46 11 Propuesta de un proyecto piloto
Fase 2: Relación datos LiDAR-datos ópticos y/o radar: en esta fase las
estimaciones realizadas en el paso anterior serán consideradas como los datos de referencia (verdad terreno) y se desarrollarán los modelos estadísticos que permitan relacionar estos datos con los datos obtenidos a partir de otros sensores que permitan obtener una cobertura completa del país, tales como imágenes de satélite o datos radar. En este sentido pueden considerarse dos enfoques, el
53
primero consiste en una estratificación del país por tipos de bosque y estado de los mismos para posteriormente asignarles un valor representativo (por ejemplo la media) obtenido de las estimaciones realizadas en la fase 1. El segundo enfoque sigue un método estadístico en el que se establecerán relaciones entre las estimaciones obtenidas en la fase 1 y los datos proporcionados por diversos sensores de teledetección.
Conversión de valores de biomasa a carbono: la estimación del carbono
partir de los datos de biomasa estimados en el paso anterior se realizará mediante la aplicación de coeficientes de densidad específicos para las especies presentes en la zona. En caso de que estos coeficientes no estén disponibles se aplicará un valor genérico de 0.5.
Generación de cartografía: Se generará la cartografía correspondiente a la
distribución de biomasa y de carbono en la zona de estudio a escala 1:250,000 – 1:500,000.
Divulgación de resultados: Se realizarán tareas de difusión de los resultados
del proyecto incluyendo seminarios, presentaciones en congresos, artículos, así como a través de la web.
Estimación de costes: como en el caso anterior, la estimación de costes en
un proyecto de estas características resulta muy complicada debido a los condicionantes que pueden plantearse en función del área de estudio seleccionada, ya que de esto dependerá la configuración del vuelo (alturas de vuelo, número de pasadas a realizar), así como el trabajo de campo de campo a realizar. Por lo tanto a continuación se presenta una estimación que debe tenerse en cuenta únicamente a modo de orientación:
54
Item Coste $
Adquisición y filtrado de datos LiDAR
400-550 US$/km2
Adquisición datos multiespectrales
Landsat: disponibles gratuitamente. MODIS: disponibles gratuitamente.
Datos Radar
Radarsat-1: 3,600-4,500 CAD (dólar canadiense). Hay que añadir costes de
programación, entre 135-1,350 CAD Radarsat-2:3,600-8,400 CAD. Hay
que añadir costes de programación, entre 120-3,600 CAD
Trabajo de campo
Dificultad alta: 549.1 $/parcela Dificultad media: 229.5 $/parcela Dificultad baja: 153.0 $/parcela
Análisis información 100 -300 US$/km2
Divulgación de resultados 70,000
Equipamiento (ordenadores, material para la realización de medidas en campo,
GPS, etcétera)
50,000
Viajes/reuniones 20,000
55
En cuanto al tiempo de ejecución se estiman los siguientes tiempos aproximados. Como se mencionó anteriormente deben considerarse únicamente a modo orientativo:
Item Meses
Adquisición datos LiDAR 6
Adquisición de datos
multiespectrales y RaDAR 1.5
Trabajo de campo
Dificultad alta: 1 parcela diaria Dificultad media: 2 parcelas diarias Dificultad baja: 4 parcelas diarias
Análisis datos campo
3-6
Análisis información y generación
de modelos 25-30
Total 30-36
Tabla 10: Estimación de tiempo de ejecución de un proyecto para la estimación de biomasa mediante datos LiDAR a nivel local
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12 Referencias
Andersen, H.-E., McGaughey, R.J., & Reutebuch, S.E. (2005). Estimating forest canopy fuel parameters using LIDAR data. Remote Sensing of Environment, 94, 441-449
Andersen, H.E., Reutebuch, S.E., & McGaughey, R.J. (2006). A rigorous assessment of tree height measurements obtained using airborne lidar and conventional field methods. Canadian Journal of Remote Sensing, 32, 355-366
Antonarakis, A.S., Richards, K.S., & Brasington, J. (2008). Object-based land cover classification using airborne LiDAR. Remote Sensing of Environment, 112, 2988-2998
Baccini, A., Laporte, N., Goetz, S.J., Sun, M., & Dong, H. (2008). A first map of tropical Africa’s above-ground biomass derived from satellite imagery. Environmental Research Letters, 3, doi:10.1088/1748-9326/1083/1084/045011
Baltsavias, E.P. (1999). Airborne laser scanning: basic relations and formulas. ISPRS Journal of Photogrammetry & Remote Sensing, 54, 199-214
Banskota, A., Wynne, R.H., Johnson, P., & Emessiene, B. (2011). Synergistic use of very high-frequency radar and discrete-return lidar for estimating biomass in temperate hardwood and mixed forests. Annals of Forest Science, DOI 10.1007/s13595-13011-10023-13590
Bortolot, Z.J., & Wynne, R.H. (2005). Estimating forest biomass using small footprint LiDAR data: An individual tree-based approach that incorporates training data. ISPRS Journal of Photogrammetry and Remote Sensing, 59, 342-360
Böttcher, H., Eisbrenner, K., Fritz S., Kindermann, G., Kraxner, F., McCallum, I. & Obersteiner, M. (2009) An assessment of monitoring requirements and costs of 'Reduced Emissions from Deforestation and Degradation'. Carbon Balance and Management.
