Evaluating the changes in Gavkhuni Wetland using MODIS satellite images in 2000-2016

Subject Areas : Environment

Maryam  Zarei ¹ (MSc of Combating Desertification, Agriculture and Natural Resources Department, Ardakan University, Yazd, Iran)
Mahdi  Tazeh ² (Assistant Professor, Department of Nature Engineering, Faculty of Agriculture & Natural Resources, Ardakan University, P.O. Box 184, Ardakan, Iran)
Vahid  Moosavi ³ (Assistant Professor, Tarbiat Modares University, Tehran, Iran)
Saeideh  Kalantari ⁴ (Assistant Professor, Department of Nature Engineering, Faculty of Agriculture & Natural Resources, Ardakan University, P.O. Box 184, Ardakan, Iran)

Received: 2021-01-22 Accepted : 2021-03-29 Published : 2021-03-01

Keywords: MODIS, satellite imagery, Gavkhuni Wetland, Desert surface,

Abstract :

Background and objectives:The changes in desert areas depend on climate condition and water balance of upstream watershed. satellite image can help us in distinguishing the trend of areas of Playa wetland.And with achieving these trend, both the status of the non-conventional water resources will be identified and this information can be used in wind erosion management.Materials and methods:In the present study, the changes in Gavkhuni Wetland was evaluated using MODIS satellite images from 2000 to 2016. For this purpose, after performing the required modifications on the satellite images, they were classified and their changes in studies time intervals were detected. Since the changes of desert areas depend on the humidity variations, the TVDI، MTVDI، VTCI indices were calculated to enhance the satellite images. The indices along with the bands of MODIS images were used in classification. The classification was done in August and March (maximum changes in desert areas and wet age) during 16 years. Due to the large number of used images, coding in MATLAB software was used to facilitate calculation of these parameters.Results and conclusion:The results indicated that on August and March, the desert areas faced the descending precipitation, which led to reducing water right. In the studied intervals, in 78.98% of the study areas, no changes were observed and the maximum changes (15%) was for a wet edge. Evaluating the validity of the maps revealed that the Kappa coefficient and total validation were respectively 95% and 96%.

References:

Abutalebi Nasrabadi, M., Mokhtari, M. H., Hakimzadeh, M. A., & Shahmoradi, S. (2020). Estimation of the soil moisture using thermal inertia and MODIS satellite data imagery: A case study of Mortazieh area. The Journal of Geographical Research on Desert Areas, 8(1), 55-80.    

Afrasyabi, S., Tazeh, M., Taghizadeh Mehrjardi, R., & Kalantari, S. (2019). Performance of two measurement methods of pin meter and laser disto meter in the measurement of microtopography Created by desert pavement. Desert Ecosystem Engineering Journal, 8(22), 1-14.       

Adegoke, J. O., & Carleton, A. M. (2002). Relations between soil moisture and satellite vegetation indices in the US Corn Belt. Journal of hydrometeorology, 3(4), 395-405. https://doi.org/10.1175/1525-7541(2002)003

Alavi-Panah, S. K. (2003). Application of remote sensing in geosciences. Soil Science), Tehran: Tehran University Press..

Carlson, T. (2007). An overview of the" triangle method" for estimating surface evapotranspiration and soil moisture from satellite imagery. Sensors, 7(8), 1612-1629.https://doi.org/10.3390/s7081612 https://doi.org/10.1016/j.apgeog.2010.10.002

Chen, C. F., Son, N. T., Chang, L. Y., & Chen, C. C. (2011). Monitoring of soil moisture variability in relation to rice cropping systems in the Vietnamese Mekong Delta using MODIS data. Applied Geography, 31(2), 463-475. https://doi.org/10.1016/j.apgeog.2010.10.002

Chen, C. F., Son, N. T., Chang, L. Y., & Chen, C. C. (2011). Monitoring of soil moisture variability in relation to rice cropping systems in the Vietnamese Mekong Delta using MODIS data. Applied Geography, 31(2), 463-475. https://doi.org/10.1016/j.apgeog.2010.10.002

Fathizad, H., & Tazeh, M. (2016). Assessment of pixel-based classification (ARTMAP fuzzy Neural Networks and Decision Tree) and Object-Oriented methods for land use mapping (Case study: Meymeh, Ilam province).

