Remote sensing: base mapping

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Remote sensing: base mapping Paul Aplin Progress in Physical Geography 2003 27: 275 DOI: 10.1191/0309133303pp381pr The online version of this article can be found at: http://ppg.sagepub.com/content/27/2/275

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Progress in Physical Geography 27,2 (2003) pp. 275–283

Remote sensing: base mapping Paul Aplin School of Geography, The University of Nottingham, University Park, Nottingham NG7 2RD, UK

I

Introduction

Much geographical analysis is underpinned by the need for base maps. Base mapping is an obvious, but often neglected, function of remote sensing. Recent developments mean that remote sensing is in a stronger position than ever to contribute to the base mapping needs of the geographical community. Therefore, this paper begins by examining the rôle remote sensing has to play in this area. Particular attention is paid to the increasing availability and use of fine spatial resolution satellite sensor imagery and the data requirements of geographical information systems (GIS). This is followed by a broader discussion on the various mapping applications of remote sensing and, finally, a brief summary of general developments in the field. II

Fine spatial resolution satellite sensors

Commonly, the term ‘base map’ is used to refer to a standard map product generated by a mapping agency. This definition seems somewhat restricted, though, so a base map is defined here as a core spatial data set providing a foundation for spatial investigation. Traditionally, base maps have been produced from aerial photographs using photogrammetric techniques (Holland and Allan, 2001; Teng and Fairbairn, 2002). Aerial photographs are well suited for this purpose because of their relatively fine spatial detail. While such traditional procedures remain commonplace, alternative data sources are now also being adopted for base mapping. In particular, satellite sensor imagery has been tested for this purpose (Arai et al., 2001). The main factor driving this trend is the emergence of fine spatial resolution satellite sensors. In September 1999, the (now well-known) IKONOS satellite sensor was launched, providing 1 m spatial resolution panchromatic imagery and 4 m spatial resolution multispectral imagery (Jiao et al., 2001). The implications for base mapping are clear: this level of spatial detail is sufficient to enable the production of relatively accurate base maps (Li, 2000; Hanley and Fraser, 2001). © Arnold 2003

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The launch of IKONOS was an important landmark for remote sensing since this was the first of a new era of fine spatial resolution satellite sensors (Gibson, 2001; Tanaka and Sugimura, 2001). (Prior to this, the most spatially detailed spaceborne imagery in common use was 10 m spatial resolution panchromatic imagery and 20 m spatial resolution multispectral imagery from the Satellite Pour l’Observation de la Terre (SPOT) High Resolution Visible (HRV) instrument.) IKONOS was followed by the launch of a less well-known satellite sensor, EROS A1, in December 2000. Like IKONOS, EROS A1 produces 1 m spatial resolution panchromatic imagery (Topaz et al., 1999) and is, therefore, capable of generating relatively accurate base maps. However, a particularly significant development for base mapping occurred in October 2001 when the QuickBird satellite sensor was launched. QuickBird produces 0.7 m spatial resolution panchromatic imagery and 2.8 m spatial resolution multispectral imagery. This represents the most spatially detailed spaceborne imagery available widely and, therefore, holds the greatest potential for base mapping. Fine spatial resolution satellite sensor imagery holds several strengths for base mapping. In addition to their fine spatial detail, these spaceborne images have relatively little geometric distortion (Zhou and Li, 2000; Fraser et al., 2002b) since satellites tend to be stable imaging platforms. (In comparison, airborne remote sensing can be affected significantly by geometric distortion due to panoramic effects, aircraft movement and so on.) In fact, prior to sale, image vendors can be requested to provide ‘orthorectified’ images, whereby geometric distortion is removed and geographical referencing is provided (Ahn et al., 2001). As a result, fine spatial resolution satellite sensor imagery can produce accurate, spatially detailed maps. For instance, 0.7 m spatial resolution QuickBird imagery enables routine mapping at a scale of 1:25 000. Other benefits of using these spaceborne data sources for base mapping include the relatively low cost (compared with aerial or ground surveys) and relatively fast data supply. While several fine spatial resolution satellite sensors are already operating successfully, more are planned. OrbView-3, an instrument with the same spatial and spectral characteristics as IKONOS (Mulawa, 2000), is tentatively scheduled for launch in 2003. Although OrbView-3 may not offer new imaging capabilities in terms of spatial resolution (Cox and Rye, 1999), it is likely to increase competition between image vendors, possibly leading to better customer services, lower costs and so on. Perhaps more significant for base mapping is the development of a successor to IKONOS. Space Imaging, the organization responsible for IKONOS, has been awarded a license to develop an instrument capable of producing 0.5 m spatial resolution panchromatic imagery and 2 m spatial resolution multispectral imagery. The launch is planned to take place around 2005 or 2006. Clearly, such imagery will enable more detailed base mapping than is currently possible from spaceborne instruments. Aside from base mapping, fine spatial resolution satellite sensor imagery holds potential for many other uses. For instance, IKONOS imagery has been used for various purposes, and reports of some such investigations are beginning to appear in the literature, following the widespread uptake of these data from 2000. Unsurprisingly, given the fine spatial detail of IKONOS imagery, there has been a strong focus on the analysis of relatively small features, such as buildings (Mueller and Segl, 2001; Fraser et al., 2002a), roads (Amini et al., 2002; Yoon et al., 2002) and, more generally, urban areas (Tatem et al., 2001; Sugumaran et al., 2002). Other applications of IKONOS imagery have Downloaded from ppg.sagepub.com at UNIVERSIDAD DE SEVILLA on October 10, 2011

