Enhanced Built-up and Bareness Index (EBBI) for Mapping Built-up and Bare Land In Urban Area.

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Remote Sensing — Editors

Journal Contact

Remote Sensing Editorial Office

MDPI AG, Klybeckstrasse 64, 4057 Basel, Switzerland E-Mail: remotesensing@mdpi.com

Research Geographer-15, U. S. Geological Survey (USGS), USGS Western Geographic Science Center (WGSC), 2255, N. Gemini Dr., Flagstaff, AZ 86001, USA

Tel. +94-11-2788924; Fax: +1 928 556 7112 Website: http://profile.usgs.gov/pthenkabail E-Mail: thenkabail@gmail.com

Interests: 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 resources management, (i) environments, (j) vegetation, and (k) characterization of large river basins and deltas

Contribution

Associate Editor

Prof. Dr. Clement Atzberger

Head Surveying, Remote Sensing & Land Information (IVFL), University of Natural Resources and Life Sciences, Vienna (BOKU), Peter Jordan Strasse 82, 1190 Vienna, Austria

Fax: +43 1 47654 5142

Website: http://www.rali.boku.ac.at/ivfl.html E-Mail: clement.atzberger@boku.ac.at

Interests: imaging spectroscopy; time series analysis; radiative transfer modeling (forward and inverse); retrieval of

vegtetation biophysical variables; vegetation monitoring

Contribution

Associate Editor Dr. Nicolas Baghdadi

Maison de la Télédétection, Irstea - UMR TETIS, 500 rue JF Breton, 34093 Montpellier Cedex 05, France Website: http://www.tetis.teledetection.fr/

E-Mail: nicolas.baghdadi@teledetection.fr

Interests: SAR images applied to soil (surface roughness, soil moisture, texture); Lidar and Forest (canopy height and

biomass); SAR images and biomass

Associate Editor Dr. Ioannis Gitas

Laboratory of Forest Management and Remote Sensing, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, Thessaloniki, Greece

Fax: +30 2310 992677 Website: http://fmrs.web.auth.gr/ E-Mail: igitas@for.auth.gr

Interests: forest fires; pre-fire planning and post-fire assessment; land use/land cover mapping; soil erosion risk

assessment/desertification; other environmental applications of remote sensing and GIS

Contribution

Associate Editor Dr. Richard Gloaguen

Remote Sensing Group, Helmholtz Institute Freiberg, TU Bergakademie Freiberg, Bernhard von-Cotta Str., 2, D-09599 Freiberg, Germany

Website: http://tu-freiberg.de/remote-sensing-group E-Mail: r.gloaguen@hzdr.de

Interests: earth sciences; remote sensing/photogrammetry; tectonic geomorphology; vegetation physical properties;

hydrological cycle

Contribution

Associate Editor

Prof. Dr. Alfredo R. Huete

Plant Functional Biology and Climate Change Cluster, School of Environment, University of Technology Sydney, 15 Broadway Road Ultimo, NSW 2007, Australia

Website: http://www.c3.uts.edu.au/ E-Mail: alfredo.huete@uts.edu.au

Interests: biophysical remote sensing; phenology; satellite products; carbon and water fluxes; land use science; drought

studies

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Associate Editor Dr. Yoshio Inoue

National Institute for Agro-Environmental Sciences (NIAES), Tsukuba, Ibaraki 305-8604, Japan Tel. +81-29-838-8222

Website: http://www.niaes.affrc.go.jp/researcher/inoue_y_e.html E-Mail: yinoue@affrc.go.jp

Interests: plant eco-physiology; remote sensing, modeling, agro-ecosystem; precision farming; GIS Contribution

Associate Editor Dr. Josef Kellndorfer

Senior Scientist, The Woods Hole Research Center, 149 Woods Hole Road, Falmouth, MA 02540, USA Fax: +1 (508) 444 1840

Website: http://whrc.org/about/cvs/jkellndorfer.html E-Mail: jkellndorfer@whrc.org

Interests: REDD; remote sensing; forests; climate change; high performance computing; SAR imaging; data fusion; time

series analysis

Associate Editor Dr. Norman Kerle

Department of Earth Systems Analysis (ESA), Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 6, Hengelosestraat 99, 7500 AA Enschede, Netherlands

Website: http://www.itc.nl/resumes/kerle E-Mail: n.kerle@utwente.nl

Interests: damage assessment; vulnerability; disaster risk management; UAV; resilience; recovery; OBIA; object-oriented

analysis; VGI

Associate Editor

Dr. Alexander A. Kokhanovsky

EUMETSAT, Eumetsat Allee 1, D-64295 Darmstadt, Germany E-Mail: Alexander.Kokhanovsky@eumetsat.int

Interests: remote sensing; light scattering; radiative transfer; inverse problems; atmospheric optics; retrieval of aerosol and

cloud properties from spaceborne observations

Contribution

Associate Editor Dr. Richard Müller

German Meteorological Service CM-SAF, Frankfurter Straße 135, 63067 Offenbach, Germany Fax: +49 (0) 69 8062 4955

Website: http://www.cmsaf.dwd.de/ E-Mail: richard.mueller@dwd.de

Interests: remote sensing of surface radiation; clouds and aerosols; sensor calibration; methods for \"merging\" in-situ data

with remote sensing data

Contribution

Associate Editor Dr. Parth Sarathi Roy

University Centre of Earth & Space Science, Hyderabad Central University P.O Central University, Gachibouli, Hyderabad 500046, India

Tel. +918008504546 E-Mail: psroy13@gmail.com

Interests: land use and land cover change; landscape ecology; biodiversity; land dynamic modelling; assessment of

ecological services

Associate Editor

Prof. Dr. Randolph H. Wynne

Department of Forest Resources and Environmental Conservation, Virginia Tech, Cheatham Hall, RM 319, 310 West Campus Dr, Blacksburg, VA 24061, USA

Tel. 540-231-7811

Website: http://frec.vt.edu/people/faculty/faculty_folder/wynne.html E-Mail: wynne@vt.edu

Interests: applications of remote sensing to forestry; natural resource management; ecological modeling; and earth system

science

Contribution

Associate Editor Prof. Dr. Xiao-Hai Yan

Center for Remote Sensing (CRS), College of Earth, Ocean and Environment, University of Delaware, 209 Robinson Hall, Newark, DE 19716, USA

Website: http://www.ceoe.udel.edu/people/profile.aspx?xiaohai E-Mail: xiaohai@udel.edu

Interests: satellite oceanography/ocean remote sensing; Physical oceanography/ocean circulation/climate change; remote

sensing of estuaries, coastal and open ocean waters; remote sensing image processing; air-sea interactions and upper ocean dynamics; mirowave remote sensing (altimeter, scatterometer and SAR); environmental remote sensing

