. Daily minimum and maximum temperatures were
extracted from the nearby weather station, used as the macroclimate temperature in our
linear models (see equation above), and the mean statistics were plotted. We also calculated
the resulting daily offsets to macroclimate (T<sub>micro</sub> - T<sub>macro</sub>).

Finally, we tested the assumption that the equilibrium can be considered constant over the
whole study area by generating random equilibrium values following a normal distribution
for each plot, using the mean and standard deviation of the observed equilibria. Using these
random equilibrium values resulted in very similar, although slightly less well fitted
temperature predictions (Appendix 4). We hence determined that using a constant
373 equilibrium fixed at the median introduced minimal error to temperature predictions, while

374 vastly enhancing the ease and repeatability of this methodology.

375

376 3. Results

377 3.1 Computing slope and equilibrium from hourly temperatures

378 Temperatures recorded from June to September 2021 by the nearby weather station ranged

379 from 4.1°C to 31.8°C, while it ranged from -1.1°C to 40.8°C across our 53 microclimatic

380 sensors. The total range of air temperatures recorded simultaneously across our 53 sensors

381 (i.e. difference between warmest and coolest temperature recorded by any sensor at a given

382 time) averaged 5.5°C, with a maximum range of 18.2°C.

383 The sensor-specific linear regressions between macroclimate temperature and microclimate

384 temperature to extract the slope and equilibrium parameters had very good fits (median R² =

385 93 %, ranging from 80.3 to 96 %; Fig. S1). We also compared these R² from linear regressions

386 and the equivalent metric (the proportion of deviance explained) from non-linear GAMs. The

387 fits were slightly better for GAMs, but overall very similar (mean difference = 0.2 %,

388 maximum difference = 1 %), validating the linearity of the relationship between

389 macroclimate and microclimate (Appendix 2).

390 The equilibrium was not exactly constant, ranging from 8.7 to 22.2°C around its median at

391 17.5°C (except for one outlier at -102.5°C, resulting from a plot with a temperature highly

392 coupled to macroclimate, i.e. a slope very close to 1, here 0.993). However, the equilibrium

393 had a narrow quasi-normal distribution, with a standard deviation of 2.4°C around the mean
394 of 17.7°C without the outlier mentioned above (Fig. S1). As expected, the mean and median
395 values of the equilibrium were close to the mean macroclimate value over the period
396 (18.19°C). The log of the slope — of the regression between microclimate and macroclimate
397 — ranged from -0.38 to +0.36 (Fig. S1).

398

399
400
# 3.2. Identifying the optimal resolution to explain microclimate buffering or amplification

401 Forest stand structure explained a large proportion of the observed variation in the log of
402 the slope parameter, but with great disparities depending on the LiDAR-derived metric used
403 as a predictor and the spatial resolution chosen to derive it (Fig. 3). The variable log(Hmax)
404 had a very high predictive power (R² of 76 % to 80 %) from 1 m to 10 m-radius around the
405 sensor location. The variables PAI and VCI both had lower explanatory power below a 5 m
406 radius. Remarkably, while the explanatory power of log(Hmax) and VCI dropped after a 10
407 m-radius, PAI maintained roughly the same predictive power from 5 m to 70 m radius, even
408 peaking at 20 m radius (Fig. 3). When the three predictors were used simultaneously in a
409 single model, the model explaining most of the observed variation in the log of the slope was
410 the one with LiDAR-derived predictors calculated for a 5 m radius (Table S1; R² = 91 %), albeit
411 we note that from 3 m to 10 m, the R² remained very similar.
<figure><img src="image_9.png" /><figcaption>**Fig. 3.** Proportion of the observed variation in the log of the slope parameter (extracted from the linear relationship between microclimate and macroclimate temperatures) explained by each of the three LiDAR-derived predictors, separately (i.e. using simple linear regressions) and jointly (i.e. using a multiple linear regression). The proportion of explained variance was computed separately for each buffer radius: every metre from 1 to 10 m and then every 10 m from 10 to 100 m. The best radius (5 m, R² = 91 %) is highlighted with an arrow.</figcaption></figure>

Irrespective of the spatial resolution (radius size) used, the three metrics had a negative effect on the log of the slope; taller trees and stands with high PAI and VCI values were more
422 buffered. The PAI and VCI variables had linear effects, while the maximum height had a non-linear effect after back transformation (Fig. 4).
423

<figure><img src="image_10.png" /></figure>

424 **Fig. 4.** Effect of LiDAR-derived variables related to stand structure on the log of the slope between microclimate and macroclimate temperatures. All LiDAR-derived variables depicted in this figure were computed for a 5 m radius buffer. Only univariate model predictions are illustrated here (see Fig. S5 for the predictions of the multivariate model). The log-transformation of the maximum height allowed us to linearize the relationship, and to use a simple linear model to predict microclimate effect. Here, the prediction is depicted with a back transformation – $exp(log(Hmax))$ – to show directly the effect of maximum height on the log of the slope parameter.

