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Identification and map** of Algerian island vegetation using high-resolution images (Pléiades and SPOT 6/7) and random forest modeling

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Abstract

Despite their proximity to the coast, few studies have focused on identifying and map** the vegetation of Algerian islands and islets. To fill this lacuna, our work, using satellite images and machine learning methods, is mainly aimed at identifying and map** the main vegetation groups on a few islands, while evaluating the effectiveness of the random forest classifier, which is effectively used in the study of the vegetation of large areas. However, despite the high heterogeneity of their vegetation cover, the use of very high-resolution images (Pléaides and SPOT 6/7), through the fusion bands and derived bands (NDVI), has allowed the elaboration of a fairly precise vegetation map that can be used for the preparation of management and protection plans for these habitats. Our methodological approach revealed very satisfactory results, having allowed the identification of the plant communities inventoried in the field, while showing high accuracy values, ranging from 0.642 for the halophilic group of Asteriscus to 1 for the endemic Chasmophyte group of the Habibas archipelago (Pléiades images). The groups identified from SPOT 6/7 images show accuracy values between 0.67 for the Mediterranean cliff formations on Garlic Islet and 1 for the two formations (shrubby and herbaceous) of the Skikda islands. Our methodological approach, and notwithstanding the great heterogeneity and the very small surface areas of our islands and islets, has led to very satisfactory results, reflected with good overall accuracy and kappa index values (for Pléiades: overall accuracy > 92% and kappa index > 0.90; for SPOT 6/7: overall accuracy > 83% and kappa index > 0.80).

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References

  • Al-Rubiey, I. J. (2017). Increase the intelligibility of multispectral image using pan-sharpening techniques for many remotely sensed images. Ibn AL-Haitham Journal For Pure and Applied Science, 28(3).

  • Bachet, F., Benhaj, S., Bernard, F., Delauge, J., Harmelin, J., Mante, A., et al. (2007). Réserve des Iles Habibas. Notes naturalistes. Petites Îles de Méditerranée. (pp. 107): Conservatoire de l’espace littoral et des rivages lacustres. République Française.

  • Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011

    Article  Google Scholar 

  • Benhamiche-Hanifi, S. (2013). Caractérisation de la flore insulaire de quelques îlots de la côte Algérienne. PhD, Abderrahmane MIRA, Bejaia,

  • Benhamiche-Hanifi, S., & Moulaï, R. (2012). Analyse des phytocénoses des systèmes insulaires des régions de Béjaia et de Jijel (Algérie) en présence du Goéland leucophée (Larus michahellis). Revue D’écologie, 67(4), 375–397.

    Google Scholar 

  • Blanquet, J. B. (1952). Phytosociologie Appliquée. Sigma, 116, 156–161.

    Google Scholar 

  • Blondel, J., & Aronson, J. (1999). Biology and wildlife of the Mediterranean region: Oxford University Press, USA.

  • Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32.

    Article  Google Scholar 

  • Brennan, J. R., Johnson, P. S., & Hanan, N. P. (2020). Comparing stability in random forest models to map Northern Great Plains plant communities in pastures occupied by prairie dogs using Pleiades imagery. Biogeosciences, 17(5), 1281–1292. https://doi.org/10.5194/bg-17-1281-2020

    Article  Google Scholar 

  • Buddenbaum, H., Schlerf, M., & Hill, J. (2005). Classification of coniferous tree species and age classes using hyperspectral data and geostatistical methods. International Journal of Remote Sensing, 26(24), 5453–5465.

    Article  Google Scholar 

  • Carpenter, G. A., Gopal, S., Macomber, S., Martens, S., Woodcock, C. E., & Franklin, J. (1999). A neural network method for efficient vegetation map**. Remote Sensing of Environment, 70(3), 326–338.

    Article  Google Scholar 

  • Colditz, R. (2015). An evaluation of different training sample allocation schemes for discrete and continuous land cover classification using decision tree-based algorithms. Remote Sensing, 7(8), 9655–9681. https://doi.org/10.3390/rs70809655

    Article  Google Scholar 

  • Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35–46.

    Article  Google Scholar 

  • Congalton, R. G., & Green, K. (2019). Assessing the accuracy of remotely sensed data: principles and practices. CRC press.

  • Coops, N. C., Wulder, M. A., & Iwanicka, D. (2009a). Exploring the relative importance of satellite-derived descriptors of production, topography and land cover for predicting breeding bird species richness over Ontario. Canada. Remote Sensing of Environment, 113(3), 668–679.

