1. R. Stirzaker, R. Vertessy, and A. Sarre. Trees, Water, and Salt: An Australian Guide to Using Trees for Healthy Catchments and Productive Farms. Rural Industries Research and Development Corporation, RIRDC, 2002. Note: NOTE: Good layman descriptions of hill slopes interms of hydrologic parameters, such as concavity, etc. Nice figures. I have a hard time collecting all of the bibliogrpahic info since this is not a normal published book. It is a publication of the Austrialian government, but it is not free, so you can simply download it. Go to (http://www.agric.uwa.edu.au/soils/tw&s/) to view text of the chapters. I printed out Chapter 2. I found most of the bibliographic information be serach ing for trees, water, and salt on the CSIRO Web site. Chapter 2 makes the point that shape (concavity) of the slope affects local water table depth and vegetation.
   

   @book{RefWorks:752,
   author={R. Stirzaker and R. Vertessy and A. Sarre},
   year={2002},
   title={Trees, Water, and Salt: An Australian Guide to Using Trees for Healthy Catchments and Productive Farms},
   publisher={Rural Industries Research and Development Corporation},
   address={RIRDC},
   note={NOTE: Good layman descriptions of hill slopes interms of hydrologic parameters, such as concavity, etc. Nice figures. I have a hard time collecting all of the bibliogrpahic info since this is not a normal published book. It is a publication of the Austrialian government, but it is not free, so you can simply download it. Go to (http://www.agric.uwa.edu.au/soils/tw&s/) to view text of the chapters. I printed out Chapter 2. I found most of the bibliographic information be serach ing for "trees, water, and salt" on the CSIRO Web site. Chapter 2 makes the point that shape (concavity) of the slope affects local water table depth and vegetation.} 
   }
   

1. George Alan Blackburn. Remote sensing of forest pigments using airborne imaging spectrometer and LIDAR imagery. rse, 82(2-3):311-321, 2002. [WWW ] Abstract:

   This study created and tested predictive models developed using airborne imaging spectrometer and light detection and ranging (LIDAR) instruments for estimating the concentrations of photosynthetic pigments in broad-leaved and coniferous forest plantations. Data were acquired using a Compact Airborne Spectrographic Imager (CASI) and an Airborne Laser Terrain Mapping (ALTM) 1020 instrument in midsummer for study sites in the New Forest, England, along with concomitant in situ measurements of canopy properties. The stands used displayed a wide variation in the biophysical and biochemical properties of interest. When employing the imaging spectrometer data alone, there were no relationships between any spectral variables (band reflectance, band ratios, or first derivatives of reflectance) and canopy biophysical and biochemical properties when both broad-leaved and coniferous stands were analysed as a combined data set. However, for the broad-leaved stands alone, curvilinear relationships were found between the wavelength position of the red edge (small lambda, GreekRE) and pigment concentrations per unit ground area (e.g., R2=0.88** for chlorophyll a [Chl a]) and per unit leaf mass (e.g., R2=0.76** for Chl a). The predictive value of these models was somewhat limited; for example, the root mean squared error (RMSE) was 300 mg m-2 (27% of the mean) for Chl a concentration per unit ground area and 1.17 mg g-1 (24% of the mean) for Chl a concentration per unit leaf mass. A ratio of a near-infrared and a green band (865 nm/553 nm) was linearly related to leaf area index (LAI) of the broad-leaved stands (R2=0.71**) and the regression model was a reasonable predictor of the LAI for the independent test sites (RMSE=0.88; 18.6% of the mean). Canopy height information derived from the ALTM data was used to mask out canopy gap areas from the CASI imagery of each stand. This process had limited impact on the relationships between spectral and canopy variables for the broad-leaved stands, and small lambda, GreekRE remained unrelated to pigment concentrations per unit ground area for the coniferous stands. However, the masking process substantially improved the strength of the relationship between small lambda, GreekRE and pigment concentrations per unit leaf mass for the coniferous stands (e.g., for Chl a R2=0.85**; RMSE of PREDICTION=0.84 mg g-1 [22% of the mean]). Therefore, the study demonstrates that for broad-leaved stands, spectral models can be applied to imaging spectrometer data to quantify forest pigments and LAI with moderate accuracy. For coniferous stands, the use of LIDAR data to remove canopy gap areas from the CASI imagery considerably increases the accuracy of spectral predictive models for quantifying pigment concentrations per unit leaf mass.

   
   

