PDF
Abstract
Forests play a vital role in mitigating climate change through their physiological functions and metabolic processes, including their ability to convert solar energy into biomolecules. However, further research is necessary to elucidate how structural characteristics of a forest and topographic settings influence energy conversion and surface temperature of a forest. In this study, we investigated a beech forest in central Germany using airborne laser scanning (ALS) point cloud data and land surface temperature (LST) data derived from Landsat 9 satellite imagery. We constructed 30 m × 30 m plots across the study area (approximately 17 km2) to align the spatial resolution of the satellite imagery with the ALS data. We analyzed topographic variables (surface elevation, aspect and slope), forest attributes (canopy cover, canopy height, and woody area index), as well as forest structural complexity, quantified by the box-dimension (Db). Our analysis revealed that LST is significantly influenced by both forest attributes and topographic variables. A multiple linear regression model demonstrated an inverse relationship (R2 = 0.38, AIC = 8105) between LST and a combination of Db, elevation, slope, and aspect. However, the model residuals exhibited significant spatial dependency, as indicated by Moran’s I test. To address this, we applied a spatial autoregressive model, which effectively accounted for spatial autocorrelation and improved the model fit (AIC = 746). Our findings indicate that elevation exerts the most substantial influence on LST, followed by forest structural complexity, slope, and aspect. We conclude that forest management practices that enhance structural complexity can effectively reduce land surface temperatures in forested landscapes.
Keywords
Airborne laser scanning
/
Topography
/
Box-dimension
/
Landsat satellite imagery
/
Land surface temperature
Cite this article
Download citation ▾
Prakash Basnet, Simon Grieger, Birgitta Putzenlechner, Dominik Seidel.
Forests with high structural complexity contribute more to land surface cooling: empirical support for management for complexity.
Journal of Forestry Research, 2025, 36(1): DOI:10.1007/s11676-025-01855-6
| [1] |
AckerSA, SabinTE, GanioLM, McKeeWA. Development of old-growth structure and timber volume growth trends in maturing Douglas-fir stands. For Ecol Manag, 1998, 104(1–3): 265-280
|
| [2] |
AmthorJS. From sunlight to phytomass: on the potential efficiency of converting solar radiation to Phyto-energy. New Phytol, 2010, 188(4): 939-959
|
| [3] |
Anderson C (2021) Landsat 8–9 Calibration and Validation (Cal/Val) Algorithm Description Document (ADD) Version 4.0
|
| [4] |
ArseniouG, MacFarlaneDW. Fractal dimension of tree crowns explains species functional-trait responses to urban environments at different scales. Ecol Appl, 2021, 31(4): e02297
|
| [5] |
ArzbergerS, EgererM, SudaM, AnnighöferP. Thermal regulation potential of urban green spaces in a changing climate: winter insights. Urban Urban Green, 2024, 100 128488
|
| [6] |
BennieJ, HuntleyB, WiltshireA, HillMO, BaxterR. Slope, aspect and climate: spatially explicit and implicit models of topographic microclimate in chalk grassland. Ecol Model, 2008, 216(1): 47-59
|
| [7] |
BivandR, MilloG, PirasG. A review of software for spatial econometrics in R. Mathematics, 2021, 9(11): 1276
|
| [8] |
BivandR, PirasG. Comparing implementations of estimation methods for spatial econometrics. J Stat Soft, 2015, 63(18): 1-36
|
| [9] |
BonanGB. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science, 2008, 320(5882): 1444-1449
|
| [10] |
CamarrettaN, EhbrechtM, SeidelD, WenzelA, ZuhdiM, MerkMS, SchlundM, ErasmiS, KnohlA. Using airborne laser scanning to characterize land-use systems in a tropical landscape based on vegetation structural metrics. Remote Sens, 2021, 13(23): 4794
|
| [11] |
CookM, SchottJ, MandelJ, RaquenoN. Development of an operational calibration methodology for the Landsat thermal data archive and initial testing of the atmospheric compensation component of a land surface temperature (LST) product from the archive. Remote Sens, 2014, 6(11): 11244-11266
|
| [12] |
