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Landscape change assessment and its prediction in a mountainous gradient with diverse land-uses

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Abstract

Land-use change is one of the main threats to the environment and natural resources nowadays. Dealing with the impacts of landscape change and its better management needs up-to-date information and data about the rate and amount of changes in different land-use types and spatial configurations. Remote sensing and landscape assessment provide diverse metrics for analyzing spatiotemporal changes over different scales. The purpose of this study is to quantifying the spatiotemporal analysis of landscape change index (LCI) during 1984 to 2016, and predicting the trend of land-use in 2030 using CA–Markov model. Toward this attempt, the Meshgin-Shahr area was chosen as a region with high potential in agriculture and tourism that has undergone many changes in recent decades. Landsat images were used to change detection, and landscape indices were calculated. Also, the LCI has been quantified in different time intervals under study. Also, the CA–Markov model was employed to predict the trend of changes in 2032. The values of CONTAG_MN, AREA_MN, CONNECT, and edge density (ED) landscape metrics have been increased at the class level. Assessing the trend of changes in the landscape metrics showed that the number of patch (NP), patch density (PD), largest patch index (LPI), ED, and total edge (TE) had a decreasing trend at the landscape level, which shows a significant decrease from 2008 to 2016. Accordingly, the value of the LCI was 6.67 in 2008–2016 as the most considerable value among consequent periods. Eventually, the LCI for the predicted period (2016–2032) equals 4.97. The result provides a basis for predicting the landscape characteristics in the future and will help managers to develop effective policies for the conservation of landscape integrity and connectivity.

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Data Availability Statement

The dataset generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  • Abdolalizadeh, Z., Ebrahimi, A., & Mostafazadeh, R. (2019). Landscape pattern change in Marakan protected area Iran. Regional Environmental Change, 19(6), 1683–1699.

    Google Scholar 

  • Adiguzel, F., Cetin, M., Dogan, M., Gungor, S., Kose, M., Bozdogan Sert, E., & Kaya, E. (2022). The assessment of the thermal behavior of an urban park surface in a dense urban area for planning decisions. Environmental Monitoring and Assessment, 194(7), 519. https://doi.org/10.1007/s10661-022-10172-y

    Article  CAS  PubMed  Google Scholar 

  • Aksoy, T., Dabanli, A., Cetin, M., Kurkcuoglu, M. A. S., Cengiz, A. M., Cabuk, S. N., Agacsapan, B., & Cabuk, A. (2022). Evaluation of comparing urban area land use change with Urban Atlas and CORINE data. Environmental Science and Pollution Research, 29, 28995–29015. https://doi.org/10.1007/s11356-021-17766-y

    Article  PubMed  Google Scholar 

  • Alaei, N., Mostafazadeh, R., Esmaliouri, A., Sharari, M., & Hazbavi, Z. (2020). Assessment and comparison of landscape connectivity in Koozehtopraghi Watershed, Ardabil Province Iranian. Journal of Applied Ecology, 8(4), 19–34. (In Persian).

    Google Scholar 

  • Alphan, H., & Aşur, F. (2021). Geospatial analysis of lake scenery as an indicator for the environment: The city of van (Turkey) and its surroundings. Environmental and sustainability indicators, 9, 100091. https://doi.org/10.1016/j.indic.2020.100091

    Article  Google Scholar 

  • Antrop, M. (2004). Landscape change and the urbanization process in Europe. Landscape and Urban Planning, 67(1–4), 9–26. https://doi.org/10.1016/s0169-2046(03)00026-4

    Article  Google Scholar 

  • Apan, A., Raine, S., & Paterson, M. (2002). Mapping and analysis of changes in the riparian landscape structure of the Lockyer Valley catchment, Queensland, Australia. Journal of Landscape and Urban Planning, 59(1), 43–57.

    Google Scholar 

  • Arekhi, S., & Komaki, B. (2015). Detecting and assessing desertification using landscape metrics in GIS environment (Case study: Ain-e-khosh Region, Iran). Environmental Resources Research, 3(2), 122–138. https://doi.org/10.22069/ijerr.2015.2709

    Article  Google Scholar 

  • Bautista, S., Mayor, A. G., Bourakhouadar, J., & Bellot, J. (2007). Plant spatial pattern predicts hillslope runoff and erosion in a Semiarid Mediterranean landscape. Ecosystems, 10, 987–998.