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included studies of plants (Bachmann et al., 2001) and trees (Franklin et al., 2001), and elevation mapping (Toutin, 2001). III

Geographical information system requirements

Base maps are an integral part of geographical information systems (GIS). That is, since spatial data are acknowledged widely to be a central component of GIS (Longley et al., 2001), accurate base maps provide a foundation for successful GIS-based spatial investigation. Commonly, GIS base maps have been constructed from digital vector map data and, often, standard topographic map products (e.g., those produced by mapping agencies (Holland and Allan, 2001)) have been used for this purpose. Such data hold certain benefits for base mapping in that they are accurate, readily available and in a familiar map format. However, these data have the drawbacks that they may be expensive, sometimes prohibitively so, they may be out of date, and vector data are unsuitable for certain types of spatial investigation (Chang, 2002). In these circumstances, other forms of data are required for base mapping and, often, remotely sensed imagery may be suitable. Satellite sensor imagery has various advantages for GIS base mapping. Generally, these data are relatively inexpensive, up-to-date images are readily available (since new images are acquired frequently), and raster image data are particularly useful for various forms of spatial analysis (Chang, 2002). However, while some GIS favour the raster data model, others use a vector approach, which can lead to problems involving data conversion and so on. Recently, though, GIS have been developed with advanced raster/vector integration capabilities, enabling accurate analysis of raster and/or vector data (Smith and Fuller, 2001). Fine spatial resolution satellite sensor imagery can be particularly useful for GIS base mapping (Gibson, 2001), although only where accuracy requirements do not exceed a certain level. As mentioned in the previous section, 0.7 m spatial resolution panchromatic QuickBird imagery enables mapping at a scale of 1:25 000. This is unlikely to be sufficiently detailed for routine topographic mapping in many countries. For instance, the UK’s national mapping agency, the Ordnance Survey, produces standard map products at scales of 1:1250. However, in other countries where mapping is less advanced (e.g., countries with remote, unpopulated areas), mapping at relatively small scales such as 1:25 000 may be sufficient (Chen et al., 2002). More generally, every individual GIS study has its own specific data requirements, depending on the nature and scope of investigation. In many cases, fine spatial resolution satellite sensor imagery may be suitable for base mapping (Arai et al., 2001). Alternatively, where spatial accuracy requirements are relatively low, coarser spatial resolution satellite sensor imagery may be adequate (Almeida-Filho and Shimabukuro, 2002; Guindon and Edmonds, 2002; Wylie et al., 2002). Generally, remote sensing has long been used as a source of data for GIS-based investigation. This trend continues and notable recent developments include combined use of remote sensing and GIS to monitor land cover change (Petit and Lambin, 2001; Weng, 2002; Xiuwan, 2002) and glacial advance (Gao and Liu, 2001; Mennis and Fountain, 2001), to map vegetation (Hoersch et al., 2002) and wildlife habitats (Hansen et al., 2001), to assess crop damage (Silleos et al., 2002) and soil erosion (Jain and Goel, 2002), to Downloaded from ppg.sagepub.com at UNIVERSIDAD DE SEVILLA on October 10, 2011

