Spatial Analysis of Expansion of Farmland in the Protected Area of Kavir using Ecosystem Management Approach and its forecast in the coming years

Document Type : Research Paper

Authors

1 Ph.D. student of Environmental planning, Department of Environmental Planning, Management and Education, Kish International Campus, University of Tehran, Tehran, Iran

2 Professor of Geomorphology, Department of Environmental Planning, Management and Education, Faculty of Environment, University of Tehran, Tehran, Iran

3 Assistant Professor of Environment, Department of Environmental Planning, Management and Education, Faculty of Environment, University of Tehran, Tehran, Iran

10.29252/aridbiom.2023.19071.1904

Abstract

This study was conducted to investigate the expansion of Farmland in the protected area of Kavir. In order to achieve this approach, an area of ​​33933 ha was selected in the northern part of the region, where Farmlands are only expanded in this part. Land use maps were prepared for 1986, 1994, 2002, 2013 and 2020 using the images of Landsat's TM and OLI sensors and SVM algorithm by coding in Google Earth Engine. LUCC were calculated by the LCM model. The land use map for 2054 was predicted by CA Markov. Finally, the habitat changes were evaluated by landscape metrics. The results showed that the validation coefficient of SVM was more than 0.98. The land uses were divided into three classes: Farmlands, desert scrublands, and shrub pastures. The area of ​​Farmland increased from 434.5 ha in 1986 to 4,243 ha in 2020, which has increased by 3,809 ha, of which 3,067 ha were related to the conversion of shrublands to agriculture and 822 ha were related to the conversion of shrub pastures to agriculture. Farmland has been extended towards the center of the district and along the Golu River. The most changes and increase in the cultivated area were related to the period from 2002 to 2013 (2104 ha). The forecast results for the year 2054 also indicate that the Farmlands will expand downstream along the Golu River and compared to 2020, about 2787 hectares of Farmlands will grow, of which about 2010 hectares are related to bush conversion. Shrublands to agriculture and 725 ha of it will be related to the conversion of shrub pastures to agriculture. The number and weighted profile of the shape of patches of shrubland and shrub meadows increases, the average area, contiguity and size of the patches decreases, which indicates destruction and decomposition.

