Investigation of Land use Changes and Climatic Components in Meshkinshahr City

Subject Areas : Application of computer in water and soil issues

Batool Zeynali ¹ (Associate Professor, Department of Natural Geography, Faculty of Literature and Humanities, University of Mohaghegh Ardabili, Ardabil, Iran.)
Elham Mollanouri ² (Ph.D. Student in Hydrology and Meteorology, Mohaghegh Ardabili University, Ardabil, Iran.)
Shiva Safary ³ (MSc of Mohaghegh Ardabili University, Ardabil, Iran.)

Received: 2022-07-31 Accepted : 2022-10-16 Published : 2023-03-21

Keywords: land use, Climate Change, TOTRAM algorithm, NDVI,

Abstract :

Introduction: Climate change has negative effects on water resources and human societies, especially in arid and semi-arid regions. Remote sensing is widely used in studies such as monitoring the environment, agriculture, and climate of the earth. This technology makes it possible to research and investigate such studies in large areas with the high spatial and temporal resolution, especially inaccessible areas or areas that do not have ground measurement stations (such as synoptic stations) provides. Considering the expansion of the cold semi-desert climate in the northwestern region of the country and the changes in land use in the Meshkinshahr region in recent years, the purpose of this research is to investigate the interrelationship between climatic components of temperature, precipitation, and soil moisture with land use changes. Methods: Meshkinshahr city is located in the northwest of Iran and is one of the important cities of Ardabil province. In the present research, the studied area using satellite images of Landsat 5 for 2002 and Landsat 8 for 2021 in seven classes of irrigated agriculture, rain-fed agriculture, residential area, water areas, snow cover, good pasture, and poor pasture for investigation of the changes in land-use has been classified. In the following, temperature and precipitation components (along with verification with ground data) respectively using the single-channel algorithm and GPM database and soil moisture using the optical thermal algorithm (TOTRAM) by applying LST and NDVI parameters as a time series were reviewed for the years 2002, 2006, 2011, 2016, 2019 and 2021. Result: By examining the resulting maps, it has been observed that there have been significant changes in different land uses, and most of these changes have been related to the increase of irrigated agricultural lands and poor pastures, and the loss of quality pastures. Also, temperature and precipitation fluctuations in the studied years are quite clear and do not have a regular trend, so for example, in 2011, a sudden increase in precipitation to the amount of 46 mm is observed. But the minimum temperature compared to its maximum in different years shows a greater increase compared to 2002. Vegetation and soil fertility do not have a particular upward or downward trend and show different values ​​in different years, but the maximum vegetation cover, i.e. value 1 in the case of the NDVI index, is observed in 2002. Conclusion: According to the results and fluctuations in the values ​​of climatic components, especially sudden changes in some years, signs of climate change can be observed in the region, but a definite opinion cannot be expressed regarding the trend of land use changes and climate changes. Examining the results shows that land use changes, especially in the agricultural sector, can be affected by climatic conditions. The dependence of climatic components on each other and the influence of soil moisture and vegetation on these components are other results of this study. So, with the increase in temperature, we see a decrease in the amount of humidity and the level of vegetation. The times when soil moisture is higher than in other years, due to the cooling role of evaporation, low temperature, and vegetation cover are favorable. As a result, there is a clear dependence between the different components and the change in one parameter affects the other parameters, but it seems that the temperature component plays a more colorful role than the other parameters.