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identify geological features (Lunden et al., 2001), and to manage forest (Bugg et al., 2002) and water (Fortin et al., 2001; Khan et al., 2001) resources. IV

Thematic mapping

This paper has demonstrated that remote sensing has much potential for producing base maps, either topographic (Pieraccini et al., 2001; Teng and Fairbairn, 2002) or according to other core spatial properties such as reflectance (Hammer et al., 2001). More generally, remotely sensed imagery can be used to map a wide range of thematic spatial distributions. Indeed, thematic mapping is perhaps the most common function of remote sensing. Recent applications in this area include various forms of vegetation mapping (Muchoney and Williamson, 2001; Milne and Dong, 2002), such as analysis of the biophysical properties of aquatic vegetation (Costa et al., 2002). Other vegetation studies have focused specifically on mapping forest distributions (Hame et al., 2001), for instance through the use of texture measures (Saura and Miguel-Ayanz, 2002). Various different forms of remotely sensed data have been used for vegetation mapping. While most investigations have used traditional sources of optical and radar imagery, there are some recent examples of the use of videography (Hess et al., 2002) and multi-angle imagery (Zhang et al., 2002). Other land surfaces have been mapped through remote sensing, including soil (Dwivedi, 2001; Ben-Dor et al., 2002) and snow (Hall et al., 2001), and remotely sensed images have even been used to map archaeological remains (Harrower et al., 2002). In addition to land-based mapping, remote sensing is used extensively to map oceanographic properties, including bathymetry (Liceaga-Correa and Euan-Avila, 2002; Tripathi and Rao, 2002), currents (Frasier and Camps, 2001) and even ocean surface winds (Piepmeier and Gasiewski, 2001; Hasager et al., 2002). Flood events on land are also investigated using remotely sensed imagery (Brivio et al., 2002). While there are many examples of the application of remote sensing for thematic mapping, other studies have focused on certain technical issues related to mapping. For instance, Smith and Atkinson (2001) consider map-based geometric rectification, while there have been several investigations related to assessing the accuracy of thematic maps (Stehman, 2001; Laba et al., 2002). There is also considerable interest in sub-pixel analysis, used to increase the spatial detail of thematic mapping from remotely sensed imagery (Aplin and Atkinson, 2001; Verhoeye and De Wulf, 2002; Tatem et al., 2002). More generally, Dierking and Skriver (2002) used multitemporal imagery to generate thematic maps for change detection. V

General developments in remote sensing

Perhaps the most significant event to take place recently in the field of remote sensing was the launch of ENVISAT in March 2002. Developed at considerable cost by the European Space Agency (ESA), with input from various countries over more than a decade, ENVISAT represents a major remote sensing initiative. The payload includes ten instruments, providing data continuity for the earlier ESA European Remote Sensing Satellite missions (ERS-1 and -2) and offering new capabilities for advanced Downloaded from ppg.sagepub.com at UNIVERSIDAD DE SEVILLA on October 10, 2011

