Long-Term Time Series Analysis of Land Cover Changes in an Arid Environment Using Landsat Data:(A Case Study Of Hamoun Biosphere Reserve, Iran)

Document Type : Research Paper

Authors

1 Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.

2 Faculty of Agriculture, University of Torbat Heydarieh, Torbat Heydarieh, Iran.

3 Area de Edafoloxía, Departamento de Bioloxía Vexetal e Ciencia do Solo, Facultade de Ciencias, Universaide de Vigo, As Lagoas s/n, 32004, Ourense, Spain.

4 Department of Hydrology and Water Resources Management, Faculty of Meteorology, Environment & Arid Land Agriculture, King Abdulaziz University Jeddah, 21589. Saudi Arabia.

10.22059/jdesert.2023.93547

Abstract

Change detection of lakes is important to monitor ecosystem health and wind erosion process in arid environments. The main purpose of this research is to evaluate unsupervised classification based on vegetation indices to monitor Land cover changes (LCCs). The Hamoun Biosphere Reserve is located in the east of Iran and is considered one of the most important wetlands in the center of the Iran Plateau. To detect land cover changes, using Landsat images from the 1990s, 2000s, 2010s and 2020s ground control points (GCP) and spectral profiles, four major land cover classes were obtained (sparse vegetation, dense vegetation, bare land, and water bodies). To create AOIs, the pure pixels were selected using obtained spectral profiles of the main land types by GCPs in 2020. The separability of representative AOIs by classes was examined by Jeffries–Matsushita distances and scattering ellipse parameters. A maximum likelihood classifier (MLC) was applied to Landsat images in 2020 with an overall accuracy of 93% and a Kappa statistic of 0.90. Subsequently, based on Soil Adjusted Vegetation Index (SAVI) maps, as additional input data, unsupervised classification was used to classify the same images in 2020.  The observed accuracy and kappa statistic of the used classification technique was up to 0.91 and 0.89 respectively. The finding indicated that in 2000, the area of arid land increased (90% of all areas) and became a major land use type, whereas water bodies (74% of all areas in the 1990s) reached zero in this year. Yearly water body changes revealed a severe dryness condition in this wetland. After 2000, in most cases in subsequent years, the water body completely dried up and in the seasonally flooded years, it did not exceed 10% of the total wetland’s area. On the other hand, before 2000, on average, 60% of the wetland’s area was dominated by the water class. Our study showed that in the time series without GCP for monitoring past changes, an unsupervised SAVI-based technique could provide acceptable accuracy in this region.

Keywords

References

References
Abdollahi, A., Liu, Y., Pradhan, B., Huete, A., Dikshit, A., Tran, N.N., 2022. Short-time-series grassland mapping using Sentinel-2 imagery and deep learning-based architecture. Egypt J Remote Sens Space Sci, 25(3), 673-685. https://doi.org/10.1016/j.ejrs.2022.06.002 
Ahmad, A., Quegan, S., 2012. Analysis of maximum likelihood classification on multispectral data. Applied Mathematical Sciences. 6(129), 6425-36.
Ahmed, K.R., Akter, S., Marandi, A., Schüth, C.A., 2021. Simple and robust wetland classification approach by using optical indices, unsupervised and supervised machine learning algorithms. Remote Sens Appl Soc Environ, 23, 100569. https://doi.org/10.1016/j.rsase.2021.100569
Ajaj, Q.M., Pradhan, B., Noori, A.M., Jebur, M.N. 2017. Spatial monitoring of desertification extent in western Iraq using Landsat images and GIS. Land Degradation & Development, 28(8), 2418-2431.
