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Remote Sensing — Editors
Journal ContactRemote Sensing Editorial OfficeMDPI AG, Klybeckstrasse 64, 4057 Basel, SwitzerlandE-Mail: [email protected]. +41 61 683 77 34; Fax: +41 61 302 89 18
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Editor-in-ChiefDr. Prasad S. ThenkabailResearch Geographer-15, U. S. Geological Survey (USGS), USGS Western Geographic Science Center (WGSC), 2255, N.Gemini Dr., Flagstaff, AZ 86001, USATel. +94-11-2788924; Fax: +1 928 556 7112 Website: http://profile.usgs.gov/pthenkabailE-Mail: [email protected]: hyperspectral remote sensing, remote sensing expertise in a number of areas including: (a) global croplands, (b)agriculture, (c) water resources, (d) wetlands, (e) droughts, (f) land use/land cover, (g) forestry, (h) natural resourcesmanagement, (i) environments, (j) vegetation, and (k) characterization of large river basins and deltasContribution
Associate EditorProf. Dr. Clement AtzbergerHead Surveying, Remote Sensing & Land Information (IVFL), University of Natural Resources and Life Sciences, Vienna(BOKU), Peter Jordan Strasse 82, 1190 Vienna, AustriaFax: +43 1 47654 5142 Website: http://www.rali.boku.ac.at/ivfl.htmlE-Mail: [email protected]: imaging spectroscopy; time series analysis; radiative transfer modeling (forward and inverse); retrieval ofvegtetation biophysical variables; vegetation monitoringContribution
Associate EditorDr. Nicolas BaghdadiMaison de la Télédétection, Irstea - UMR TETIS, 500 rue JF Breton, 34093 Montpellier Cedex 05, FranceWebsite: http://www.tetis.teledetection.fr/E-Mail: [email protected]: SAR images applied to soil (surface roughness, soil moisture, texture); Lidar and Forest (canopy height andbiomass); SAR images and biomass
Associate EditorDr. Ioannis GitasLaboratory of Forest Management and Remote Sensing, School of Forestry and Natural Environment, Aristotle University ofThessaloniki, Thessaloniki, GreeceFax: +30 2310 992677 Website: http://fmrs.web.auth.gr/E-Mail: [email protected]: forest fires; pre-fire planning and post-fire assessment; land use/land cover mapping; soil erosion riskassessment/desertification; other environmental applications of remote sensing and GISContribution
Associate EditorDr. Richard GloaguenRemote Sensing Group, Helmholtz Institute Freiberg, TU Bergakademie Freiberg, Bernhard von-Cotta Str., 2, D-09599Freiberg, GermanyWebsite: http://tu-freiberg.de/remote-sensing-groupE-Mail: [email protected]: earth sciences; remote sensing/photogrammetry; tectonic geomorphology; vegetation physical properties;hydrological cycleContribution
Associate EditorProf. Dr. Alfredo R. HuetePlant Functional Biology and Climate Change Cluster, School of Environment, University of Technology Sydney, 15Broadway Road Ultimo, NSW 2007, AustraliaWebsite: http://www.c3.uts.edu.au/E-Mail: [email protected]: biophysical remote sensing; phenology; satellite products; carbon and water fluxes; land use science; droughtstudies
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Associate EditorDr. Yoshio InoueNational Institute for Agro-Environmental Sciences (NIAES), Tsukuba, Ibaraki 305-8604, JapanTel. +81-29-838-8222 Website: http://www.niaes.affrc.go.jp/researcher/inoue_y_e.htmlE-Mail: [email protected]: plant eco-physiology; remote sensing, modeling, agro-ecosystem; precision farming; GISContribution
Associate EditorDr. Josef KellndorferSenior Scientist, The Woods Hole Research Center, 149 Woods Hole Road, Falmouth, MA 02540, USAFax: +1 (508) 444 1840 Website: http://whrc.org/about/cvs/jkellndorfer.htmlE-Mail: [email protected]: REDD; remote sensing; forests; climate change; high performance computing; SAR imaging; data fusion; timeseries analysis
Associate EditorDr. Norman KerleDepartment of Earth Systems Analysis (ESA), Faculty of Geo-Information Science and Earth Observation (ITC), Universityof Twente, P.O. Box 6, Hengelosestraat 99, 7500 AA Enschede, NetherlandsWebsite: http://www.itc.nl/resumes/kerleE-Mail: [email protected]: damage assessment; vulnerability; disaster risk management; UAV; resilience; recovery; OBIA; object-orientedanalysis; VGI
Associate EditorDr. Alexander A. KokhanovskyEUMETSAT, Eumetsat Allee 1, D-64295 Darmstadt, GermanyE-Mail: [email protected]
Interests: remote sensing; light scattering; radiative transfer; inverse problems; atmospheric optics; retrieval of aerosol andcloud properties from spaceborne observationsContribution
Associate EditorDr. Richard MüllerGerman Meteorological Service CM-SAF, Frankfurter Straße 135, 63067 Offenbach, GermanyFax: +49 (0) 69 8062 4955 Website: http://www.cmsaf.dwd.de/E-Mail: [email protected]: remote sensing of surface radiation; clouds and aerosols; sensor calibration; methods for \"merging\" in-situ datawith remote sensing dataContribution
Associate EditorDr. Parth Sarathi RoyUniversity Centre of Earth & Space Science, Hyderabad Central University P.O Central University, Gachibouli, Hyderabad500046, IndiaTel. +918008504546 E-Mail: [email protected]: land use and land cover change; landscape ecology; biodiversity; land dynamic modelling; assessment ofecological services
Associate EditorProf. Dr. Randolph H. WynneDepartment of Forest Resources and Environmental Conservation, Virginia Tech, Cheatham Hall, RM 319, 310 WestCampus Dr, Blacksburg, VA 24061, USATel. 540-231-7811 Website: http://frec.vt.edu/people/faculty/faculty_folder/wynne.htmlE-Mail: [email protected]: applications of remote sensing to forestry; natural resource management; ecological modeling; and earth systemscienceContribution
Associate EditorProf. Dr. Xiao-Hai YanCenter for Remote Sensing (CRS), College of Earth, Ocean and Environment, University of Delaware, 209 Robinson Hall,Newark, DE 19716, USAWebsite: http://www.ceoe.udel.edu/people/profile.aspx?xiaohaiE-Mail: [email protected]: satellite oceanography/ocean remote sensing; Physical oceanography/ocean circulation/climate change; remotesensing of estuaries, coastal and open ocean waters; remote sensing image processing; air-sea interactions and upperocean dynamics; mirowave remote sensing (altimeter, scatterometer and SAR); environmental remote sensingContribution
Senior Assistant EditorMs. Xuanxuan GuanMDPI Tongzhou Office, Room 2207, Jincheng Center, No. 21 Cuijingbeili, Tongzhou District, Beijing 101101, ChinaFax: +86 10 59011089 E-Mail: [email protected]
Editorial ManagerMs. Delia Costache
MDPI AG, Klybeckstrasse 64, CH-4057 Basel, SwitzerlandE-Mail: [email protected]
Former Editor-in-ChiefProf. Dr. Wolfgang Wagner * Research Group Remote Sensing, Department of Geodesy and Geoinformation (GEO), Vienna University of Technology (TUWien), Gusshausstrasse 27-29, 1040 Vienna, AustriaFax: +43 1 58801 12299 Website: http://www.ipf.tuwien.ac.at/Interests: remote sensing; geophysical parameter retrieval; airborne laser scanning; full-waveform lidar; radar remotesensing; soil moisture* Founding Editor-in-Chief and Editor-in-Chief up to 2 September 2011 Contribution
For further MDPI contacts, see here.
