Wetland monitoring using classification trees and SPOT-5 seasonal time series

Remote Sensing of Environment

Volumn 114, Issue 3, 2010, Pages 552-562

  Davranche, Aurélie ^a   Lefebvre, Gaëtan ^b   Poulin, Brigitte ^b  

^a UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

^b STATION BIOLOGIQUE DE LA TOUR DU VALAT   (France)

Author keywords

Camargue; Classification tree; Multispectral indices; Multitemporal indices; Phragmites australis; Remote sensing; SPOT 5; Submerged macrophytes; Wetland monitoring

Indexed keywords

CAMARGUE; CLASSIFICATION TREES; MULTI-SPECTRAL; MULTI-TEMPORAL; PHRAGMITES AUSTRALIS; SPOT-5; SUBMERGED MACROPHYTES;

MONITORING; REMOTE SENSING; TIME SERIES; TURBIDITY; WASTEWATER RECLAMATION; WETLANDS;

VEGETATION;

CLASSIFICATION; DIGITIZATION; ENVIRONMENTAL MONITORING; HYDROLOGICAL REGIME; LAND COVER; MACROPHYTE; MARSH; MULTISPECTRAL IMAGE; NDVI; RADIOMETER; REEDBED; REFLECTANCE; SATELLITE DATA; SPOT; SUBMERGED VEGETATION; TIME SERIES;

BOUCHES DU RHONE; CAMARGUE; FRANCE; PROVENCE-ALPES-COTE D'AZUR;

CHARA; MYRIOPHYLLUM SPICATUM; PECTINATUS; PHRAGMITES AUSTRALIS; POTAMOGETON PECTINATUS; RUPPIA MARITIMA;

EID: 74349113700     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2009.10.009     Document Type: Article

References (57)

