Modelling past and future land use and land cover dynamics in the Nakambe River Basin, West Africa
Tóm tắt
Understanding land use and cover (LULC) dynamic is of great importance to sustainable development in Africa where deforestation is a common problem. This study aimed to assess the historical and future dynamics of LULC in the Nakambé River Basin. Landsat images were used to determine LULC dynamics for the years 1990, 2005 and 2020 using Random Forest classification system in Google Earth Engine while the predicted LULC of 2050 was simulated using the Markov Chain and Multi-Layer-Perceptron neural network in Land Change Modeler. The findings showed significant changes in LULC patterns. From 1990 to 2020, woodland and shrubland decreased by − 45% and − 68%, respectively, while water body, cropland and bare land/built-up increased by 233%, 51%, and 75%, correspondingly. From 2020 to 2050, the results revealed that under the Business-as-usual scenario, bare land/built-up and water bodies could continue to increase by 99% and 1%, respectively. However, cropland, shrubland, and woodland could decrease by − 32.61%, − 33.91%, and − 46.86%, respectively. Under the afforestation scenario, the contrary of Business-as-usual could occur. While woodland, shrubland, and cropland would increase by 22.24%, 51.57%, and 18.13%, correspondingly, between 2020 and 2050, the area covered by water bodies and bare land/built-up will decrease by − 6.16% and − 39.04%, respectively. The results of this research give an insight into past and future LULC dynamics in the Nakambé River Basin and suggest the need to strengthen the policies and actions for better land management in the region.
Tài liệu tham khảo
Akinyemi FO (2021) Vegetation trends, drought severity and land use-land cover change during the growing season in semi-arid contexts. Remote Sensing 13(5):836. https://doi.org/10.3390/rs13050836
Akpoti K, Antwi E, Kabo-bah A (2016) Impacts of rainfall variability, land use and land cover change on stream flow of the black Volta Basin, West Africa. Hydrology 3(3):26. https://doi.org/10.3390/hydrology3030026
Baatuuwie BN (2015) Multi-dimensional approach for evaluating land degradation in the savanna belt of the white volta basin. PhD dissertation, KNUST, Ghana.
Balist J, Malekmohammadi B, Jafari HR, Nohegar A, Geneletti D (2022) Detecting land use and climate impacts on water yield ecosystem service in arid and semi-arid areas. A study in Sirvan River Basin-Iran. Appl Water Sci 12(1):1–14. https://doi.org/10.1007/s13201-021-01545-8
Barnieh AB, Jia L, Menenti M, Zhou J, Zeng Y (2020) Mapping Land Use Land Cover Transitions at Different Spatiotemporal Scales in West Africa. Sustainability 12(20):8565. https://doi.org/10.3390/su12208565
Belemsobgo U, Kafando P, Adouabou BA, Nana S, Coulibaly S, Gnoumou A (2010) Le réseau d’Aires Protégées. In: Thiombiano A, Kampmann D (eds) Atlas de la Biodiversité de l’Afrique de l’Ouest. Tome II: Burkina Faso. Ouagadougou et Francfortt-sur-le Main, BIOTA, Berlin, p 592
Bessah E, Raji AO, Taiwo OJ, Agodzo SK, Ololade OO, Strapasson A (2020) Hydrological responses to climate and land use changes: The paradox of regional and local climate effect in the Pra River Basin of Ghana. J Hydrol Reg Stud 27(23):100654. https://doi.org/10.1016/j.ejrh.2019.100654
Bozkaya AG, Balcik FB, Goksel C, Esbah H (2015) Forecasting landcover growth using remotely sensed data: a case study of the Igneada protection area in Turkey. Environ Monit Assess. https://doi.org/10.1007/s10661-015-4322-z
Braimoh AK, Vlek PLG (2004) Land-cover change analyses in the Volta Basin of Ghana. Earth Interact 8:21
Bullock EL, Healey SP, Yang Z, Oduor P, Gorelick N, Omondi S, Ouko E, Cohen WB (2021) Three decades of land cover change in East Africa. Land 10(2):150. https://doi.org/10.3390/land10020150
Cherlet M, Hutchinson C, Reynolds J, Hill J, Sommer S, Von Maltitz G (2018) World Atlas of Desertification. Publications Office of the European Union, Luxembourg. https://doi.org/10.2760/9205 (978-92-79-75350-3, JRC111155)
CILSS (2016) Les Paysages de l’Afrique de l’Ouest : Une Fenêtre sur un Monde en Pleine Évolution. U.S. Geological Survey EROS, 47914 252nd St, Garretson, SD 57030, UNITED STATES.
