Digital Remote Sensing within the Field of Land Change Science: Past, Present and Future Directions

The availability of repeat digital, synoptic measures of the earth’s surface has resulted in remote sensing of the earth’s surface forming the basis of many land change science (LCS) research questions. This article reviews passive digital remote sensing for the use of LCS and discusses the past, present and future directions for remote sensing applications within the LCS field. Rigorous detection of land cover change provides the foundation for improved understandings of human–environment interactions. Change detection includes the monitoring and assessing of land cover conversions and modifications that have become imperative to LCS. Generally land cover change analyses rely on digital remotely sensed data, in particular, passive satellite imagery. We aim to provide a general overview of the remote sensing methodologies used commonly for land cover change analyses and then consider more novel approaches for monitoring and assessing land cover change using the available technology. Finally, some of the main limitations to development in this field will also be discussed.

[1]  R. Kauth,et al.  The tasselled cap - A graphic description of the spectral-temporal development of agricultural crops as seen by Landsat , 1976 .

[2]  P. Sellers Canopy reflectance, photosynthesis and transpiration , 1985 .

[3]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[4]  D. Leckie Synergism of synthetic aperture radar and visible/infrared data for forest type discrimination. , 1990 .

[5]  D. Peddle,et al.  Image texture processing and data integration for surface pattern discrimination , 1991 .

[6]  Jesslyn F. Brown,et al.  Development of a land-cover characteristics database for the conterminous U.S. , 1991 .

[7]  D. Quattrochi,et al.  Measurement and analysis of thermal energy responses from discrete urban surfaces using remote sensing data , 1994 .

[8]  G. Foody,et al.  Sub-pixel land cover composition estimation using a linear mixture model and fuzzy membership functions , 1994 .

[9]  C. Justice,et al.  Development of vegetation and soil indices for MODIS-EOS , 1994 .

[10]  K. Johnsson Segment-based land-use classification from SPOT satellite data , 1994 .

[11]  G. Foody Ordinal-level classification of sub-pixel tropical forest cover , 1994 .

[12]  Robert A. Schowengerdt,et al.  On the estimation of spatial-spectral mixing with classifier likelihood functions , 1996, Pattern Recognit. Lett..

[13]  Jerry C. Ritchie,et al.  Remote sensing applications to hydrology: airborne laser altimeters , 1996 .

[14]  M. Thompson,et al.  A standard land-cover classification scheme for remote-sensing applications in South Africa , 1996 .

[15]  W. Salas,et al.  Mapping deforestation and secondary growth in Rondonia, Brazil, using imaging radar and thematic mapper data☆ , 1997 .

[16]  T. Carlson,et al.  On the relation between NDVI, fractional vegetation cover, and leaf area index , 1997 .

[17]  R. Lucas,et al.  Non-linear mixture modelling without end-members using an artificial neural network , 1997 .

[18]  L. Wald,et al.  Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images , 1997 .

[19]  Eric F. Lambin,et al.  Land-cover changes in sub-saharan Africa (1982–1991): Application of a change index based on remotely sensed surface temperature and vegetation indices at a continental scale , 1997 .

[20]  Christine Pohl,et al.  Multisensor image fusion in remote sensing: concepts, methods and applications , 1998 .

[21]  J. Townshend,et al.  Global land cover classifications at 8 km spatial resolution: The use of training data derived from Landsat imagery in decision tree classifiers , 1998 .

[22]  Michael A. Wulder,et al.  Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters , 1998 .

[23]  C. Wessman,et al.  Textural Analysis of Historical Aerial Photography to Characterize Woody Plant Encroachment in South African Savanna , 1998 .

[24]  Giles M. Foody,et al.  Detection of partial land cover change associated with the migration of inter-class transitional zones , 1999 .

[25]  Christopher O. Justice,et al.  The EOS land validation core sites: background information and current status , 1999 .

[26]  W. Cohen,et al.  Surface lidar remote sensing of basal area and biomass in deciduous forests of eastern Maryland, USA , 1999 .

