Evaluating the effect of ongoing conservation policies and ‎forest cover changes in Iranian Zagros ‎forests based on a Land ‎‎Transformation Model: transition to forest or deforestation?‎


  • Hadi Beygi Heidarlou Department of Forest Engineering, Forest Management Planning and Terrestrial Measurements, Faculty of Silviculture and Forest Engineering, Transilvania University of Brașov, Sirul Beethoven 1, 500123 Brasov, Romania
  • Abbas Banj Shafiei Forestry Department, Faculty of Natural Resources, Urmia University, 165 Urmia, Iran
  • Amin Tayyebi Data Science Manager, Exelon Utility, Chicago, IL, USA
  • Stelian Alexandru BORZ Department of Forest Engineering, Forest Management Planning and Terrestrial Measurements, Transilvania University of Brasov




Conservation, Deforestation, Forecasting model, Land use/cover ‎change, ANNs.


In recent decades, Zagros forests from western Iran have experienced dramatic changes in cover and structure. Conservation policies, on the other hand, have existed or are being implemented in these forests since 2002 to prevent deforestation. There is, however, the question on how effective were the conservation policies in preventing forest loss. The goal of this study was to analyze the effect of conservation policies in preventing forest loss, as well as to forecast their future effectiveness. Since the spatio-temporal changes in forest cover, land-use and its patterns occur in a non-linear way, this study was based on the use of Land Transformation Model (LTM). Using geographic information systems (GIS) and artificial neural networks (ANNs), this model forecasts future forest changes for the next 30 years. Three scenarios were used for this purpose, in which the input patterns included the years 2002-2012, 2002-2022, and 2012-2022. Based on these, deforestation was predicted for the next three decades using 14 variables. Assuming no changes in the implementation of conservation policies in the Zagros forests, the model was characterized by a consistent accuracy and indicated a projected pattern of increased deforestation over the next years in the region. In other words, by the ongoing conservation policies, the net deforestation overtakes net reforestation. It appears that to stop further forest degradation, Iran's Forestry Service decision-makers must implement improved forest conservation policies.


Areerachakul S., Sanguansintukul S., 2010. Classification and regression trees and MLP neural network to classify water quality of ‎canals in Bangkok, Thailand. International Journal of Intelligent Computing Research 1(1/2): 43-50.‎ https://doi.org/10.20533/ijicr.2042.4655.2010.0004

Arekhi S., 2014. Prediction of spatial land use changes based on LCM in a GIS environment (A case study of Sarabeleh (Ilam), Iran. ‎Iranian Journal of Forest and Range Protection Research 12(1): 1-19.‎

Armenteras, D, Cabrera, E, Rodríguez, N, Retana, J (2013) National and regional determinants of tropical deforestation in Colombia. ‎Regional Environmental Change 13(6): 1181-1193.‎ https://doi.org/10.1007/s10113-013-0433-7

Baumann M., Radeloff V.C., Avedian V., Kuemmerle T., 2015. Land-use change in the Caucasus during and after the ‎Nagorno-Karabakh conflict. Regional Environmental Change 15: 1703-1716.‎ https://doi.org/10.1007/s10113-014-0728-3

Beygi Heidarlou H., Banj Shafiei A., Erfanian M., Tayyebi A., Alijanpour A., 2020a. Armed conflict and land-use changes: Insights ‎from Iraq-Iran war in Zagros forests. Forest Policy and Economics 118: 1-10.‎ https://doi.org/10.1016/j.forpol.2020.102246

Beygi Heidarlou H., Banj Shafiei A., Erfanian M., Tayyebi A., Alijanpour A., 2020b. Underlying driving forces of forest cover ‎changes due to the implementation of preservation policies in Iranian northern Zagros forests. International Forestry Reviewn 22(2): 241-256.‎ https://doi.org/10.1505/146554820829403531