Editorial Board
Dr. Devrim AkcaDepartment of Civil Engineering, Isik University, TR-34980 Sile, Istanbul, TurkeyFax: +90 216 712 1474 Website: http://www2.isikun.edu.tr/personel/akcaInterests: sensor calibration; 3D city modeling; laserscanning; photogrammetry; machine vision; cultural and naturalheritage applications; high accuracy object measurement and 3D modelingContribution: Special Issue: Remote Sensing in Natural and Cultural Heritage
Dr. Sreekala BajwaDepartment of Agricultural and Biosystems Engineering North Dakota State University NDSU Dept. 7620 PO Box 6050,Fargo, ND 58108, USATel. +1 701 231 7265; Fax: +1 701 231 1008 Website: http://www.ndsu.edu/aben/personnel/bajwa/Interests: remote sensing; precision agriculture; unmanned aerial systems; bio-composites
Prof. Dr. Heiko BalzterHolder of the Royal Society Wolfson Research Merit Award, Centre for Landscape and Climate Research, Department ofGeography, University of Leicester, Bennett Building, University Road, Leicester LE1 7RH, UKFax: +44 116 252 3854 Website: http://www.le.ac.uk/clcrInterests: land cover / land use change; spatial-temporal scaling; land/atmosphere interactions; data assimilation; syntheticaperture radar (SAR); SAR interferometry; SAR polarimetry; ground-based, airborne and spaceborne light detection andranging (LIDAR); digital elevation models; carbon accounting; forest structure and biomass mapping; vegetation phenology;fire and burned area mappingContribution: Special Issue: Remote Sensing and GIS for Habitat Quality MonitoringIn other journals: Special Issue: Land Use Change Feedbacks with Climate
Dr. Agnes BegueCIRAD-UMR TETIS, Maison de la Télédétection, 500 Rue Jean François Breton, 34093 Montpellier, FranceTel. +33 4 67 54 87 54 Interests: Remote sensing for agriculture applications (yield estimation, cropland mapping, precision agriculture, zoning,cropping practices) Use of multi-source data (image time series, crop model simulations and expertise
Prof. Dr. James CampbellGeography Department, 220 Stanger Street, 115 Major Williams Hall, Virginia Tech, Blacksburg, VA 24061, USATel. 540.231.5841 Website: http://geography.vt.edu/people/campbell.htmInterests: agricultural systems (crop rotation, tillage assessment, yield estimation); soil variability; land use/land coverchange; coastal reclamation; urban systems (microclimates, impervious surfaces, drainage)
Prof. Dr. Toby N. CarlsonProfessor Emeritus of Meteorology, Penn State University, 617 Walker Building, University Park, PA 16802, USATel. (814) 863-1582; Fax: +1 814 865 3663 Website: http://www.met.psu.edu/people/tncInterests: satellite remote sensing applications to regional planning; modeling of evapotranspiration over plant canopies;land surface processes
Dr. Matthew ClarkDepartment of Geography and Global Studies & Center for Interdisciplinary Geospatial Analysis (CIGA), Sonoma StateUniversity, 1801 E. Cotati Ave, Rohnert Park, CA 94928, USATel. 707-664-2558 Website: http://www.sonoma.edu/geoglobal/home/faculty/matthew-clark.htmlInterests: remote sensing; geographic information systems; biogeography
Dr. Roberto ColomboRemote Sensing of Environmental Dynamics Lab., Dept. of Earth and Environmental Sciences, University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milan, ItalyWebsite: http://telerilevamento.disat.unimib.it/Interests: remote sensing; earth observation; environmental modelling; imaging spectroscopy; field spectroscopy;chlorophyll fluorescence; land surface phenology; bio-geophysical remote sensing
Dr. Jose DematteSoil Science Department, Luiz de Queiroz College of Agriculture, São Paulo, University of Piracicaba, SP 13418-900, BrazilWebsite: http://www.en.esalq.usp.br/Interests: remote sensing applied to soils; soil spectroscopy, digital soil mapping, RS from ground to space, precisionagriculture
Prof. Dr. Ralph DubayahDepartment of Geography, 2181 LeFrak Hall, University of Maryland, College Park MD 20742, USAFax: +1 301 314 9299 Website: http://www.geog.umd.edu/facultyprofile/Dubayah/RalphInterests: lidar remote sensing; terrestrial carbon cycling; biodiversity and habitat
Prof. Dr. Giles M. FoodySchool of Geography, University of Nottingham, Nottingham, NG7 2RD, UKFax: +44 1159515249 Website: http://www.nottingham.ac.uk/~lgzwww/contacts/points/teaching.phtml?name=foodyInterests: land cover; image classification; ecology; GIS
Dr. Chandra GiriResearch Physical Scientist, USGS Earth Resources Observation and Science (EROS) Center, Nicholas School of theEnvironment/Duke University , A321 LSRC, Box 90328, Durham, NC 27708-0328, USATel. 6055942835; Fax: +1 605 594 6529 Website: http://eros.usgs.gov/Interests: mangrove forests mapping and monitoring using high resolution satellite data; global and continental land covermapping and monitoring using multi-spectral, multi-temporal, and multi-platform remotely sensed data; image pre-processing, classification, and validation using cloud computingContribution: Special Issue: Remote Sensing of Mangroves: Observation and Monitoring
Prof. Dr. Anatoly GitelsonIsrael Institute of Technology, 812 Rabin Hall, Technion City, Haifa 320000, Israel; School of Natural Resources, Universityof Nebraska - Lincoln, Lincoln, NE 68583, USATel. 402 5608430 Website: http://www.calmit.unl.edu/people/agitelson2/Interests: remote sensing of vegetation and water quality
Prof. Dr. Douglas G. GoodinRemote Sensing Research Laboratory, Department of Geography, Kansas State University, Manhattan, KS 66506, USATel. 785-532-3411; Fax: +1 785 532 7310 Website: http://www.k-state.edu/rssg/doug.htmInterests: environmental health and infectious disease; biophysical and thematic remote sensing; geospatial modeling
Dr. Eileen H. HelmerUSDA Forest Service, International Institute of Trop Forestry, 1201 Calle Ceiba, Río Piedras, PR 00926, USAInterests: tropical forest; secondary tropical forest; forest inventory; image time series; forest change detection; forestdynamics; forest phenology; vegetation mapping; vegetation classification; image composites; image mosaics; clouddetection
Dr. Benjamin KoetzDirectorate of Earth Observation Programmes, European Space Agency, Via Galileo Galilei, 00044 Frascati, ItalyFax: +39 06 941 80 552 Interests: remote sensing of ecosystem structure and processes; multi-temporal optical remote sensing for vegetationmonitoring; radiative transfer modeling; fusion of multi-source dataContribution: Special Issue: Earth Observation for Water Resource Management in AfricaSpecial Issue: Lessons Learned from the SPOT4 (Take5): Experiment in Preparation for Sentinel-2
Dr. Felix KoganEnvironmental Monitoring Branch, National Oceanic and Atmospheric Administration, 5200 Auth Rd, Camp Springs, MD20746, USAWebsite: http://www.star.nesdis.noaa.gov/star/Kogan_F.phpInterests: remote sensing; ecosystems; climate and weather impact assessments; land cover/land use change; monitoringdroughts, desertification and deforestation; mosquito-borne diseases; productivity of land landscape; some issues ofagriculture and forestry; agricultural meteorology and climatology; and environmental zoning
Prof. Dr. Raphael M. KudelaOcean Sciences Department, University of California, A-461 Earth & Marine Sciences Bldg., CA 95064, Santa Cruz, USAFax: +1 831 459-4882 Website: http://people.ucsc.edu/~kudela/Interests: ecological modeling and remote sensing; satellite oceanography; phytoplankton ecology and harmful algalbloomsContribution: Special Issue: Remote Sensing of Phytoplankton