    Google Scholar 

  • Bell, S. (2012). Landscape: Pattern (p. 348p). Routledge.

    Google Scholar 

  • Botequila, L., Miller-Jozeph, M., & Jack, A. (2006). Measuring landscapes: A planner’s handbook (p. 245). Island Press.

    Google Scholar 

  • Brandt J (2000) Demands for future landscapes research on multifunctional landscapes. In Proceedings of the conference on multifunctional landscapes interdisciplinary approaches to landscape research and management, Roskilde.

  • Castillo, E. M., García-Martin, A., Aladrén, L. A. L., & De Luis, M. (2015). Evaluation of forest cover change using remote sensing techniques and landscape metrics in Moncayo natural park (Spain). Applied Geography, 62(1), 247–255.

    Google Scholar 

  • Cetin, M. (2020). Climate comfort depending on different altitudes and land use in the urban areas in Kahramanmaras City. Air Quality, Atmosphere & Health. https://doi.org/10.1007/s11869-020-00858-y

    Article  Google Scholar 

  • Cetin, M., Isik Pekkan, O., Bilge Ozturk, G., Kurkcuoglu, M. A. S., Kucukpehlivan, T., & Cabuk, A. (2022). Examination of the change in the vegetation around the Kirka Boron Mine site by using remote sensing techniques. Water, Air and Soil Pollution, 233, 254. https://doi.org/10.1007/s11270-022-05738-y

    Article  CAS  Google Scholar 

  • Civco, D. L. (1989). Topographic normalization of Landsat thematic mapper digital imagery. Photogrammetric Engineering Remote Sensing, 55(9), 1303–1309.

    Google Scholar 

  • Cushman, S., & McGarigal, K. (2008). Landscape metrics, scales of resolution. Book: Designing green landscapes. Springer.

    Google Scholar 

  • Esfandiyari Darabad, F., Hamzeei, M., Alaei, N., & Mostafazadeh, R. (2021). Spatial variations of landscape metrics in riparian area vegetation of Gharesou River reaches under the effect of different land uses Ardabil Province. Geographical Planning of Space, 10(38), 219–234. (In Persian).

    Google Scholar 

  • Fahsi, A., Tsegaye, T., Tadesse, W., & Coleman, T. (2000). Incorporation of digital elevation models with Landsat-TM data to improve land cover classification accuracy. Forest Ecology and Management, 128(1–2), 57–64. https://doi.org/10.1016/s0378-1127(99)00272-8

    Article  Google Scholar 

  • Farina, A. (1998). Principles and methods in landscape ecology. Chapman and Hall.

    Google Scholar 

  • Farina, A. (2009). Ecology cognition and landscape. Linking natural and social system. Springer.

    Google Scholar 

  • Fiener, P., Auerswald, K., & Van Oost, K. (2011). Spatio-temporal patterns in land-use and management affecting surface runoff response of agricultural catchments-a review. Earth-Science Reviews, 106(1), 92–104.

    Google Scholar 

  • Fischer, J., & Lindenmayer, D. B. (2007). Landscape modification and habitat fragmentation: A synthesis. Global Ecology and Biogeography, 16(3), 265–280. https://doi.org/10.1111/j.1466-8238.2007.00287.x

    Article  Google Scholar 

  • Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1), 185–201. https://doi.org/10.1016/s0034-4257(01)00295-4

    Article  Google Scholar 

  • Foody, G. M. (2004). Thematic map comparison. Photogrammetric Engineering & Remote Sensing, 70(5), 627–633. https://doi.org/10.14358/pers.70.5.627

    Article  Google Scholar 

  • Ghorbani, A., Hazbavi, Z., Mostafazadeh, R., & Alaei, N. (2020). Analysis the relationship between landscape metrics and soil erosion of Koozehtopraghi Watershed, Ardabil Province. Journal of Geography and Environmental Hazards, 9(4), 65–91.

    Google Scholar 

  • Gilabert, M. A., Conse, C., Maselli, F., & Bottai, L. (1993). Topographic normalization of TM scenes through the use of atompospheric correction method and digital terrain models. Photogrammetric Engineering and Remote Sensing, 59, 1745.

    Google Scholar 

  • Gökyer, Ercan. (2013). Understanding landscape structure using landscape metrics. In Ö. Murat (Ed.), Advances in landscape architecture. IntechOpen.