433
434 The R² of the model was 89 %, the RMSE was 0.059 and the MAE was 0.046 after a leave-one-out cross validation procedure (Fig. 5). Residuals of the models showed no spatial autocorrelation (Fig. S7).

437
<figure><img src="image_11.png" /><figcaption>**Fig. 5.** Observed versus predicted log of the slope, from the best model with all three LiDAR-derived metrics at a 5-m buffer size, after a leave-one-out cross validation procedure. The colour gradient corresponds to the maximum height calculated around each plot with a 5-m buffer.</figcaption></figure>

## 3.2. Mapping the buffering or amplification effect

Although the vast majority of the forest understory (91.4 % of the 275,500 10-m pixels covering the forest) had a buffered temperature compared to the standardised weather station (green colours on Fig. 6a), significant parts (5.9 %) of the forest had a coupled temperature that did not deviate significantly from the macroclimate (beige colours in Fig. 6a). Areas with low or sparse canopies (2.7 %) even showed the reverse trend, an
450 amplification of temperature with more extreme temperatures than recorded by the weather station (yellow colour in Fig. 6a).

452 Some areas showed more uncertainty in temperature predictions (darker red colours in Fig. 6b), typically in situations where the model had to extrapolate beyond the conditions observed in the calibration range. We indeed have a larger range of predicted log of the slope (-0.59 to +0.56) compared to the range of observed log of the slope (-0.38 to +0.36).

456 This extrapolation concerned either stands with taller trees or denser and more complex vegetation layering or, alternatively, areas with very low, sparse and single layering of vegetation cover, such as road verges. The median standard error across the whole forest was 0.012, and the maximum 0.069 (Fig. 6, S6).

460

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

461
Fig. 6. Map of understory air temperature buffering or amplification over the forest of Blois
(a), green for buffering, yellow for amplification, along with a map of the uncertainty of the
predictions (b) by mapping the standard error – darker red colours suggest a higher
uncertainty in predictions.

466

467

3.3. Predicting understory temperatures

Predicted understory temperatures (calculated from the predicted slope and median
equilibrium) were indeed related to the matching observed temperatures of our 53 plots (R²
= 91 %, Fig. 7). The RMSE was 1.2°C, and MAE was 0.9°C. Plots with amplified temperature
had the greatest errors (Fig. 7, S10). If we separate plots were microclimate temperature is
buffered (N = 45) from those where it is amplified (N = 7), model performance metrics
showed an error in predicted hourly temperatures almost twice as large for amplified plot
(R² = 87.9 %, RMSE = 1.95°C, MAE = 1.44°C, Fig. S10) compared to buffered plots (R² = 92.3
%, RMSE = 1.06°C, MAE = 0.79°C).

476
<figure><img src="image_14.png" /><figcaption>**Fig. 7.** Predicted versus observed hourly temperatures (random subset of n = 10,000 for each), separately for plots with a buffered temperature (log of the slope < 0, N = 45 plots, left) and with an amplified temperature (log of the slope > 0, N = 7 plots, right). Note that no plots have an exactly coupled temperature (log of the slope = 0). The colour gradient is the same as Fig. 6: green for buffered, and yellow for amplified plots. The model R², RMSE and MAE were calculated separately. For a similar figure with all plots at once, see Fig. S10.</figcaption></figure>

Finally, as an illustration, we predicted the temperature on the 14th of August across the entire forest, focusing on the coolest ($T_{macro} = 14.3°C$) and warmest ($T_{macro} = 31.8°C$) hours of that day (5:00 and 15:00, respectively), according to the nearby weather station. At 05:00, the total range of predicted microclimate temperature was 3.8°C – from 11.9 to 15.7°C). At 15:00, the range was 17.2°C – from 25.4 to 42.6°C (Fig. 8). We also predicted the mean of daily statistics over the studied period across the forest: mean minimum temperature
491 ranged from 8.4 to 14.6°C and mean maximum temperature from 21.4 to 29.9°C (Fig. S8).

