A machine learning approach to monitoring and forecasting spatio-temporal dynamics of land cover in Cox's Bazar district, Bangladesh from 2001 to 2019

Abdi, 2020, Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data, GIScience \& Remote Sens., 57, 1, 10.1080/15481603.2019.1650447 Abdullah, 2018, Quantifying the spatiotemporal patterns of forest degradation in a fragmented, rapidly urbanizing landscape: a case study of Gazipur, Bangladesh, Remote Sens. Appl. Soc. Environ., 13, 457 Adepoju, 2019, Vegetation response to recent trends in climate and Landuse dynamics in a typical humid and dry tropical region under global change, Adv. Meteorol., 2019, 1, 10.1155/2019/4946127 Agency, N. G., et al., “World geodetic system 1984,” pp. 0–2, 2005. Ahammed, 2010, Impact of tourism in Cox's Bazar, Bangladesh, North, 92 Al Balasmeh, 2020, Effect of temperature and precipitation on the vegetation dynamics of high and moderate altitude natural forests in India, J. Indian Soc. Remote Sens., 48, 121, 10.1007/s12524-019-01065-8 Al Kafy, 2020, Modelling future land use land cover changes and their impacts on land surface temperatures in Rajshahi, Bangladesh, Remote Sens. Appl. Soc. Environ., 18 Al Sultan, 2008, Innovative satellite image map of R. Alkhabrta Area, Saudi Arabia using high resolution image, 37, 1355 Alshari, 2021, Development of classification system for LULC using remote sensing and GIS, Glob. Transitions Proc., 2, 8, 10.1016/j.gltp.2021.01.002 Ashworth, 2000, Morphological evolution and dynamics of a large, sand braid-bar, Jamuna River, Bangladesh, Sedimentology, 47, 533, 10.1046/j.1365-3091.2000.00305.x Bao, 2021, The sensitivity of vegetation cover to climate change in multiple climatic zones using machine learning algorithms, Ecol. Indic., 124, 10.1016/j.ecolind.2021.107443 Belgiu, 2016, Random forest in remote sensing: a review of applications and future directions, ISPRS J. Photogramm. Remote Sens., 114, 24, 10.1016/j.isprsjprs.2016.01.011 Boavida-Portugal, 2016, Exploring the impacts of future tourism development on land use/cover changes, Appl. Geogr., 77, 82, 10.1016/j.apgeog.2016.10.009 Boretti, 2019, Reassessing the projections of the World Water Development Report, npj Clean Water, 2, 15, 10.1038/s41545-019-0039-9 Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324 Bucała, 2014, The impact of human activities on land use and land cover changes and environmental processes in the Gorce Mountains (Western Polish Carpathians) in the past 50 years, J. Environ. Manage., 138, 4, 10.1016/j.jenvman.2014.01.036 Carlet, 2017, Greening U.S. legacy cities: urban agriculture as a strategy for reclaiming vacant land, Agroecol. Sustain. Food Syst., 41, 887, 10.1080/21683565.2017.1311288 Chen, 2019, China and India lead in greening of the world through land-use management, Nat. Sustain., 2, 122, 10.1038/s41893-019-0220-7 Diez, 2018, Introduction, 1 Dunne, 2019, One third of the world's new vegetation is in India and China, data shows | World Economic Forum,”, Carbon Brief “ee.ImageCollection.errorMatrix | Google Earth Engine.” https://developers.google.com/earth-engine/apidocs/ee-imagecollection-errormatrix (accessed May 25, 2021). “Error Matrix.” https://gis.humboldt.edu/OLM/Courses/GSP_216_Online/lesson6-2/error-matrix.html (accessed May 25, 2021). FAO-FRA, “Forest cover mapping & monitoring with Noaa-Avhrr & other coarse spatial,” 2000. Garg, S. “Impact of overpopulation on land use pattern,” 2017, pp. 137–154. Ghosh, 2014, Random forest classification of urban landscape using Landsat archive and ancillary data: combining seasonal maps with decision level fusion, Appl. Geogr., 48, 31, 10.1016/j.apgeog.2014.01.003 Gibbs, 2010, Tropical forests were the primary sources of new agricultural land in the 1980s and 1990s, Proc. Natl. Acad. Sci. USA, 107, 16732, 10.1073/pnas.0910275107 Gillanders, 2008, Multitemporal remote sensing of landscape dynamics and pattern change: describing natural and anthropogenic trends, Progr. Phys. Geogr., 32, 503, 10.1177/0309133308098363 Google Earth Engine, “Supervised Classification,” 2021. https://developers.google.com/earth-engine/guides/classification. Han, 2012, 3 - Data preprocessing, 83 Helm, 2020, The environmental impacts of the coronavirus, Environ. Resour. Econ., 76, 21, 10.1007/s10640-020-00426-z Ho, 1995, Random decision forests, 1, 278 “India: In Uttar Pradesh, farmers green their barren lands.” (accessed Aug. 03, 2021) https://www.worldbank.org/en/news/feature/2006/10/18/india-in-uttar-pradesh-farmers-green-their-barren-lands (accessed Aug. 03, 2021). Kluyver, 2016, Jupyter Notebooks – a publishing format for reproducible computational workflows, 87 Landsberg, 1997, 2 - Forest biomes of the World, 19 Liu, 2018, Multi-perspective