Contribution

Senior Assistant Editor Ms. Xuanxuan Guan

MDPI Tongzhou Office, Room 2207, Jincheng Center, No. 21 Cuijingbeili, Tongzhou District, Beijing 101101, China Fax: +86 10 59011089

E-Mail: xuanxuan.guan@mdpi.com

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MDPI AG, Klybeckstrasse 64, CH-4057 Basel, Switzerland E-Mail: costache@mdpi.com

Former Editor-in-Chief Prof. Dr. Wolfgang Wagner *

Research Group Remote Sensing, Department of Geodesy and Geoinformation (GEO), Vienna University of Technology (TU Wien), Gusshausstrasse 27-29, 1040 Vienna, Austria

Fax: +43 1 58801 12299

Website: http://www.ipf.tuwien.ac.at/

Interests: remote sensing; geophysical parameter retrieval; airborne laser scanning; full-waveform lidar; radar remote

sensing; 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 Akca

Department of Civil Engineering, Isik University, TR-34980 Sile, Istanbul, Turkey Fax: +90 216 712 1474

Website: http://www2.isikun.edu.tr/personel/akca

Interests: sensor calibration; 3D city modeling; laserscanning; photogrammetry; machine vision; cultural and natural

heritage applications; high accuracy object measurement and 3D modeling

Contribution: Special Issue: Remote Sensing in Natural and Cultural Heritage

Dr. Sreekala Bajwa

Department of Agricultural and Biosystems Engineering North Dakota State University NDSU Dept. 7620 PO Box 6050, Fargo, ND 58108, USA

Tel. +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 Balzter

Holder of the Royal Society Wolfson Research Merit Award, Centre for Landscape and Climate Research, Department of Geography, University of Leicester, Bennett Building, University Road, Leicester LE1 7RH, UK

Fax: +44 116 252 3854 Website: http://www.le.ac.uk/clcr

Interests: land cover / land use change; spatial-temporal scaling; land/atmosphere interactions; data assimilation; synthetic

aperture radar (SAR); SAR interferometry; SAR polarimetry; ground-based, airborne and spaceborne light detection and ranging (LIDAR); digital elevation models; carbon accounting; forest structure and biomass mapping; vegetation phenology; fire and burned area mapping

Contribution: Special Issue: Remote Sensing and GIS for Habitat Quality Monitoring In other journals:

Special Issue: Land Use Change Feedbacks with Climate

Dr. Agnes Begue

CIRAD-UMR TETIS, Maison de la Télédétection, 500 Rue Jean François Breton, 34093 Montpellier, France Tel. +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 Campbell

Geography Department, 220 Stanger Street, 115 Major Williams Hall, Virginia Tech, Blacksburg, VA 24061, USA Tel. 540.231.5841

Website: http://geography.vt.edu/people/campbell.htm

Interests: agricultural systems (crop rotation, tillage assessment, yield estimation); soil variability; land use/land cover

change; coastal reclamation; urban systems (microclimates, impervious surfaces, drainage)

Prof. Dr. Toby N. Carlson

Professor Emeritus of Meteorology, Penn State University, 617 Walker Building, University Park, PA 16802, USA Tel. (814) 863-1582; Fax: +1 814 865 3663

Website: http://www.met.psu.edu/people/tnc

Interests: satellite remote sensing applications to regional planning; modeling of evapotranspiration over plant canopies;

land surface processes

Dr. Matthew Clark

Department of Geography and Global Studies & Center for Interdisciplinary Geospatial Analysis (CIGA), Sonoma State University, 1801 E. Cotati Ave, Rohnert Park, CA 94928, USA

Tel. 707-664-2558

Website: http://www.sonoma.edu/geoglobal/home/faculty/matthew-clark.html

Interests: remote sensing; geographic information systems; biogeography

Dr. Roberto Colombo

Remote Sensing of Environmental Dynamics Lab., Dept. of Earth and Environmental Sciences, University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milan, Italy

Website: 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 Dematte

Soil Science Department, Luiz de Queiroz College of Agriculture, São Paulo, University of Piracicaba, SP 13418-900, Brazil Website: http://www.en.esalq.usp.br/

Interests: remote sensing applied to soils; soil spectroscopy, digital soil mapping, RS from ground to space, precision

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Prof. Dr. Ralph Dubayah

Department of Geography, 2181 LeFrak Hall, University of Maryland, College Park MD 20742, USA Fax: +1 301 314 9299

Website: http://www.geog.umd.edu/facultyprofile/Dubayah/Ralph

Interests: lidar remote sensing; terrestrial carbon cycling; biodiversity and habitat

Prof. Dr. Giles M. Foody

School of Geography, University of Nottingham, Nottingham, NG7 2RD, UK Fax: +44 1159515249

Website: http://www.nottingham.ac.uk/~lgzwww/contacts/points/teaching.phtml?name=foody

Interests: land cover; image classification; ecology; GIS

Dr. Chandra Giri

Research Physical Scientist, USGS Earth Resources Observation and Science (EROS) Center, Nicholas School of the Environment/Duke University , A321 LSRC, Box 90328, Durham, NC 27708-0328, USA

Tel. 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 cover

mapping and monitoring using multi-spectral, multi-temporal, and multi-platform remotely sensed data; image pre-processing, classification, and validation using cloud computing

Contribution: Special Issue: Remote Sensing of Mangroves: Observation and Monitoring

Prof. Dr. Anatoly Gitelson

Israel Institute of Technology, 812 Rabin Hall, Technion City, Haifa 320000, Israel; School of Natural Resources, University of Nebraska - Lincoln, Lincoln, NE 68583, USA

Tel. 402 5608430

Website: http://www.calmit.unl.edu/people/agitelson2/

Interests: remote sensing of vegetation and water quality

Prof. Dr. Douglas G. Goodin

Remote Sensing Research Laboratory, Department of Geography, Kansas State University, Manhattan, KS 66506, USA Tel. 785-532-3411; Fax: +1 785 532 7310

Website: http://www.k-state.edu/rssg/doug.htm

Interests: environmental health and infectious disease; biophysical and thematic remote sensing; geospatial modeling

Dr. Eileen H. Helmer

USDA Forest Service, International Institute of Trop Forestry, 1201 Calle Ceiba, Río Piedras, PR 00926, USA

Interests: tropical forest; secondary tropical forest; forest inventory; image time series; forest change detection; forest

dynamics; forest phenology; vegetation mapping; vegetation classification; image composites; image mosaics; cloud detection

Dr. Benjamin Koetz

Directorate of Earth Observation Programmes, European Space Agency, Via Galileo Galilei, 00044 Frascati, Italy Fax: +39 06 941 80 552

Interests: remote sensing of ecosystem structure and processes; multi-temporal optical remote sensing for vegetation

monitoring; radiative transfer modeling; fusion of multi-source data

Contribution: Special Issue: Earth Observation for Water Resource Management in Africa