   @Article{blackburn02,
   author = {George Alan Blackburn},
   title = {Remote sensing of forest pigments using airborne imaging spectrometer and LIDAR imagery},
   journal = rse,
   year = {2002},
   volume = {82},
   pages = {311-321},
   number = {2-3},
   url = {http://www.sciencedirect.com/science/article/B6V6V-460417K-1/1/e8fa885c024c494a5e0fc20b9a1a432f},
   keyword = {},
   abstract = {This study created and tested predictive models developed using airborne imaging spectrometer and light detection and ranging (LIDAR) instruments for estimating the concentrations of photosynthetic pigments in broad-leaved and coniferous forest plantations. Data were acquired using a Compact Airborne Spectrographic Imager (CASI) and an Airborne Laser Terrain Mapping (ALTM) 1020 instrument in midsummer for study sites in the New Forest, England, along with concomitant in situ measurements of canopy properties. The stands used displayed a wide variation in the biophysical and biochemical properties of interest. When employing the imaging spectrometer data alone, there were no relationships between any spectral variables (band reflectance, band ratios, or first derivatives of reflectance) and canopy biophysical and biochemical properties when both broad-leaved and coniferous stands were analysed as a combined data set. However, for the broad-leaved stands alone, curvilinear relationships were found between the wavelength position of the red edge (small lambda, GreekRE) and pigment concentrations per unit ground area (e.g., R2=0.88** for chlorophyll a [Chl a]) and per unit leaf mass (e.g., R2=0.76** for Chl a). The predictive value of these models was somewhat limited; for example, the root mean squared error (RMSE) was 300 mg m-2 (27% of the mean) for Chl a concentration per unit ground area and 1.17 mg g-1 (24% of the mean) for Chl a concentration per unit leaf mass. A ratio of a near-infrared and a green band (865 nm/553 nm) was linearly related to leaf area index (LAI) of the broad-leaved stands (R2=0.71**) and the regression model was a reasonable predictor of the LAI for the independent test sites (RMSE=0.88; 18.6% of the mean). Canopy height information derived from the ALTM data was used to mask out canopy gap areas from the CASI imagery of each stand. This process had limited impact on the relationships between spectral and canopy variables for the broad-leaved stands, and small lambda, GreekRE remained unrelated to pigment concentrations per unit ground area for the coniferous stands. However, the masking process substantially improved the strength of the relationship between small lambda, GreekRE and pigment concentrations per unit leaf mass for the coniferous stands (e.g., for Chl a R2=0.85**; RMSE of PREDICTION=0.84 mg g-1 [22% of the mean]). Therefore, the study demonstrates that for broad-leaved stands, spectral models can be applied to imaging spectrometer data to quantify forest pigments and LAI with moderate accuracy. For coniferous stands, the use of LIDAR data to remove canopy gap areas from the CASI imagery considerably increases the accuracy of spectral predictive models for quantifying pigment concentrations per unit leaf mass. },
   
   
   
   }
   




2. J. C. Brock and C. W. Wright. Initial results from a test of the NASA experimental advanced airborne research lidar (EAARL) for the study of coral reef ecosystems. Proceedings 7th International Conference on Remote Sensing for Marine and Coastal Environments, 2002.
   

   @article{RefWorks:793,
   author={J. C. Brock and C. W. Wright},
   year={2002},
   title={Initial results from a test of the NASA experimental advanced airborne research lidar (EAARL) for the study of coral reef ecosystems},
   journal={Proceedings 7th International Conference on Remote Sensing for Marine and Coastal Environments} 
   }
   




3. J. C. Brock, C. W. Wright, A. H. Sallenger, W. B. Krabill, and R. N. Swift. Basis and methods of NASA Airborne Topographic Mapper lidar surveys for coastal studies. Journal of Coastal Research, 18(1):1-13, 2002.
   

   @article{RefWorks:792,
   author={J. C. Brock and C. W. Wright and A. H. Sallenger and W. B. Krabill and R. N. Swift},
   year={2002},
   title={Basis and methods of NASA Airborne Topographic Mapper lidar surveys for coastal studies},
   journal={Journal of Coastal Research},
   volume={18},
   number={1},
   pages={1-13} 
   }
   




4. E. Chuvieco, D. Riano, I. Aguado, and D. Cocero. Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: applications in fire danger assessment. ijrs, 23(11):2145-2162, 2002. Abstract:

   The objective of this paper was to define indices based on reflectance measurements performed by the Landsat Thematic Mapper (TM) sensor for estimating water content of live Mediterranean fuels for fire danger estimation. Seven Landsat TM images were processed and correlated with fuel moisture content (FMC) of several live species of Mediterranean grassland and shrubland. Raw bands were converted to reflectances, and several indices potentially related to water content were calculated from them. Pearson r correlation coefficients and linear regression analysis were computed in order to estimate FMC. Those indices based on the short wave infrared bands (SWIR: 1.4-2.5 m) and on the contrast between this band and the near-infrared band offered the best estimations. For grassland, the integral of visible and SWIR bands provided the highest correlation, but also raw reflectances and Normalized Difference Vegetation Indices (NDVIs) provide significant r values (r2 above 0.8). For shrub species, indices that include SWIR reflectances performed much better than NDVI, because the SWIR band is more sensitive to water absorption, whereas NDVI estimates FMC indirectly, only from the effects of chlorophyll changes due to water variation content and leaf area index. The most significant relations were found with the derivatives of bands 4-5 and 2-3, and again the integral of visible and SWIR bands. Multiple regression analysis provided adjusted r2 values of 0.84 for grasslands and 0.74 for shrublands. Average errors of 23.45-40 $\%$ in the estimation of FMC for grasslands were found, depending on which variables were included in the multiple regression. For the FMC estimation of shrub species, errors were lower (from 7.94 to 19.40 $\%$ ), since the range of FMC values was also lower.

