J. For. Sci., 2019, 65(1):27-32 | DOI: 10.17221/121/2018-JFS

Assessment of LST and NDMI indices using MODIS and Landsat images in Karun riparian forestOriginal Paper

Marjan Firoozy Nejad*, Amin Zoratipour
Department of Range and Watershed Management, Faculty of Natural Resources, Khuzestan Agricultural Sciences and Natural Resources University, Ahvaz, Iran

Riparian forest plays a significant role in ecosystems. Also, research on land surface temperature and soil moisture is essential in earth science and forest studies. Because measuring methods are difficult to apply in large areas and especially in dense forests, in this study normalized difference moisture index (NDMI) and land surface temperature (LST) were estimated using the infrared thermal method by data of Landsat 8 and Moderate Resolution Imaging Spectroradiometer (MODIS) in the Karun riparian forest that is of ecological importance in the Khuzestan province of Iran. The results showed that the accuracy for estimated NDMI and LST was appropriate (root mean square error = 3.45). In addition, the used polynomial support vector machine algorithm for classification by four classes (forest, agriculture, river, and others) and the validity of classification in these areas were suitable (overall accuracy = 95%, kappa coefficient = 0.93). Also, the NDMI index was dependent on changes in LST and Pearson coefficients were 0.94 and 0.84 for Landsat 8 and MODIS data, respectively. The average temperature of the area was obtained as 43.22 and 42.77 for Landsat 8 and MODIS, respectively. Finally, more protection of this forest against LST enhancement and reduction in soil moisture is necessary.

Keywords: moisture index; temperature; forestry; river; climate; infrared thermal method

Published: January 31, 2019  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Nejad MF, Zoratipour A. Assessment of LST and NDMI indices using MODIS and Landsat images in Karun riparian forest. J. For. Sci. 2019;65(1):27-32. doi: 10.17221/121/2018-JFS.
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Bernstein L.S., Jin X., Gregor B., Adler-Golden S.M. (2012): Quick atmospheric correction code: Algorithm description and recent upgrades. Optical Engineering, 51: 111719. Go to original source...
    2. Bhamare S.M., Agone V. (2011): Change detection of surface temperature and its consequence using multi-temporal remote sensing data and GIS application to Tapi Basin of India. In: Proceedings of the Global Conference on Global Warming 2011, Lisbon, July 11-14, 2011: 1-12.
    3. Congalton R.G., Green K. (1999): Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Boca Raton, Lewis Publishers: 160. Go to original source...
    4. Emami H., Mojaradi B., Safari A. (2016): An alternative scaling method for cross-validation of land surface temperature from remote sensing data: A case study of Fars province. Journal of Geomatics Science and Technology, 6: 1-17. (in Persian)
    5. Feizizadeh B., Didehban K., Gholamnia K. (2016): Extraction of land surface temperature (LST) based on Landsat satellite images and split window algorithm study area: Mahabad catchment. Scientific-Research Quarterly of Geographical Data (SEPEHR), 25: 171-181. (in Persian)
    6. Firoozy Nejad M. (2013): Evaluation of ETM+ and IRSLISSIII sensor potential for riparian forest mapping (case study: Maroon River riparian forest, Khuzestan). [MSc Thesis.] Behbahan, Khatam University of Technology: 100.
    7. Firoozy Nejad M., Zoratipour A. (2018): Interactions of land surface temperature (LST) in increasing or decreasing vegetation. In: Akrami M., Moazami S., Neamati A.R: Proceedings of the First Congress of Water, Soil and Environmental Sciences, Tehran, Mar 2, 2018: 1-10.
    8. Firoozy Nejad M., Torahi A.A., Rai S.C. (2017): Comparing the capability of ETM+ and LISS-III imagery in riparian forest mapping: A case study of Maroon river, Iran. Lebanese Science Journal, 18: 226-233. Go to original source...
    9. Hosseini Z., Zerang N., Hajilo M., Dadashi M., Tiba A. (2014): Investigating relationship between land surface temperature (LST) and vegetation using Landsat images (case study: Isfahan). In: Almodaresi S.A.: Proceedings of the First Conference on the Use of Advanced Models Spatial Analysis Remote Sensing and GIS, Yazd, Feb 24, 2015: 1-11.
    10. Khanmohammadi F., Homaee M., Noroozi A.A. (2015): Soil moisture estimating with NDVI and land surface temperature and normalized moisture index using MODIS images. Journal of Soil and Water Resources Conservation, 4: 37-45.
    11. Kheradmand V., Jalali Majidi M., Khazami S. (2015): Investigating the possibility of estimating soil moisture from satellite images using the thermal bands. In: Proceedings of the International Congress on the Ability of the Community in the Field of Management, Economics, Entrepreneurship and Cultural Engineering, Tehran, Nov 4, 2016: 1-11.
    12. Kozlowski T.T. (2002): Physiological-ecological impacts of flooding on riparian forest ecosystems. Wetlands, 22: 550-561. Go to original source...
    13. Laliberte A.S., Browning D.M., Rango A. (2012): A comparison of three feature selection methods for object-based classification of sub-decimeter resolution UltraCam-L imagery. International Journal of Applied Earth Observation and Geoinformation, 15: 70-78. Go to original source...
    14. Latifi H., Oladi J., Saroei S., Jalilvand H. (2007): Evaluating ETM+ satellite data for mapping forest cover - shrublandpasture classes (case study: Zalemrood, Mazandaran). Journal of Science and Technology of Agriculture and Natural Resources, 40: 439-447.
    15. Mountrakis G., Im J., Ogole C. (2011): Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66: 247-259. Go to original source...
    16. Naiman R.J., Décamps H. (1997): The ecology of interfaces: Riparian zones. Annual Review of Ecology and Systematics, 28: 621-658. Go to original source...
    17. Torahi A., Firoozy Nejad M., Abdolkhani A. (2017): Evaluation of the performance of SVM algorithms and ML in land use mapping of riparian forests using OLI sensor (case study: Maroun Behbahan riparian forests). Iranian Remote Sensing & GIS, 9: 49-62. (in Persian)
    18. Vaez Mousavi A., Mokhtarzadeh M. (2015): Estimation of land surface temperature using MODIS data. In: Gheragozlo A.: Geomatics 94 National Conference & Exhibition, Tehran, May 17, 2015: 1-9.

    This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY NC 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content