    Article  Google Scholar 

  • Coops, N. C., Wulder, M. A., & Iwanicka, D. (2009b). Large area monitoring with a MODIS-based Disturbance Index (DI) sensitive to annual and seasonal variations. Remote Sensing of Environment, 113(6), 1250–1261.

    Article  Google Scholar 

  • Cutler, R., Edwards, T. C., Jr., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., et al. (2007). Random forests for classification in ecology. Ecology, 88(11), 2783–2792.

    Article  Google Scholar 

  • de Bie, C. A. J. M., Khan, M. R., Smakhtin, V. U., Venus, V., Weir, M. J. C., & Smaling, E. M. A. (2011). Analysis of multi-temporal SPOT NDVI images for small-scale land-use map**. International Journal of Remote Sensing, 32(21), 6673–6693. https://doi.org/10.1080/01431161.2010.512939

    Article  Google Scholar 

  • de Montmollin, B., & Strahm, W. (Eds.). (2005). The Top 50 Mediterranean Island Plants: Wild plants at the brink of extinction, and what is needed to save them. IUCN.

  • Delanoe, O. M., & deOlivier, L. B. (1996). Conservation de la flore des iles Méditerranéennes, 1: Stratégie d’action. IUCN.

    Google Scholar 

  • Dorigo, W., Lucieer, A., Podobnikar, T., & Čarni, A. (2012). Map** invasive Fallopia japonica by combined spectral, spatial, and temporal analysis of digital orthophotos. International Journal of Applied Earth Observation and Geoinformation, 19, 185–195.

    Article  Google Scholar 

  • Du, P., Samat, A., Waske, B., Liu, S., & Li, Z. (2015). Random forest and rotation forest for fully polarized SAR image classification using polarimetric and spatial features. ISPRS Journal of Photogrammetry and Remote Sensing, 105, 38–53. https://doi.org/10.1016/j.isprsjprs.2015.03.002

    Article  Google Scholar 

  • Duplan, L. (1952). La région de Bougie. 19 ème congrès Géol. Intern. Mong. Rég., 1er Série, 17.

  • Espel, D., Courty, S., Auda, Y., Sheeren, D., & Elger, A. (2020). Submerged macrophyte assessment in rivers: An automatic map** method using Pleiades imagery. Water Research, 186, 116353. https://doi.org/10.1016/j.watres.2020.116353

    Article  CAS  Google Scholar 

  • Fasbender, D., Radoux, J., & Bogaert, P. (2008). Bayesian data fusion for adaptable image pansharpening. IEEE Transactions on Geoscience and Remote Sensing, 46(6), 1847–1857.

    Article  Google Scholar 

  • Foody, G. M. (2004). Thematic map comparison. Photogrammetric Engineering & Remote Sensing, 70(5), 627–633.

    Article  Google Scholar 

  • Foody, G. M., & Cutler, M. E. (2003). Tree biodiversity in protected and logged Bornean tropical rain forests and its measurement by satellite remote sensing. Journal of Biogeography, 30(7), 1053–1066.

    Article  Google Scholar 

  • Frampton, W. J., Dash, J., Watmough, G., & Milton, E. J. (2013). Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS Journal of Photogrammetry and Remote Sensing, 82, 83–92. https://doi.org/10.1016/j.isprsjprs.2013.04.007

    Article  Google Scholar 

  • Gamon, J., Penuelas, J., & Field, C. (1992). A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment, 41(1), 35–44.

    Article  Google Scholar 

  • Gao, B.-C. (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266.

    Article  Google Scholar 

  • Ghermaoui, M., Hassaine, K., & Moulaï, R. (2016). Influence du Goéland leucophée Larus michahellis sur les formations végétales ouvertes du littoral de Rachgoun (Ouest Oranie, Algérie).

  • Gholizadeh, H., Gamon, J. A., Zygielbaum, A. I., Wang, R., Schweiger, A. K., & Cavender-Bares, J. (2018). Remote sensing of biodiversity: Soil correction and data dimension reduction methods improve assessment of α-diversity (species richness) in prairie ecosystems. Remote Sensing of Environment, 206, 240–253. https://doi.org/10.1016/j.rse.2017.12.014

    Article  Google Scholar 

  • Gil, A., & Abadi, M. (2015). Using very high resolution satellite imagery for land cover map** in Pico da Vara nature reserve (S. Miguel Island, Archipelago of the Azores, Portugal). In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), (pp. 3329–3332). IEEE.