Alavi Zadeh, S., Monazzam Esmaeel Pour, A., Hossein Zadeh Kermani, M., 2013, Possibility study of areas with potential cultivation of saffron in Kashmar plain using GIS, Saffron Agronomy & Technology, 1 (1): 71-95. https://doi.org/ 10.22048/jsat.2013.4812
Alavipanah, S.K., Amiri, R., Khodaei, K., 2007. The use of spectral signatures in extracting information from water quality parameters in the lake Urmia, Iran. International Symposium on Physical Measurements and Signatures in Remote Sensing, Davos, Switzerland. Available at: https://www.isprs.org/proceedings/xxxvi/7-c50/papers/P76.pdf
Ali, M.Z., Qazi, W., Aslam, N., 2018. A comparative study of ALOS-2 PALSAR and landsat-8 imagery for land cover classification using maximum likelihood classifier. The Egyptian Journal of Remote Sensing and Space Science, 21, 29-35.  https://doi.org/10.1016/j.ejrs.2018.03.003
Alloghani, M., Al-Jumeily, D., Mustafina, J., Hussain, A., Aljaaf, A,J., 2020. A systematic review on supervised and unsupervised machine learning algorithms for data science. Supervised and unsupervised learning for data science. Springer, Cham,  Switzerland,  pp 3-21. https://doi.org/10.1007/978-3-030-22475-2_1
Almalki, R., Khaki, M., Saco, P.M., Rodriguez, J.F., 2022. Monitoring and mapping vegetation cover changes in arid and semi-arid areas using remote sensing technology: a review. Remote Sensing, 14(20), 5143. https://doi.org/10.3390/rs14205143
Alshahrani, H.M., Al-Wesabi, F.N., Al Duhayyim, M., Nemri, N., Kadry, S., Alqaralleh, B.A., 2021. An automated deep learning based satellite imagery analysis for ecology management. Ecol Inform, 66, 101452. https://doi.org/10.1016/j.ecoinf.2021.101452
Bai, J., Chen, X., Li, J., Yang, L., Fang, H., 2011. Changes in the area of inland lakes in arid regions of central Asia during the past 30 years. Environ Monit Assess, 178(1), 247-256. https://doi.org/10.1007/s10661-010-1686-y
Bashiri, M., Maroosi, M., Salari, A., Ghodoosi, M., 2018, Climatic zonation and land suitability determination for saffron in the Khorasan-Razavi province using data mining algorithms, Saffron Agronomy & Technology, 5 (4): 379-392. https://doi.org/10.22048/jsat.2017.60768.1189
Berry, M.W., Mohamed, A., Yap, B.W., 2020. Supervised and unsupervised learning for data science, 1st ed, Springer Nature, pp 3-21. https://doi.org/10.1007/978-3-030-22475-2
Bezerra, A.C., da Silva, J.L.B., de Albuquerque Moura, G.B., Lopes, P.M.O., Nascimento. C.R., et al., 2022. Dynamics of land cover and land use in Pernambuco (Brazil): Spatio-temporal variability and temporal trends of biophysical parameters. Remote Sensing Applications: Society and Environment, 25, 100677.
Chen, H., Liu, H., Chen, X., Qiao, Y., 2020. Analysis on impacts of hydro-climatic changes and human activities on available water changes in Central Asia. Science of the Total Environment, 737, 139779.
Chen, Y., Guerschman, J.P., Cheng, Z., Guo, L., 2019. Remote sensing for vegetation monitoring in carbon capture storage regions: A review. Applied energy, 240, 312-326.
Chouari, W., 2021.  Wetland land cover change detection using multitemporal Landsat data: a case study of the Al-Asfar wetland, Kingdom of Saudi Arabia. Arab J Geosci, 14(6), 1-14. https://doi.org/10.1007/s12517-021-06815-y
Chughtai, A.H., Abbasi, H., Karas, I.R. 2021. A review on change detection method and accuracy assessment for land use land cover. Remote Sensing Applications: Society and Environment, 22, 100482.
Condeça, J., Nascimento, J., Barreiras, N. 2022. Monitoring the storage volume of water reservoirs using Google Earth Engine. Water Resources Research, 58(3), e2021WR030026. https://doi.org/10.1029/2021WR030026
Dabboor, M., Howell, S., Shokr, M., Yackel, J., 2014. The Jeffries–Matusita distance for the case of complex Wishart distribution as a separability criterion for fully polarimetric SAR data. International Journal of Remote Sensing, 35(19), 6859-6873. DOI:10.1080/01431161.2014.960614
Dahmardeh, M., Piri, I., 2012. Importance of Hamoon Lake on rural development in Sistan region. Am J Econ, 2(6), 96-100. DOI: 10.5923/j.economics.20120206.01
Dahmardeh, M., Shahraki, J., Akbari, A., 2019. Economic assessment of environmental damages caused by drying up of Hamoon wetland in Sistan region. Journal of Natural Environmental Hazards, 8(19), 209-228. doi: 10.22111/jneh.2018.22176.1321
Dahmardeh, M., Yazdani, S., Piri, E., 2009. The socio-economic effects of Hamoon lake in Sistan region of Iran. J Food Agric Environ, 7(2), 799-802.
