1. Jantz SM, Barker B, Brooks TM, Chini LP, Huang Q, Moore RM, et al. Future habitat loss and extinctions driven by land‐use change in biodiversity hotspots under four scenarios of climate‐change mitigation. Conserv Biol. 2015;29(4):1122-31.
2. Xu L, Chen SS, Xu Y, Li G, Su W. Impacts of land-use change on habitat quality during 1985–2015 in the Taihu Lake Basin. Sustainability. 2019;11(13):3513.
3. Allan JR, Watson JE, Di Marco M, O’Bryan CJ, Possingham HP, Atkinson SC, et al. Hotspots of human impact on threatened terrestrial vertebrates. PLoS Biol. 2019;17(3):e3000158.
4. Ndegwa Mundia C, Murayama Y. Analysis of land use/cover changes and animal population dynamics in a wildlife sanctuary in East Africa. Remote Sensing. 2009;1(4):952-70.
5. Clark NE, Boakes EH, McGowan PJ, Mace GM, Fuller RA. Protected areas in South Asia have not prevented habitat loss: a study using historical models of land-use change. PloS one. 2013;8(5):e65298.
6. Rahdari V, Maleki Najafabadi S, Afsari K, Abtin A, Piri H, Fakhire A. Monitoring the changes in land use and land cover of Hamon Wildlife Sanctuary during the 1986 to 2009 using satellite images and geographic information systems. Iranian Remote Sensing & GIS. 2011;3(2):59-70.
7. Najafi Z, Darvishsefat A, Fatehi P, Attarod P. Time series analysis of vegetation dynamic trend using Landsat data in Tehran Megacity. Iranian Journal of Forest. 2020;12(2):257-70.
8. Foroutan S, Islamzadeh N. The Study of Mazandaran Province Forest and Rangeland Vegetation Changes Trend by Satellite Images. Plant Ecosystem Conservation. 2022;9(19):197-215.
9. Baskaya S. Distribution and principal threats to Caucasian black grouse Tetrao mlokosiewiczi in the Eastern Karadeniz Mountains in Turkey. Wildlife Biology. 2003;9(4):377-83.
10. Storch I. Conservation status of grouse worldwide: an update. Wildlife Biology. 2007;13(sp1):5-12.
11. Darvishi A, Fakheran S, Soffianian A, Ghorbani M. Quantifying landscape spatial pattern changes in the Caucasian Black Grouse (Tetrao Mlokosiewiczi) Habitat in Arasbaran biosphere reserve. Iranian journal of applied ecology. 2014;2(5):27-38.
12. Kaboodvandpour S, Shiriazar J. Modeling the habitat desirability of the Black Grouse species (Tetrao mlokosiewiczi) in Arasbaran biosphere reserve by maximum entropy method. International Congress of Developing Agriculture, Natural Resources, Environment and Tourism of Iran. 2019:14-6.
13. Darvishi A, Fakheran S, Soffianian A. Monitoring landscape changes in Caucasian black grouse (Tetrao mlokosiewiczi) habitat in Iran during the last two decades. Environmental monitoring and assessment. 2015;187(7):443.
14. Etzold J. Analyses of vegetation and human impacts in the habitat of the Caucasian Black Grouse Tetrao mlokosiewiczi in the Greater Caucasus/Azerbaijan. Archiv für Naturschutzund Landschaftsforschung. 2005;44:7-36.
15. Ghanbari S, Nasiri V, Mohammadi Y. Effects of developmental level on forest area changes of rural areas in Arasbaran by satellite images. Plant Ecosystem Conservation. 2019;7(14):291-312.
16. Ghanbari S, Turvey ST. Local ecological knowledge provides novel evidence on threats and declines for the Caucasian grouse Lyrurus mlokosiewiczi in Arasbaran Biosphere Reserve, Iran. People and Nature. 2022;4(6):1536-46.
17. Sagheb-Talebi K, Pourhashemi M, Sajedi T. Forests of Iran: A Treasure from the Past, a Hope for the Future: Springer; 2014.
18. Sasanifar S, Alijanpour A, Shafiei AB, Rad JE, Molaei M, Azadi H. Forest protection policy: Lesson learned from Arasbaran biosphere reserve in Northwest Iran. Land Use Policy. 2019;87:104057.
19. Ghanbari S, Sefidi K, Álvarez-Álvarez P. Vegetation and Forest Complexity Analysis of the Caucasian Grouse (Lyrurus mlokosiewiczi) Habitats in the Lesser Caucasus Mountain. Forests. 2023;14(2):353.