  • Gil, A., Fonseca, C., Lobo, A., & Calado, H. (2012). Linking GMES Space Component to the development of land policies in Outermost Regions—the Azores (Portugal) case-study. European Journal of Remote Sensing, 45(1), 263–281. https://doi.org/10.5721/EuJRS20124524

    Article  Google Scholar 

  • Gil, A., Lobo, A., Abadi, M., Silva, L., & Calado, H. (2013). Map** invasive woody plants in Azores Protected Areas by using very high-resolution multispectral imagery. European Journal of Remote Sensing, 46(1), 289–304. https://doi.org/10.5721/EuJRS20134616

    Article  Google Scholar 

  • Gil, A., Yu, Q., Abadi, M., & Calado, H. (2014). Using ASTER multispectral imagery for map** woody invasive species in Pico da Vara Natural Reserve (Azores Islands, Portugal). Revista Árvore, 38(3), 391–401.

    Article  Google Scholar 

  • Gil, A., Yu, Q., Lobo, A., Lourenço, P., Silva, L., & Calado, H. (2011). Assessing the effectiveness of high resolution satellite imagery for vegetation map** in small islands protected areas. Journal of Coastal Research, 1663–1667.

  • Gillet, F. (2000). La Phytosociologie synusiale intégrée. Guide méthodologique. Laboratoire d’écologie végétale et de phytosociologie de l’Université de Neuchatel, Inst. Bot (pp. 68). Université de Neuchâtel (Suisse).

  • Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58(3), 289–298.

    Article  Google Scholar 

  • Gu, Z., Ju, W., Li, L., Li, D., Liu, Y., & Fan, W. (2013). Using vegetation indices and texture measures to estimate vegetation fractional coverage (VFC) of planted and natural forests in Nan**g city. China. Advances in Space Research, 51(7), 1186–1194. https://doi.org/10.1016/j.asr.2012.11.015

    Article  CAS  Google Scholar 

  • Guan, H., Li, J., Chapman, M., Deng, F., Ji, Z., & Yang, X. (2013). Integration of orthoimagery and lidar data for object-based urban thematic map** using random forests. International Journal of Remote Sensing, 34(14), 5166–5186.

    Article  Google Scholar 

  • Jago, R. A., Cutler, M. E., & Curran, P. J. (1999). Estimating canopy chlorophyll concentration from field and airborne spectra. Remote Sensing of Environment, 68(3), 217–224.

    Article  Google Scholar 

  • Jensen, J. (2005). Digital image processing: a remote sensing perspective. Upper Saddle River, NJ: sPrentice Hall.

  • Jhonnerie, R., Siregar, V. P., Nababan, B., Prasetyo, L. B., & Wouthuyzen, S. (2015). Random forest classification for mangrove land cover map** using Landsat 5 TM and Alos Palsar Imageries. Procedia Environmental Sciences, 24, 215–221. https://doi.org/10.1016/j.proenv.2015.03.028

    Article  Google Scholar 

  • Jones, H. G., & Vaughan, R. A. (2010). Remote sensing of vegetation: Principles, techniques, and applications: Oxford university press.

  • Karlson, M., Ostwald, M., Reese, H., Sanou, J., Tankoano, B., & Mattsson, E. (2015). Map** tree canopy cover and aboveground biomass in Sudano-Sahelian Woodlands using Landsat 8 and random forest. Remote Sensing, 7(8), 10017–10041. https://doi.org/10.3390/rs70810017

    Article  Google Scholar 

  • Lachouri, T., Mouloudj, L., & Moulai, R. E. (2016). Contribution à l’étude de la diversité des milieux insulaires de la région de Collo (Skikda).

  • Lagabrielle, E., Rouget, M., Payet, K., Wistebaar, N., Durieux, L., Baret, S., et al. (2009). Identifying and map** biodiversity processes for conservation planning in islands: A case study in Réunion Island (Western Indian Ocean). Biological Conservation, 142(7), 1523–1535. https://doi.org/10.1016/j.biocon.2009.02.022

    Article  Google Scholar 

  • Lefebvre, A., Nabucet, J., Corpetti, T., Courty, N., & Hubert-Moy, L. (2016). Extraction of Urban Vegetation with Pleiades Multiangular Images., 10008, 100080H. https://doi.org/10.1117/12.2241162

    Article  Google Scholar 

  • Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823–870. https://doi.org/10.1080/01431160600746456

    Article  Google Scholar 

  • Lucas, K. L., & Carter, G. A. (2008). The use of hyperspectral remote sensing to assess vascular plant species richness on Horn Island. Mississippi. Remote Sensing of Environment, 112(10), 3908–3915.