   
   

   @Article{chuvieco02,
   author = {E. Chuvieco and D. Riano and I. Aguado and D. Cocero},
   title = {Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: applications in fire danger assessment},
   journal = ijrs,
   year = {2002},
   volume = {23},
   pages = {2145-2162},
   number = {11},
   url = {},
   keyword = {},
   abstract = {The objective of this paper was to define indices based on reflectance measurements performed by the Landsat Thematic Mapper (TM) sensor for estimating water content of live Mediterranean fuels for fire danger estimation. Seven Landsat TM images were processed and correlated with fuel moisture content (FMC) of several live species of Mediterranean grassland and shrubland. Raw bands were converted to reflectances, and several indices potentially related to water content were calculated from them. Pearson r correlation coefficients and linear regression analysis were computed in order to estimate FMC. Those indices based on the short wave infrared bands (SWIR: 1.4-2.5 m) and on the contrast between this band and the near-infrared band offered the best estimations. For grassland, the integral of visible and SWIR bands provided the highest correlation, but also raw reflectances and Normalized Difference Vegetation Indices (NDVIs) provide significant r values (r2 above 0.8). For shrub species, indices that include SWIR reflectances performed much better than NDVI, because the SWIR band is more sensitive to water absorption, whereas NDVI estimates FMC indirectly, only from the effects of chlorophyll changes due to water variation content and leaf area index. The most significant relations were found with the derivatives of bands 4-5 and 2-3, and again the integral of visible and SWIR bands. Multiple regression analysis provided adjusted r2 values of 0.84 for grasslands and 0.74 for shrublands. Average errors of 23.45-40 $\%$ in the estimation of FMC for grasslands were found, depending on which variables were included in the multiple regression. For the FMC estimation of shrub species, errors were lower (from 7.94 to 19.40 $\%$ ), since the range of FMC values was also lower.},
   
   
   
   }
   




5. J. B. Drake, R. O. Dubayah, D. B. Clark, R. G. Knox, J. B. Blair, M. A. Hofton, R. L. Chazdon, J. F. Weishampel, and S. Prince. Estimation of tropical forest structural characteristics using large-footprint lidar. Remote Sensing of Environment, 79(2-3):305-319, 2002.
   

   @article{RefWorks:800,
   author={J. B. Drake and R. O. Dubayah and D. B. Clark and R. G. Knox and J. B. Blair and M. A. Hofton and R. L. Chazdon and J. F. Weishampel and S. Prince},
   year={2002},
   title={Estimation of tropical forest structural characteristics using large-footprint lidar},
   journal={Remote Sensing of Environment},
   volume={79},
   number={2-3},
   pages={305-319} 
   }
   




6. Jason B. Drake, Ralph O. Dubayah, David B. Clark, Robert G. Knox, J. Bryan Blair, Michelle A. Hofton, Robin L. Chazdon, John F. Weishampel, and Stephen D. Prince. Estimation of tropical forest structural characteristics using large-footprint lidar. rse, 79:305-319, 2002. [WWW ] Abstract:

   Quantification of forest structure is important for developing a better understanding of how forest ecosystems function. Additionally, estimation of forest structural attributes, such as aboveground biomass (AGBM), is an important step in identifying the amount of carbon in terrestrial vegetation pools and is central to global carbon cycle studies. Although current remote sensing techniques recover such tropical forest structure poorly, new large-footprint lidar instruments show great promise. As part of a prelaunch validation plan for the Vegetation Canopy Lidar (VCL) mission, the Laser Vegetation Imaging Sensor (LVIS), a large-footprint airborne scanning lidar, was flown over the La Selva Biological Station, a tropical wet forest site in Costa Rica. The primary objective of this study was to test the ability of large-footprint lidar instruments to recover forest structural characteristics across a spectrum of land cover types from pasture to secondary and primary tropical forests. LVIS metrics were able to predict field-derived quadratic mean stem diameter (QMSD), basal area, and AGBM with R2 values of up to .93, .72, and .93, respectively. These relationships were significant and nonasymptotic through the entire range of conditions sampled at the La Selva. Our results confirm the ability of large-footprint lidar instruments to estimate important structural attributes, including biomass in dense tropical forests, and when taken along with similar results from studies in temperate forests, strongly validate the VCL mission framework.

   
   

   @Article{drake02,
   author = {Jason B. Drake and Ralph O. Dubayah and David B. Clark and Robert G. Knox and J. Bryan Blair and Michelle A. Hofton and Robin L. Chazdon and John F. Weishampel and Stephen D. Prince},
   title = {Estimation of tropical forest structural characteristics using large-footprint lidar},
   journal = rse,
   year = 2002,
   volume = {79},
   pages = {305-319},
   keyword = {},
   url = {http://www.sciencedirect.com/science/article/B6V6V-44R1BH4-G/1/60d06e497b9bc5a0a87686d087f82a06},
   abstract = {Quantification of forest structure is important for developing a better understanding of how forest ecosystems function. Additionally, estimation of forest structural attributes, such as aboveground biomass (AGBM), is an important step in identifying the amount of carbon in terrestrial vegetation pools and is central to global carbon cycle studies. Although current remote sensing techniques recover such tropical forest structure poorly, new large-footprint lidar instruments show great promise. As part of a prelaunch validation plan for the Vegetation Canopy Lidar (VCL) mission, the Laser Vegetation Imaging Sensor (LVIS), a large-footprint airborne scanning lidar, was flown over the La Selva Biological Station, a tropical wet forest site in Costa Rica. The primary objective of this study was to test the ability of large-footprint lidar instruments to recover forest structural characteristics across a spectrum of land cover types from pasture to secondary and primary tropical forests. LVIS metrics were able to predict field-derived quadratic mean stem diameter (QMSD), basal area, and AGBM with R2 values of up to .93, .72, and .93, respectively. These relationships were significant and nonasymptotic through the entire range of conditions sampled at the La Selva. Our results confirm the ability of large-footprint lidar instruments to estimate important structural attributes, including biomass in dense tropical forests, and when taken along with similar results from studies in temperate forests, strongly validate the VCL mission framework.},
   