[27]  Charles M. Schweik,et al.  The Use of Spectral Mixture Analysis to Study Human Incentives, Actions, and Environmental Outcomes , 1999 .

[28]  S. Ochoa-Gaona,et al.  Land use and deforestation in the highlands of Chiapas, Mexico , 2000 .

[29]  Stuart E. Marsh,et al.  A Landscape Approach for Detecting and Evaluating Change in a Semi-Arid Environment , 2000 .

[30]  Sassan Saatchi,et al.  The use of decision tree and multiscale texture for classification of JERS-1 SAR data over tropical forest , 2000, IEEE Trans. Geosci. Remote. Sens..

[31]  E. Lambin,et al.  Land-Cover-Change Trajectories in Southern Cameroon , 2000 .

[32]  J. Strobl,et al.  Object-Oriented Image Processing in an Integrated GIS/Remote Sensing Environment and Perspectives for Environmental Applications , 2000 .

[33]  A. Karnieli,et al.  A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region , 2001 .

[34]  M. Ramsey,et al.  Monitoring urban land cover change: An expert system approach to land cover classification of semiarid to arid urban centers , 2001 .

[35]  G. Foody Monitoring the magnitude of land-cover change around the southern limits of the Sahara , 2001 .

[36]  R. Fuller,et al.  An integrated approach to land cover classification: An example in the Island of Jersey , 2001 .

[37]  Andrew T. Hudak,et al.  Textural analysis of high resolution imagery to quantify bush encroachment in Madikwe Game Reserve, South Africa, 1955-1996 , 2001 .

[38]  Frédéric Achard,et al.  Tropical forest mapping from coarse spatial resolution satellite data: Production and accuracy assessment issues , 2001 .

[39]  P. Gamba,et al.  Joint analysis of SAR, LIDAR and aerial imagery for simultaneous extraction of land cover, DTM and 3D shape of buildings , 2002 .

[40]  R. Dubayah,et al.  Estimation of tropical forest structural characteristics using large-footprint lidar , 2002 .

[41]  Antonio Di Gregorio,et al.  Parametric land cover and land-use classifications as tools for environmental change detection , 2002 .

[42]  PETER H. VERBURG,et al.  Modeling the Spatial Dynamics of Regional Land Use: The CLUE-S Model , 2002, Environmental management.

[43]  E. Lambin,et al.  Proximate Causes and Underlying Driving Forces of Tropical Deforestation , 2002 .

[44]  Bo R. Döös Population growth and loss of arable land , 2002 .

[45]  Qihao Weng Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. , 2002, Journal of environmental management.

[46]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[47]  Christopher O. Justice,et al.  Special issue on the moderate resolution imaging spectroradiometer (MODIS): a new generation of land surface monitoring , 2002 .

[48]  David B. Lobell,et al.  Per-Pixel Analysis of Forest Structure , 2003 .

[49]  E. Davidson,et al.  Classifying successional forests using Landsat spectral properties and ecological characteristics in eastern Amazônia , 2003 .

[50]  Matthew D. Turner,et al.  Methodological Reflections on the Use of Remote Sensing and Geographic Information Science in Human Ecological Research , 2003 .

[51]  Charles M Schweik,et al.  Using Satellite Imagery to Locate Innovative Forest Management Practices in Nepal , 2003, Ambio.

[52]  M. Fladeland,et al.  Remote sensing for biodiversity science and conservation , 2003 .

[53]  Thomas Blaschke,et al.  A comparison of three image-object methods for the multiscale analysis of landscape structure , 2003 .

[54]  C. Topp,et al.  Forecasting the environmental and socio-economic consequences of changes in the Common Agricultural Policy , 2003 .

[55]  Limin Yang,et al.  Urban Land-Cover Change Detection through Sub-Pixel Imperviousness Mapping Using Remotely Sensed Data , 2003 .

