Spatial and temporal analysis of surface temperature in the Apacheta micro-basin using Landsat thermal data

Authors

DOI:

https://doi.org/10.4995/raet.2020.13855

Keywords:

brightness temperature, NDSI, vegetation cover fraction, emissivity, soil surface temperature

Abstract

High Andean ecosystems, such as grasslands and peatlands, are fragile and, due to the effects of climate change, their sustainability is being jeopardized. A key factor hampering sustainable management efforts from the government and communities, is the lack or scarcity of in-situ eco-hydrological and climate data. In that sense, remote sensing techniques offers a powerful alternative for the assessment of the evolution of these ecosystems, by providing a holistic view of the territory. The objective of this work is to determine both the spatial and temporal evolution of the local atmospheric temperature of the Apacheta micro-basin in Ayacucho over the past 34 years, using the soil surface temperature (SST) as a proxy. For this, thermal data of Landsat series (TM, ETM+ and TIRS sensors), covering the period from 1985 to 2018, were used. The TSS estimates were made from the emissivity correction of the brightness temperatures at the top of the atmosphere, considering the negligible atmospheric effect due to the conditions of high atmospheric transmissivity in the study area. The results show a positive trend of the SST with an increase of 4.9 °C, equivalent to 27.5% of the SST. Trends are higher (5.8 °C) in the snowy areas (equivalent to 35.3% of the TSS in the whole micro-basin). The SST in the snow area explains the 83.6% of the behavior of the snow cover derived by the NDSI, with a decreasing surface as SST increase.

Downloads

Download data is not yet available.

Author Biographies

W. Moncada, Universidad Nacional de San Cristóbal de Huamanga

Profesor Asociado a Dedicación Exclusiva del Área de Física del Departamento académico de Matemática y Física, Facultad de Ingeniería de Minas, Geología y Civil. Adscrito a la Escuela Profesional de Matemática y Física de la Universidad Nacional de San Cristóbal de Huamanga. Investigador en la aplicación de técnicas de teledetección por satélite, Sistemas de Información Geográfica, Gestión de Recursos Hídricos y Ambientales

B. Willems, Centro de Competencia del Agua

Fundador y Director del Centro de Competencias del Agua. Director del MBA en Gestión Integral del Agua (CCA-UPCH) Director del Expo Agua Perú Director del Programa Agua-Andes Especialista en la gestión de proyectos interdisciplinarios e inter-institucionales. Investigador en la aplicación de técnicas de teledetección por satélite al estudio de los ecosistemas y recursos hídricos, modelamiento físico de procesos sociales y económicos.

References

Aguilar, H., Mora, R., Vargas, C. 2014. Metodología para la corrección atmosférica de imágenes Aster, Rapideye, Spot 2 y Landsat 8 con el módulo Flaash del software Envi. Revista Geográfica de América Central, 2(53), 39-59. https://doi.org/10.15359/rgac.2-53.2

Aguilar, J., Espinoza, R., Espinoza, J.C., Rojas, J., Willems, B.L., Leyva, W.M. 2019. Elevationdependent warming of land surface temperatures in the Andes assessed using MODIS LST time series (2000–2017). International Journal of Applied Earth Observation and Geoinformation, 77, 119- 128. https://doi.org/10.1016/j.jag.2018.12.013

Araghi, A., Mousavi-Baygi, M., Adamowski, J. 2017. Detecting soil temperature trends in Northeast Iran from 1993 to 2016. Soil and Tillage Research, 174, 177-192. https://doi.org/10.1016/j.still.2017.07.010

Artis, D. A., Carnahan, W.H. 1982. Survey of emissivity variability in thermography of urban areas. Remote Sensing of Environment, 12(4), 313-329. https://doi.org/10.1016/0034-4257(82)90043-8

Arvidson, T., Barsi, J., Jhabvala, M., Reuter, D. 2013. Landsat and Thermal Infrared Imaging. En C. Kuenzer & S. Dech (Eds.), Thermal Infrared Remote Sensing: Sensors, Methods, Applications (pp. 177-196). Springer Netherlands. https://doi.org/10.1007/978-94-007-6639-6_9

Avdan, U., Jovanovska, G. 2016. Algorithm for Automated Mapping of Land Surface Temperature Using Landsat 8 Satellite Data. Journal of Sensors, 2016, 1480307-1480307. https://doi.org/10.1155/2016/1480307

Carlson, T.N., Ripley, D.A. 1997. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, 62(3), 241-252. https://doi.org/10.1016/S0034-4257(97)00104-1

Caselles, E., Abad, F.J., Valor, E., Caselles, V. 2011. Automatic Generation of Land Surface Emissivity Maps. Climate Change - Research and Technology for Adaptation and Mitigation, 15. https://doi.org/10.5772/24968

Chi, Y., Sun, J., Sun, Y., Liu, S., Fu, Z. 2020. Multitemporal characterization of land surface temperature and its relationships with normalized difference vegetation index and soil moisture content in the Yellow River Delta, China. Global Ecology and Conservation, 23, e01092. https://doi.org/10.1016/j.gecco.2020.e01092

