Surface temperature and states of the vegetation of the forest of Polylepis spp, district of San Marcos de Rocchac, Huancavelica – Peru
DOI:
https://doi.org/10.29019/enfoqueute.v11n3.592Keywords:
Polylepis; LST; Vegetation States; TVX; Jenks NaturalAbstract
The effect of surface temperature on the state of the vegetation in the forest of Polylepis spp and to relate it is the objective of this research. As methodology 9 satellite images of the Landsat 8 OLI / TIRS Sensor were used, evaluated using remote sensing, applying Pearson r correlation and statistical t student hypothesis. The following results were obtained: the relationships during the 9 months of the year 2018 - - 2019 between LST - NDVI r = 0.11, t = 0.29; LST - NDWI r = -0.43, t = 1.27; LST - SAVI r = 0.13, t = 0.34 and LST - MSI r = 0.56, t = 1.77; the average ratio of 9 images classified in Jenks Natural Breaks values between LST - NDVI r = 0.99, t = 47.12; LST - NDWI r = -0.98, t = 28.93; LST - SAVI r = -0.99, t = 65.39 and LST - MSI r = 0.99, t = 30.39; and the effect of “TVX” for NDVI (East: -0.0778 / 0.0549; West: 0.6434 / -0.0120), NDWI (West: -0.6128 / -0.0463; East: 0.3057 / 0.0474), SAVI (West: 0.4089 / 0.0232; East: -0.0073 / -0.0011) and MSI (East: 0.5565 / 0.1856; West: 1.3993 / 0.0362). In conclusion, it is confirmed that during the 9 months that the monitoring lasted, there was no statistical correlation and that on average of the 9 images classified in Jenks Natural Breaks there is a correlation; TVX confirmed the influence of surface temperature on the state of vegetation within the forest over time.
Downloads
References
Almeida, A. (2015). Procesamiento Digital De Imágenes Multiespectrales Landsat 8, Para Aplicaciones Agronómicas En La Subcuenca Del Río Guayllabamba (Universidad Central del Ecuador). Obtenido dehttp://www.dspace.uce.edu.ec/bitstream/25000/6489/3/T-UCE-0004-17.pdf
Alshaikh, A. (2015). Vegetation Cover Density and Land Surface Temperature Interrelationship Using Satellite Data, Case Study of Wadi Bisha, South KSA. Advances in Remote Sensing, 04(03), 248–262. https://doi.org/10.4236/ars.2015.43020
Anandababu, D., Purushothaman, B. M., y Suresh Babu, S. (2018). Estimation of Land Surface Temperature using LANDSAT 8 Data. International Journal of Advance Research, 4(2), 177–186. Obtenido dewww.IJARIIT.com
Anbazhagan, S., y Paramasivam, C. R. (2016). Statistical Correlation between Land Surface Temperature (LST) and Vegetation Index (NDVI) using Multi-Temporal Landsat TM Data. International Journal of Advanced Earth Science and Engineering, 5(1), 333–346. https://doi.org/10.23953/cloud.ijaese.204
Braun, G. (1997). The Use of Digital Methods in Assessing Forest Patterns in an Andean Environment: The Polylepis Example. Mountain Research and Development, 17(3), 253. https://doi.org/10.2307/3673852
Bravo, F. (2017). Teledetección espacial (Primera Ed). Obtenido de https://acolita.com/wp-content/uploads/2018/01/Teledeteccion_espacial_ArcGeek.pdf
Campomanes, Y. (2017). Escenario de distribución de los bosques de Polylepis al 2030 frente a los elementos climatológicos de Temperatura y Precipitación, en el distrito de Pomabamba -Ancash, utilizando Maxent y GIS, 2017. Universidad César Vallejo.
Carnahan, W. H., y Larson, R. C. (1990). An analysis of an urban heat sink. Remote Sensing of Environment, 33(1), 65–71. https://doi.org/10.1016/0034-4257(90)90056-R
Carvajal, A. F., y Pabón, J. D. (2014). Temperatura de la superficie terrestre en diferentes tipos de cobertura de la Región Andina Colombiana. Sociedade y Natureza, 26(1), 95–112. https://doi.org/10.1590/1982-451320140107
Contreras, O. (2019). Identificación De La Especie Polylepis reticulata Mediante Teledetección En Las Zonas Alto Andinas Del Ecuador. Escuela Superior Politécnica de Chimborazo.