Dr. Claudia KuenzerHead of Land Surface Dynamics Research Group of DLR, German Remote Sensing Data Center, DFD, Earth ObservationCenter, EOC, German Aerospace Center, DLR, Oberpfaffenhofen, 32234 Wessling, GermanyFax: +49 8153 28 1458 Website: http://www.eoc.dlr.deInterests: remote sensing applications for land and water resources management, land cover and land use change, timeseries analyses, geosciences, linkage of natural and social sciences
Dr. Rosa LasaponaraCNR-IMAA (National Research Council, Institute for Environmental Analysis), C.da S. Loya, 85050 Tito Scalo (PZ), ItalyFax: +39 0971 427222 Website: http://www.imaa.cnr.it/index.php?option=com_content&task=view&id=378&Itemid=220&lang=enInterests: remote sensing; data processing; microwave sensor design; analytical methods, modeling, readout and softwarefor sensors; sensor technology and new sensor principlesContribution: Special Issue: New Perspectives of Remote Sensing for Archaeology
Dr. Henrique Lorenzo
Close-range Remote Sensing & Photogrammetry Group, University of Vigo, EUET Forestal, Campus A Xunqueira s/n,36005 Pontevedra, SpainTel. +34 647343152; Fax: +34 986 801 907 Website: http://webs.uvigo.es/grupotf1/Interests: ground-penetrating radar; close-range photogrammetry; terrestrial laser scanner; cultural heritage applicationsContribution: Special Issue: Close-Range Remote Sensing by Ground Penetrating Radar
Dr. Arko LucieerSchool of Land and Food, Discipline of Geography and Spatial Sciences, University of Tasmania, Private Bag 76, Hobart,TAS 7001, AustraliaTel. +61362262140; Fax: +61 (0)3 6226 2989 Website: http://www.lucieer.netInterests: environmental and quantitative remote sensing; unmanned aerial vehicles (UAVs); UAV sensor integration;hyperspectral, multispectral, and thermal image processing; image texture measures; classification and machine learning;object-based image analysis; change detection; terrain analysis techniquesContribution: Special Issue: UAV-Based Remote Sensing Methods for Modeling, Mapping, and Monitoring Vegetation andAgricultural Crops
Dr. Nicola MasiniCNR-IBAM (National Research Council, Institute for Archaeological and Architectural Heritage), C.da S. Loya, 85050 TitoScalo (PZ), ItalyFax: +39 0971 427333 Website: http://www.ibam.cnr.it/englishversion/Masini.htmInterests: remote sensing for archaeology; Lidar; archaeogeophisics; non invasive tests for historical buildingContribution: Special Issue: New Perspectives of Remote Sensing for Archaeology
Dr. Andrew McGonigleSchool of Geography, University of Sheffield, Sheffield S10 2TN, UKFax: +44 114 222 7961 Website: http://www.shef.ac.uk/geography/staff/mcgonigle_andrew/Interests: volcano remote sensing; ground based remote sensing
Prof. Dr. Assefa M. MelesseDepartment of Earth and Environment, AHC-5-390, Florida International University, 11200 SW 8th Street, Miami, FL 33199,USAFax: +1-305-348-3877. Website: http://faculty.fiu.edu/~melessea/Interests: watershed modelling; sediment dynamics, climate change, evapotranspiration and energy fluxes; systemanalysis; remote sensing hydrologyContribution: Special Issue: Land Surface FluxesIn other journals: Special Issue: Remote Sensing of Natural Resources and the EnvironmentSpecial Issue: Hydrologic System Analysis, Patterns, and Predictions for Arid and Semi-arid Environment
Dr. Deepak R. MishraDepartment of Geography, University of Georgia, 210 Field Street, Rm 212B, Athens, GA 30602, USAWebsite: http://geography.uga.edu/directory/profile/mishra-deepak/Interests: Water quality (inland waters, estuaries, coastal, and open ocean waters); Wetlands health, productivity, andcarbon sequestration; Benthic habitat mapping, Cyber-innovated environmental sensingContribution: Special Issue: Remote Sensing of Water ResourcesSpecial Issue: Remote Sensing in Coastal Environments
Prof. Dr. Jose MorenoLaboratory for Earth Observation, Department of Earth Physics and Thermodynamics, Faculty of Physics, University ofValencia, C/ Dr. Moliner, 50, 46100 Burjassot, Valencia, SpainTel. +34 96 3543112; Fax: +34 96 354 33 85 Website: http://ipl.uv.es/?q=users/josemorenoInterests: optical remote sensing; imaging spectroscopy; vegetation fluorescence; vegetation biophysical parameters; landsurface applications; optical reflectance/fluorescence models; retrieval methods; design of future earth observation missions;dynamical vegetation models; calibration/validation field campaigns
Prof. Dr. L. Monika MoskalSchool of Environmental and Forest Sciences, College of the Environment, University of Washington, Box 352100, SeattleWA 98195-2100, USA. Director, UW Precision Forestry Cooperative and Remote Sensing and Geospatial AnalysisLaboratoryWebsite: http://faculty.washington.edu/lmmoskal/Interests: ALS/TLS LiDAR; precision forestry; hyperspatial remote sensing; ecosystem servicesContribution: In other journals: Special Issue: LiDAR and Other Remote Sensing Applications in Mapping and Monitoring of Forests Structure and Biomass
Prof. Dr. Soe MyintSchool of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85287, USATel. 480-965-6514; Fax: +49-480-965-8313 Website: http://geoplan.asu.edu/myintInterests: remote sensing; GIS; geospatial statistics; land use land cover change and prediction; assessment andmonitoring of drought, land degradation, and desertification; landscape fragmentation; urban environmental modelingincluding urban water use and climate analysis; forest characterization including coastal environments; disasterassessment, recovery, and monitoring; agriculture water use, evapotranspiration, and surface energy analysis; spatialmodeling; and classification algorithm developmentContribution: Special Issue: Thermal Remote Sensing Applications: Present Status and Future Possibilities
Dr. Markus NetelerHead of GIS and Remote Sensing Unit, Department of Biodiversity and Molecular Ecology, Research and Innovation Centre,Fondazione Edmund Mach, Via E. Mach, 1 - 38010 S. Michele all'Adige (TN), ItalyWebsite: http://gis.cri.fmach.it/neteler
Interests: remote sensing for environmental risk assessment; Free and Open Source GIS development; remote sensing foreco-health
Prof. Dr. Janet NicholDepartment of Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon Hong KongWebsite: http://www.lsgi.polyu.edu.hk/RSRG/Interests: remote sensing of urban areas (including urban heat islands, aerosol retrieval and urban enviromental quality);ecological and habitat mapping, biomass and carbon storage estimation of forests; land cover monitoring, satellite sensors(small satellites, visible and thermal infrared sensors); integration of remote sensing and GIS; data visualisationContribution: Special Issue: Thermal Remote Sensing Applications: Present Status and Future Possibilities
Prof. Dr. Gonzalo Pajares MartinsanzDepartment Software Engineering and Artificial Intelligence, Faculty of Informatics, University Complutense of Madrid, 28040Madrid, SpainTel. +34.1.3947546 Website: http://www.fdi.ucm.es/profesor/pajares/Interests: computer vision; image processing; pattern recognition; 3D image reconstruction, spatio-temporal image changedetection and track movement; fusion and registering from imaging sensors; superresolution from low-resolution imagesensorsContribution: Special Issue: Unmanned Aerial Vehicles (UAVs) based Remote SensingIn other journals: Special Issue: State-of-the-Art Sensors Technology in SpainSpecial Issue: Sensors in Agriculture and ForestrySpecial Issue: Collaborative SensorsSpecial Issue: Sensor-Based Technologies and Processes in Agriculture and ForestrySpecial Issue: State-of-the-Art Sensors Technology in Spain 2013Special Issue: Sensors for Fluid Leak DetectionSpecial Issue: Agriculture and Forestry: Sensors, Technologies and ProceduresSpecial Issue: Sensors in Agriculture and ForestrySpecial Issue: State-of-the-Art Sensors Technology in Spain 2015Special Issue: Image Processing in Agriculture and ForestrySpecial Issue: Image and Video Processing in MedicineSpecial Issue: Unmanned Aerial Vehicles in GeomaticsSpecial Issue: Imaging: Sensors and Technologies