Dozier, J. 1989. Spectral signature of alpine snow cover from the Landsat thematic mapper. Remote Sensing of Environment, 28, 9-22. https://doi.org/10.1016/0034-4257(89)90101-6

Gutman, G., Ignatov, A. 1998. The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. International Journal of Remote Sensing, 19(8), 1533- 1543. https://doi.org/10.1080/014311698215333

Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., Ferreira, L.G. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1), 195-213. https://doi.org/10.1016/S0034-4257(02)00096-2

ITT Visual Information Solutions. 2009. ENVI Atmospheric Correction Module: QUAC and FLAASH User’s Guide, Version 4.7, pp. 44. http://www.harrisgeospatial.com/portals/0/pdfs/ envi/Flaash_Module.pdf

Jiménez-Muñoz, J.C., Sobrino, J.A., Skoković, D., Mattar, C., Cristóbal, J. 2014. Land Surface Temperature Retrieval Methods From Landsat-8 Thermal Infrared Sensor Data. IEEE Geoscience and Remote Sensing Letters, 11(10), 1840-1843. https://doi.org/10.1109/LGRS.2014.2312032

Mendoza, J.N. 2014. Implementación de un método operativo para la estimación de la temperatura superficial terrestre en la Región Callao usando datos de las imágenes satelitales. Universidad Nacional del Callao, 53. http://repositorio.unac.edu.pe/handle/UNAC/966

Moncada, W., Pereda, A., Aldana, C., Masias, M., Jiménez, J. 2015. Cuantificación hidrográfica de la cuenca del río Cachi-Ayacucho, mediante imágenes satelitales. Instituto de Investigación Científica e innovación Tecnológica de la UNSCH, II.

Moncada, W., Willems, B., Rojas, J. 2020. Estimación de estadíos estacionales a partir de parámetros climáticos medidos en la estación meteorológica de la microcuenca Apacheta, Región Ayacucho, 2000 al 2018. Revista de Investigación de Física. UNMSM, 23(2), 17-25. https://fisica.unmsm.edu.pe/ rif/previo_files/2020-2/03moncada.pdf

Pereda, A., Moncada, W., Verde, L. 2018. Respuesta nival de la cabecera de cuenca Cachi-Apacheta de Ayacucho: Vol. I. Editorial Académica Española. https://www.morebooks.shop/store/es/book/ respuesta-nival-de-la-cabecera-de-cuenca-cachiapacheta-de-ayacucho/isbn/978-620-2-12620-5

Quispe, B.J., Révolo, R.H. 2020. Temperatura superficial y estado de la vegetación del bosque de Polylepis spp, distrito de San Marcos de Rocchac, Huancavelica – Perú. Enfoque UTE, 11(3), 69-86. https://doi.org/10.29019/enfoque.v11n3.592

Rudjord, Due, 2012. Evaluation of FLAASH atmospheric correction (SAMBA/10/12; p. 24). Norwegian Computing Center. http://publications. nr.no/1338298623/Rudjord-Trier_FLAASH_2012. pdf

Santos, B. 2016. Cubierta Nival y Temperaturas de Superficie en Sierra Nevada a través del tratamiento digital de imágenes de satélite [Tesis Doctoral, Universitat de Barcelona]. http://diposit.ub.edu/dspace/handle/2445/108441

Sayão, V.M., Demattê, J.A.M., Bedin, L.G., Nanni, M.R., Rizzo, R. 2018. Satellite land surface temperature and reflectance related with soil attributes. Geoderma, 325, 125-140. https://doi.org/10.1016/j.geoderma.2018.03.026

Sobrino, J., Jiménez, J., Paolini, L. 2004. Land surface temperature retrieval from Landsat TM 5. Remote Sensing of Environment, 90(4), 434-440. https://doi.org/10.1016/j.rse.2004.02.003

Solman, S.A., Nuñez, M.N., Cabré, M.F. 2008. Regional climate change experiments over southern South America. I: Present climate. Climate Dynamics, 30(5), 533-552. https://doi.org/10.1007/s00382-007-0304-3

USGS, Landsat Collections, Landsat Missions. Consultado el 14 de octubre de 2019, de https://www.usgs.gov/land-resources/nli/landsat.

Vuille, M., Bradley, R.S. 2000. Mean annual temperature trends and their vertical structure in the tropical Andes. Geophysical Research Letters, 27(23), 3885- 3888. https://doi.org/10.1029/2000GL011871

Weng, Q., Lu, D., 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

Xu, C., Qu, J.J., Hao, X., Zhu, Z., Gutenberg, L. 2020. Surface soil temperature seasonal variation estimation in a forested area using combined satellite observations and in-situ measurements. International Journal of Applied Earth Observation and Geoinformation, 91, 102156. https://doi.org/10.1016/j.jag.2020.102156

Zhang, A., Liu, X., Di, W. 2009. Derivation of the green vegetation fraction from TM data of three gorges area. Procedia Earth and Planetary Science, 1(1), 1152- 1157. https://doi.org/10.1016/j.proeps.2009.09.177

Published

2020-12-28

Issue

Section

Practical cases