Dhingra, S., Kumar, D., 2019. A review of remotely sensed satellite image classification. International Journal of Electrical and Computer Engineering, 9(3), 1720. doi: 10.11591/ijece.v9i3.pp.1720-1731
Ehsani, A.H., Shakeryari, M., 2021. Monitoring of wetland changes affected by drought using four Landsat satellite data and Fuzzy ARTMAP classification method (case study Hamoun wetland, Iran). Arabian Journal of Geosciences, 14, 1-14. https://doi.org/10.1007/s12517-020-06320-8
Gaikwad, S.V., Vibhute, A.D., Kale, K.V., Dhumal, R.K., Nagne, A.D., Mehrotra, S.C., Varpe, A.B.,  Surase, R.R., 2019. Drought severity identification and classification of the land pattern using Landsat 8 data based on spectral indices and maximum likelihood algorithm. Microelectronics, Electromagnetics and Telecommunications, Springer, Singapore, pp 517-524. https://doi.org/10.1007/978-981-13-1906-8_53
Gao, L., Wang, X., Johnson, B.A., Tian, Q., Wang, Y., et al., 2020. Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review. ISPRS J Photogramm Remote Sens,  159, 364-377. https://doi.org/10.1016/j.isprsjprs.2019.11.018
Gill, T., Collett, L., Armston, J., Eustace, A., Danaher, T., Scarth, P., Flood, N., Phinn, S., 2010. Geometric correction and accuracy assessment of Landsat-7 ETM+ and Landsat-5 TM imagery used for vegetation cover monitoring in Queensland, Australia from 1988 to 2007. J Spat Sci,  55(2), 273-87. https://doi.org/10.1080/14498596.2010.521977
Gong, P., Wang,  J., Yu, L., Zhao, Y., Zhao, Y., et al., 2013. Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data. Int. J Remote Sens, 34(7), 2607-2654. https://doi.org/10.1080/01431161.2012.748992
Groisman, P., Bulygina, O., Henebry, G., Speranskaya, N., Shiklomanov, A., et al., 2018. Dryland belt of Northern Eurasia: contemporary environmental changes and their consequences. Environ Res Lett, 13(11), 115008. https://doi.org/10.1088/1748-9326/aae43c
Hamidianpour, M., Jahanshahi, S.M.A.,  Kaskaoutis, D.G., Rashki, A., Nastos, P.G., 2021. Climatology of the Sistan Levar wind: At-mospheric dynamics driving its onset, duration and withdrawal. Atmos Res, 260, 105711. https://doi.org/10.1016/j.atmosres.2021.105711
Hansen, M.C., Loveland, T.R., 2012. A review of large area monitoring of land cover change using Landsat data. Remote sensing of Environment, 122, 66-74.
Huete, A.R. 1988. A soil-adjusted vegetation index (SAVI). Remote sensing of environment, 25(3), 295-309.
Janparvar, M., Salehabadi, R.,  Zargari,  M., 2017.  Migration crisis caused by short-term droughts in Sistan and Baluchistan province. Geography,15(52), 183-199.
Jensen, J.R., 2018. Introductory digital image processing: a Remote sensing perspective (Pearson series in geographic information science) 4th Edition, Prentice-Hall Inc, UK.
Jiang, H., Xu, X., Guan, M., Wang, L., Huang, Y., Jiang, Y., 2015. Determining the contributions of climate change and human activities to vegetation dynamics in agro-pastural transitional zone of northern China from 2000 to 2015. Sci Total Environ, 718, 134871. https://doi.org/10.1016/j.scitotenv.2019.134871
Jiang, Z., Jiang, W., Ling, Z., Wang, X., Peng, K., Wang, C., 2021. Surface water extraction and dynamic analysis of baiyangdian lake based on the google earth engine platform using sentinel-1 for reporting sdg 6.6. 1 indicators. Water, 13(2), 138.  https://doi.org/10.3390/w13020138
Kalidhas, M., Sivakumar, R., 2022. Image processing and Supervised Classification of LANDSAT data for Flood Impact Assessment on Land Use and Land Cover. 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS), 437-440, IEEE. doi: 10.1109/ICTACS56270.2022.9988164.