Dr. George P. PetropoulosDepartment of Geography and Earth Sciences, University of Aberystwyth, Old College, King Street Llandinam Building,Room H4 Aberystwyth, Ceredigion SY23 3DB, UKWebsite: http://www.aber.ac.uk/en/iges/staff/academic-staff/gep9/Interests: Earth Observation; GIS; multi- and hyper- spectral remote sensing; land use/cover mapping; change detection;natural hazards; fires; floods; land surface interactions; evapotranspiration; soil moisture; land surface temperature; landbiosphere modelling; Soil Vegetation Atmosphere Transfer (SVAT) models; EO algorithms benchmarking; sensitivityanalysis
Dr. Ruiliang PuSchool of Geosciences, University of South Florida, 4202 E. Fowler Ave., NES 107, Tampa, FL 33620, USATel. 813-974-1508; Fax: 813-974-4808 Website: http://rpu.myweb.usf.eduInterests: wildfire detection and mapping; land use/land cover change detection and mapping; mapping with satellite remotesensing and GISContribution: In other journals: Special Issue: Mapping and Assessing Natural Disasters Using Geospatial Technologies
Prof. Dr. Jiaguo QiCenter for Global Change & Earth Observations, Michigan State University, 218 Manly Miles Building, 1405 S. HarrisonRoad, East Lansing, MI 48823, USATel. +1-517-353-8736 Website: http://www.globalchange.msu.edu/qi.htmlInterests: Dr. Qi has a broad interest in developing techniques and models to address global change issues, including:remote sensing and geospatial technologies; environmental monitoring, assessment and modeling; land use and land coverchange dynamic assessments; and decision support systems for agriculture. Dr. Qi’s primary professional focus has beenon the generation of information and knowledge from satellite images using process-based models and geospatialtechnologies. Over the past decade, he developed geospatial modeling tools to guide rangeland management and cropirrigation scheduling, incorporated satellite-based biophysical attributes to improve climate modeling and predictions,ingested land use and land cover information in biogeochemical models to improve greenhouse gas emissions and nitrogenleaching, applied geospatial technologies to characterize landscape patterns for large scale ecological assessment,developed innovative way of using free satellite images for improved cropland detection and production estimation, andrecently developed innovative approaches to integrate human, environment and climate to understand the coupling nature ofhuman and environment for sustainable development in developing countries in East and Southeast Asia, Central Asia andEast and West Africa
Prof. Dr. Dale A. QuattrochiEarth Science Office, ZP11, Marshall Space Flight Center, NASA, Huntsville, AL 35812 USATel. 256-961-7887; Fax: +1 256 961 7788 Interests: thermal remote sensing; urban heat island analysis; geospatial techniques and remote sensing; land use/landcover changeContribution: Special Issue: Thermal Remote Sensing Applications: Present Status and Future PossibilitiesIn other journals: Special Issue: Remote Sensing of Land Surface Properties, Patterns and Processes
Prof. Dr. Daniele RiccioUniversità degli Studi di Napoli Federico II, Faculty of Engineering, Department of Electrical Engineering and InformationTechnology, Via Claudio 21, 80125 Napoli, ItalyFax: +39 0817685925 Website: http://www.docenti.unina.it/Daniele.RiccioInterests: remote sensing; electromagnetic scattering; synthetic aperture radar; radar; microwave imaging
Contribution: In other journals: Special Issue: Synthetic Aperture Radar (SAR)
Prof. Dr. Dar RobertsGeography Department, University of California, Santa Barbara, Santa Barbara, CA 93106, USATel. (805)880-2531; Fax: +1-805-893-2578 Website: http://www.geog.ucsb.edu/people/faculty/dar-roberts.htmlInterests: remote sensing of vegetation, geology, ecology, and ecophysiology
Dr. Duccio RocchiniGIS and Remote Sensing Unit, Department of Biodiversity and Molecular Ecology, Research and Innovation Centre,Fondazione Edmund Mach, Via Mach 1, 38010 San Michele all'Adige (TN), ItalyFax: +39 3491425786 Website: http://gis.fem-environment.eu/rocchini/Interests: ecological Informatics; ecological heterogeneity and biodiversity estimate by satellite imagery; Free and OpenSource Software for spatial ecology; statistical analysis of spatial and ecological dataContribution: Special Issue: Ecological Status and Change by Remote SensingSpecial Issue: Earth Observation for Ecosystems Monitoring in Space and TimeIn other journals: Special Issue: Geospatial Monitoring and Modelling of Environmental Change
Dr. Raad A. SalehEarth Resources Observation and Science (EROS) Center, US Geological Survey (USGS), 47914 252nd Street Sioux Falls,SD 57198, USAFax: +1 605 594 6906 Website: http://astrogeology.usgs.govInterests: satellite sensing systems; sensor networks; high-resolution EOS; multi- and hyper-spectral imaging; multi-dimensional image analysis algorithms; geo-referencing; planetary mapping; digital photogrammetry
Dr. Gabriel Senay1 Research Physical Scientist: USGS, Center for Earth Resource Observation & Science (EROS), Mundt Federal Building,47914 252nd Street, Sioux Falls, SD 57198-0001, USA2 South Dakota State University, GISc Center of Excellence (GIScCE), USAFax: +1-605-594-6925. Interests: regional water balance assessment and monitoringContribution: Special Issue: Land Surface FluxesIn other journals: Special Issue: Hydrologic System Analysis, Patterns, and Predictions for Arid and Semi-arid Environment
Dr. Christopher SmallLamont Doherty Earth Observatory, 304b Oceanography, Palisades, NY 10964, USAWebsite: http://www.ldeo.columbia.edu/user/smallInterests: geophysics; land surface processes; remote sensing; population and environment
Dr. Lenio Soares GalvaoDivisão de Sensoriamento Remoto, Instituto Nacional de Pesquisas Espaciais (INPE), Caixa Postal 515, Av. dosAstronautas, 1758, Bairro Jardim da Granja, 12245-970 São José dos Campos, SP, BrazilWebsite: http://www.dsr.inpe.br/dsr/lenio/Interests: hyperspectral remote sensing, reflectance spectroscopy, vegetation, soils and rocks
Dr. Salvatore StramondoIstituto Nazionale di Geofisica e Vulcanologia, National Earthquake Center, Remote Sensing Laboratory, Via di VignaMurata 605, 00143 Rome, ItalyFax: +39 06 51860507 Interests: remote sensing; synthetic aperture radar interferometry; multitemporal SAR interferometry; remote sensing fornatural disaster mitigation and monitoringContribution: Special Issue: Remote Sensing in Seismology
Dr. Anton VrielingNatural Resources Department, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O.Box 217, 7500 AE Enschede, The NetherlandsWebsite: http://www.itc.nl/about_itc/resumes/vrieling.aspxInterests: remote sensing; time series analysis; agriculture; food security; soil erosion
Prof. Dr. Lizhe WangInstitute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No.9 Dengzhuang South Road, HaidianDistrict, Beijing 100094, ChinaTel. +86 10 8217 8070 Website: http://www.escience.cn/people/lzwangEN/index.html;jsessionid=2F9BF4288D3B0200C2E74EA96DB4354C-n2Interests: Digital Earth; Remote Sensing Image Processing; High Performance Geocomputing