Beygi Heidarlou H., Banj Shafiei A., Erfanian M., Tayyebi A., Alijanpour A., 2022. Forecasting deforestation and forest recovery ‎using Land Transformation Model‎‎ (LTM) in Iranian Zagros forests. Forest Research and Development 7(4): 527-544.‎ 10.30466/jfrd.2021.53873.1572

Beygi Heidarlou H., Banj Shafiei A., Erfanian M., Tayyebi A., Alijanpour A., 2019. Effects of preservation policy on land use changes in ‎Iranian Northern Zagros forests. Land Use Policy 81: 76-90.‎ https://doi.org/10.1016/j.landusepol.2018.10.036

Breiman L., 1999. Random forests—random features, Technical Report 567. Statistics Department, University of California, Berkeley, 29 p.

Brown D.E., Corruble V., Pittard C.L., 1993. A comparison of decision tree classifiers with backpropagation neural networks for ‎multimodal classification problems. Pattern Recognition 26(6): 953-961.‎ https://doi.org/10.1016/0031-3203(93)90060-A

Dávalos L.M., Bejarano A.C., Hall M.A., Correa H.L., Corthals A., Espejo O.J., 2011. Forests and drugs: Coca-driven deforestation in ‎tropical biodiversity hotspots. Environmental Science & Technology 45(4): 1219-1227.‎ https://doi.org/10.1021/es102373d

Duncan S.I., Pynne J., Parsons E.I., Fletcher Jr R.J., Austin J.D., Castleberry S.B., Conner L.M., Gitzen R.A., Barbour M., McCleery R.A., ‎‎2020. Land use and cover effects on an ecosystem engineer. Forest Ecology and Management 456: 117642.‎ https://doi.org/10.1016/j.foreco.2019.117642

e Silva L.P., Xavier A.P.C., da Silva R.M., Santos C.A.G., 2020. Modeling land cover change based ‎on an artificial neural network for a semiarid river basin in northeastern Brazil. Global Ecology ‎and Conservation 21: e00811. https://doi.org/10.1016/j.gecco.2019.e00811‎

FAO, 2020. Global Forest Resources Assessment (FRA) 2020 report Iran. Rome, 54 p.

Gaur S., Mittal A., Bandyopadhyay A., Holman I., Singh R., 2020. Spatio-temporal analysis of land ‎use and land cover change: a systematic model inter-comparison driven by integrated modelling ‎techniques. International Journal of Remote Sensing 41(23): 9229-9255. ‎https://doi.org/10.1080/01431161.2020.1815890‎

Ge D., Long H., Zhang Y., Ma L., Li T., 2018. Farmland transition and its influences on grain ‎production in China. Land Use Policy 70: 94-105. ‎https://doi.org/10.1016/j.landusepol.2017.10.010‎

Geyer L., DeWald C., 1973. Feature lists and confusion matrices. Perception & Psychophysics 14 (3): 471-482.‎ https://doi.org/10.3758/BF03211185

Gómez-Ossa, LF, Botero-Fernández, V (2017) Application of artificial neural networks in modeling deforestation associated with new ‎road infrastructure projects. Dyna 84(201): 68-73.‎ https://doi.org/10.15446/dyna.v84n201.54310

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(20): 20669-20688. ‎https://doi.org/10.1007/s11356-019-05127-9‎

Heathcote I.W., 1998. Integrated watershed management: Principles and practices. john wiley& sons. Inc. New York, 464 p.

Heidari Zahiri N., Amirnejad H., Hosseini Yekani S.A., 2015. The economic contribution of forest resources use to rural livelihoods ‎‎(Case study: Hezar Jarib area of Behshahr City). Iranian Journal of Agricultural Economics and Development Research 46(2): ‎‎207-215.‎ https://doi.org/10.22059/ijaedr.2015.54886

Hostert P. Kuemmerle T., Prishchepov A., Sieber A., Lambin E.F., Radeloff V.C., 2011. Rapid land use change after socio-economic ‎disturbances: the collapse of the Soviet Union versus Chernobyl. Environmental Research Letters 6(4): 045201.‎ https://doi.org/10.1088/1748-9326/6/4/045201

