Forest canopy density assessment using different approaches – Review A., Panagiotidis D., Surový P. (2017): Forest canopy density assessment using different approaches – Review. J. For. Sci., 63: 107-116.
download PDF
Crown canopy is a significant regulator of forest, affecting microclimate, soil conditions and having an undeniable role in a forest ecosystem. Among the different materials and approaches that have been used for the estimation of crown canopy, satellite based methods are among the most successful methods regarding cost-saving efforts and different kinds of options for measuring the crown canopy. Different types of satellite sensors can result in different outputs due to their various spectral and spatial resolution, even when using the same methodologies. The aim of this review is to assess different remote sensing methods for forest crown canopy density assessment.
Abbasi M. (2001): Investigating the possibility of Fagus orientalis stand type mapping using ETM+ data in Chalir district – Nowshahr. [MSc Thesis.] Tehran, University of Tehran: 114.
Abdi O., Akbari H., Sosani J., Shirvani Z. (2009): Comparison of vegetation indicators for determination of crown canopy of Zagros forest using ETM+ data. In: Abbasi A. (ed.): Geomatic Conference, Tehran, May 10–11, 2009: 12.
Azizi Z., Najafi A., Sohrabi H. (2008): Forest canopy density estimating, using satellite images. In: Chen J., Jiang J., Peled A. (eds): Proceedings of the 21st ISPRS Congress, Commission VIII, Beijing, July 3–11, 2008: 1127–1130.
Baatz M., Benz U., Dehghani S., Heynen M., Höltje A., Hofmann P., Lingenfelder I., Mimler M., Sohlbach M., Weber M., Willhauck G. (2004): Definiens Enterprise Image Intelligence Suite 7 for Windows – installation and administration guide. Available at (accessed Mar 7, 2006).
Banerjee K., Panda S., Bandyopadhyay J., Jain M.K. (2014): Forest canopy density mapping using advance geospatial technique. International Journal of Innovative Science, Engineering & Technology, 1: 358–363.
Baret F., Guyot G. (1991): Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 35, 161-173
Benz Ursula C., Hofmann Peter, Willhauck Gregor, Lingenfelder Iris, Heynen Markus (2004): Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58, 239-258
Birth Gerald S., McVey George R. (1968): Measuring the Color of Growing Turf with a Reflectance Spectrophotometer1. Agronomy Journal, 60, 640-
Boles Stephen H, Xiao Xiangming, Liu Jiyuan, Zhang Qingyuan, Munkhtuya Sharav, Chen Siqing, Ojima Dennis (2004): Land cover characterization of Temperate East Asia using multi-temporal VEGETATION sensor data. Remote Sensing of Environment, 90, 477-489
Bonyad A. (2005): Multitemporal satellite image database classification for land cover inventory and mapping. Journal of Applied Sciences, 5: 835–837.
Breiman L. (2001): Random forests. Machine Learning, 45: 5–32.
Breiman L. (2002): Using models to infer mechanisms. IMS Wald lecture 2. Available at (accessed Sept 19, 2002).
BUSCHMANN C., NAGEL E. (1993): In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation. International Journal of Remote Sensing, 14, 711-722
Kristine Butera M. (1983): Remote Sensing of Wetlands. IEEE Transactions on Geoscience and Remote Sensing, GE-21, 383-392
Carleer A.P., Wolff E. (2006): Region-based classification potential for land-cover classification with very high spatial resolution satellite data. In: Lang S., Blaschke T., Schöpfer E. (eds): Proceedings of the 1st International Conference on Object-based Image Analysis, Salzburg, July 4–5, 2006: 1–6.
CLARK M, ROBERTS D, CLARK D (2005): Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales. Remote Sensing of Environment, 96, 375-398
Conchedda Giulia, Durieux Laurent, Mayaux Philippe (2008): An object-based method for mapping and change analysis in mangrove ecosystems. ISPRS Journal of Photogrammetry and Remote Sensing, 63, 578-589
Cortez P., Morais A. (2007): A data mining approach to predict forest fires using meteorological data. In: Neves J., Santos M.F., Machado J.M. (eds): Proceedings of the EPIA 2007 – Portuguese Conference on Artificial Intelligence, Guimarães, Dec 3–7, 2007: 512–523.
