Ahmadi Nadoushan, M, Sofianian, A & Khajehaldin, SJAD 2009, Land cover mapping of Arak city using artificial neural network and maximum likelihood classifiers, Physical Geography Research Quarterly, 69: 83–98.
Ahmadi Sani, N 2008, Potentiality of ASTER images for forest density mapping in Zagros (Case study: Marivan forests). Iranian Journal of Natural Resources, 61: 603–614.
Baig, MHA 2014, Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance. Remote Sensing Letters, 5: 423–431.
Bannari, A 1995, A review of vegetation indices. Remote Sensing Reviews, 13: 95–120.
Baret, F, Jacquemoud, S & Hanocq, JF 1993, The soil line concept in remote sensing. Remote Sensing Reviews, 7: 65–82.
Bazrafkan, AM, PB & Fathi, P 2015, Capability of Liss III data for forest canopy density mapping in Zagros forests (Case study: Marivan Forests), Iranian Journal of Forest, 6: 387–401.
Bazrafkan, A, Pir Bavaghar, M & Fathi, P 2014, Capability of Liss III data for forest canopy density mapping in Zagros forests (Case study: Marivan Forests), Iranian Journal of Forest, 6: 387–401.
Brown De Colstoun, EC 2003, National Park vegetation mapping using multitemporal Landsat 7 data and a decision tree classifier, Remote Sensing of Environment, 85: 316–327.
Carpenter, GA 1997, Art neural networks for remote sensing: vegetation classification from landsat tm and terrain Data. IEEE Transactions on Geoscience and Remote Sensing, 35: 308–325.
Chagas, C, daS, Vieira, CAO & Fernandes Filho, EI 2013, Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping. Revista Brasileira de Ciência do Solo, 37: 339–351.
Chavez, PSJ 1996, Image-Based Atmospheric Corrections-Revisited and Improved. Photogrammetric Engineering & Remote Sensing, 62: 1025–1035.
Crist, EP & Cicone, RC 1984, A physically-based transformation of Thematic Mapper data---The TM Tasseled Cap. IEEE Transactions on Geoscience and Remote sensing, 22: 256–263.
Davis, PA 2002, Evaluation of airborne image data for mapping riparian vegetation within the Grand Canyon. US Geological Survey, 470p.
Domac, A & Suezen, ML 2006, Integration of environmental variables with satellite images in regional scale vegetation classification. International Journal of Remote Sensing, 27: 1329–1350.
Doostan, R & Alijani, B 2016, Climate Change of Iran : A Synoptic Approach. Journal of geography and regional developement, 13: 21–26.
Dorren, LKA, Maier, B & Seijmonsbergen, AC 2003, Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification, Forest Ecology and Management, 183: 31–46.
Eastman Ronald, J 2006, IDRISI Andes Guide to GIS and Image Processing, Clark Labs, Clark University, 284p.
Erbek, FS, Özkan, C & Taberner, M 2004, Comparison of maximum likelihood classification method with supervised artificial neural network algorithms for land use activities. International Journal of Remote Sensing, 25: 1733–1748.
Fassnacht, FE, LiL & Fritz, A 2015, Mapping degraded grassland on the Eastern Tibetan Plateau with multi-temporal Landsat 8 data - where do the severely degraded areas occur? International Journal of Applied Earth Observation and Geoinformation, 42: 115–127.
Fatehi, P 2015, Estimation of Alpine Forest Structural Variables from Imaging Spectrometer Data. Remote Sensing, 7: 16315–16338.
Foody, GM 2002, Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80: 185–201.
Franklin, SE 2001, Remote sensing for sustainable forest management, CRC Press, 424p.
Frazier, RJ 2014, Characterization of aboveground biomass in an unmanaged boreal forest using Landsat temporal segmentation metrics. ISPRS Journal of Photogrammetry and Remote Sensing, 92,: 137–146.
Ghazanfari, H 2003, Evaluation of growth and changes in diameter distribution of Quercus libani and Quercus infectoria stands to present forest adjustment pattern in Baneh region (Case study: Hawarakhol). University of Tehran, 82p.