Special Issue: Lessons Learned from the SPOT4 (Take5): Experiment in Preparation for Sentinel-2

Dr. Felix Kogan

Environmental Monitoring Branch, National Oceanic and Atmospheric Administration, 5200 Auth Rd, Camp Springs, MD 20746, USA

Website: http://www.star.nesdis.noaa.gov/star/Kogan_F.php

Interests: remote sensing; ecosystems; climate and weather impact assessments; land cover/land use change; monitoring

droughts, desertification and deforestation; mosquito-borne diseases; productivity of land landscape; some issues of agriculture and forestry; agricultural meteorology and climatology; and environmental zoning

Prof. Dr. Raphael M. Kudela

Ocean Sciences Department, University of California, A-461 Earth & Marine Sciences Bldg., CA 95064, Santa Cruz, USA Fax: +1 831 459-4882

Website: http://people.ucsc.edu/~kudela/

Interests: ecological modeling and remote sensing; satellite oceanography; phytoplankton ecology and harmful algal

blooms

Contribution: Special Issue: Remote Sensing of Phytoplankton

Dr. Claudia Kuenzer

Head of Land Surface Dynamics Research Group of DLR, German Remote Sensing Data Center, DFD, Earth Observation Center, EOC, German Aerospace Center, DLR, Oberpfaffenhofen, 32234 Wessling, Germany

Fax: +49 8153 28 1458 Website: http://www.eoc.dlr.de

Interests: remote sensing applications for land and water resources management, land cover and land use change, time

series analyses, geosciences, linkage of natural and social sciences

Dr. Rosa Lasaponara

CNR-IMAA (National Research Council, Institute for Environmental Analysis), C.da S. Loya, 85050 Tito Scalo (PZ), Italy Fax: +39 0971 427222

Website: http://www.imaa.cnr.it/index.php?option=com_content&task=view&id=378&Itemid=220&lang=en

Interests: remote sensing; data processing; microwave sensor design; analytical methods, modeling, readout and software

for sensors; sensor technology and new sensor principles

Contribution: Special Issue: New Perspectives of Remote Sensing for Archaeology

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Close-range Remote Sensing & Photogrammetry Group, University of Vigo, EUET Forestal, Campus A Xunqueira s/n, 36005 Pontevedra, Spain

Tel. +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 applications Contribution: Special Issue: Close-Range Remote Sensing by Ground Penetrating Radar

Dr. Arko Lucieer

School of Land and Food, Discipline of Geography and Spatial Sciences, University of Tasmania, Private Bag 76, Hobart, TAS 7001, Australia

Tel. +61362262140; Fax: +61 (0)3 6226 2989 Website: http://www.lucieer.net

Interests: 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 techniques

Contribution: Special Issue: UAV-Based Remote Sensing Methods for Modeling, Mapping, and Monitoring Vegetation and Agricultural Crops

Dr. Nicola Masini

CNR-IBAM (National Research Council, Institute for Archaeological and Architectural Heritage), C.da S. Loya, 85050 Tito Scalo (PZ), Italy

Fax: +39 0971 427333

Website: http://www.ibam.cnr.it/englishversion/Masini.htm

Interests: remote sensing for archaeology; Lidar; archaeogeophisics; non invasive tests for historical building Contribution: Special Issue: New Perspectives of Remote Sensing for Archaeology

Dr. Andrew McGonigle

School of Geography, University of Sheffield, Sheffield S10 2TN, UK Fax: +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. Melesse

Department of Earth and Environment, AHC-5-390, Florida International University, 11200 SW 8th Street, Miami, FL 33199, USA

Fax: +1-305-348-3877.

Website: http://faculty.fiu.edu/~melessea/

Interests: watershed modelling; sediment dynamics, climate change, evapotranspiration and energy fluxes; system

analysis; remote sensing hydrology

Contribution: Special Issue: Land Surface Fluxes In other journals:

Special Issue: Remote Sensing of Natural Resources and the Environment

Special Issue: Hydrologic System Analysis, Patterns, and Predictions for Arid and Semi-arid Environment

Dr. Deepak R. Mishra

Department of Geography, University of Georgia, 210 Field Street, Rm 212B, Athens, GA 30602, USA Website: http://geography.uga.edu/directory/profile/mishra-deepak/

Interests: Water quality (inland waters, estuaries, coastal, and open ocean waters); Wetlands health, productivity, and

carbon sequestration; Benthic habitat mapping, Cyber-innovated environmental sensing

Contribution: Special Issue: Remote Sensing of Water Resources

Special Issue: Remote Sensing in Coastal Environments

Prof. Dr. Jose Moreno

Laboratory for Earth Observation, Department of Earth Physics and Thermodynamics, Faculty of Physics, University of Valencia, C/ Dr. Moliner, 50, 46100 Burjassot, Valencia, Spain

Tel. +34 96 3543112; Fax: +34 96 354 33 85 Website: http://ipl.uv.es/?q=users/josemoreno

Interests: optical remote sensing; imaging spectroscopy; vegetation fluorescence; vegetation biophysical parameters; land

surface applications; optical reflectance/fluorescence models; retrieval methods; design of future earth observation missions; dynamical vegetation models; calibration/validation field campaigns

Prof. Dr. L. Monika Moskal

School of Environmental and Forest Sciences, College of the Environment, University of Washington, Box 352100, Seattle WA 98195-2100, USA. Director, UW Precision Forestry Cooperative and Remote Sensing and Geospatial Analysis Laboratory

Website: http://faculty.washington.edu/lmmoskal/

Interests: ALS/TLS LiDAR; precision forestry; hyperspatial remote sensing; ecosystem services Contribution: In other journals:

Special Issue: LiDAR and Other Remote Sensing Applications in Mapping and Monitoring of Forests Structure and Biomass

Prof. Dr. Soe Myint

School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85287, USA Tel. 480-965-6514; Fax: +49-480-965-8313

Website: http://geoplan.asu.edu/myint

Interests: remote sensing; GIS; geospatial statistics; land use land cover change and prediction; assessment and

monitoring of drought, land degradation, and desertification; landscape fragmentation; urban environmental modeling including urban water use and climate analysis; forest characterization including coastal environments; disaster assessment, recovery, and monitoring; agriculture water use, evapotranspiration, and surface energy analysis; spatial modeling; and classification algorithm development

Contribution: Special Issue: Thermal Remote Sensing Applications: Present Status and Future Possibilities

Dr. Markus Neteler

Head 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), Italy

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Interests: remote sensing for environmental risk assessment; Free and Open Source GIS development; remote sensing for

eco-health

Prof. Dr. Janet Nichol

Department of Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon Hong Kong Website: 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 visualisation

Contribution: Special Issue: Thermal Remote Sensing Applications: Present Status and Future Possibilities

