Using the Google Earth Engine cloud-computing platform to assess the long-term spatial temporal dynamics of land use and land cover within the Letaba watershed, South Africa

References

• Abijith D, Saravanan S. 2022. Assessment of land use and land cover change detection and prediction using remote sensing and CA Markov in the northern coastal districts of Tamil Nadu, India. Environ Sci Pollut Res. 29(57):86055–86067.
   PubMed Web of Science ®Google Scholar
• Acharya TD, Yang I. 2015. Exploring landsat 8. Int J IT, Eng Appl Sci Res (IJIEASR). 4(4):4–10.
   Google Scholar
• Agariga F, Abugre S, Appiah M. 2021. Spatio-temporal changes in land use and forest cover in the Asutifi North District of Ahafo Region of Ghana, (1986–2020). Environ Chall. 5:100209. doi: 10.1016/j.envc.2021.100209.
   Google Scholar
• Alemayehu F, Taha N, Nyssen J, Girma A, Zenebe A, Behailu M, Deckers S, Poesen J. 2009. The impacts of watershed management on land use and land cover dynamics in Eastern Tigray (Ethiopia). Resour Conserv Recycl. 53(4):192–198. doi: 10.1016/j.resconrec.2008.11.007.
   Web of Science ®Google Scholar
• Amsalu A, Stroosnijder L, de Graaff J. 2007. Long-term dynamics in land resource use and the driving forces in the Beressa watershed, highlands of Ethiopia. J Environ Manage. 83(4):448–459. doi: 10.1016/j.jenvman.2006.04.010.
   PubMed Web of Science ®Google Scholar
• Azzari G, Lobell DB. 2017. Landsat-based classification in the cloud: an opportunity for a paradigm shift in land cover monitoring. Remote Sens Environ. 202:64–74. doi: 10.1016/j.rse.2017.05.025.
   Web of Science ®Google Scholar
• Berry NJ, Phillips OL, Lewis SL, Hill JK, Edwards DP, Tawatao NB, Ahmad N, Magintan D, Khen CV, Maryati M, et al. 2010. The high value of logged tropical forests: lessons from northern Borneo. Biodivers Conserv. 19(4):985–997. doi: 10.1007/s10531-010-9779-z.
   Web of Science ®Google Scholar
• Bufebo B, Elias E. 2021. Land use/land cover change and its driving forces in Shenkolla watershed, south Central Ethiopia. Sci World J. 2021:1–13. doi: 10.1155/2021/9470918.
   Google Scholar
• Cai J, Luo J, Wang S, Yang S. 2018. Feature selection in machine learning: a new perspective. Neurocomputing. 300:70–79. doi: 10.1016/j.neucom.2017.11.077.
   Web of Science ®Google Scholar
• Ceballos G, Davidson A, List R, Pacheco J, Manzano-Fischer P, Santos-Barrera G, Cruzado J. 2010. Rapid decline of a grassland system and its ecological and conservation implications. PLoS One. 5(1):e8562. doi: 10.1371/journal.pone.0008562.
   PubMed Web of Science ®Google Scholar
• Chaves ME, Soares AR, Mataveli GA, Sánchez AH, Sanches ID. 2023. A semi-automated workflow for lulc mapping via sentinel-2 data cubes and spectral indices. Automation. 4(1):94–109. doi: 10.3390/automation4010007.
   Google Scholar
• Chen Y, Shuai J, Zhang Z, Shi P, Tao F. 2014. Simulating the impact of watershed management for surface water quality protection: a case study on reducing inorganic nitrogen load at a watershed scale. Ecol Eng. 62:61–70. doi: 10.1016/j.ecoleng.2013.10.023.
   Web of Science ®Google Scholar
• Cui J, Zhu M, Liang Y, Qin G, Li J, Liu Y. 2022. Land use/land cover change and their driving factors in the Yellow River Basin of Shandong Province based on google earth Engine from 2000 to 2020. IJGI. 11(3):163. doi: 10.3390/ijgi11030163.