Boudreau, J., Nelson, R.F., Margolis, H.A., Beaudoin, A., Guindon, L., & Kimes, D.S. (2008). Regional aboveground forest biomass using airborne and spaceborne LiDAR in Québec. Remote Sensing of Environment, 112, 3876-3890
Coren, F., & Sterzai, P. (2006). Radiometric correction in laser scanning. International Journal of Remote Sensing, 27, 3097-3104
Chen, Q., Laurin, G.V., Battles, J.J., & Saah, D. (2012). Integration of airborne lidar and vegetation types derived from aerial photography for mapping aboveground live biomass. Remote Sensing of Environment, 121, 108-117
Drake, J.B., Dubayah, R.O., Clark, D.B., Knox, R.G., Blair, J.B., Hofton, M.A., Chazdon, R.L., Weishampel, J.F., & Prince, S. (2002). Estimation of tropical forest
57
structural characteristics using large-footprint lidar. Remote Sensing of Environment, 79, 305-319
Dubayah, R., & Drake, J.B. (2000). Lidar Remote Sensing for Forestry Applications. Journal of Forestry
FAO (2006). Global Forest Resources Assessment 2005. Progress towards sustainable forest management. In F.F.P. 147 (Ed.)
Finney, M.A. (1998). FARSITE: Fire Area Simulator Model development and evaluation. Research Paper RMRS-RP-4. In: USDA Forest Service. Rocky Mountain Research Station, Odgen, UT,
Frazer, G., Trofymow, J., & Lertzman, K. (1997). A method for estimating canopy openess, effective leaf area index and photosynthetically active photon flux density using hemispherical photography and computerized image analysis techniques. In. Tech. rep., Information Report BC-X-373, Natural Resources Canada, Canadian Forest Service, Pacific Forestry Centre, Victoria, BC
García-Gutiérrez, J., Mateos-García, D., & Riquelme-Santos, J.C. (2011). EVOR- STACK: A label-dependent evolutive stacking on remote sensing data fusion. Neurocomputing, 74
García, M., Riaño, D., Chuvieco, E., & Danson, F.M. (2010). Estimating biomass carbon stocks for a Mediterranean forest in Spain using height and intensity LiDAR data. Remote Sensing of Environment, 114, 816-830
García, M., Riaño, D., Chuvieco, E., Salas, F.J., & Danson, F.M. (2011). Multispectral and LiDAR data fusion for fuel type mapping using Support Vector Machine and decision rules. Remote Sensing of Environment, 115, 1369–1379
Gatziolis, D., & Andersen, H.E. (2008). A guide to LIDAR data acquisition and processing for the forests of the Pacific Northwest. In. Gen. Tech. Rep. PNW-GTR- 768. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 32 p
Gaveau, D.L.A., & Hill, R.A. (2003). Quantifying canopy height underestimation by laser pulse penetration in small-footprint airborne laser scanning data. Canadian Journal of Forest Research, 29, 650-657
Gibbs, H.K., Brown, S., Niles, J.O., & Foley, J.A. (2007). Monitoring and estimating tropical forest carbon stocks: Making REDD a reality. Environmental Research Letters, 2:045023
Gobakken, T., Naesset, E., Nelson, R., Bollandsas, O.M., Gregoire, T.G., Stahl, G., Holm, S., Orka, H.O., & Astrup, R. (2012). Estimating biomass in Hedmark County, Norway using national forest inventory field plots and airborne laser scanning. Remote Sensing of Environment, 123, 443-456
58
Gonzalez, P., Asner, G.P., Battles, J.J., Lefsky, M.A., Waring, K.M., & Palace, M. (2010). Forest carbon densities and uncertainties from Lidar, QuickBird, and field measurements in California. Remote Sensing of Environment, 114, 1561-1575