Kabba V.T.S., Li J., 2011. Analysis of land use and land cover changes, and their ecological implications in Wuhan, China. Journal of ‎Geography and Geology 3(1): 104.‎ https://doi.org/10.5539/jgg.v3n1p104

Kamusoko C., Gamba J., 2015. Simulating urban growth using a random forest-cellular automata (RF-CA) model. ISPRS International ‎Journal of Geo-Information 4(2): 447-470.‎ https://doi.org/10.3390/ijgi4020447

Katila P., Colfer C.J.P., De Jong W., Galloway G., Pacheco P., Winkel G. (Eds.). (2019). Sustainable ‎Development Goals. Cambridge University Press, 654 p.‎

Kavzoglu T., Mather P.M., 2000. Using feature selection techniques to produce smaller neural networks with better generalisation ‎capabilities, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the ‎Planet: The Role of Remote Sensing in Managing the Environment. IEEE, 7: 3069-3071. https://doi.org/‎10.1109/IGARSS.2000.860339

Lambin E.F., Geist H.J., 2008. Land-use and land-cover change: local processes and global impacts. Springer Science & Business ‎Media, 221 p.

Lambin E.F., Geist H.J., Lepers E., 2003. Dynamics of land-use and land-cover change in tropical regions. Annual Review of Environment ‎and Resources 28(1): 205-241.‎ https://doi.org/‎10.1146/annurev.energy.28.050302.105459

Legdou A., Chafik H., Amine A., Lahssini S., Berrada M., 2020. A random forest-cellular automata modeling approach to predict ‎future forest cover change in Middle Atlas Morocco, under anthropic, biotic and abiotic parameters, International ‎Conference on Image and Signal Processing. Springer, 12119: 91-100, https://doi.org/10.1007/978-3-030-51935-3_10

Lek S., Delacoste M., Baran P., Dimopoulos I., Lauga J., Aulagnier S., 1996. Application of neural networks to modelling nonlinear ‎relationships in ecology. Ecological Modelling 90(1): 39-52.‎ https://doi.org/10.1016/0304-3800(95)00142-5

Lek-Ang S., Deharveng L., Lek S., 1999. Predictive models of collembolan diversity and abundance in a riparian habitat. Ecological ‎Modelling 120(2-3): 247-260.‎ https://doi.org/10.1016/S0304-3800(99)00106-4

Li X-g., Liang C-h., Wang Y-s., LV J-j., 2013. Predicting landscape patterns of Lianhe Delta Wetland by CA–Markov model. ‎Environmental Science & Technology 36(5): 188-192.‎

Liu X., Liang X., Li X., Xu X., Ou J., Chen Y., Li S., Wang S., Pei F., 2017. A future land use simulation model (FLUS) for simulating ‎multiple land use scenarios by coupling human and natural effects. Landscape and Urban Planning 168: 94-116.‎ https://doi.org/10.1016/j.landurbplan.2017.09.019

López-Carr, D (2021) A review of small farmer land use and deforestation in tropical forest frontiers: Implications for conservation ‎and sustainable livelihoods. Land 10(11): 1113.‎ https://doi.org/10.3390/land10111113

Mallard F., François D., 2013. Effectiveness of the legal framework for natural areas protection relative to French road projects. Land ‎Use Policy 30(1): 582-591.‎ https://doi.org/10.1016/j.landusepol.2012.05.006

Mas J-F., Puig H., Palacio JL., Sosa-López A., 2004. Modelling deforestation using GIS and artificial neural networks. Environmental ‎Modelling & Software 19(5): 461-471.‎ https://doi.org/10.1016/S1364-8152(03)00161-0

Mondal P., Southworth J., 2010. Protection vs. commercial management: Spatial and temporal analysis of land cover changes in the ‎tropical forests of Central India. Forest Ecology and Management 259(5): 1009-1017.‎ https://doi.org/10.1016/j.foreco.2009.12.007