Prof. Dr. Gonzalo Pajares Martinsanz

Department Software Engineering and Artificial Intelligence, Faculty of Informatics, University Complutense of Madrid, 28040 Madrid, Spain

Tel. +34.1.3947546

Website: http://www.fdi.ucm.es/profesor/pajares/

Interests: computer vision; image processing; pattern recognition; 3D image reconstruction, spatio-temporal image change

detection and track movement; fusion and registering from imaging sensors; superresolution from low-resolution image sensors

Contribution: Special Issue: Unmanned Aerial Vehicles (UAVs) based Remote Sensing In other journals:

Special Issue: State-of-the-Art Sensors Technology in Spain

Special Issue: Sensors in Agriculture and Forestry

Special Issue: Collaborative Sensors

Special Issue: Sensor-Based Technologies and Processes in Agriculture and Forestry

Special Issue: State-of-the-Art Sensors Technology in Spain 2013

Special Issue: Sensors for Fluid Leak Detection

Special Issue: Agriculture and Forestry: Sensors, Technologies and Procedures

Special Issue: Sensors in Agriculture and Forestry

Special Issue: State-of-the-Art Sensors Technology in Spain 2015

Special Issue: Image Processing in Agriculture and Forestry

Special Issue: Image and Video Processing in Medicine

Special Issue: Unmanned Aerial Vehicles in Geomatics

Special Issue: Imaging: Sensors and Technologies

Dr. George P. Petropoulos

Department of Geography and Earth Sciences, University of Aberystwyth, Old College, King Street Llandinam Building, Room H4 Aberystwyth, Ceredigion SY23 3DB, UK

Website: http://www.aber.ac.uk/en/iges/staff/academic-staff/gep9/

Interests: Earth Observation; GIS; multi- and hyperspectral remote sensing; land use/cover mapping; change detection;

natural hazards; fires; floods; land surface interactions; evapotranspiration; soil moisture; land surface temperature; land biosphere modelling; Soil Vegetation Atmosphere Transfer (SVAT) models; EO algorithms benchmarking; sensitivity analysis

Dr. Ruiliang Pu

School of Geosciences, University of South Florida, 4202 E. Fowler Ave., NES 107, Tampa, FL 33620, USA Tel. 813-974-1508; Fax: 813-974-4808

Website: http://rpu.myweb.usf.edu

Interests: wildfire detection and mapping; land use/land cover change detection and mapping; mapping with satellite remote

sensing and GIS

Contribution: In other journals:

Special Issue: Mapping and Assessing Natural Disasters Using Geospatial Technologies

Prof. Dr. Jiaguo Qi

Center for Global Change & Earth Observations, Michigan State University, 218 Manly Miles Building, 1405 S. Harrison Road, East Lansing, MI 48823, USA

Tel. +1-517-353-8736

Website: http://www.globalchange.msu.edu/qi.html

Interests: 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 cover change dynamic assessments; and decision support systems for agriculture. Dr. Qi’s primary professional focus has been on the generation of information and knowledge from satellite images using process-based models and geospatial technologies. Over the past decade, he developed geospatial modeling tools to guide rangeland management and crop irrigation 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 nitrogen leaching, 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, and recently developed innovative approaches to integrate human, environment and climate to understand the coupling nature of human and environment for sustainable development in developing countries in East and Southeast Asia, Central Asia and East and West Africa

Prof. Dr. Dale A. Quattrochi

Earth Science Office, ZP11, Marshall Space Flight Center, NASA, Huntsville, AL 35812 USA Tel. 256-961-7887; Fax: +1 256 961 7788

Interests: thermal remote sensing; urban heat island analysis; geospatial techniques and remote sensing; land use/land

cover change

Contribution: Special Issue: Thermal Remote Sensing Applications: Present Status and Future Possibilities In other journals:

Special Issue: Remote Sensing of Land Surface Properties, Patterns and Processes

Prof. Dr. Daniele Riccio

Università degli Studi di Napoli Federico II, Faculty of Engineering, Department of Electrical Engineering and Information Technology, Via Claudio 21, 80125 Napoli, Italy

Fax: +39 0817685925

Website: http://www.docenti.unina.it/Daniele.Riccio

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Contribution: In other journals:

Special Issue: Synthetic Aperture Radar (SAR)

Prof. Dr. Dar Roberts

Geography Department, University of California, Santa Barbara, Santa Barbara, CA 93106, USA Tel. (805)880-2531; Fax: +1-805-893-2578

Website: http://www.geog.ucsb.edu/people/faculty/dar-roberts.html

Interests: remote sensing of vegetation, geology, ecology, and ecophysiology

Dr. Duccio Rocchini

GIS 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), Italy

Fax: +39 3491425786

Website: http://gis.fem-environment.eu/rocchini/

Interests: ecological Informatics; ecological heterogeneity and biodiversity estimate by satellite imagery; Free and Open

Source Software for spatial ecology; statistical analysis of spatial and ecological data

Contribution: Special Issue: Ecological Status and Change by Remote Sensing

Special Issue: Earth Observation for Ecosystems Monitoring in Space and Time In other journals:

Special Issue: Geospatial Monitoring and Modelling of Environmental Change

Dr. Raad A. Saleh

Earth Resources Observation and Science (EROS) Center, US Geological Survey (USGS), 47914 252nd Street Sioux Falls, SD 57198, USA

Fax: +1 605 594 6906

Website: http://astrogeology.usgs.gov

Interests: satellite sensing systems; sensor networks; high-resolution EOS; and hyper-spectral imaging;

multi-dimensional image analysis algorithms; geo-referencing; planetary mapping; digital photogrammetry

Dr. Gabriel Senay

1_{Research Physical Scientist: USGS, Center for Earth Resource Observation & Science (EROS), Mundt Federal Building,}

47914 252nd Street, Sioux Falls, SD 57198-0001, USA

2_{South Dakota State University, GISc Center of Excellence (GIScCE), USA}

Fax: +1-605-594-6925.

