An integrated approach of remote sensing and geospatial analysis for modeling and predicting the impacts of climate change on food security
Ortiz-Bobea, A., Ault, T. R., Carrillo, C. M., Chambers, R. G. & Lobell, D. B. Anthropogenic climate change has slowed global agricultural productivity growth. Nat. Clim. Chang. 11, 306–312 (2021).Article
ADS
Google Scholar
Garajeh, M. K. & Feizizadeh, B. A comparative approach of data-driven split-window algorithms and MODIS products for land surface temperature retrieval. Appl. Geomat. 13, 715–733 (2021).Article
Google Scholar
Alizadeh-Choobari, O., Ahmadi-Givi, F., Mirzaei, N. & Owlad, E. Climate change and anthropogenic impacts on the rapid shrinkage of Lake Urmia. Int. J. Climatol. 36, 4276–4286 (2016).Article
Google Scholar
Rembold, F., Kerdiles, H., Lemoine, G. & Perez-Hoyos, A. Impact of El Niño on agriculture in Southern Africa for the 2015/2016 main season. Joint Research Centre (JRC) MARS Bulletin–Global Outlook Series. European Commission, Brussels (2016).Zampieri, M., Ceglar, A., Dentener, F. & Toreti, A. Wheat yield loss attributable to heat waves, drought and water excess at the global, national and subnational scales. Environ. Res. Lett. 12, 064008 (2017).Article
ADS
Google Scholar
Toté, C. et al. Evaluation of the SPOT/VEGETATION Collection 3 reprocessed dataset: Surface reflectances and NDVI. Remote Sens. Environ. 201, 219–233 (2017).Article
ADS
Google Scholar
Solomon, N. et al. Environmental impacts and causes of conflict in the Horn of Africa: A review. Earth Sci. Rev. 177, 284–290 (2018).Article
ADS
Google Scholar
Dresse, A., Fischhendler, I., Nielsen, J. Ø. & Zikos, D. Environmental peacebuilding: Towards a theoretical framework. Coop. Confl. 54, 99–119 (2019).Article
Google Scholar
Vos, R., Jackson, J., James, S. & Sánchez, M. V. Refugees and Conflict-Affected People: Integrating Displaced Communities into Food Systems. 2020 Global Food Policy Report, 46–53 (2020).Zulfiqar, F., Navarro, M., Ashraf, M., Akram, N. A. & Munné-Bosch, S. Nanofertilizer use for sustainable agriculture: Advantages and limitations. Plant Sci. 289, 110270 (2019).Article
CAS
Google Scholar
Viana, C. M. & Rocha, J. Evaluating dominant land use/land cover changes and predicting future scenario in a rural region using a memoryless stochastic method. Sustainability 12, 4332 (2020).Article
Google Scholar
Vasile, A. J., Popescu, C., Ion, R. A. & Dobre, I. From conventional to organic in Romanian agriculture—Impact assessment of a land use changing paradigm. Land Use Policy 46, 258–266 (2015).Article
Google Scholar
Veloso, A. et al. Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications. Remote Sens. Environ. 199, 415–426 (2017).Article
ADS
Google Scholar
Samasse, K., Hanan, N. P., Tappan, G. & Diallo, Y. Assessing cropland area in West Africa for agricultural yield analysis. Remote Sens. 10, 1785 (2018).Article
ADS
Google Scholar
Van Esse, H. P., Reuber, T. L. & van der Does, D. Genetic modification to improve disease resistance in crops. New Phytol. 225, 70–86 (2020).Article
Google Scholar
FAO. The Future of Food and Agriculture—Trends and Challenges (FAO, 2017).
Google Scholar
Müller, B. et al. Modelling food security: Bridging the gap between the micro and the macro scale. Glob. Environ. Chang. 63, 102085 (2020).Article
Google Scholar
Food and Agriculture Organization of the United Nations. Forest Management and Conservation Agriculture: Experiences of Smallholder Farmers in the Eastern Region of Paraguay (FAO, 2013).
Google Scholar
FAO Food Price Index. World Food Situation (FAO, 2021).
