Évaluation des modifications de l'occupation des sols dans l'Est-Kavango en Namibie selon une approche multi-dates par objet
DOI :
https://doi.org/10.19182/bft2020.344.a31897Mots-clés
période, modification de l'occupation des sols, classification de l'occupation des sols, Landsat, segmentation multi-dates, trajectoire, classification par l'objet, NamibieRésumé
Les modifications de l'occupation des sols représentent une problématique mondiale, mais leurs effets peuvent être particulièrement sévères dans les pays en développement comme la Namibie, où elles perturbent le fonctionnement des écosystèmes et, de ce fait, la soutenabilité du développement économique. Les conditions arides en Namibie, dues à la faible pluviométrie et aux taux élevés d'évapotranspiration, amplifiées par les feux de brousse qui sévissent chaque année, se traduisent par un paysage hétérogène caractérisé par un mélange d'arbres, d'arbustes et de plantes herbacées. De ce fait, les cartes d'occupation des sols sont souvent imprécises à l'échelle du pixel. Cependant, et malgré leur précision relativement élevée, l'analyse d'images par objets est encore loin d'être largement utilisée pour les forêts tropicales sèches de l'Afrique australe. Cette étude vise à évaluer les modifications de l'occupation des sols selon une approche multi-dates par l'objet, afin d'en déterminer l'ampleur et la dynamique dans ce paysage hétérogène de l'Est-Kavango, région de Namibie où le couvert végétal est parmi les plus denses. La segmentation multi-dates, les valeurs moyennes des courbes et la différenciation des images ont permis de détecter les modifications de l'occupation des sols sur quatre périodes (1990, 2000, 2009 et 2016). Pour toutes les périodes, la modification la plus fréquente est la conversion de forêts en terres agricoles. En 1990, les forêts recouvraient 58 % des terres de l'étude pour 55 % en 2016, tandis que, sur la même période, la superficie des terres agricoles doublait, passant de 3 % en 1990 à 6 % en 2016. Les résultats obtenus par l'approche novatrice adoptée pour cette étude sont prometteurs à la lumière des méthodes traditionnelles, où la détection de modifications postérieures au classement peut être erronée. La méthode utilisée peut donc être recommandée pour le suivi à long terme des modifications de l'occupation et l'utilisation des sols dans les zones caractérisées par des conditions environnementales biophysiques similaires.
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Références
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Kamwi J. M., Cho M., Kaetsch C., Manda S., Graz F., Chirwa P., 2018. Assessing the spatial drivers of land use and land cover change in the protected and communal areas of the Zambezi Region, Namibia. Land, 7 (4): 131. https://doi.org/10.3390/land7040131
Kamwi J. M., Chirwa P. W. C., Manda S. O. M., Graz P. F., Kätsch C., 2015. Livelihoods, land use and land cover change in the Zambezi Region, Namibia. Population and Environment, 37 (2): 207-230. https://doi.org/10.1007/s11111-015-0239-2
Krueger O., Fischer J., 1994. Correction of aerosol influence in Landsat 5 thematic mapper data. GeoJournal, 32: 61. https://doi.org/10.1007/bf00806358
Mango L. M., Melesse A. M., McClain M. E., Gann D., Setegn G., 2011. Land use and climate change impacts on the hydrology of the upper Mara River Basin, Kenya: results of a modeling study to support better resource management. Hydrology and Earth System Sciences, 15: 2245-2258. https://doi.org/10.5194/hess-15-2245-2011
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Namibia Meteorological Service, 2014. Daily rainfall entry for seasonal monitoring and production of climate bulletin. Microsoft Excel data file. Windhoek, Namibia.