Kang, S., Lee, G., Togtokh, C., Jang, K., 2015. Characterizing regional precipitation-driven lake area change in Mongolia. Journal of Arid Land, 7, 146-158. https://doi.org/10.1007/s40333-014-0081-x
Karkon Varnosfaderani, M.,  Kharazmi, R.,  Nazari Samani, A., Rahdari, M.R., Matinkhah, S.H., Aslinezhad, N., 2017. Distribution changes of woody plants in Western Iran as monitored by remote sensing and geographical information system: a case study of Zagros forest. J For Res, 28(1),145-153. https://doi.org/10.1007/s11676-016-0295-1
Karmaoui, A., Messouli, M., Khebiza, Y.M., Ifaadassan, I., 2014. Environmental vulnerability to climate change and anthropogenic impacts in dryland,(pilot study: Middle Draa Valley, South Morocco). J Earth Sci Clim Change, 11, 002. http://dx.doi.org/10.4172/2157-7617.S11-002
Kaselimi, M., Voulodimos, A., Daskalopoulos, I., Doulamis, N., Doulamis, A., 2022. A vision transformer model for convolution-free multilabel classification of satellite imagery in deforestation monitoring. IEEE Transactions on Neural Networks and Learning Systems. doi: 10.1109/TNNLS.2022.3144791.
Kennedy, R.E., Townsend, P.A., Gross, J.E., Cohen, W.B.,  Bolstad, P.,  Wang, Y.Q., Adams, P., 2009. Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects. Remote Sens Environ, 113(7), 1382-1396. https://doi.org/10.1016/j.rse.2008.07.018
Khalid, H.W., Khalil, R.M.Z., Qureshi, M.A. 2021. Evaluating spectral indices for water bodies extraction in western Tibetan Plateau. The Egyptian Journal of Remote Sensing and Space Science, 24(3), 619-634. https://doi.org/10.1016/j.ejrs.2021.09.003
Kharazmi, R., Panidi, E.A., Karkon, V.M. 2016. Assessment of dry land ecosystem dynamics based on time series of satellite images. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 13(5), 214-23. DOI: 10.21046/2070-7401-2016-13-5-214-223
Kharazmi, R., Tavili, A., Rahdari, M.R.,  Chaban, L., Panidi, E.,  Rodrigo-Comino, J., 2018.  Monitoring and assessment of seasonal land cover changes using remote sensing: a 30-year (1987–2016) case study of Hamoun Wetland, Iran. Environ Monit Assess, 190, 356 https://doi.org/10.1007/s10661-018-6726-z
Kumar, S., Arya, S., 2021. Change detection techniques for land cover change analysis using spatial datasets: A review. Remote Sensing in Earth Systems Sciences, 4(3), 172-185. https://doi.org/10.1007/s41976-021-00056-z
Li ,W.,  Hsu, C.Y., 2020. Automated terrain feature identification from remote sensing imagery: a deep learning approach. Int J Geogr Inf Sci, 34(4), 637-660. https://doi.org/10.1080/13658816.2018.1542697
Li, J., Pei, Y., Zhao, S., Xiao, R., Sang, X., Zhan, C., 2020. A review of remote sensing for environmental monitoring in China. Remote Sensing, 12(7), 1130.
Li, Q., Zhang, C., Shen, Y., Jia, W., Li, J., 2016. Quantitative assessment of the relative roles of climate change and human activities in desertification processes on the Qinghai-Tibet Plateau based on net primary productivity. Catena, 147, 789-796.
Liu, H., Gong, P., Wang. J., Clinton, N.,  Bai, Y., Liang, S., 2020. Annual dynamics of global land cover and its long-term changes from 1982 to 2015. Earth Syst Sci Data, 12(2), 1217-1243. https://doi.org/10.5194/essd-12-1217-2020
Liu, P., 2015. A survey of remote-sensing big data. Front Environ Sci, 3, 45. https://doi.org/10.3389/fenvs.2015.00045
Long, W., Srihann, S., 2004. Land cover classification of SSC image: unsupervised and supervised classification using ERDAS Imagine, Geosci. Remote. Sens. Lett. IEEE International Geoscience and Remote Sensing Symposium, 4, 2707-2712. https://doi.org/10.1109/IGARSS.2004.1369859
Lu, S., Wang, Y., Zhou, J., Hughes, A.C., Li, M., et al., 2022. Active water management brings possibility restoration to degraded lakes in dryland regions: a case study of Lop Nur, China. Scientific Reports, 12(1), 18578. https://doi.org/10.1038/s41598-022-23462-9
Lv, Z., Huang, H., Li, X., Zhao, M., Benediktsson, J.A., Sun, W., Falco, N., 2022. Land cover change detection with heterogeneous remote sensing images: Review, progress, and perspective. Proceedings of the IEEE, 110(12), 1976-1991. doi: 10.1109/JPROC.2022.3219376.