   Google Scholar
• de Sousa MDC, Veloso GV, Gomes LC, Fernandes-Filho EI, de Oliveira TS. 2021. Spatio-temporal dynamics of land use changes of an intense anthropized basin in the Brazilian semi-arid region. Remote Sens Appl: Soc Environ. 24:100646. doi: 10.1016/j.rsase.2021.100646.
   Google Scholar
• Delalay M, Tiwari V, Ziegler AD, Gopal V, Passy P. 2019. Land-use and land-cover classification using Sentinel-2 data and machine-learning algorithms: operational method and its implementation for a mountainous area of Nepal. J Appl Rem Sens. 13(01):1. doi: 10.1117/1.JRS.13.014530.
   Google Scholar
• Du Plessis A, Harmse T, Ahmed F. 2014. Quantifying and predicting the water quality associated with land cover change: a case study of the Blesbok Spruit Catchment, South Africa. Water. 6(10):2946–2968. doi: 10.3390/w6102946.
   Web of Science ®Google Scholar
• Dube T, Mutanga O. 2015. Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgeni catchment, South Africa. ISPRS J Photogramm Remote Sens. 101:36–46. doi: 10.1016/j.isprsjprs.2014.11.001.
   Web of Science ®Google Scholar
• Dube T, Mutanga O, Adam E, Ismail R. 2014. Intra-and-inter species biomass prediction in a plantation forest: testing the utility of high spatial resolution spaceborne multispectral rapideye sensor and advanced machine learning algorithms. Sensors (Basel). 14(8):15348–15370. doi: 10.3390/s140815348.
   PubMed Web of Science ®Google Scholar
• DWAF. 2004. Internal Strategic Perspective: luvuvhu/Letaba Water Management Area. Report No. PWMA 02/000/00/0304. Pretoria, South Africa: Department of Water Affairs and Forestry.
   Google Scholar
• DWAF. 2006. Letaba catchment reserve determination study. February 2006. Pretoria, South Africa: department of Water Affairs and Forestry.
   Google Scholar
• Forkuor G, Dimobe K, Serme I, Tondoh JE. 2018. Landsat-8 vs. Sentinel-2: examining the added value of sentinel-2’s red-edge bands to land-use and land-cover mapping in Burkina Faso. GIScience Remote Sens. 55(3):331–354. doi: 10.1080/15481603.2017.1370169.
   Web of Science ®Google Scholar
• Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R. 2017. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens Environ. 202:18–27. doi: 10.1016/j.rse.2017.06.031.
   Web of Science ®Google Scholar
• Grecchi RC, Gwyn QHJ, Bénié GB, Formaggio AR, Fahl FC. 2014. Land use and land cover changes in the Brazilian Cerrado: a multidisciplinary approach to assess the impacts of agricultural expansion. Appl Geogr. 55:300–312. doi: 10.1016/j.apgeog.2014.09.014.
   Web of Science ®Google Scholar
• Gxokwe S, Dube T, Mazvimavi D. 2022. Leveraging Google Earth Engine platform to characterize and map small seasonal wetlands in the semi-arid environments of South Africa. Sci Total Environ. 803:150139. doi: 10.1016/j.scitotenv.2021.150139.
   PubMed Web of Science ®Google Scholar
• Hailu S, Tena A, Tibebu K, Gete Z. 2021. Evaluating ecosystems services values due to land use transformation in the Gojeb watershed, Southwest Ethiopia. Environ Syst Res. 10(1).
   Google Scholar
• Hoque MZ, Islam I, Ahmed M, Hasan SS, Prodhan FA. 2022. Spatio-temporal changes of land use land cover and ecosystem service values in coastal Bangladesh. Egypt J Remote Sens Space Sci. 25(1):173–180. doi: 10.1016/j.ejrs.2022.01.008.