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analysis of the atmosphere, land, sea and ice caps (Levrini and Brooker, 2000; Gessner et al., 2001; Benveniste et al., 2002). Other important launches include the National Aeronautics and Space Administration (NASA) mission, Aqua, in May 2002. Aqua, comprising six diverse remote sensing instruments (Imaoka et al., 2000; Chapman, 2001; Pagano et al., 2002), forms part of NASA’s Earth Science Enterprise, a long-term initiative to study global environmental change (Bordi and Scolese, 2001). Other topical issues in remote sensing include image data standards and, in particular, the lack of generic standards. This formed the primary focus of a major ‘Digital Imagery Roundtable’ discussion in February 2002, convened in London by the Association of Geographic Information, the Remote Sensing and Photogrammetry Society (RSPSoc) and the Royal Institute of Chartered Surveyors. As a result of intense interest, a follow up meeting on data standards was held by the RSPSoc in October 2002, with further debate likely to follow. From the research community, there is much interest in the use of remote sensing for the measurement of biophysical properties (Jakubauskas et al., 2001; North, 2002; Cihlar et al., 2002). The growing interest in this area has been stimulated both by new imaging capabilities and increased understanding of environmental processes. Consequently, the Remote Sensing of Environment journal published a special issue on the subject in early 2002 (Goetz, 2002). Finally, results from the Global Rain Forest Mapping (GRFM) project, undertaken by the National Space Development Agency of Japan (NASDA) and affiliated organizations since 1995, have been brought together in an early 2002 issue of the International Journal of Remote Sensing (Rosenqvist et al., 2002). The goal of the GRFM is to acquire and mosaic Synthetic Aperture Radar (SAR) imagery from the Japanese Earth Resources Satellite (JERS-1) for the Earth’s major tropical regions. These data have been made freely available by NASDA in an effort to stimulate research that contributes towards rainforest understanding and conservation (Mayaux et al., 2002; Santos et al., 2002). References Ahn, C.-H., Cho, S.-I. and Jeon, J.C. 2001: Orthorectification software applicable for IKONOS high resolution images: GeoPixel-Ortho. Proceedings international geoscience and remote sensing symposium (IGARSS) 2001 1. Sydney: The Institute of Electrical and Electronics Engineers, Inc (IEEE), 555–57. Almeida-Filho, R. and Shimabukuro, Y.E. 2002: Digital processing of a Landsat-TM time series for mapping and monitoring degraded areas caused by independent gold miners. Remote Sensing of Environment 79, 42–50. Amini, J., Lucas, C., Saradjian, M.R., Azizi, A. and Sadeghian, S. 2002: Fuzzy logic system for road identification using Ikonos images. Photogrammetric Record 17, 493–504. Aplin, P. and Atkinson, P.M. 2001: Sub-pixel land cover mapping for per-field classification. International Journal of Remote Sensing 22, 2853–58.

Arai, C., Shikada, M., Iwamura, K. and Fujii, K. 2001: Research on real-time revision of base map using remote sensing and RTK-GPS. Proceedings international geoscience and remote sensing symposium (IGARSS) 2001 5. Sydney: IEEE, 2463–65. Bachmann, C.M., Donato, T., Dubois, K., Fusina, R.A., Bettenhausen, M., Porter, J.H. and Truitt, B.R. 2001: Automatic detection of an invasive plant species on a barrier island in the Virginia coast reserve using HYMAP and IKONOS imagery. Proceedings international geoscience and remote sensing symposium (IGARSS) 2001 5. Sydney: IEEE, 2172–74. Ben-Dor, E., Patkin, K., Banin, A. and Karnieli, A. 2002: Mapping of several soil properties using DAIS-7915 hyperspectral scanner data – a case study over clayey soils in Israel. International Journal of Remote Sensing 23,