[56]  L. P. C. Verbeke,et al.  Using genetic algorithms in sub-pixel mapping , 2003 .

[57]  Curt H. Davis,et al.  A combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areas , 2003, IEEE Trans. Geosci. Remote. Sens..

[58]  Richard R. Forster,et al.  Fusion of hyperspectral and radar data using the IHS transformation to enhance urban surface features , 2003 .

[59]  José A. Sobrino,et al.  Land surface temperature retrieval from LANDSAT TM 5 , 2004 .

[60]  Ioannis Z. Gitas,et al.  A performance evaluation of a burned area object-based classification model when applied to topographically and non-topographically corrected TM imagery , 2004 .

[61]  Volker Walter,et al.  Object-based classification of remote sensing data for change detection , 2004 .

[62]  Steven W. Running,et al.  Effects of precipitation and soil water potential on drought deciduous phenology in the Kalahari , 2004 .

[63]  T. Dawson,et al.  Quantifying forest above ground carbon content using LiDAR remote sensing , 2004 .

[64]  Louis R. Iverson,et al.  Applications of satellite remote sensing to forested ecosystems , 1989, Landscape Ecology.

[65]  Arturo Ruiz-Luna,et al.  Land use, land cover changes and coastal lagoon surface reduction associated with urban growth in northwest Mexico , 2003, Landscape Ecology.

[66]  N. Coops,et al.  High Spatial Resolution Remotely Sensed Data for Ecosystem Characterization , 2004 .

[67]  G. J. Hay,et al.  A multiscale framework for landscape analysis: Object-specific analysis and upscaling , 2001, Landscape Ecology.

[68]  C. Justice,et al.  Land change science : observing, monitoring and understanding trajectories of change on the Earth's surface , 2004 .

[69]  A. Rango,et al.  Object-oriented image analysis for mapping shrub encroachment from 1937 to 2003 in southern New Mexico , 2004 .

[70]  Peng Gong,et al.  Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery , 2004 .

[71]  Jan Dempewolf,et al.  Mapping regional land cover with MODIS data for biological conservation: Examples from the Greater Yellowstone Ecosystem, USA and Pará State, Brazil , 2004 .

[72]  R. Latifovic,et al.  Large area forest classification and biophysical parameter estimation using the 5-Scale canopy reflectance model in Multiple-Forward-Mode , 2004 .

[73]  Hanqiu Xu,et al.  Remote sensing of the urban heat island and its changes in Xiamen City of SE China. , 2004, Journal of environmental sciences.

[74]  Benjamin L Turner Land Change Science , 2004 .

[75]  H. Nagendra,et al.  Assessing the impact of Celaque National Park on forest fragmentation in western Honduras , 2004 .

[76]  D. Lu,et al.  Change detection techniques , 2004 .

[77]  J. Southworth An assessment of Landsat TM band 6 thermal data for analysing land cover in tropical dry forest regions , 2004 .

[78]  C. Tucker,et al.  North American vegetation patterns observed with the NOAA-7 advanced very high resolution radiometer , 1985, Vegetatio.

[79]  M. Ashton,et al.  Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests , 2004 .

[80]  A. Formaggio,et al.  Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data , 2005 .

[81]  K. Price,et al.  Relations between NDVI, Grassland Production, and Crop Yield in the Central Great Plains , 2005 .

[82]  G. Hay,et al.  An automated object-based approach for the multiscale image segmentation of forest scenes , 2005 .

[83]  Wolfgang Kainz,et al.  Reasoning about changes of land covers with fuzzy settings , 2005 .

[84]  Edwin W. Pak,et al.  An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data , 2005 .

[85]  K. Soudani,et al.  Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands , 2006 .

[86]  Niklaus E. Zimmermann,et al.  Improving land change detection based on uncertain survey maps using fuzzy sets , 2007, Landscape Ecology.

[87]  Eric F. Lambin,et al.  Land-Use and Land-Cover Change , 2006 .