    Google Scholar 

  • Griffith, J. A. (2002). Geographic techniques and recent applications of remote sensing to landscape-water quality studies. Water, Air, & Soil Pollution, 138(1–4), 181–197.

    CAS  Google Scholar 

  • Guan, D., Zhao, Z., & Tan, J. (2019). Dynamic simulation of land-use change based on logistic-CA-Markov and WLC-CA-Markov models: A case study in three gorges reservoir area of Chongqing, China. Environmental Science and Pollution Research, 26, 20669–20688. https://doi.org/10.1007/s11356-019-05127-9

    Article  PubMed  Google Scholar 

  • Hamad, R., Balzter, H., & Kolo, K. (2018). Predicting land-use/land cover changes using a CA-Markov model under two different scenarios. Sustainability, 10(10), 3421. https://doi.org/10.3390/su10103421

    Article  Google Scholar 

  • Hazbavi, Z., Moradzadeh, V., Esmali-Ouri, A., Mostafazadeh, R., & Zarei, S. H. (2022). Promoting the concept and assessment approaches of human disturbance. Environmental Sciences Studies, 7(3), 5216–5239.

    Google Scholar 

  • Herold, M., Scepan, J., & Clarke, K. C. (2002). The use of remote sensing and landscape metrics to describe structures and changes in urban land-uses. Environment and Planning A, 34(8), 1443–1458. https://doi.org/10.1068/a3496

    Article  Google Scholar 

  • Hersperger, A. M., & Bürgi, M. (2009). Going beyond landscape change description: Quantifying the importance of driving forces of landscape change in a Central Europe case study. Land-Use Policy, 26(3), 640–648. https://doi.org/10.1016/j.landusepol.2008.08.015

    Article  Google Scholar 

  • Hersperger, A. M., Gennaio Franscini, M.-P., & Kübler, D. (2013). Actors, decisions and policy changes in local urbanization. European Planning Studies, 22(6), 1301–1319. https://doi.org/10.1080/09654313.2013.783557

    Article  Google Scholar 

  • Herzog, F., Lausch, A., Müller, E., Thulke, H.-H., Steinhardt, U., & Lehmann, S. (2001). Landscape metrics for assessment of landscape destruction and rehabilitation. Environmental Management, 27(1), 91–107. https://doi.org/10.1007/s002670010136

    Article  CAS  PubMed  Google Scholar 

  • Huang, C., Geiger, E. L., & Kupfer, J. A. (2006). Sensitivity of landscape metrics to classification scheme. International Journal of Remote Sensing, 27(14), 2927–2948. https://doi.org/10.1080/01431160600554330

    Article  Google Scholar 

  • Hussain, S., Mubeen, M., Ahmad, A., Akram, W., Hammad, H. M., Ali, M., Masood, N., Amin, A., Umar-Farid, H., Sultana, S. R., Fahad, S., Wang, D., & Nasim, W. (2019). Using GIS tools to detect the land-use/land cover changes during forty years in Lodhran District of Pakistan. Environmental Science and Pollution Research, 27, 39676–39692. https://doi.org/10.1007/s11356-019-06072-3

    Article  PubMed  Google Scholar 

  • Jensen, J. R. (1996). Introductory digital image processing: A remote sensing perspective. Englewood Cliffs, Prentice-Hall.

    Google Scholar 

  • Kearns, F. R., Kelly, N. M., Carter, J. L., & Resh, V. H. (2005). A method for the use of landscape metrics in freshwater research and management. Landscape Ecology, 20, 113–125.

    Google Scholar 

  • Keshtkar, H., & Voit, W. A. (2016). Spatiotemporal analysis of landscape change using an integrated Markov chain and cellular automata models. Earth System and Environment, 2(10), 1–13.

    Google Scholar 

  • Khan, I., Javed, T., Khan, A., Lei, H., Muhammad, I., Ali, I., & Huo, X. (2019). Impact assessment of land-use change on surface temperature and agricultural productivity in Peshawar-Pakistan. Environmental Science and Pollution Research, 26, 33076–33085. https://doi.org/10.1007/s11356-019-06448-5

    Article  PubMed  Google Scholar 

  • Khavarian, H., Aghaei, M., Mostafazadeh, R., & Rabiei, H. (2022). Evaluation of land use change predictions using CA-Markov model and management scenarios. Computers in earth and environmental sciences: Artificial intelligence and advanced technologies in hazards and risk management (pp. 105–115). Elsevier.

