Modeling and Forecasting the Risk of Forest Degradation with Geomod Model (Case study: Chālus and Nowshahr counties )

Document Type : Research Article - Case Study

Author

Associate Professor, Department of Geography, Faculty of , Human Sciences , Golestan University, Gorgan, Iran.

Abstract

The aim of this research is to investigate land use changes in the past and evaluate the effectiveness of the Geomod model in modeling and predicting land use changes. Forecasting the state of forest use change for the year 2021 was done using the land use maps of 1989 and 2001 with the help of geomed model and based on logistic regression. For this purpose, the spatial variables of distance from the road, distance from the river, distance from rural areas, distance from urban areas, distance from the edge of the forest, elevation, slope, and slope direction were used as factors affecting changes in logistic regression. The Kappa coefficient obtained from the classification of 1989, 2000 and 2021 was 0.88, 0.91 and 0.87, respectively. Evaluation of logistic regression performance using two indices of Pseudo-R2 and ROC with values of 0.34 and 0.85 for the first period and 0.35 and 0.87 for the second period, respectively, showing the relative agreement of the obtained model with real changes and the appropriate ability of the model in estimating forest changes in the past 32 years. The results showed that in the period of 1989-2021, about 33,853 hectares of forest area has been reduced, and in the period of 2021-2051, the area of forests will decrease by 15,047 hectares. The results related to the simulation of the land cover map of the year 2021 showed that the Geomod model has a high ability and capability in modeling land cover changes.

Highlights

- Monitoring and investigation of land use changes in forest areas provides acceptable information for efficient management of these resources.

- The results of this research showed that during the years 1989 to 2021, the studied area has undergone extensive changes, and the majority of these changes were in the direction of forest degradation, and the reason for the decrease in the extent of forest lands in the studied period was the development of residential areas and agricultural lands in it the area.

