[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Home::
Journal Information::
Articles archive::
For Authors::
For Reviewers::
Registration::
Contact us::
Site Facilities::
::
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..
:: Volume 7, Issue 15 (2-2020) ::
PEC 2020, 7(15): 333-356 Back to browse issues page
Highlighting the Importance of the Vegetation Variable on DistributedLand surface temperature on different land use/land cover in Javanrud city range
Peyman Karami , Kamran Shayesteh * , Mina Esmaeili
, ka_shayesteh@yahoo.com
Abstract:   (3437 Views)
In environmental models, Vegetations are considered as an important part in controlling environmental changes. To determine the importance of vegetation on land surface temperature (LST), preliminary preprocessing was performed on Landsat 8 image and a split window procedure was used to determine surface temperature. Temperature difference with the surrounding synoptic stations was estimated to be about 2.5 °C, which confirmed the accuracy of the computational temperature.In addition to Normalized difference vegetation index (NDVI), other variables such as Normalized difference water index (NDWI), elevation, Aspect, Landuse/Landcover (LU/LC) and heat loading index were used to show the effect of vegetation on temperature. LU/LC map of city area was obtained by using a 7 categories training samples by Neural Network Classification (MLP). Total Kappa index was calculated 0.82. Then, the points were randomly extracted by stratified sampling method separated for 7 LU/LC categories and divided into two groups of testing and training. Values of the mentioned variables were extracted for these points and random forest regression was used to investigate their effect on surface temperature. Performance of the model was evaluated and validated using mean absolute error (MAE), mean square error (MSE) and root mean squared error (RMSE). Based on sensitivity analysis by Mean decrease accuracy, NDVI, Aspect and NDWI had the highest effect on surface temperature distribution. Highest surface temperature with 29 °C belonged to uncoated rangelands and its lowest with 25 °C belonged to human-made areas, including Javanrud and peripheral villages. The results of this study showed that the NDVI plays a very important role in land surface temperature changes.
 
Keywords: Vegetation cover, spilt window, NDWI, Random forest regression, Javanrud
Full-Text [PDF 1248 kb]   (793 Downloads)    
Type of Study: Research | Subject: Special
Received: 2019/03/6 | Accepted: 2019/06/10 | Published: 2020/03/18
References
1. Aggarwal, S., Misra, M. 2018. Comparison of NDVI, NDBI as indicators of surface heat island effects for Bangalore and New Delhi: Case Study. In Remote Sensing Technologies and Applications in Urban Environments III, 10793: 1079314.
2. Coll, C., Caselles, V., Valor, E., Rubio, E. 2003. Validation of temperature emissivity separation and split window methods from TIM’s data and ground measurements. Remote Sensing of Environment, 85: 232-242.
3. Deng, Y., Wang, S., Bai, X., Tian, Y., Wu, L., Xiao, J., Qian, Q. 2018. Relationship among land surface temperature and LUCC, NDVI in typical karst area. Scientific reports, 8(1): 641.
4. García-Haro, F. J., Sommer, S., Kemper, T.2005. Variable multiple end member spectralmixture analysis (VMESMA), InternationalJournal of Remote Sensing, 26:2135-2162.
5. Guo, G., Zhou, X., Wu, Z., Xiao, R. and Chen, Y. 2016. Characterizing the impact of urban morphology heterogeneity on land surface temperature in Guangzhou, China. Journal of Environmental modelling & software,84:427-439.
6. Hereher, M.E. 2017. Effect of land use/cover change on land surface temperatures-The Nile Delta, Egypt. Journal of African Earth Sciences, 126:75-83.
7. Jamei E., Rajagopalan P., Seyedmahmoudian M. Jamei Y. 2016. Review on the Impact of UrbanGeometry and Pedestrian Level Greening on Outdoor Thermal Comfort, Renewable and Sustainable Energy Reviews, 54 (2016): 1002-1017.
8. Kayet, N., Pathak, K., Chakrabarty, A., Sahoo, S.2016. Spatial impact of land use/land cover change on surface temperature distribution in Saranda Forest, Jharkhand. Modeling Earth Systems and Environment, 2(3):127.
9. Kurc, S. A., Small, E. E.2007. Soil moisture variations and ecosystem‐scale fluxes of water and carbon in semiarid grassland and shrubland. Water Resources Research, 43(6).13pp.
10. Mackey, C. W., Lee, X., Smith, R. B.2012.Remotely sensing the cooling effects of city scale efforts to reduce urban heat island, Building and Environment,1(49):348-358.
11. McFeeters, S.L.1996. The use of the Normal Different water index (NDWI) in the Delineation of open water feature. International journal of remote sesnseing.17 (7):1425-1432
12. Malik, M. S., Shukla, J. P.,Mishra, S. 2019. Relationship of LST, NDBI and NDVI using Landsat-8 data in Kandaihimmat Watershed, Hoshangabad, India.Indian Journal of Geo-Marine Sciences.48(1):25-31.
13. Mallick, J., Kant, Y., Bharath, B. D. 2008. Estimation of land surface temperature over Delhi using Landsat–7 ETM+. Journal of the Indian geography :union:, 12 (3):131–140.
14. McCune, B., Keon, D. 2002. Equations for potential annual direct incident radiation and heat load, Journal of Vegetation Science, 13(4): 603-606.
15. Panda, S.,Jain, M.K . 2017. Effects of Green Space Spatial Distribution on Land Surface Temperature: Implications for Land Cover Change as Environmental Indices. 10(02):180-184.
16. Rodriguez-Galiano, V. F., Chica-Olmo, M., Abarca-Hernandez, F., Atkinson, P. M., Jeganathan, C. 2012. RandomForest classification of Mediterranean land covers using multi-seasonal imagery and multi-seasonal texture. Journal of Remote Sensing of Environment121: 93-107.
17. Siddique, N. P.,Ghaffar, A.2019. Spatial and Temporal relationship between NDVI and Land Surface Temperature of Faisalabad city from 2000-2015. European Online Journal of Natural and Social Sciences, 8(1): 55.
18. Villegas, J. C., Breshears, D. D., Zou, C. B., Royer, P. D.2010. Seasonally pulsed heterogeneity in microclimate: phenology and cover effects along deciduous grassland–forest continuum. Vadose Zone Journal, 9(3): 537-547.
19. Yue, Y., Wang, K., Bing, Z., Chen, Z., Jiao, Q., Bo, L., Chen, H.2009. Exploring the relationship between vegetation spectra and eco-geo-environmental conditions in Karst region, Southwest China. Environ. Monit. Assess, 160(1-4): 157–168.
Send email to the article author

Add your comments about this article
Your username or Email:

CAPTCHA


XML   Persian Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Karami P, Shayesteh K, Esmaeili M. Highlighting the Importance of the Vegetation Variable on DistributedLand surface temperature on different land use/land cover in Javanrud city range. PEC 2020; 7 (15) :333-356
URL: http://pec.gonbad.ac.ir/article-1-559-en.html


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 7, Issue 15 (2-2020) Back to browse issues page
مجله حفاظت زیست بوم گیاهان Journal of Plant Ecosystem Conservation
Persian site map - English site map - Created in 0.05 seconds with 37 queries by YEKTAWEB 4645