NBSAP, 2014. Namibia’s Second National Biodiversity Strategy Action Plan 2013-2022. http://www.met.gov.na/files/files/Namibia%E2%80%99s%20Second%20National%20Biodiversity%20Strategy%20and%20Action%20Plan%20(NBSAP%202)%20%202013%20-%202022.pdf
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Pröpper M., Vollan B., 2013. Beyond Awareness and Self-Governance: Approaching Kavango Timber Users’ Real-Life Choices. Land, 2: 392-418. https://doi.org/10.3390/land2030392
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Stellmes M., Frantz D., Finckh M., Revermann R., Roder A., Hill J., 2013. Fire frequency, fire seasonality and fire intensity within the Okavango region derived from MODIS fire products. Biodiversity and Ecology, 5: 351-362. https://doi.org/10.7809/b-e.00288
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Verhegghen A., Ernst C., Defourny P., Beuchle R., 2010. Automated land cover mapping and independent change detection in tropical forest using multi-temporal high-resolution data set. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38 (4).
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Wingate V., Phinn S., Kuhn N., Bloemertz L., Dhanjal-Adams K., 2016. Mapping Decadal Land Cover Changes in the Woodlands of North Eastern Namibia from 1975 to 2014 Using the Landsat Satellite Archived Data. Remote Sensing, 8 (8): 681. https://doi.org/10.3390/rs8080681
Zha Y., Gao J., Ni S., 2003. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery, International Journal of Remote Sensing, 24 (3): 583-594. https://doi.org/10.1080/01431160304987
Biggs R., Simons H., Bakkenes M., Scholes R. J., Eickhout B., Vuuren D. van, Alkemade R., 2008. Scenarios of biodiversity loss in southern Africa in the 21st century. Global Environmental Change, 18: 296-309. https://doi.org/10.1016/j.gloenvcha.2008.02.001
Dirzo R., Young H. S., Galletti Y. M., Ceballos G., Nick J. B., Collen B., 2014. Defaunation in the Anthropocene. Science, 345: 401-406. https://doi.org/10.1126/science.1251817
Campbell J., Wynne R., 2011. Introduction to Remote Sensing (5th ed.). New York, USA, The Guilford Press, 305 p.
Cracknell A. P., 2001. The exciting and totally unanticipated success of the AVHRR in applications for which it was never intended. Advances in Space Research, 28 (1): 233-240. https://doi.org/10.1016/s0273-1177(01)00349-0
De Cauwer V., Geldenhuys C. J., Aerts R., Kabajani M., Muys B., 2016. Patterns of forest composition and their long-term environmental drivers in the tropical dry forest transition zone of southern Africa. Forest Ecosystems, 3 (1): 23. https://doi.org/10.1186/s40663-016-0080-9
De Cauwer V., 2015. Towards estimation of growing stock for the timber tree Pterocarpus angolensis in Namibia. Presented at the DAAD World Forestry Congress Workshop Bridging the Gap between Forest Information Needs and Forest Inventory Capacity, 7-12 September, Pietermaritzburg, South Africa.
Desclée B., Bogaert P., Defourny P., 2006. Forest change detection by statistical object-based method. Remote Sensing of Environment, 102 (1-2): 1-11. https://doi.org/10.1016/j.rse.2006.01.013
De Vries B., Pratihast A., Verbesselt J., Kooistra L., Herold M., 2016. Characterizing Forest Change Using Community-Based Monitoring Data and Landsat Time Series. PLoS ONE, 11 (3): e0147121. https://doi.org/10.1371/journal.pone.0147121
Directorate of Forestry, 2013. Forest inventory data, 2002-2013. Microsoft Excel data file. Windhoek, Namibia, Ministry of Agriculture, Water and Forestry.
Directorate of Forestry, 2012. Community Forestry in Namibia. Windhoek, Namibia, Ministry of Agriculture, Water and Forestry. https://doi.org/10.1371/journal.pone.0147121
Ernst C., Verhegghen A., Bodart C., Mayaux P., de Wasseige C., Bararwandika A., et al., 2010. Congo basin forest cover change estimate for 1990, 2000 and 2005 by Landsat interpretation using an automated object-based processing chain. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38 (4).