Ghazanfari, H 2004, Traditional Forest Management and its Application to Encourage Public Participation for Sustainable Forest Management in the Northern Zagros Mountains of Kurdistan Province, Iran. Scandinavian Journal of Forest Research, 19: 65–71.
Gong, PPuR & Chen, J 1996, Elevation and Forest-Cover Data Using Neural Networks. Photogrammetric Engineering & Remote Sensing, 62: 1249–1260.
Hanara Khaliani, J 2013, Financial estimation and design of forestry incentive programs aimed at improving customary forest management (Case study: Baneh forests, northern Zagros). Iranian Journal of Forest, 5: 295–308.
Henareh Khalyani, A 2013, Deforestation and landscape structure changes related to socioeconomic dynamics and climate change in Zagros forests. Journal of Land Use Science, 8: 321–340.
Hu, X & Weng, Q 2009, Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks. Remote Sensing of Environment, 113: 2089–2102.
Jayakumar, S, Arockiasamy, DI & Britto, SJ 2000, Estimates of current status of forest types in Kolli Hill using remote sensing. Journal of the Indian Society of Remote Sensing, 28: 141-151.
Jia, X & Richards, JA 1994, Efficient maximum likelihood classification for imaging spectrometer data sets. IEEE Transactions on Geoscience and Remote Sensing, 32: 274–281.
Joshi, C 2006, Remotely sensed estimation of forest canopy density: A comparison of the performance of four methods. International Journal of Applied Earth Observation and Geoinformation, 8: 84–95.
Joshi, PK, Kumar, M & Agrawal, D 2004, Geospatial network analysis for path optimisation in solid waste management - a case study of Haridwar (India). Journal of the Indian Society of Remote Sensing, 32: 387–392.
Kadmon, R & Harari-kremer, R 1999, Studying Long-Term Vegetation Dynamics Using Digital Processing of Historical Aerial Photographs. Remote Sens. Environ, 68: 164–176.
Karjalainen, E, Sarjala, T & Raitio, H 2009, Promoting human health through forests: overview and major challenges. Environmental Health and Preventive Medicine, 15: 1-8.
Kavzoglu, T & Mather, PM 2003, The use of backpropagating artificial neural networks in land cover classification. International Journal of Remote Sensing, 24: 4907–4938.
Kavzoglu, T & Reis, S 2008, Performance Analysis of Maximum Likelihood and Artificial Neural Network Classifiers for Training Sets with Mixed Pixels. GIScience & Remote Sensing, 45: 330–342.
Knorn, J 2009, Land cover mapping of large areas using chain classification of neighboring Landsat satellite images. Remote Sensing of Environment, 113: 957–964.
De Laet, V, Paulissen, E & Waelkens, M 2007, Methods for the extraction of archaeological features from very high-resolution Ikonos-2 remote sensing imagery, Hisar (southwest Turkey). Journal of Archaeological Science, 34: 830–841.
Lawley, V 2016, Site-based and remote sensing methods for monitoring indicators of vegetation condition: An Australian review. Ecological Indicators, 60: 1273–1283.
Lippmann, R 1987, An introduction to computing with neural nets. IEEE ASSP Magazine, 4: 4–22.
Lu, D & Weng, Q 2007, A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28: 823–870.
Mendoza, EH 2004, Land use/land cover mapping in Brazilian Amazon using neural network with aster/terra data. In Proc. Geo-Imagery Bridging Continents. Citeseer, 2004: 123–126.
Moradi, A 2009, The ability of SPOT-HRG and IRS-LISSIII data to identify and separate pollarding classes in north Zagros (Case study: Pollarded forests of Baneh in Kurdistan). Iranian Journal of Forest and Poplar Research, 17: 463–450.
Noormets, A 2015, Effects of forest management on productivity and carbon sequestration : A review and hypothesis q. Forest Ecology and Management, 355: 124–140.
