Exploring Renewable Energy Resources Using Remote Sensing and GIS—A Review

Renewable energy has received noteworthy attention during the last few decades. This is partly due to the fact that fossil fuels are depleting and the need for energy is soaring because of the growing population of the world. This paper attempts to provide an idea of what is being done by researchers in remote sensing and geographical information system (GIS) field for exploring the renewable energy resources in order to get to a more sustainable future. Several studies related to renewable energy resources viz. geothermal energy, wind energy, hydropower, biomass, and solar energy, have been considered in this paper. The focus of this review paper is on exploring how remote sensing and GIS-based techniques have been beneficial in exploring optimal locations for renewable energy resources. Several case studies from different parts of the world which use such techniques in exploring renewable energy resource sites of different kinds have also been included in this paper. Though each of the remote sensing and GIS techniques used for exploration of renewable energy resources seems to efficiently sell itself in being the most effective among others, it is important to keep in mind that in actuality, a combination of different techniques is more efficient for the task. Throughout the paper, many issues relating to the use of remote sensing and GIS for renewable energy are examined from both current and future perspectives and potential solutions are suggested. The authors believe that the conclusions and recommendations drawn from the case studies and the literature reviewed in the present study will be valuable to renewable energy scientists and policymakers.

[1]  Ashwani Kumar Aggarwal,et al.  Assessment of Energy–Population–Urbanization Nexus with Changing Energy Industry Scenario in India , 2019, Land.

[2]  Mubashir Husain Rehmani,et al.  Energy replenishment using renewable and traditional energy resources for sustainable wireless sensor networks: A review , 2015 .

[3]  Ashwani Kumar Aggarwal,et al.  Population–Urbanization–Energy Nexus: A Review , 2019, Resources.

[4]  John W. Lund,et al.  CHARACTERISTICS , DEVELOPMENT AND UTILIZATION OF GEOTHERMAL RESOURCES , 2007 .

[5]  Hermann Kaufmann,et al.  Remarkable Urban Uplift in Staufen im Breisgau, Germany: Observations from TerraSAR-X InSAR and Leveling from 2008 to 2011 , 2013, Remote. Sens..

[6]  Thuy Le Toan,et al.  Relating forest biomass to SAR data , 1992, IEEE Trans. Geosci. Remote. Sens..

[7]  David J. Schneider,et al.  Exploring the limits of identifying sub-pixel thermal features using ASTER TIR data , 2010 .

[8]  N. E. Goldstein,et al.  Using surface displacement and strain observations to determine deformation at depth, with an application to Long Valley Caldera, California , 1988 .

[9]  P. S. Roy,et al.  Biomass estimation using satellite remote sensing data—An investigation on possible approaches for natural forest , 1996, Journal of Biosciences.

[10]  Hubert Fabriol,et al.  Monitoring and modeling land subsidence at the Cerro Prieto Geothermal Field, Baja California, Mexico, using SAR interferometry , 1999 .

[11]  C. T. Hoanh,et al.  The delta machine: water management in the Vietnamese Mekong Delta in historical and contemporary perspectives , 2009 .

[12]  Jeffry D. Harrison,et al.  Onshore Wind Power Systems (ONSWPS): A GIS-based tool for preliminary site-suitability analysis , 2012 .

[13]  Ali M. Baniyounes.  Renewable Energy Potential in Jordan , 2017 .

[14]  Joseph N. Moore,et al.  Vegetal-spectral anomaly detection at the Cove Fort-Sulphurdale thermal anomaly, Utah, USA: Implications for use in geothermal exploration , 2003 .

[15]  Mark Coolbaugh,et al.  Mineral mapping in the Pyramid Lake basin: hydrothermal alteration, chemical precipitates and geothermal energy potential. , 2010 .

[16]  Hui Lin,et al.  An overview on SAR measurements of sea surface wind , 2008 .

[17]  V. D. Assimakopoulos,et al.  Comparative study of various correlations in estimating hourly diffuse fraction of global solar radiation , 2006 .

[18]  Md. Bodruddoza Mia,et al.  Monitoring heat flux using Landsat TM/ETM + thermal infrared data — A case study at Karapiti (‘Craters of the Moon’) thermal area, New Zealand , 2012 .

[19]  Gülçin Büyüközkan,et al.  Selection of the strategic alliance partner in logistics value chain , 2008 .

