Red-edge vegetation indices for detecting and assessing disturbances in Norway spruce dominated mountain forests

International Journal of Applied Earth Observation and Geoinformation

Volumn 37, Issue , 2015, Pages 90-99

  Adamczyk, Joanna ^a   Osberger, Antonia ^b  

^a WARSAW UNIVERSITY OF LIFE SCIENCES   (Poland)

^b UNIVERSITY OF SALZBURG   (Austria)

Author keywords

Forest disturbance; Mountain regions; Obia; Rapideye; Red edge reflectance; Worldview 2

Indexed keywords

ACCURACY ASSESSMENT; DETECTION METHOD; DISTURBANCE; EDGE EFFECT; ENVIRONMENTAL MONITORING; IMAGE CLASSIFICATION; MONTANE FOREST; MOUNTAIN REGION; RAPIDEYE; VEGETATION INDEX; WORLDVIEW;

NORWAY;

PICEA ABIES;

EID: 84945252577     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2014.10.013     Document Type: Article

References (29)

1. Baatz, M., Schäpe, A., 2000. Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. In: Proceedings of the 12th Symposium for Applied Geographic Information Processing (Angewandte Geographische Informationsverarbeitung XII. AGIT 2000), Salzburg, Austria, pp. 12-23.
2. Blackburn, G.A., 1998. Quantifying chlorophylls and carotenoids at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sens. Environ. 66 (3), 273-285.
3. CEC, 1993. Commission Regulation (EEC) No 926/93 of 1 April 1993. OJL 100, 26.4.1993., pp. 1-47.
4. Cohen, W.B., Goward, S.N., 2004. Landsat's role in ecological applications of remote sensing. Bioscience 54 (6), 535-545.
5. Datt, B., 1998. Remote sensing of chlorophyll a, chlorophyll b, chlorophyll a + b, and total carotenoid content in eucalyptus leaves. Remote Sens. Environ. 66 (2), 111-121.
6. Datt, B., 1999. Visible/near infrared reflectance and chlorophyll content in eucalyptus leaves. Int. J. Remote Sens. 20 (14), 2741-2759.
7. Dragut, L, Tiede, D., Levick, S.R., 2010. ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. Int. J. Geogr. Inf. Sci. 24, 859-871.
8. European Environment Agency (EEA), 2009. Habitats Directive Article 17. In: Reporting for habitat code 9110. Denmark. http://forum.eionet.europa.eu/x_habitat-art17report/library/datasheets/habitats/forests/forests/9110-luzulofagetum/download/1/9110-Luzulo-Fagetum%20beech%20forests.pdf?action= view (18.09.14).
9. European Commission, 2006. Assessment, Monitoring and Reporting Under Article 17 of the Habitats Directive: Explanatory Notes and Guidelines. European Commission, Brussels.
10. Gitelson, A.A., Keydan, G.P., Merzlyak, M.N., 2006. Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophys. Res. Lett. 3,3, http://dx.doi.org/10.1029/2006GL026457.
11. Henrich, V., Krauss, G., Götze, C, Sandow, C, 2012. IDB: www.indexdatabase.de, Entwicklung einer Datenbank für Fernerkundungsindizes. AK Fernerkundung, Bochum.
12. Herrmann, I., Karnieli, A., Bonfil, D.J., Cohen, Y., Alchanatis, V., 2010. SWIR-based spectral indices for assessing nitrogen content in potato fields. Int. J. Remote Sens. 31 (19), 5127-5143, http://dx.doi.org/10.1080/01431160903283892.
13. Lang, S., 2008. Object-based image analysis for remote sensing applications: modelling reality - dealing with complexity. In: Blaschke, T., Lang, S., Hay, G.J. (Eds.), Object-Based Image Analysis. Springer, Berlin, pp. 3-27.
14. Le Maire, G., Francois, C, Dufrene, E., 2004. Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sens. Environ. 89 (1), 1-28.
15. Lichtenthaler, H.K., 1996. Vegetation stress: an introduction to the stress concept in plants. J. Plant Physiol. 148, 4-14, http://dx.doi.org/10.1016/S0176-1617(96)80287-2.
16. Masek, J.G., Huang, C, Wolfe, R., Cohen, W., Hall, F., Kutler, J., Nelson, P., 2008. North American forest disturbance mapped from a decadal Landsat record. Remote Sens. Environ. 112 (6), 2914-2926.
17. Matsushita, B., Yang, W., Chen, J., Onda, Y., Qiu, G., 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.
18. Meigs, G.W., Kennedy, R.E., Cohen, W.B., 2011. A Landsat time series approach to characterize bark beetle and defoliator impacts on tree mortality and surface fuels in conifer forests. Remote Sens. Environ. 115, 3707-3718, http://dx.doi.org/j.rse.2011.09.009.
19. Nagendra, H., Lucas, R., Honrado, J.P., Jongman, R.H.G., Tarantino, C, Adamo, M., Mairota, P., 2013. Remote sensing for conservation monitoring: assessing protected areas, habitat extent, habitat condition, species diversity, and threats. Ecol. Indic. 33, 45-59.
20. Osberger, A., Tiede, D., Lang, S., 2013. Forest disturbance monitoring system based on high spatial resolution satellite images in the Kalkalpen National Park. In: Conference Volume of the 5th Symposium for Research in Protected Areas.
21. Price, M.F., Gratzer, G., Duguma, LA., Kohler, T., Maselli, D., Romeo, R. (Eds.), 2011. Mountain Forests in a Changing World - Realizing Values, addressing challenges. FAO/MPS and SDC, Rome.
22. Radu, S., 2007. Nature conservation: concept. In: Practice, in: Gafta, D., Akeroyd, J. (Eds.), The Ecological Role of Deadwood in Natural Forests. Springer, Berlin, pp. 137-141.
23. Richter, R., Schlapfer, D., 2002. Geo-atmospheric processing of airborne imaging spectrometry data. Part 2. Atmospheric/topographic correction. Int. J. Remote Sens. 23, 2631-2649.
24. Rondeux, J., Sanchez, C, 2009. Review of indicators and field methods for monitoring biodiversity within national forest inventories. Core variable. Deadwood. Environ. Monit. Assess. 164, 617-630.
25. Schuster, C, Förster, M., Kleinschmit, B., 2012. Testing the red edge channel for improving land-use classifications based on high-resolution multi-spectral satellite data. Int. J. Remote Sens. 33 (17), 5583-5599, http://dx.doi.org/10.1080/01431161.2012.666812.
26. Tucker, C.J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8 (2), 127-150, http://dx.doi.org/10.1016/0034-4257(79)90013-0.
27. Vanden Borre, J., Paelinckx, D., Mücher, C.A., Kooistra, L., Haest, B., De Blust, G., Schmidt, A.M., 2011. Integrating remote sensing in Natura 2000 habitat monitoring: prospects on the way forward. J. Nat. Conserv., 116-125, http://dx.doi.org/10.1016/jjnc.2010.07.003.
28. Schuck, A., Meyer, P., Menke, N., Lier, M., Lindner, M., 2004. Forest biodiversity indicator: dead wood - a proposed approach towards operationalising the MCPFE indicator. In: Marchetti, M. (Ed.), Monitoring and Indicators of Forest Biodiversity in Europe - From Ideas to Operationality, 51. EFI Proceedings, pp. 49-77.
29. Verrelst, J., Schaepman, M.E., Koetz, B., Kneubuhler, M., 2008. Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data. Remote Sens. Environ. 112 (5), 2341, http://dx.doi.org/10.1016/j.rse.2007.11.001.