    Google Scholar 

  • Kilicoglu, C., Cetin, M., Aricak, B., & Sevik, H. (2020). Site selection by using the multi-criteria technique—a case study of Bafra Turkey. Environmental Monitoring and Assessment, 45, 4561. https://doi.org/10.1007/s10661-020-08562-1

    Article  Google Scholar 

  • Krajewski, P., Solecka, I., & Mastalska-Cetera, B. (2017). Landscape change index as a tool for spatial Analysis. IOP Conference Series: Materials Science and Engineering, 245, 072014. https://doi.org/10.1088/1757-899x/245/7/072014

    Article  Google Scholar 

  • Krajewski, P., Solecka, I., & Mrozik, K. (2018). Forest landscape change and preliminary study on its driving forces in Ślęża landscape park (Southwestern Poland) in 1883–2013. Sustainability, 10(12), 4526. https://doi.org/10.3390/su10124526

    Article  Google Scholar 

  • Kumar, M., Denis, D. M., Singh, S. K., Szabó, S., & Suryavanshi, S. (2018). Landscape metrics for assessment of land cover change and fragmentation of a heterogeneous watershed. Remote Sensing Applications: Society and Environment, 10, 224–233. https://doi.org/10.1016/j.rsase.2018.04.002

    Article  Google Scholar 

  • Lausch, A., & Herzog, F. (2002). Applicability of landscape metrics for the monitoring of landscape change: Issues of scale, resolution and interpretability. Journal of Ecological Indicators, 2, 3–15.

    Google Scholar 

  • Leitao, A. B., & Ahren, J. (2002). Appling landscape ecological concept and metrics in sustainable planning. Landscape and Urban Planning., 59(2), 65–93.

    Google Scholar 

  • Levers, C., Butsic, V., Verburg, P. H., Müller, D., & Kuemmerle, T. (2016). Drivers of changes in agricultural intensity in Europe. Land-Use Policy, 58, 380–393. https://doi.org/10.1016/j.landusepol.2016.08.013

    Article  Google Scholar 

  • Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823–870. https://doi.org/10.1080/01431160600746456

    Article  Google Scholar 

  • Mcgarigal K (2001) Landscape metrics for categorical map patterns. https://www.umass.edu/landeco/teaching/landscape_ecology/schedule/chapter9_metrics.pdf

  • Mcgarigal, K., & Barbara, M. (1995). Spatial pattern analysis program for quantifying landscape structure. General Technical Report PNW-GTR, 134, 351.

    Google Scholar 

  • Mcgarigal, K., & Marks, B. J. (1995). FRAGSTATS: Spatial pattern analysis program for quantifying landscape structure. General Technical Report PNW-GTR, 351, 134.

    Google Scholar 

  • Mcgarigal, K., Sam, C., & Maile, N. (2002). FRAGSTATS: Spatial pattern analysis program for categorical maps. Computer software program produced by the authors at the University of Massachusetts, 6, 691–703.

    Google Scholar 

  • Meneses, B., Reis, E., Vale, M., & Reis, R. (2018). Modelling land use and land cover changes in Portugal. A Multi-Scale and Multi-Temporal Approach. Finisterra (revista Portuguesa De Geografia), 107, 3–26.

    Google Scholar 

  • Mirzaei, S., Esmali Ouri, A., Mostafazadeh, R., Ghorbani, A., & Mirzaei, S. (2018). Flood hydrograph simulation and analysis of its components with landscape metrics in Amoughin Watershed Ardabil Province. Iranian Journal of Ecohydrology, 5(2), 357–372.

    Google Scholar 

  • Mohajane, M., Essahlaoui, A., Oudija, F., El-Hafyani, M., Hmaidi, A. E., El-Ouali, A., Randazzo, G., & Teodoro, A. C. (2018). Land-use/land cover (LULC) using Landsat data series (MSS, TM, ETM+ and OLI) in Azrou forest, in the Central Middle Atlas of Morocco. Environments, 5(12), 131. https://doi.org/10.3390/environments5120131

    Article  Google Scholar 

  • Mohanrajan, S. N., Loganathan, A., & Manoharan, P. (2020). Survey on land-use/land cover (LU/LC) change analysis in remote sensing and GIS environment: Techniques and challenges. Environmental Science and Pollution Research, 27, 29900–29926. https://doi.org/10.1007/s11356-020-09091-7

    Article  PubMed  Google Scholar 

  • Mondal, M., Surabuddin-Sharma, N., Kappas, M., & Gorg, P. (2019). CA-Markov modeling of land use land cover dynamics and sensitivity analysis to identify sensitive parameters. Remote Sensing and Spatial Information Sciences, 2(3), 723–729.