De Sousa, S. B., y Júnior, L. G. F. (2012). Relação entre temperatura de superfície terrestre, índices espectrais e classes de cobertura da terra no município de Goiânia (GO). RA’E GA - O Espaco Geografico Em Analise, 26(26), 75–99. https://doi.org/10.5380/raega.v26i0.30151
Dourojeanni P. (2008). Distribución Y Conectividad De Bosques Alto Andinos (Polylepis) En La Cuenca Alta Del Río Pativilca (Pontificia Universidad Católica del Perú). Obtenido de http://tesis.pucp.edu.pe/repositorio/handle/20.500.12404/628
Fjeldså, J. (1993). The avifauna of the polylepis woodlands of the Andean highlands: the efficiency of basing conservation priorities on patterns of endemisn. Bird Conservation International, 3, 37–55.
Galvão, L. S., Formaggio, A. R., y Tisot, D. A. (2005). Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data. Remote Sensing of Environment, 94(4), 523–534. https://doi.org/10.1016/j.rse.2004.11.012
Gilabert, M. A., González-Piqueras, J., y García-Haro, J. (1997). Acerca de los índices de vegetación. Revista de Teledetección, Vol. 8(May 2014), 1–10. Obtenido de https://www.researchgate.net/publication/39195330_Acerca_de_los_indices_de_vegetacion
Gonzaga, C. (2014). Aplicación de Índices de Vegetación Derivados de Imágenes Satelitales Landsat 7 ETM + y ASTER para la Caracterización de la Cobertura Vegetal en la Zona Centro de la Provincia De Loja, Ecuador. Universidad Nacional de La Plata.
Gonzaga, C. (2015). Aplicación de índices de vegetación derivados de imágenes satelitales para análisis de coberturas vegetales en la provincia de Loja , Ecuador. Cedemaz, 5(1), 30–41. Obtenido de http://revistas.unl.edu.ec/index.php/cedamaz/article/view/43/41
Goward, S. N., Waring, R. H., Dye, D. G., Yang, J., Applications, S. E., May, N., … Waring, R. H. (1994). Ecological Remote Sensing At Otter: Satellite Macroscale Observations ’. 4(2), 322–343. https://doi.org/10.2307/1941937
Hardisky, M. A., Klemas, V., y Smart, R. M. (1983). The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of Spartina alterniflora canopies. Photogrammetric Engineering y Remote Sensing, 49(1), 77–83. Obtenido dehttps://www.asprs.org/wp-content/uploads/pers/1983journal/jan/1983_jan_77-83.pdf
Hoch, G., y Körner, C. (2005). Growth, demography and carbon relations of Polylepis trees at the world’s highest treeline. Functional Ecology, 19(6), 941–951. https://doi.org/10.1111/j.1365-2435.2005.01040.x
Hong, S., Lakshmi, V., y Small, E. E. (2007). Relationship between vegetation biophysical properties and surface temperature using multisensor satellite data. Journal of Climate, 20(22), 5593–5606. https://doi.org/10.1175/2007JCLI1294.1
Huete, A. R. (1988). A Soil-Adjusted Vegetation Index (SAVI). Remote Sensing of Environment, 25(2), 295–309. https://doi.org/10.1006/geno.1994.1397
Ito, E., Lim, S., Pol, S., Tith, B., Pith, P., Khorn, S., … Araki, M. (2007). Use of ASTER Optical Indices to Estimate Spatial Variation in Tropical Seasonal Forests on the West Bank of the Mekong River, Cambodia. Forest Environments in the Mekong River Basin, 232–240. https://doi.org/10.1007/978-4-431-46503-4_21
Jepsen, J. U., Hagen, S. B., Høgda, K. A., Ims, R. A., Karlsen, S. R., Tømmervik, H., y Yoccoz, N. G. (2009). Monitoring the spatio-temporal dynamics of geometrid moth outbreaks in birch forest using MODIS-NDVI data. Remote Sensing of Environment, 113(9), 1939–1947. https://doi.org/10.1016/j.rse.2009.05.006
Kessler, M. (2006). Bosques de Polylepis. Botánica Económica de Los Andes Centrales, 110–120. Obtenido de http://www.beisa.dk/Publications/BEISA Book pdfer/Capitulo 07.pdf
Mejia Rios, A. A. (2014). Metodologá para la cartografia de bosques del genero Polylepis, aplicando Geomatica (Universidad Nacional Agraria la Molina).