Harding, D.J., Lefsky, M.A., Parker, G.G., & Blair, J.B. (2001). Laser altimeter canopy height profiles: methods and validation for closed-canopy, broadleaf forests. Remote Sensing of Environment, 76, 283-297
Hawbaker, T.J., Keuler, N.S., Lesak, A.A., Gobakken, T., Contrucci, K., & Radeloff, V.C. (2009). Improved estimates of forest vegetation structure and biomass with a LiDAR-optimized sampling design. JOURNAL OF GEOPHYSICAL RESEARCH, 114, doi:10.1029/2008JG000870
Hill, R.A., & Thomson, A.G. (2005). Mapping woodland species composition and structure using airborne spectral and LiDAR data. In (pp. 3763 - 3779): Taylor & Francis
Höfle, B., & Pfeifer, N. (2007). Correction of laser scanning intensity data: Data and model-driven approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 62, 415-433
Holmgren, J., Johansson, F., Olofsson, K., Olsson, H., & Glimskär, A. (2008). Estimation of crown coverage using airborne laser scanning. In, Hill, R., Rosette, J. & Suárez, J. (Eds.) 8th international conference on LiDAR applications in forest assessment and inventory. SilviLaser. Heriot-Watt University, Edinburgh, UK
Holmgren, J., & Persson, A. (2004). Identifying species of individual trees using airborne laser scanner. Remote Sensing of Environment, 90, 415-423
Hollaus, M., Wagner, W., Eberhöfer, C., & Karel, W. (2006). Accuracy of large- scale canopy heights derived from LiDAR data under operational constraints in a complex alpine environment. ISPRS Journal of Photogrammetry and Remote Sensing, 60, 323-338
Hopkinson, C., & Chasmer, L. (2009). Testing LiDAR models of fractional cover across multiple forest ecozones. Remote Sensing of Environment, 113, 275-288
Houghton, R.A. (2005). Aboveground forest biomass and the global carbon balance. Global Change Biology, 11, 945:958
Hudak, A.T., Crookston, N.L., Evans, J.S., Hall, D.E., & Falkowski, M.J. (2008). Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data. Remote Sensing of Environment, 112, 2232-2245
Hudak, A.T., Lefsky, M.A., Cohen, W.B., & Berterretche, M. (2002). Integration of lidar and Landsat ETM+ data for estimating and mapping forest canopy height. Remote Sensing of Environment, 82, 397-416
59
Hudak, A.T., Strand, E.K., Vierling, L.A., Byrne, J.C., Eitel, J.U.H., Martinuzzi, S., & Falkowski, M.J. (2012). Quantifying aboveground forest carbon pools and fluxes from repeat LiDAR surveys. Remote Sensing of Environment, 123, 25-40
Huising, E.J., & Gomes Pereira, L.M. (1998). Errors and accuracy estimates of laser data acquired by various laser scanning systems for topographic applications. ISPRS Journal of Photogrammetry and Remote Sensing, 53, 241-261
Hurtt, G.C., Dubayah, R., Drake, J.B., Moorcroft, P., Pacala, S., & Fearon, M. (2004). Beyond Potential Vegetation: Combining Lidar Remote Sensing and a Height-Structured Ecosystem Model for Improved Estimates of Carbon Stocks and Fluxes. Ecological applications, 14, 873-883
Hyde, P., Dubayah, R., Walker, W., Blair, J.B., Hofton, M., & Hunsaker, C. (2006). Mapping forest structure for wildlife habitat analysis using multi-sensor (LiDAR, SAR/InSAR, ETM+, Quickbird) synergy. Remote Sensing of Environment, 102, 63-73
Hyyppä, I., Hyyppä, H., Litkey, P., Yu, X., Haggrén, H., Ronnholm, P., Pyysalo, U., Pitkanen, J., & Maltamo, M. (2004). Algorithms and methods of airborne laser- scanning for forest measurements. In M. Thies, B. Koch, H. Spiecker, & H. Weinacker (Eds.), International Archives of Photogrammetry, Remote Sensing, and the Spatial Information Sciences, Vol. XXXVI-w2.