Downloaded from ppg.sagepub.com at UNIVERSIDAD DE SEVILLA on October 10, 2011

280

Remote sensing: base mapping

1043–62. Benveniste, J., Resti, A., Roca, M., MilagroPerez, M.P. and Levrini, G. 2002: ENVISAT radar altimeter system. Proceedings of the International Society for Optical Engineering (SPIE) 4544, 71–82. Bordi, F. and Scolese, C.J. 2001: Overview of Earth science missions at the Goddard Space Flight Center. Proceedings of the International Society for Optical Engineering (SPIE) 4540, 1–10. Brivio, P.A., Colombo, R., Maggi, M. and Tomasoni, R. 2002: Integration of remote sensing data and GIS for accurate mapping of flooded areas. International Journal of Remote Sensing 23, 429–42. Bugg, A.L., Spencer, R.D. and Lee, A. 2002: Applying GIS for developing regional forest agreements in Australia. Photogrammetric Engineering and Remote Sensing 68, 241–50. Chang, K.-T. 2002: Introduction to geographic information systems. New York: McGraw-Hill. Chapman, J.J. 2001: CERES aqua instrument simulator for command and upload validation. Proceedings of the International Society for Optical Engineering (SPIE) 4169, 309–16. Chen, J.M., Pavlic, G., Brown, L., Cihlar, J., Leblanc, S.G., White, H.P., Hall, R.J., Peddle, D.R., King, D.J. and Trofymow, J.A. 2002: Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements. Remote Sensing of Environment 80, 165–84. Cihlar, J., Chen, J., Li, Z., Latifovic, R., Fedosejevs, G., Adair, M., Park, W., Fraser, R., Trishchenko, A. and Guindon, B. 2002: GeoComp-n, an advanced system for the processing of coarse and medium resolution satellite data. Part 2: Biophysical products for Northern ecosystems. Canadian Journal of Remote Sensing 28, 21–44. Costa, M.P.F., Niemann, O., Novo, E. and Ahern, F. 2002: Biophysical properties and mapping of aquatic vegetation during the hydrological cycle of the Amazon floodplain using JERS-1 and Radarsat. International Journal of Remote Sensing 23, 1401–26. Cox, S.M. and Rye, G. 1999: OrbView 2, 3, and 4. Proceedings of the International Society for Optical Engineering (SPIE) 3870, 674–75. Dierking, W. and Skriver, H. 2002: Change detection for thematic mapping by means of airborne multitemporal polarimetric SAR imagery. IEEE Transactions on Geoscience and

Remote Sensing 40, 618–36. Dwivedi, R.S. 2001: Soil resources mapping: a remote sensing perspective. Remote Sensing Reviews 20, 89–122. Fortin, J.-P., Turcotte, R., Massicotte, S., Moussa, R., Fitzback, J. and Villeneuve, J.-P. 2001: Distributed watershed model compatible with remote sensing and GIS data. II: Application to Chaudiere watershed. Journal of Hydrologic Engineering 6, 100–108. Franklin, S.E., Wulder, M.A. and Gerylo, G.R. 2001: Texture analysis of IKONOS panchromatic data for Douglas-fir forest age class separability in British Columbia. International Journal of Remote Sensing 22, 2627–32. Fraser, C.S., Baltsavias, E. and Gruen, A. 2002a: Processing of Ikonos imagery for submetre 3D positioning and building extraction. ISPRS Journal of Photogrammetry and Remote Sensing 56, 177–94. Fraser, C.S., Hanley, H.B. and Yamakawa, T. 2002b: Three-dimensional geopositioning accuracy of Ikonos imagery. Photogrammetric Record 17, 465–80. Frasier, S.J. and Camps, A.J. 2001: Dual-beam interferometry for ocean surface current vector mapping. IEEE Transactions on Geoscience and Remote Sensing 39, 401–14. Gao, J. and Liu, Y. 2001: Applications of remote sensing, GIS and GPS in glaciology: a review. Progress in Physical Geography 25, 520–40. Gessner, R., Smith, D.J., Kolm, M., Endemann, M.J. and Gare, P. 2001: MIPAS onboard ENVISAT: flight model performance, calibration, and characterization. Proceedings of the International Society for Optical Engineering (SPIE) 4169, 133–43. Gibson, L. 2001: Automation technology with IKONOS satellite imagery. Proceedings of the International Society for Optical Engineering (SPIE) 4540, 650–60. Goetz, S. 2002: Recent advances in remote sensing of biophysical variables: An overview of the special issue. Remote Sensing of Environment 79, 145–46. Guindon, B. and Edmonds, C.M. 2002: Largearea land-cover mapping through scene-based classification compositing. Photogrammetric Engineering and Remote Sensing 68, 589–96. Hall, D.K., Foster, J.L., Salomonson, V.V., Klein, A.G. and Chien, J.Y.L. 2001: Development of a technique to assess snow-cover mapping errors from space. IEEE Transactions on Geoscience and