Dr. Dongdong WangDepartment of Geographical Sciences, University of Maryland, 2181 Samuel J. LeFrak Hall, College Park, MD 20742, USAWebsite: http://www.geog.umd.edu/facultyprofile/Wang/DongdongInterests: quantitative remote sensing; surface radiation budget; satellite data integration; satellite data degradation; wildfireand climate change
Dr. Lars T. WaserSwiss National Forest Inventory, Swiss Federal Research Institute WSL, Zuercherstr. 111, CH-8903 Birmensdorf,SwitzerlandTel. +41 44 739 2292; Fax: +41 44 739 2215 Website: http://www.wsl.ch/info/mitarbeitende/waser/index_ENInterests: national forest inventory; spatially estimating forest parameters; tree species; growing stock; change detection;biodiversity; LiDAR; airborne and spaceborne; image-matching; canopy height model; land cover/land use change
Prof. Dr. Qihao WengCenter for Urban and Environmental Change, Department of Geography, Geology, and Anthropology, Indiana StateUniversity, Terre Haute, IN 47809, USATel. +1 812 237 2255; Fax: +1 812 237 8029 Website: http://isu.indstate.edu/qweng/Interests: urban remote sensing; thermal remote sensing; digital image processing; remote sensing and GIS integrationContribution: In other journals: Special Issue: Remote Sensing of Land Surface Properties, Patterns and Processes
Dr. Iain H. WoodhouseEdinburgh Earth Observatory, School of GeoSciences, Geography Building, Drummond, Edinburgh EH8 9XP, UKFax: +44 (0) 131 650 2524 Website: http://www.geos.ed.ac.uk/people/person.html?indv=186Interests: radar remote sensing; polar decomposition methods for visualising SAR data; novel visualisation techniques forthe analysis of multichannel remote sensing data; DEM generation and regional scale geomorphology; synergistic remotesensing of vegetation; macroecology and telemacroscopicsContribution: Special Issue: Microwave Remote Sensing
Dr. Pablo J. Zarco-TejadaQuantaLab Remote Sensing Laboratory, Instituto de Agricultura Sostenible (IAS), Consejo Superior de InvestigacionesCientíficas (CSIC), Alameda del Obispo, s/n, E-14004 Córdoba, SpainFax: +(34) 957 499 252 Website: http://quantalab.ias.csic.esInterests: hyperspectral remote sensing imagery for vegetation stress monitoring, water stress detection with thermalimagery; pre-visual indicators of stress; chlorophyll fluorescence; precision agricultureContribution: Special Issue: UAV-Based Remote Sensing Methods for Modeling, Mapping, and Monitoring Vegetation andAgricultural Crops
Dr. Raul Zurita-MillaFaculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede,The NetherlandsWebsite: http://www.itc.nl/about_itc/resumes/zurita-milla.aspxInterests: spatio-temporal analysis; time series, process modelling and integration of remote sensing and GIS forapplications in phenology, agriculture, land use/land cover, epidemiology and public health
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Article: Mapping Bush Encroaching Species by Seasonal Differences in Hyperspectral ImageryRemote Sens. 2010, 2(6), 1416-1438; doi:10.3390/rs2061416Received: 15 March 2010 / Revised: 12 April 2010 / Accepted: 19 May 2010 / Published: 27 May 2010Cited by 11 | PDF Full-text (1034 KB)
Article: Evidence of Hydroperiod Shortening in a Preserved System of Temporary PondsRemote Sens. 2010, 2(6), 1439-1462; doi:10.3390/rs2061439Received: 8 April 2010 / Revised: 17 May 2010 / Accepted: 21 May 2010 / Published: 1 June 2010Cited by 9 | PDF Full-text (2782 KB)
Article: Changes in Croplands as a Result of Large Scale Mining and the Associated Impact on Food Security StudiedUsing Time-Series Landsat ImagesRemote Sens. 2010, 2(6), 1463-1480; doi:10.3390/rs2061463Received: 10 April 2010 / Revised: 25 May 2010 / Accepted: 26 May 2010 / Published: 1 June 2010Cited by 4 | PDF Full-text (8621 KB)
Article: Comparison of Area-Based and Individual Tree-Based Methods for Predicting Plot-Level Forest AttributesRemote Sens. 2010, 2(6), 1481-1495; doi:10.3390/rs2061481Received: 15 April 2010 / Revised: 20 May 2010 / Accepted: 24 May 2010 / Published: 2 June 2010Cited by 17 | PDF Full-text (243 KB)
Article: Medium Spatial Resolution Satellite Imagery to Estimate Gross Primary Production in an Urban AreaRemote Sens. 2010, 2(6), 1496-1507; doi:10.3390/rs2061496Received: 6 April 2010 / Revised: 10 May 2010 / Accepted: 21 May 2010 / Published: 3 June 2010Cited by 6 | PDF Full-text (1064 KB)
Article: Change Detection Accuracy and Image Properties: A Study Using Simulated DataRemote Sens. 2010, 2(6), 1508-1529; doi:10.3390/rs2061508Received: 14 April 2010 / Revised: 27 May 2010 / Accepted: 2 June 2010 / Pub lished: 3 June 2010Cited by 7 | PDF Full-text (3988 KB)
Article: Investigation on the Patterns of Global Vegetation Change Using a Satellite-Sensed Vegetation IndexRemote Sens. 2010, 2(6), 1530-1548; doi:10.3390/rs2061530Received: 2 April 2010 / Revised: 18 May 2010 / Accepted: 21 May 2010 / Published: 3 June 2010Cited by 4 | PDF Full-text (724 KB)
Article: Analysis and Modeling of Urban Land Cover Change in Setúbal and Sesimbra, PortugalRemote Sens. 2010, 2(6), 1549-1563; doi:10.3390/rs2061549Received: 24 March 2010 / Revised: 31 May 2010 / Accepted: 2 June 2010 / Published: 9 June 2010Cited by 20 | PDF Full-text (1081 KB)
Article: Assessment of Light Environment Variability in Broadleaved Forest Canopies Using Terrestrial Laser ScanningRemote Sens. 2010, 2(6), 1564-1574; doi:10.3390/rs2061564Received: 27 April 2010 / Revised: 1 June 2010 / Accepted: 7 June 2010 / Published: 14 June 2010Cited by 5 | PDF Full-text (602 KB)
Article: On the Exportability of Robust Satellite Techniques (RST) for Active Volcano MonitoringRemote Sens. 2010, 2(6), 1575-1588; doi:10.3390/rs2061575Received: 10 April 2010 / Revised: 27 May 2010 / Accepted: 8 June 2010 / Pub lished: 17 June 2010Cited by 7 | PDF Full-text (1072 KB)
Article: Monitoring Global Croplands with Coarse Resolution Earth Observations: The Global Agriculture Monitoring (GLAM)ProjectRemote Sens. 2010, 2(6), 1589-1609; doi:10.3390/rs2061589Received: 19 April 2010 / Revised: 7 June 2010 / Accepted: 8 June 2010 / Published: 18 June 2010Cited by 22 | PDF Full-text (1281 KB)
Article: Static Calibration and Analysis of the Velodyne HDL-64E S2 for High Accuracy Mobile ScanningRemote Sens. 2010, 2(6), 1610-1624; doi:10.3390/rs2061610Received: 7 June 2010 / Revised: 17 June 2010 / Accepted: 18 June 2010 / Published: 22 June 2010Cited by 17 | PDF Full-text (520 KB)
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Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Sw itzerland RSS E-Mail Table of Contents Alert
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Remote Sens. 2010, 2, 1496-1507; doi:10.3390/rs2061496
Remote Sensing ISSN 2072-4292
www.mdpi.com/journal/remotesensing Article
Medium Spatial Resolution Satellite Imagery to Estimate Gross Primary Production in an Urban Area
A. Rahman As-syakur 1,2,*, Takahiro Osawa 2 and I. Wayan S. Adnyana 1, 2
1 Environmental Research Center, Udayana University, PB Sudirman Street, Denpasar, Bali 80232,
Indonesia; E-Mail: [email protected] 2 Center for Remote Sensing and Ocean Science (CReSOS), Udayana University. PB Sudirman
Street, Post Graduate Building, Denpasar, Bali 80232, Indonesia; E-Mail: [email protected]
* Author to whom correspondence should be addressed; E-Mail: [email protected];
Tel.: +62-361-236-221; Fax: +62-361-236-180.
Received: 6 April 2010; in revised form: 10 May 2010 / Accepted: 21 May 2010 /
Published: 3 June 2010
Abstract: Remote sensing data with medium spatial resolution can provide useful
information about Gross Primary Production (GPP), especially on the scale of urban areas.