Google Scholar
Sishodia, R. P., Ray, R. L. & Singh, S. K. Applications of remote sensing in precision agriculture: A review. Remote Sens. 12, 3136 (2020).Article
ADS
Google Scholar
Weiss, M., Jacob, F. & Duveiller, G. Remote sensing for agricultural applications: A meta-review. Remote Sens. Environ. 236, 111402 (2020).Article
ADS
Google Scholar
Feizizadeh, B., Garajeh, M. K., Blaschke, T. & Lakes, T. An object based image analysis applied for volcanic and glacial landforms mapping in Sahand Mountain, Iran. CATENA 198, 105073 (2021).Article
Google Scholar
Wen, W., Timmermans, J., Chen, Q. & van Bodegom, P. M. A review of remote sensing challenges for food security with respect to salinity and drought threats. Remote Sens. 13, 6 (2020).Article
ADS
Google Scholar
Feizizadeh, B., Omarzadeh, D., Kazemi Garajeh, M., Lakes, T. & Blaschke, T. Machine learning data-driven approaches for land use/cover mapping and trend analysis using Google Earth Engine. J. Environ. Plan. Manag. https://doi.org/10.1080/09640568.2021.2001317 (2021).Article
Google Scholar
Westerveld, J. J. et al. Forecasting transitions in the state of food security with machine learning using transferable features. Sci. Total Environ. 786, 147366 (2021).Article
ADS
CAS
Google Scholar
Anderson, R., Bayer, P. E. & Edwards, D. Climate change and the need for agricultural adaptation. Curr. Opin. Plant Biol. 56, 197–202 (2020).Article
Google Scholar
Baniya, B., Tang, Q., Xu, X., Haile, G. G. & Chhipi-Shrestha, G. Spatial and temporal variation of drought based on satellite derived vegetation condition index in Nepal from 1982–2015. Sensors 19, 430 (2019).Article
ADS
Google Scholar
Kubitza, C., Krishna, V. V., Schulthess, U. & Jain, M. Estimating adoption and impacts of agricultural management practices in developing countries using satellite data. A scoping review. Agron. Sustain. Dev. 40, 1–21 (2020).Article
Google Scholar
Lees, T., Tseng, G., Atzberger, C., Reece, S. & Dadson, S. Deep learning for vegetation health forecasting: a case study in Kenya. Remote Sens. 14, 698 (2022).Article
ADS
Google Scholar
Khanian, M., Serpoush, B. & Gheitarani, N. Balance between place attachment and migration based on subjective adaptive capacity in response to climate change: The case of Famenin County in Western Iran. Clim. Dev. 11, 69–82 (2019).Article
Google Scholar
Khanian, M., Marshall, N., Zakerhaghighi, K., Salimi, M. & Naghdi, A. Transforming agriculture to climate change in Famenin County, West Iran through a focus on environmental, economic and social factors. Weather Clim. Extremes 21, 52–64 (2018).Article
Google Scholar
Leroux, L. et al. Crop monitoring using vegetation and thermal indices for yield estimates: Case study of a rainfed cereal in semi-arid West Africa. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 9, 347–362 (2015).Article
ADS
Google Scholar
Sun, J. et al. Multilevel deep learning network for county-level corn yield estimation in the us corn belt. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 13, 5048–5060 (2020).Article
ADS
Google Scholar
Tian, H. et al. An LSTM neural network for improving wheat yield estimates by integrating remote sensing data and meteorological data in the Guanzhong Plain, PR China. Agric. Forest Meteorol. 310, 108629 (2021).Article
ADS
Google Scholar
Weng, Y., Chang, S., Cai, W. & Wang, C. Exploring the impacts of biofuel expansion on land use change and food security based on a land explicit CGE model: A case study of China. Appl. Energy 236, 514–525 (2019).Article
ADS
Google Scholar
Rojas, O., Rembold, F., Royer, A. & Negre, T. Real-time agrometeorological crop yield monitoring in Eastern Africa. Agron. Sustain. Dev. 25, 63–77 (2005).Article
Google Scholar
Rembold, F. et al. ASAP: A new global early warning system to detect anomaly hot spots of agricultural production for food security analysis. Agric. Syst. 168, 247–257 (2019).Article
Google Scholar
Gohar, A. A., Cashman, A. & El-bardisy, H. A. H. Modeling the impacts of water-land allocation alternatives on food security and agricultural livelihoods in Egypt: Welfare analysis approach. Environ. Dev. 39, 100650 (2021).Article
Google Scholar