Hyyppä, J., & Inkinen, M. (1999). Detecting and estimating attributes for single trees using laser scanner. The Photogrammetric Journal of Finland,, 16, 27-42
Hyyppä, J., Kelle, O., Lehikoinen, M., & Inkinen, M. (2001). A Segmentation- Based Method to Retrieve Stem Volume Estimates from 3-D Tree Height Models Produced by Laser Scanners. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 39, 969 - 975
Koetz, B., Morsdorf, F., van der Linden, S., Curt, T., & Allgöwer, B. (2008). Multi-source land cover classification for forest fire management based on imaging spectrometry and LiDAR data. Forest Ecology and Management, 256, 263-271
Kronseder, K., Ballhorn, U., V., B., & Siegert, F. (2012). Above ground biomass estimation across forest types at different degradation levels in Central Kalimantan using LiDAR data. International Journal of Applied Earth Observation and Geoinformation, 18, 37-48
Latifi, H., Nothdurft, A., & Koch, B. (2010). Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest:Application of multiple optical/LiDAR-derived predictors. Forestry, 83, 395-407
Lefsky, M.A., Cohen, W.B., Acker, S.A., Parker, G.G., Spies, T.A., & Harding, D. (1999a). Lidar Remote Sensing of the Canopy Structure and Biophysical Properties
60
of Douglas-Fir Western Hemlock Forests. Remote Sensing of Environment, 70, 339- 361
Lefsky, M.A., Harding, D., Cohen, W.B., Parker, G., & Shugart, H.H. (1999b). Surface Lidar Remote Sensing of Basal Area and Biomass in Deciduous Forests of Eastern Maryland, USA. Remote Sensing of Environment, 67, 83-98
Lefsky, M.A., Turner, D.P., Guzy, M., & Cohen, W.B. (2005). Combining lidar estimates of aboveground biomass and Landsat estimates of stand age for spatially extensive validation of modeled forest productivity. Remote Sensing of Environment, 95, 549-558
Lim, K., & Treitz, P. (2004). Estimation of Above ground Forest Biomass from Airborne Discrete Return Laser Scanner Data Using Canopy-based Quantile Estimators. Scandinavian Journal of Forest Research, 19, 558-570
Lim, K., Treitz, P., Wulder, M., St-Onge, B., & Flood, M. (2003). LiDAR remote sensing of forest structure. Progress in Physical Geography, 27, 88-106
Lin, Y., Jaakkola, A., Hyyppä, J., & Kaartinen, H. (2010). From TLS to VLS: Biomass Estimation at Individual Tree Level. Remote Sensing, 2, 1864- 1879;doi:1810.3390/rs2081864
Lovell, J.L., Jupp, D.L.B., Culvenor, D.S., & Coops, N.C. (2003). Using airborne and ground-based ranging lidar to measure canopy structure in Australian forests. Canadian Journal of Remote Sensing, 29, 607-622
Lucas, R.M., Cronin, N., Lee, A., Moghaddam, M., Witte, C., & Tickle, P. (2006). Empirical relationships between AIRSAR backscatter and LiDAR-derived forest biomass, Queensland, Australia. Remote Sensing of Environment, 100, 407-425
Lucas, R.M., Lee, A.C., & P.J., B. (2008). Retrieving forest biomass through integration of CASI and LiDAR data. International Journal of Remote Sensing, 29, 1553-1577
Maas, H.-G. (2001). On the use of reflectance data for laserscanner strip adjustment. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 34, 53-56
Maier, B., Tiede, D., & Dorren, L. (2008). Characterising Mountain Forest Structure using Landscape metrics on LiDAR-based Canopy Surface Models. In T. Blaschke, S. Lang, & G. Hay (Eds.), Object-Based Image Analysis - Spatial concepts for knowledge-driven remote sensing applications. Berlin: Springer, 625-643