analysis of vegetation cover changes and driving factors of long time series based on climate and terrain data in Hanjiang River Basin, China, Arab. J. Geosci., 11, 509, 10.1007/s12517-018-3756-3 Loveland, 2016, Landsat 8: the plans, the reality, and the legacy, Remote Sens. Environ., 185, 1, 10.1016/j.rse.2016.07.033 “LP DAAC - MCD12Q1.” https://lpdaac.usgs.gov/products/mcd12q1v006/ (accessed May 26, 2021). Mahmon, 2015, Differences of image classification techniques for land use and land cover classification, IEEE Control Syst. Soc., 6 Mansour, 2020, Monitoring land use and land cover changes in the mountainous cities of Oman using GIS and CA-Markov modelling techniques, Land Use Policy, 91, 10.1016/j.landusepol.2019.104414 McHugh, 2012, Interrater reliability: the kappa statistic, Biochem. Medica, 22, 276, 10.11613/BM.2012.031 Nurwanda, 2020, The prediction of city expansion and land surface temperature in Bogor City, Indonesia, Sustain. Cities Soc., 52, 10.1016/j.scs.2019.101772 Okello, 2015, Impact of population growth and climate change on the freshwater resources of Lamu Island, Kenya, Water, 7 Pan, L., Gu, L., Ren, R. and Yang, S. “Land cover classification based on machine learning using UAV multi-spectral images,” Aug. 2020, vol. 11501, p. 50, doi: 10.1117/12.2566128. “Population and housing census,” 2011. Raju, R.S., Sudarsana Raju, G. and Rajasekhar, M. “Land use/land cover change detection analysis using supervised classification, remote sensing and GIS In Mandavi river basin, YSR Kadapa District, Andhra Pradesh, India,” vol. 9, no. 3, pp. 46–54, 2018. Rashid, 2020, Spatiotemporal changes of vegetation and land surface temperature in the refugee camps and its surrounding areas of Bangladesh after the Rohingya influx from Myanmar, Environ. Dev. Sustain. Roy, 2014, Landsat-8: science and product vision for terrestrial global change research, Remote Sens. Environ., 145, 154, 10.1016/j.rse.2014.02.001 Roy, 2021, Comparison of temporal changes in urban settlements and land surface temperature in Rangpur and Gazipur Sadar, Bangladesh after the establishment of city corporation, Remote Sens. Appl. Soc. Environ., 23 Roy, B. “Assessment of vegetation health in Saint Martin's Island, Bangladesh using remote sensing & GIS,” vol. 7, pp. 89–93, 2020, doi:10.6084/m9.figshare.12674426. Roy, 2021, Optimum machine learning algorithm selection for forecasting vegetation indices: MODIS NDVI & EVI, Remote Sens. Appl. Soc. Environ., 23 Roy, B. “Land use & socio-environmental studies of saint Martin's Island in 2020Roy, B. (2021). Land Use & Socio-Environmental Studies Of Saint Martin's Island in 2020. https://doi.org/10.6084/m9.figshare.14376347,” 2021. Sammut, 2010, Mean squared error, 653 Santé, 2010, Cellular automata models for the simulation of real-world urban processes: a review and analysis, Landsc. Urban Plan., 96, 108, 10.1016/j.landurbplan.2010.03.001 Scepan, 2001, Thematic validation of global land cover data sets-procedures and interpretation methods, 3, 1119 Shi, 2017, A review of simulation-based urban form generation and optimization for energy-driven urban design, Build. Environ., 121, 119, 10.1016/j.buildenv.2017.05.006 Singh, 2012, Towards an integrated generative design framework, Des. Stud., 33, 185, 10.1016/j.destud.2011.06.001 Stout, 1990, Climate and water, Eos, Trans. Am. Geophys. Union, 71, 339, 10.1029/90EO00112 Swamidass, P. M. Ed., “MAPE (mean absolute percentage error)Mean absolute percentage error (Mape),” in Encyclopedia of Production and Manufacturing Management, Boston, MA: Springer US, 2000, p. 462. Talukdar, 2020, Land-use land-cover classification by machine learning classifiers for satellite observations—a review, Remote Sens., 12, 10.3390/rs12071135 Trucco, 2019, Validation, 157 van Leeuwen, 2020, Machine learning techniques for land use/land cover classification of medium resolution optical satellite imagery focusing on temporary inundated areas, J. Environ. Geogr., 13, 43, 10.2478/jengeo-2020-0005 Van Rossum, 1995 Viera, 2005, Understanding interobserver agreement: the kappa statistic. Fam Med 2005;37(5):360-63, Fam. Med., 37, 360 Von Neumann, J. “THE GENERAL AND LOGICAL THEORY OF AUTOMATA.” Wolfram, 2002, 5 Wu, 2019, Hyperparameter optimization for machine learning models based on Bayesian optimizationb, J. Electron. Sci. Technol., 17, 26 Wu, 2020, Monitoring tropical forest degradation and restoration with satellite remote sensing: a test using Sabah Biodiversity Experiment, Adv. Ecol. Res., 62, 117, 10.1016/bs.aecr.2020.01.005 Zaimes, 2019, Assessing the impact of dams on riparian and deltaic vegetation using remotely-sensed vegetation indices and Random Forests modelling, Ecol. Indic., 103, 630, 10.1016/j.ecolind.2019.04.047