1. Arzandeh S., and Wang J. Monitoring the change of Phragmites distribution using satellite data. Canadian Journal of Remote Sensing 29 (2003) 24-35
2. Baker C., Lawrence R., Montagne C., and Patten D. Mapping wetlands and riparian areas using Landsat ETM+ imagery and decision-tree-based models. Wetlands 26 (2006) 465-474
3. Baret F., and Weiss M. Can-Eye: Processing digital photographs for canopy structure characterization, Tutorial (2004). http://www.avignon.inra.fr/can_eye/page2.htm http://www.avignon.inra.fr/can_eye/page2.htm
4. Bradley B.A., and Fleishman E. Can remote sensing of land cover improve species distribution modelling?. Journal of Biogeography 35 (2008) 1158-1159
5. Breiman L., Friedman J.H., Olshen R., and Stone C. Classification and regression trees (1984), Chapman & Hall, New York, USA
6. Brostaux, Y. (2005). Etude du classement par forêt aléatoire d'échantillons perturbés à forte structure d'intéraction. PhD thesis, Faculté des sciences agronomiques de Gembloux, Communauté Française de Belgique.
7. Brown de Colstoun E.C., Story M.H., Thompson C., Commisso K., Smith T.G., and Irons J.R. National Park vegetation mapping using multi-temporal Landsat 7 data and a decision tree classifier. Remote Sensing of Environment 85 (2003) 316-327
8. Caillaud, L., Guillaumont, B., & Manaud, F. (1991). Essai de discrimination des modes d'utilisation des marais maritimes par analyse multitemporelle d'images SPOT. Application aux marais maritimes du Centre Ouest. IFREMER, (H4.21) 485. 24 p.
9. Chauvelon, P., 2009. Gestion Intégrée d'une Zone humide littorale méditerranéenne aménagée : contraintes, limites et perspectives pour l'Ile de CAMargue (GIZCAM). Programme LITEAU 2, Ministère de l'Ecologie, de l'Energie, du Développement durable et de l'Aménagement du Territoire, Final report, Tour du Valat, 84 pp.
10. Congalton R.G., and Green K. Assessing the accuracy of remotely sensed data: Principles and practices (1999), CRC Press, Boca Raton, FL
11. Davranche, A., Lefebvre, G., & Poulin, B. (2009). Radiometric normalization of multi-temporal SPOT 5 images for wetland monitoring : accuracy of pseudo-invariant features vs. 6 S atmospheric model. Photogrammetric engineering and remote sensing, 75, 723-729.
12. DongMei C., and Douglas S. The effect of training strategies on supervised classification at different spatial resolutions. Photogrammetric Enginering and Remote Sensing 68 (2002) 1155-1161
13. Esposito F., Malerba D., Semeraro G., and Tamma V. The effects of pruning methods on the predictive accuracy of induced decision trees. Applied Stochastic Models in Business and Industry 15 (1999) 277-299
14. Gao B.G. NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment 58 (1996) 257-266
15. Ghioca-Robrecht D.M., Johnston C.A., and Tulbure M.G. Assessing the use of multiseason Quickbird imagery for mapping invasive species in a Lake Erie coastal marsh. Wetlands 28 (2008) 1028-1039
16. Girard M.C., and Girard C.M. Traitement des données en télédétection (1999), Éditions Dunod, Paris
17. Gond V., Bartholome E., Ouattara F., Nonguierma A., and Bado L. Surveillance et cartographie des plans d'eau et des zones humides et inondables en régions arides avec l'instrument VEGETATION embarqué sur SPOT-4. International Journal of Remote Sensing 25 (2004) 987-1004
18. Hanqiu X. Modification of Normalized Difference Water Index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing 27 (2006) 3025-3033
19. Harrell F.E.J. Regression modeling strategies. With applications to linear models, logistic regression, and survival analysis. Springer Series in Statistics (2001), Springer, New York
20. Huete A.R. A Soil-Adjusted Vegetation Index (SAVI). Remote sensing of Environment 25 (1988) 295-309
21. Hunt E.R., and Rock B.N. Detection of changes in leaf water content using near- and middle-infrared reflectances. Remote Sensing of Environment 30 (1989) 43-54
22. Johnston R.M., and Barson M.M. Remote sensing of Australian wetlands: An evaluation of Landsat TM data for inventory and classification. Australian Journal of Marine and Freshwater Research 4 (1993) 235-252
23. Lawrence R., Bunn A., Powell S., and Zambon M. Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis. Remote Sensing of Environment 90 (2004) 331-336
24. Lillesand T.M., and Kiefer R.W. Remote sensing and image interpretation. 2nd edition (1987), John Wiley and Sons, New York
25. Lieutaud A., and Puech C. Méthode de traitement d'images adaptée au capteur SPOT pour une cartographie quantitative des herbiers lagunaires submergés. Bulletin de la Société française de photogrammétrie et de télédétection 141 (1996) 115-120
26. Liu C., Frazier P., and Kumar L. Comparative assessment of the measures of thematic classification accuracy. Remote Sensing of Environment 107 (2007) 606-616
27. MacAlister C., and Mahaxay M. Mapping wetlands in the Lower Mekong Basinfor wetland resource and conservation management using Landsat ETM images and field survey data. Journal of Environmental Management 90 (2009) 2130-2137
28. Maheu-Giroux M., and De Bois S. Mapping the invasive species Phragmites australis in linear wetland corridors. Aquatic Botany 83 (2005) 310-320