Keywords

Main Subjects

References

Abdollahi, S., Nasiri, V. (2021). Forest change Detecting and predicting in Gilan province using satellite images and geomed. Environmental research and technology. 7(5): 141-151. [In Persian].
Adedeji, O.H., Tope-Ajayi, O.O., & Abegunde, O.L. (2015). Assessing and Predicting Changes in the Status of Gambari Forest Reserve, Nigeria Using Remote Sensing an GIS Techniques, Journal of Geographic Information System, 7: 301-318.
Ahmadi-Ndosh, M., Sufianian, A. (2008). Revealing and predicting the changes in the land cover of Arak city. 88th Geomatic Conference. Country Mapping Organization. Tehran. [In Persian]
Amini, M.R., Shtai, S., Muairi, M.H., & Ghazanfari, H. (2006). Investigation of Zagros forest extent changes and its relationship with physiographic and anthropogenic factors using GIS and RS, case study: Armarde Baneh forests. Master's Thesis in Forestry, under the guidance of Dr. Shatai, Gorgan University, 157 p. [In Persian] 
Andaryani, S., Sloan, S., Nourani, V., & Keshtkar, H. (2021). The utility of a hybrid GEOMOD-Markov Chain model of land-use change in the context of highly water-demanding agriculture in a semi-arid region. Ecological Informatics. 64: 1-12.
Bagheri, R., & Shataee, S.H. (2010). Modeling forest areas decreases, using logistic regression (case study: Chehl-Chay catchment, Golestan province), Iranian Journal of Forest, 2(3): 243-252. [In Persian]
Brown, S., Hall, M., Andrasko, K., Ruiz, F., Marzoli, W., Guerrero, G., Masera, O., Dushku, A., DeJong, B., & Cornell J. (2007). Baselines for land-use change in the tropics: application to avoided deforestation projects. Mitigation and Adaptation Strategies for Global Change, 12(6): 1001-1026.
Cabral, P., Zamyatin, A. (2006). Three land change models for urban dynamics analysis in SintraCascais area. In: Proceedings of First Workshop of the EARSEL SIG on Urban Remote Sensing: Challenges and solutions. Humboldt-Universität zu Berlin, 2-3 March.
Clark, W.A.V., Peter, L.H. (1986). Statistical Methods for Geographers. 1st Edition. Chichester:
DeFries, R., Belward, A. (2000). Global and regional land cover characterization from satellite data: an introduction to the Special Issue. International Journal of Remote Sensing, 21(6-1): 1083-1092.
Eastman, J.R. (2006). IDRISI Andes. Guide to GIS and Image Processing. Clark Labs, Clark University, Worcester, MA.
Eastman, J.R. (2012). Idrisi Production, Clark Labs-Clark University IDRISI Selva Tutorial.
Echeverria, C., Coomes, D.A., Hall, M., & Newton, A.C. (2008). Spatially explicit models to analyze forest loss and fragmentation between 1976 and 2020 in southern Chile. Ecological Modelling, 212(3): 439-449.
Eman A.A., Bharti, W.G. (2022). Modeling Land Use Change in Sana’a City of Yemen with MOLUSCE. Journal of Sensors. Vol 2022: 1-15.
Fraser, R.H., Abuelgasim, A., & Latifovic, R. (2005). A method for detecting large-scale forest cover change using coarse  spatial  resolution imagery, Remote  Sensing  of  Environment,  95: 414-427.
Galdavi, S., Mohammadzadeh, M., Salman Mahini, A., & Najafi Nejad, A. (2013). Urban Change Detection Using Multi-Temporal Remotely Sensed imagery (Case study: Gorgan Area, Northern Iran). Environment & Urbanization ASIA. 4: 339-348.
Geri, F., Amici, V., & Rocchini, D. (2011). Spatiallybased accuracy assessment of forestation prediction in a complex Mediterranean landscape. Journal of Applied Geography, 31(3): 881-890
Ghorbanian Khyberi, W., Mirsengari, M.M., & Armin, Mohsen. (2016). Prediction of forest use changes in Chalus River watershed, Journal of Remote Sensing and Geographical Information System in Natural Resources, 8(2). pp. 79-91. [In Persian]
Heydarizadi, Z., Mohammadi Behbahani, A. (2018). Evaluating the effectiveness of Geomod and LCM models in simulating land use changes (case study: Abu Ghoir plain, Dehlran, Ilam province).  Scientific Journal of Pasture and Desert Research in Iran, 62(3), Pages 660-673. [In Persian]
http://www.ncc.org.ir [In Persian]
Irmadi, N., Turmudi, R.W., Jaka, S., Ratna, S.D., & Sri, L. (2018). Comparing of Land Change Modeler and Geomod Modeling for the Assessment of Deforestation (Case Study: Forest Area at Poso Regency, Central Sulawesi Province). International Journal of Advanced Engineering, Management and Science (IJAEMS). 4(8): 597-607.
Joybarian Shushtri, S., Shaishte, K., Gholamali Fard, M., Azari, M., & Juan Ignacio, L.M. (2016). The stratum of land surface parameters and spatial processes of change in evaluating the efficiency of the GEOMOD model (case study: Neka River watershed). Geography and environmental sustainability, 24: 73-63. [In Persian]
Kamyab, H., Mahini, S., Hosseini, A., & Gholamalifard, M. (2010). Adoption of informationbased approach using logistic regression methodology for modeling urban development in Gorgan. Journal of Ecology, 36(54): 89-96. [In Persian]
Khoi, D.D., Murayama, Y. (2010). Forecasting areas vulnerable to forest conversion in the Tam Dao National Park Region, Vietnam, Journal of Remote Sensing, 2: 1249-1272.
Landis, J., Koch, G. (1977). The measurement of observer agreement for categorical data. Biometrics. 33: 159–174.
Linkie, M., Smith, R.J. Smith., & Leader-Williams, N. (2004). Mapping and predicting deforestation patterns in the lowlands of Sumatra. Biodiversity and Conservation. 13(10), 1809-1818.
Lo, C.P., Quattrochi, D.A. (2003). Land-Use and Land-Cover Change, Urban Heat Island Phenomenon, and Health Implications:  A Remote Sensing Approach, Photogrammetric Engineering and Remote Sensing, 69:  1053-1063.
Mallinis, G., Emmanoloudis, D., Giannakopoulos, V., Maris, F., & Koutsias, N. (2011). Mapping and interpreting historical land cover/land use changes in a Natura 2000 site using earth observational data: The case of Nestos delta, Greece, Applied geography, 31(1): 312-320.
Marzoli, W., Guerrero, G., Masera, O., Dushku, A., & DeJong, B. (2007). Baselines for land-use change in the tropics: application to avoided deforestation projects. Mitig Adapt Start Glob Chenge. 12: 1001- 1026.