   
   
   }
   




7. Jason B. Drake, Ralph O. Dubayah, Robert G. Knox, David B. Clark, and J. B. Blair. Sensitivity of large-footprint lidar to canopy structure and biomass in a neotropical rainforest. rse, 81(2-3):378-392, 2002. [WWW ] Abstract:

   Accurate estimates of the total biomass in terrestrial vegetation are important for carbon dynamics studies at a variety of scales. Although aboveground biomass is difficult to quantify over large areas using traditional techniques, lidar remote sensing holds great promise for biomass estimation because it directly measures components of canopy structure such as canopy height and the vertical distribution of intercepted canopy surfaces. In this study, our primary goal was to explore the sensitivity of lidar to differences in canopy structure and aboveground biomass in a dense, neotropical rainforest. We first examined the relationship between simple vertical canopy profiles derived from field measurements and the estimated aboveground biomass (EAGB) across a range of field plots located in primary and secondary tropical rainforest and in agroforesty areas. We found that metrics from field-derived vertical canopy profiles are highly correlated (R2 up to .94) with EAGB across the entire range of conditions sampled. Next, we found that vertical canopy profiles from a large-footprint lidar instrument were closely related with coincident field profiles, and that metrics from both field and lidar profiles are highly correlated. As a result, metrics from lidar profiles are also highly correlated (R2 up to .94) with EAGB across this neotropical landscape. These results help to explain the nature of the relationship between lidar data and EAGB, and also lay the foundation to explore the generality of the relationship between vertical canopy profiles and biomass in other tropical regions.

   
   

   @Article{drakedub02,
   author = {Jason B. Drake and Ralph O. Dubayah and Robert G. Knox and David B. Clark and J. B. Blair},
   title = {Sensitivity of large-footprint lidar to canopy structure and biomass in a neotropical rainforest},
   journal = rse,
   year = {2002},
   volume = {81},
   pages = {378-392},
   number = {2-3},
   url = {http://www.sciencedirect.com/science/article/B6V6V-45NP347-M/1/c6fc2a8d23c943aeb3c22434783e4658},
   keyword = {},
   abstract = {Accurate estimates of the total biomass in terrestrial vegetation are important for carbon dynamics studies at a variety of scales. Although aboveground biomass is difficult to quantify over large areas using traditional techniques, lidar remote sensing holds great promise for biomass estimation because it directly measures components of canopy structure such as canopy height and the vertical distribution of intercepted canopy surfaces. In this study, our primary goal was to explore the sensitivity of lidar to differences in canopy structure and aboveground biomass in a dense, neotropical rainforest. We first examined the relationship between simple vertical canopy profiles derived from field measurements and the estimated aboveground biomass (EAGB) across a range of field plots located in primary and secondary tropical rainforest and in agroforesty areas. We found that metrics from field-derived vertical canopy profiles are highly correlated (R2 up to .94) with EAGB across the entire range of conditions sampled. Next, we found that vertical canopy profiles from a large-footprint lidar instrument were closely related with coincident field profiles, and that metrics from both field and lidar profiles are highly correlated. As a result, metrics from lidar profiles are also highly correlated (R2 up to .94) with EAGB across this neotropical landscape. These results help to explain the nature of the relationship between lidar data and EAGB, and also lay the foundation to explore the generality of the relationship between vertical canopy profiles and biomass in other tropical regions.},
   
   
   
   }
   




8. M. Flood. From commercial data to commercial products: research priorities in the commercial sector. International Workshop on Three-dimensional Analysis of Forest Structure and Terrain using LiDAR technology, Mar 12-15 2002.
   

   @article{RefWorks:804,
   author={M. Flood},
   year={2002},
   month={Mar 12-15},
   title={From commercial data to commercial products: research priorities in the commercial sector},
   journal={International Workshop on Three-dimensional Analysis of Forest Structure and Terrain using LiDAR technology} 
   }
   




9. F. J. Garcia-Haro and S. Sommer. A fast canopy reflectance model to simulate realistic remote sensing scenarios. rse, 81(2-3):205-227, 2002. [WWW ] Abstract:

   A model for light interaction has been developed to compute spectral and bidirectional reflectance from discontinuous canopies approximated by an arbitrary configuration of plants. The model assumes certain principles of geometric models, e.g., that sensor integrates the radiance reflected from tree plants, shaded soil, and illuminated soil. However, the model attempts to compensate for errors due to multiple scattering in vegetation canopies that lead to nonlinear mixing. In contrast to geometric models, tree crowns are treated as porous (partially transmitting), geometric bodies. Reflectance of canopy and shadowed ground are nonlinear mixtures of leaves and background signatures, which are moreover influenced by parameters of canopy, such as leaf area index (LAI), coefficient of attenuation, and leaves transmittance. Optical parameters are not constant but stochastic variables are controlled by a certain texture in canopy envelopes, and roughness and relief in surface background. The model may also be run using backgrounds presenting variable topography and comprising different landscape features on imported real images. The model predicts the basic features of the Bidirectional Reflectance Factor (BRF), i.e., bowl shape and the hotspot, but unlike common models, it is well suited to address the spectral and spatial domains. For example, the model provides a fast and efficient strategy to derive hyperspectral images at appropriate spatial resolutions (e.g., regional scale) over a wide range of ecosystems.