Maas, S.J., Nithya, R., 2010. Normalizing and Converting Image DC Data Using Scatter Plot Matching. Remote Sensing 2(7), 1644-1661. https://doi.org/10.3390/rs2071644
Maleki, S., Koupaei, S.S., Soffianian, A., Saatchi, S.,  Pourmanafi, S.,  Rahdari, V., 2019.  Human and climate effects on the Hamoun wetlands. Weather Clim Soc, 11(3), 609-622. https://doi.org/10.1175/WCAS-D-18-0070.1
Malinowski, R., Lewiński, S., Rybicki, M., Gromny, E., Jenerowicz, M., Krupiński, M., Nowakowski, A., Wojtkowski, C., Krupiński, M., Krätzschmar, E., Schauer, P., 2020. Automated production of a land cover/use map of Europe based on Sentinel-2 imagery. Remote Sensing, 12(21), 3523.  https://doi.org/10.3390/rs12213523
Mansourmoghaddam, M., Ghafarian Malamiri, H.R., Rousta, I., Olafsson, H., Zhang, H. 2022. Assessment of Palm Jumeirah Island’s construction effects on the surrounding water quality and surface temperatures during 2001–2020. Water, 14(4), 634.
Mansourmoghaddam, M., Rousta, I., Zamani, M., Mokhtari, M. H., Karimi Firozjaei, M., Alavipanah, S. K. 2021. Study and prediction of land surface temperature changes of Yazd city: assessing the proximity and changes of land cover, Journal of RS and GIS for Natural Resources, 12(4), 1-27. doi: 10.30495/girs.2021.682083
Mansourmoghaddam, M., Rousta, I., Zamani, M., Olafsson, H. 2023. Investigating and predicting Land Surface Temperature (LST) based on remotely sensed data during 1987–2030 (A case study of Reykjavik city, Iceland). Urban Ecosystems, 1-23.
Mason, I.M., Guzkowska, M.A.J., Rapley, C.G., 1994. The response of lake levels and areas to climatic change. Clim Change,  27, 161–197. https://doi.org/10.1007/BF01093590
Miri, A., Ahmadi, H., Ghanbari, A., Moghaddamnia, A., 2007. Dust storms impacts on air pollution and public health under hot and dry climate. Int J Energy Environ Eng, 2(1), 101-105.
Mishra, V.N., Prasad, R., Kumar, P., Gupta, D.K., Srivastava, P.K., 2017. Dual-polarimetric C-band SAR data for land use/land cover classification by incorporating textural information. Environmental Earth Sciences, 76(1),1-16. https://doi.org/10.1007/s12665-016-6341-7
Mohammady, M., Moradi, H.R., Zeinivand, H., Temme, A.J.A.M., 2015. A comparison of supervised, unsupervised and synthetic land use classification methods in the north of Iran. Int J Environ Sci Technol, 12(5),1515-1526. https://doi.org/10.1007/s13762-014-0728-3
Mousavi, S.A., Fakhireh, A., Shahryari, A., 2014. Assessment of changes trend of land cover with use of remote sensing data in Hamoon wetlands. J Biodivers Conserv Environ Sci, 4(5), 146-156.
