Leveraging Big Data and Analytics to Improve Food, Energy, and Water System Sustainability

With the world population projected to grow significantly over the next few decades, and in the presence of additional stress caused by climate change and urbanization, securing the essential resources of food, energy, and water is one of the most pressing challenges that the world faces today. There is an increasing priority placed by the United Nations (UN) and US federal agencies on efforts to ensure the security of these critical resources, understand their interactions, and address common underlying challenges. At the heart of the technological challenge is data science applied to environmental data. The aim of this special publication is the focus on big data science for food, energy, and water systems (FEWSs). We describe a research methodology to frame in the FEWS context, including decision tools to aid policy makers and non-governmental organizations (NGOs) to tackle specific UN Sustainable Development Goals (SDGs). Through this exercise, we aim to improve the “supply chain” of FEWS research, from gathering and analyzing data to decision tools supporting policy makers in addressing FEWS issues in specific contexts. We discuss prior research in each of the segments to highlight shortcomings as well as future research directions.

[1]  Ranga Raju Vatsavai Gaussian multiple instance learning approach for mapping the slums of the world using very high resolution imagery , 2013, KDD.

[2]  A. Neef,et al.  Sustainable development and the water–energy–food nexus: A perspective on livelihoods , 2015 .

[3]  J. Pittock,et al.  The energy-water nexus: managing the links between energy and water for a sustainable future. , 2010 .

[4]  P. D’Odorico,et al.  Accelerated deforestation driven by large-scale land acquisitions in Cambodia , 2015 .

[5]  R. R. Vatsavai,et al.  Complex settlement pattern extraction with multi-instance learning , 2013, Joint Urban Remote Sensing Event 2013.

[6]  Teuvo Kohonen,et al.  The self-organizing map , 1990, Neurocomputing.

[7]  M. Kurian The water-energy-food nexus: Trade-offs, thresholds and transdisciplinary approaches to sustainable development , 2017 .

[8]  Morton J. Canty,et al.  Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL and Python, Third Edition , 2014 .

[9]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[10]  Akira Ishii,et al.  Methods of the Water-Energy-Food Nexus , 2015 .

[11]  I. Theilade,et al.  Rural household incomes and land grabbing in Cambodia , 2015 .

[12]  T. Cochrane,et al.  Land use change uncertainty impacts on streamflow and sediment projections in areas undergoing rapid development: A case study in the Mekong Basin , 2018 .

[13]  Chong-Yu Xu,et al.  Possible influence of ENSO on annual maximum streamflow of the Yangtze River, China , 2007 .

[14]  D. Conway,et al.  Tracing the water-energy-food nexus: description, theory and practice , 2015 .

[15]  Alex Smajgl,et al.  The water-food-energy Nexus - Realising a new paradigm , 2016 .

[16]  Chengquan Huang,et al.  Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges , 2012, Int. J. Digit. Earth.

[17]  Michael Batty,et al.  Cities and fractals: simulating growth and form , 1991 .

[18]  Terry L. Sohl,et al.  Regional characterization of land cover using multiple sources of data , 1998 .

[19]  Shiliang Sun,et al.  A survey of multi-view machine learning , 2013, Neural Computing and Applications.

[20]  Jane Southworth,et al.  Tourism, forest conversion, and land transformations in the Angkor basin, Cambodia , 2009 .

[21]  Ashbindu Singh,et al.  Review Article Digital change detection techniques using remotely-sensed data , 1989 .

[22]  V. Singh,et al.  Hydrological Cycles, Models, and Applications to Forecasting , 2017 .

[23]  Dolf Gielen,et al.  Considering the energy, water and food nexus: Towards an integrated modelling approach , 2011 .

[24]  Sovan Lek,et al.  A comparison of self-organizing map algorithm and some conventional statistical methods for ecological community ordination , 2001 .

[25]  Varun Chandola,et al.  Machine learning for energy-water nexus: challenges and opportunities , 2018, Big Earth Data.