References:

Alavipanah, K. (2018). Thermal Remote Sensing. Tehran University Publications, 506 pp https://press.ut.ac.ir/book_1807.html [in Persian]

Asghari saraskanroud, S., Safari, S., & Mollanouri, E. (2021). Estimation of the levels of Groundwater Aquifers under the Influence of Land-Use Changes by Using GRACE Satellite Data. Geography and Environmental Planning, 32(4), 65-86. [in Persian]

Asghari Saraskanrod, S., Esfandayari Darabad, F., Mollanouri, E., & Safary, S. (2022). Investigation of Soil Surface Moisture in Ardabil City Using Landsat 8 and Sentile 1 satellite Data. Journal of Water and Soil Resources Conservation, 11(4), 89-103. [in Persian] 

Adab, H. (2017).  Estimation of the Instantaneous Soil Surface Moisture Content in Cold Seasons by using Optical and Thermal Remote Sensing Data under Clear Sky. Water and Soil Sci, 21(2), 175-191. [in Persian]

Asghari, S., & Emami H. (2019). Monitoring the earth surface temperature and relationship land use with surface temperature using of OLI and TIRS Image. Scientific Journals Management System, 19 (53), 195-215. [in Persian]

Bahri, M., & Dastorani, M. (2017). Evaluation of the Impacts of Climate and Land Use Changes on Hydrological Responses of Eskandari Basin in the Future Decade. Journal of Geography and Environmental Hazards, 6(2), 37-57. [in Persian]

Bai, J., Cui, Q., Zhang, W., & Meng, L. (2019). An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data. 1–20.

Ebrahimy, H., Rasuly, A., & Ahmadpour, A. (2019). Modeling dynamic changes of Land Use with Object Based Image Analysis and CA-Markov approach (Case study: Shiraz city). Scientific- Research Quarterly of Geographical Data (SEPEHR), 27(108), 137-149. [in Persian]

Chung, E.S., Park, K., & Lee, K.S. (2011). The relative impacts of climate change and urbanization on the hydrological response of a Korean urban watershed. Hydrol. Process, 25, 544–560, doi:10.1002/hyp.7781.

Cooley, T., Anderson, G. P., Felde, G. W., Hoke, M. L., Ratkowski, A. J., Chetwynd, J. H., Gardner, J. A., Adler-Golden, S. M., Matthew, M. W., Berk, A., Bernstein, L. S., Acharya, P. K., Miller, D., & Lewis, P. (2002). FLAASH, a MODTRAN4-based atmospheric correction algorithm, its applications and validation. International Geoscience and Remote Sensing Symposium (IGARSS), 3(C), 1414–1418. https://doi.org/10.1109/igarss.2002.1026134

Eskandari Damane, H., Zehtabian, G., Khosravi, H., Azarnivand, H., & Barati, A. (2020). Simulation and forecasting of climatic components of temperature and precipitation in arid regions (Case study: Minab plain). Geography, 18(66), 110-128. [in Persian]

Fathololoumi, S., Vaezi, A., Alavipanah, S., & Ghorbani, A. (2020). Modeling the Influence of Biophysical Properties and Surface Topography on the Spatial Distribution of Soil Moisture in the Summer: A Case Study of Balikhli-Chay Watershed. Iranian journal of Ecohydrology, 7(3), 563-581. doi: 10.22059/ije.2020.299783.1307. [in Persian]

Farrokhzadeh, B., Choobeh, S., Nouri, H., & Goodarzi, M. (2018). Study of climate change and land use changes impacts on surface runoff: Balighlo Chai Watershed in Ardebil. Watershed Engineering and Management, 10(3), 318-331. [in Persian]

Hanson, H., & Gray, E. (2013). Global Precipitation Measurement: Core Observatory. 1–20.

Skofronick-Jackson, G., Petersen, W. A., Berg, W., Kidd, C., Stocker, E. F., Kirschbaum, D. B., Kakar, R., Braun, S. A., Huffman, G. J., Iguchi, T., Kirstetter, P. E., Kummerow, C., Meneghini, R., Oki, R., Olson, W. S., Takayabu, Y. N., Furukawa, K., & Wilheit, T. (2017). The global precipitation measurement (GPM) mission for science and Society. Bulletin of the American Meteorological Society, 98(8), 1679–1695. https://doi.org/10.1175/BAMS-D-15-00306.1