Downloaded from ppg.sagepub.com at UNIVERSIDAD DE SEVILLA on October 10, 2011

P. Aplin

Remote Sensing 39, 432–38. Hame, T., Stenberg, P., Andersson, K., Rauste, Y., Kennedy, P., Folving, S. and Sarkeala, J. 2001: AVHRR-based forest proportion map of the Pan-European area. Remote Sensing of Environment 77, 76–91. Hammer, P.D., Johnson, L.F., Strawa, A.W., Dunagan, S.E., Higgins, R.G., Brass, J.A., Slye, R.E., Sullivan, D.V., Smith, W.H. and Lobitz, B.M. 2001: Surface reflectance mapping using interferometric spectral imagery from a remotely piloted aircraft. IEEE Transactions on Geoscience and Remote Sensing 39, 2499–506. Hanley, H.B. and Fraser, C.S. 2001: Geopositioning accuracy of Ikonos imagery: indications from two dimensional transformations. Photogrammetric Record 17, 317–30. Hansen, M.J., Franklin, S.E., Woudsma, C.G. and Peterson, M. 2001: Caribou habitat mapping and fragmentation analysis using Landsat MSS, TM, and GIS data in the North Columbia Mountains, British Columbia, Canada. Remote Sensing of Environment 77, 50–65. Harrower, M., McCorriston, J. and Ocher, E.A. 2002: Mapping the roots of agriculture in southern Arabia: the application of satellite remote sensing, global positioning system and geographic information system. Archaeological Prospection 9, 35–42. Hasager, C.B., Frank, H.P. and Furevik, B.R. 2002: On offshore wind energy mapping using satellite SAR. Canadian Journal of Remote Sensing 28, 80–89. Hess, L.L., Novo, E.M.L.M., Slaymaker, D.M., Holt, J., Steffen, C., Valeriano, D.M., Mertes, L.A.K., Krug, T., Melack, J.M. and Gastil, M. 2002: Geocoded digital videography for validation of land cover mapping in the Amazon basin. International Journal of Remote Sensing 23, 1527–56. Hoersch, B., Braun, G. and Schmidt, U. 2002: Relation between landform and vegetation in alpine regions of Wallis, Switzerland. A multiscale remote sensing and GIS approach. Computers, Environment and Urban Systems 26, 113–39. Holland, D.A. and Allan, L.E. 2001: The Digital National Framework and digital photogrammetry at Ordnance Survey. Photogrammetric Record 17, 291–301. Imaoka, K., Shibata, A., Nakagawa, N., Nakajima, Y., Haijima, H., Tanaka, H. and Imatani, T. 2000: Airborne microwave radiometer for AMSR and AMSR-E project.

281

Proceedings of the International Society for Optical Engineering (SPIE) 4152, 303–309. Jain, S.K. and Goel, M.K. 2002: Assessing the vulnerability to soil erosion of the Ukai Dam catchments using remote sensing and GIS. Hydrological Sciences Journal 47, 31–40. Jakubauskas, M., Kindscher, K. and Debinski, D. 2001: Spectral and biophysical relationships of montane sagebrush communities in multitemporal SPOT XS data. International Journal of Remote Sensing 22, 1767–78. Jiao, Z., Li, X., Wang, J. and Yan, G. 2001: Classification-based fusion of IKONOS 1-m high-resolution panchromatic image and 4-m multi-spectral images. Proceedings international geoscience and remote sensing symposium (IGARSS) 2001 2. Sydney: IEEE, 703–705. Khan, M.A., Gupta, V.P. and Moharana, P.C. 2001: Watershed prioritization using remote sensing and geographical information system: a case study from Guhiya, India. Journal of Arid Environments 49, 465–76. Laba, M., Gregory, S.K., Braden, J., Ogurcak, D., Hill, E., Fegraus, E., Fiore, J. and DeGloria, S.D. 2002: Conventional and fuzzy accuracy assessment of the New York Gap Analysis Project land cover map. Remote Sensing of Environment 81, 443–55. Levrini, G. and Brooker, G. 2000: The ENVISAT data products. Proceedings international geoscience and remote sensing symposium (IGARSS) 2000 3. Honolulu: IEEE, 1198–201. Li, R. 2000: A study of the potential attainable geometric accuracy of IKONOS satellite imagery. International Archives of Photogrammetry and Remote Sensing 33, 587–95. Liceaga-Correa, M.A. and Euan-Avila, J.I. 2002: Assessment of coral reef bathymetric mapping using visible Landsat Thematic Mapper data. International Journal of Remote Sensing 23, 3–14. Longley, P.A., Goodchild, M.F., Maguire, D.J. and Rhind, D.W. 2001: Geographic Information Systems and science. Chichester: John Wiley & Sons, Ltd. Lunden, B., Wang, G. and Wester, K. 2001: A GIS based analysis of data from Landsat TM, airborne geophysical measurements, and digital maps for geological remote sensing in the Stockholm region, Sweden. International Journal of Remote Sensing 22, 517–32. Mayaux, P., De Grandi, G.F., Rauste, Y., Simard, M. and Saatchi, S. 2002: Large-scale vegetation