Keywords


[1]. Arekhi, S. (2015). Application of Landscape Metrics in Assessing Land Use Changes' Trend by Using Remote Sensing and GIS Case study: Dehloran Desert Area. Geography and Development, 13(40), 59-68 (in Farsi).
[2]. Awad, M. (2021, December). Google Earth Engine (GEE) cloud computing based crop classification using radar, optical images and Support Vector Machine Algorithm (SVM). In 2021 IEEE 3rd International Multidisciplinary Conference on Engineering Technology (IMCET) (pp. 71-76). IEEE.
[3]. Chen, A., Yang, X., Guo, J., Zhang, M., Xing, X., Yang, D., Jiang, L. (2022). Dynamic of land use, landscape, and their impact on ecological quality in the northern sand-prevention belt of China. Journal of Environmental Management, 317, 115351.
[4]. Chisanga, C. B., Shepande, C. C., & Nkonde, E. (2022). CA-Markov Approach in Dynamic Modelling of LULCC Using ESA CCI Products over Zambia.‏
[5]. De Moraes, M. C. P., de Mello, K., & Toppa, R. H. (2017). Protected areas and agricultural expansion: Biodiversity conservation versus economic growth in the Southeast of Brazil. Journal of Environmental Management, 188, 73-84.‏
[6]. Farashiani, M., Yarmand, H., Kazerani, F., Farahani, S., amani, M., & Alinejad, M. (2021). Conservation of planted rangelands and forests in desert areas of the country: Challenges and solutions. Iran Nature, 6(4), 23-32 (in Farsi)
[7]. Feizizadeh, B., Blaschke, T., Nazmfar, H., Akbari, E., & Kohbanani, H. R. (2013). Monitoring land surface temperature relationship to land use/land cover from satellite imagery in Maraqeh County, Iran. Journal of Environmental Planning and Management, 56(9), 1290-1315.‏
[8]. Gaurav Singh, V., Singh, S. K., Kumar, N., & Singh, R. P. (2022). Simulation of land use/land cover change at a basin scale using satellite data and Markov Chain model. Geocarto International, (just-accepted), 1-23.‏
[9]. Kaboli, M. (2014). Habitat Evaluation of Wild Sheep (Ovis orientalis) in Kavir National Park using Ecological Niche Factor Analysis Method. Journal of Natural Environment67(2), 185-194. (in Farsi)
[10]. Khoshnood Motlagh, S., Sadoddin, A., Haghnegahdar, A., Razavi, S., Salmanmahiny, A., & Ghorbani, K. (2021). Analysis and prediction of land cover changes using the land change modeler (LCM) in a semiarid river basin, Iran. Land Degradation & Development, 32(10), 3092-3105.‏
[11]. Kline, O., & Joshi, N. K. (2020). Mitigating the effects of habitat loss on solitary bees in agricultural ecosystems. Agriculture, 10(4), 115.‏
[12]. Lark, T. J., Spawn, S. A., Bougie, M., & Gibbs, H. K. (2020). Cropland expansion in the United States produces marginal yields at high costs to wildlife. Nature communications, 11(1), 1-11.‏
[13]. Leta, M. K., Demissie, T. A., & Tränckner, J. (2021). Modeling and prediction of land use land cover change dynamics based on land change modeler (LCM) in Nashe watershed, upper Blue Nile Basin, Ethiopia. Sustainability, 13(7), 3740.‏
[14]. Li, K., Feng, M., Biswas, A., Su, H., Niu, Y., & Cao, J. (2020). Driving factors and future prediction of land use and cover change based on satellite remote sensing data by the LCM model: a case study from Gansu province, China. Sensors, 20(10), 2757.‏
[15]. Lira, P. K., Tambosi, L. R., Ewers, R. M., & Metzger, J. P. (2012). Land-use and land-cover change in Atlantic Forest landscapes. Forest Ecology and Management, 278, 80-89.‏
[16]. Mishra, V. N., Rai, P. K., & Mohan, K. (2014). Prediction of land use changes based on land change modeler (LCM) using remote sensing: A case study of Muzaffarpur (Bihar), India. Journal of the Geographical Institute "Jovan Cvijic", SASA, 64(1), 111-127.
[17]. Mwabumba, M., Yadav, B. K., Rwiza, M. J., Larbi, I., & Twisa, S. (2022). Analysis of land use and land-cover pattern to monitor dynamics of Ngorongoro world heritage site (Tanzania) using hybrid cellular automata-Markov model. Current Research in Environmental Sustainability, 4, 100126.‏
[18]. Nhung, N. T. T. (2021). How to develop agriculture and protect the environment around protected areas: A case analysis of Xuan Thuy National Park, Vietnam (Doctoral dissertation, Gembloux Agro-Bio Tech-Université de Liège,​Gembloux,​​ Belgique).‏
[19]. Pech-May, F., Aquino-Santos, R., Rios-Toledo, G., & Posadas-Durán, J. P. F. (2022). Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine. Sensors, 22(13), 4729.
[20]. Pricope, N. G., & Binford, M. W. (2012). A spatio-temporal analysis of fire recurrence and extent for semi-arid savanna ecosystems in southern Africa using moderate-resolution satellite imagery. Journal of environmental management, 100, 72-85.‏
[21]. Rafaai, N. H., Abdullah, S. A., & Reza, M. I. H. (2020). Identifying factors and predicting the future land-use change of protected area in the agricultural landscape of Malaysian peninsula for conservation planning. Remote Sensing Applications: Society and Environment, 18, 100298.‏
[22]. Rao, Y., Zhou, M., Ou, G., Dai, D., Zhang, L., Zhang, Z., Yang, C. (2018). Integrating ecosystem services value for sustainable land-use management in semi-arid region. Journal of Cleaner Production, 186, 662-672.‏
[23]. Rukundo, E., Liu, S., Dong, Y., Rutebuka, E., Asamoah, E. F., Xu, J., & Wu, X. (2018). Spatio-temporal dynamics of critical ecosystem services in response to agricultural expansion in Rwanda, East Africa. Ecological Indicators, 89, 696-705.‏
[24]. Shao, S., Yu, M., Huang, Y., Wang, Y., Tian, J., & Ren, C. (2022). Towards a Core Set of Landscape Metrics of Urban Land Use in Wuhan, China. ISPRS International Journal of Geo-Information, 11(5), 281.
[25]. Shelestov, A., Lavreniuk, M., Kussul, N., Novikov, A., & Skakun, S. (2017). Exploring Google Earth Engine platform for big data processing: Classification of multi-temporal satellite imagery for crop mapping. Frontiers in Earth Science, 5, 17.
[26]. Shetty, S. (2019). Analysis of machine learning classifiers for LULC classification on Google Earth engine (Master's thesis, University of Twente).
[27]. Thiam, S., Salas, E. A. L., Hounguè, N. R., Almoradie, A. D. S., Verleysdonk, S., Adounkpe, J. G., & Komi, K. (2022). Modelling Land Use and Land Cover in the Transboundary Mono River Catchment of Togo and Benin Using Markov Chain and Stakeholder’s Perspectives. Sustainability,  14(7), 4160.‏
[28]. Wang, Q., & Wang, H. (2022). An integrated approach of logistic-MCE-CA-Markov to predict the land use structure and their micro-spatial characteristics analysis in Wuhan metropolitan area, Central China. Environmental Science and Pollution Research, 29(20), 30030-30053.
[29]. Wang, Q., Guan, Q., Lin, J., Luo, H., Tan, Z., & Ma, Y. (2021). Simulating land use/land cover change in an arid region with the coupling models. Ecological Indicators, 122, 107231.‏
[30]. Wang, S., & Zheng, X. (2022). Dominant transition probability: combining CA-Markov model to simulate land use change. Environment, Development and Sustainability, 1-19.
[31]. Yeh, A. G. O., & Li, X. (1997). An integrated remote sensing and GIS approach in the monitoring and evaluation of rapid urban growth for sustainable development in the Pearl River Delta, China. International Planning Studies, 2(2), 193-210.