   Web of Science ®Google Scholar
• Huang H, Chen Y, Clinton N, Wang J, Wang X, Liu C, Gong P, Yang J, Bai Y, Zheng Y, et al. 2017. Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine. Remote Sens Environ. 202:166–176. doi: 10.1016/j.rse.2017.02.021.
   Web of Science ®Google Scholar
• Izadi S, Sohrabi H. 2021. Using Bayesian kriging and satellite images to estimate above-ground biomass of Zagros mountainous forests. In Forest resources resilience and conflicts. Amsterdam, The Netherlands: Elsevier; p. 193–201.
   Google Scholar
• Jansen LJ, Di Gregorio A. 2002. Parametric land cover and land-use classifications as tools for environmental change detection. Agric Ecosyst Environ. 91(1-3):89–100. doi: 10.1016/S0167-8809(01)00243-2.
   Web of Science ®Google Scholar
• Kandekar VU, Pande CB, Rajesh J, Atre AA, Gorantiwar SD, Kadam SA, Gavit B. 2021. Surface water dynamics analysis based on sentinel imagery and Google Earth Engine Platform: a case study of Jayakwadi dam. Sustain Water Resour Manag. 7(3):44. doi: 10.1007/s40899-021-00527-7.
   Web of Science ®Google Scholar
• Kerr J, Chung K. 2002. Evaluating watershed management projects. Water Policy. 3(6):537–554. doi: 10.1016/S1366-7017(02)00016-8.
   Google Scholar
• Kombate A, Folega F, Atakpama W, Dourma M, Wala K, Goïta K. 2022. Characterization of land-cover changes and forest-cover dynamics in Togo between 1985 and 2020 from Landsat images using Google Earth Engine. Land. 11(11):1889. doi: 10.3390/land11111889.
   Web of Science ®Google Scholar
• Kulithalai Shiyam Sundar P, Deka PC. 2022. Spatio-temporal classification and prediction of land use and land cover change for the Vembanad Lake system, Kerala: a machine learning approach. Environ Sci Pollut Res Int. 29(57):86220–86236. doi: 10.1007/s11356-021-17257-0.
   PubMed Web of Science ®Google Scholar
• Kumar L, Mutanga O. 2018. Google Earth Engine applications since inception: usage, trends, and potential. Remote Sens. 10(10):1509. doi: 10.3390/rs10101509.
   Google Scholar
• Kyere I, Astor T, Graß R, Wachendorf M. 2019. Multi-temporal agricultural land-Cover mapping using single-year and multi-year models based on Landsat imagery and IACS data. Agronomy. 9(6):309. doi: 10.3390/agronomy9060309.
   Google Scholar
• Le Maitre DC, Blignaut JN, Clulow A, Dzikiti S, Everson CS, Görgens AH, Gush MB. 2020. Impacts of plant invasions on terrestrial water flows in South Africa. Biol Invasions S Afr:431–457. Cham: Springer International Publishing.
   Google Scholar
• Lee JSH, Wich S, Widayati A, Koh LP. 2016. Detecting industrial oil palm plantations on Landsat images with Google Earth Engine. Remote Sens Appl: Soc Environ. 4:219–224. doi: 10.1016/j.rsase.2016.11.003.
   Google Scholar
• 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(7):3740. doi: 10.3390/su13073740.
   Web of Science ®Google Scholar
• Lin L, Hao Z, Post CJ, Mikhailova EA, Yu K, Yang L, Liu J. 2020. Monitoring land cover change on a rapidly urbanizing island using Google Earth Engine. Appl Sci. 10(20):7336. doi: 10.3390/app10207336.
   Google Scholar
• Liping C, Yujun S, Saeed S. 2018. Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—a case study of a hilly area, Jiangle, China. PLoS One. 13(7):e0200493. doi: 10.1371/journal.pone.0200493.