Cook M (2014) Atmospheric Compensation for a Landsat Land Surface Temperature Product. Rochester Institute of Technology
|
| [13] |
CoopsNC, TompaskiP, NijlandW, RickbeilGJM, NielsenSE, BaterCW, StadtJJ. A forest structure habitat index based on airborne laser scanning data. Ecol Indic, 2016, 67: 346-357
|
| [14] |
DavisonS, DonoghueDNM, GaliatsatosN. The effect of leaf-on and leaf-off forest canopy conditions on LiDAR derived estimations of forest structural diversity. Int J Appl Earth Obs Geoinf, 2020, 92 102160
|
| [15] |
Dietz M, Meyer P, Schmidt M (2014) Weserhänge. Nordwestdeutsche Forstliche Versuchsanstalt (NW-FVA) and Landesbetrieb HESSEN-FORST, Göttingen and Kassel, Germany
|
| [16] |
DonfackLS, RöllA, EllsäßerF, EhbrechtM, IrawanB, HölscherD, KnohlA, KreftH, SiahaanEJ, SundawatiL, StieglerC, ZempDC. Microclimate and land surface temperature in a biodiversity enriched oil palm plantation. For Ecol Manag, 2021, 497 119480
|
| [17] |
DorjiY, IsasaE, PierickK, CabralJS, TobgayT, AnnighöferP, SchuldtB, SeidelD. Insights into the relationship between hydraulic safety, hydraulic efficiency and tree structural complexity from terrestrial laser scanning and fractal analysis. Trees, 2024, 38(1): 221-239
|
| [18] |
EhbrechtM, SchallP, AmmerC, SeidelD. Quantifying stand structural complexity and its relationship with forest management, tree species diversity and microclimate. Agric for Meteor, 2017, 242: 1-9
|
| [19] |
EllisonD, MorrisCE, LocatelliB, SheilD, CohenJ, MurdiyarsoD, GutierrezV, van NoordwijkM, CreedIF, PokornyJ, GaveauD, SpracklenDV, TobellaAB, IlstedtU, TeulingAJ, GebrehiwotSG, SandsDC, MuysB, VerbistB, SpringgayE, SugandiY, SullivanCA. Trees, forests and water: cool insights for a hot world. Glob Environ Change, 2017, 43: 51-61
|
| [20] |
ElmesA, RoganJ, WilliamsC, RatickS, NowakD, MartinD. Effects of urban tree canopy loss on land surface temperature magnitude and timing. ISPRS J Photogramm Remote Sens, 2017, 128: 338-353
|
| [21] |
FeyisaGL, DonsK, MeilbyH. Efficiency of parks in mitigating urban heat island effect: an example from Addis Ababa. Landsc Urban Plan, 2014, 123: 87-95
|
| [22] |
GodinhoS, GilA, GuiomarN, CostaMJ, NevesN. Assessing the role of Mediterranean evergreen oaks canopy cover in land surface albedo and temperature using a remote sensing-based approach. Appl Geogr, 2016, 74: 84-94
|
| [23] |
GoodwinNR, CoopsNC, CulvenorDS. Assessment of forest structure with airborne LiDAR and the effects of platform altitude. Remote Sens, 2006, 103: 140-152
|
| [24] |
HeTT, HuYH, GuoAD, ChenYW, YangJ, LiMM, ZhangMX. Quantifying the impact of urban trees on land surface temperature in global cities. ISPRS J Photogramm Remote Sens, 2024, 210: 69-79
|
| [25] |
HeJL, ZhaoW, LiAN, WenFP, YuDJ. The impact of the terrain effect on land surface temperature variation based on Landsat-8 observations in mountainous areas. Int J Remote Sens, 2019, 40(5–6): 1808-1827
|
| [26] |
HuangB, LiY, LiuY, HuXP, ZhaoWW, CherubiniF. A simplified multi-model statistical approach for predicting the effects of forest management on land surface temperature in Fennoscandia. Agric for Meteor, 2023, 332 109362
|
| [27] |
Ihlen V (2019) Landsat 8 Data Users Handbook (LSDS-1574) Version 5.0
|
| [28] |
IrwinLAK, CoopsNC, RiofríoJ, et al.. Prioritizing commercial thinning: quantification of growth and competition with high-density drone laser scanning. Forestry: Int J Forest Res, 2024
|
| [29] |
JacobsonMZFundamentals of Atmospheric Modeling, 20052Cambridge, Cambridge University Press
|
| [30] |
JasechkoS, SharpZD, GibsonJJ, Jean BirksS, YiY, FawcettPJ. Terrestrial water fluxes dominated by transpiration. Nature, 2013, 496(7445): 347-350
|
| [31] |
JiangJ, TianGJ. Analysis of the impact of land use/land cover change on land surface temperature with remote sensing. Procedia Environ Sci, 2010, 2: 571-575
|
| [32] |
JungMC, DysonK, AlbertiM. Urban landscape heterogeneity influences the relationship between tree canopy and land surface temperature. Urban Urban Green, 2021, 57 126930
|
| [33] |