Dey NN, Al Rakib A, Al K, Raikwar V (2021) Geospatial modelling of changes in land use/land cover dynamics using Multi-layer perception Markov chain model in Rajshahi City, Bangladesh. Environ Chall 4:100148. https://doi.org/10.1016/j.envc.2021.100148
DGRE (2010) Etat des lieux de la gestion des ressources en eau du bassin du Nakanbé: Rapport final. https://eaunakanbe.bf/wp-content/uploads/2019/06/Rapport-etat-des-lieux-des-RE-du-Nakanbé-de-2010-Final.pdf. Accessed 11 Dec 2021
Dimobe K, Goetze D, Ouédraogo A, Forkuor G, Wala K, Porembski S, Thiombiano A (2017) Spatio-temporal dynamics in land use and habitat fragmentation within a protected area dedicated to tourism in a Sudanian Savanna of West Africa. J Landsc Ecol 10(1):75–95. https://doi.org/10.1515/jlecol-2017-0011
Eastman JR (2020) TerrSet geospatial monitoring and modeling system, Tutorial Version 2020v.19.0. Clark University, Worcester
FAO (2010) Rapport principal: evaluation des ressources forestières mondiales 2010. Etude FAO, Forêt
Fattore C, Abate N, Faridani F, Masini N, Lasaponara R (2021) Google earth engine as multi-sensor open-source tool for supporting the preservation of archaeological areas: the case study of flood and fire mapping in Metaponto, Italy. Sensors 21(5):1791. https://doi.org/10.3390/s21051791
Feng D, Zhao Y, Yu L, Li C, Wang J, Clinton N, Bai Y, Belward A, Zhu Z, Gong P (2016) Circa 2014 African land-cover maps compatible with FROM-GLC and GLC2000 classification schemes based on multi-seasonal Landsat data. Int J Remote Sens 37(19):4648–4664. https://doi.org/10.1080/01431161.2016.1218090
Findell KL, Berg A, Gentine P, Krasting JP, Lintner BR, Malyshev S, Santanello JA, Shevliakova E (2017) The impact of anthropogenic land use and land cover change on regional climate extremes. Nat Commun 8(1):989. https://doi.org/10.1038/s41467-017-01038-w
Fitzgerald RW, Lees BG (1994) Assessing the classification accuracy of multisource remote sensing data. Remote Sens Environ 47(3):362–368. https://doi.org/10.1016/0034-4257(94)90103-1
Floreano IX, de Moraes LAF (2021) Land use/land cover (LULC) analysis (2009–2019) with Google Earth Engine and 2030 prediction using Markov-CA in the Rondônia State, Brazil. Environ Monit Assess 193(4):239. https://doi.org/10.1007/s10661-021-09016-y
Forkuor G (2014) Agricultural land use mapping in west africa using multi-sensor satellite imagery. PhD dissertation, Julius-Maximilians-Universität, Würzburg.