Most models of ecosystem carbon exchange that are based on remote sensing use some
form of the light use efficiency (LUE) model. The aim of this work is to analyze the
distribution of annual GPP in the urban area of Denpasar, Bali. Additional analysis using
two types of satellite data (ALOS/AVNIR-2 and Aster) addresses the impact of spatial
resolution on the detection of various ecosystem processes in Denpasar. Annual GPP
estimated using ALOS/AVNIR-2 varied from 0.13 gC m−2 yr−1 to 2,586.18 gC m−2 yr−1.
Meanwhile, the Aster estimate varied from 0.14 gC m−2 yr−1 to 2,595.26 gC m−2 yr−1. GPP
as measured by ALOS/AVNIR-2 was lower than that from Aster because ALOS/AVNIR-2
has medium spatial resolution and a smaller spectral range than Aster. Variations in land
use may influence the measured value of GPP via differences in vegetation type,
distribution, and photosynthetic pathway type. The medium spatial resolution of the remote
sensing data is crucial for discriminating different land cover types in heterogeneous urban
areas. Given the heterogeneity of land cover over Denpasar, ALOS/AVNIR-2 detects a
smaller maximum value of GPP than Aster, but the annual mean GPP from
ALOS/AVNIR-2 is higher than that from Aster. Based on comparisons with previous
work, we find that ALOS/AVNIR-2 and Aster satellite data provided more accurate
estimates of maximum GPP in Denpasar and in the tropical Kalimantan-Indonesia and
Amazon forest than estimates derived from the MODIS GPP product (MOD17).
OPEN ACCESS
Remote Sens. 2010, 2
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Keywords: ALOS/AVNIR-2; Aster; gross primary production; spatial resolution
1. Introduction
The most important global interactions between the biosphere and atmosphere involve the transfer
of energy, water, and carbon. Carbon is assimilated by the biosphere through photosynthesis and
released through autotrophic and heterotrophic respiration [1]. Emissions and re-absorption of carbon
gases from natural ecosystems were in a state of equilibrium for millions of years; however, this
balance has been disturbed by human activities. Consequently, atmospheric concentrations of carbon
dioxide (CO2) have been increasing and are widely believed to be responsible for global warming [2].
Understanding the drivers of the spatial and temporal patterns of surface-atmosphere CO2 exchange is
therefore crucial to improving predictions of future concentrations of atmospheric CO2 [3]. To achieve
this goal, plant cover and corresponding surface CO2 uptake must be monitored on a large scale. Such
data will facilitate accurate estimates of regional and global carbon budgets and, ultimately, more
accurate prediction of carbon source-sink relationships and atmospheric CO2 concentrations [4].
Remote sensing can be used to estimate surface-atmosphere CO2 exchange such as gross primary
productivity (GPP).
GPP is the total carbon assimilated by vegetation [5]. Based on biological processes GPP is the sum
of net primary productivity (NPP) with respiration. GPP can be estimated by combining remote
sensing with carbon cycle processing [6]. GPP have been estimated based on biophysical parameters
derived from vegetation indices (such as the normalized difference vegetation index, NDVI),
land-cover data, and light-use-efficiency parameters [7]. Most models of ecosystem carbon exchange
that are based on remote sensing use some form of the light use efficiency (LUE) model. The LUE
model states that carbon exchange is a function of the amount of light energy absorbed by vegetation
and the efficiency with which that light energy is used to fix carbon [8]. Monteith [9] developed a
method for estimating plant productivity from observations of APAR and estimates of LUE.
Remotely-sensed optical signatures have proved useful for estimating a range of ecological
variables including leaf area index (LAI) and the absorptivity of photosynthetically active radiation
(APAR) [10,11]. The fraction of absorbed photosynthetically active radiation (fAPAR) driven by
vegetation cover is related to the NDVI. The strong relationship between NDVI and fAPAR has been
examined in detail through theoretical and experimental analyses [10,12,13]. The NDVI has become a
popular tool for assessing different aspects of plant processes while simultaneously determining spatial
variation in vegetation cover [14].
Previous work in the Kalimantan tropical forest estimated a GPP value of between 2,859 and
3,227 gC m−2 yr−1 [15], and research in an Amazonian tropical forest showed a GPP of
3,040 gC m−2 yr−1 [1]. The seasonal dynamics of GPP prediction from satellite data were similar to
those of GPP from observations. Seasonally-integrated GPP observations over an eight-month period
accounted for 98% of the annual GPP prediction [16]. In a tropical evergreen forest in the Brazilian
Amazon, prediction of GPP from MODIS satellite data was consistent with GPP estimation from eddy
flux tower measurements [17,18], with a GPP prediction from MODIS of about 2,977 gC m−2 year−1 [17].
Research in the Labanan Concession Area in the East Kalimantan area of Indonesia showed an annual
range of GPP from 1,710 to 2,635 gC m−2 year−1 based on MODIS satellite data [19]. Estimates of
Remote Sens. 2010, 2
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GPP from Landsat were linearly related to daytime maize GPP measurements with root mean squared
error less than 1.58 gC m−2 d−1 over a GPP range of 1.88 to 23.1 gC m−2 d−1 [20].
Denpasar represent an urban city in Bali. Remote sensing is a tool for mapping and monitoring
urban areas. Application of remote sensing data for urban areas requires imagery with moderate until
high spatial resolution. Remote sensing is a powerful tool for mapping and monitoring regional
landscapes, and high-resolution imagery has proved effective for understanding land properties (e.g.,
ecosystems and hydrology) [21] and vegetative cover [22] in urban areas. Landsat imagery with a
moderate spatial resolution of 30 m has been effectively used to classify homogeneous landscapes;
however, the accuracy of such techniques may diminish in regions with highly heterogeneous
landscapes [22]. Urban areas provide unique challenges to satellite remote sensing techniques. In
urban landscapes, accurate flux measurements are complicated by surface heterogeneity, and NDVI is
less useful for estimating CO2 exchange [23]. The operational potential of urban remote sensing
depends on the capacity of remote sensing to capture small-scale objects over heterogeneous surfaces
in urban areas [24]. We therefore require imagery with spatial resolution higher than that of Landsat to
classify and identify the heterogeneous landscapes characteristic of urban areas. ALOS/AVNIR-2 and
Aster are two types of images that have higher spatial resolution than that of Landsat. ALOS/AVNIR-2
has a spatial resolution of 10 m and Aster 15 m in the red and near infrared (NIR) bands. The spatial
resolution of the remote sensing data is crucial for discriminating fluxes for the different land cover
types and hence avoiding significant errors due to application of a land surface model to a mixed pixel
containing large contrasts in vegetation cover [25].
The aim of this research is to analyze the potential of GIS and remote sensing for estimating GPP,
in particular over urban scales. In this study, we focus our analysis on the distribution of annual GPP,
which effectively measures CO2 assimilation by vegetation in urban areas. We further use two types of
satellite data (ALOS/AVNIR-2 and Aster) to assess the impact of spatial resolution on the detection
various ecosystem processes in Denpasar, an urban city in Bali Island, Indonesia.
2. Research Methods
2.1. Research Location and Materials
The research was conducted in Denpasar, a city in the Province of Bali, Indonesia, located at
8°36′56″S–8°42′01″S and 115°10′23″E–115°16′27″E (Figure 1) The location is between two
neighborhood regencies namely Badung and Gianyar Regencies. Denpasar city population reached
608,595 people in 2007. The condition of oblique topography is from north to south with the height
level of 0–75 m above sea level, while the inclination slope morphology is from 0 to 5%. Denpasar has
tropical climate with monthly mean temperature around 24–32 °C and monthly mean precipitation
around 13–358 mm. The dominant land use in Denpasar is the settlement that has an area of
7,179.17 ha.
Remote Sens. 2010, 2
1499
Figure 1. The research location: Denpasar, Bali, Indonesia.