Maltamo, M., Eerikainen, K., Pitkanen, J., Hyyppa, J., & Vehmas, M. (2004). Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions. Remote Sensing of Environment, 90, 319-330
61
Mallet, C., & Bretar, F. (2009). Full-waveform topographic lidar: State-of-the- art. ISPRS Journal of Photogrammetry and Remote Sensing, 64, 1-16
Mascaro, J., Detto, M., Asner, G.P., & Muller-Landau, H.C. (2011). Evaluating uncertainty in mapping forest carbon with airborne LiDAR. Remote Sensing of Environment, 115, 3770-3774
Morsdorf, F., Kotz, B., Meier, E., Itten, K.I., & Allgower, B. (2006). Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction. Remote Sensing of Environment, 104, 50-61
Muss, J.D., Mladenoff, D.J., & Townsend, P.A. (2011). A pseudo-waveform technique to assess forest structure using discrete lidar data. Remote Sensing of Environment, 115, 824-835
Mutlu, M., Popescu, S.C., Stripling, C., & Spencer, T. (2008). Mapping surface fuel models using lidar and multispectral data fusion for fire behavior. Remote Sensing of Environment, 112, 274-285
Muukkonen, P., & Heiskanen, J. (2007). Biomass estimation over a large area based on standwise forest inventory data and ASTER and MODIS satellite data: A possibility to verify carbon inventories. Remote Sensing of Environment, 107, 617- 624
Næsset, E. (1997). Determination of mean tree height of forest stands using airborne laser scanner data. ISPRS Journal of Photogrammetry and Remote Sensing, 52, 49-56
Naesset, E., & Bjerknes, K.-O. (2001). Estimating tree heights and number of stems in young forest stands using airborne laser scanner data. Remote Sensing of Environment, 78, 328-340
Næsset, E., & Gobakken, T. (2008). Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser. Remote Sensing of Environment, 112, 3079-3090
Nelson, R. (2010). Model effects on GLAS-based regional estimates of forest biomass and carbon. International Journal of Remote Sensing, 31, 1359-1372
Nelson, R., Krabill, W., & MacLean, G. (1984). Determining forest canopy characteristics using airborne laser data. Remote Sensing of Environment, 15, 201- 212
Nelson, R., Krabill, W., & Tonelli, J. (1988). Estimating forest biomass and volume using airborne laser data. Remote Sensing of Environment, 24, 247-267
Nelson, R., Valenti, M.A., Short, A., & Keller, C. (2003). A multiple resource inventory of Delaware using airborne laser data. Bioscience, 35, 981-992
62
Nyström, M., Holmgren, J., & Olsson, H. (2012). Prediction of tree biomass in the forest–tundra ecotone using airborne laser scanning. Remote Sensing of Environment, 123, 271-279
Packalén, P., Pitkänen, J., & Maltamo, M. (2008). Comparison of individual tree detection and canopy height distribution approaches: a case study in Finland. In R. Hill, J. Rosette, & J. Suárez (Eds.), 8th international conference on LiDAR applications in forest assessment and inventory. SilviLaser. Heriot-Watt University, Edinburgh, UK
Parker, G.G. (1995). Structure and microclimate of forest canopies. Forest Canopies-A review of Research on a Biological Frontier (M. Lowman and N. Nadkarni, Eds.), Academic, San Diego
Pascual, C., García-Abril, A., Cohen, W.B., & Martín-Fernández, S. (2010). Relationship between LiDAR-derived forest canopy height and Landsat images. International Journal of Remote Sensing, 31, 1261-1280
Patenaude, G., Hill, R.A., Milne, R., Gaveau, D.L.A., Briggs, B.B.J., & Dawson, T.P. (2004). Quantifying forest above ground carbon content using LiDAR remote sensing. Remote Sensing of Environment, 93, 368-380