492 Daily offsets (microclimate minus macroclimate) of maximum temperatures from all days

493 from June to September ranged from -5°C to +13.2°C, and for minimum temperature from -

494 6.4°C to +4.5°C (Fig. S9).

495

<figure><img src="image_15.png" /></figure>

496

497 **Fig. 8.** Predicted air temperature at 1-metre high for (a) the warmest ($T_{macro} = 31.8°C$) and (b)

498 the coldest ($T_{macro} = 14.3°C$) time of the warmest day, according to the nearby weather

499 station, during summer 2021 (14th of August, 15:00 and 05:00 respectively) in the forest of

500 Blois. The spatial resolution of the predictions is 10 m. Note that the colour scale

501 corresponds to different ranges of temperature in (a) and (b). See Fig. S8 for similar maps,
but instead of point-in-time temperature, two statistics of understory air temperature: the mean of daily maxima and the mean of daily minima during summer 2021.

## 4. Discussion

### 4.1. Using LiDAR to relate forest structure to temperature buffering or amplification

We showed that three metrics derived from airborne LiDAR that describe forest structure – maximum canopy height, plant area index (PAI), and vertical complexity index (VCI) – can be sufficient to map understory air temperature. Errors increased under very open vegetation cover, similarly to former findings from George et al. (2015), with errors in predicted temperatures doubling (from 1 to 2°C) in amplified habitats compared to buffered ones.

However, our rather simple linear model with LiDAR predictors had a very good explanatory power on microclimate ($R^2$ = 91 %), resulting in good performance as well in hourly temperature predictions, validated against observations ($R^2$ = 91 % over all plots, 89 % in amplified plots, 92 % in buffered plots).

The effects of our three predictors are consistent with what has been reported so far in the scientific literature. First, maximum height is known to drive microclimate temperature (Frey et al., 2016; George et al., 2015; Kašpar et al., 2021). Here, using the log transformation, we confirm a non-linear effect of vegetation height on the magnitude of the buffering effect: an increase in the first metres of height has a greater impact on microclimate, similarly to former work by Jucker et al. (2018). Second, leaf or plant area index (LAI or PAI) are among
the best predictors of microclimate temperature beneath trees canopies (Hardwick et al., 2015; Jucker et al., 2018). Note that whenever the term LAI is employed, it often actually is since discriminating leaves from trunks and branches from airborne LiDAR point clouds or other methods remains challenging. One of the main advantages of PAI is that it integrates not only the top canopy, but also the vegetation layering underneath (Lenoir et al., 2022), information that is lacking when considering canopy structure from 2D optical imagery or photogrammetric products (Marsh et al., 2022). The sub-canopy and understory layers are indeed significant drivers of microclimate (Kovács et al., 2017; Stickley and Fraterrigo, 2021). Third, VCI complements this information, also accounting for vegetation layering underneath the upper canopy, but rather focusing on the evenness of its vertical distribution. Even though VCI was used to describe forest succession (van Ewijk et al., 2011), to our knowledge, it has not yet been used as a microclimate predictor. We argue for its use as a major metric quantifying the vertical complexity in future endeavours to model microclimate.

A taller forest stand with massive aboveground and well-distributed foliage can be compared to a thicker, denser, and multi-layered insulating material, resulting in an enhanced buffering capacity (De Frenne et al., 2021; Frey et al., 2016; Jucker et al., 2018). On the other hand, our study shows that a forest with a low canopy and reduced PAI and VCI has the opposite effect on temperature. Microclimate amplification is a known phenomenon (Woods et al., 2021), but it is much less often considered than buffering, for which we hypothesise two main reasons. First, open areas are often discarded because low cost microclimate sensors, even if shielded, are less reliable in exposed conditions and prone to overheating (Maclean et al., 2021; Terando et al., 2017). Nevertheless, organisms are also subjected to overheating if they stand in full sun, and microclimate temperature sensors placed directly near the habitat
547 of interest could be more ecologically relevant than standardised, unbiased weather stations
548 (Ashcroft, 2018). The warmer or cooler temperatures we recorded in open areas are thus
549 likely to be relevant from a living organism perspective, even if prone to higher
550 measurement uncertainties – and even if living organism are likely to have different thermal
551 properties than temperature sensors. Second, open areas are often used as references,
552 proxies for macroclimate, and are not the focus of most forest microclimate studies.
553 However, open or sparsely vegetated areas are very common either in managed
554 (regeneration stages, roads, intraforest clearings) or natural (canopy gaps, savannah, rock
555 boulders) forested landscapes (Kovács et al., 2017; Pincebourde and Suppo, 2016; Tymen et
556 al., 2017) and deserve to be studied as components of forest environments. Moreover, close
557 to edges or under a certain threshold of PAI, even forested areas can show patterns of
558 temperature amplification (von Arx et al., 2013; Williamson et al., 2021). Beyond forests,
559 open habitats such as crops, peatlands or deserts have a temperature that may deviate
560 substantially from weather station temperature and exhibit strong spatial heterogeneity in
561 microclimates as well. We therefore recommend a generalised approach, integrating both
562 buffered and amplified microclimates in future investigations.

