Investigating land use changes and trends of hydro morphological indicators on the area and volume of the Ovan Lake's water zone based on the time series of Landsat data
Subject Areas : environmental economy
Morteza Karimi
1
,
Hadi Modabberi
2
*
,
Babak Razdar
3
1 - Researcher of Water Resources Monitoring Department of Jihad University Environmental Research Institute
2 - Assistant Professor of Water Resources Monitoring Department of Jihad University Environmental Research Institute
3 - پژوهشگر پژوهشکده محیط زیست جهاد دانشگاهی
Keywords: Wetland water area, Remote sensing, Hydrological indicators, Ovan Lake,
Abstract :
One of the most important approaches to preserve and restore wetlands, is identifying environmental changes from past to present and developing an integrated management plan to control these changes and decision-making to provide solutions for improving the condition of these valuable ecosystems. Ovan Lake, as one of the beautiful and touristic landscapes in the forbidden hunting area of Eastern Qazvin, has distinct mountain habitats and various species of wildlife. By employing remote sensing techniques for a 30-year period, the process of changes and land use in the hydrological unit leading to Ovan Lake were identified and the trend of their changes was obtained quantitatively in this research. Then, the effect of the related hydromorphological indicators on the area and volume of the lake was investigated. The results showed that, according to the Modified Normalized Difference Water Index (MNDWI), the average area of the lake water zone was 8.15 hectares over the past eight years and based on univariate regressions, its hydrological regime is mainly related to two important factors of precipitation and evaporation. According to the univariate regressions demonstrate a significant relationship between the lake's hydrological regime and precipitation/evaporation rates. The evaporation parameter also showed a logical trend during the statistical years, so that the area and volume of the water zone of the lake has decreased by the increase of evaporation from the free surface of the water. Also, the results of multivariate regression between lake water volume and rainfall and evaporation components showed that the lake volume is more correlated with rainfall. But in contrast, evaporation changes with a greater slope or rate.
1- Asghari, S., Jalilyan, R., Pirouzinejad, N., Madadi, A., & Yadegari, M. (2020). Evaluation of Water Extraction Indices Using Landsat Satellite Images (Case Study: Gamasiab River of Kermanshah). Journal of Geographical Sciences, 20(58), 53-70. (In Persian)
2- Azareh, A., Sardooi, E.R., Gholami, H., Mosavi, A.H., Shahdadi, A., & Barkhori, S. (2021). Detection and prediction of lake degradation using landscape metrics and remote sensing dataset. Environ Sci Pollution Res 28, 27283–27298.
3- Ballanti, L., Byrd, B., Woo, I., & Ellings, C. (2017). Remote sensing for wetland mapping and historical change detection at the Nisqually River Delta. Sustainability. 9(11): 1-32.
4- Banko, G. (1998). A review of assessing the accuracy of classifications of remotely sensed data and of methodsincluding remote sensing data in forest inventory. IIASAI, International Institue for Applied Systems Analysis, A-2361.
5- Feng, L., Han, X., Hu, C., & Chen, X. (2016). Four decades of wetland changes of the largest freshwater lake in China: Possible linkage to the Three Gorges Dam? Remote Sensing of Environment, 176: 43-55.
6- Feyisa, GL., Meilby, H., Fensholt, R., & Proud S. R. (2014). Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment, 140: 23-35.
7- Forkuor, G., Conrad, C., Thiel, M., Zoungrana, B., & Tondoh, J. (2017). Multiscale Remote Sensing to Map the Spatial Distribution and Extent of Cropland in the Sudanian Savanna of West Africa. Remote Sens, 9, 839.
8- Haghigh Khomami, M., Bonyad, A. E., & Panahandeh, M. (2023). Wetland Water Surface Area Identification and Evaluation affected by climate change Based on Landsat Data and NDWI Indices. Journal of Soil and Water Research, 54 (1), 173-192. (In Persian).
9- Haghigh Khomami, M., Tajaddod, M. J., Ravanbakhsh., M., & Jamalzad, F. (2021). Vegetation classification based on wetland index using object based classification of satellite images (Case study: Anzali wetland). Journal of RS & GIS for Natural Resources, 54 (1), 173-192. (In Persian).
10- Jamali, A., Mahdianpari, M., Brisco, B., Granger, J., Mohammadimanesh, F., & Salehi, B. (2021). Wetland Mapping Using Multi- Spectral Satellite Imagery and Deep Convolutional Neural Networks: A Case Study in Newfoundland and Labrador, Canada. Can. J. Remote Sens. 47(2): 243–260.
11- Johnston, R. & Barson, M. (1993). Remote-sensing of Australian wetlands — an evaluation of
Landsat Tm data for inventory and classification. AUST J MAR FRESH. RES, 44, 235–252.
12- Kazemirad, L., & Modaberi, H. (2023). Evaluation of Climatic parameters in Ovan Lake affected by climate change. Journal of Environmental science studies. 8(3): 6936-6942.
13- Lima-Quispe, N., Escobar, M., Albertus, J., Wickel, M., & Purkey, D. (2021). Untangling the effects of climate variability and irrigation management on water levels in Lakes Titicaca and Poop´o. Journal of Hydrology, Regional Studies, 37,100927.
14- Manandhar, S., Dev, S., Lee, Y. H., Winkler, S., & Meng, Y.S. (2018). Systematic study of weather variables for rainfall detection. IGARSS 2018- IEEE International Geoscience and Remote Sensing Symposium, 3027- 3030
15- McFeeters S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing Letters, 17(7), 1425-1432.
16- Modabberi, H., & Shokoohi, A. (2019). Determining Anzali Wetland Environmental Water Requirement Using Eco-Hydrologic Methods. Iran-Water Resources Research, 15(3), 91-104. (In Persian).
17- Modaberi, H., & Shokoohi, A. (2020). Evaluating the Effects of Reducing Environmental Water Requirement of Anzali Wetland on its Ecological Services in an IWRM Framework. Journal of Ecohydrology, 7(2). 481-496. (In Persian).
18- Mohammadi, A., Almasieh, K., & Nayeri, N. (2021). Change detection of land cover in Meighan wetland using remote sensing technique. Animal Environment, 13(3), 45-412. (In Persian).
19- Qureshi, S., Alavipanah, S., Konyushkova, M., Mijani, N., Fathololomi, M., Firozjaei, K., & Kakroodi, A. (2020). A Remotely sensed assessment of surface ecological change over the Gomishan Wetland, Iran. Remote Sensing, 12(18): 2989.
20- Salimi, Sh., Almuktar, S. A.A.A.N., & Scholz, M. (2021). Impact of climate change on wetland ecosystems: A critical review of experimental wetlands. Journal of Environmental Management, 286, 112160.
21- Soti, V., Tran, A., Bailly, S., Puech, C., Seen, D., Begue, A. (2009). Assessing optical
earth observation systems for mapping and monitoring temporary ponds in arid
areas. Int. J. Appl. Earth Obs. Geo inf. 11 (5), 344–351.
22- Wang, L., Diao, C., Xian, G., Yin, D., Lu, Y., Zou, S., & Erickson, T.A. (2020). A summary of the special issue on remote sensing of land change science with Google Earth Engine. Remote Sens. Environ. 248, 112002.
23- Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033