The following materials were used in this work: (1) a digital image of the Denpasar area taken on
13 October 2006 from ALOS/AVNIR-2, (2) a digital image of the Denpasar area taken on 5
September 2006 from Aster, (3) a land use map of the Denpasar area in 2006 from Quick Bird, (4) a
topography map of the Denpasar region at a scale of 1:25,000 from the Indonesian National
Coordinating Agency for Surveys and Mapping (Bakosurtanal), and (5) solar radiation data from the
Indonesian Meteorology, Climatology and Geophysics Agency (BMKG). ALOS/AVNIR-2 and Aster
images were used to calculate the value of GPP. Topographic maps were used for coordinate
corrections. Meanwhile, solar radiation data was used to calculate photosynthetically active radiation
(PAR).
2.2. Radiance Correction
Digital numbers (DN) in each band of the ALOS/AVNIR-2 and Aster images were converted to
physical measurements of sensor radiance (Lsat). Conversion of DN to absolute radiance values is a
necessary procedure for comparative analysis of several images taken by different sensors [26]. Since
each sensor has its own calibration parameters used in recording the DN values, the same DN values in
two images taken by two different sensors may represent two different radiance values. For this
purpose we used the following formula, which implicitly includes a transformation of the analog signal
received at the sensor to DN stored in the resulting image pixels (Equation 1):
Lsat = (DN + a) × UCC (1)
Here, a represents the absolute calibration coefficients contained in the header file (0 for
ALOS/AVNIR-2 [27] and −1 for Aster satellite data [28]), and UCC is the Unit Conversion
Coefficient, which is different for each image band and also depends on the gain setting that was used
to acquire the image. Values of UCC for ALOS/AVNIR-2 and Aster images are given in Table 1.
Table 1. Unit Conversion Coefficient (UCC) for each band of satellite imagery.
ALOS/AVNIR-2 Aster Band No UCC Band No UCC
1 2 3 4
0.588 0.573 0.502 0.835
1 2
3N
0.676 0.708 0.862
Remote Sens. 2010, 2
1500
2.3. Data Analysis
The carbon budget is controlled by several major processes that describe the exchange of carbon
dioxide between terrestrial ecosystems and the atmosphere. Satellite remote sensing provides
consistent and systematic observations of vegetation and has played an increasingly important role in
characterizing vegetation structure and in estimating the GPP of vegetation [16]. In this work, GPP
was estimated using the following equation:
GPP = ε × fAPAR × PAR = ε × APAR (2)
PAR is restricted to the visible portion of the solar spectrum, i.e., from 400 to 700 nanometers [29].
PAR is assumed to be approximately half of the incoming solar radiation [30], with solar radiation data
from the Indonesian Meteorology, Climatology and Geophysics Agency (BMKG). The fAPAR is
related to the NDVI. NDVI has been widely used to estimate fAPAR because of the positive linear
relationship between these variables [12]. In Southeast Asian countries, this relationship can be
parameterized as [31]:
fAPAR = −0.08 + 1.075 × NDVI (3)
NDVI is computed from image data using the following formula:
Band Red Band Red InfraNear
Band Red - Band Red InfraNear NDVI
+= (4)
Light use efficiency (ε) is a biome-specific value representing the optimal potential of the
vegetation to convert PAR to GPP. Light use efficiency values are similar for all plant types and
biomes [29]. Estimation of LUE has proven problematic since it varies with vegetation type and
environmental conditions. Light use efficiency can be estimated using mechanistic models based on
leaf biochemistry and micrometeorological parameters, but these models are complex and generally
require many parameters that cannot be directly estimated by remote sensing [8]. Light use efficiency
may be assumed to be constant under non-stress conditions, but it is affected by stresses, phenological
stages, and the physical environment [10]. In some Asian countries, the value of ε has been estimated
as 1.5 gC MJ−1 [31]. Here, we compare the output of mechanistic models of light use efficiency to the
MODIS GPP product (MOD17) in the Denpasar area.
The main analysis in this research is to calculate GPP on different types of land use, but similar
measures have been applied in analyzing the entire area of research. GPP maximum and minimum
values are the maximum and minimum value of GPP in all research areas or a land use. Meanwhile the
average value of GPP is obtained from division between the total value of GPP with the number of
image pixels in all research areas or in each land use type. Analyses were carried out using ENVI 4.4
and ArcView GIS (version 3.2) software with the Spatial Analyst Extensions package.
3. Results and Discussion
3.1. Results
The two satellite datasets provide different estimates of annual GPP. With the ALOS/AVNIR-2
data, annual GPP varies from 0.13 gC m−2 yr−1 to 2,586.18 gC m−2 yr−1 with a mean of
836.23 gC m−2 yr−1. With the Aster satellite, GPP shows a minimum of 0.14 gC m−2 yr−1, a maximum
of 2,595.26 gC m−2 yr−1, and a mean of 776.83 gC m−2 yr−1. Based on ALOS/AVNIR-2 data, total GPP
Remote Sens. 2010, 2
1501
per year in Denpasar is 52,421.46 tC yr−1 and covers an area of 6,267.56 ha; the equivalent measures
based on Aster satellite data are 59,355.49 tC yr−1 and 7,647.84 ha (Table 2). The GPP pixel value
distribution from the ALOS/AVNIR-2 satellite data is dominated by low-GPP pixels (<250 gC m−2 yr−1),
which cover an area of 1,236.62 ha. The area covered decreases with increasing GPP; the highest GPP
pixels (2,250–2,587 gC m−2 yr−1) cover an area of 17.17 ha (Table 3). Similarly, the GPP pixel
distribution from the Aster satellite data is also dominated by low-GPP pixels (<250 gC m−2 yr−1), in
this case covering an area of 1,694.56 ha, and area again decreases with increasing GPP. At the high
end (GPP of 2,250–2,595 gC m−2 yr−1), these pixels cover an area of 6.59 ha (Table 3). Maps of the
GPP distribution from ALOS/AVNIR-2 and Aster are shown in Figure 2. In the Denpasar area, the
maximum GPP measured by both ALOS/AVNIR-2 and Aster is smaller than the maximum GPP
derived from the MOD17 (2,707.8 gC m−2 yr−1).
Table 2. Annual GPP from the ALOS/AVNIR-2 and Aster satellites.
Satellite GPP (gC m−2 yr−1) Total GPP
tC yr−1 Max Mean Min ALOS/AVNIR-2 2,586.18 836.23 0.13 52,421.46 Aster 2,595.26 776.83 0.14 59,355.49
Table 3. Total number of pixels and area covered for a range of GPP values from the
ALOS/AVNIR-2 and Aster satellites.
GPP value (gC m−2 yr−1)
ALOS/AVNIR-2 Aster Total pixels Area (ha) Total pixels Area (ha)
< 250 123,662 1,236.62 75,314 1,694.56250–500 101,694 1,016.94 59,523 1,339.27500–750 88,378 883.78 49,242 1,107.94750–1,000 76,929 769.29 42,346 952.781,000–1,250 70,423 704.23 36,175 813.941,250–1,500 61,249 612.49 30,336 682.561,500–1,750 52,544 525.44 24,785 557.661,750–2,000 34,440 344.40 14,900 335.252,000–2,250 15,720 157.20 6,990 157.27> 2,250 1,717 17.17 293 6.59
Differences in land use can impact the measured annual GPP. The maximum GPP in the
ALOS/AVNIR-2 data (2,586.18 gC m−2 yr−1) was observed for a rice field, while the minimum
(0.13 gC m−2 yr−1) was observed for all types of land use. In the Aster data, the maximum GPP
(2,595.26 gC m−2 yr−1) was observed for forests (mangroves), while the minimum (0.14 gC m−2 yr−1)
was observed for all types of land use (Figure 3 and Table 4). Estimates of total annual GPP observed
by ALOS/AVNIR-2 and Aster over different types of land use are shown in Table 4 and Figure 4.
Remote Sens. 2010, 2
1502
Figure 2. Annual GPP distribution from two satellites: (a) ALOS/AVNIR-2 and (b) Aster.