Sisay, S., Mamuye, F., Yesuf, B., Saeed, S., Bhattacharjee, S., & Hossain, S. (2022). Analysis of Precipitation and Temperature Trends Under the Impact of Climate Change Over Ten Districts of Jimma Zone , Ethiopia. Earth Systems and Environment. https://doi.org/10.1007/s41748-022-00322-0

 Ghavami, A., Akbari, M., Karimi, A., & Yazdandad, H. (2021) Assessing impact of climate change and land-use changes on desertification in the Shirahmadprotected area of Sabzevar, in the 1st International and the 8th National Conference on Rangeland Management in Iran. [in Persian]

Hosseini, S., Ghaffarzadeh, H., Abedi, Z., & Shiry, N. (2014). Assessment of Climate Change and its Impacts on the Natural Land use of Gorgan River Basin. Journal of Natural Environment, 67(1), 25-39. [in Persian]

Harti, E., Lhissou, A., Chokmani, R., Ouzemou, K., Hassouna, J. & Bachaoui, m. (2016). Spatiotemporal monitoring of soil salinization in irrigated Tadla Plain (Morocco) using satellite spectral indices. International Journal of Applied Earth Observation and Geoinformation. 50, 64-73.

Hanson, H., & Gray, E. (2013). Global Precipitation Measurement: Core Observatory. 1–20.

Jouybari Moghaddam, Y., Akhoondzadeh, M., & Saradjian., M. R. (2015). A Novel Method for Retrieving Landsurface Emissivity from Landsat-8 Satellite Data Based on Vegetation Index. Journal of Geomatics Science and Technology. 5(1), 175-187 [in Persian]

Keikha, A., Khanlary, A., Keikha, A., & Sabouhi, M. (2020). The effect of climate change on land usage and agricultural sector performance in Mazandaran province. Journal of Environmental Science and Technology, 22(10), 93-104. [in Persian]

Majeed, M., Tariq, A., Anwar, M. M., Khan, A. M., Arshad, F., Mumtaz, F., Farhan, M., Zhang, L., Zafar, A., Aziz, M., Abbasi, S., Rahman, G., Hussain, S., Waheed, M., Fatima, K., & Shaukat, S. (2021). Monitoring of land use–Land cover change and potential causal factors of climate change in Jhelum district, Punjab, Pakistan, through GIS and multi-temporal satellite data. Land, 10(10). https://doi.org/10.3390/land10101026

Molotoks, A., Smith, P., & Dawson, T. P. (2021). Impacts of land use, population, and climate change on global food security. Food and Energy Security, 10(1), 1–20. https://doi.org/10.1002/fes3.261

Marhaento, H., Booij, M. J., & Hoekstra, A. Y. (2018). Hydrological response to future land-use change and climate change in a tropical catchment. Hydrological Sciences Journal, 63(9), 1368–1385. https://doi.org/10.1080/02626667.2018.1511054

Nazari Samani, A., Khalighi Sigaroodi, S., abdolshahnejad, M., Syadi Lotf Abadi, S., & Habibi Nokhandan, M. (2019). Determination the role of climate change and land use on future desertification status, case study: Sabzevar. Watershed Engineering and Management, 11(3), 806-818. [in Persian]

Naji Domirani, S., Ziaian Firouzabadi, P., Sadidi, J., Rahimi, K. (2016). Earth surface temperature calculation using split window algorithm and Landsat 8 satellite images (case study: Nahavand city), National Geomatics Conference. [in Persian]

Ndossi, M., & Avdan, U. (2016). Application of Open Source Coding Technologies in the Production of Land Surface Temperature (LST) Maps from Landsat: A PyQGIS Plugin, Journal remato sensing ,8(413), 1-31.

Orozco, I., Martínez, A., & Ortega, V. (2020). Assessment of the water, environmental, economic and social vulnerability of a watershed to the potential effects of climate change and land use change. Water (Switzerland), 12(6). https://doi.org/10.3390/W12061682

Pandey, R., Goswami, S., Sarup, J. & Matin, S. (2020). The thermal–optical trapezoid model‑based soil moisture estimation using Landsat‑8 data, journal Modeling Earth Systems and Environment, 1-9. https://doi.org/10.1007/s40808-020-00975-8.