Morshed S.R., Fattah M.A., Haque M.N., Morshed S.Y., 2022. Future ecosystem service value modeling with land cover dynamics by ‎using machine learning based Artificial Neural Network model for Jashore city, Bangladesh. Physics and Chemistry of the ‎Earth, Parts a/b/c 126: 103021.‎ https://doi.org/10.1016/j.pce.2021.103021

Munthali M.G., Mustak S., Adeola A., Botai J., Singh S.K., Davis N., 2020. Modelling land use and ‎land cover dynamics of Dedza district of Malawi using hybrid Cellular Automata and Markov ‎model. Remote Sensing Applications: Society and Environment 17: 100276. ‎https://doi.org/10.1016/j.rsase.2019.100276‎

Newman G., Lee J., Berke P., 2016. Using the land transformation model to forecast vacant land. Journal of Land Use Science 11(4): ‎‎450-475.‎ https://doi.org/10.1080/1747423X.2016.1162861

Olmedo M.T.C., Pontius Jr R.G., Paegelow M., Mas J-F., 2015. Comparison of simulation models in terms of quantity and allocation of ‎land change. Environmental Modelling & Software 69: 214-221.‎ https://doi.org/10.1016/j.envsoft.2015.03.003

Ordway E.M., 2015. Political shifts and changing forests: Effects of armed conflict on forest conservation in Rwanda. Global Ecology ‎and Conservation 3: 448-460.‎ https://doi.org/10.1016/j.gecco.2015.01.013

Oyebode O., 2007. Application of GIS and land use models-artificial neural network based Land Transformation Model for future ‎land use forecast and effects of urbanization within the Vermillion River Watershed. Saint Mary’s University of Minnesota ‎Central Services Press: Winona, MN, USA, 13 p.

Paruelo J., Tomasel F., 1997. Prediction of functional characteristics of ecosystems: a comparison of artificial neural networks and ‎regression models. Ecological Modelling 98(2-3): 173-186.‎ https://doi.org/10.1016/S0304-3800(96)01913-8

Pebesma E., 2018. Simple features for R: Standardized support for spatial vector data. The R Journal 10(1): 439-446. https://doi.org/10.32614/RJ-2018-009

Pflugmacher D., Rabe A., Peters M., Hostert P., 2019. Mapping pan-European land cover using Landsat spectral-temporal metrics ‎and the European LUCAS survey. Remote Sensing of Environment 221: 583-595.‎ https://doi.org/10.1016/j.rse.2018.12.001

Pijanowski B.C., Alexandridis K., Mueller D., 2006. Modelling urbanization patterns in two diverse regions of the world. Journal of Land ‎Use Science 1(2-4): 83-108.‎ https://doi.org/10.1080/17474230601058310

Pijanowski B.C., Brown D.G., Shellito B.A., Manik G.A., 2002a. Using neural networks and GIS to forecast land use changes: a land ‎transformation model. Computers, Environment and Urban Systems 26(6): 553-575.‎ https://doi.org/10.1016/S0198-9715(01)00015-1

Pijanowski B.C., Gage S., Long D.T., Cooper W., 2000. A Land Transformation Model: Integrating Policy, Socioeconomics and ‎Environmental Drivers Using a Geographic Information System, in Landscape Ecology: A Top-Down Approach. Boca ‎Raton: Lewis Publishers, pp. 183-198.