Mas, J.F., Melanie, K., Martin, P., Mara, T., Camacho, O., & Thoma, H. (2014). Inductive pattern-based land use/cover change models: A comparison of four software packages. Environmental Modelling and Software, 94-111.
Mertens, B., and Lambin, E.F. (1999). Modelling land cover dynamics: integration of fine-scale land cover data with landscape attributes. International Journal of Applied Earth Observation and Geoinformation, 1: 48–52 pp.
Moradi, S., Pahkideh, E. (2013). Studying the causes of changes in forest and non-forest land using RS and GIS (Case study: conventional territory of Joojar villageKermanshah - Iran), Life Science Journal, 10(3): 566-573. [In Persian]
Naseri Rad, S., Naghavi, H., Soosani, J., Nouredini, A.R., & Vafaei, S. (2021). Investigating the transmission potential of land use and land cover using Similarity Weighted Instance based Learning, Logistic regression and Geomod methods (Case study: Bastam basin, Selseleh city). Journal of environmental science and technology. 22(11). 121-133. [In Persian]
Nasiri, V., Darvishsefat, A.A., Shirvani, A., & Avatefi, H. (2019). Forest change detecting and modeling in Arsbaran using regression Logistic, Markov chain and geomed model. 19(65): 171-189. [In Persian]
Nozari, F., Salehi, A., & Farzin, M. (2020). Prediction of forest cover changes in Boyar Ahmad region using GEOMOD model. Jungle scientific research quarterly, 6(3), 463-476. [In Persian]
Pickard, B., Gray, J., & Meentemeyer, R. (2017). Comparing Quantity, Allocation and Configuration Accuracy of Multiple Land Change Models. Land. 6(52): 1-21.
Pijanowski, B., Pithadia, S., Shellito, B., & Alexandridis, K. (2005). Calibrating a neural network-based urban change model for two metropolitan areas of the Upper Midwest of the United States. International Journal of Geographical Information Science, 19(2), 197–215.
Poelmans, L., Romoaey, A.V. (2009). Detection and modeling spatial patterns of urban sprawl in highly fragmented areas: A case study in the Flunders region. Landscape and Urban Planning. 93: 10-19.
Pontius J.r, R.G. Cornell, J.D., & Hall, C.A.S. (2001). Modeling the spatial pattern of land- use change with GEOMOD2: Application and validation for CostaRica. Agriculture Ecosystems & Environment. 1775: 1-13.
Pontius J.r, R.G., Malanson, J. (2005). Comparison of the structure and accuracy of two land change models. International Journal of Geographical Information Science. 19(2): 243-265.
Pontius J.r, R.G., Pacheco, P. (2004). Calibration and validation of model of forest disturbance in the western ghats, India 1920- 1990. Geo Journal. 61: 325-334.
Pontius, R.G, Cornell, J.D., & Hall, C.A. (2001). Modeling the spatial pattern of land-use change with GEOMOD2: application and validation for Costa Rica. Agriculture, Ecosystems & Environment, 85(1): 191-203
Pontius, R.G. (2000). Quantification error versus location error in comparison of categorical maps. Photogrammetric Engineering and Remote Sensing, 66(8): 1011-1016.
Pontius, R.G. (2002). Statistical methods to partition effects of quantity and location during comparison of categorical maps at multiple resolutions. Photogrammetric Engineering and Remote Sensing, 68(10): 1041-1050.
Pontius, R.G., Schneider, LC. (2001). Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agriculture, Ecosystems & Environment, 85(1): 239-248.
Rafiee, R., Salman Mahiny, A., & Khorasani, N. (2009).  Assessment of changes in urban green spaces of Mashad city using satellite data, International Journal of Applied Earth Observation and Geoinformation, 11: 431-438.
Ramezani, J., & Nafiseh, R. (2014). Detecting land use and land cover changes in the 1404 horizon using the CA Markov chain model (case study: Efrain). Geographical Research Quarterly, 29(4), 84-95. [In Persian]
Rostamikia, S., Younes, J. (2017).   Find out the largest forest reserve of Fundak in Iran. Nature of Iran, Ardabil, 6(13). 7-20. [In Persian]
Russell-Smith, J., Yates, C., Edwards, A., Allen, G.E., Cook, G.D., Cooke, P., Craig, R., Heath, B., & Smith, R. (2003). “Contemporary fire regimes of northern Australia, 1997–1380:  change since Aboriginal occupancy, challenges for sustainable management,” International Journal of Wildland Fire, 12: 283-297.
Schneider, L.C., Pontius, J.r. (2001). Modeling land-use change in the Ipswich watershed, Massachusetts, USA. Agriculture Ecosystems & Environment. 85: 83-94.
Schulz, J.J., Cayuela, C., Echeverria, C., Salas, J., & Rey Benayas, J.M.  (2010). Monitoring land cover change of the dryland forest landscape of Central Chile (1975–2008), Applied Geography, 30: 436-447.
Shayesteh, K., Abedian, S., & Galdavi, S. (2018). Urban Expansion Modeling Using Logistic Regression Method based on Geomod Model Case study: Kordkuy city. Journal of geography and development. 16(51): 44-64. Journal of geography and development. [In Persian]
Smaeily, A., Ashjaei. (2019). Modeling land use change through Markoff and GIS (Case study: Qom Province). Geography and territorial Spatial Arrangement. 9(31): 153-172. [In Persian]
Soffianian, A., Ahmadi Nadoushan, M. (2010). Modelling urban changes using Geomod Model in Arak, Iran.3rd International Conference on Cartography and GIS.15-20 June. Nessebar, Bulgaria.
Wiley - Merten, B., Lambin, E.F.  (1997). spatial modeling of tropical deforestation in southern Cameroon: spatial disaggregation of diverse deforestation processes. Applied Geography, 17(2), 143-162.
Yuan F, Sawaya KE, Loeffelholz BC, Bauer ME (2005) Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing. Remote Sens Environ. 98(2–3):317–328.
Zare Garizi, A., Sheikh, V., Sadoddin, A., & Mahiny, S. (2012). Simulating the spatiotemporal changes of forest extent for the Chehelchay watershed (Golestan province), using integrated CA-Markov model, Iranian Journal of Forest and Poplar Research, 20(2): 273-285. [In Persian]

Files

Share

How to cite

Statistics