563

## 4.2. Choosing the best scale to assess forest structure and microclimate

566 Our three LiDAR-derived variables that describe forest structure all had a very high
567 explanatory power on the slope parameter when they were calculated in radii from 5 to 10
568 m ($R^2$ > 76 %), which is smaller than the representative spatial grain for similar forest
569 structure metrics (25 to 75 m resolution) determined by Atkins et al. (2023). Beyond a 10 m
570 radius, the predictive power of log(Hmax) and VCI abruptly dropped, while PAI remained
571 highly informative up to a 70 m radius. Contrary to PAI and VCI, log(Hmax) was a good
572 predictor at very fine spatial resolutions, even as low as a 1 m radius. Smith-Tripp et al.
573 (2022) used photogrammetry to calculate canopy height, and found that the best explaining
574 scale on microclimate temperature was 15 m, a slightly larger scale than the one we
575 identified, but still close to our results. Kašpar et al. (2021) and Frey et al. (2016) found
576 coarser resolutions as the most informative, but the latter only tested a resolution of 25 and
577 250 metres. The optimal resolution may vary according to the stand type, and be point-
578 density or -intensity dependent, in relation with the height of flight and LiDAR sensor
579 properties like pulse frequency. For instance, in dense tropical rainforests, comparable
580 results could be obtained only with very high-density LiDAR data. Therefore, results may not
581 be strictly comparable across studies. As such, the scale of effect of metrics should always be
582 tested when considering new airborne LiDAR datasets (Atkins et al., 2023).

583 Another potential issue is the accuracy of their Global Navigation Satellite System (GNSS),
584 which can be much lower than specified by manufacturers under forest cover (Naesset and
585 Jonmeister, 2002). When choosing very small spatial resolutions (1 to 3 m buffer) for LiDAR-
586 derived metrics, the microclimate temperature sensor may in fact, due to the poor accuracy
587 of GPS coordinates in forests, be located outside the area under consideration (Atkins et al.,
588 2023). We advise not to choose a scale that is too small, if the accuracy of sensor localisation
589 from GPS cannot match it. Instead, to further improve the resolution of microclimate
590 temperature maps, another solution could be to keep a 5 to 10-m sized buffer, but derive
591 metrics in "moving windows", a technique often used to "soften" images and to derive
592 textural information in landscape ecology (Hagen-Zanker, 2016). There are few applications
593 with LiDAR data, as processing time can be very long considering the huge datasets.

594 Whether we should always strive to the greatest possible accuracy depends on the scientific
595 question, the organisms under study or ecological patterns we want to explain, and the
596 application envisioned (Bennie et al., 2014). We do need ecologically-relevant maps, with
597 spatial resolutions meaningful for biodiversity or management (Bramer et al., 2018;
598 Zellweger et al., 2019b). For instance, a common resolution used for floristic inventories in
599 forests is 400 m² (corresponding to an 11.3-m radius). Making maps with a 2 m resolution
600 will not necessarily be useful to explain processes happening within the proximal resolution
601 of understory plant communities, although information on within-plot microclimate diversity
602 could be useful to explore plant distribution. In other situations, sub-meter resolution may
603 be relevant, for instance, if one wants to study whether tree holes or epiphytes can
604 efficiently protect tropical frogs against lethal temperatures (Scheffers et al., 2014). If so,
605 terrestrial or UAV-borne sensors can help to take an even closer look at forest structure, spot
606 such microhabitats and predict their buffering capacity (Hoffrén and García, 2023).