JunttilaS, VastarantaM, HämäläinenJ, Latva-käyräP, HolopainenM, Hernández ClementeR, HyyppäH, Navarro-CerrilloRM. Effect of forest structure and health on the relative surface temperature captured by airborne thermal imagery–case study in Norway Spruce-dominated stands in Southern Finland. Scand J for Res, 2017, 32(2): 154-165
|
| [34] |
KingMDEOS Science Plan: The State of Science in the EOS Program, 1999, Washington, DC, NASA
|
| [35] |
LeempoelK, ParisodC, GeiserC, DapràL, VittozP, JoostS. Very high-resolution digital elevation models: are multi-scale derived variables ecologically relevant?. Meth Ecol Evol, 2015, 6(12): 1373-1383
|
| [36] |
van LeeuwenM, van AardtJAN, KampeT, KrauseKA box-counting method to characterize degrees of foliage clumping using airborne and simulated LIDAR data2015Int Arch Photogramm Remote Sens Spatial Inf Sci
|
| [37] |
LiHF, CalderCA, CressieN. Beyond Moran’s I: testing for spatial dependence based on the spatial autoregressive model. Geogr Anal, 2007, 39(4): 357-375
|
| [38] |
LiZ-L, WuH, DuanS-B, et al.. Satellite remote sensing of global land surface temperature: definition, methods, products, and applications. Rev Geophys, 2023
|
| [39] |
LinY-C, LiuJD, FeiSL, HabibA. Leaf-off and leaf-on UAV LiDAR surveys for single-tree inventory in forest plantations. Drones, 2021, 5(4): 115
|
| [40] |
LiuY, HuangX, YangQQ, CaoYX. The turning point between urban vegetation and artificial surfaces for their competitive effect on land surface temperature. J Clean Prod, 2021, 292 126034
|
| [41] |
MandelbrotBBThe fractal geometry of nature, 1982RevisedSan Francisco, W.H, Freeman
|
| [42] |
MarandoF, HerisMP, ZulianG, UdíasA, MentaschiL, ChrysoulakisN, ParastatidisD, MaesJ. Urban heat island mitigation by green infrastructure in European functional urban areas. Sustain Cities Soc, 2022, 77 103564
|
| [43] |
MathesT, SeidelD, HäberleKH, PretzschH, AnnighöferP. What are we missing? Occlusion in laser scanning point clouds and its impact on the detection of single-tree morphologies and stand structural variables. Remote Sens, 2023, 15(2): 450
|
| [44] |
MauroF, HudakAT, FeketyPA, et al.. Regional modeling of forest fuels and structural attributes using airborne laser scanning data in oregon. Remote Sens, 2021, 13: 261
|
| [45] |
MessierC, PuettmannKJ, CoatesKDManaging forests as complex adaptive systems: building resilience to the challenge of global change, 2013, London, Routledge
|
| [46] |
MiuraN, JonesSD. Characterizing forest ecological structure using pulse types and heights of airborne laser scanning. Remote Sens Environ, 2010, 114(5): 1069-1076
|
| [47] |
MokhovII, AkperovMG. Tropospheric lapse rate and its relation to surface temperature from reanalysis data. Izv Atmos Ocean Phys, 2006, 42(4): 430-438
|
| [48] |
MontealegreAL, LamelasMT, de la RivaJ, et al.. Use of low point density ALS data to estimate stand-level structural variables in Mediterranean Aleppo pine forest. Forestry: Int J Forest Res, 2016
|
| [49] |
MorabitoM, CrisciA, GuerriG, MesseriA, CongedoL, MunafòM. Surface urban heat islands in Italian metropolitan cities: tree cover and impervious surface influences. Sci Total Environ, 2021, 751 142334
|
| [50] |
MossJL, DoickKJ, SmithS, ShahrestaniM. Influence of evaporative cooling by urban forests on cooling demand in cities. Urban Fores Urban Green, 2019, 37: 65-73
|
| [51] |
MuQZ, ZhaoMS, RunningSW. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens Environ, 2011, 115(8): 1781-1800
|
| [52] |
NeudamL, AnnighöferP, SeidelD. Exploring the potential of mobile laser scanning to quantify forest structural complexity. Front Remote Sens, 2022, 3 861337
|
| [53] |
ParadisE, SchliepK. Ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics, 2019, 35(3): 526-528
|
| [54] |
PebesmaE, BivandRSpatial Data Science: With Applications in R, 2023, New York, Chapman and Hall/CRC
|
| [55] |
PengXX, WuWY, ZhengYY, SunJY, HuTG, WangP. Correlation analysis of land surface temperature and topographic elements in Hangzhou. China Sci Rep, 2020, 10(1): 10451
|
| [56] |
PhanT, KappasM, TranT. Land surface temperature variation due to changes in elevation in northwest Vietnam. Climate, 2018, 6(2): 28
|
| [57] |
PrigogineIThermodynamics of irreversible processes, 1961, New York, Wiley
|
| [58] |