Interests: regional water balance assessment and monitoring Contribution: Special Issue: Land Surface Fluxes

In other journals:

Special Issue: Hydrologic System Analysis, Patterns, and Predictions for Arid and Semi-arid Environment

Dr. Christopher Small

Lamont Doherty Earth Observatory, 304b Oceanography, Palisades, NY 10964, USA Website: http://www.ldeo.columbia.edu/user/small

Interests: geophysics; land surface processes; remote sensing; population and environment

Dr. Lenio Soares Galvao

Divisão de Sensoriamento Remoto, Instituto Nacional de Pesquisas Espaciais (INPE), Caixa Postal 515, Av. dos Astronautas, 1758, Bairro Jardim da Granja, 12245-970 São José dos Campos, SP, Brazil

Website: http://www.dsr.inpe.br/dsr/lenio/

Interests: hyperspectral remote sensing, reflectance spectroscopy, vegetation, soils and rocks

Dr. Salvatore Stramondo

Istituto Nazionale di Geofisica e Vulcanologia, National Earthquake Center, Remote Sensing Laboratory, Via di Vigna Murata 605, 00143 Rome, Italy

Fax: +39 06 51860507

Interests: remote sensing; synthetic aperture radar interferometry; multitemporal SAR interferometry; remote sensing for

natural disaster mitigation and monitoring

Contribution: Special Issue: Remote Sensing in Seismology

Dr. Anton Vrieling

Natural Resources Department, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands

Website: http://www.itc.nl/about_itc/resumes/vrieling.aspx

Interests: remote sensing; time series analysis; agriculture; food security; soil erosion

Prof. Dr. Lizhe Wang

Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China

Tel. +86 10 8217 8070

Website: http://www.escience.cn/people/lzwangEN/index.html;jsessionid=2F9BF4288D3B0200C2E74EA96DB4354C-n2

Interests: Digital Earth; Remote Sensing Image Processing; High Performance Geocomputing

Dr. Dongdong Wang

Department of Geographical Sciences, University of Maryland, 2181 Samuel J. LeFrak Hall, College Park, MD 20742, USA Website: http://www.geog.umd.edu/facultyprofile/Wang/Dongdong

Interests: quantitative remote sensing; surface radiation budget; satellite data integration; satellite data degradation; wildfire

and climate change

Dr. Lars T. Waser

Swiss National Forest Inventory, Swiss Federal Research Institute WSL, Zuercherstr. 111, CH-8903 Birmensdorf, Switzerland

Tel. +41 44 739 2292; Fax: +41 44 739 2215

Website: http://www.wsl.ch/info/mitarbeitende/waser/index_EN

Interests: national forest inventory; spatially estimating forest parameters; tree species; growing stock; change detection;

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Prof. Dr. Qihao Weng

Center for Urban and Environmental Change, Department of Geography, Geology, and Anthropology, Indiana State University, Terre Haute, IN 47809, USA

Tel. +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 integration Contribution: In other journals:

Special Issue: Remote Sensing of Land Surface Properties, Patterns and Processes

Dr. Iain H. Woodhouse

Edinburgh Earth Observatory, School of GeoSciences, Geography Building, Drummond, Edinburgh EH8 9XP, UK Fax: +44 (0) 131 650 2524

Website: http://www.geos.ed.ac.uk/people/person.html?indv=186

Interests: radar remote sensing; polar decomposition methods for visualising SAR data; novel visualisation techniques for

the analysis of multichannel remote sensing data; DEM generation and regional scale geomorphology; synergistic remote sensing of vegetation; macroecology and telemacroscopics

Contribution: Special Issue: Microwave Remote Sensing

Dr. Pablo J. Zarco-Tejada

QuantaLab Remote Sensing Laboratory, Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Alameda del Obispo, s/n, E-14004 Córdoba, Spain

Fax: +(34) 957 499 252

Website: http://quantalab.ias.csic.es

Interests: hyperspectral remote sensing imagery for vegetation stress monitoring, water stress detection with thermal

imagery; pre-visual indicators of stress; chlorophyll fluorescence; precision agriculture

Contribution: Special Issue: UAV-Based Remote Sensing Methods for Modeling, Mapping, and Monitoring Vegetation and Agricultural Crops

Dr. Raul Zurita-Milla

Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands

Website: http://www.itc.nl/about_itc/resumes/zurita-milla.aspx

Interests: spatio-temporal analysis; time series, process modelling and integration of remote sensing and GIS for

applications in phenology, agriculture, land use/land cover, epidemiology and public health

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Article: Enhanced Built-Up and Bareness Index (EBBI) for Mapping Built-Up and Bare Land in an Urban Area Remote Sens. 2012, 4(10), 2957-2970; doi:10.3390/rs4102957

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Remote Sens.2012, 4, 2957-2970; doi:10.3390/rs4102957

Remote Sensing

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Article

Enhanced Built-Up and Bareness Index (EBBI) for Mapping

Built-Up and Bare Land in an Urban Area

Abd. Rahman As-syakur 1,*, I Wayan Sandi Adnyana 1,2, I Wayan Arthana 1 and I Wayan Nuarsa 1,2

1

Environmental Research Center (PPLH), Udayana University, PB Sudirman Street, Denpasar, Bali 80232, Indonesia; E-Mail: iwarthana60@yahoo.co.id

2

Faculty of Agriculture, Udayana University, Kampus Bukit Jimbaran, Bali 80361, Indonesia; E-Mails: sandiadnyana@yahoo.com (I.W.S.A.); nuarsa@ymail.com (I.W.N.)

* Author to whom correspondence should be addressed; E-Mail: ar.assyakur@pplh.unud.ac.id; Tel.: +62-361-236-221; Fax: +62-361-236-180.

Received: 20 August 2012; in revised form: 21 September 2012 / Accepted: 24 September 2012 / Published: 2 October 2012

Abstract: Remotely sensed imagery is a type of data that is compatible with the

monitoring and mapping of changes in built-up and bare land within urban areas as the impacts of population growth and urbanisation increase. The application of currently available remote sensing indices, however, has some limitations with respect to distinguishing built-up and bare land in urban areas. In this study, a new index for transforming remote sensing data for mapping built-up and bare land areas is proposed. The Enhanced Built-Up and Bareness Index (EBBI) is able to map built-up and bare land areas using a single calculation. The EBBI is the first built-up and bare land index that applies near infrared (NIR), short wave infrared (SWIR), and thermal infrared (TIR) channels simultaneously. This new index was applied to distinguish built-up and bare land areas in Denpasar (Bali, Indonesia) and had a high accuracy level when compared to existing indices. The EBBI was more effective at discriminating built-up and bare land areas and at increasing the accuracy of the built-up density percentage than five other indices.

Keywords: Enhanced Built-Up and Bareness Index; NIR; SWIR; TIR

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Remote Sens. 2012, 4 2958

1. Introduction

One of the main problems in mapping urban areas is assessing the change in land usage from non-residential to residential. Land use changes usually occur because of high urbanisation and residential development rates. These conditions result in high surface runoff, changes in micro-temperature [1], transport of water pollutants [2], and reduction in water quality [3]. In some cases, development may introduce bare land within an urban area [4]. This bare land is seasonal due to the effect of land use changes or the timing of fallow periods in paddy fields after harvesting.

Mapping the built-up and bare land in urban areas is important because the existence of these types of land can be used as an indicator of urban development and environmental quality [5]. The mapping process applies different remotely sensed data and spectral values based on the land use category. Land use mapping primarily employs the multispectral classification method; however, there are other methods that also utilise the application of the remote sensing index [1]. Chen et al. [6] classified urban land uses using several remote sensing indices in the Pearl River Delta of China with high accuracy. Indices for mapping the built-up and bare land in urban areas, such as the Normalised Difference Built-Up Index (NDBI) [7], Index-based Built-Up Index (IBI) [1], Urban Index (UI) [8], Normalised Difference Bareness Index (NDBaI) [4], and Bare soil index (BI) [9], have been employed in various studies.