Persson, A., Holmgren, J., & Soderman, U. (2002). Detecting and measuring individual trees using an airborne laser scanner. Photogrammetric Engineering and Remote Sensing, 68, 925-932
Popescu, S., Wynne, R.H., & Scrivani, J.A. (2004). Fusion of Small-Footprint Lidar and Multispectral Data to Estimate Plot-Level Volume and Biomass in Deciduous and Pine Forests in Virginia, USA. Forest Science, 50, 551-565
Popescu, S.C. (2007). Estimating biomass of individual pine trees using airborne lidar. Biomass and Bioenergy, 31, 646-655
Popescu, S.C., Wynne, R.H., & Nelson, R.F. (2002). Estimating plot-level tree heights with lidar: local filtering with a canopy-height based variable window size. Computers and Electronics in Agriculture, 37, 71-95
Riano, D., Valladares, F., Condes, S., & Chuvieco, E. (2004). Estimation of leaf area index and covered ground from airborne laser scanner (Lidar) in two contrasting forests. Agricultural and Forest Meteorology, 124, 269-275
Ritchie, J.C., Evans, D.L., Jacobs, D., Everitt, J.H., & Weltz, M.A. (1993). Measuring canopy structure with an airborne laser altimeter. Transactions of the American Society of Agricultural Engineers, 36, 1235-1238
Saatchi, S.S., Houghton, R.A., Dos Santos Alvalá, R.C., Soares, J.V., & Yu, Y. (2007). Distribution of aboveground live biomass in the Amazon basin. Global Change Biology, 13, 816-837
63
Sheng, Y. (2008). Quantifying the size of a LiDAR footprint: a set of generalized equations. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 5, 419-422
Stephens, P.R., Kimberley, M.O., Beets, P.N., Paul, T.S.H., Searles, N., Bell, A., Brack, C., & Broadley, J. (2011). Airborne scanning LiDAR in a double sampling forest carbon inventory. Remote Sensing of Environment, 117, 348-357
Sun, G., Ranson, K.J., Guo, Z., Zhang, Z., Montesano, P., & Kimes, D. (2011). Forest biomass mapping from lidar and radar synergies. Remote Sensing of Environment, 115, 2906-2916
Swatantran, A., Dubayah, R., Roberts, D., Hofton, M., & Blair, J.B. (2011). Mapping biomass and stress in the Sierra Nevada using lidar and hyperspectral data fusion. Remote Sensing of Environment, 115, 2917-2930
Thomas, R.Q., Hurtt, G.C., Dubayah, R., & Schilz, M.H. (2008). Using lidar data and a height-structured ecosystem model to estimate forest carbon stocks and fluxes over mountainous terrain. Canadian Journal of Remote Sensing, 34, S351- S363
Vain, A., Yu, X., Kaasalainen, S., & Hyyppä, J. (2010). Correcting Airborne Laser Scanning Intensity Data for Automatic Gain Control Effect. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 7, 511-514. doi:510.1109/LGRS.2010.2040578
Wang, C., & Glenn, N.F. (2008). A linear regression method for tree canopy height estimation using airborne lidar data. Canadian Journal of Remote Sensing, 34, S217-S227
Wehr, A., & Lohr, U. (1999). Airborne laser scanning—an introduction and overview. ISPRS Journal of Photogrammetry & Remote Sensing, 54, 68-82
Yu, X., Hyyppa, J., Kaartinen, H., & Maltamo, M. (2004). Automatic detection of harvested trees and determination of forest growth using airborne laser scanning. Remote Sensing of Environment, 90, 451-462
Zhao, K., Popescu, S., Meng, X., & Agca, M. (2011). Characterizing forest canopy structure with lidar composite metrics and machine learning. Remote Sensing of Environment, 115, 1978-1996
Zimble, D.A., Evans, D.L., Carlson, G.C., Parker, R.C., Grado, S.C., & Gerard, P.D. (2003). Characterizing vertical forest structure using small-footprint airborne LiDAR. Remote Sensing of Environment, 87, 171-182