   PubMed Web of Science ®Google Scholar
• Loukika KN, Keesara VR, Sridhar V. 2021. Analysis of land use and land cover using machine learning algorithms on Google Earth Engine for Munneru River Basin, India. Sustainability. 13(24):13758. doi: 10.3390/su132413758.
   Web of Science ®Google Scholar
• Lu D, Li G, Moran E, Hetrick S. 2013. Spatiotemporal analysis of land-use and land-cover change in the Brazilian Amazon. Int J Remote Sens. 34(16):5953–5978. doi: 10.1080/01431161.2013.802825.
   PubMed Web of Science ®Google Scholar
• Marks-Bielska R, Witkowska-Dabrowska M. 2021. Evaluation of changes in exclusion of arable land from agricultural production in Poland in the context of guidelines of the strategy for responsible development. ERSJ. XXIV(Special Issue 3):351–364. doi: 10.35808/ersj/2433.
   Google Scholar
• Mashala MJ, Dube T, Mudereri BT, Ayisi KK, Ramudzuli MR. 2023. An advancements in remote sensing for assessing and monitoring land use and land cover changes impacts on surface water resources in semi-arid tropical environments. Remote Sens. 15(16):926. n doi: 10.3390/rs15163926.
   Google Scholar
• Mekuriaw A. 2017. Assessing the effectiveness of land resource management practices on erosion and vegetative cover using GIS and remote sensing techniques in Melaka watershed, Ethiopia. Environ Syst Res. 6(1):1–10. doi: 10.1186/s40068-017-0093-6.
   Google Scholar
• Mellor A, Boukir S, Haywood A, Jones S. 2015. Exploring issues of training data imbalance and mislabelling on random forest performance for large area land cover classification using the ensemble margin. ISPRS J Photogramm Remote Sens. 105:155–168. doi: 10.1016/j.isprsjprs.2015.03.014.
   Web of Science ®Google Scholar
• Mendoza ME, Granados EL, Geneletti D, Pérez-Salicrup DR, Salinas V. 2011. Analysing land cover and land use change processes at watershed level: a multitemporal study in the Lake Cuitzeo Watershed, Mexico (1975–2003). Appl Geogr. 31(1):237–250. doi: 10.1016/j.apgeog.2010.05.010.
   Web of Science ®Google Scholar
• Mudereri BT, Abdel-Rahman EM, Dube T, Landmann T, Khan Z, Kimathi E, Owino R, Niassy S. 2020. Multi-source spatial data-based invasion risk modeling of Striga (Striga asiatica) in Zimbabwe. GIScience Remote Sens. 57(4):553–571. doi: 10.1080/15481603.2020.1744250.
   Web of Science ®Google Scholar
• Munthali MG, Davis N, Adeola AM, Botai JO, Kamwi JM, Chisale HL, Orimoogunje OO. 2019. Local perception of drivers of land-use and land-cover change dynamics across Dedza District, Central Malawi Region. Sustainability. 11(3):832. doi: 10.3390/su11030832.
   Web of Science ®Google Scholar
• Namugize JN, Jewitt G, Graham M. 2018. Effects of land use and land cover changes on water quality in the uMngeni river catchment, South Africa. Phys Chem Earth, Parts a/b/c. 105:247–264. doi: 10.1016/j.pce.2018.03.013.
   Web of Science ®Google Scholar
• Pan X, Wang Z, Gao Y, Dang X, Han Y. 2022. Detailed and automated classification of land use/land cover using machine learning algorithms in Google Earth Engine. Geocarto Int. 37(18):5415–5432. doi: 10.1080/10106049.2021.1917005.
   Web of Science ®Google Scholar
• Pande CB, Moharir KN, Khadri SFR, Patil S. 2018. Study of land use classification in an arid region using multispectral satellite images. Appl Water Sci. 8(5):1–11. doi: 10.1007/s13201-018-0764-0.