Forkuor G, Hounkpatin OKL, Welp G, Thiel M (2017) High resolution mapping of soil properties using remote sensing variables in south-western urkina Faso: a comparison of machine learning and multiple linear regression models. PLoS ONE 12(1):e0170478. https://doi.org/10.1371/journal.pone.0170478
Gharaibeh A, Shaamala A, Obeidat R, Al-Kofahi S (2020) Improving land-use change modeling by integrating ANN with Cellular Automata-Markov Chain model. Heliyon. https://doi.org/10.1016/j.heliyon.2020.e05092
Girma R, Fürst C, Moges A (2022) Land use land cover change modeling by integrating artificial neural network with cellular Automata-Markov chain model in Gidabo river basin, main Ethiopian rift. Environ Chall 6:100419. https://doi.org/10.1016/j.envc.2021.100419
Gislason PO, Benediktsson JA, Sveinsson JR (2006) Random forests for land cover classification. Pattern Recogn Lett 27(4):294–300. https://doi.org/10.1016/j.patrec.2005.08.011
Gupta R, Sharma LK (2020) Efficacy of Spatial Land Change Modeler as a forecasting indicator for anthropogenic change dynamics over five decades: a case study of Shoolpaneshwar Wildlife Sanctuary, Gujarat, India. Ecol Indic 112(23):106171. https://doi.org/10.1016/j.ecolind.2020.106171
Hackman KO, Gong P, Wang J (2017) New land-cover maps of Ghana for 2015 using Landsat 8 and three popular classifiers for biodiversity assessment. Int J Remote Sens 38(14):4008–4021. https://doi.org/10.1080/01431161.2017.1312619
Hackman KO, Li X, Asenso-Gyambibi D, Asamoah EA, Nelson ED (2020) Analysis of geo-spatiotemporal data using machine learning algorithms and reliability enhancement for urbanization decision support. Int J Digit Earth. https://doi.org/10.1080/17538947.2020.1805036
Hassen G, Bantider A, Legesse A, Maimbo M, Likissa D (2021) Land use and land cover change for resilient environment and sustainable development in the Ethiopian Rift Valley Region. Ochr Sr i Zasobow Nat 32:24–41. https://doi.org/10.2478/oszn-2021-0007
Hussien K, Kebede A, Mekuriaw A, Beza SA, Erena SH (2022) Modelling spatiotemporal trends of land use land cover dynamics in the Abbay River Basin, Ethiopia. Model Earth Syst Environ. https://doi.org/10.1007/s40808-022-01487-3
Idrissou M, Diekkrüger B, Tischbein B, Op de Hipt F, Näschen K, Poméon T, Yira Y, Ibrahim B (2022) Modeling the impact of climate and land use/land cover change onwater availability in an inland valley catchment in Burkina Faso. Hydrology 9:12. https://doi.org/10.3390/hydrology9010012
INSD (2020) Résultats Préliminaires du RGPH 5. http://www.insd.bf/contenu/documents_rgph5/RAPPORT_PRELIMINAIRE_RGPH_2019.pdf. Accessed 28 Jan 2022
Karambiri H, García Galiano SG, Giraldo JD, Yacouba H, Ibrahim B, Barbier B, Polcher J (2011) Assessing the impact of climate variability and climate change on runoff in West Africa: the case of Senegal and Nakambe River basins. Atmos Sci Lett 12(1):109–115. https://doi.org/10.1002/asl.317
Kim Y, Newman G, Güneralp B (2020) A review of driving factors, scenarios, and topics in urban land change models. Land 9:1–22. https://doi.org/10.3390/LAND9080246
Koko AF, Yue W, Abubakar GA, Hamed R, Alabsi AAN (2020) Monitoring and predicting spatio-temporal land use/land cover changes in Zaria City, Nigeria, through an integrated cellular automata and Markov chain model (CA-Markov). Sustainability (switzerland) 12(24):1–21. https://doi.org/10.3390/su122410452
Koubodana DH, Diekkrüger B, Näschen K, Adounkpe J, Atchonouglo K (2019) Impact of the accuracy of land cover data sets on the accuracy of land cover change scenarios in the Mono River Basin, Togo, West Africa. Int J Adv Remote Sens GIS 8(1):3073–3095. https://doi.org/10.23953/cloud.ijarsg.422
Labs C (2020) TerrSet 2020 geospatial monitoring and modeling system. Clark Labs, Clark University, Worcester
Larbi I, Forkuor G, Hountondji FCC, Agyare WA, Mama D (2019) Predictive land use change under business-as-usual and afforestation scenarios in the vea catchment, West Africa. Int J Adv Remote Sens GIS 8(1):3011–3029. https://doi.org/10.23953/cloud.ijarsg.416