607

608
4.3. Beyond LiDAR-based vegetation structure

609 Forest structure is hardly the only driver of microclimate temperature buffering: topography
610 or hydrology can play major roles as well (Greiser et al., 2018; Jucker et al., 2018; Zellweger
611 et al., 2019a). For instance, northern-oriented slopes or places characterised by higher soil
612 moisture are associated with a higher microclimate temperature buffering potential (Hoffrén
613 and García, 2023; Zellweger et al., 2019a). The distance to water bodies or rivers is another
614 major driver described in the scientific literature (McLaughlin et al., 2017). In our case, the
615 Topographic Wetness Index and distance to the nearest river were calculated and had no
616 significant effect (see Fig. S3, S4), probably because the area is fairly flat, and the forest of
617 Blois does not have any large, permanent rivers in its boundaries. However, depending on
618 the context, these variables should be included in predictive models as well.

619 Another category of relevant metrics involves landscape features, such as the proportion of
620 forest cover in a wide buffer or the distance to the nearest forest edge (Meeussen et al.,
621 2021). However, our sampling design did not allow us to test for these landscape effects on
622 the magnitude of the buffering or amplification effect. Adding the effect of edges to our
623 models would have necessitated a different protocol, with several sensors placed along a
624 gradient of edge distances, including intraforest edges such as clearings or roads that cross
625 the forest of Blois. Future endeavours to spatialise microclimate temperature or other
626 variables such as relative humidity could focus on this question of forest edges to improve
627 resulting maps.

628 Lastly, especially so in a managed forest, the buffering or amplification capacity will be
629 dynamic over time, as thinning or harvest practices will drastically impact the resulting
630 microclimate effect and its spatial distribution. Up-to-date data is required to reflect the
631 result of forestry management (Greiser et al., 2018). Unfortunately, the high costs of an
632 airborne LiDAR campaign make it hard to repeat acquisitions in time. However, other remote
633 sensing techniques such as photogrammetry may help to fill this gap and obtain a temporal
634 series of forest structure from optical images (Randin et al., 2020). Spaceborne radar data,
635 for example Sentinel-1 time-series, has the potential to provide variables sensitive to
636 changes in forest structure that have proven their utility in biodiversity modelling (Bae et al.,
637 2019).

638 Coupled with Sentinel-2 multispectral imagery, radar could deliver relevant predictors at
639 high spatial resolution (10 m) and high temporal resolution (two or more images per month).

640 Combining LiDAR with other remote sensing techniques may also be useful to obtain
641 information complementary to structure, such as canopy phenology or water status
642 (Zellweger et al., 2019b).

643

644 4.4. Applications: why map forest temperature buffering or
645 amplification?

646 We propose to model one integrative and mechanistic metric representing the microclimate
647 effect (the “log of the slope”). This approach differs from modelling offsets (the difference
648 between macroclimate and microclimate) or temperature itself (Gril et al., 2023). For
649 instance, Davis et al. (2019), Frey et al. (2016) and George et al. (2015) modelled directly
650 daily or sub-daily temperatures, or statistics such as the mean minimum and maximum
651 temperature as response variables. With our parsimonious approach, once the buffering or
652 amplification capacity is modelled, we simply need the equilibrium to infer the whole linear
653 relationship between microclimate and macroclimate. Although it is likely to contribute to
654 predictions errors, the equilibrium can be considered constant in a given site (e.g. one forest)
655 and fixed at its median, or approached by mean macroclimate. Then we have enough
656 information to easily retrieve any point-in-time or statistic of microclimate temperature. The
657 only thing needed is macroclimate temperature data with a matching temporal resolution,
either using nearby weather stations or global databases such as ERA5 or E-OBS, with hourly macroclimate temperature at coarse spatial resolutions (Cornes et al., 2018; Muñoz-Sabater et al., 2021). Spatializing this forest buffering or amplification effect can have multiple applications (Zellweger et al., 2019b). Here, we listed a few examples of how such a map can be useful, aside from inferring any microclimate temperature of the understory.

(1) To study one of the many temperature-related processes, such as thermoregulation (Milling et al., 2018), photosynthesis (Miller et al., 2021) or phenology (Jackson, 1966), as forest understory species may not only have adapted to temperature itself, but also to temperature range and variation through time.

(2) To model the thermal connectivity of habitats at the landscape level, and build current density maps based on a fine-scale spatialized buffering effect (Huang et al., 2022; Milanesi et al., 2017). Some forest-dwelling species (such as insects, birds, amphibians or ungulates) have to move daily or seasonally for foraging, mating or nesting. Here, road verges or clear-cut areas with amplified temperatures may impair the thermal connectivity for forest specialists (e.g., the buffered forest stands located east of the forest of Blois may be hard to reach for some species, as they are surrounded by areas with large temperature amplification).