Mekonnen, A., Tessema, A., Ganewo, Z. & Haile, A. Climate change impacts on household food security and farmers adaptation strategies. J. Agric. Food Res. 6, 100197 (2021).Article
Google Scholar
Hervas, A. Mapping oil palm-related land use change in Guatemala, 2003–2019: Implications for food security. Land Use Policy 109, 105657 (2021).Article
Google Scholar
Viana, C. M., Freire, D., Abrantes, P., Rocha, J. & Pereira, P. Agricultural land systems importance for supporting food security and sustainable development goals: A systematic review. Sci. Total Environ. 806, 150718 (2022).Article
ADS
CAS
Google Scholar
Bazzana, D., Foltz, J. & Zhang, Y. Impact of climate smart agriculture on food security: An agent-based analysis. Food Policy 111, 102304 (2022).Article
Google Scholar
Parven, A. et al. Impacts of disaster and land-use change on food security and adaptation: Evidence from the delta community in Bangladesh. Int. J. Disaster Risk Reduct. 78, 103119 (2022).Article
Google Scholar
Mohajane, M. et al. Land use/land cover (LULC) using landsat data series (MSS, TM, ETM+ and OLI) in Azrou Forest, in the Central Middle Atlas of Morocco. Environments 5, 131 (2018).Article
Google Scholar
Duarte, L., Teodoro, A. C., Sousa, J. J. & Pádua, L. QVigourMap: A GIS open source application for the creation of canopy vigour maps. Agronomy 11, 952 (2021).Article
Google Scholar
Tavares, P. A., Beltrão, N. E. S., Guimarães, U. S. & Teodoro, A. C. Integration of sentinel-1 and sentinel-2 for classification and LULC mapping in the urban area of Belém, eastern Brazilian Amazon. Sensors 19, 1140 (2019).Article
ADS
Google Scholar
Atuoye, K. N., Luginaah, I., Hambati, H. & Campbell, G. Who are the losers? Gendered-migration, climate change, and the impact of large scale land acquisitions on food security in coastal Tanzania. Land Use Policy 101, 105154 (2021).Article
Google Scholar
Yang, S., Gu, L., Li, X., Jiang, T. & Ren, R. Crop classification method based on optimal feature selection and hybrid CNN-RF networks for multi-temporal remote sensing imagery. Remote Sens. 12, 3119 (2020).Article
ADS
Google Scholar
Milojevic-Dupont, N. & Creutzig, F. Machine learning for geographically differentiated climate change mitigation in urban areas. Sustain. Cities Soc. 64, 102526 (2021).Article
Google Scholar
Santos, D. et al. Spectral analysis to improve inputs to random forest and other boosted ensemble tree-based algorithms for detecting NYF Pegmatites in Tysfjord, Norway. Remote Sens. 14, 3532 (2022).Article
ADS
Google Scholar
Hitouri, S. et al. Hybrid machine learning approach for gully erosion mapping susceptibility at a watershed scale. ISPRS Int. J. Geo Inf. 11, 401 (2022).Article
Google Scholar
Alvarez-Mendoza, C. I., Teodoro, A., Freitas, A. & Fonseca, J. Spatial estimation of chronic respiratory diseases based on machine learning procedures—An approach using remote sensing data and environmental variables in quito, Ecuador. Appl. Geogr. 123, 102273 (2020).Article
Google Scholar
Teodoro, A., Pais-Barbosa, J., Gonçalves, H., Veloso-Gomes, F. & Taveira-Pinto, F. Identification of beach features/patterns through image classification techniques applied to remotely sensed data. Int. J. Remote Sens. 32, 7399–7422 (2011).Article
Google Scholar
Saleem, M. H., Potgieter, J. & Arif, K. M. Automation in agriculture by machine and deep learning techniques: A review of recent developments. Precis. Agric. 22, 2053–2091 (2021).Article
Google Scholar
Carrasco, L., O’Neil, A. W., Morton, R. D. & Rowland, C. S. Evaluating combinations of temporally aggregated Sentinel-1, Sentinel-2 and Landsat 8 for land cover mapping with Google Earth Engine. Remote Sens. 11, 288 (2019).Article
ADS
Google Scholar
Kumar, L. & Mutanga, O. Google Earth Engine applications since inception: Usage, trends, and potential. Remote Sens. 10, 1509 (2018).Article
ADS
Google Scholar
Kakooei, M., Nascetti, A. & Ban, Y. in IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium 6836–6839 (IEEE).Castillo, E., Iglesias, A. & Ruiz-Cobo, R. Functional Equations in Applied Sciences (Elsevier, 2004).MATH
Google Scholar
Zhao, G., Gao, H. & Cai, X. Estimating lake temperature profile and evaporation losses by leveraging MODIS LST data. Remote Sens. Environ. 251, 112104 (2020).Article