   Web of Science ®Google Scholar
• Piao Y, Jeong S, Park S, Lee D. 2021. Analysis of land use and land cover change using time-series data and random forest in North Korea. Remote Sens. 13(17):3501. doi: 10.3390/rs13173501.
   Web of Science ®Google Scholar
• Pontius RG, Jr, Millones M. 2011. Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. Int J Remote Sens. 32(15):4407–4429. doi: 10.1080/01431161.2011.552923.
   Web of Science ®Google Scholar
• Pullanikkatil D, Palamuleni LG, Ruhiiga TM. 2016. Land use/land cover change and implications for ecosystems services in the Likangala River Catchment, Malawi. Phys Chem Earth Parts A/B/C. 93:96–103. doi: 10.1016/j.pce.2016.03.002.
   Web of Science ®Google Scholar
• Querner EP, Froebrich J, de Clercq W, Jovanovic N. 2016. Effect of water use by smallholder farms in the Letaba basin: a case study using the SIMGRO model (No. 2715). Alterra, Wageningen-UR.
   Google Scholar
• Rotich B, Kindu M, Kipkulei H, Kibet S, Ojwang D. 2022. Impact of land use/land cover changes on ecosystem service values in the cherangany hills water tower, Kenya. Environ Chall. 8:100576. doi: 10.1016/j.envc.2022.100576.
   Google Scholar
• Salazar A, Baldi G, Hirota M, Syktus J, McAlpine C. 2015. Land use and land cover change impacts on the regional climate of non-Amazonian South America: a review. Glob Planet Change. 128:103–119. doi: 10.1016/j.gloplacha.2015.02.009.
   Web of Science ®Google Scholar
• Shafizadeh-Moghadam H, Khazaei M, Alavipanah SK, Weng Q. 2021. Google Earth Engine for large-scale land use and land cover mapping: an object-based classification approach using spectral, textural and topographical factors. GIScience Remote Sens. 58(6):914–928. doi: 10.1080/15481603.2021.1947623.
   Web of Science ®Google Scholar
• Shen Z, Wang Y, Su H, He Y, Li S. 2022. A bi-directional strategy to detect land use function change using time-series Landsat imagery on Google Earth Engine: a case study of Huangshui River Basin in China. Sci Remote Sens. 5:100039. doi: 10.1016/j.srs.2022.100039.
   Google Scholar
• Shiferaw H, Bewket W, Alamirew T, Zeleke G, Teketay D, Bekele K, Schaffner U, Eckert S. 2019. Implications of land use/land cover dynamics and Prosopis invasion on ecosystem service values in Afar Region, Ethiopia. Sci Total Environ. 675:354–366. doi: 10.1016/j.scitotenv.2019.04.220.
   PubMed Web of Science ®Google Scholar
• Sidhu N, Pebesma E, Câmara G. 2018. Using Google Earth Engine to detect land cover change: Singapore as a use case. Eur J Remote Sens. 51(1):486–500. doi: 10.1080/22797254.2018.1451782.
   Web of Science ®Google Scholar
• Sumari NS, Cobbinah PB, Ujoh F, Xu G. 2020. On the absurdity of rapid urbanization: spatio-temporal analysis of land-use changes in Morogoro, Tanzania. Cities. 107:102876. doi: 10.1016/j.cities.2020.102876.
   Web of Science ®Google Scholar
• Tian Y, Yin K, Lu D, Hua L, Zhao Q, Wen M. 2014. Examining land use and land cover spatiotemporal change and driving forces in Beijing from 1978 to 2010. Remote Sens. 6(11):10593–10611. doi: 10.3390/rs61110593.
   Google Scholar
• Tolentino FM, Galo M. 2021. Selecting features for LULC simultaneous classification of ambiguous classes by artificial neural network. Remote Sens Appl: Soc Environ. 24:100616. doi: 10.1016/j.rsase.2021.100616.
   Google Scholar
• Uniyal B, Jha MK, Verma AK, Anebagilu PK. 2020. Identification of critical areas and evaluation of best management practices using SWAT for sustainable watershed management. Sci Total Environ. 744:140737. doi: 10.1016/j.scitotenv.2020.140737.