Fathizad, H., Tazeh, M., Kalantari, S., & Shojaei, S. (2017). The investigation of spatiotemporal variations of land surface temperature based on land use changes using NDVI in southwest of Iran. Journal of African Earth Sciences, 134, 249-256. https://doi.org/10.1016/j.jafrearsci.2017.06.007

Farayola, A. M., Hasan, A. N., & Ali, A. (2021). Efficient photovoltaic MPPT system using Coarse Gaussian support vector machine and artificial neural network techniques. International Journal of Innovative Computing Information and Control (IJICIC), 14(1), 323-329.

Goward, S. N., Xue, Y., & Czajkowski, K. P. (2002). Evaluating land surface moisture conditions from the remotely sensed temperature/vegetation index measurements: An exploration with the simplified simple biosphere model. Remote sensing of environment, 79(2-3), 225-242. https://doi.org/10.1016/S0034-4257(01)00275-9

Guan, X., Li, J., & Booty, W. G. (2020). Monitoring Lake Simcoe water clarity using Landsat-5 TM images. Water resources management, 25(8), 2015-2033. https://doi.org/10.1007/s11269-011-9792-3

Han, Y., Wang, Y., & Zhao, Y. (2010). Estimating soil moisture conditions of the greater Changbai Mountains by land surface temperature and NDVI. IEEE Transactions on Geoscience and Remote Sensing, 48(6), 2509-2515. https://doi.org/10.1109/TGRS.2010.2040830

Kogan, F. N. (2000). Contribution of remote sensing to drought early warning. Early warning systems for drought preparedness and drought management, 75-87.

Kousari, M. R., Naeini, S., & MA, T. M. and Frozeh, MR (2010). Sensitivity analysis of some equation for estimation of time of concentration in watersheds. Arid Biom Scientific and Research Journal, 1(1), 57-66. https://doi.org/10.1016/j.quaint.2010.05.011

Legates, D. R. (2018). An introduction. https://doi.org/10.1111/0033-0124.00220

Lu, D., Mausel, P., Brondizio, E., & Moran, E. (2004). Change detection techniques. International journal of remote sensing, 25(12), 2365-2401. https://doi.org/10.1080/0143116031000139863

Mallick, K., Bhattacharya, B. K., & Patel, N. K. (2009). Estimating volumetric surface moisture content for cropped soils using a soil wetness index based on surface temperature and NDVI. Agricultural and Forest Meteorology, 149(8), 1327-1342. https://doi.org/10.1016/j.agrformet.2009.03.004

Mountrakis, G., Im, J., & Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), 247-259. https://doi.org/10.1016/j.isprsjprs.2010.11.001

Sandholt, I., Rasmussen, K., & Andersen, J. (2002). A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of environment, 79(2-3), 213-224. https://doi.org/10.1016/S0034-4257(01)00274-7

Tazeh, M., Asadi, M., Taghizadeh, R., Kalantari, S., & Sadeghinia, M. (2018). Evaluation of geomorphometry indices in semi-automatic separation of the geomorphological types in desert areas (case study: west north of Ardekan). Iranian Journal of Range and Desert Research, 25(1).

Tucker, C. J. (1996). History of the use of AVHRR data for land applications. In Advances in the use of NOAA AVHRR data for land applications (pp. 1-19). Springer, Dordrecht. https://doi.org/10.1007/978-94-009-0203-9_1

Wang, H., Li, X., Long, H., Xu, X., & Bao, Y. (2010). Monitoring the effects of land use and cover type changes on soil moisture using remote-sensing data: A case study in China's Yongding River basin. Catena, 82(3), 135-145. https://doi.org/10.1016/j.catena.2010.05.008

Wang, X., & Xie, H. (2005). Relation between groundbased soil moisture and satellite image based NDVI. Earth and Environmental Science Department, University of Texas at San Antonio. https://doi.org/10.1016/j.jhydrol.2007.03.022

Wang, X., Xie, H., Guan, H., & Zhou, X. (2007). Different responses of MODIS-derived NDVI to root-zone soil moisture in semi-arid and humid regions. Journal of hydrology, 340(1-2), 12-24. https://doi.org/10.1016/j.jhydrol.2007.03.022

Yousefi, S., Khatami, R., Mountrakis, G., Mirzaee, S., Pourghasemi, H. R., & Tazeh, M. (2015). Accuracy assessment of land cover/land use classifiers in dry and humid areas of Iran. Environmental monitoring and assessment, 187(10), 1-10. https://doi.org/10.1007/s10661-015-4847-1