   
   

   @Article{garcia02,
   author = {F. J. Garcia-Haro and S. Sommer},
   title = {A fast canopy reflectance model to simulate realistic remote sensing scenarios },
   journal = rse,
   year = {2002},
   volume = {81},
   pages = {205-227},
   number = {2-3},
   url = {http://www.sciencedirect.com/science/article/B6V6V-45NP347-4/1/0e1cf3d02f83e37913e21b965234bf85},
   keyword = {},
   abstract = {A model for light interaction has been developed to compute spectral and bidirectional reflectance from discontinuous canopies approximated by an arbitrary configuration of plants. The model assumes certain principles of geometric models, e.g., that sensor integrates the radiance reflected from tree plants, shaded soil, and illuminated soil. However, the model attempts to compensate for errors due to multiple scattering in vegetation canopies that lead to nonlinear mixing. In contrast to geometric models, tree crowns are treated as porous (partially transmitting), geometric bodies. Reflectance of canopy and shadowed ground are nonlinear mixtures of leaves and background signatures, which are moreover influenced by parameters of canopy, such as leaf area index (LAI), coefficient of attenuation, and leaves transmittance. Optical parameters are not constant but stochastic variables are controlled by a certain texture in canopy envelopes, and roughness and relief in surface background. The model may also be run using backgrounds presenting variable topography and comprising different landscape features on imported real images. The model predicts the basic features of the Bidirectional Reflectance Factor (BRF), i.e., bowl shape and the hotspot, but unlike common models, it is well suited to address the spectral and spatial domains. For example, the model provides a fast and efficient strategy to derive hyperspectral images at appropriate spatial resolutions (e.g., regional scale) over a wide range of ecosystems.},
   
   
   
   }
   




10. E. Grall-Maës and P. Beauseroy. Mutual Information-Based Feature Extraction on the Time-Frequency Plane. IEEE Trans. Sig. Proc., 50(4):779-790, 2002.
    

    @article{RefWorks:805,
    author={E. Grall-Maës and P. Beauseroy},
    year={2002},
    title={Mutual Information-Based Feature Extraction on the Time-Frequency Plane},
    journal={IEEE Trans. Sig. Proc.},
    volume={50},
    number={4},
    pages={779-790} 
    }
    




11. R. A. Hill, G. S. Smith, R. M. Fuller, and N. Veitch. Landscape modelling using airborne multi-spectral and laser scanning data. ijrs, 23(11):2327-2334, 2002. [WWW ] Abstract:

    By integrating multi-spectral and elevation data from airborne sensors (CASI and ALTM) and adopting a parcel-based approach, a progression is achieved from land-cover classification to landscape modelling. This work involved data integration, per-parcel classification, knowledge-based correction and the derivation of landscape objects. For a 1 km2 study area, a 14 land-cover class vector dataset was generated in which the parcels relate to landscape objects and contain information on their structure and 'terrain' context. At a 1 m spatial resolution, the correspondence between land-cover mapped using the airborne sensor data and identified by Countryside Survey 2000 field surveyors was 88 $\%$ .

    
    

    @Article{hill02,
    author = {R. A. Hill and G. S. Smith and R. M. Fuller and N. Veitch},
    title = {Landscape modelling using airborne multi-spectral and laser scanning data},
    journal = ijrs,
    year = {2002},
    volume = {23},
    pages = {2327-2334},
    number = {11},
    url = {http://taylorandfrancis.metapress.com/link.asp?id=fcwl61c0vcv4hf01},
    keyword = {},
    abstract = {By integrating multi-spectral and elevation data from airborne sensors (CASI and ALTM) and adopting a parcel-based approach, a progression is achieved from land-cover classification to landscape modelling. This work involved data integration, per-parcel classification, knowledge-based correction and the derivation of landscape objects. For a 1 km2 study area, a 14 land-cover class vector dataset was generated in which the parcels relate to landscape objects and contain information on their structure and 'terrain' context. At a 1 m spatial resolution, the correspondence between land-cover mapped using the airborne sensor data and identified by Countryside Survey 2000 field surveyors was 88 $\%$ .},
    
    
    
    }
    




12. R.A. Hill and N. Veitch. Landscape visualisation: rendering a virtual reality simulation from airborne laser altimetry and multi-spectral scanning data.. ijrs, 23:3307-3309, 2002.
    

    @Article{hill02land,
    author = {R.A. Hill and N. Veitch},
    title = {Landscape visualisation: rendering a virtual reality simulation from airborne laser altimetry and multi-spectral scanning data.},
    journal = ijrs,
    year = {2002},
    volume = {23},
    pages = {3307-3309},
    number = {},
    url = {},
    keyword = {},
    abstract = {},
    
    
    
    }
    




13. Andrew T. Hudak, Michael A. Lefsky, Warren B. Cohen, and Mercedes Berterretche. Integration of lidar and Landsat ETM+ data for estimating and mapping forest canopy height. rse, 82(2-3):397-416, 2002. [WWW ] Abstract:

    Light detection and ranging (lidar) data provide accurate measurements of forest canopy structure in the vertical plane; however, current lidar sensors have limited coverage in the horizontal plane. Landsat data provide extensive coverage of generalized forest structural classes in the horizontal plane but are relatively insensitive to variation in forest canopy height. It would, therefore, be desirable to integrate lidar and Landsat data to improve the measurement, mapping, and monitoring of forest structural attributes. We tested five aspatial and spatial methods for predicting canopy height, using an airborne lidar system (Aeroscan) and Landsat Enhanced Thematic Mapper (ETM+) data: regression, kriging, cokriging, and kriging and cokriging of regression residuals. Our 200-km2 study area in western Oregon encompassed Oregon State University's McDonald¯Dunn Research Forest, which is broadly representative of the age and structural classes common in the region. We sampled a spatially continuous lidar coverage in eight systematic patterns to determine which lidar sampling strategy would optimize lidar¯Landsat integration in western Oregon forests: transects sampled at 2000, 1000, 500, and 250 m frequencies, and points sampled at these same spatial frequencies. The aspatial regression model results, regardless of sampling strategy, preserved actual vegetation pattern, but underestimated taller canopies and overestimated shorter canopies. The spatial models, kriging and cokriging, produced less biased results than regression but poorly reproduced vegetation pattern, especially at the sparser (2000 and 1000 m) sampling frequencies. The spatial model predictions were more accurate than the regression model predictions at locations <200 m from sample locations. Cokriging, using the ETM+ panchromatic band as the secondary variable, proved slightly more accurate than kriging. The integrated models that kriged or cokriged regression residuals were preferable to either the aspatial or spatial models alone because they preserved the vegetation pattern like regression yet improved estimation accuracies above those predicted from the regression models alone. The 250-m point sampling strategy proved most optimal because it oversampled the landscape relative to the geostatistical range of actual spatial variation, as indicated by the sample semivariograms, while making the sample data volume more manageable. We concluded that an integrated modeling strategy is most suitable for estimating and mapping canopy height at locations unsampled by lidar, and that a 250-m discrete point sampling strategy most efficiently samples an intensively managed forested landscape in western Oregon.

    
    

    @Article{hudak02,
    author = {Andrew T. Hudak and Michael A. Lefsky and Warren B. Cohen and Mercedes Berterretche},
    title = {Integration of lidar and Landsat ETM+ data for estimating and mapping forest canopy height },
    journal = rse,
    year = {2002},
    volume = {82},
    pages = {397-416},
    number = {2-3},
    url = {http://www.sciencedirect.com/science/article/B6V6V-4619P8M-1/1/5e59b7c4c7ddc2fbdc033e4688cfffb9},
    keyword = {},
    abstract = {Light detection and ranging (lidar) data provide accurate measurements of forest canopy structure in the vertical plane; however, current lidar sensors have limited coverage in the horizontal plane. Landsat data provide extensive coverage of generalized forest structural classes in the horizontal plane but are relatively insensitive to variation in forest canopy height. It would, therefore, be desirable to integrate lidar and Landsat data to improve the measurement, mapping, and monitoring of forest structural attributes. We tested five aspatial and spatial methods for predicting canopy height, using an airborne lidar system (Aeroscan) and Landsat Enhanced Thematic Mapper (ETM+) data: regression, kriging, cokriging, and kriging and cokriging of regression residuals. Our 200-km2 study area in western Oregon encompassed Oregon State University's McDonald¯Dunn Research Forest, which is broadly representative of the age and structural classes common in the region. We sampled a spatially continuous lidar coverage in eight systematic patterns to determine which lidar sampling strategy would optimize lidar¯Landsat integration in western Oregon forests: transects sampled at 2000, 1000, 500, and 250 m frequencies, and points sampled at these same spatial frequencies. The aspatial regression model results, regardless of sampling strategy, preserved actual vegetation pattern, but underestimated taller canopies and overestimated shorter canopies. The spatial models, kriging and cokriging, produced less biased results than regression but poorly reproduced vegetation pattern, especially at the sparser (2000 and 1000 m) sampling frequencies. The spatial model predictions were more accurate than the regression model predictions at locations <200 m from sample locations. Cokriging, using the ETM+ panchromatic band as the secondary variable, proved slightly more accurate than kriging. The integrated models that kriged or cokriged regression residuals were preferable to either the aspatial or spatial models alone because they preserved the vegetation pattern like regression yet improved estimation accuracies above those predicted from the regression models alone. The 250-m point sampling strategy proved most optimal because it oversampled the landscape relative to the geostatistical range of actual spatial variation, as indicated by the sample semivariograms, while making the sample data volume more manageable. We concluded that an integrated modeling strategy is most suitable for estimating and mapping canopy height at locations unsampled by lidar, and that a 250-m discrete point sampling strategy most efficiently samples an intensively managed forested landscape in western Oregon. },
    
    
    
    }
    




14. Michael A. Lefsky, Warren B. Cohen, Geoffrey G. Parker, and David J. Harding. Lidar Remote Sensing for Ecosystem Studies. BioScience, 52(1):19-30, 2002. Abstract:

    LIDAR, AN EMERGING REMOTE SENSING TECHNOLOGY THAT DIRECTLY MEASURES THE THREE-DIMENSIONAL DISTRIBUTION OF PLANT CANOPIES, CAN ACCURATELY ESTIMATE VEGETATION STRUCTURAL ATTRIBUTES AND SHOULD BE OF PARTICULAR INTEREST TO FOREST, LANDSCAPE, AND GLOBAL ECOLOGISTS