[26]  T. Cochrane,et al.  The Flood Pulse as the Underlying Driver of Vegetation in the Largest Wetland and Fishery of the Mekong Basin , 2013, AMBIO.

[27]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[28]  Johannes R. Sveinsson,et al.  Random Forests for land cover classification , 2006, Pattern Recognit. Lett..

[29]  E. Ostrom A General Framework for Analyzing Sustainability of Social-Ecological Systems , 2009, Science.

[30]  Thomas G. Dietterich,et al.  Explanation-Based Learning and Reinforcement Learning: A Unified View , 1995, Machine Learning.

[31]  A. Winsor Sampling techniques. , 2000, Nursing times.

[32]  Mahesh Pal,et al.  Random forest classifier for remote sensing classification , 2005 .

[33]  Jixian Zhang Multi-source remote sensing data fusion: status and trends , 2010 .

[34]  C. Ringler,et al.  The nexus across water, energy, land and food (WELF): potential for improved resource use efficiency? , 2013 .

[35]  Hui Lin,et al.  Radar interferometry offers new insights into threats to the Angkor site , 2017, Science Advances.

[36]  Jeroen C. J. H. Aerts,et al.  Sensitivity of river discharge to ENSO , 2010 .

[37]  Thomas R. Loveland,et al.  A review of large area monitoring of land cover change using Landsat data , 2012 .

[38]  Alexei Novikov,et al.  Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping , 2017, Front. Earth Sci..

[39]  Badrinath Roysam,et al.  Image change detection algorithms: a systematic survey , 2005, IEEE Transactions on Image Processing.

[40]  Kenneth Grogan,et al.  A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring , 2016, Remote. Sens..

[41]  E. Ostrom A diagnostic approach for going beyond panaceas , 2007, Proceedings of the National Academy of Sciences.

[42]  Rabi H. Mohtar,et al.  Water–energy–food (WEF) Nexus Tool 2.0: guiding integrative resource planning and decision-making , 2015 .

[43]  Peijun Du,et al.  Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features , 2015 .

[44]  Joseph F. Knight,et al.  Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota , 2013, Remote. Sens..

[45]  Mikel D. Petty Modeling and Validation Challenges for Complex Systems , 2018, Engineering Emergence.

[46]  Dengsheng Lu,et al.  Mapping soil erosion risk in Rondônia, Brazilian Amazonia: using RUSLE, remote sensing and GIS , 2004 .

[47]  W. Cohen,et al.  Landsat's Role in Ecological Applications of Remote Sensing , 2004 .

[48]  Albert Albers,et al.  Modeling and validation , 2005 .

[49]  Arianna Traviglia,et al.  Uncovering Angkor: Integrated Remote Sensing Applications in the Archaeology of Early Cambodia , 2012 .

[50]  H. Hoff Understanding the nexus : Background paper for the Bonn2011 Nexus Conference , 2011 .

[52]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[53]  Dacheng Tao,et al.  A Survey on Multi-view Learning , 2013, ArXiv.

[54]  Shashi Shekhar,et al.  Spatial computing perspective on food energy and water nexus , 2016, Journal of Environmental Studies and Sciences.

[55]  Thanapon Piman,et al.  Quantifying changes in flooding and habitats in the Tonle Sap Lake (Cambodia) caused by water infrastructure development and climate change in the Mekong Basin. , 2012, Journal of environmental management.

[56]  Alberto M. Mestas-Nuñez,et al.  The Atlantic Multidecadal Oscillation and its relation to rainfall and river flows in the continental U.S. , 2001 .

[57]  N. Hishamunda,et al.  Commercial aquaculture in Southeast Asia: Some policy lessons , 2009 .

[58]  Les Kaufman,et al.  The Multiscale Integrated Model of Ecosystem Services (MIMES): Simulating the interactions of coupled human and natural systems , 2015 .

[59]  F. Lindsay,et al.  Dynamics of urban growth in the Washington DC metropolitan area, 1973-1996, from Landsat observations , 2000 .