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Analysis of land surface temperature on pastureland areas with different management using Landsat 8 data
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Paper Abstract

The aim of this study was to analyze the LST on pasture areas with different systems of production and management based on survey with farmers and remote sensing data during four development cycles (2013-2017). The study was carried out on western Sao Paulo state, Brazil, which is known as its traditional pastureland areas. We analyzed different type of production system such as extensive, semi-extensive and intensive pasture. The band 10 from Landsat 8 Thermal Infrared Sensor (TIRS) was used to retrieve the LST, and to verify the inter-seasonal productivity variation in function of management and temperature we calculated Net Primary Production - NPP. By the survey and visual analysis, we could classify the pasture areas as degraded and non-degraded area. For this classification, only pasture in degradation or maintenance phase was identified. Degraded pasture in the first or second cycle was recovered after proper management. However, the inverse effect was also verified, areas in maintenance became degraded pasture. It occurred not only because of the management practices but also because of extreme meteorological conditions. For those considered degraded pasture we verified an average temperature of 27°C - 29°C, and areas with the proper management the average temperature where 24°C - 26°C. On farms which were verified lost of productivity in function of degradation the temperature raised around 2°C. For this study, the use of remote sensing data to retrieve Land Surface Temperature shows to be an additional tool for monitoring pasture areas in function of management and productivity.

Paper Details

Date Published: 21 October 2019
PDF: 7 pages
Proc. SPIE 11149, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI, 111491B (21 October 2019); doi: 10.1117/12.2535593
Show Author Affiliations
Gleyce K. D. A. Figueiredo, Univ. Estadual de Campinas (Brazil)
Roberto B. Santos, Univ. Estadual de Campinas (Brazil)
Julianne C. Oliveira, Univ. Estadual de Campinas (Brazil)
Rubens A. C. Lamparelli, Univ. Estadual de Campinas (Brazil)

Published in SPIE Proceedings Vol. 11149:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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