29. Mathevet R., and Sandoz A. L'exploitation du roseau et les mesures agri-environnementales dans le delta du Rhône. Revue de l'Economie Méridionale 47 185-186 (1999) 101-122
30. McFeeters S.K. The use of the Normalised Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing 17 (1996) 1425-1432
31. McIver D.K., and Friedl M.A. Using prior probabilities in decision-tree classification of remotely sensed data. Remote Sensing of Environment 81 (2002) 253-261
32. Muller S.V., Walker D.A., Nelson F.E., Auerbach N.A., Bockheim J.G., Guyer S., and Sherba D. Accuracy assessment of a landcover map of the Kuparuk River basin, Alaska: Considerations for remote regions. Photogrammetric Engineering and Remote Sensing 64 (1998) 619-628
33. Özesmi, S. L. (2000). Satellite Remote Sensing of Wetlands and a Comparison of Classification Techniques, PhD Thesis, University of Minnesota.
34. Özesmi S.L., and Bauer M.E. Satellite remote sensing of wetlands. Wetlands Ecology and Management 10 (2002) 381-402
35. Pearson R.L., and Miller L.D. Remote mapping of standing crop biomass for estimation of the productivity of the short-grass Prairie, Pawnee National Grasslands, Colorado. Processing of the 8th International Symposium on Remote Sensing of Environment, ERIM, Ann Arbor, MI (1972) 1357-1381
36. Pinnel, N. (2007). A method for mapping submerged macrophytes in lakes using hyperspectral remote sensing. Phd Thesis, Technische Universität München.
37. Ramsey E.W., and Laine S.C. Comparison of Landsat Thematic Mapper and high resolution aerial photography to identify change in complex coastal wetlands. Journal of Coastal Research 13 (1997) 281-292
38. Richardson A.J., and Everitt J.H. Using spectra vegetation indices to estimate rangeland productivity. Geocarto International 1 (1992) 63-69
39. Rondeaux G., Steven M., and Baret F. Optimization of Soil-Adjusted Vegetation Indices. Remote Sensing of Environment 55 (1996) 95-107
40. Rouse J.W., Haas R.H., Schell J.A., and Deering D.W. Monitoring vegetation systems in the great plains with ERTS. Third ERTS Symposium, NASA SP-351 vol. 1 (1973) 309-317
41. Rutchey K., and Vilchek L. Air photointerpretation and satellite imagery analysis techniques for mapping cattail coverage in a northern Everglades impoundment. Photogrammetric Engineering and Remote Sensing 65 (1999) 85-191
42. Sader S.A., Ahl D., and Wen-Shu L. Accuracy of Landsat-TM and GIS rule-based methods for forest wetland classification in Maine. Remote Sensing of the Environment 53 (1995) 133-144
43. Schmidt K.S., and Skidmore A.K. Spectral discrimination of vegetation types in a coastal wetland. Remote Sensing of Environment 85 (2003) 92-108
44. Silvestri S., Marani M., Settle J., Benvenuto F., and Marani A. Salt marsh vegetation radiometry: Data analysis and scaling. Remote Sensing of Environment 2 (2002) 473-482
45. Sokal R.R., and Rohlf F.J. Biometry: The principles and practices of statistics in biological research (1995), W.H. Freeman, NY
46. Spot Image (2008). Preprocessing levels and location accuracy. Technical information, www.spotimage.com
47. Story M., and Congalton R.G. Accuracy assessment: A user's perspective. Photogrammetric Engineering and Remote Sensing 52 (1986) 397-399
48. Tadjudin S., and Landgrebe D.A. A decision tree classifier design for high-dimensional data with limited training samples. Proceedings of the IGARSS '96 Symposium (1996), Lincoln, NE 790-793
49. Tamisier A., and Grillas P. A review of habitat changes in the Camargue: An assessment of the effects of the loss of biological diversity on the wintering waterfowl community. Biological Conservation 70 (1994) 39-47
50. Therneau T.M., and Atkinson E.J. An introduction to recursive partitioning using the RPART routines (1997), Mayo Foundation. http://www.mayo.edu/hsr/techrpt/61.pdf http://www.mayo.edu/hsr/techrpt/61.pdf
51. Thomson J., Mac Nally R., Fleishman E., and Horrocks G. Predicting bird species distributions in reconstructed landscapes. Conservation Biology 21 (2007) 752-766
52. Valley R.D., Drake M.T., and Anderson C.S. Evaluation of alternative interpolation techniques for the mapping of remotely-sensed submersed vegetation abundance. Aquatic Botany 81 (2005) 13-25
53. Valta-Hulkkonen K., Pellikka P., Tanskanen H., Ustinov A., and Sandman O. Digital false colour aerial photographs for discrimination of aquatic macrophyte species. Aquatic Botany 75 (2003) 71-88
54. Vermote E., Tanre D., Deuze J.L., Herman M., and Morcrette J.J. Second simulation of the satellite signal in the Solar Spectrum, 6S: An overview. IEEE Transactions on Geoscience and Remote Sensing 35 (1997) 675-686
55. Vis C., Hudon C., and Carignan R. An evaluation of approaches used to determine the distribution and biomass of emergent and submerged aquatic macrophytes over large spatial scales. Aquatic Botany 77 (2003) 187-201
56. Wintle B.A., Elith J., and Potes J. Fauna habitat modelling and mapping: A review and case study in the Lower Hunter Central Coast of NSW. Austral Ecology 30 (2005) 719-738
57. Wright C., and Gallant A. Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data. Remote Sensing of Environment 107 (2007) 582-605