ADS
Google Scholar
Mu, Q., Zhao, M. & Running, S. W. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 115, 1781–1800 (2011).Article
ADS
Google Scholar
Monteith, J. L. in Symposia of the society for experimental biology 205–234 (Cambridge University Press (CUP) Cambridge).Kidd, C. et al. So, how much of the Earth’s surface is covered by rain gauges?. Bull. Am. Meteorol. Soc. 98, 69–78 (2017).Article
ADS
Google Scholar
Huffman, G. J. et al. NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG). In Algorithm Theoretical Basis Document (ATBD) Version 4 (2015).Zhang, W., Cao, H. & Liang, Y. Plant uptake and soil fractionation of five ether-PFAS in plant-soil systems. Sci. Total Environ. 771, 144805 (2021).Article
ADS
CAS
Google Scholar
Jiang, S. et al. Effects of clouds and aerosols on ecosystem exchange, water and light use efficiency in a humid region orchard. Sci. Total Environ. 811, 152377 (2022).Article
ADS
CAS
Google Scholar
Ghimire, C., Bruijnzeel, L., Lubczynski, M. & Bonell, M. Negative trade-off between changes in vegetation water use and infiltration recovery after reforesting degraded pasture land in the Nepalese Lesser Himalaya. Hydrol. Earth Syst. Sci. 18, 4933–4949 (2014).Article
ADS
Google Scholar
Zhang, J., Chen, H., Fu, Z. & Wang, K. Effects of vegetation restoration on soil properties along an elevation gradient in the karst region of southwest China. Agric. Ecosyst. Environ. 320, 107572 (2021).Article
CAS
Google Scholar
Yan, W. Y., Shaker, A. & El-Ashmawy, N. Urban land cover classification using airborne LiDAR data: A review. Remote Sens. Environ. 158, 295–310 (2015).Article
ADS
Google Scholar
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).Article
ADS
CAS
Google Scholar
Zhang, C. et al. Joint deep learning for land cover and land use classification. Remote Sens. Environ. 221, 173–187 (2019).Article
ADS
Google Scholar
Interdonato, R., Ienco, D., Gaetano, R. & Ose, K. DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn. ISPRS J. Photogramm. Remote. Sens. 149, 91–104 (2019).Article
ADS
Google Scholar
Nyamekye, C., Ghansah, B., Agyapong, E. & Kwofie, S. Mapping changes in artisanal and small-scale mining (ASM) landscape using machine and deep learning algorithms—a proxy evaluation of the 2017 ban on ASM in Ghana. Environ. Chall. 3, 100053 (2021).Article
Google Scholar
Rahmati, O. et al. Land subsidence modelling using tree-based machine learning algorithms. Sci. Total Environ. 672, 239–252 (2019).Article
ADS
CAS
Google Scholar
Kingma, D. P. & Ba, J. Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980 (2014).Gupta, A. A comprehensive guide on deep learning optimizers. Analytics Vidhya. Dostopno na: https://www.analyticsvidhya.com/blog/2021/10/acomprehensive-guide-on-deep-learningoptimizers/#:~:text=An%20optimizer%20is%20a%20function,loss%20and%20improve%20the%20accuracy [22 May 2022] (2021).Reddy, V. K. & AV, R. K. Multi-channel neuro signal classification using Adam-based coyote optimization enabled deep belief network. Biomed. Signal Process. Control 77, 103774 (2022).Article
Google Scholar
Pulatov, B., Linderson, M.-L., Hall, K. & Jönsson, A. M. Modeling climate change impact on potato crop phenology, and risk of frost damage and heat stress in northern Europe. Agric. For. Meteorol. 214, 281–292 (2015).Article
ADS
Google Scholar
Parker, L., Pathak, T. & Ostoja, S. Climate change reduces frost exposure for high-value California orchard crops. Sci. Total Environ. 762, 143971 (2021).Article
ADS
CAS
Google Scholar
Svystun, T., Lundströmer, J., Berlin, M., Westin, J. & Jönsson, A. M. Model analysis of temperature impact on the Norway spruce provenance specific bud burst and associated risk of frost damage. For. Ecol. Manage. 493, 119252 (2021).Article
Google Scholar
Kheybari, S., Rezaie, F. M. & Farazmand, H. Analytic network process: An overview of applications. Appl. Math. Comput. 367, 124780 (2020).MATH
Google Scholar
Saaty, T. The Analytic Hierarchy Process: Planning, Priority Setting Resource Allocation (McGraw-Hill, 1980).MATH
Google Scholar
Saaty, T. L. & Ozdemir, M. S. The Encyclicon-Volume 1: A Dictionary of Decisions with Dependence and Feedback Based on the Analytic Network Process (RWS Publications, 2021).