    Google Scholar 

  • Mostafazadeh, R., Jafari, A., & Keivan-behjou, F. (2018). Comparing the rangelands structure and degradation of landscape connectivity in Iril sub-watersheds, Ardabil Province. Iranian Journal of Applied Ecology, 7(1), 41–53. (in Persian).

    Google Scholar 

  • Mukhopdhaya, S. (2016). Land use and land cover change modelling using CA-Markov case study: Deforestation analysis of Doon valley. Agroecology and Natural Resource Management, 3(1), 1–5.

    Google Scholar 

  • Nazarnejad, H., Hosseine, M., & Mostafazadeh, R. (2019). Analysis of land-use change in Balanjchai watershed (Urmia) using landscape metrics. Geography and Development Iranian Journal, 17(54), 75–90.

    Google Scholar 

  • Nouri, J., Gharagozlu, A., Arjmandi, R., Faryadi, S., & Adl, M. (2014). Predicting urban land use changes using a CA-Markov model. Arabian Journal of Geosciences, 39(7), 5565–5573.

    Google Scholar 

  • Onwuka, S., Eneche, P., & Ismail, N. (2017). Geospatial modeling and prediction of land use/cover dynamics in Onitsha metropolis, Nigeria: A subpixel approach. Applied Science & Technology, 22(6), 1–18.

    Google Scholar 

  • Pal, M., & Mather, P. M. (2003). An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sensing of Environment, 86(4), 554–565. https://doi.org/10.1016/s0034-4257(03)00132-9

    Article  Google Scholar 

  • Patil, S., & Jamgade, M. (2019). Land change prediction using Markov change multi-layer perceptron in Navi Mumbai, Maharashtra India. Innovative Technology and Exploring Engineering (IJITEE), 8(10), 484–490.

    Google Scholar 

  • Penghui, J., Manchun, L., & Liang, C. H. (2020). Dynamic response of agricultural productivity to landscape structure changes and its policy implications of Chinese farmland conservation. Resources, Conservation & Recycling, 156, 104724. https://doi.org/10.1016/j.resconrec.2020.104724

    Article  Google Scholar 

  • Peter, M., Vitousek Harold, A., Lubchenco, M. J., & Melillo, J. M. (1997). Human domination of earth’s ecosystems. Science, 277, 494–499.

    Google Scholar 

  • Rasoulzadeh, A., Hazbavi, Z., Azizi Mobaser, J., Mostafazadeh, R., & Alaei, N. (2022). Hot spots analysis of landscape metrics and runoff landscape index in Ardabil province Iranian. Journal of Applied Ecology, 11(1), 77–96. (In Persian).

    Google Scholar 

  • Reddy, C., Singh, S., Dadhwal, V., Jhacs, R., & Diwakar, P. (2017). Predictive modelling of the spatial pattern of past and future forest cover change in India. Earth System Science, 126(8), 1–16.

    CAS  Google Scholar 

  • Regmi, R., Saha, S., & Balla, M. (2014). Geospatial analysis of land-use land cover change predictive modeling at Phewa Lake Watershed of Nepal. International Journal of Current Engineering and Technology, 4, 2617–2627.

    Google Scholar 

  • Sahin, G., Cabuk, S. N., & Cetin, M. (2022). The change detection in coastal settlements using image processing techniques: A case study of Korfez. Environmental Science and Pollution Research, 29(10), 15172–15187. https://doi.org/10.1007/s11356-021-16660-x

    Article  PubMed  Google Scholar 

  • Shafizadeh-Moghadam, H., Minaei, F., Talebi-Khiyavi, H., Tingting, X., & Homaee, M. (2022). Synergetic use of multi-temporal Sentinel-1, Sentinel-2, NDVI, and topographic factors for estimating soil organic carbon. CATENA, 212(1), 106077.

    CAS  Google Scholar 

  • Shi, Y., & Xiao, J. (2008). Evaluating Landscape changing due to urbanization using remote sensing data: A case study of Shijiazhung, China. International Workshop on Geoscience and Remote Sensing, IEEE Computer Society Washington, DC, USA, 21(22), 508–511.