Mousazadeh, R., Ghaffarzadeh, H., Nouri, J., Gharagozlou, A., Farahpour, M., 2015. Land use change detection and impact assessment in Anzali international coastal wetland using multi-temporal satellite images. Environ Monit Assess, 187(12), 1-11. https://doi.org/10.1007/s10661-015-4900-0
Muttitanon, W., Tripathi, N.K., 2005. Land use/land cover changes in the coastal zone of Ban Don Bay, Thailand using Landsat 5 TM data. Int J Remote Sens, 26(11), 2311-23. https://doi.org/10.1080/0143116051233132666
Nagendra, H.,  Lucas, R., Honrado, J.P., Jongman, R.H., Tarantino, C., Adamo, M.,  Mairota, P., 2013. Remote sensing for conservation monitoring: Assessing protected areas, habitat extent, habitat condition, species diversity, and threats. Ecol Indic, 33, 45-59. https://doi.org/10.1016/j.ecolind.2012.09.014
Nutini, F., Boschetti, M., Brivio, P.A., Bocchi, S., Antoninetti, M., 2013. Land-use and land-cover change detection in a semi-arid area of Niger using multi-temporal analysis of Landsat images. International journal of remote sensing, 34(13), 4769-4790. https://doi.org/10.1080/01431161.2013.781702
Opedes, H., Mücher, S., Baartman, J.E., Nedala, S., Mugagga, F., 2022. Land Cover Change Detection and Subsistence Farming Dynamics in the Fringes of Mount Elgon National Park, Uganda from 1978–2020. Remote Sensing, 14(10), 2423. https://doi.org/10.3390/rs14102423
Othman, Y., Steele, C., St Hilaire, R., 2018. Surface Reflectance Climate Data Records (CDRs) is a reliable Landsat ETM+ source to study chlorophyll content in pecan orchards. J Indian Soc Remote Sens, 46(2), 211-218. https://doi.org/10.1007/s12524-017-0690-x
Pande, C.B., 2022. Land use/land cover and change detection mapping in Rahuri watershed area (MS), India using the google earth engine and machine learning approach. Geocarto International, 1-21. https://doi.org/10.1080/10106049.2022.2086622
Pandey, P.C., Koutsias, N., Petropoulos, G.P., Srivastava, P.K., Ben Dor, E., 2021. Land use/land cover in view of earth observation: data sources, input dimensions, and classifiers—a review of the state of the art. Geocarto International, 36(9), 957-988. https://doi.org/10.1080/10106049.2019.1629647
Petropoulos, G.P., Kalivas, D.P., Griffiths, H.M., Dimou, P.P., 2015. Remote sensing and GIS analysis for mapping spatio-temporal changes of erosion and deposition of two Mediterranean river deltas: The case of the Axios and Aliakmonas rivers, Greece. Int J of Applied Earth Observation and Geoinformation, 35, 217-228.  https://doi.org/10.1016/j.jag.2014.08.004
Rad, A.M., Kreitler, J.,  Sadegh, M., 2021. Augmented Normalized Difference Water Index for improved surface water monitoring. Environ Model Softw, 140, 105030. https://doi.org/10.1016/j.envsoft.2021.105030
Rahimi, A., Breuste, J., 2021. Why is Lake Urmia drying up? Prognostic modeling with land-use data and artificial neural network. Frontiers in Environmental Science, 9, 603916. https://doi.org/10.3389/fenvs.2021.603916
Rashki, A., Kaskaoutis, D.G., Goudie, A.S., Kahn, R.A., 2013.  Dryness of ephemeral lakes and consequences for dust activity: the case of the Hamoun drainage basin, southeastern Iran. Sci Total Environ, 463, 552-564. https://doi.org/10.1016/j.scitotenv.2013.06.045
Rastegar-Pouyani, N., Gholamifard, A., Karamiani, R., Bahmani, Z., Mobaraki, A., Abtin, E., Faizi, H., Heidari, N., Takesh, M., Sayyadi, F., Ahsani, N., Browne, R.K. 2015. Sustainable Management of the Herpetofauna of the Iranian Plateau and coastal Iran. Amphib Reptile Conserv, 9(1), 1-15.
Rezaei, R., & Ghofranfarid, M. 2018. Rural households’ renewable energy usage intention in Iran: extending the unified theory of acceptance and use of technology. Renewable Energy, 122, 382–391.
Rousta, I., Sarif, M.O., Gupta, R.D., Olafsson, H., Ranagalage, M., Murayama, Y., Zhang, H., Mushore, T.D. 2018. Spatiotemporal analysis of land use/land cover and its effects on surface urban heat island using Landsat data: A case study of Metropolitan City Tehran (1988–2018). Sustainability, 10(12), 4433.
