Relationship between land surface temperature and fraction of anthropized area in the Atlantic forest region, Brazil

Autoři: Raianny L. N. Wanderley aff001;  Leonardo M. Domingues aff002;  Carlos A. Joly aff003;  Humberto R. da Rocha aff001
Působiště autorů: Universidade de São Paulo, Instituto de Energia e Ambiente, Programa de Pós-Graduação em Ciência Ambiental, São Paulo, Brazil aff001;  Universidade de São Paulo, Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Departamento de Ciências Atmosféricas, Laboratório de Clima e Biosfera, São Paulo, Brazil aff002;  Universidade de Campinas, Instituto de Biologia, Departamento de Biologia Vegetal, Campinas, São Paulo, Brazil aff003
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
prolekare.web.journal.doi_sk: 10.1371/journal.pone.0225443


There is growing evidence that modification of tropical forests to pasture or other anthropic uses (anthropization) leads to land surface warming at local and regional scales; however, the degree of this effect is unknown given the dependence on physiographic and atmospheric conditions. We investigated the dependence of satellite land surface temperature (LST) on the fraction of anthropized area index, defined as the fraction of non-forested percentual area within 120m square boxes, sampled over a large tropical forest dominated ecosystem spatial domain in the Atlantic Forest biome, southeastern Brazil. The LST estimated at a 30 m resolution, showed a significant dependence on elevation and topographic aspect, which controlled the average thermal regime by 2~4°C and 1~2°C, respectively. The correction of LST by these topographic factors allowed to detect a dependence of LST on the fraction of non-forested area. Accordingly, the relationship between LST and the fraction of non-forested area showed a positive linear relationship (R2 = 0.63), whereby each 25% increase of non-forest area resulted in increased 1°C. As such, increase of the maximum temperature (~4°C) would occur in the case of 100% increase of non-forested area. We conclude that our study area, composed to Atlantic forest, appears to show regulatory characteristics of temperature attenuation as a local climatic ecosystem service, which may have mitigation effects on the accelerated global warming.

Klíčová slova:

Brazil – Deforestation – Ecosystems – Forest ecology – Forests – Surface temperature – Topography – Tropical forests


1. Ellison D, Morris CE, Localettir B, Sheil D, Cohen J, Murdiyarso D, et al. Trees, forests and water: Cool insights for a hot world. Global Environ Change. 2017;43: 51–61.

2. Laurance WF. Conserving the hottest of the hotspots. Biol Conserv. 2009;142: 1137–1137.

3. Culf AD, Esteves JL, Filho AOM, da Rocha HR. Radiation, temperature and humidity over forest and pasture in amazonia. In: Gash J, Nobre CA, Roberts J, Victoria R, editors. Amazon deforestation and climate. 1st ed. Chichester: John Wiley and Sons; 1996. pp. 175–192.

4. da Rocha HR, Manzi A, Shuttleworth WJ. Evapotranspiration. In: Keller M, Bustamante M, Gash J, Silva Dias P, editors. Amazonia and Global Change. Washington, DC: American Geophysical Union; 2009. pp. 261–272.

5. Watson RT, Noble IR, Bolin B, Ravindranath NH, Verardo DJ, Dokken DJ, editors. Land Use, Land-Use Change, and Forestry: A Special Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press; 2000.

6. Bala G, Caldeira K, Wickett M, Phillips TJ, Lobell DB, Delire C, et al. Combined climate and carbon-cycle effects of large-scale deforestation. Proc Natl Acad Sci USA. 2007;104: 6550–6555. doi: 10.1073/pnas.0608998104 17420463

7. Saad SI, Rocha HR, Dias MAFS, Rosolem R. Can the Deforestation Breeze Change the Rainfall in Amazonia? A Case Study for the BR-163 Highway Region. Earth Interact. 2010;214: 1–25.

8. Oyama MD, Nobre CA. A new climate-vegetation equilibrium state for Tropical South America. Geophys Res Lett. 2003;30(23): 2199–2203.

9. Spracklen DV, Garcia-Carreras L. The impact of Amazonian deforestation on Amazon basin rainfall. Geophys Res Lett. 2015;42(21): 9546–9552.

10. Ewers RM, Banks-Leite C. Fragmentation Impairs the Microclimate Buffering Effect of Tropical Forests. PLoS ONE. 2013;8(3): e58093. doi: 10.1371/journal.pone.0058093 23483976

11. Wang Y, Hu BKH, Myint SW, Feng C, Chow WTL, Passy PF. Patterns of land change and their potential impacts on land surface temperature change in Yangon, Myanmar. Science of the Total Environment. 2018; 643: 738–750. doi: 10.1016/j.scitotenv.2018.06.209 29957438

12. Prata AJ, Caselles V, Coll C, Sobrino JA, Ottlé C. Thermal remote sensing of land surface temperature from satellites: Current status and future prospects. Remote Sens Rev. 1995;12(3–4): 175–224.