    Article  Google Scholar 

  • Madonsela, S., Cho, M. A., Ramoelo, A., & Mutanga, O. (2017). Remote sensing of species diversity using Landsat 8 spectral variables. ISPRS Journal of Photogrammetry and Remote Sensing, 133, 116–127. https://doi.org/10.1016/j.isprsjprs.2017.10.008

    Article  Google Scholar 

  • Maire, R., & Wilczek, E. (1936). Florule des iles Habibas. Bulletin de la Société d’Histoire Naturelle de l’Afrique du Nord,, 26bis, 61–78.

  • Makhamreh, Z. (2018). Derivation of vegetation density and land-use type pattern in mountain regions of Jordan using multi-seasonal SPOT images. Environmental Earth Sciences, 77(10). https://doi.org/10.1007/s12665-018-7534-z

  • Massetti, A., Sequeira, M. M., Pupo, A., Figueiredo, A., Guiomar, N., & Gil, A. (2016). Assessing the effectiveness of RapidEye multispectral imagery for vegetation map** in Madeira Island (Portugal). European Journal of Remote Sensing, 49(1), 643–672. https://doi.org/10.5721/EuJRS20164934

    Article  Google Scholar 

  • Médail, F. (2013). The unique nature of Mediterranean island floras and the future of plant conservation. In Cardona Pons E., Estaún Clarisó I., Comas Casademont M. & Fraga i Arguimbau P.(eds.). Islands and plants: preservation and understanding of flora on Mediterranean islands. 2nd Botanical Conference in Menorca. Recerca, (Vol. 20, pp. 325–350).

  • Médail, F. (2017). The specific vulnerability of plant biodiversity and vegetation on Mediterranean islands in the face of global change. Regional Environmental Change, 17(6), 1775–1790. https://doi.org/10.1007/s10113-017-1123-7

    Article  Google Scholar 

  • Médail, F., & Myers, N. (2004). Mediterranean basin. Hotspots revisited: Earth’s biologically richest and most endangered terrestrial ecoregions, 144–147.

  • Melville, B., Lucieer, A., & Aryal, J. (2018). Object-based random forest classification of Landsat ETM+ and WorldView-2 satellite imagery for map** lowland native grassland communities in Tasmania, Australia. International Journal of Applied Earth Observation and Geoinformation, 66, 46–55. https://doi.org/10.1016/j.jag.2017.11.006

    Article  Google Scholar 

  • Miles, V. V., Bobylev*, L. P., Maximov, S. V., Johannessen, O. M., & Pitulko, V. M. (2003). An approach for assessing boreal forest conditions based on combined use of satellite SAR and multi-spectral data. International Journal of Remote Sensing, 24(22), 4447–4466.

  • Moffiet, T., Armston, J. D., & Mengersen, K. (2010). Motivation, development and validation of a new spectral greenness index: A spectral dimension related to foliage projective cover. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 26–41. https://doi.org/10.1016/j.isprsjprs.2009.08.002

    Article  Google Scholar 

  • Mutanga, O., Adam, E., & Cho, M. A. (2012). High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm. International Journal of Applied Earth Observation and Geoinformation, 18, 399–406. https://doi.org/10.1016/j.jag.2012.03.012

    Article  Google Scholar 

  • Nagler, P., Daughtry, C., & Goward, S. (2000). Plant litter and soil reflectance. Remote Sensing of Environment, 71(2), 207–215.

    Article  Google Scholar 

  • Ngie, A., & Ahmed, F. (2018). Estimation of Maize grain yield using multispectral satellite data sets (SPOT 5) and the random forest algorithm. South African Journal of Geomatics, 7(1), 11. https://doi.org/10.4314/sajg.v7i1.2

    Article  Google Scholar 

  • Ngo, K. D., Lechner, A. M., & Vu, T. T. (2020). Land cover map** of the Mekong Delta to support natural resource management with multi-temporal Sentinel-1A synthetic aperture radar imagery. Remote Sensing Applications: Society and Environment, 17, 100272. https://doi.org/10.1016/j.rsase.2019.100272

    Article  Google Scholar 

  • Niculescu, S., Billey, A., & Talab Ou Ali, H. (2018). Random forest classification using Sentinel-1 and Sentinel-2 series for vegetation monitoring in the Pays de Brest (France). 6. https://doi.org/10.1117/12.2325546

  • Niculescu, S., Boissonnat, J.-B., Lardeux, C., Roberts, D., Hanganu, J., Billey, A., et al. (2020). Synergy of high-resolution radar and optical images satellite for identification and map** of wetland macrophytes on the Danube Delta. Remote Sensing, 12(14), 2188.