Oetter, DR 2001, Land cover mapping in an agricultural setting using multiseasonal Thematic Mapper data. Remote Sensing of Environment, 76: 139–155.
Pandey, VK 2006, Delineation and parameterization of banikdih watershed using remote sensing and avswat model. Journal of the Indian Society of Remote Sensing, 34: 143–152.
Petropoulos, GP 2010, A Comparison of Spectral Angle Mapper and Artificial Neural Network Classifiers Combined with Landsat TM Imagery Analysis for Obtaining Burnt Area Mapping. Sensors , 10: 1967-1985.
Pourshakouri Aladeh, F 2005, Separating the northern border of Caspian forests using multi-temporal satellite images (Case study: Kalachai forests), University of Tehran, 93p.
Ranjbar, H & Honarmand, M 2004, Integration and analysis of airborne geophysical and ETM+ data for exploration of porphyry type deposits in the Central Iranian Volcanic Belt using fuzzy classification. International Journal of Remote Sensing, 25: 4729–4741.
Rezayan, F & Erfanifard, Y 2016, Estimating biophysical parameters of Persian oak coppice trees using UltraCam-D airborne imagery in Zagros semi-arid woodlands. Journal of Arid Environments, 133: 10–18.
Richardson, AJ & Wiegand, CL 1977, Distinguishing vegetation from soil background information. Photogrammetric engineering and remote sensing, 43(12): 1541–1552.
Rocchini, D 2013, Uncertainty in ecosystem mapping by remote sensing. Computers & Geosciences, 50: 128–135.
Roy, PS & Ravan, SA 1996, Biomass estimation using satellite remote sensing data—An investigation on possible approaches for natural forest. Journal of Biosciences, 21: 535–561.
Satterwhite, MB & Ponder Henley, J 1987, Spectral characteristics of selected soils and vegetation in Northern Nevada and their discrimination using band ratio techniques. Remote Sensing of Environment, 23(2): 155–175.
Schultz, B 2015, Self-guided segmentation and classification of multi-temporal Landsat 8 images for crop type mapping in Southeastern Brazil. Remote Sensing, 7: 14482–14508.
Soffianian, AR 2011, No Title. Journal of RS and GIS for Natural Resources, 2: 1–11.
Stone, MG 1998, Forest-type mapping by photo-interpretation: A multi-purpose base for Tasmania’s forest management. Tasforests, 10:15–32.
Thakur, T, Swamy, SL & Nain, AS 2014, Composition, structure and diversity characterization of dry tropical forest of Chhattisgarh using satellite data. Journal of Forestry Research, 25: 819–825.
USGS 2014, No Title. Available at: ear.
Ustin, SL & Gamon, JA 2010, Remote sensing of plant functional types. New Phytologist, 186: 795–816.
Wolter, PT 1995, Improved forest classification in the northern Lake States using multi-temporal landsat imagery. Photogrammetric Engineering and Remote Sensing, 61: 1129–1143.
Wulder, MA 2008, Landsat continuity: Issues and opportunities for land cover monitoring. Remote Sensing of Environment, 112(3): 955–969.
Xie, Y, Sha, Z & YuM 2008, Remote sensing imagery in vegetation mapping: a review. Journal of Plant Ecology, 1: 9–23.
Xu, B, Gong, P & PuR 2003, Crown closure estimation of oak savannah in a dry season with Landsat TM imagery: Comparison of various indices through correlation analysis. International Journal of Remote Sensing, 24: 1811–1822.
Yuan, F 2005, Land cover classification and change analysis of the Twin Cities (Minnesota) metropolitan area by multitemporal Landsat remote sensing. Remote Sensing of Environment, 98: 317–328.
Zhang, J & Foody, GM 2001, Fully-fuzzy supervised classification of sub-urban land cover from remotely sensed imagery: Statistical and artificial neural network approaches. International Journal of Remote Sensing, 22: 615–628.
Zhou, J 2016, Comparison modeling for alpine vegetation distribution in an arid area. Environmental Monitoring and Assessment, 188: 408-420.