Discrimination of crown closure of forest ecosystems using different remotely sensed data: a case study of Kızılcasu planning unit

References

• Andersen HE, McGaughey R, Reutebuch SE. 2005. Estimating forest canopy fuel parameters using LIDAR data. Remote Sensing Environ. 94:441–449.
   Web of Science ®Google Scholar
• Berberoğlu S, Satir O, Atkinson PM. 2009. Mapping percentage tree cover from Envisat MERIS data using linear and nonlinear techniques. Int J Remote Sensing. 30 (18):4747–4766.
   Web of Science ®Google Scholar
• Chen L, Wang J, Fu B, Qiu Y. 2001. Land-use change in a small catchment of northern Loess Plateau, China, Agriculture. Ecosystems Environ. 86:163–172.
   Web of Science ®Google Scholar
• Chubey MS, Franklin SE, Wulder MA. 2006. Object-based analysis of IKONOS-2 imagery for extraction of forest inventory parameters. Photogrammetric Eng. Remote Sensing. 72 (4):383–394.
   Web of Science ®Google Scholar
• Cracknell AP. 1998. Synergy in remote sensing: what's in a pixel? Int J Remote Sensing. 19:2025–2047.
   Web of Science ®Google Scholar
• Çakır G, Sivrikaya F, Keleş S. 2008. Forest cover change and fragmentation using Landsat data in Maçka State Forest Enterprise in Turkey. Environ Monit Assess. 137:51–66.
   PubMed Web of Science ®Google Scholar
• Defries RS, Hansen MC, Townshend JRG. 2000. Global continuous fields of vegetation characteristics: a linear mixture model applied to multi-year 8 km AVHRR data. Int J Remote Sensing. 21:1389–1414.
   Web of Science ®Google Scholar
• Erdas. 2014. Erdas 2014 Software, USA.
   Google Scholar
• Foody GM. 1992. A fuzzy sets approach to the representation of vegetation continua from remotely sensed data, an example from lowland heath. Photogrammetric Eng Remote Sensing. 58:221–225.
   Web of Science ®Google Scholar
• Foody GM. 1996. Approaches for the production and evaluation of fuzzy land cover classifications from remotely sensed data. Int J Remote Sensing. 17:1317–1340.
   Web of Science ®Google Scholar
• Foody GM. 1999. The continuum of classification fuzziness in thematic mapping. Photogrammetric Eng Remote Sensing. 65(4): 443–451.
   Web of Science ®Google Scholar
• Franklin SE, Hall RJ, Smith L, Gerylo GR. 2003. Discrimination of conifer height, age and crown closure classes using Landsat-5 TM imagery in the Canadian Northwest Territories. Int J Remote Sensing. 24:1823–1834.
   Web of Science ®Google Scholar
• Gemmel FM. 1995. Effects of forest cover, terrain, and scale on timber volume estimation with Thematic Mapper data in a Rocky Mountain site. Remote Sensing Environ. 51:291–305.
   Web of Science ®Google Scholar
• Graetz RD. 1990. Remote sensing of terrestrial structure: an ecologist's pragmatic view, in Remote Sensing of Biosphere Functioning. Ecological Stud. 79:5–30 (New York: Springer Verlag).
   Google Scholar
• Günlü A, Başkanet EZ. 2015. Estimating crown closure of forest stands using Landsat TM data: A case study from Turkey. Environ Eng Manag J. 14(1):183–193.
   Web of Science ®Google Scholar
• Günlü A, Sivrikaya F, Başkent EZ, Keleş S, Çakir G, Kadiogullari AI. 2008. Estimation of stand type parameters and land cover using Landsat-7 ETM image: a case study from Turkey. Sensors. 8:2509–2525.
   PubMed Web of Science ®Google Scholar
• Hansen MC, Defries RS, Townshend JRG, Carroll M, Dimiceli C, Sohlberg RA. 2003. Global percent tree cover at a spatial resolution of 500 metres: first results of the MODIS vegetation continuous fields algorithm. Earth Interactions. 7:1–15.
   Web of Science ®Google Scholar
• Hyyppa J, Hyyppa H, Inkinen M, Engdahl M, Linko S, Zhu YH. 2000. Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. For Ecol Manag 128:109–120.
   Web of Science ®Google Scholar
• Iverson LR, Cook EA, Graham RL. 1989. A technique for extrapolating and validating forest cover across large regions: calibrating AVHRR data with TM data. Int J Remote Sensing. 10:1805–1812
   Web of Science ®Google Scholar
• Jensen JR, Qiu F, Mınhe J. 1999. Predictive modeling of coniferous forest age using statistical and artificial neural network approaches applied to remote sensor data. Int J Remote Sensing. 20:2805–2822.
   Web of Science ®Google Scholar
• Kammerbauer J, Ardon C. 1999. Land use dynamics and landscape change pattern in a typical watershed in the hillside region of central Honduras. Agric Ecosystems Environ. 75:93–100.
   Web of Science ®Google Scholar
• Keleş S, Sivrikaya F, Çakır G, Köse S. 2008. Urbanization and forest cover change in regional directorate of Trabzon forestry from 1975 to 2000 using Landsat data. Environ Monit Assess. 140:1–14.
   PubMed Web of Science ®Google Scholar
• Poulain M, Pena M, Schmidt A, Schmidt Schulte HA. 2010. Relationship between forest variables and remote sensing data in a Nothafagus pumilio forest. Geocarto Int. 25:25–43.
   Web of Science ®Google Scholar
• Pu R, Xu B, Gong P. 2003. Oakwood crown closure estimation by unmixing Landsat TM data. Int J Remote Sensing. 24:4433–4445.
   Web of Science ®Google Scholar
• Reese H, Nilsson M, Sandström P, Olsson H. 2002. Applications using estimates of forest parameters derived from satellite and forest inventory data. Comput Electron Agric. 37:37–55.
   Web of Science ®Google Scholar
• Sader SA, Hepinstall DJH, Coan M, Soza C. 2001. Forest change monitoring of a remote biosphere reserve. Int J Remote Sensing. 22:1937–1950.
   Web of Science ®Google Scholar
• Spanner MA, Pierce L, Running SW, Peterson DL. 1990. Remote sensing of temperate coniferous forest leaf area index: the influence of canopy closure, understory vegetation and background reflectance. Int J Remote Sensing. 11:95–111.
   Web of Science ®Google Scholar
• Wang F. 1990. Fuzzy supervised classification of remote. IEEE Trans Geoscience Remote Sensing. 28:194–201.
   Web of Science ®Google Scholar