Land surface temperature anomalies in response to changes in forest cover

Abstract

Land cover/use changes specially the forest cover changes affect the local surface temperature (LST) of the earth. In this study, a combination of remote sensing and GIS techniques was used to scrutinize the interactions between LST anomalies and deforestation in Sardasht County, NW Iran. The land cover/use change layers of the study area were extracted from Landsat satellite imagery based on Binary Encoding classification and change detection technique. The radiometric correction analysis were done for each Landsat image to derive LST map layers. According to the results, a descending trend in forest cover with a total 2560 ha decline in area and an ascending trend of about 4 degrees rise in surface temperature values on both forest and non-forest areas were detected in the study area from 1984 to 2017. The temporal and spatial analysis yielded high rates of reverse temporal correlation (-0.81) between forest areas and LST anomalies while the correlation value of 0.76 was found for non-forest areas and LST. The regression analysis of the values confirmed the correlation results to be trustable at 99 percent. It was also found that the deforested areas of the study area correlate with the LST rise spatially with a very high correlation (0.98) from which a tangible interaction of the parameters can be inferred.

Keywords

• Iran
• Land Surface Temperature
• Sardasht
• Zagros Forest

References

1. Amiri R., Weng Q., Alimohammadi A. and Alavipanah S.K (2009). Spatial–temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran. Remote sensing of environment, 113(12), pp.2606-2617.
2. Anandababu D., Purushothaman BM. and Babu SS (2018). Estimation of Land Surface Temperature using LANDSAT 8 Data. International Journal of Advance Research, Ideas and Innovations in Technology. 4(2):177-86.
3. Arslan M., Zahid R.and Ghauri B (2016). Assessing the occurrence of drought based on NDVI, LST and rainfall pattern during 2010–2014. Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International (pp. 4233-4236). IEEE.
4. Artis D.A. and Carnahan W.H (1982). Survey of emissivity variability in thermography of urban areas. Remote Sensing of Environment, 12(4), pp.313-329.
5. Bakar S.B.A., Pradhan B., Lay U.S. and Abdullahi S (2016). Spatial assessment of land surface temperature and land use/land cover in Langkawi Island. In IOP Conference Series: Earth and Environmental Science.Vol. 37, No. 1, p. 012064.
6. Beygi Heidarlou H., Banj Shafiei A., Erfanian M., Tayyebi. and Alijanpour A (2015). Detection of land cover changes in Sardasht during time period of 1993 to 2016. The International Conference on Natural Resources Management in Developing Countries, Iran, Tehran, 25 Feb. 2015.
7. Cetin M., Kavzoglu T. and Musaoglu N (2004). Classification of multi-spectral, multi-temporal and multi-sensor images using principal components analysis and artificial neural networks: Beykoz case. In Proceedings XXth International Society for Photogrammetry and Remote Sensing-Congress, pp. 12- 23.
8. Chavez Jr. and Pat S (1988). An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote sensing of environment 24, no. 3: 459-479.

9. Ding H. and Shi W (2013). Land-use/land-cover change and its influence on surface temperature: a case study in Beijing City. International Journal of Remote Sensing, 34(15), pp.5503-5517.
10. Entezari A., Ahmadi A., Aliabadi K., Khosravian M. and Ebrahimi M (2016). Monitoring Land Surface Temperature and Evaluating Change Detection Land Use (Case Study:Parishan Lake Basin), Hydrogeomorphology, v2[8],113-139.
11. Foody GM. (2002). Status of land cover classification accuracy assessment Remote Sensing of Environment 80:185-201 doi:10.1016/S0034-4257(01)00295-4.
12. Gidey E., Dikinya O., Sebego R., Segosebe E. and Zenebe A (2018). Analysis of the long-term agricultural drought onset, cessation, duration, frequency, severity and spatial extent using Vegetation Health Index (VHI) in Raya and its environs, Northern Ethiopia. Environmental Systems Research, 7(1), p.13.
13. Gilmore S., Saleem A. and Dewan A. (2015). Effectiveness of DOS (Dark-Object Subtraction) method and water index techniques to map wetlands in a rapidly urbanising megacity with Landsat 8 data. Research@ Locate'15, pp.100-108.
14. Karnieli A., Agam N., Pinker R.T., Anderson M., Imhoff M.L., Gutman G.G., Panov N. and Goldberg A. (2010). Use of NDVI and land surface temperature for drought assessment: Merits and limitations. Journal of climate, 23(3), pp.618-633.
15. Kumar M. and Singh R.K. (2013). Digital Image Processing of Remotely Sensed Satellite Images for Information Extraction. In Conference on Advances in Communication and Control Systems (CAC2S).
16. Lu D., Mausel P., Brondizio E. and Moran E. (2004). Change detection techniques. International journal of remote sensing. 1; 25(12):2365-401.
17. Manoharan V.S., Welch R.M. and Lawton R.O. (2009). Impact of deforestation on regional surface temperatures and moisture in the Maya lowlands of Guatemala. Geophysical Research Letters, 36(21).
18. Markham B.L. and Barker J.L. (1987). Thematic Mapper bandpass solar exoatmospheric irradiances. International Journal of remote sensing 8, no. 3 517-523. Research Systems, Inc. ENVI tutorials. Research Systems, September, 2001 Edition. gers.uprm.edu/geol6225/pdfs/envy_tutorial.pdf (Accessed 05.09.2018).
19. Shooshtari SJ. and Gholamalifard M. (2015). Scenariobased land cover change modeling and its implications for landscape pattern analysis in the Neka Watershed, Iran Remote Sensing Applications: Society and Environment 1:1-19
20. Son N.T., Chen C.F., Chen C.R., Chang L.Y. and Minh V.Q (2012). Monitoring agricultural drought in the Lower Mekong Basin using MODIS NDVI and land surface temperature data. International Journal of Applied Earth Observation and Geoinformation, 18, pp.417-427.
21. Sruthi S. and Aslam M.M. (2015). Agricultural drought analysis using the NDVI and land surface temperature data; a case study of Raichur district. Aquatic Procedia, 4, pp.1258-1264.
22. Waters R., Allen R., Bastiaanssen W., Tasumi M. and Trezza R. (2002). Surface energy balance algorithms for land, Idaho implementation, advanced training and user’s manual. NASA, USA.
23. Xiao H. and Weng Q. (2007). The impact of land use and land cover changes on land surface temperature in a karst area of China. Journal of environmental management, 85(1), pp.245-257.
24. Xie H. and Xiaohua T. (2012). An inproved binary encoding algorithm for classification of hyperspectral images. In Hyperspectral Image and Signal Processing (WHISPERS), 2012 4th Workshop on, pp. 1-4. IEEE.
25. Zhang X., Yamaguchi Y., Li F., He B. and Chen Y. (2017). Assessing the impacts of the 2009/2010 drought on vegetation indices, normalized difference water index, and land surface temperature in Southwestern China. Advances in Meteorology. Volume 2017, Article ID 6837493, 9 pages.