[20]  Borut Zalik,et al.  Wind resource assessment using airborne LiDAR data and smoothed particle hydrodynamics , 2017, Environ. Model. Softw..

[21]  Xia Li,et al.  Using spatial information technologies to select sites for biomass power plants : A case study in Guangdong Province, China , 2008 .

[22]  G. Cochrane,et al.  APPLICATION OF SATELLITE IMAGERY TO EXPLORE AND MONITOR GEOTHERMAL SYSTEMS , 2004 .

[23]  Onisimo Mutanga,et al.  Remote Sensing of Above-Ground Biomass , 2017, Remote. Sens..

[24]  Frede Blaabjerg,et al.  Renewable energy resources: Current status, future prospects and their enabling technology , 2014 .

[25]  M. Chaichan,et al.  Modelling of Daily Solar Energy System Prediction using Support Vector Machine for Oman , 2016 .

[26]  H. Guillard,et al.  A method for the determination of the global solar radiation from meteorological satellite data , 1986 .

[27]  Ian K. G. Boothroyd,et al.  Ecological characteristics and management of geothermal systems of the Taupo Volcanic Zone, New Zealand , 2009 .

[28]  Seval Sözen,et al.  Assessment of renewable energy potential and policy in Turkey – Toward the acquisition period in European Union , 2015 .

[29]  J. Riva,et al.  Using Remote Sensing to Estimate a Renewable Resource: Forest Residual Biomass , 2012 .

[30]  Ning Zhang,et al.  Geothermal area detection using Landsat ETM+ thermal infrared data and its mechanistic analysis - A case study in Tengchong, China , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[31]  W. Cohen,et al.  Lidar remote sensing of above‐ground biomass in three biomes , 2002 .

[32]  B. R. Karthikeya,et al.  Wind resource assessment for urban renewable energy application in Singapore , 2016 .

[33]  A. Finkral,et al.  From renewable energy to fire risk reduction: a synthesis of biomass harvesting and utilization case studies in US forests , 2009 .

[34]  Yasuhiro Fujimitsu,et al.  Mapping hydrothermal altered mineral deposits using Landsat 7 ETM+ image in and around Kuju volcano, Kyushu, Japan , 2012, Journal of Earth System Science.

[35]  W. Calvin,et al.  Detection of geothermal anomalies using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared images at Bradys Hot Springs, Nevada, USA , 2007 .

[36]  Enrico Barbier,et al.  Geothermal energy technology and current status: an overview , 2002 .

[37]  Robert A. Garrott,et al.  Development and comparison of Landsat radiometric and snowpack model inversion techniques for estimating geothermal heat flux , 2008 .

[39]  Cristina Catita,et al.  Extending solar potential analysis in buildings to vertical facades , 2014, Comput. Geosci..

[40]  O. M. Johannessen,et al.  Determination of wind energy from SAR images for siting windmill locations , 1998 .

[41]  K. Pruess.  Enhanced geothermal systems (EGS) using CO2 as working fluid - A novelapproach for generating renewable energy with simultaneous sequestration of carbon , 2006 .

[42]  Wataru Takeuchi,et al.  PALSAR 50 m Mosaic Data Based National Level Biomass Estimation in Cambodia for Implementation of REDD+ Mechanism , 2013, PloS one.

[43]  R. Suzuki,et al.  Natural Forest Biomass Estimation Based on Plantation Information Using PALSAR Data , 2014, PloS one.

[44]  Lin Ma,et al.  Assessing the potential of urban wind energy in a major UK city using an analytical model , 2013 .

[45]  W. Cohen,et al.  Lidar Remote Sensing for Ecosystem Studies , 2002 .

[46]  F. Kruse.  Mapping surface mineralogy using imaging spectrometry , 2012 .

[47]  Eli A. Silver,et al.  Hyperspectral Mineral Mapping in Support of Geothermal Exploration: Examples from Long Valley Caldera, CA and Dixie Valley, NV, USA , 2004 .

[48]  Yuan Lin,et al.  Self-Powered, Wireless, Remote Meteorologic Monitoring Based on Triboelectric Nanogenerator Operated by Scavenging Wind Energy. , 2016, ACS applied materials & interfaces.

[49]  Cheryl Jaworowski,et al.  Use of ASTER and MODIS thermal infrared data to quantify heat flow and hydrothermal change at Yellowstone National Park , 2012 .