R Core Team_R: A Language and Environment for Statistical Computing_, 2023, Vienna, Austria, R Foundation for Statistical Computing
|
| [59] |
RichterR, HutengsC, WirthC, BannehrL, VohlandM. Detecting tree species effects on forest canopy temperatures with thermal remote sensing: the role of spatial resolution. Remote Sens, 2021, 13(1): 135
|
| [60] |
RobertsDA, DennisonPE, RothKL, DudleyK, HulleyG. Relationships between dominant plant species, fractional cover and land surface temperature in a Mediterranean ecosystem. Remote Sens Environ, 2015, 167: 152-167
|
| [61] |
ŠafandaJ. Ground surface temperature as a function of slope angle and slope orientation and its effect on the subsurface temperature field. Tectonophysics, 1999, 306(3–4): 367-375
|
| [62] |
SarkarN, ChaudhuriBB. An efficient differential box-counting approach to compute fractal dimension of image. IEEE Trans Syst Man Cybern, 1994, 24(1): 115-120
|
| [63] |
Sayler K (2022) Landsat 9 Data Users Handbook (LSDS-2082) Version 1.0
|
| [64] |
Sayler K (2023) Landsat 8–9 Collection 2 (C2) Level 2 Science Product (L2SP) Guide (LSDS-1619) Version 5.0
|
| [65] |
SchneiderED, KayJJ. Complexity and thermodynamics towards a new ecology. Futures, 1994, 26(6): 626-647
|
| [66] |
SchwaabJ, MeierR, MussettiG, SeneviratneS, BürgiC, DavinEL. The role of urban trees in reducing land surface temperatures in European cities. Nat Commun, 2021, 12(1): 6763
|
| [67] |
SeidelD. A holistic approach to determine tree structural complexity based on laser scanning data and fractal analysis. Ecol Evol, 2017, 8(1): 128-134
|
| [68] |
SeidelD, AmmerC. Towards a causal understanding of the relationship between structural complexity, productivity, and adaptability of forests based on principles of thermodynamics. For Ecol Manage, 2023, 544: 121238
|
| [69] |
SeidelD, AnnighöferP, EhbrechtM, MagdonP, WöllauerS, AmmerC. Deriving stand structural complexity from airborne laser scanning data—What does it tell us about a forest?. Remote Sens, 2020, 12(11): 1854
|
| [70] |
SeidelD, AnnighöferP, StiersM, ZempCD, BurkardtK, EhbrechtM, WillimK, KreftH, HölscherD, AmmerC. How a measure of tree structural complexity relates to architectural benefit-to-cost ratio, light availability, and growth of trees. Ecol Evol, 2019, 9(12): 7134-7142
|
| [71] |
SuYX, LiuLY, WuJP, ChenXZ, ShangJL, CiaisP, ZhouGY, LafortezzaR, WangYP, YuanWP, WangYL, ZhangHO, HuangGQ, HuangNS. Quantifying the biophysical effects of forests on local air temperature using a novel three-layered land surface energy balance model. Environ Int, 2019, 132 105080
|
| [72] |
TodaM, KnohlA, LuyssaertS, HaraT. Simulated effects of canopy structural complexity on forest productivity. For Ecol Manag, 2023, 538 120978
|
| [73] |
WangCC, RenZB, DongYL, ZhangP, GuoYJ, WangWJ, BaoGD. Efficient cooling of cities at global scale using urban green space to mitigate urban heat island effects in different climatic regions. Urban for Urban Green, 2022, 74 127635
|
| [74] |
WangXJ, ScottCE, DallimerM. High summer land surface temperatures in a temperate city are mitigated by tree canopy cover. Urban Clim, 2023, 51 101606
|
| [75] |
WangLM, TianFQ, WangXF, YangYZ, WeiZW. Attribution of the land surface temperature response to land-use conversions from bare land. Glob Planet Change, 2020, 193 103268
|
| [76] |
WetherleyEB, McFaddenJP, RobertsDA. Megacity-scale analysis of urban vegetation temperatures. Remote Sens EnviRon, 2018, 213: 18-33
|
| [77] |
Wickham H (1964) ggplot2—elegant graphics for data analysis
|
| [78] |
ZennerEK, HibbsDE. A new method for modeling the heterogeneity of forest structure. For Ecol Manag, 2000, 129(1–3): 75-87
|
| [79] |
ZhaoKG, SuarezJC, GarciaM, HuTX, WangC, LondoA. Utility of multitemporal lidar for forest and carbon monitoring: tree growth, biomass dynamics, and carbon flux. Remote Sens Environ, 2018, 204: 883-897
|
| [80] |
ZiterCD, PedersenEJ, KucharikCJ, TurnerMG. Scale-dependent interactions between tree canopy cover and impervious surfaces reduce daytime urban heat during summer. Proc Natl Acad Sci USA, 2019, 116(15): 7575-7580
|
Funding
Georg-August-Universität Göttingen (1018)
RIGHTS & PERMISSIONS
The Author(s)