    
    

    @Article{lefsky02,
    author = {Michael A. Lefsky and Warren B. Cohen and Geoffrey G. Parker and David J. Harding},
    title = {Lidar Remote Sensing for Ecosystem Studies},
    journal = {BioScience},
    year = {2002},
    volume = {52},
    number = {1},
    pages = {19-30},
    abstract = {LIDAR, AN EMERGING REMOTE SENSING TECHNOLOGY THAT DIRECTLY MEASURES THE THREE-DIMENSIONAL DISTRIBUTION OF PLANT CANOPIES, CAN ACCURATELY ESTIMATE VEGETATION STRUCTURAL ATTRIBUTES AND SHOULD BE OF PARTICULAR INTEREST TO FOREST, LANDSCAPE, AND GLOBAL ECOLOGISTS},
    
    
    
    }
    




15. Kevin Lim, Paul Treitz, Mike Wulder, Benoit St-Onge, and Martin Flood. LiDAR remote sensing of forest structure. ppg, 2002. [WWW ] Keyword(s): remote sensing, LiDAR, laser altimetry, forest structure, biomass. Abstract:

    Light detection and ranging (LiDAR) technology provides horizontal and vertical information at high spatial resolutions and vertical accuracies. Forest attributes such as canopy height can be directly retrieved from LiDAR data. Direct retrieval of canopy height provides opportunities to model above ground biomass and canopy volume. Access to the vertical nature of forest ecosystems also offers new opportunities for enhanced forest monitoring, management and planning.

    
    

    @Article{lim_ppg02,
    author = {Kevin Lim and Paul Treitz and Mike Wulder and Benoit St-Onge and Martin Flood},
    title = {LiDAR remote sensing of forest structure},
    journal = ppg,
    year = {2002},
    volume = {},
    pages = {},
    number = {},
    url = {/paper/laser/PPG_LiDAR_2001_submitted.pdf},
    keyword = {remote sensing; LiDAR; laser altimetry; forest structure; biomass},
    abstract = {Light detection and ranging (LiDAR) technology provides horizontal and vertical information at high spatial resolutions and vertical accuracies. Forest attributes such as canopy height can be directly retrieved from LiDAR data. Direct retrieval of canopy height provides opportunities to model above ground biomass and canopy volume. Access to the vertical nature of forest ecosystems also offers new opportunities for enhanced forest monitoring, management and planning.},
    
    
    
    }
    




16. Hans-Gerd Maas. Methods for Measuring Height and Planimetry Discrepancies in Airborne Laserscanner Data. pers, 68(9):933-940, 2002. [WWW ] Abstract:

    Airborne laserscanning (or lidar) has become a very important technique for the acquisition of digital terrain model data. Beyond this, the technique is increasingly being used for the acquisition of point clouds for 3D modeling of a wide range of objects, such as buildings, vegetation, or electrical power lines. As an active technique, airborne laserscanning offers a high reliability even over terrain with poor image contrast. The precision of the technique is often specified to be on the order of one to two decimeters. By reason of its primary use in digital terrain modeling, examinations of the precision potential of airborne laserscanning have so far been concentrated on the height precision. With the use of the technique for general 3D reconstruction tasks and the increasing resolution of laserscanner systems, the planimetric precision of laserscanner point clouds becomes an important issue. In addition to errors in the laser distance meter and the deflecting mirror system, the error budget of airborne laserscanning instruments is strongly influenced by the GPS/INS systems used for sensor pose (position and orientation) determination. Errors of these systems often lead to the deformation of laserscanner data strips and may become evident as discrepancies in the overlap region between neighboring strips in a block of laserscanner data. The paper presents least-squares matching implemented on a TIN structure as a general tool for the determination of laser-scanner strip discrepancies in all three coordinate directions, using both height and reflectance data. Practical problems of applying matching techniques to 2.5D laserscanner point clouds are discussed and solved, and the success of the technique is shown on the basis of several datasets. Applying least-squares matching techniques to dense laserscanner data in a TIN structure, strip discrepancies can be determined with centimeter precision for the height coordinate and decimeter precision for the planimetric coordinates.

    
    

    @Article{maas02,
    author = {Hans-Gerd Maas},
    title = {Methods for Measuring Height and Planimetry Discrepancies in Airborne Laserscanner Data},
    journal = pers,
    year = {2002},
    volume = {68},
    pages = {933-940},
    number = {9},
    url = {http://www.asprs.org/asprs/publications/pe&rs/2002journal/september/abstracts.html#933},
    keyword = {},
    abstract = {Airborne laserscanning (or lidar) has become a very important technique for the acquisition of digital terrain model data. Beyond this, the technique is increasingly being used for the acquisition of point clouds for 3D modeling of a wide range of objects, such as buildings, vegetation, or electrical power lines. As an active technique, airborne laserscanning offers a high reliability even over terrain with poor image contrast. The precision of the technique is often specified to be on the order of one to two decimeters. By reason of its primary use in digital terrain modeling, examinations of the precision potential of airborne laserscanning have so far been concentrated on the height precision. With the use of the technique for general 3D reconstruction tasks and the increasing resolution of laserscanner systems, the planimetric precision of laserscanner point clouds becomes an important issue. In addition to errors in the laser distance meter and the deflecting mirror system, the error budget of airborne laserscanning instruments is strongly influenced by the GPS/INS systems used for sensor pose (position and orientation) determination. Errors of these systems often lead to the deformation of laserscanner data strips and may become evident as discrepancies in the overlap region between neighboring strips in a block of laserscanner data. The paper presents least-squares matching implemented on a TIN structure as a general tool for the determination of laser-scanner strip discrepancies in all three coordinate directions, using both height and reflectance data. Practical problems of applying matching techniques to 2.5D laserscanner point clouds are discussed and solved, and the success of the technique is shown on the basis of several datasets. Applying least-squares matching techniques to dense laserscanner data in a TIN structure, strip discrepancies can be determined with centimeter precision for the height coordinate and decimeter precision for the planimetric coordinates. },
    