    Google Scholar 

  • Singh, S. K., Srivastava, P. K., Szilard, S., Petropoulos, G. P., Gupta, M., & Islam, M. (2016). Landscape transform and spatial metrics for mapping spatiotemporal land cover dynamics using earth observation data-sets. Geocarto International. https://doi.org/10.1080/10106049.2015.1130084

    Article  Google Scholar 

  • Smits, P. C., Dellepiane, S. G., & Schowengerdt, R. A. (1999). Quality assessment of image classification algorithms for land-cover mapping: A review and a proposal for a cost-based approach. International Journal of Remote Sensing, 20(8), 1461–1486. https://doi.org/10.1080/014311699212560

    Article  Google Scholar 

  • Solon, J., Chmielewski, T. J., Myga-Piątek, U., & Kistowski, M. (2015). Identification and assessment of polish landscapes-stages and methods of proceedings in the process of landscape audit in voivodships. Problems of Landscape Ecology, XL, 55–76.

    Google Scholar 

  • Su, Sh., Ma, X., & Xiao, R. (2014). Agricultural landscape pattern changes in response to urbanization atecoregional scale. Ecological Indicators, 40(1), 10–18.

    Google Scholar 

  • Szilassi, P., Jordan, G., Van Rompaey, A., & Csillag, G. (2006). Impacts of historical land-use changes on erosion and agricultural soil properties in the Kali Basin at Lake Balaton, Hungary. CATENA, 68, 98–108.

    Google Scholar 

  • Talebi Khiavi, H., & Mostafazadeh, R. (2021). Land use change dynamics assessment in the Khiavchai region, the hillside of Sabalan mountainous area. Arab Journal of Geosciences, 14, 2257. https://doi.org/10.1007/s12517-021-08690-z

    Article  Google Scholar 

  • Talebi Khiavi, H., Mostafazadeh, R., Asaadi, M. A., & Asbaghian-Namini, S. K. (2022). Temporal land use change and its economic values under competing driving forces in a diverse land use configuration. Arabian Journal of Geosciences, 15, 1597. https://doi.org/10.1007/s12517-022-10890-0

    Article  Google Scholar 

  • Talebi khiavi, H., Zabihi, M., & Mostafazadeh, R. (2017). Effects of land-use management scenarios on soil erosion rate using GIS-based USLE Model in Yamchi Dam Watershed, Ardabil. Journal of Science and Technology of Agriculture and Natural Resources Water Soil Science, 21(2), 221–234. (in Persian).

  • Tang, J., Wang, L., & Yao, Z. (2008). Analyses of urban landscape dynamics using multi-temporal satellite images: A comparison of two petroleumoriented cities. Landscape and Urban Planning, 87(4), 269–278.

    Google Scholar 

  • Thomlinson, J. R., Bolstad, P. V., & Cohen, W. B. (1999). Coordinating methodologies for scaling landcover classifications from site-specific to global: Steps toward validating global map products. Remote Sensing of Environment, 70(1), 16–28.

    Google Scholar 

  • Weng, Q. (2002a). Land-use change analysis in the Zhujiang Delta of China using satellite remote sensing GIS and Stochastic Modelling. Environmental Management, 64(3), 274–284.

    Google Scholar 

  • Weng, Q. (2002b). Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. Journal of Environmental Management, 64(3), 273–284. https://doi.org/10.1006/jema.2001.0509

    Article  PubMed  Google Scholar 

  • Yuan, J., Cohen, M.J., Kaplan, D.A. et al. (2015). Linking metrics of landscape pattern to hydrological process in a lotic wetland. Landscape Ecol 30, 1893–1912. https://doi.org/10.1007/s10980-015-0219-z

    Article  Google Scholar 

  • Zurlini, G., & Girardin, P. (2008). Introduction to the special issue on ecological indicators at multiple scales. Ecological Indicators, 8(6), 781–782.

    Google Scholar 

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  1. Department of Natural Resources, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran

    Raoof Mostafazadeh & Hossein Talebi Khiavi

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Mostafazadeh, R., Talebi Khiavi, H. Landscape change assessment and its prediction in a mountainous gradient with diverse land-uses. Environ Dev Sustain 26, 3911–3941 (2024). https://doi.org/10.1007/s10668-022-02862-x

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