This study aims to demonstrate the utility of Markov chain analysis and geospatial technology to assess point-based fragmentation. It has been observed that unprecedented urbanization is primarily responsible for abrupt changes in the land system of cities. Such abrupt changes may lead to fragmentation of urban landscape and its neighbourhood areas. Generally, fragmentation begins with point locations and may convert into line- and polygon-based fragmentations. This is necessary to understand that fragmentation has negative consequences on the habitat suitability and ecosystem of a city. Consequently, this may also affect the environment in the context of sustainability. Hence, the present work proposes a framework to identify the possible hot spots of point-based fragmentation. This may help in containing landscape fragmentation. It begins with identification of zones which are more vulnerable to fragmentation using Markov chain analysis. Next, the zone-based vulnerability analysis has been downscaled to point-based fragmentation using block statistics-based spatial overlay operation. Results of the proposed framework have been validated through field survey. It has been found that landscape is fragmenting in the identified hot spots by the proposed framework. Effective measures for containing point-based fragmentation in these places may be adopted to avoid the conversion of point-based fragmentation into either line-based or polygon-based fragmentation.
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Abuelaish, B., & Olmedo, M. T. C. (2016). Scenario of land use and land cover change in the Gaza Strip using remote sensing and GIS models. Arabian Journal of Geosciences,9, 274.
Atasoy, M. (2018). Monitoring the urban green spaces and landscape fragmentation using remote sensing: A case study in Osmaniye, Turkey. Environmental Monitoring and Assessment,190, 713.
Batistella, M., Brondizio, E. D., & Moran, E. F. (2000). Comparative analysis of landscape fragmentation in Rondônia, Brazilian Amazon. International Archives of Photogrammetry and Remote Sensing,33, 148–155.
Bennett, A. F., & Saunders, D. (2011). Habitat fragmentation and landscape change. In N. S. Sodhi & P. R. Ehrlich (Eds.), Conservation biology for all (pp. 88–106). Berlin: ResearchGate.
Bidgoli, R. D., Koohbanani, H., & Yazdani, M. (2018). Investigation on ecosystem degradation induced by LULC changes using landscape pattern indices analysis. Arabian Journal of Geosciences,11(16), 443.
Castillo, R. F. D. (2015). A conceptual framework to describe the ecology of fragmented landscapes and implications for conservation and management. Ecological Applications,25(6), 1447–1455.
Chowdhury, M., Hasan, M. E., & Abdullah-Al-Mamun, M. M. (2018). Land use/land cover change assessment of Halda watershed using remote sensing and GIS. The Egyptian Journal of Remote Sensing and Space Sciences. https://doi.org/10.1016/j.ejrs.2018.11.003.
Desmet, P. G. (2018). Using landscape fragmentation thresholds to determine ecological process targets in systematic conservation plans. Biological Conservation,221, 257–260.
Dewan, A. M., Yamaguchi, Y., & Rahman, M d Z. (2012). Dynamics of land use/cover changes and the analysis of landscape fragmentation in Dhaka Metropolitan, Bangladesh. GeoJournal,77, 315–330.
Esbah, H., Erdogan, M. A., Akin, A., & Tanriover, A. (2011). Cellular automata-Markov chain and landscape metrics for landscape planning. ITU,8, 63–79.
Ewers, R. M., & Didham, R. K. (2007). Habitat fragmentation: Panchreston or paradigm? Trends in Ecology and Evolution,22(10), 511.
Fahrig, L. (2003). Effects of habitat fragmentation on biodiversity. Annual Review of Ecology Evolution and Systematics,34, 487–515.
Giulio, M. D., Holderegger, R., & Tobias, S. (2009). Effects of habitat and landscape fragmentation on humans and biodiversity in densely populated landscapes. Journal of Environmental Management,90, 2959–2968.
Gülçin, D., & Yılmaz, K. T. (2017). The assessment of landscape fragmentation in an agricultural environment: Degradation or contribution to ecosystem services? Fresenius Environmental Bulletin,25(12), 7941–7950.
Jaeger, J. A. G., Raumer, H. S., Esswein, H., Müller, M., & Schmidt-Lüttmann, M. (2007). Time series of landscape fragmentation caused by transportation infrastructure and urban development: A case study from Baden-Württemberg, Germany. Ecology and Society,12(1), 22.