Pijanowski B.C., Gage S.H., Long D.T., Cooper W.E., 2020. A land transformation model for the ‎Saginaw Bay Watershed. In Landscape Ecology, CRC Press, ‎pp. 183-198.‎

Pijanowski B.C., Machemer T., Gage S., Long D., Cooper W., Edens T., 1995. A land transformation model: integration of policy, ‎socioeconomics and ecological succession to examine pollution patterns in watershed. Report to the Environmental Protection ‎Agency, 72-83 p.‎

Pijanowski B.C., Shellito B., Pithadia S., Alexandridis K., 2002b. Forecasting and assessing the impact of urban sprawl in coastal ‎watersheds along eastern Lake Michigan. Lakes & Reservoirs: Research & Management 7(3): 271-285.‎ https://doi.org/10.1046/j.1440-1770.2002.00203.x

Pijanowski B.C., Tayyebi A., Delavar M., Yazdanpanah M., 2009. Urban expansion simulation using geospatial information system ‎and artificial neural networks. International Journal of Environmental Research 3: 493-502.‎

Pijanowski B.C., Tayyebi A., Doucette J., Pekin, B.K., Braun D., Plourde J., 2014. A big data urban growth simulation at a national scale: ‎configuring the GIS and neural network based land transformation model to run in a high performance computing (HPC) ‎environment. Environmental Modelling & Software 51: 250-268.‎ https://doi.org/10.1016/j.envsoft.2013.09.015

Pontius Jr R.G., Schneider L.C., 2001. Land-cover change model validation by an ROC method for the Ipswich watershed, ‎Massachusetts, USA. Agriculture, Ecosystems & Environment 85(1-3): 239-248.‎ https://doi.org/10.1016/S0167-8809(01)00187-6

Rahnama M.R., Wyatt R., 2021. Projecting Land use change with neural network and GIS in northern Melbourne for 2014–2050. ‎Australian Geographer 52(2): 149-170.‎ https://doi.org/10.1080/00049182.2021.1920088

Reardon T., Vosti S.A., 1995. Links between rural poverty and the environment in developing countries: asset categories and ‎investment poverty. World Development 23(9): 1495-1506.‎ https://doi.org/10.1016/0305-750X(95)00061-G

Riutta T., Slade E.M., Morecroft M.D., Bebber D.P., Malhi Y., 2014. Living on the edge: quantifying the structure of a fragmented forest ‎landscape in England. Landscape Ecology 29(6): 949-961.‎ https://doi.org/10.1007/s10980-014-0025-z

Roozitalab M.H., Siadat H., Farshad A., 2018. The soils of Iran. Springer, Switzerland, 255 p. https://doi.org/10.1007/978-3-319-69048-3

Roudgarmi P., Mahdiraji M.T.A., 2020. Current challenges of laws for preservation of forest and ‎rangeland, Iran. Land Use Policy 99: 105002. https://doi.org/10.1016/j.landusepol.2020.105002‎

Sagheb Talebi K., Sajedi T., Pourhashemi M., 2014. Forests of Iran. A treasure from the past, a hope for the future, Springer, Dordrecht, 152 p. https://doi.org/10.1007/978-94-007-7371-4

Schürmann A., Kleemann J., Fürst C., Teucher M., 2020. Assessing the relationship between land ‎tenure issues and land cover changes around the Arabuko Sokoke Forest in Kenya. Land Use ‎Policy 95: 104625. https://doi.org/10.1016/j.landusepol.2020.104625‎

Shooshtari S.J., Gholamalifard M., 2015. Scenario-based land cover change modeling and its implications for landscape pattern ‎analysis in the Neka Watershed, Iran. Remote Sensing Applications: Society and Environment 1: 1-19.‎ https://doi.org/10.1016/j.rsase.2015.05.001

Song D-X., Huang C., Sexton J.O., Channan S., Feng M., Townshend J.R., 2015. Use of Landsat and Corona data for mapping forest ‎cover change from the mid-1960s to 2000s: Case studies from the Eastern United States and Central Brazil. ISPRS Journal of ‎Photogrammetry and Remote Sensing 103: 81-92.‎ https://doi.org/10.1016/j.isprsjprs.2014.09.005

Sotoudeh Foumani B., Rostami Shahraji T., Mohammadi Limaei S., 2017. Role of political power in forest administration policy of ‎Iran. Caspian Journal of Environmental Sciences 15(2): 181-199.‎ https://doi.org/10.22124/cjes.2017.2374

