Mapping stand volumes of Pinus halepensis Mill in a semi-arid region using satellite imagery of the Sénalba Chergui forest in north-central Algeria

Journal of Arid Environments

Volumn 92, Issue , 2013, Pages 63-75

  Mouissa, H ^a   Fournier, R A ^b  

^a UNIVERSITY OF DJELFA   (Algeria)

^b UNIVERSITÉ DE SHERBROOKE   (Canada)

Author keywords

Aleppo pine; Forest mapping; Ordinary linear regression; Reduced major axis regression; Semi arid environment; Stand volume

Indexed keywords

AKAIKE INFORMATION CRITERION; CONIFEROUS TREE; DATA SET; FOREST INVENTORY; LANDSAT; MAPPING; NUMERICAL MODEL; SATELLITE IMAGERY; SEMIARID REGION; VEGETATION MAPPING;

ALGERIA; DJELFA; SENALBA CHERGUI FOREST;

EID: 84874570765     PISSN: 01401963     EISSN: 1095922X     Source Type: Journal    
DOI: 10.1016/j.jaridenv.2013.01.008     Document Type: Article

References (42)

1. Anderson D.R. Model Based Inference in the Life Sciences: a Primer on Evidence 2008, Springer, 184 pp.
2. Ardö J. Volume quantification of coniferous forest compartments using spectral radiance recorded by Landsat Thematic Mapper. International Journal of Remote Sensing 1992, 13:1779-1786.
3. Baatz M., Schäpe A. Multiresolution Segmentation - an optimization approach for high quality multi-scale image segmentation. Angewandte Geographische Informations Verarbeitung. XII. Beitrage Zum AGIT-symp 2000, 12-23. Wichmann-Verlag, Salzburg, Heidelberg. T. Strobl (Ed.).
4. Bannari A., Morin D., Bonn F., Huete A.R. A review of vegetation indices. Remote Sensing Reviews 1995, 13:95-120.
5. Bannari, A., 1996. La télédétection et les indices de végétation pour la détection de la végétation éparse et moyennement dense: Cas de l'environnement urbain. Université de Sherbrooke, Quebec, Canada, Unpublished doctoral dissertation.
6. BNEF Etude du milieu de la forêt domaniale Sénalba Chergui (Wilaya de Djelfa) 1984, Bureau National des Etudes Forestiers, Ministère de l'agriculture et de la révolution agraire (MARA), Algérie, pp. 1-25.
7. BNEF Résultats de l'inventaire par série de la forêt domaniale de Sénalba Chergui (Wilaya de Djelfa) 1984, Bureau National des Etudes Forestiers, Ministère de l'agriculture et de la révolution agraire (MARA), Algérie, pp. 89-126.
8. Chakali G. Stratégie d'attaque de l'hylésine Tomicus destruens (Wollaston 1865) (Coleoptera: Scolytidae) sur le pin d'Alep en zone semi-aride (Algérie, Djelfa). Annales de la Société Entomologique de France 2007, l43:129-137.
9. Cohen W.B., Spies T.A., Fiorella M. Estimating the age and structure of forests in a multi-ownership landscape of western Oregon, U.S.A. International Journal of Remote Sensing 1995, 16:721-746.
10. Cohen W.B., Maiersperger T.K., Spies T.A., Oetter D.R. Modelling forest cover attributes as continuous variables in a regional context with Thematic Mapper data. International Journal of Remote Sensing 2001, 22:2279-2310.
11. Cohen W.B., Maiersperger T.K., Gower S.T., Turner D.P. An improved strategy for regression of biophysical variables and Landsat ETM+ data. Remote Sensing of Environment 2003, 84:561-571.
12. Cohen W.B., Goward S.N. Landsat's role in ecological applications of remote sensing. Bioscience 2004, 54:535-545.
13. Cohen W.B., Spies T.A. Estimating structural attributes of Douglas-fir/Western hemlock forest stands from Landsat and Spot imagery. Remote Sensing of Environment 1992, 1:1-17.
14. Curran P., Hay A.M. The importance of measurement error for certain procedures in remote sensing at optical wavelengths. Photogrammetric Engineering and Remote Sensing 1986, 52:229-241.
15. DGF Politique forestière nationale et stratégie d'aménagement et de développement durable des ressources forestières et alfatières 2007, Rapport provisoire de la Direction Générale des Forêts, Ministère de l'agriculture et du développement rural (MADR), Algérie, pp. 1-3.
16. Foody G.M., Boyd D.S., Cutler M.E.J. Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions. Remote Sensing of Environment 2003, 85:463-474.
17. Franco-Lopez H., Ek A.R., Bauer M.E. Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method. Remote Sensing of Environment 2001, 77:251-274.