Downloaded from ppg.sagepub.com at UNIVERSIDAD DE SEVILLA on October 10, 2011

282

Remote sensing: base mapping

maps derived from the combined L-band GRFM and C-band AMP wide area radar mosaics of Central Africa. International Journal of Remote Sensing 23, 1261–82. Mennis, J.L. and Fountain, A.G. 2001: A spatiotemporal GIS database for monitoring Alpine glacier change. Photogrammetric Engineering and Remote Sensing 67, 967–74. Milne, A.K. and Dong, Y. 2002: Vegetation mapping using JERS-1 SAR mosaic for northern Australia. International Journal of Remote Sensing 23, 1475–86. Muchoney, D. and Williamson, J. 2001: A gaussian adaptive resonance theory neural network classification algorithm applied to supervised land cover mapping using multitemporal vegetation index data. IEEE Transactions on Geoscience and Remote Sensing 39, 1969–77. Mueller, M. and Segl, K. 2001: House detection in IKONOS data using region- and edge-based segmentation. Proceedings of the International Society for Optical Engineering (SPIE) 4472, 500–507. Mulawa, D. 2000: Preparations for the on-orbit geometric calibration of the OrbView 3 and 4 satellites. International Archives of Photogrammetry and Remote Sensing 33, 209–13. North, P.R. 2002: Estimation of fAPAR, LAI, and vegetation fractional cover from ATSR-2 imagery. Remote Sensing of Environment 80, 114–21. Pagano, T.S., Elliot, D.A., Gunson, M.R., Aumann, H.H., Gaiser, S.L., Dehghani, N. and Overoye, K. 2002: Operational readiness for the atmospheric infrared sounder (AIRS) on the Earth Observing System Aqua spacecraft. Proceedings of the International Society for Optical Engineering (SPIE) 4483, 35–43. Petit, C.C. and Lambin, E.F. 2001: Integration of multi-source remote sensing data for land cover change detection. International Journal of Geographical Information Science 15, 785–803. Piepmeier, J.R. and Gasiewski, A.J. 2001: Highresolution passive polarimetric microwave mapping of ocean surface wind vector fields. IEEE Transactions on Geoscience and Remote Sensing 39, 606–22. Pieraccini, M., Luzi, G. and Atzeni, C. 2001: Terrain mapping by ground-based interferometric radar. IEEE Transactions on Geoscience and Remote Sensing 39, 2176–81.