NDBI and UI are based on the high-speed mapping of built-up or bare land areas. Nevertheless, these two indices are unable to verify the distribution of built-up versus bare land areas [7,10,11]. He et al. [10] stated that this inability was due to the high complexity of the spectral response patterns to vegetation, bare land, and built-up areas, particularly in terms of the pixel combinations in areas with heterogenic objects.

This paper introduces a new index for mapping built-up and bare land areas. This index, referred to as the Enhanced Built-Up and Bareness Index (EBBI), is able to map and distinguish built-up and bare land areas and was tested by mapping these land use categories in the city of Denpasar (Bali, Indonesia). The EBBI mapping results were compared to the results of other remote sensing indices. The relationship between the amount of built-up cover from EBBI and that from other remote sensing indices was also determined. The results of this study provide an alternate and more accurate remote sensing index for mapping built-up and bare land areas.

2. Research Location

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Remote Sens. 2012, 4 2959

field, perennial plant, bare land, forest (mangrove), dry land, and shrub. Settlement is the dominant land use, followed by paddy field and perennial plant areas [15].

Figure 1. Research Location.

3. Method

3.1. Enhanced Built-Up and Bareness Index (EBBI)

The EBBI is a remote sensing index that applies wavelengths of 0.83 μm, 1.65 μm, and 11.45 μm, (NIR, SWIR, and TIR, respectively) to Landsat ETM+ images. These wavelengths were selected based on the contrast reflection range and absorption in built-up and bare land areas. According to Herold et al. [16], the reflectance values of built-up areas are higher due to the longer sensor wavelengths. The NIR wavelength, which corresponds to band 4 in Landsat ETM+ and band 5 in SWIR, is associated with a high contrast level for detecting built-up and bare land areas, as shown in Figure 2. In addition, in bands 4 and 5, there is an inverse reflectance ratio with respect to detecting built-up or bare land areas compared to vegetation. Vegetation has a high reflectance in band 4, but the reflectance of built-up or bare land in band 4 is low. In contrast, in band 5, there is high reflectance when detecting built-up areas compared with vegetated areas [17]. NIR and SWIR were used for mapping built-up areas in a study conducted by Zha et al. [7] when developing the NDBI.

Zhao and Chen [4] utilised Landsat ETM+ band 5 (SWIR) and band 6 (TIR) to generate the NDBaI. The NDBaI is an index used to map bare land areas. The TIR can distinguish high and low levels of albedo in built-up objects [4]. According to Weng [5], the utilisation of TIR channels is very effective for mapping built-up areas based on a low albedo, which eliminates the effect of shadows and water, while a high albedo demonstrates built-up and bare land areas clearly. The TIR channel also exhibits a high level of contrast for vegetation. The temperature of a built-up area is 10–12 degrees higher than that of vegetation [18]. Therefore, the combination of NIR, MIR, and TIR (Landsat ETM+ bands 4, 5, and 6) wavelengths makes it possible to improve the mapping method for built-up and bare land areas relative to previously existing remote sensing indices.

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Remote Sens. 2012, 4 2960

water pixels as well as negative values for vegetation and positive values for built-up and barren pixels. This outcome allows for easy distinguishing between built-up and bare land areas.

Figure 2. Spectral profiles for seven classes of land cover in Denpasar city. Values were derived from an average of 20 pixels for each class of land cover.

0 40 80 120 160 200

Band 1 Band 2 Band 3 Band 4 Band 5 Band 61 Band 62 Band 7

Channel

D

ig

it

a

l

N

u

m

b

e

r

Forest (Mangrove)

Paddy field

Paddy field at water Residential

Road

Water Bare land

To achieve a higher level of contrast, an improvement in the mathematical operations was introduced into the equation used to calculate the EBBI. The EBBI applies a root function to cluster the numbers that contrast identical objects based on the different levels of reflectance values. To obtain an index value of −1~1, the multiplied factor is divided by ten. The EBBI is calculated from the image data using the following formula:

6 Band 5

Band 10

4 Band -5 Band EBBI

+

= (1)

3.2. Data and Analysis

3.2.1. Data

The remote sensing data used in this study were Landsat ETM+ data acquired on 21 May 2002 in Path 116 and Row 66. The spectral wavelength ranges and the spatial resolution bands of the data are presented in Table 1 [19]. The accuracy levels of the analyses performed using several remote sensing indices were compared to the distribution of built-up and bare land areas in the IKONOS imagery analysis. The IKONOS image used for comparison was acquired on 22 April 2002. The interpretation of the distribution of built-up and bare land originated from the Regional Planning and Development Board of Denpasar in 2003. The classification of the IKONOS image had an overall accuracy rate of 96.4%.

3.2.2. Analysis

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Remote Sens. 2012, 4 2961

EBBI analysis were compared to the results of remote sensing analyses that used several other indices: the IBI, NDBI, UI, NDBaI and NDVI (Normalised Difference Vegetation Index) [20]. The IBI, NDBI, and UI are indices for quickly mapping built-up areas. In contrast, the NDVI is a well-known spectral index for rapidly mapping the distribution of vegetation and a variety of conditions over land surfaces. Previously, Matthias and Martin [21] applied NDVI for mapping the impervious area of an urban area of Germany. The formulas for the indices that were compared to the EBBI results are as follows:

[

]

where the pixel value used in each channel is the digital number. In this study, the EBBI and NDBal utilised band 6 of the Landsat ETM+ images; band 6 has a 60 m spatial resolution, which was rescaled to 30 m prior to transforming the data into an index.

Table 1. Spectral Bands of Landsat ETM+.

Channel Spectral Range (μm)

Spatial

Resolution (m)

Electromagnetic

Region Application

Band 1 0.45–0.52 30 Visible Blue Coastal water mapping, differentiation of vegetation from soils

Band 2 0.53–0.61 30 Visible Green Assessment of vegetation vigour

Band 3 0.63–0.69 30 Visible Red Chlorophyll absorption for vegetation differentiation

Band 4 0.78–0.90 30 Near Infrared Biomass surveys and delineation of water bodies

Band 5 1.55–1.75 30 Short Wave

Infrared Vegetation and soil moisture measurements

Band 6 10.4–12.5 60 Thermal Infrared Land surface temperature

Band 7 2.09–2.35 30 Short Wave

Infrared Hydrothermal mapping

Band 8 0.52–0.90 15 Panchromatic Visual interpretation

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Remote Sens. 2012, 4 2962

Serangan Island. To determine the accuracy of the EBBI, this study examined bare land located in Serangan Island. The validation process was accomplished by comparing the built-up and bare land areas determined by the indices (EBBI, IBI, NDBI, UI, NDBaI and NDVI) with the results from IKONOS. Comparative results were based on the differences in the areal percentage, which were used to determine the level of accuracy.