Culvenor D.S. (2003): Extracting individual tree information. A survey of techniques for high spatial resolution imagery. In: Wulder M.A., Franklin S.E. (eds): Remote Sensing of Forest Environments: Concepts and Case Studies. Boston, Kluwer Academic Publishers: 255–277.
Dean A. M., Smith G. M. (2003): An evaluation of per-parcel land cover mapping using maximum likelihood class probabilities. International Journal of Remote Sensing, 24, 2905-2920
Deka Jyotishman, Tripathi Om Prakash, Khan Mohamed Latif (2013): Implementation of Forest Canopy Density Model to Monitor Tropical Deforestation. Journal of the Indian Society of Remote Sensing, 41, 469-475
Durbha Surya S., King Roger L., Younan Nicolas H. (2007): Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer. Remote Sensing of Environment, 107, 348-361
Ernst C.L., Hoffer R.M. (1979): Digital Processing of Remotely Sensed Data for Mapping Wetland Communities. LARS Technical Report 122079. West Lafayette, Purdue University: 119.
Yan Gao, Mas J. ‐F., Maathuis B. H. P., Xiangmin Zhang, Van Dijk P. M. (2006): Comparison of pixel‐based and object‐oriented image classification approaches—a case study in a coal fire area, Wuda, Inner Mongolia, China. International Journal of Remote Sensing, 27, 4039-4055
Benito Garzón Marta, Sánchez de Dios Rut, Sainz Ollero Helios (2008): Effects of climate change on the distribution of Iberian tree species. Applied Vegetation Science, 11, 169-178
Gemmell Fraser (1999): Estimating Conifer Forest Cover with Thematic Mapper Data Using Reflectance Model Inversion and Two Spectral Indices in a Site with Variable Background Characteristics. Remote Sensing of Environment, 69, 105-121
Ghazanfari H. (1996): Investigation of the application of satellite data for classifying forest types in the forests managed by Mazandaran wood and paper company. [MSc Thesis.] Gorgan, University of Gorgan: 124.
Gitelson Anatoly A., Kaufman Yoram J., Stark Robert, Rundquist Don (2002): Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 80, 76-87
Godinho Sérgio, Guiomar Nuno, Machado Rui, Santos Pedro, Sá-Sousa Paulo, Fernandes J. P., Neves Nuno, Pinto-Correia Teresa (2016): Assessment of environment, land management, and spatial variables on recent changes in montado land cover in southern Portugal. Agroforestry Systems, 90, 177-192
Hadi F., Wikantika K., Sumarto I. (2004): Implementation of forest canopy density model to monitor forest fragmentation in Mt. Simpang and Mt. Tilu Nature Reserves, West Java, Indonesia. In: Sumarto I. (ed.): Proceedings of the 3rd FIG Regional Conference, Jakarta, Oct 3–7, 2004: 9–20.
Hay G.L., Castilla G. (2008): Geographic object-based image analysis (GEOBIA): A new name for a new discipline. In: Blaschke T., Lang S., Hay G.J. (eds): Object-Based Image Analysis – Spatial Concepts for Knowledge-Driven Remote Sensing Applications. Berlin, Heidelberg, Springer-Verlag: 93–112.
Hines Mark E., Pelletier Ramona E., Crill Patrick M. (1993): Emissions of sulfur gases from marine and freshwater wetlands of the Florida Everglades: Rates and extrapolation using remote sensing. Journal of Geophysical Research, 98, 8991-
Hosseini S.Z., Khajeddin S.J., Azarnivand H., Khalilpour S.A. (2004): Land use mapping using ETM+ data (case study: Chamestan area, Iran). In: Altan O. (ed.): Proceedings of the 20th ISPRS Congress, Commission VII, Istanbul, July 12–23, 2004: 391–393.
Huang C., Yang L., Wylie B., Homer C. (2001): A strategy for estimating tree canopy density using Landsat 7 ETM+ and high resolution images over large areas. Available at (accessed Mar 1, 2008).
Huete A.R (1988): A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 295-309
Huguenin R.L., Karaska M.A., Blaricom D.V., Jensen J.R. (1997): Subpixel classification of bald cypress and tupelo gum trees in Thematic Mapper imagery. Photogrammetric Engineering & Remote Sensing, 63: 717–725.