Leta MK, Demissie TA, Tränckner J (2021) Modeling and prediction of land use land cover change dynamics based on land change modeler (LCM) in Nashe Watershed, Upper Blue Nile Basin, Ethiopia. Sustainability 13:3740. https://doi.org/10.3390/su13073740
Liu C, Li W, Zhu G, Zhou H, Yan H, Xue P (2020) Land Use/land cover changes and their driving factors in the Northeastern Tibetan Plateau based on geographical detectors and Google earth engine: a case study in Gannan Prefecture. Remote Sens 12(19):3139. https://doi.org/10.3390/rs12193139
Mahe G, Paturel JE, Servat E, Conway D, Dezetter A (2005) The impact of land use change on soil water holding capacity and river flow modelling in the Nakambe River, Burkina-Faso. J Hydrol 300(1–4):33–43. https://doi.org/10.1016/j.jhydrol.2004.04.028
Mechal A, Takele T, Meten N, Deyassa G, Degu Y (2022) A modeling approach for evaluating the impacts of Land Use/Land Cover change for Ziway Lake Watershed hydrology in the Ethiopian Rift. Modeling Earth Systems and Environment. https://doi.org/10.1007/s40808-022-01472-w
Mehrabi A, Khabazi M, Almodaresi SA, Nohesara M, Derakhshani R (2019) Land use changes monitoring over 30 years and prediction of future changes using multi-temporal landsat imagery and the land change modeler tools in rafsanjan city (Iran). Sustain Dev Mt Territ 11(1):26–35. https://doi.org/10.21177/1998-4502-2019-11-1-26-35
Midekisa A, Holl F, Savory DJ, Andrade-Pacheco R, Gething PW, Bennett A, Sturrock HJW (2017) Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing. PLoS ONE 12(9):e0184926. https://doi.org/10.1371/journal.pone.0184926
Ministère de l’Environnement et du Developpement Durable (2015) Mécanisme Spécial de Dons ( DGM ) pour les Peuples Autochtones et les Communautés Locales. 122. https://www.iucn.org/sites/dev/files/import/downloads/cges_dgm_burkina_faso_revu_ccp_17mai15.pdf. Accessed 26 Feb 2022
Näschen K, Diekkrüger B, Evers M, Höllermann B, Steinbach S, Thonfeld F (2019) The impact of land use/land cover change (LULCC) on water resources in a tropical catchment in Tanzania under different climate change scenarios. Sustainability (switzerland). https://doi.org/10.3390/su11247083
Nery T, Sadler R, Solis-Aulestia M, White B, Polyakov M, Chalak M (2016) Comparing supervised algorithms in land use and land cover classification of a landsat time-series. In: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 8(23): 5165–5168. https://doi.org/10.1109/IGARSS.2016.7730346
Nowak DJ, Greenfield EJ (2020) The increase of impervious cover and decrease of tree cover within urban areas globally (2012–2017). Urban for Urban Green. https://doi.org/10.1016/j.ufug.2020.126638
Nut N, Mihara M, Jeong J, Ngo B, Sigua G, Prasad PVV, Reyes MR (2021) Land Use and land cover changes and its impact on soil erosion in stung Sangkae catchment of Cambodia. Sustainability 13(16):9276. https://doi.org/10.3390/su13169276
Nyamekye C, Thiel M, Schönbrodt-Stitt S, Zoungrana B, Amekudzi L (2018) Soil and water conservation in Burkina Faso, West Africa. Sustainability 10(9):3182. https://doi.org/10.3390/su10093182
Okafor GC, Annor T, Odai SN, Larbi I (2019) Land use landcover change monitoring and projection in the Dano Catchment, Southwest Burkina Faso. Int J Adv Remote Sens GIS 9(1):3185–3204. https://doi.org/10.23953/cloud.ijarsg.445
PNUE (2004) Loss and Damage: The Role of Ecosystem Services. In Earth Interactions (Vol. 8, Issue 23). http://collections.unu.edu/view/UNU:5614. Accessed 10 Feb 2022
Pontius GR (2000) Quantification error versus location error in comparison of categorical maps. Photogramm Eng Remote Sens 66:1011–1016
Prasomsup W, Piyatadsananon P, Aunphoklang W, Boonrang A (2020) Extraction Technic for Built-up Area Classification in Landsat 8 Imagery. Int J Environ Sci Dev 11(1):15–20. https://doi.org/10.18178/ijesd.2020.11.1.1219
Ramezan CA, Warner TA, Maxwell AE, Price BS (2021) Effects of training set size on supervised machine-learning land-cover classification of large-area high-resolution remotely sensed data. Remote Sens 13(3):368. https://doi.org/10.3390/rs13030368