Peng, W., Wang J., Zhang, J. & Zhang, Y. (2020). Soil moisture estimation in the transition zone from the Chengdu Plain region to the Longmen Mountains by field measurements and LANDSAT 8 OLI/TIRS-derived indices. Arabian Journal of Geosciences, 1-15. https://doi.org/10.1007/s12517-020-5152-z.

Prashant, K. S., George, P. P., & Yann, H. K. (2016). Satellite Soil Moisture Retrieval Techniques and Applications, 411 pp.

Raziei, T. (2017). Identification of the temperature regimes of Iran using multivariate methods. Iranian Journal of Geophysics, 11(2), 15-35. [in Persian]

Sari Sarraf, B., Naghizadeh, H., Rasouly, A., Jahanbakhsh, S., & Babaeyan, I. (2019). Modeling and spatial analysis of snow depth in Northern Iran based on database from European Centre for Medium-Range Weather Forecasts (ECMWF). Physical Geography Research Quarterly, 51(4), 651-671. doi: 10.22059/jphgr.2019.268047.1007289. [in Persian]

Sekertekin, A., & Bonafoni, S. (2020). Land surface temperature retrieval from Landsat 5, 7, and 8 over rural areas: Assessment of different retrieval algorithms and emissivity models and toolbox implementation. Remote Sensing, 12(2). https://doi.org/10.3390/rs12020294

Sadeghi, M., Babaeian, E., Tuller, M. & Jones, S. (2017). The optical trapezoid model: A novel approach to remote sensing of soil moisture applied to Sentinel-2 and Landsat-8 observations, Journal Remote Sensing of Environment. http://dx.doi.org/10.1016/j.rse.2017.05.041 t, 52-68.

Shafian, S. & maas, S. (2015). Improvement of the Trapezoid Method Using Raw Landsat Image Digital Count Data for Soil Moisture Estimation in the Texas (USA) High Plains, Sensors, doi:10.3390/s150101925, 1925-1944.

Srivastava, P.K., Han, D., Rico-Ramirez, M.A., Al-Shrafany, D., & Islam, T. (2013). Data fusion techniques for improving soil moisture deficit using SMOS satellite and WRF-NOAH land surface model. Water Resour. Manage. 27, 5069–5087.

Skofronick-Jackson, G., Petersen, W. A., Berg, W., Kidd, C., Stocker, E. F., Kirschbaum, D. B., Kakar, R., Braun, S. A., Huffman, G. J., Iguchi, T., Kirstetter, P. E., Kummerow, C., Meneghini, R., Oki, R., Olson, W. S., Takayabu, Y. N., Furukawa, K., & Wilheit, T. (2017). The global precipitation measurement (GPM) mission for science and Society. Bulletin of the American Meteorological Society, 98(8), 1679–1695. https://doi.org/10.1175/BAMS-D-15-00306.1

Tang, Z.; Engel, B.; Pijanowski, B.; & Lim, K. (2005). Forecasting land use change and its environmental impact at a watershed scale. J. Environ. Manag, 76, 35–45.

Xu, R., Tian, F., Yang, L., Hu, H., Lu, H., & Hou, A. (2017). Ground validation of GPM IMERG and trmm 3B42V7 rainfall products over Southern Tibetan plateau based on a high-density rain gauge network. Journal of Geophysical Research, 122(2), 910–924. https://doi.org/10.1002/2016JD025418

Yadav, S., Singh, P., Pal Singh Jadaun, S., Kumar, N. & Upadhyay, R. (2019). SOIL MOISTURE ANALYSIS OF LALITPUR DISTRICT UTTAR PRADESH INDIA USING LANDSAT AND SENTINEL DATA The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3(6), 1-7.