Rosenqvist, Å., Shimada, M., Chapman, B.D., Dutra, L.V., Saatchi, S. and Tanaka, O. 2002: [The Global Rain Forest Mapping Project:] Introduction from the guest editors. International Journal of Remote Sensing 23, 1215. Santos, J.R., Lacruz, M.S.P., Araujo, L.S. and Keil, M. 2002: Savanna and tropical rainforest biomass estimation and spatialization using JERS-1 data. International Journal of Remote Sensing 23, 1217–30. Saura, S. and Miguel-Ayanz, J.S. 2002: Forest cover mapping in Central Spain with IRS-WIFS images and multi-extent textual-contextual measures. International Journal of Remote Sensing 23, 603–608. Silleos, N., Perakis, K. and Petsanis, G. 2002: Assessment of crop damage using space remote sensing and GIS. International Journal of Remote Sensing 23, 417–28. Smith, D.P. and Atkinson, S.F. 2001: Accuracy of rectification using topographic map versus GPS ground control points. Photogrammetric Engineering and Remote Sensing 67, 565–70. Smith, G.M. and Fuller, R.M. 2001: An integrated approach to land cover classification: an example in the Island of Jersey. International Journal of Remote Sensing 22, 3123–42. Stehman, S.V. 2001: Statistical rigor and practical utility in thematic map accuracy assessment. Photogrammetric Engineering and Remote Sensing 67, 727–34. Sugumaran, R., Zerr, D. and Prato, T. 2002: Improved urban land cover mapping using multi-temporal IKONOS images for local government planning. Canadian Journal of Remote Sensing 28, 90–95. Tanaka, S. and Sugimura, T. 2001: Cover. A new frontier of remote sensing from IKONOS images. International Journal of Remote Sensing 22, 1–6. Tatem, A.J., Lewis, H.G., Atkinson, P.M. and Nixon, M.S. 2001: Super-resolution mapping of urban scenes from IKONOS imagery using a Hopfield neural network. Proceedings international geoscience and remote sensing symposium (IGARSS) 2001 7. Sydney: IEEE, 3203–205. –––– 2002: Super-resolution land cover pattern prediction using a Hopfield neural network. Remote Sensing of Environment 79, 1–14. Teng, C.H. and Fairbairn, D. 2002: Comparing expert systems and neural fuzzy systems for object recognition in map dataset revision. International Journal of Remote Sensing 23, 555–68.

Downloaded from ppg.sagepub.com at UNIVERSIDAD DE SEVILLA on October 10, 2011

P. Aplin

Topaz, J.M., Porat, I., Blasberger, A., Stavitsky, D. and Freiman, D. 1999: Cameras for the EROS Earth remote sensing satellite constellation. Proceedings of the International Society for Optical Engineering (SPIE) 3870, 436–40. Toutin, T. 2001: DEM generation from new vir sensors: IKONOS, ASTER and Landsat-7. Proceedings international geoscience and remote sensing symposium (IGARSS) 2001 3. Sydney: IEEE, 973–75. Tripathi, N.K. and Rao, A.M. 2002: Bathymetric mapping in Kakinada Bay, India, using IRS-1D LISS-III data. International Journal of Remote Sensing 23, 1013–26. Verhoeye, J. and De Wulf, R. 2002: Land cover mapping at sub-pixel scales using linear optimization techniques. Remote Sensing of Environment 79, 96–104. Weng, Q. 2002: Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. Journal of Environmental Management 64, 273–84. Wylie, B.K., Meyer, D.J., Tieszen, L.L. and

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Mannel, S. 2002: Satellite mapping of surface biophysical parameters at the biome scale over the North American grasslands. Remote Sensing of Environment 79, 266–78. Xiuwan, C. 2002: Using remote sensing and GIS to analyse land cover change and its impacts on regional sustainable development. International Journal of Remote Sensing 23, 107–24. Yoon, T., Park, W. and Kim, T. 2002: Semiautomatic road extraction from IKONOS satellite image. Proceedings of the International Society for Optical Engineering (SPIE) 4545, 320–28. Zhang, Y., Tian, Y., Myneni, R.B., Knyazikhin, Y. and Woodcock, C.E. 2002: Assessing the information content of multiangle satellite data for mapping biomes. Remote Sensing of Environment 80, 418–34. Zhou, G. and Li, R. 2000: Accuracy evaluation of ground points from IKONOS high-resolution satellite imagery. Photogrammetric Engineering and Remote Sensing 66, 1103–12.

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