Muñoz, P. (2013). Apuntes de Teledetección: Índices de vegetación. In Centro de información de recursos Naturales (Pimera Edi). https://doi.org/http://bibliotecadigital.ciren.cl/bitstream/handle/123456789/26389/Tema %20Indices %20de %20vegetaci %C3 %B3n %2C %20Pedro %20Mu %C3 %B1oz %20A.pdf?sequence=1yisAllowed=y
Orhan, O., Ekercin, S., y Dadaser-Celik, F. (2014). Use of Landsat land surface temperature and vegetation indices for monitoring drought in the Salt Lake Basin Area, Turkey. The Scientific World Journal, 2014(December), 13. https://doi.org/10.1155/2014/142939
Pacheco, M., Franco, P., Cáceres, C., Navarro, M., y Jove, C. (2018). Aplicación De Técnicas SIG Para La Cobertura Superficial Y Distribución Del Bosque De Polylepis En La Zona Andina De Moquegua 2018. 17(2), 26–32. https://doi.org/10.33326/26176033.2018.23.753
Rock, B. N., Vogelmann, J. E., Williams, D. L., Vogelmann, A. F., y Hoshizaki, T. (1986). Remote Detection of Forest Damage. BioScience, 36(7), 439–445. https://doi.org/10.2307/1310339
Rouse, J. W., Hass, R. H., Schell, J. A., y Deering, D. W. (1973). Monitoring vegetation systems in the great plains with ERTS. Third Earth Resources Technology Satellite (ERTS) Symposium, 1, 309–317. https://doi.org/citeulike-article-id:12009708
Segovia-Salcedo, M., Domic, A., Boza, T., y Kessler, M. (2018). Situación taxonómica de las especies del género Polylepis. Implicancias para los estudios ecológicos, la conservación y la restauración de sus bosques. Ecología Austral, 28, 188–201. https://doi.org/https://doi.org/10.25260/EA.18.28.1.1.527
Silva Laurentino, M. L. (2014). Aplicaciones de la teledetección en el análisis de daños en masas de coníferas en la provincia de Burgos - España (Universidad de Valladolid). Obtenido de https://uvadoc.uva.es/bitstream/10324/6649/1/TFM-L187.pdf
Smith, M. J. de, Goodchild, M. F., y Longley, P. A. (2018). Geospatial Analysis A Comprehensive Guide to Principles Techniques and Software Tools (Sixth Edit). https://doi.org/www.spatialanalysisonline.com
Speranza, F. C., y Zerda, H. R. (2002). Potencialidad De Los Índices De Vegetación Para La Discriminación De Coberturas Forestales. (1), 1–10. Obtenido de https://www.academia.edu/13450382/POTENCIALIDAD_DE_LOS_ÍNDICES_DE_VEGETACIÓN_PARA_LA_DISCRIMINACIÓN_DE_COBERTURAS_FORESTALES
Stimson, H. C., Breshears, D. D., Ustin, S. L., y Kefauver, S. C. (2005). Spectral sensing of foliar water conditions in two co-occurring conifer species: Pinus edulis and Juniperus monosperma. Remote Sensing of Environment, 96(1), 108–118. https://doi.org/10.1016/j.rse.2004.12.007
USGS. (2019). LANDSAT 8 (L8) DATA USERS HANDBOOK. In USGS (Ed.), Department of the Interior, U.S. Geological Survey (Version 4., Vol. 4). Obtenido de https://prd-wret.s3-us-west-2.amazonaws.com/assets/palladium/production/atoms/files/LSDS-1574_L8_Data_Users_Handbook_v4.0.pdf
Weng, Q., Lu, D., y Schubring, J. (2004). Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89(4), 467–483. https://doi.org/10.1016/j.rse.2003.11.005
Zutta, B. R., Rundel, P. W., Saatchi, S., Casana, J. D., Gauthier, P., Soto, A., … Buermann, W. (2012). Prediciendo la distribución de Polylepis: bosques Andinos vulnerables y cada vez más importantes. Revista Peruana de Biologia, 19(2), 205–212. https://doi.org/10.15381/rpb.v19i2.849
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 Enfoque UTE
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The articles and research published by the UTE University are carried out under the Open Access regime in electronic format. This means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, without asking prior permission from the publisher or the author. This is in accordance with the BOAI definition of open access. By submitting an article to any of the scientific journals of the UTE University, the author or authors accept these conditions.
The UTE applies the Creative Commons Attribution (CC-BY) license to articles in its scientific journals. Under this open access license, as an author you agree that anyone may reuse your article in whole or in part for any purpose, free of charge, including commercial purposes. Anyone can copy, distribute or reuse the content as long as the author and original source are correctly cited. This facilitates freedom of reuse and also ensures that content can be extracted without barriers for research needs.
This work is licensed under a Creative Commons Attribution 3.0 International (CC BY 3.0).
The Enfoque UTE journal guarantees and declares that authors always retain all copyrights and full publishing rights without restrictions [© The Author(s)]. Acknowledgment (BY): Any exploitation of the work is allowed, including a commercial purpose, as well as the creation of derivative works, the distribution of which is also allowed without any restriction.
Enfoque UTE - Facultad de Ciencias de la Ingeniería e Industrias - Universidad UTE