The percentage of built-up areas was obtained from the IKONOS image analysis. The percentage of built-up area refers to the proportion of built-up area within each pixel. Fifty built-up locations were randomly selected, and polygon sampling of the data was conducted based on the number of Landsat pixels in the image (30 × 30 m). The percentage of built-up areas was correlated to the remote sensing index to determine the relationship between the percentage of built-up areas and the remote sensing indices. The mean bias error (MBE) and the root mean square error (RMSE) [22,23] were used to calculate the systematic and non-systematic errors of each of the transformation indices. MBE is a good measure of model bias and is simply the average of the differences in the set. The RMSE was used to find the average error value between the remote sensing indices and the percentage of built-up areas.

4. Results

The transformation of bands 4, 5, and 6 of Landsat ETM+ was examined in this study. This study investigated the mapping of built-up and bare land areas by EBBI transformation. The EBBI transformation was compared to other transformation indices. The results of the EBBI transformation were compared to the results obtained using IKONOS imagery to determine the levels of accuracy and effectiveness. In addition, the results of comparing the relationship between the EBBI and the percentages of built-up area coverage and remote sensing indices are detailed in this section.

Transforming remote sensing data into an index value required the use of a control index value as a reference index to distinguish the different types of land cover. In this case, the reference was the limit of the index value for each type of transformation index, as shown in Table 2.

Table 2. Limitation of the index value for each type of index transformation in determining non-built-up, built-up, and bare land areas.

Remote Sensing Indices Built-Up Area Bare Land

EBBI (Enhanced Built-Up and Bareness Index) 0.100–0.350 >0.350

IBI (Index-based Built-Up Index) [1] 0.018–0.308 >0.308

NDBI (Normalised Difference Built-Up Index) [6] 0.100–0.300 >0.300

UI (Urban Index) >0 -

NDBaI (Normalised Difference Bareness Index) [4] - >−0.150

4.1. Mapping Built-Up and Bare Land

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Remote Sens. 2012, 4 2963

Table 3. The built-up area from each type of index/remote sensing data transformation.

Remote Sensing Indices Built-Up Area (ha)

EBBI 5,898.60

IBI 6,239.70

NDBI 5,934.33

UI 4,146.84

NDBaI -

IKONOS 6,629.04

The areas of bare land in Serangan Island determined using the remote sensing indices are presented in Table 4. The area of bare land on Serangan Island as determined by the EBBI is 173.93 ha, which corresponds to 75.11% of the bare land area obtained from IKONOS imagery (231.39 ha). This percentage is higher than the bare land area determined using IBI and NDBI. However, the percentage of bare land indicated by the NDBaI is higher than that determined by EBBI. The overall spatial distribution of built-up and bare land areas obtained from the remote sensing indices and IKONOS data are shown in Figure 3.

Table 4. The bare land area in Serangan Island determined using each type of

index/remote sensing data transformation.

Remote Sensing Indices Bare Land Area (ha)

EBBI 173.79

IBI 105.21

NDBI 100.89

UI -

NDBaI 197.82

IKONOS 231.39

The accuracy levels with respect to the percentage of built-up area for each type of transformation index from IKONOS data are shown in Table 5. The built-up area determined using the EBBI transformation was similar to that determined using the IKONOS data, which was assumed to represent the actual built-up area. A high level of accuracy was demonstrated when the IKONOS results were compared with the NDBI and UI transformations and negligible differences were observed between the IKONOS results and the IBI results. The percentage of similarity between the built-up area determined using the EBBI transformation compared to that from IKONOS was 69.65%; of this value, 25.49% of the IKONOS built-up area was transformed by EBBI into non-built-up area and 4.85% was transformed by EBBI into bare land area.

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Remote Sens. 2012, 4 2964

Figure 3. The spatial distribution of built-up areas and bare land is shown for each type of index/remote sensing data transformation: (a) EBBI, (b) IBI, (c) NDBI, (d) UI, (e) NDBaI, and (f) IKONOS imagery.

Table 5. The percent accuracy of the built-up area obtained from each type of index transformation.

Explanation EBBI (%) IBI (%) NDBI (%) UI (%)

Non-built-up area from indices vs.

built-up area from IKONOS 25.49 21.71 25.05 47.11

Built-up area from indices vs.

built-up Area from IKONOS 69.65 70.18 67.60 52.89

Bare land area from indices vs.

built-up Area from IKONOS 4.85 8.11 7.34 –

Total built-up area from indices vs.

Total built-up area from IKONOS 88.98 94.13 89.52 62.56

(a) (b) (c)

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Remote Sens. 2012, 4 2965

Table 6. The percent accuracies of the bare land area obtained from each type of index transformation.

Explanation EBBI (%) IBI (%) NDBI (%) NDBaI (%)

Non-bare land area from indices vs. bare land from IKONOS 13.73 12.10 13.15 31.74 Built-up area from indices vs. bare land area from IKONOS 23.45 49.59 50.72 – Bare land area from indices vs. built-up area from IKONOS 62.82 38.31 36.13 68.26 Total bare land area from indices vs. total bare land area from

IKONOS 75.11 45.47 43.60 85.49

When the average accuracy levels of the EBBI, NDBI, and IBI results were compared to those of the built-up and bare land area values obtained from IKONOS, the EBBI was found to exhibit the highest accuracy of all the indices. The EBBI exhibited an average accuracy level of 66.24%, which is higher than those of IBI and NDBI, which had average accuracy rates of 54.25% and 51.87%, respectively. The UI is an index that cannot detect bare land areas, while the NDBaI could not be used because it cannot map built-up areas.

The EBBI can be utilised directly as a highly accurate index for mapping built-up and bare land areas. Although the relative accuracy of the EBBI is lower than that of the IBI for mapping built-up areas and that of the NDBaI for mapping the distribution of bare land areas, the average accuracy level of EBBI represents a substantial improvement in the simultaneous mapping built-up and bare land areas.

4.2. Relationship between the Transformed Remote Sensing Indices and the Percentage of Built-Up Area

Figure 4 shows the relationship between the percentage of built-up area and the transformation indices (EBBI, IBI, NDBI, UI, NDBaI, and NDVI). The correlation coefficient and linear equation for each of the indices is indicated on the plot. A cross-plot of each index and the percentage of built-up area confirmed the close relationship between the EBBI results with the percentage of actual built-up areas. The relationship between the percentage of built-up areas and the EBBI results shows a high degree of correlation (r = 0.70). This correlation is higher than that of the five other remote sensing indices; the correlation values of the IBI, NDBI, UI, NDBaI, and NDVI are 0.61, 0.60, 0.59, 0.53, and 0.56, respectively.