   PubMed Web of Science ®Google Scholar
• Verde N, Kokkoris IP, Georgiadis C, Kaimaris D, Dimopoulos P, Mitsopoulos I, Mallinis G. 2020. National scale land cover classification for ecosystem services mapping and assessment, using multitemporal copernicus EO data and Google Earth Engine. Remote Sens. 12(20):3303. doi: 10.3390/rs12203303.
   Google Scholar
• Wang Y, He Y, Li J, Jiang Y. 2022. Evolution simulation and risk analysis of land use functions and structures in ecologically fragile watersheds. Remote Sens. 14(21):5521. doi: 10.3390/rs14215521.
   Google Scholar
• Xiong J, Thenkabail PS, Gumma MK, Teluguntla P, Poehnelt J, Congalton RG, Yadav K, Thau D. 2017. Automated cropland mapping of continental Africa using Google Earth Engine cloud computing. ISPRS J Photogramm Remote Sens. 126:225–244. doi: 10.1016/j.isprsjprs.2017.01.019.
   Web of Science ®Google Scholar
• Yangouliba GI, Zoungrana BJ-B, Hackman KO, Koch H, Liersch S, Sintondji LO, Dipama J-M, Kwawuvi D, Ouedraogo V, Yabré S, et al. 2023. Modelling past and future land use and land cover dynamics in the Nakambe River Basin, West Africa. Model Earth Syst Environ. 9(2):1651–1667. doi: 10.1007/s40808-022-01569-2.
   Web of Science ®Google Scholar
• Yesuph AY, Dagnew AB. 2019. Land use/cover spatiotemporal dynamics, driving forces and implications at the Beshillo catchment of the Blue Nile Basin, North Eastern Highlands of Ethiopia. Environ Syst Res. 8(1):1–30. doi: 10.1186/s40068-019-0148-y.
   Google Scholar
• Zavaleta ES, Hulvey KB. 2004. Realistic species losses disproportionately reduce grassland resistance to biological invaders. Science. 306(5699):1175–1177. doi: 10.1126/science.1102643.
   PubMed Web of Science ®Google Scholar
• Zeferino LB, de Souza LFT, do Amaral CH, Fernandes Filho EI, de Oliveira TS. 2020. Does environmental data increase the accuracy of land use and land cover classification? Int J Appl Earth Obs Geoinf. 91:102128. doi: 10.1016/j.jag.2020.102128.
   Web of Science ®Google Scholar
• Zhang C, Di L, Lin L, Li H, Guo L, Yang Z, Eugene GY, Di Y, Yang A. 2022. Towards automation of in-season crop type mapping using spatiotemporal crop information and remote sensing data. Agric Syst. 201:103462. doi: 10.1016/j.agsy.2022.103462.
   Web of Science ®Google Scholar
• Zhang C, Zhang H, Zhang L. 2021. Spatial domain bridge transfer: an automated paddy rice mapping method with no training data required and decreased image inputs for the large cloudy area. Comput Electron Agric. 181:105978. doi: 10.1016/j.compag.2020.105978.
   Web of Science ®Google Scholar
• Zhao Y, An R, Xiong N, Ou D, Jiang C. 2021. Spatio-temporal land-use/land-cover change dynamics in coastal plains in Hangzhou Bay Area, China from 2009 to 2020 Using Google Earth Engine. Land. 10(11):1149. doi: 10.3390/land10111149.
   Web of Science ®Google Scholar
• Zurqani HA, Post CJ, Mikhailova EA, Schlautman MA, Sharp JL. 2018. Geospatial analysis of land use change in the Savannah River Basin using Google Earth Engine. Int J Appl Earth Obs Geoinf. 69:175–185. doi: 10.1016/j.jag.2017.12.006.
   Web of Science ®Google Scholar