Esetlili M. T., Balcik F. B., Şanli F. B., Üstüner M., Kalkan K., Göksel C., et al., 2018. Comparison of Object and Pixel-Based Classifications for Mapping Crops Using Rapideye Imagery: A Case Study of Menemen Plain, Turkey. International Journal of Environment and Geoinformatics, 5 (2): 231-243. https://doi.org/10.30897/ijegeo.442002
FAO, 2001. Nutrition Country Profiles – Namibia. Rome, Italy, FAO. http://www.fao.org/tempref/AG/agn/nutrition/ncp/nam.pdf
Flood N., 2014. Continuity of reflectance data between Landsat-7 ETM+ and Landsat-8 OLI, for both Ttop-of-atmosphere and surface reflectance: A study in the Australian landscape. Remote Sensing, 6 (9): 7952-7970. https://doi.org/10.3390/rs6097952
Herold M., Mayaux P., Woodcock C. E., Baccini B., Schmullius C., 2008. Some challenges in global land cover mapping: An assessment of agreement and accuracy in existing 1 km datasets. Remote Sensing of Environment, 112 (5): 2538-2556. https://doi.org/10.1016/j.rse.2007.11.013
IPCC, 2003. Good practice guidance for land use, land-use change and forestry. In: Penman J., Gytarsky M., Hiraishi T., Krug T., Kruger D., Pipatti R., et al. (eds). The Intergovernmental Panel on Climate Change. Hayama, Kanagawa. Japan. https://doi.org/10.1007/springerreference_28950
Jawak S. D., Wankhede S. F., Luis A. J., 2018. Comparison of Pixel and Object-Based Classification Techniques for Glacier Facies Extraction. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42 (5). https://doi.org/10.5194/isprs-archives-XLII-5-543-2018
Juniati E., Arrofigoh E. N., 2017. Comparison of Pixel-Based and Object-Based Classification Using Parameters and Non-Parameters Approach for The Pattern Consistency of Multi Scale Land Cover. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42 (2)/W7. https://doi.org/10.5194/isprs-archives-xlii-2-w7-765-2017
Kamwi J. M., Cho M., Kaetsch C., Manda S., Graz F., Chirwa P., 2018. Assessing the spatial drivers of land use and land cover change in the protected and communal areas of the Zambezi Region, Namibia. Land, 7 (4): 131. https://doi.org/10.3390/land7040131
Kamwi J. M., Chirwa P. W. C., Manda S. O. M., Graz P. F., Kätsch C., 2015. Livelihoods, land use and land cover change in the Zambezi Region, Namibia. Population and Environment, 37 (2): 207-230. https://doi.org/10.1007/s11111-015-0239-2
Krueger O., Fischer J., 1994. Correction of aerosol influence in Landsat 5 thematic mapper data. GeoJournal, 32: 61. https://doi.org/10.1007/bf00806358
Mango L. M., Melesse A. M., McClain M. E., Gann D., Setegn G., 2011. Land use and climate change impacts on the hydrology of the upper Mara River Basin, Kenya: results of a modeling study to support better resource management. Hydrology and Earth System Sciences, 15: 2245-2258. https://doi.org/10.5194/hess-15-2245-2011
MAWF (Ministry of Agriculture, Water and Forestry), 2008. Green Schemes farms of Namibia (No. 1). Windhoek, Namibia.
Mendelsohn J., Jarvis A., Roberts C., Robertson T., 2002. Atlas of Namibia. A Portrait of the Land and its People. Windhoek, Namibia, Ministry of Environment and Tourism. https://onlinelibrary.wiley.com/doi/abs/10.1002/mmnz.20040800111
Mendelsohn J., 2009. Land use in Kavango: Past, Present and Future. OKACOM. Research and information Services of Namibia. Retrieved from http://www.the-eis.com/data/literature/Mendelsohn_Land%20use%20OKavango.pdf
MLR (Ministry of Land Reform), 2015. Integrated Regional Land Use Plan for the Kavango East Region Baseline Report (Volume 1). Windhoek, Namibia.