The scatterplot indicates that the relationship between the EBBI results and the percentage of built-up area is generally higher than those of the other indices. In addition, statistical error analyses also demonstrated that EBBI generally produced lower values than the other indices, except with regard to MBE values, for which NDBaI produced higher values than EBBI. EBBI had an MBE value of 25.37%, while the MBE values of the IBI, NDBI, UI, NDBaI and NDVI were 60.03, 71.66, 57.84,

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Figure 4. Scatterplots of the built-up area percentage vs. the remote sensing transformation indices. (a) EBBI, (b) IBI, (c) NDBI, (d) UI, (e) NDBaI, and (f) Normalised Difference Vegetation Index (NDVI) .

Figure 5. Mean bias error (MBE) and root mean square error (RMSE) statistical values

between the percentage of built-up area and the remote sensing transformation indices.

-20

EBBI IBI NDBI UI NDBaI NDVI

Remote sensing indices

Urban remote sensing indices are generally used to distinguish built-up and bare land areas based on spectral values with a low level of accuracy because of the high degree of homogeneity in these land-use categories, especially in urban areas. However, utilisation of the EBBI, which is the first built-up and bare land index based on thermal infrared data, can increase the accuracy of mapping built-up and bare

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Remote Sens. 2012, 4 2967

land areas. The EBBI was also found to be very effective in distinguishing built-up and bare land areas, which is one of the major limitations of applying built-up area indices based on remotely sensed data. The use of three infrared channels (NIR, SWIR, and TIR) that reflect different contrasts in detecting built-up, bare land, and vegetation areas is responsible for the high accuracy level compared to other built-up area indices.

The application of TIR channels, which exhibit high emissivity in built-up areas, is one of the reasons that EBBI is a better index for distinguishing between classes of land use. The high emissivity in built-up areas is caused by the types of materials that are predominately found in these areas, such as roofing and building materials. In contrast, the emissivity of bare soil is determined by the level of soil moisture and by the mineral constituents of the soil [24]. Built-up areas exhibit higher heat conductivity than bare soil areas, resulting in the higher emissivity and albedo values of thermal infrared waves in built-up areas relative to bare soil areas.

The EBBI not only exhibits a better accuracy than other indices, but also was able to distinguish the amount of built-up areas with a higher correlation than other indices used in urban areas. The EBBI generally produced lower statistical errors regarding the measurement the built-up area percentage. The correlation levels between the percentage of built-up areas and the results of the other indices were all positive, with the exception of the results of NDVI. In addition, the MBE and RMSE values were generally lower than those determined by the other indices, with the exception of the MBE values, for which the value produced by NDBaI was better than that produced by EBBI. The utilisation of TIR channels and the improvement of the mathematical operations in the applied root function enhanced the statistical score regarding the percentage of built-up areas determined by the indices. TIR was also able to capture the temperature radiation released by buildings. Areas with relatively high building densities will emit larger amounts of temperature radiation. The root function applied in the EBBI contrasted identical objects based on different levels of reflectance values. The negative correlation exhibited by the NDVI was observed because this index is usually applied to rapidly map the distribution of vegetation. In NDVI, a smaller value indicates less vegetation, which results in a negative correlation with the amount of built-up areas. The relationships observed between the remote sensing indices and the amount of built-up area were similar to the results of previous studies, such as those of Widyasamratri [25] and Sukristiyanti et al. [11], who found the correlation coefficient between the UI-built-up area density in Semarang city to be 0.56 and 0.59, respectively.

The high degree of land homogeneity in built-up and bare land areas resulted in a low level of accuracy. Some regions in built-up areas were detected as non-built-up or bare land areas or were observed as noise in EBBI and NDBI images (Figure 3(a,c)). The spectral response of built-up areas showed a higher reflectance in the SWIR compared with the NIR wavelength range. However, in urban areas with heterogeneous landscapes, this factor is not always relevant. Drier vegetation or areas with mixed vegetation and buildings in a particular pixel can exhibit an even higher reflectance in the SWIR wavelength range than in the NIR range [26], thus leading to confusion with areas of built-up land. According to Yüksel et al. [27], accurate map processing in urban landscapes is further complicated by surface heterogeneity. Improving the spatial resolution may improve the accuracy of remote sensing in capturing small-scale objects over heterogeneous surfaces in urban areas [28].

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Remote Sens. 2012, 4 2968

fluctuations in the centre of the town are substantial and may cover many different land use types. This creates a problem for subdividing urban areas into generalised classes of urban land uses and activities based on the spectral values of each individual pixel. Therefore, the accuracy of such techniques may decrease in regions with highly heterogeneous landscapes [29].

All the indices could not perfectly differentiate between built-up and bare land because both of these land types show similar spectral responses in all Landsat ETM+ bands. However, the EBBI was found to be superior to the other indices in distinguishing between built-up and bare land due to the use of TIR channels. Visual inspection of the extraction results showed that confusion mainly occurs between built-up areas and bare land areas. Mixed regions were detected by the EBBI as built-up in bare land areas in regions with a highly heterogeneous landscape, which exhibit high values in NIR and SWIR because of the drier vegetation. Generally, all of the findings of this study demonstrated that the EBBI could be utilised as an alternative and more accurate remote sensing index to distinguish built-up areas from other land use areas and determine the cover percentage of built-up areas.

Data from built-up areas are very important for determining correlations between land use and environmental conditions, such as air and water quality and water runoff. It expected that in the future, the EBBI could be used in various applications associated with the utilisation of remote sensing data, specifically in the field of urban remote sensing. There are a number of potential applications for the EBBI, such as monitoring land use changes, following the development of cities, determining the condition of the urban environment, and calculating the urban population.

6. Conclusions

In this study, a new index for transforming remote sensing data was proposed and evaluated for mapping built-up and bare land areas. The index was able to map built-up and bare land areas with a single calculation and was referred to as the Enhanced Built-up and Bareness Index (EBBI). The EBBI is the first built-up and bare land index that uses the NIR, SWIR, and TIR channels simultaneously. The EBBI was applied in Denpasar, Indonesia, to distinguish between built-up and bare land in an urban area, and the results obtained by the EBBI were compared with those determined by other available remote sensing indices.

The use of the EBBI was found to improve the accuracy of mapping built-up and bare land areas. The EBBI was also effective at discriminating between built-up and bare land areas in an urban area. With regard to distinguishing between built-up and bare land areas with a single calculation, the EBBI showed an average accuracy level of 66.24%, which is higher than those of the IBI and NDBI (54.25% and 51.87%, respectively). The advantage of the EBBI is that it can be used not only to map built-up and bare land areas but is also capable of transforming the percentages of built-up land in urban area. Comparisons of the relationships between the percentages of built-up obtained from the EBBI and the five other remote sensing indices showed that the EBBI determines the built-up density percentage with higher accuracy (0.70) than the other indices.