20. Rouse JW, Haas RH, Schell JA, Deering DW. Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec Publ. 1974;351(1):309.
21. Qi J, Chehbouni A, Huete AR, Kerr YH, Sorooshian S. A modified soil adjusted vegetation index. Remote Sens Environ. 1994;48(2):119-26.
22. Huete A, Liu H, Batchily K, Van Leeuwen W. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sens Environ. 1997;59(3):440-51.
23. Muchsin F, Dirghayu D, Prasasti I, Rahayu M, Fibriawati L, Pradono K, et al., editors. Comparison of atmospheric correction models: FLAASH and 6S code and their impact on vegetation indices (case study: paddy field in Subang District, West Java). IOP Conference Series: Earth and Environmental Science; 2019: IOP Publishing.
24. Cooley T, Anderson GP, Felde GW, Hoke ML, Ratkowski AJ, Chetwynd JH, et al., editors. FLAASH, a MODTRAN4-based atmospheric correction algorithm, its application and validation. IEEE international geoscience and remote sensing symposium; 2002: IEEE.
25. Berk A, Anderson GP, Bernstein LS, Acharya PK, Dothe H, Matthew MW, et al., editors. MODTRAN4 radiative transfer modeling for atmospheric correction. Optical spectroscopic techniques and instrumentation for atmospheric and space research III; 1999: SPIE.
26. Yang M, Hu Y, Tian H, Khan FA, Liu Q, Goes JI, et al. Atmospheric correction of airborne hyperspectral CASI data using polymer, 6S and FLAASH. Remote Sensing. 2021;13(24):5062.
27. Wang Z, Xia J, Wang L, Mao Z, Zeng Q, Tian L, et al. Atmospheric correction methods for GF-1 WFV1 data in hazy weather. Journal of the Indian Society of Remote Sensing. 2018;46:355-66.
28. Shalaby A, Tateishi R. Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt. Applied geography. 2007;27(1):28-41.
29. Günlü A, Sivrikaya F, Baskent EZ, Keles S, Çakir G, Kadiogullari Aİ. Estimation of stand type parameters and land cover using Landsat-7 ETM image: A case study from Turkey. Sensors. 2008;8(4):2509-25.
30. Serra P, Pons X, Saurí D. Land-cover and land-use change in a Mediterranean landscape: a spatial analysis of driving forces integrating biophysical and human factors. Applied geography. 2008;28(3):189-209.
31. Heidarlou HB, Mirshekarlou AK, Lopez-Carr D, Borz SA. Conservation policy and forest transition in Zagros forests: Statistical analysis of human welfare, biophysical, and climate drivers. Forest Policy and Economics. 2024;161:103177.
32. Rash A, Mustafa Y, Hamad R. Quantitative assessment of Land use/land cover changes in a developing region using machine learning algorithms: A case study in the Kurdistan Region, Iraq. Heliyon. 2023;9(11).
33. Ribeiro MP, de Mello K, Valente RA. How can forest fragments support protected areas connectivity in an urban landscape in Brazil? Urban Forestry & Urban Greening. 2022;74:127683.
34. Negassa MD, Mallie DT, Gemeda DO. Forest cover change detection using Geographic Information Systems and remote sensing techniques: a spatio-temporal study on Komto Protected forest priority area, East Wollega Zone, Ethiopia. Environmental Systems Research. 2020;9:1-14.
35. Gottschalk TK, Ekschmitt K, İsfendiyaroglu S, Gem E, Wolters V. Assessing the potential distribution of the Caucasian black grouse Tetrao mlokosiewiczi in Turkey through spatial modelling. Journal of Ornithology. 2007;148(4):427-34.
36. Sefidi K, Ghanbari S. Quantitative analysis of woody plants within the habitat of Caucasian black grouse (Lyrurus mlokosiewiczi Taczanowski) in the Arasbaran Forests, Iran. Iranian Journal of Forest and Poplar Research. 2021;29(3):301-13.
37. Faridi E, Naseri D. Providing a habitat model for black male Caucasians grouse (Tetrao mlokosiewiczi) using geographical information system (GIS). Journal of Environmental Science and Technology. 2019;21(5):263-72.
38. Behruzi Rad B. biology of caucasian black grouse. 3rd national conference on biology and natural science of Iran. 2016:8.
39. Karami A, Feghhi J. Investigation of Quantitative metrics to protect the landscape in land use by sustainable pattern (Case study: Kohgiluyeh and Boyer Ahmad). Journal of Environmental Studies. 2012;37(60):79-88.