Rodrguez Eraso N, Armenteras-Pascual D, Retana Alumbreros J (2013) Land use and land cover change in the Colombian Andes: dynamics and future scenarios. J Land Use Sci 8(2):154–174. https://doi.org/10.1080/1747423X.2011.650228
Rwanga SS, Ndambuki JM (2017) Accuracy assessment of land use/land cover classification using remote sensing and GIS. Int J Geosci 08(04):611–622. https://doi.org/10.4236/ijg.2017.84033
Shade C, Kremer P (2019) Article predicting land use changes in philadelphia following green infrastructure policies. Land. https://doi.org/10.3390/land8020028
Shetty S (2019) Analysis of machine learning classifiers for LULC classification on google earth engine analysis of machine learning classifiers for LULC classification on Google Earth Engine. Master Thesis in Geo-information Science and Earth Observation, Enschede, The Netherlands, pp 1–65
Sibanda S, Ahmed F (2021) Modelling historic and future land use/land cover changes and their impact on wetland area in Shashe sub-catchment, Zimbabwe. Model Earth Syst Environ 7(57):70. https://doi.org/10.1007/s40808-020-00963-y
Sinha RK, Eldho TI, Subimal G (2020) Assessing the impacts of land use/land cover and climate change on surface runoff of a humid tropical river basin in Western Ghats, India. Int J River Basin Manag. https://doi.org/10.1080/15715124.2020.1809434
Tanksali A, Soraganvi VS (2021) Assessment of impacts of land use/land cover changes upstream of a dam in a semi-arid watershed using QSWAT. Model Earth Syst Environ 7:2391–2406. https://doi.org/10.1007/s40808-020-00978-5
Thiam S, Salas EAL, Hounguè NR, Almoradie ADS, Verleysdonk S, Adounkpe JG, Komi K (2022) Modelling land use and land cover in the transboundary mono river catchment of Togo and Benin using Markov chain and stakeholder’s perspectives. Sustainability 14:4160. https://doi.org/10.3390/su14074160
Thiombiano AN (2011) Variabilité Climatique et Impacts sur les Ressources en eau au Burkina Faso: étude de cas du Bassin Hydrographique du Fleuve Nakambé. Master Thesis in environment, Université de Moncton, Canada. Available from: https://www.collectionscanada.gc.ca/obj/thesescanada/vol2/002/MR81178.PDF?is_thesis¼1&oclc_number¼812063916. Accessed 13 Mar 2021
UNEP-GEF Volta Project (2013) Volta Basin Transboundary Diagnostic Analysis : http://gefvolta.iwlearn.org/project-resources/studies-reports/tda-final/regional-tda/volta-basin-tda-english. Accessed 20 Sept 2021
Winkler K, Fuchs R, Rounsevell M, Herold M (2021) Global land use changes are four times greater than previously estimated. Nat Commun 12:2501. https://doi.org/10.1038/s41467-021-22702-2
Yang C, Wei T, Li Y (2022) Simulation and spatio-temporal variation characteristics of LULC in the context of urbanization construction and ecological restoration in the yellow river basin. Sustainability 14:789. https://doi.org/10.3390/su14020789
Yira Y, Diekkrüger B, Steup G, Bossa AY (2016) Modeling land use change impacts on water resources in a tropical West African catchment (Dano, Burkina Faso). J Hydrol 537:187–199
Yira Y, Diekkrüger B, Steup G, Yaovi Bossa A (2017) Impact of climate change on hydrological conditions in a tropical West African catchment using an ensemble of climate simulations. Hydrol Earth Syst Sci 21:2143–2161
Yonaba R, Biaou AC, Koïta M, Tazen F, Mounirou LA, Zouré CO, Queloz P, Karambiri H, Yacouba H (2021) A dynamic land use/land cover input helps in picturing the Sahelian paradox: Assessing variability and attribution of changes in surface runoff in a Sahelian watershed. Sci Total Environ 757(23):143792. https://doi.org/10.1016/j.scitotenv.2020.143792
Yu L, Liang L, Wang J, Zhao Y, Cheng Q, Hu L, Liu S, Yu L, Wang X, Zhu P, Li X, Xu Y, Li C et al (2014) Meta-discoveries from a synthesis of satellite-based land-cover mapping research. Int J Remote Sens 35(13):4573–4588. https://doi.org/10.1080/01431161.2014.930206
Zoungrana B, Conrad C, Amekudzi L, Thiel M, Da E, Forkuor G, Löw F (2015) Multi-Temporal Landsat Images and Ancillary Data for Land Use/Cover Change (LULCC) Detection in the Southwest of Burkina Faso, West Africa. Remote Sens 7(9):12076–12102. https://doi.org/10.3390/rs70912076