Myneni R., Hall F., Sellers P., Marshak A., 1995. The Interpretation of spectral vegetation Indexes. Transaction Journal of Geoscience and Remote Sensing, 33 (2): 481-486. https://doi.org/10.1109/36.377948
Namibia Meteorological Service, 2014. Daily rainfall entry for seasonal monitoring and production of climate bulletin. Microsoft Excel data file. Windhoek, Namibia.
NBSAP, 2014. Namibia’s Second National Biodiversity Strategy Action Plan 2013-2022. http://www.met.gov.na/files/files/Namibia%E2%80%99s%20Second%20National%20Biodiversity%20Strategy%20and%20Action%20Plan%20(NBSAP%202)%20%202013%20-%202022.pdf
NSA (Namibia Statistics Agency), 2012. Namibia Population and Housing Main Report. Windhoek, Namibia.
Pröpper M., Gröngröft A., Falk T., Eschenbach A., Fox T., Gessner U., et al., 2010. Causes and perspectives of land-cover change through expanding cultivation in Kavango. Biodiversity in Southern Africa, 3: 1-31. http://researchspace.csir.co.za/dspace/handle/10204/4831
Pröpper M., Vollan B., 2013. Beyond Awareness and Self-Governance: Approaching Kavango Timber Users’ Real-Life Choices. Land, 2: 392-418. https://doi.org/10.3390/land2030392
Röder A., Pröpper M., Stellmes M., Schneibel A., Hill J., 2015. Assessing urban growth and rural land use transformations in a cross-border situation in Northern Namibia and Southern Angola. Land Use Policy, 42 (2015): 340-354.
https://doi.org/10.1016/j.landusepol.2014.08.008
Schultz M., Shapiro A., Clevers J. G. P. W., Beech C., Herold M., 2018. Forest Cover and Vegetation Degradation Detection in the Kavango Zambezi Transfrontier Conservation Area Using BFAST Monitor. Remote Sensing, 10 (11): 1850. https://doi.org/10.3390/rs10111850
Stellmes M., Frantz D., Finckh M., Revermann R., Roder A., Hill J., 2013. Fire frequency, fire seasonality and fire intensity within the Okavango region derived from MODIS fire products. Biodiversity and Ecology, 5: 351-362. https://doi.org/10.7809/b-e.00288
Trimble, 2014. eCognition® Developer Reference Book. Munich, Germany, Trimble Germany GmbH, 258 p.
Tucker C. J., Pinzon J. E., Brown M. E., Slayback D. A., Pak E. W., Mahoney R., et al., 2005. An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. International Journal of Remote Sensing, 26 (20): 4485-4498. https://doi.org/10.1080/01431160500168686
Verhegghen A., Ernst C., Defourny P., Beuchle R., 2010. Automated land cover mapping and independent change detection in tropical forest using multi-temporal high-resolution data set. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38 (4).
Verlinden A., Laamanen R., 2006. Modeling woody vegetation resources using Landsat TM imagery in northern Namibia. The Southern African Forestry Journal, 207 (1): 27-39. https://doi.org/10.2989/10295920609505250
Viera A. J., Garret J. M., 2005. Understanding Interobserver Agreement: The Kappa Statistic. Family Medicine, 37: 360-363. https://www.ncbi.nlm.nih.gov/pubmed/15883903
Wingate V., Phinn S., Kuhn N., Bloemertz L., Dhanjal-Adams K., 2016. Mapping Decadal Land Cover Changes in the Woodlands of North Eastern Namibia from 1975 to 2014 Using the Landsat Satellite Archived Data. Remote Sensing, 8 (8): 681. https://doi.org/10.3390/rs8080681
Zha Y., Gao J., Ni S., 2003. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery, International Journal of Remote Sensing, 24 (3): 583-594. https://doi.org/10.1080/01431160304987
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2020-07-21
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MUHOKO, E. ., de WASSEIGE, C. ., & DE CAUWER, V. . (2020). Évaluation des modifications de l’occupation des sols dans l’Est-Kavango en Namibie selon une approche multi-dates par objet . BOIS & FORETS DES TROPIQUES, 344, 17–32. https://doi.org/10.19182/bft2020.344.a31897
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