13. Peng SS, Piao S, Zeng Z, Ciais P, Zhou L, Li LZX, et al. Afforestation in China cools local land surface temperature. Proc Natl Acad Sci PNAS. 2014;111(8): 2915–2919. doi: 10.1073/pnas.1315126111 24516135

14. Li Y, Zhao M, Motesharrei S, Mu Q, Kalnay E, Li S. Local cooling and warming effects of forests based on satellite observations. Nature Commun. 2015; 6: 6603.

15. Nascimento RL. Análise comparativa dos componentes do saldo de radiação em áreas de pastagem e floresta na Amazônia. 16 de março de 2012. 71 f. Dissertation, Universidade Federal de Campina Grande. 2012 (in Portuguese).

16. Bastiaanssen WGM, Menenti M, Feddes RA, Holtslag AAM. A remote sensing surface energy balance algorithm for land (SEBAL) 1. Formulation. J Hydrol. 1998;212–213: 198–212.

17. Bastiaanssen WGM, Pelgrum H, Wang J, Ma Y, Moreno JF, Roenrink GJ, et al. A remote sensing surface energy balance algorithm for land (SEBAL) 2. Validation. J Hydrol. 1998;212–213: 213–229.

18. Connors JP, Galletti CS, Chow WT. Landscape configuration and urban heat island effects: Assessing the relationship between landscape characteristics and land surface temperature in Phoenix, Arizona. Landsc Ecol. 2013;28: 271–283.

19. Jenerette G, Harlan S, Brazel A, Jones N, Larissa L, Stefanov WL. Regional relationships between surface temperature, vegetation, and human settlement in a rapidly urbanizing ecosystem. Landsc Ecol. 2007;14(3): 353.

20. Kong F, Yin H, James P, Hutyra LR, He HS. Effects of spatial pattern of greenspace on urban cooling in a large metropolitan area of eastern China. Landsc Urban Plan. 2014;128: 34–47.

21. Martin TCM, da Rocha HR, Joly CA, Freitas HC, Wanderley RN, da Silva JM. Climate variability in a complex terrain basin using a high resolution weather station network in southeastern Brazil. Int J Climatol. 2018: Forthcoming. doi: 10.1002/joc.5797

22. He J, Zhao W, Li A, Wen F, Yu D. The impact of the terrain effect on land surface temperature variation based on Landsat-8 observations in mountainous areas. International Journal of Remote Sensing. 2018: 1366–5901.

23. Ranzini, M. Modelagem hidrológica de uma microbacia floresta da serra do mar, SP, com o modelo TOPMODEL: simulação do comportamento hidrológico em função do corte raso. Doctorate thesis, Escola de Engenharia de São Carlos, Universidade de São Paulo. 2002. (in Portuguese).

24. Cicco V, Arcova FCS, Ranzini M, Santos JBA, Forti MC. Recursos hídricos na Mata Atlântica: estudo de caso do Laboratório de Hidrologia Florestal Walter Emmerich, Cunha-SP. Anais I Seminário de Recursos Hídricos da Bacia Hidrográfica do Paraíba do Sul: O eucalipto e o Ciclo Hidrológico. 2007. pp. 25–33 (in Portuguese).

25. Microsoft Corporation. Bing Maps Aerial. 2011.

26. NASA Jet Propulsion Laboratory. 2015, NASA Shuttle Radar Topography Mission 1 Arc Second. Ver. 3.0 Global. 24°S46′W. NASA EOSDIS Land Processes DAAC, USGS Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota. Accessed from: Cited 25 January 2018.

27. Allen R, Bastiaanssen W, Waters R, Tasumi M, Trezza R. Surface energy balance algorithms for land (SEBAL), Idaho implementation–Advanced training and user’s manual, ver. 1.0. 2002.

28. Huete AR. Adjusting vegetation indices for soil influences. Int Agrophys. 1988;4(4): 367–376.

29. Tucker CJ, Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Envir. 1979;8, 127–150.

30. Tasumi M. Progress in operational estimation of regional evapotranspiration using satellite imagery. PhD Thesis, University of Idaho. 2003.

31. Chander G, Markham B. Revised Landsat 5—TM Radiometric Calibration Procedures and Postcalibration Dynamic Ranges. IEEE Trans Geosci Remote Sens. 2003;41: 2674–2677.

32. NASA Applied Remote Sensing Training (ARSET). Advanced Webinar: Land Cover Classification with Satellite. Online Training. 31 January 2017 to 7 February 2017. Available from:

33. Environmental Systems Research Institute (ESRI), 2014. ArcGIS Desktop 10.6.1. Spatial Analyst.

34. Environmental Systems Research Institute (ESRI), 2014. ArcGIS Desktop 10.6.1. Spatial Analyst.

35. Minder JR, Mote PW, Lundquist JD. Surface temperature lapse rates over complex terrain: Lessons from the Cascade Mountains, J. Geophys. Res., 2010; 115, D14122, doi: 10.1029/2009JD013493

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2019 Číslo 12