(3) To improve species distribution and redistribution models with climate change, and to spot thermal microrefugia with buffered climatic conditions (Lenoir et al., 2017). Species distribution models incorporating microclimate may yield great discrepancies compared to the usual ones based on macroclimatic grids (Haesen et al. 2023; Stickley and Fraterrigo, 2023). Local extinction risks and thermophilization, i.e., the gain of warm-adapted and loss of cold-adapted taxa, may be mitigated by the thermal insulating function of forest canopies
681 (Zellweger et al., 2020). This is especially relevant to the conservation of threatened
682 understory species facing climate change, and to prioritise suitable refuges that may
683 represent long-term microrefugia. Adding directly a metric quantifying the buffering or
684 amplification effect as a predictor of species distributions might be a great step forward.

685 (4) To assist forest management decisions under climate change. For foresters, this kind of
686 map may help, for instance, to evaluate the risk for tree regeneration, as young seedlings
687 and saplings are especially sensitive to climatic extremes (von Arx et al., 2013). Sessile oaks
688 seedlings have been shown to be sensitive to local microclimatic gradients, in terms of
689 germination, survival and performance (Meeussen et al., 2022). We found that all
690 regeneration stages in our study forest, with low or sparse canopy, were subjected to strong
691 amplification of temperature extremes. This means more exposure to frost or heat shock, in
692 addition to the risks of drought associated with high temperatures. Forestry has a central
693 role in mitigating local effects of global warming through microclimate regulation
694 (Christiansen et al., 2022; Greiser et al., 2018). Another "burning issue" in forest
695 management is wildfire control, and these areas of amplified temperature extremes should
696 be especially at risk, deserving close surveillance (Laurance, 2004).

697 (5) To make informed decisions about landscape planning for a human-liveable future,
698 especially in the case of urban forests such as the forest of Blois. Predictive models may
699 supply managers with site-specific, quantitative advice on how to strengthen urban forest
700 buffering capacity by manipulating forest structures. A map of forest microclimate effect
701 may also help to select cool spots that may act as cooling recreational areas to humans
702 during heatwaves, and create safe havens and walkable paths to escape the dreaded urban
703 heat island effect (Gillerot et al., 2022).
704 In brief, this study brings interesting prospects towards efficiently obtaining maps of forest
705 structure and microclimates, as airborne LiDAR point clouds are becoming more and more
706 available in Europe and beyond (Moudrý et al., 2022). We hope our flexible approach will be
707 applied across different biomes and forest types, with LiDAR or other remote sensing
708 technologies, for high-resolution maps of forest thermal environments.

709

710

Acknowledgements

711 We deeply thank all interns who contributed to temperature data collection in the field,
712 namely Hugo Mahier, Germain Vital, Ambre Châline, and Hugo Hayé. We also thank the
713 French Forest Office (ONF) and all ONF agents who helped with the project.

714 We thank Jérôme Chaves, Rebecca Senior and one anonymous reviewer, for their insightful
715 comments and suggestions which strengthened and clarified our paper.

716 J.L. acknowledges funding from the Centre National de la Recherche Scientifique (CNRS),
717 under the framework of the Mission pour les Initiatives Transverses et Interdisciplinaires
718 (MITI, Défi INFINITI 2018: MORFO project), and the Agence Nationale de la Recherche (ANR),
719 under the framework of the young investigators' funding scheme (JCJC Grant N°ANR-19-
720 CE32-0005-01: IMPRINT project) which funded E.G.'s PhD. S.H. received funding from a FLOF
721 fellowship of the KU Leuven (project nr. 3E190655).

722
723 Conflict of Interest

724 None.

725

726 Author's contributions

727 JL, EGM, FS and RM designed and performed the experiment, and collected field data along

728 with BB, EG and VLR. EGM, ML and SD handled the collection and treatment of LiDAR data.

729 EG performed the analysis with ML, JL and RM. GD, JL, KVM, RM, SD and SH participated in

730 the writing lead by EG.

731 All authors significantly contributed to this study and agreed to the submission of this draft.

732

733 Data and Code Availability

734 The R Markdown script and data are available online on Figshare (private link for the review

735 process: https://figshare.com/s/6f9beb675f3ea927b48d).

736

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