    Article  Google Scholar 

  • Niculescu, S., & Lam, C. N. (2019). Geographic object-based image analysis of changes in land cover in the coastal zones of the Red River Delta (Vietnam). Journal of Environmental Protection, 10(3), 413–430.

    Article  Google Scholar 

  • Ouma, Y. O., Tetuko, J., & Tateishi, R. (2008). Analysis of co-occurrence and discrete wavelet transform textures for differentiation of forest and non-forest vegetation in very-high-resolution optical-sensor imagery. International Journal of Remote Sensing, 29(12), 3417–3456.

    Article  Google Scholar 

  • Ozenda, P. (1982). Les végétaux dans la biosphère (Masson ed.). Paris.

  • Panitsa, M., Tzanoudakis, D., & Sfenthourakis, S. (2008). Turnover of plants on small islets of the eastern Aegean Sea within two decades. Journal of Biogeography, 35(6), 1049–1061.

    Article  Google Scholar 

  • Pantaleoni, E., Wynne, R., Galbraith, J., & Campbell, J. (2009). Map** wetlands using ASTER data: A comparison between classification trees and logistic regression. International Journal of Remote Sensing, 30(13), 3423–3440.

    Article  Google Scholar 

  • Pignatti, S., Cavalli, R. M., Cuomo, V., Fusilli, L., Pascucci, S., Poscolieri, M., et al. (2009). Evaluating Hyperion capability for land cover map** in a fragmented ecosystem: Pollino National Park. Italy. Remote Sensing of Environment, 113(3), 622–634.

    Article  Google Scholar 

  • Price, J. C. (2003). Comparing MODIS and ETM+ data for regional and global land classification. Remote Sensing of Environment, 86(4), 491–499.

    Article  Google Scholar 

  • Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., & Rigol-Sanchez, J. P. (2012). An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67, 93–104. https://doi.org/10.1016/j.isprsjprs.2011.11.002

    Article  Google Scholar 

  • Rouse, J. W., Haas, R. H., Schell, J. A., Deering, D. W., & Harlan, J. C. (1974). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. NASA/GSFC Type III Final Report, Greenbelt, Md, 371.

  • Sadran, G. (1952). Les roches cristallines du littoral Oranais: XIXe Congrès Géologique International.

  • Sarker, L. R., & Nichol, J. E. (2011). Improved forest biomass estimates using ALOS AVNIR-2 texture indices. Remote Sensing of Environment, 115(4), 968–977. https://doi.org/10.1016/j.rse.2010.11.010

    Article  Google Scholar 

  • Setyowati, H. A., Murti B S, S. H., & Sukentyas, E. S. (2016). Effectiveness of vegetation index transformation for land use identifying and map** in the area of oil palm plantation based on SPOT-6 imagery (case study: PT.Tunggal Perkasa Plantations, Air Molek, Indragiri Hulu). IOP Conference Series: Earth and Environmental Science, 47, 012033. https://doi.org/10.1088/1755-1315/47/1/012033

  • Shultis, J. K., & Myneni, R. (1988). Radiative transfer in vegetation canopies with anisotropic scattering. Journal of Quantitative Spectroscopy and Radiative Transfer, 39(2), 115–129.

    Article  Google Scholar 

  • Smits, P. C., Dellepiane, S. G., & Schowengerdt, R. A. (1999). Quality assessment of image classification algorithms for land-cover map**: A review and a proposal for a cost-based approach. International Journal of Remote Sensing, 20(8), 1461–1486. https://doi.org/10.1080/014311699212560

    Article  Google Scholar 

  • Stehman, S. V. (1997). Estimating standard errors of accuracy assessment statistics under cluster sampling. Remote Sensing of Environment, 60(3), 258–269.

    Article  Google Scholar 

  • Topouzelis, K., & Psyllos, A. (2012). Oil spill feature selection and classification using decision tree forest on SAR image data. ISPRS Journal of Photogrammetry and Remote Sensing, 68, 135–143.