[88]  Zhong Lu,et al.  Multiple Baseline Radar Interferometry Applied to Coastal Land Cover Classification and Change Analyses , 2006 .

[89]  Jin Chen,et al.  Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction , 2006 .

[90]  Nadège Martiny,et al.  Compared regimes of NDVI and rainfall in semi‐arid regions of Africa , 2006 .

[91]  D. Quattrochi,et al.  A multi-scale approach to urban thermal analysis , 2006 .

[92]  J. Zhang,et al.  Change analysis of land surface temperature based on robust statistics in the estuarine area of Pearl River (China) from 1990 to 2000 by Landsat TM/ETM+ data , 2007 .

[93]  E. Lambin,et al.  The emergence of land change science for global environmental change and sustainability , 2007, Proceedings of the National Academy of Sciences.

[94]  T. Downing,et al.  Global Desertification: Building a Science for Dryland Development , 2007, Science.

[95]  Patrick Hostert,et al.  A method to detect and correct single-band missing pixels in Landsat TM and ETM+ data , 2008, Comput. Geosci..

[96]  Benjamin Koetz,et al.  Multi-source land cover classification for forest fire management based on imaging spectrometry and LiDAR data , 2008 .

[97]  Daniel E. Irwin,et al.  Estimating proportional change in forest cover as a continuous variable from multi-year MODIS data , 2008 .

[98]  Jane Southworth,et al.  Application of multi-scale spatial and spectral analysis for predicting primate occurrence and habitat associations in Kibale National Park, Uganda , 2008 .

[99]  S. Dobrowski,et al.  Mapping mountain vegetation using species distribution modeling, image-based texture analysis, and object-based classification , 2008 .

[100]  W. Cohen,et al.  North American forest disturbance mapped from a decadal Landsat record , 2008 .

[101]  Yichun Xie,et al.  Remote sensing imagery in vegetation mapping: a review , 2008 .

[102]  R. Platt,et al.  An Evaluation of an Object-Oriented Paradigm for Land Use/Land Cover Classification , 2008 .

[103]  D. Roy,et al.  Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data , 2008 .

[104]  Frédéric Achard,et al.  Remote Sensing of Land-Cover and Land-Use Dynamics , 2008 .

[105]  A. Daniels,et al.  Milpa imprint on the tropical dry forest landscape in Yucatan, Mexico: Remote sensing & field measurement of edge vegetation , 2008 .

[106]  M. Hodgson,et al.  Object-Based Land Cover Classification Using High-Posting-Density LiDAR Data , 2008 .

[107]  L. Zhang,et al.  Using a hybrid fuzzy classifier (HFC) to map typical grassland vegetation in Xilin River Basin, Inner Mongolia, China , 2008 .

[108]  Thomas Rudel,et al.  Meta-analyses of case studies: A method for studying regional and global environmental change , 2008 .

[109]  Alexander Moffett,et al.  A Dynamic Graph Automata Approach to Modeling Landscape Change in the Andes and the Amazon , 2009 .

[110]  R. M. Prol-Ledesma,et al.  Three decades of land use variations in Mexico City , 2009 .

[111]  Josiane Zerubia,et al.  Hierarchical Multiple Markov Chain Model for Unsupervised Texture Segmentation , 2009, IEEE Transactions on Image Processing.

[112]  Hua Liu,et al.  Scaling Effect on the Relationship between Landscape Pattern and Land Surface Temperature : A Case Study of Indianapolis, United States , 2009 .

[113]  S. Myint,et al.  Modelling land‐cover types using multiple endmember spectral mixture analysis in a desert city , 2009 .

[114]  G. Petropoulos,et al.  A review of Ts/VI remote sensing based methods for the retrieval of land surface energy fluxes and soil surface moisture , 2009 .

[115]  Eric F. Lambin,et al.  Land-use and land-cover change : local processes and global impacts , 2010 .

[116]  S. Bhaskaran,et al.  Per-pixel and object-oriented classification methods for mapping urban features using Ikonos satellite data , 2010 .