Svoboda J., Štych P., Laštovička J., Paluba D., Kobliuk N., 2022. Random Forest Classification of ‎Land Use, Land-Use Change and Forestry (LULUCF) Using Sentinel-2 Data—A Case Study of ‎Czechia. Remote Sensing 14(5): 1189. https://doi.org/10.3390/rs14051189‎

Tang Z., Engel B., Pijanowski B.C., Lim K., 2005. Forecasting land use change and its environmental impact at a watershed scale. ‎Journal of Environmental Management 76(1): 35-45.‎ https://doi.org/10.1016/j.jenvman.2005.01.006

Tayyebi A., Pekin B.K., Pijanowski B.C., Plourde J.D., Doucette J.S., Braun D., 2013. Hierarchical modeling of urban growth across the ‎conterminous USA: developing meso-scale quantity drivers for the Land Transformation Model. Journal of Land Use Science ‎‎ 8(4): 422-442.‎ https://doi.org/10.1080/1747423X.2012.675364

Tayyebi A., Pijanowski B.C., 2014. Modeling multiple land use changes using ANN, CART and MARS: Comparing tradeoffs in ‎goodness of fit and explanatory power of data mining tools. International Journal of Applied Earth Observation and ‎Geoinformation 28: 102-116.‎ https://doi.org/10.1016/j.jag.2013.11.008

Tayyebi A., Pijanowski B.C., Pekin B.K., 2015. Land use legacies of the Ohio River Basin: Using a spatially explicit land use change ‎model to assess past and future impacts on aquatic resources. Applied Geography 57: 100-111.‎ https://doi.org/10.1016/j.apgeog.2014.12.020

Tayyebi A., Pijanowski B.C., Tayyebi A.H., 2011. An urban growth boundary model using neural networks, GIS and radial ‎parameterization: An application to Tehran, Iran. Landscape and Urban Planning 100(1-2): 35-44.‎ https://doi.org/10.1016/j.landurbplan.2010.10.007

Tayyebi A.H., Tayyebi A., Khanna N., 2014. Assessing uncertainty dimensions in land-use change models: using swap and ‎multiplicative error models for injecting attribute and positional errors in spatial data. International Journal of Remote Sensing ‎‎ 35(1): 149-170.‎ https://doi.org/10.1080/01431161.2013.866293

Thyagharajan K.K., Vignesh T., 2019. Soft computing techniques for land use and land cover ‎monitoring with multispectral remote sensing images: a review. Archives of Computational ‎Methods in Engineering 26(2): 275-301. https://doi.org/10.1007/s11831-017-9239-y

Valero Medina J.A., Alzate Atehortúa B.E., 2019. Comparison of maximum likelihood, support vector machines, and random forest ‎techniques in satellite images classification. Tecnura 23(59): 3-10.‎ https://doi.org/10.14483/22487638.14826

Veldkamp A., Lambin E.F., 2001. Predicting land-use change. Agriculture, Ecosystems & Environment, 85(1-3): 1-6. https://doi.org/10.1016/S0167-8809(01)00199-2

Verburg P.H., Schot P.P., Dijst M.J., Veldkamp A., 2004. Land use change modelling: current practice and research priorities. ‎GeoJournal 61(4): 309-324.‎ https://doi.org/10.1007/s10708-004-4946-y

Warth B., Marohn C., Asch F., 2020. Modelling land use change effects on ecosystem functions in ‎African Savannas–A review. Global Food Security 26: 100421. ‎https://doi.org/10.1016/j.gfs.2020.100421‎

Yu W., Zang S., Wu C., Liu W., Na X., 2011. Analyzing and modeling land use land cover change (LUCC) in the Daqing City, China. ‎Applied Geography 31(2): 600-608.‎ https://doi.org/10.1016/j.apgeog.2010.11.019

Živković Ž., Mihajlović I., Nikolić D., 2009. Artificial neural network method applied on the nonlinear multivariate problems. Serbian ‎journal of management 4(2): 143-155.‎






Research article