    Article  Google Scholar 

  • Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150.

    Article  Google Scholar 

  • Véla, E. (2017). Commentaires sur la flore de l’île Rachgoun (Nord-Ouest Algérie). Revue d’écologie.

  • Véla, E., Bouguaham, A., & Moulaï, R. (2012). Découverte d’Allium commutatum Guss.(Alliaceae) en Algérie. NOTAS TAXONÓMICAS Y COROLÓGICAS PARA LA FLORA DE LA PENÍNSULA IBÉRICA Y EL MAGREB. NOTAS 182–190, 32, 291.

  • Véla, E., Delauge, F. (2007). Etude de la végétation des îles Habibas. In F. Bachet, S. Benhaj, F. Bernard, J. Delauge, J. Harmelin, A. Mante, et al., Réserve des Iles Habibas. Notes naturalistes. Petites Îles de Méditerranée. (pp. 107). Conservatoire de l’espace littoral et des rivages lacustres. République Française.

  • Véla, E., Hill, B., & Della-Casa, S. (2008). Mission exploratoire à Skikda: Petites îles de Méditerranée 08. Conservatoire de l’espace littoral et des rivages lacustres, Aix-en-Provence.

  • Véla, E., Saatkamp, A., & Pavon, D. (2013). Flora of Habibas islands (NW Algeria): richness, persistence and taxonomy. In Islands and plants: preservation and understanding of flora on Mediterranean islands. 2nd Botanical Conference in Menorca, (pp. 271–287).

  • Ville, L. (1857). Notice minéralogique sur les provinces d’Alger et d’Oran. 1 vol. in-4", 349 p. Impr. Impér., Paris.

  • Vogiatzakis, I. N., Pungetti, G., & Mannion, A. M. (2008). Mediterranean island landscapes: natural and cultural approaches (Vol. 9): Springer Science & Business Media.

  • Walter, J. (2006). Méthode d’étude de la végétation. Méthode du relevé floristique: introduction (première partie). (pp. 1–23): Rapport Technique Institut de Botanique-Faculté des Sciences de la Vie.

  • Wang, H., Zhao, Y., Pu, R., & Zhang, Z. (2015). Map** Robinia pseudoacacia forest health conditions by using combined spectral, spatial, and textural information extracted from IKONOS imagery and random forest classifier. Remote Sensing, 7(7), 9020–9044. https://doi.org/10.3390/rs70709020

    Article  Google Scholar 

  • Whittaker, R. J., & Fernández-Palacios, J. M. (2007). Island biogeography: Ecology, evolution, and conservation: Oxford University Press.

  • **: A review. Journal of Plant Ecology, 1(1), 9–23.

    Article  Google Scholar 

  • Yahi, N., Vela, E., Benhouhou, S., De Belair, G., & Gharzouli, R. (2012). Identifying important plants areas (key biodiversity areas for plants) in northern Algeria. Journal of threatened taxa, 2753–2765.

  • Yuhendra, A. I., Sumantyo, J. T. S., & Kuze, H. (2012). Assessment of pan-sharpening methods applied to image fusion of remotely sensed multi-band data. International Journal of Applied Earth Observation and Geoinformation, 18, 165–175. https://doi.org/10.1016/j.jag.2012.01.013

    Article  Google Scholar 

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Acknowledgements

The authors gratefully acknowledge LETG Brest UMR 6554 and GEOSUD for providing us with the SPOT and Pléiades satellite images used in this work.

Funding

The authors would like to thank the Scientific Research and Technological Development Direction (DGRSDT-Algeria) and the Algerian Ministry of higher education for their financing.

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Authors and Affiliations

  1. Laboratoire de Zoologie Appliquée et d’Ecophysiologie Animale, Faculté des Sciences de la Nature et de la Vie, Université de Bejaia, Bejaia, 06000, Algérie

    Mohamed Hamimeche & Riadh Moulaï

  2. UMR 6554, Western Brittany University, CNRS, LETG, CNRS, BrestParis, France

    Simona Niculescu & Antoine Billey

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  1. Mohamed Hamimeche
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  2. Simona Niculescu
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Correspondence to Mohamed Hamimeche.

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Hamimeche, M., Niculescu, S., Billey, A. et al. Identification and map** of Algerian island vegetation using high-resolution images (Pléiades and SPOT 6/7) and random forest modeling. Environ Monit Assess 193, 617 (2021). https://doi.org/10.1007/s10661-021-09429-9

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