18. Franklin J. Thematic mapper analysis of coniferous forest structure and composition. International Journal of Remote Sensing 1986, 7:1287-1301.
19. Franklin J., Hiernaux P.H.Y. Estimating foliage and woody biomass in Sahelian and Sudanian woodlands using a remote sensing model. International Journal of Remote Sensing 1991, 12:1387-1404.
20. Gasparri N.I., Parmuchi M.G., Bono J., Karszenbaum H., Montenegro C.L. Assessing multi-temporal Landsat 7 ETM+ images for estimating above-ground biomass in subtropical dry forests of Argentina. Journal of Arid Environments 2010, 74:1262-1270.
21. Halitim A. Sols des régions arides d'Algérie 1988, Office des publications universitaires, Algérie, pp. 120-135.
22. Healey S.P., Cohen W.B., Zhiqiang Y., Krankina O.N. Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection. Remote Sensing of Environment 2005, 97:301-310.
23. Huete A.R. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 1988, 25:295-309.
24. Husch B., Beers T.W., Kershaw J.A. Forest Mensuration 2003, John Wiley and Sons, Inc., New Jersey, USA, p. 443. fourth ed.
25. Larose D.L. Discovering Knowledge in Data: an Introduction to Data Mining 2005, John Wiley and Sons, Hoboken, New Jersey.
26. Larsson H. Linear regressions for canopy cover estimation in Acacia woodlands using Landsat-TM, -MSS and SPOT HRV XS data. International Journal of Remote Sensing 1993, 14:2129-2136.
27. Lu D. Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon. International Journal of Remote Sensing 2005, 26:2509-2525.
28. Mäkelä H., Pekkarinen A. Estimation of forest stand volumes by Landsat TM imagery and stand-level field-inventory data. Forest Ecology and Management 2004, 196:245-255.
29. Mallinis G., Koutsias N., Tsakiri-Strati M., Karteris M. Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site. ISPRS Journal of Photogrammetry and Remote Sensing 2008, 63:237-250.
30. McRoberts R.E., Tomppo E.O. Remote sensing support for national forest inventories. Remote Sens. Environ. 2007, 110:412-419.
31. Powell S.L., Cohen W.B., Healey S.P., Kennedy R.E., Moisen G.G., Pierce K.B., Ohmann J.L. Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: a comparison of empirical modeling approaches. Remote Sensing of Environment 2010, 114:1053-1068.
32. Puhr C.B., Donoghue D.N.M. Remote sensing of upland conifer plantations using Landsat TM data: a case study from Galloway, south-west Scotland. International Journal of Remote Sensing 2000, 4:633-646.
33. Rondeaux G., Steven M., Baret F. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment 1996, 55:95-107.
34. Rouse J.W., Haas R.W., Shell S.S., Seering D.W., Harlan J.C. Monitoring the Vernal Advancement of Retrogradation (Greenwave Effect) of Natural Vegetation 1974, NASA/GSFC Type III Final Report, Greenbelt, Maryland, USA, 164 pp.
35. Salvador R., Pons X. On the applicability of Landsat TM images to Mediterranean forest inventories. Forest Ecology and Management 1998, 104:193-208.
36. Schroeder P., Brown S., Mo J., Birdsey R., Cieszewski C. Biomass estimation for temperate broadleaf forests of the Unites States using inventory data. Forest Science 1997, 3:424-434.
37. Seigue A. La forêt circumméditerranéenne et ses problèmes 1985, 173-195. G.P. Maisonneuve & Larose, France.
38. Tomppo E., Olsson H., Ståhl G., Nilsson M., Hagner O., Katila M. Combining national forest inventory field plots and remote sensing data for forest databases. Remote Sensing of Environment 2008, 112:1982-1999.
39. Triepke F.J., Brewer C.K., Leavell D.M., Novak S.J. Mapping forest alliances and associations using fuzzy systems and nearest neighbor classifiers. Remote Sensing of Environment 2008, 112:1037-1050.
40. WEKA (Waikato Environment for Knowledge Analysis), 2011. The University of Waikato. http://www.cs.waikato.ac.nz/ml/weka/index.html.
41. West P.W. Application of regression analysis to inventory data with measurements on successive occasions. Forest Ecology and Management 1995, 71:227-234.
42. Witten I.H., Frank E. Data Mining, Practical Machine Learning Tools and Techniques 2005, Morgan Kaufmann Publishers, San Francisco.