Sahabi, H., Moallem Banhangi, F., 2022, Evaluation the Impact Climatic Parameters on Flowering Behaviourand Yield Of Saffron (Crocus sativus L.) in Razaviand Southern Khorasan Provinces, Saffron Agronomy & Technology, 9 (4), 357-373. https://doi.org/10.22048/jsat.2021.283088.1423
Sanadgol, M., Asghari Lafmejani, S., Fazelnia, G., Pirani, M., 2022. Physical and environmental constraints of entrepreneurship in the villages surrounding the border and Hamoon International Wetland (The study of District Ghorghori, Hirmand County). Journal of Studies in Entrepreneurship and Sustainable Agricultural Development, 9(2),  37-58. doi: 10.22069/jead.2022.19746.1552
Șerban, R.,D., Jin, H., Șerban, M,, Luo, D., Wan, Q., Jin, X., Ma, Q., 2020.  Mapping thermokarst lakes and ponds across permafrost landscapes in the headwater area of yellow river on northeastern Qinghai-Tibet plateau. Int J Remote Sens, 41, 7042–7067. https://doi.org/10.1080/01431161.2020.1752954
Sharifikia, M., 2013. Environmental challenges and drought hazard assessment of Hamoun Desert Lake in Sistan region, Iran, based on the time series of satellite imagery. Natural hazards, 65(1), 201-217. https://doi.org/10.1007/s11069-012-0353-8
Soliman, G.A.A., Soussa, H., 2011. Wetland change detection in Nile swamps of southern Sudan using multitemporal satellite imagery. J Appl Remote Sens, 5(1), 053517. https://doi.org/10.1117/1.3571009
Sonnenschein, R., Kuemmerle, T., Udelhoven, T., Stellmes, M., Hostert, P., 2011. Differences in Landsat-based trend analyses in dry-lands due to the choice of vegetation estimate. Remote Sens Environ, 115(6), 1408-1420. https://doi.org/10.1016/j.rse.2011.01.021
Stavi, I., Pulido Fernández, M., Argaman, E. 2023. Long-term passive restoration of severely degraded drylands—divergent impacts on soil and vegetation: An Israeli case study. Journal of Geographical Sciences, 33(3), 529-546.
Swain, P.H., Davis, S.M. 1978. Remote Sensing: The Quantitative Approach. New York: McGraw-Hill.
Tahsin, S., Medeiros, S.C., Singh, A., 2021. Consistent Long-Term Monthly Coastal Wetland Vegetation Monitoring Using a Virtual Satellite Constellation. Remote Sens, 13(3), 438. https://doi.org/10.3390/rs13030438
Talukdar, S., Mankotia, S., Shamimuzzaman, M., Mahato, S., 2021. Wetland‐inundated area modeling and monitoring using supervised and machine learning classifiers. Advances in Remote Sensing for Natural Resource Monitoring, 5, 346-365.   https://doi.org/10.1002/9781119616016.ch17
Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y.A., Rahman, A., 2020. Land-use land-cover classification by machine learning classifiers for satellite observations—A review. Remote Sensing, 12(7), 1135. https://doi.org/10.3390/rs12071135
Tmušić, G., Manfreda, S., Aasen, H., James, M.R., Gonçalves, G., et al., 2020. Current practices in UAS-based environmental monitoring. Remote Sensing, 12(6), 1001. https://doi.org/10.3390/rs12061001
Vekerdy, Z., Dost, R.J.J., Reinink, G., Partow, H., 2006. History of environmental change in the Sistan Basin based on satellite image analysis: 1976-2005. UNEP Post-Conflict Branch Geneva,  https://wedocs.unep.org/20.500.11822/7690
Venter, Z.S., Scott, S.L., Desmet, P,G., Hoffman, M.T., 2020. Application of Landsat-derived vegetation trends over South Africa: Potential for monitoring land degradation and restoration. Ecol Indic, 113,106206. https://doi.org/10.1016/j.ecolind.2020.106206
Viennois, G., Proisy, C., Feret, J.B., Prosperi, J., Sidik, F., Rahmania, R., Longépé, N., Germain, O., Gaspar, P., 2016. Multitemporal analysis of high-spatial-resolution optical satellite imagery for mangrove species mapping in Bali, Indonesia. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(8), 3680-3686. 10.1109/JSTARS.2016.2553170
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 Sensing of Environment, 248,112002. https://doi.org/10.1016/j.rse.2020.112002
Wang, S., He, Y., Hu, S., Ji, F., Wang, B., Guan, X., Piccolroaz, S., 2021.  Enhanced warming in global dryland lakes and its drivers. Remote Sens, 14(1), 86. https://doi.org/10.3390/rs14010086
Wang, Y., Ma, J., Xiao, X., Wang, X., Dai, S., Zhao, B., 2019. Long-term dynamic of Poyang Lake surface water: A mapping work based on the Google Earth Engine cloud platform. Remote Sensing,11(3), 313. https://doi.org/10.3390/rs11030313
Wulder, M.A., Roy, D.P., Radeloff, V.C., Loveland, T.R., Anderson, M.C., et al., 2022. Fifty years of Landsat science and impacts. Remote Sensing of Environment, 280, 113195. doi: 10.1016/j.rse.2022.113195
Xie, S., Liu, L., Zhang, X., Yang, J., 2022. Mapping the annual dynamics of land cover in Beijing from 2001 to 2020 using Landsat dense time series stack. ISPRS Journal of Photogrammetry and Remote Sensing, 185, 201-218.