IVERSON L. R., COOK E. A., GRAHAM R. L. (1989): A technique for extrapolating and validating forest cover across large regions Calibrating AVHRR data with TM data. International Journal of Remote Sensing, 10, 1805-1812
Jamalabad M.S., Akbar A.A. (2004): Forest canopy density monitoring, using satellite images. In: Altan O. (ed.): Proceedings of the 20th ISPRS Congress, Commission VII, Istanbul, July 12–23, 2004: 244–249.
Joshi Chudamani, Leeuw Jan De, Skidmore Andrew K., Duren Iris C. van, van Oosten Henk (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
Korhonen Lauri, Korhonen Kari, Rautiainen Miina, Stenberg Pauline (2006): Estimation of forest canopy cover: a comparison of field measurement techniques. Silva Fennica, 40, -
Lee J.K., Park R.A. (1992): Application of geoprocessing and simulation modelling to estimate impacts of sea level rise on the northeast coast of Florida. Photogrammetric Engineering & Remote Sensing, 58: 1579–1586.
Liu Xue-Hua, Skidmore A.K., Van Oosten H. (2002): Integration of classification methods for improvement of land-cover map accuracy. ISPRS Journal of Photogrammetry and Remote Sensing, 56, 257-268
Lo C.P., Watson L.J. (1998): The influence of geographic sampling methods on vegetation map accuracy evaluation in a swampy environment. Photogrammetric Engineering & Remote Sensing, 64: 1189–1200.
Mahboob Juwairia (2012): Forest crown closure assessment using multispectral satellite imagery. AFRICAN JOURNAL OF AGRICULTURAL RESEEARCH, 7, -
Matsushita Bunkei, Yang Wei, Chen Jin, Onda Yuyichi, Qiu Guoyu (2007): Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to Topographic Effects: A Case Study in High-density Cypress Forest. Sensors, 7, 2636-2651
Mirakhorlo K.H. (2003): Land cover/land use mapping of the Northern forests of Iran using Landsat ETM+ data. Iranian Journal of Forest and Poplar Research, 1: 1–11. (in Persian)
Moeinazad Tehrani S.M., Darvishsefat A.A., Namiraniyan M. (2008): Evaluation of FCD model for estimation of forest density using Landsat 7 imagery (case study: Chalus Forest). Iranian Journal of Forest and Poplar Research, 16: 124–138. (in Persian)
Næsset Erik, Gobakken Terje, Holmgren Johan, Hyyppä Hannu, Hyyppä Juha, Maltamo Matti, Nilsson Mats, Olsson Håkan, Persson Åsa, Söderman Ulf (2004): Laser scanning of forest resources: the nordic experience. Scandinavian Journal of Forest Research, 19, 482-499
Nandy S., Joshi P. K., Das K. K. (2003): Forest canopy density stratification using biophysical modeling. Journal of the Indian Society of Remote Sensing, 31, 291-297
Ostapowicz K., Lakes T., Kozak J. (2010): Modelling of land cover change using support vector machine. In: Painho M., Santos M.Y., Pundt H. (eds): Proceedings of the 13th AGILE International Conference on Geographic Information Science, Guimarães, May 10–14, 2010.
Ozesmi S.L., Bauer M. (2002): Satellite remote sensing of wetlands. Wetlands Ecology and Management, 10: 381–402.
Pakkhesal E., Bonyad A.E. (2013): Classification and delineating natural forest canopy density using FCD Model (case study: Shafarud area of Guilan). Iranian Journal of Forest and Poplar Research, 21: 99–114. (in Persian)
Pal Mahesh, Mather Paul M (2003): An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sensing of Environment, 86, 554-565
Pinty B., Verstraete M. M. (1992): GEMI: a non-linear index to monitor global vegetation from satellites. Vegetatio, 101, 15-20
Pitkänen Juho (2001): Individual tree detection in digital aerial images by combining locally adaptive binarization and local maxima methods. Canadian Journal of Forest Research, 31, 832-844
Qi J., Chehbouni A., Huete A.R., Kerr Y.H., Sorooshian S. (1994): A modified soil adjusted vegetation index. Remote Sensing of Environment, 48, 119-126
Ramtin Nia K. (1997): Preparing forest cover type map using digital satellite information in Kheiroudkenar Forest – Nowshahr. [MSc Thesis.] Tehran, University of Tehran: 96. (in Persian)
Rautiainen Miina, Stenberg Pauline, Nilson Tiit (2005): Estimating canopy cover in Scots pine stands. Silva Fennica, 39, -
Rikimaru A., Roy P., Miyatake S. (2002): Tropical forest cover density mapping. Tropical Ecology, 43: 39–47.