[50]  Zechun Hu,et al.  Photovoltaic and solar power forecasting for smart grid energy management , 2015 .

[51]  Gergely Szabó,et al.  Automated registration of potential locations for solar energy production with Light Detection And Ranging (LiDAR) and small format photogrammetry , 2016 .

[52]  Scott L. Powell,et al.  Review of Alternative Methods for Estimating Terrestrial Emittance and Geothermal Heat Flux for Yellowstone National Park Using Landsat Imagery , 2010 .

[53]  Harald van der Werff,et al.  Geologic remote sensing for geothermal exploration: A review , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[54]  Ingo Sass,et al.  Damage to the historic town of Staufen (Germany) caused by geothermal drillings through anhydrite-bearing formations , 2010 .

[55]  A. Lovett,et al.  Land Use Implications of Increased Biomass Production Identified by GIS-Based Suitability and Yield Mapping for Miscanthus in England , 2009, BioEnergy Research.

[56]  A. Juwarkar,et al.  CARBON SEQUESTRATION POTENTIAL IN ABOVEGROUND BIOMASS OF NATURAL RESERVE FOREST OF CENTRAL INDIA . , 2011 .

[57]  Debendra Chandra Baruah,et al.  Assessment of hydropower potential using GIS and hydrological modeling technique in Kopili River basin in Assam (India) , 2010 .

[58]  M. Parada,et al.  Contribution of ground surface altitude difference to thermal anomaly detection using satellite images: Application to volcanic/geothermal complexes in the Andes of Central Chile , 2012 .

[59]  Hang Liu,et al.  Monitoring wind farms occupying grasslands based on remote-sensing data from China’s GF-2 HD satellite—A case study of Jiuquan city, Gansu province, China , 2017 .

[60]  Torben Mikkelsen,et al.  A spinner‐integrated wind lidar for enhanced wind turbine control , 2013 .

[61]  G. Štumberger,et al.  Rating of roofs’ surfaces regarding their solar potential and suitability for PV systems, based on LiDAR data , 2013 .

[62]  D. Voivontas,et al.  Aessessment of biomass potential for power production: a GIS based method , 2001 .

[63]  Liming Zhou,et al.  Satellite Observations of Wind Farm Impacts on Nocturnal Land Surface Temperature in Iowa , 2014, Remote. Sens..

[64]  Charles W. Forsberg,et al.  Sustainability by combining nuclear, fossil, and renewable energy sources , 2009 .

[65]  Gokhan Kilic,et al.  Testing of wind turbine towers using wireless sensor network and accelerometer , 2015 .

[66]  M. Ramsey,et al.  Exploration of geothermal systems using hyperspectral thermal infrared remote sensing , 2013 .

[67]  Daeyoung Kim,et al.  A comparison of ground-based LiDAR and met mast wind measurements for wind resource assessment over various terrain conditions , 2016 .

[68]  I. Ozturk,et al.  The effect of renewable energy consumption on economic growth: Evidence from top 38 countries , 2016 .

[69]  C. Reinhart,et al.  A method for predicting city-wide electricity gains from photovoltaic panels based on LiDAR and GIS data combined with hourly Daysim simulations , 2013 .

[70]  Anupma Prakash,et al.  Quantifying the heat flux and outflow rate of hot springs using airborne thermal imagery: Case study from Pilgrim Hot Springs, Alaska , 2013 .

[71]  Ha T. Nguyen,et al.  Quantifying Rooftop Solar Photovoltaic Potential for Regional Renewable Energy Policy , 2010, Comput. Environ. Urban Syst..

[72]  M. Al-Mukhtar,et al.  Modeling Water Quality Parameters Using Data-Driven Models, a Case Study Abu-Ziriq Marsh in South of Iraq , 2019, Hydrology.

[73]  Robert J. Mellors,et al.  Land subsidence in the Cerro Prieto Geothermal Field, Baja California, Mexico, from 1994 to 2005 , 2011 .

[74]  Jin-Hee Lee,et al.  Site location analysis for small hydropower using geo-spatial information system , 2010 .

[75]  Noël Djongyang,et al.  A review of geophysical methods for geothermal exploration , 2015 .

[76]  Mark Simons,et al.  Deformation and seismicity in the Coso geothermal area, Inyo County, California: Observations and modeling using satellite radar interferometry , 2000 .