Xu, D., Song, A., Li, D., Ding, X., Wang, Z., 2019. Assessing the relative role of climate change and human activities in desertification of North China from 1981 to 2010. Frontiers of Earth Science, 13, 43-54. https://doi.org/10.1007/s11707-018-0706-z
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.
Xu, Z., Xu, S., Qian, C., Klette, R., 2019.  Accurate ellipse extraction in low-quality images. 16th International Conference on Machine Vision Applications (MVA), IEEE, pp. 1-5.  doi: 10.23919/MVA.2019.8757970 
Yang, L., Driscol, J., Sarigai, S., Wu, Q., Chen, H., Lippitt, C.D., 2022. Google earth engine and artificial intelligence (ai): a comprehensive review. Remote Sensing, 14(14), 3253. https://doi.org/10.3390/rs14143253
Yang, X., Lo, C.P., 2022. Using a time series of satellite imagery to detect land use and land cover changes in the Atlanta, Georgia metropolitan area. Int J Remote Sens, 23 (9),1775–1798. doi:10.1080/01431160110075802
Yang, X., Wang, N., He, J., Hua, T., Qie, Y., 2020. Changes in area and water volume of the Aral Sea in the arid Central Asia over the period of 1960–2018 and their causes. Catena, 191, 104566. https://doi.org/10.1016/j.catena.2020.104566
Yousefi, S., Mirzaee, S., Almohamad, H., Al Dughairi, A.A., Gomez, C., Siamian, N., Alrasheedi, M., Abdo H.G., 2022. Image classification and land cover mapping using sentinel-2 imagery: optimization of SVM parameters. Land, 11(7), 993. https://doi.org/10.3390/land11070993
Yuan, F., Sawaya, K.E., Loeffelholz, B.C.,  Bauer, M.E., 2005. Land cover classification and change analysis of the Twin Cities (Min-nesota) Metropolitan Area by multitemporal Landsat remote sensing. Remote Sens Environ, 98(2-3), 317-328. https://doi.org/10.1016/j.rse.2005.08.006
Yuan, Q., Shen, H., Li, T., Li, Z., Li, S., Jiang, Y., Xu, H., Tan, W., Yang, Q., Wang, J., Gao, J., 2020. Deep learning in environmental remote sensing: Achievements and challenges. Remote Sensing of Environment, 241, 111716.
Zhai, M., 2019. Inversion of organic matter content in wetland soil based on Landsat 8 remote sensing image. J Vis Commun Image Represent, 64,102645. https://doi.org/10.1016/j.jvcir.2019.102645
Zhai, Y., Roy, D.P., Martins, V.S., Zhang, H.K., Yan, L., Li, Z., 2022. Conterminous United States Landsat-8 top of atmosphere and surface reflectance tasseled cap transformation coefficients. Remote Sensing of Environment, 274, 112992. doi: 10.1016/j.rse.2022.112992
Zhao, Y., Su, D., Bao, Y., Yang, W., Zhao, C., Bai, Y., Zhao, Y., 2017. Dynamic monitoring of fractional vegetation cover of eco-function area of grassland on northern foot of Yinshan Mountains through remote sensing technology. Res Environ Sci, 30(2), 240-248. http://dx.doi.org/10.13198/j.issn.1001-6929.2017.01.35
Zhu, Y., Zhang, Y., Zheng, Z., Liu, Y., Wang, Z., et al., 2022. Converted vegetation type regulates the vegetation greening effects on land surface albedo in arid regions of China. Agricultural and Forest Meteorology, 324, 109119.
Zhu, Z., Zhang, J., Yang, Z., Aljaddani, A.H., Cohen, W.B., Qiu, S., Zhou, C., 2020. Continuous monitoring of land disturbance based on Landsat time series. Remote Sens Environ, 238, 111-116. https://doi.org/10.1016/j.rse.2019.03.009
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Volume 28, Issue 1
June 2023
Pages 123-144

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