Rikimaru A., Utsuki Y., Yamashita S. (1999): Basic study of the maximum logging volume estimation for consideration of forest resources using time series FCD (forest canopy density) model. In: Harris R. (ed.): Asian Conference on Remote Sensing, Manila, Nov 16–20, 1998: 6.
Rouse J.W., Haas R.H., Schell J.A., Deering D.W. (1973): Monitoring vegetation systems in the Great Plains with ERTS. In: Fraden S.C., Marcanti E.P., Becker M.A. (eds): 3rd ERTS-1 Symposium, NASA SP-351, Washington, D.C., Dec 10–14, 1973: 309–317.
Sarvas R. (1953): Measurement of the crown closure of the stand. Communicationes Instituti Forestalis Fenniae, 41: 1–13.
Solberg A.H.S., Jain A.K., Taxt T. (): Multisource classification of remotely sensed data: fusion of Landsat TM and SAR images. IEEE Transactions on Geoscience and Remote Sensing, 32, 768-778
Seong J.C., Usery E.L. (2001): Assessing raster representation accuracy using a scale factor model. Photogrammetric En-gineering & Remote Sensing, 67: 1185–1191.
Shahvali-Kouhshour A., Pir-Bavaghar M., Fathi P. (2012): Forest cover density mapping in sparse and semi dense forests using forest canopy density model (case study: Marivan forests). Journal of RS and GIS for Natural Resources (Journal of Applied RS and GIS Techniques in Natural Resource Science), 3: 373–383.
Shao Guofan, Shugart Herman H., Zhao Guang, Zhao Shidong, Wang Shaoxian, Schaller Jörg (1996): Forest cover types derived from Landsat Thematic Mapper imagery for Changbai Mountain area of China. Canadian Journal of Forest Research, 26, 206-216
Shataee S.H. (2003): Investigation of the possibility of forest type mapping using satellite information (a case study of Kheiroudkenar forest – Nowshahr). [Ph.D. Thesis.] Tehran, University of Tehran: 155.
Shataee Shaban, Kalbi Syavash, Fallah Asghar, Pelz Dieter (2012): Forest attribute imputation using machine-learning methods and ASTER data: comparison of k -NN, SVR and random forest regression algorithms. International Journal of Remote Sensing, 33, 6254-6280
Sheeren David, Fauvel Mathieu, Josipović Veliborka, Lopes Maïlys, Planque Carole, Willm Jérôme, Dejoux Jean-François (2016): Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series. Remote Sensing, 8, 734-
Shirian R. (1997): Vegetation mapping of Golestan National Park using GIS and Landsat TM data. [MSc Thesis.] Gorgan, University of Gorgan: 99.
Sripada Ravi P., Heiniger Ronnie W., White Jeffrey G., Meijer Alan D. (2006): Aerial Color Infrared Photography for Determining Early In-Season Nitrogen Requirements in Corn. Agronomy Journal, 98, 968-
StatSoft, Inc. (2010): Statistica. Electronic textbook. Available at (accessed Nov 20, 2010).
Taefi M. (2006): Evaluation and Optimization of FCD Model in Order to Estimate Forest Canopy Density Using Merger Data Method and Image Index. Tehran, Nasir al-Din Tusi University: 95.
Tucker Compton J. (1979): Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8, 127-150
Walton J.T. (2008): Sub pixel urban land cover estimation: Comparing cubist, random forests, and support vector regression. Photogrammetric Engineering & Remote Sensing, 74: 1213–1222.
Wang F. (): Fuzzy classification of remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 28, 194-201
Yi G.C., Risley D., Koneff M., Davis C. (1994): Development of Ohio’s GIS-based wetland inventory. Journal of Soil and Water Conservation, 49: 23–28.
Zhang R., Ma J. (2008): An improved SVM method P‐SVM for classification of remotely sensed data. International Journal of Remote Sensing, 29, 6029-6036
download PDF

© 2022 Czech Academy of Agricultural Sciences | Prohlášení o přístupnosti