Figure 3. Annual GPP for different types of land use from ALOS/AVNIR-2 and Aster.
0
500
1,000
1,500
2,000
2,500
3,000
Settlement
Ricefield
Forest
(Mangrove)
Shrub
Perennial
plant
Dryland
Bareland
Land use
GPP
(gC
/m2/
yr) Aster Max
Aster MeanAster MinAvnir-2 MaxAvnir-2 MeanAvnir-2 Min
Table 4. Total annual GPP for different types of land use in the ALOS/AVNIR-2 and Aster data.
Land use Area
(Ha)
GPP (gC m−2 yr−1) Total GPP (tC yr−1)
ALOS/AVNIR-2 Aster ALOS/
AVNIR-2 Aster
Max Mean Min Max Mean Min
Settlement 7,179.17 2,511.43 540.49 0.13 2,353.91 492.44 0.14 12,675.23 15,992.84
Rice field 2,616.34 2,586.18 1,030.08 0.13 2,371.86 1,020.65 0.14 20,254.15 22,571.65
Forest (Mangrove) 700.69 2,501.92 1,123.58 0.13 2,595.26 1,177.40 0.14 6,255.51 7,081.16
Shrub 81.10 2,427.54 882.11 0.13 2,305.14 794.37 0.14 469.55 460.96
Perennial plant 961.75 2,456.39 1,034.77 0.13 2,257.76 989.24 0.14 8,300.74 8,567.52
Dry land 263.26 2,414.05 893.46 0.13 2,261.80 830.61 0.14 1,888.87 1,930.72
Bare land 827.39 2,489.12 771.56 0.13 2,244.33 648.17 0.14 2,577.41 2,750.65
3.2. Discussion
In the Denpasar area of Bali, the GPP measured by the ALOS/AVNIR-2 and Aster satellites is
smaller than that from the MODIS MOD17 product. This difference is primarily an artifact of the
larger spectral range of MODIS. For MODIS, the spectral range is 0.05 micrometers for the red band
a) b)
Remote Sens. 2010, 2
1503
and 0.035 micrometers for the NIR band. Meanwhile, ALOS/AVNIR-2 has a spectral range of 0.08
micrometers in the red band and 0.13 micrometers in the NIR band, and Aster has a spectral range of
0.06 micrometers in the red band and 0.08 micrometers in the NIR band. The smaller spectral ranges
of the ALOS/AVNIR-2 and Aster instruments increase the capability of these sensors to detect object
on the surface. As spectral range decreases, the sensors lose the ability to map fine spectral features
and to distinguish details [32]. The spectral range is directly related to both the material that is being
identified by the sensor and the contrast between that material and the background materials [33].
Figure 4. Total annual GPP for different types of land use in the ALOS/AVNIR-2 and Aster data.
0
5,000
10,000
15,000
20,000
25,000
Settlement
Ricefield
Forest
(Mangrove)
Shrub
Perennial
plant
Dryland
Bareland
Land use
GPP
(tC
/yr)
ALOSAster
The top-of-atmosphere (TOA) reflectance in band 4 of AVNIR-2 appears to be lower than the TOA
reflectance from exogenous sensor bands centered at 860 nm. This trait can be explained by the
significant water vapor and dioxygen absorption that occurs in this band [27]. This difference in TOA
reflectance may be responsible for the different estimate of annual GPP from ALOS/AVNIR-2.
Different values of GPP may also reflect differences in land use, which include differences in
vegetation type, percent vegetation cover and dissemination. The measured vegetation index is related
to the percent cover of vegetation in a given region [10,29]. Forests (mangroves) and rice fields are
two types of land use that typically result in a higher mean GPP due to extensive and homogeneous
vegetation cover. In contrast, areas dominated by settlements tend to have a higher maximum GPP but
a lower mean GPP because the vegetation indices and GPP estimates of a given pixel are based on the
average spectral value of that pixel. Denpasar is a unique city because it contains a “holy area” in the
center of town that is characterized by extensive vegetation cover. The result is a high maximum GPP
associated with settlement land use in Denpasar. Additional complication in the city comes from the
fact that factories emitting CO2 may be hidden beneath the same type of roof as found in residential
areas. Additionally, canopy height fluctuations in the center of town are substantial and may cover
many different types of land use. This creates a problem for subdividing urban areas into generalized
classes of urban land use and activity based on the spectral values of each individual pixel [23].
The higher spatial resolution of ALOS/AVNIR-2 improves the detection of specific land use
features, such as settlements, that lead to highly heterogeneous landscapes. As a result,
ALOS/AVNIR-2 measured a higher GPP from settlement land use than Aster. In general, flux
measurements are complicated in urban areas by surface heterogeneity, and NDVI becomes less
important for scaling the CO2 exchange [23].
Remote Sens. 2010, 2
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The lower spatial resolution of Aster results in a higher total annual GPP from settlement land use
than that measured by ALOS/AVNIR-2. The Aster satellite detects sparse vegetation around a
settlement as a pixel with a low vegetation index rather than as a residential pixel. In other words,
increased pixel size (or decreased spatial resolution) results in the loss of image detail [32]. Satellite
data with high spatial resolution may be able to narrow the gap that currently exists between field
measurements and remotely-sensed data from coarse-resolution satellites [34]. Improving spatial
resolution is key to enhancing our ability to map detailed, scale-dependent variation. However, the
image has high spatial resolution which has the disadvantage of having low spectral range, as also has
been discussed previously by Lizarazo [35]. This condition causes the choice of image resolution is
important to support the research goals.
4. Conclusion
Annual GPP measured by the ALOS/AVNIR-2 satellite is lower than that from Aster because
ALOS/AVNIR-2 has a higher spatial resolution and smaller spectral range than Aster. Total GPP per
year in Denpasar was 52,421.46 tC yr−1 as estimated by ALOS/AVNIR-2 and 59,355.49 tC yr−1 as
estimated by Aster.
The medium spatial resolution of the remote sensing data is crucial for discriminating different land
cover types in urban areas. Because of the heterogeneous land cover, the maximum value of GPP from
ALOS/AVNIR-2 was smaller than that from Aster. Meanwhile, the annual mean GPP from
ALOS/AVNIR-2 was higher than that from Aster because the higher spatial resolution of
ALOS/AVNIR-2 results in improved detection of vegetation cover and conditions.
Estimates of GPP are affected by land use patterns. In particular, forests (mangroves) and rice fields
are characterized by higher mean GPP. ALOS/AVNIR-2 estimates GPP as 1,123.58 gC m−2 yr−1 in
forests (mangroves) and 1,030.08 gC m−2 yr−1 in rice fields; the totals from Aster are 1,177.40 gC m−2 yr−1
and 1,020.65 gC m−2 yr−1, respectively. The lowest mean GPP was observed in land with settlements
and was 540.49 gC m−2 yr−1 in the ALOS/AVNIR-2 data and 492.44 gC m−2 yr−1 in the Aster data.
The maximum GPP measured by ALOS/AVNIR-2 and Aster was smaller than the maximum from
the MODIS GPP product (MOD17) in the Denpasar area, over a tropical peat swamp forest in central
Kalimantan-Indonesia, and over a tropical forest in central Amazonia, Brazil.
Differences in spatial and spectral resolution affect the accuracy of object detection. For
heterogeneous areas such as those containing settlements, satellites with high spatial resolution are
necessary to detect detailed features. For homogeneous areas such as forests (mangroves) and rice
fields, high spectral resolution is recommended.
Further research is needed to more accurately validate ALOS/AVNIR-2 and Aster GPP estimates.
In particular, these satellites should be tested in areas with eddy flux towers so that satellite results can
be directly compared to accurate in situ data.
Acknowledgments
Support for this work was provided by a JAXA, CReSOS and Indonesian department of national
education (Diknas). Yasuhiro Sugimori and Abe Susanto kindly provided the image data and
research costs.
Remote Sens. 2010, 2
1505
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