Google Scholar
Saaty, T. L. Fundamentals of the analytic network process—Dependence and feedback in decision-making with a single network. J. Syst. Sci. Syst. Eng. 13, 129–157 (2004).Article
ADS
Google Scholar
Chung, K. L. Markov Chains with Stationary Transition Probabilities 5–11 (Springer, 1960).
Google Scholar
Mokarram, M., Pourghasemi, H. R., Hu, M. & Zhang, H. Determining and forecasting drought susceptibility in southwestern Iran using multi-criteria decision-making (MCDM) coupled with CA–Markov model. Sci. Total Environ. 781, 146703 (2021).Article
ADS
CAS
Google Scholar
Maleki, T., Koohestani, H. & Keshavarz, M. Can climate-smart agriculture mitigate the Urmia Lake tragedy in its eastern basin?. Agric. Water Manag. 260, 107256 (2022).Article
Google Scholar
Rahmani, J. & Danesh-Yazdi, M. Quantifying the impacts of agricultural alteration and climate change on the water cycle dynamics in a headwater catchment of Lake Urmia Basin. Agric. Water Manag. 270, 107749 (2022).Article
Google Scholar
Schmidt, M., Gonda, R. & Transiskus, S. Environmental degradation at Lake Urmia (Iran): Exploring the causes and their impacts on rural livelihoods. GeoJournal 86, 2149–2163 (2021).Article
Google Scholar
Eimanifar, A. & Mohebbi, F. Urmia Lake (northwest Iran): A brief review. Saline Syst. 3, 1–8 (2007).Article
Google Scholar
Shadkam, S., Ludwig, F., van Oel, P., Kirmit, Ç. & Kabat, P. Impacts of climate change and water resources development on the declining inflow into Iran’s Urmia Lake. J. Great Lakes Res. 42, 942–952 (2016).Article
Google Scholar
Chaudhari, S., Felfelani, F., Shin, S. & Pokhrel, Y. Climate and anthropogenic contributions to the desiccation of the second largest saline lake in the twentieth century. J. Hydrol. 560, 342–353 (2018).Article
ADS
Google Scholar
Khazaei, B. et al. Climatic or regionally induced by humans? Tracing hydro-climatic and land-use changes to better understand the Lake Urmia tragedy. J. Hydrol. 569, 203–217 (2019).Article
ADS
Google Scholar
Schulz, S., Darehshouri, S., Hassanzadeh, E., Tajrishy, M. & Schüth, C. Climate change or irrigated agriculture—What drives the water level decline of Lake Urmia. Sci. Rep. 10, 1–10 (2020).Article
Google Scholar
Azarnivand, A., Hashemi-Madani, F. S. & Banihabib, M. E. Extended fuzzy analytic hierarchy process approach in water and environmental management (case study: Lake Urmia Basin, Iran). Environ. Earth Sci. 73, 13–26 (2015).Article
ADS
Google Scholar
Bonham-Carter, G. F. & Bonham-Carter, G. Geographic Information Systems for Geoscientists: Modelling with GIS (Elsevier, 1994).
Google Scholar
Garajeh, M. K. et al. An automated deep learning convolutional neural network algorithm applied for soil salinity distribution mapping in Lake Urmia, Iran. Sci. Total Environ. 778, 146253 (2021).Article
ADS
CAS
Google Scholar More