Jafari, M., Majedi, M., Seyed, M. M., Alesheikh, A. A., & Zarkesh, M. K. (2017). Dynamic simulation of urban expansion through a CA-Markov model Case study: Hyrcanian region, Gilan, Iran. European Journal of Remote Sensing,49(1), 513–529.
Joint EEA-FOEN Report. (2011). Landscape fragmentation in Europe.
Karimi, H., Jafarnezhad, J., Khaledi, J., & Ahmadi, P. (2018). Monitoring and prediction of land use/land cover changes using CA-Markov model: A case study of Ravansar County in Iran. Arabian Journal of Geosciences,11, 592.
Keshtkar, H., & Voigt, W. (2015). A spatiotemporal analysis of landscape change using an integrated Markov chain and cellular automata models. Modeling Earth Systems and Environment,2, 10.
Kumar, S., Radhakrishnan, N., & Mathew, S. (2014). Land use change modelling using a Markov model and remote sensing. Geomatics, Natural Hazards and Risk,5, 1947–5713.
Llausàs, A., & Nogué, J. (2012). Indicators of landscape fragmentation: The case for combining ecological indices and the perceptive approach. Ecological Indicators,10, 85–91.
Mirkatouli, J., Hosseini, A., & Neshat, A. (2015). Analysis of land use and land cover spatial pattern based on Markov chains modelling. City, Territory and Architecture,2, 4.
Mishra, V. N., & Rai, P. K. (2016). A remote sensing aided multi-layer perceptron-Markov chain analysis for land use and land cover change prediction in Patna district (Bihar), India. Arabian Journal of Geosciences,9, 249.
Mitchell, M. G. E., Suarez-Castro, A. F., Martinez-Harms, M., Maron, M., McAlpine, C., Gaston, K. J., et al. (2015). Reframing landscape fragmentation’s effects on ecosystem services. Trends in Ecology & Evolution,30(4), 190–198.
Nasiri, V., Darvishsefat, A. A., Rafiee, R., Shirvany, A., & Hemat, M. A. (2019). Land use change modeling through an integrated multi-layer perceptron neural network and Markov chain analysis (case study: Arasbaran region, Iran). Journal of Forest Research,30(3), 943–957.
Parsa, V. A., Yavari, A., & Nejadi, A. (2016). Spatio-temporal analysis of land use/land cover pattern changes in Arasbaran Biosphere Reserve: Iran Model. Earth Systems and Environment,2, 178.
Paul, S. S., Li, J., Wheate, R., & Li, Y. (2018). Application of object-oriented image classification and Markov chain modeling for land use and land cover change analysis. Journal of Environmental Informatics,31(1), 30–40.
Shu-juan, L., Yu-zheng, S., Zhi-hu, S., Feng-you, W., & Yu-wen, L. (2005). Landscape pattern and fragmentation of natural secondary forests in the eastern mountainous region, northeast China: A case study of Mao'ershan forests in Heilongjiang Province. Journal of Forestry Research,16(1), 35–38.
Shukla, A., & Jain, K. (2019). Critical analysis of spatial–temporal morphological characteristic of urban landscape. Arabian Journal of Geosciences,12, 112.
Tang, J., Wang, L., & Yao, Z. (2005). Spatio-temporal urban landscape change analysis using the Markov chain model and a modified genetic algorithm. International Journal of Remote Sensing,28, 3255–3271.
The authors are grateful to the editor and anonymous reviewers for their valuable suggestions which will certainly help to enhance the quality of proposed work. Authors are also thankful to the Vice- Chancellor and Head of the Department for providing facilities to perform this investigation in Geo-Informatics lab of the Department of Geography, North-Eastern Hill University, Shillong-India.
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Imchen, Z., Chowdhury, P., Mukherjee, A.B. et al. Assessment of Point-Based Fragmentation Using Geospatial Technology and Markov Chain Analysis: A Case Study of Kamrup Districts (Rural and Metro), Assam, India. J Indian Soc Remote Sens (2020). https://doi.org/10.1007/s12524-019-01098-z
- Point-based fragmentation
- Markov chain analysis
- Geospatial technology
- Block statistics
- Land system