Evaluating the Thermal Spatial Distribution Signature for Environmental Management and Vegetation Health Monitoring

Anibal Gusso Anibal Gusso, Mauricio Roberto Veronez, Fernanda Robinson, Vanessa Roani, Rafaela Christ Da Silva


An accurate evaluation of the environment while cities are still growing economically is highly necessary for reliable assessment of ecosystem conditions. This research evaluates a comparison between the pattern of Land Surface Temperature (LST) distribution and Enhanced Vegetation Index 2 (EVI-2) during the summer season as an indicator of development conditions in an Area of Environmental Protection (AEP) that is under pressure from the surrounding urban environment in São Leopoldo, Brazil. A TSDS (Thermal Spatial Distribution Signature) procedure using Thermal infrared (TIR) data obtained from Landsat-5 Thematic Mapper (TM) was applied to evaluate vegetation coverage conditions. A set of six images were used to analyze vegetation development of an AEP between 1985 and 2009. Our analysis suggests that there is a strong relationship between the spatial distributions of LST and its pattern of vegetation coverage conditions. The LST variance exhibited differences in the two studied periods. A decreasing trend was observed in the variance averages from 1.04 to 0.35, which is associated to higher LST occurrences and a wider range of LST distribution in the first period than in the second. The results indicate that the LST distribution variance close to 1.0 can be associated with several level of vegetation degradation. In addition, a variance below 0.5, inside the studied AEP during summertime, is associated with better conditions of vegetation coverage. In this manner, the TSDS procedure was considered a simple yet effective procedure for the timely diagnosis of AEP.


Land Surface Temperature; TSDS; Remote Sensing; Area of Environmental Protection; Wetland; Vegetation Coverage

Full Text: PDF


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

*2016 Journal Impact Factor was established by dividing the number of articles published in 2014 and 2015 with the number of times they are cited in 2016 based on Google Scholar, Google Search and the Microsoft Academic Search. If ‘A’ is the total number of articles published in 2014 and 2015, and ‘B’ is the number of times these articles were cited in indexed publications during 2016 then, journal impact factor = A/B. To know More: (http://en.wikipedia.org/wiki/Impact_factor)