    
    
    }
    




17. Kerry McIntosh and Amnon Krupnik. Integration of laser-derived DSMs and matched image edges for generating an accurate surface model. jprs, 53(3):167-176, 2002. [WWW ] Keyword(s): Data integration, airborne laser scanner, Edge matching, DSM, Surface reconstruction, Surface discontinuity modelling. Abstract:

    Airborne laser altimetry is a highly efficient and accurate method of obtaining data for the determination of visible surface topography. With minimal processing, the laser data can provide coordinates of points on the visible surface with high spatial frequency and precision. Although this technology has benefits compared to photogrammetric techniques, there are limiting factors due to the laser data having no structural and textural information. These limitations are significant in low-density laser data and may be overcome by utilizing both laser altimetry and photogrammetrically derived data in the surface determination process. The research described in this paper has been undertaken to accurately determine the visible surface in urban areas using airborne laser scanner data and digital aerial images. Edges detected and matched in aerial images are used to refine the digital surface model (DSM) produced from airborne laser scanner data. The laser data and the edge information are merged to exploit the benefits of each dataset, facilitating the generation of an accurate surface model. This model provides a better representation of surface discontinuities, especially building walls. The paper presents the algorithms developed and shows that the surface accuracy is improved by 49 $\%$ and 15 $\%$ for the two tested areas, respectively.

    
    

    @Article{mcintosh02,
    author = {Kerry McIntosh and Amnon Krupnik},
    title = {Integration of laser-derived DSMs and matched image edges for generating an accurate surface model},
    journal = jprs,
    year = {2002},
    volume = {53},
    pages = {167-176},
    number = {3},
    url = {http://www.sciencedirect.com/science/article/B6VF4-450HDXJ-1/1/92424dbf6ba9ffcea7c4bf552cc7e09b},
    keyword = {Data integration, airborne laser scanner, Edge matching, DSM, Surface reconstruction, Surface discontinuity modelling },
    abstract = {Airborne laser altimetry is a highly efficient and accurate method of obtaining data for the determination of visible surface topography. With minimal processing, the laser data can provide coordinates of points on the visible surface with high spatial frequency and precision. Although this technology has benefits compared to photogrammetric techniques, there are limiting factors due to the laser data having no structural and textural information. These limitations are significant in low-density laser data and may be overcome by utilizing both laser altimetry and photogrammetrically derived data in the surface determination process. The research described in this paper has been undertaken to accurately determine the visible surface in urban areas using airborne laser scanner data and digital aerial images. Edges detected and matched in aerial images are used to refine the digital surface model (DSM) produced from airborne laser scanner data. The laser data and the edge information are merged to exploit the benefits of each dataset, facilitating the generation of an accurate surface model. This model provides a better representation of surface discontinuities, especially building walls. The paper presents the algorithms developed and shows that the surface accuracy is improved by 49 $\%$ and 15 $\%$ for the two tested areas, respectively.},
    
    
    
    }
    




18. Kevin J. McMaster. Effects of Digital Elevation Model Resolution on Derived Stream Network Positions. AGU Water Resources Research, 38(4):13-1 - 13-8, 2002.
    

    @article{RefWorks:753,
    author={Kevin J. McMaster},
    year={2002},
    title={Effects of Digital Elevation Model Resolution on Derived Stream Network Positions},
    journal={AGU Water Resources Research},
    volume={38},
    number={4},
    pages={13-1 - 13-8} 
    }
    




19. Erik Naesset. Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. rse, 80:88-99, 2002. [WWW ] Abstract:

    The mean tree height, dominant height, mean diameter, stem number, basal area, and timber volume of 144 georeferenced field sample plots were estimated from various canopy height and canopy density metrics derived by means of a small-footprint laser scanner over young and mature forest stands using regression analysis. The sample plots were distributed systematically throughout a 1000-ha study area, and the size of each plot was 200 m2. On the average, the distance between transmitted laser pulses was 0.9 m on the ground. The plots were divided into three strata according to age class and site quality. The stratum-specific regressions explained 82-95 $\%$ , 74-93 $\%$ , 39-78 $\%$ , 50-68 $\%$ , 69-89 $\%$ , and 80-93 $\%$ of the variability in ground-truth mean height, dominant height, mean diameter, stem number, basal area, and volume, respectively. A proposed practical two-stage procedure for prediction of corresponding characteristics of entire forest stands was tested. Sixty-one stands within the study area, with an average size of 1.6 ha each, were divided into 200 m2 regular grid cells. The six examined characteristics were predicted for each grid cell from the corresponding laser data utilizing the estimated regression equations. Average values for each stand was computed. Most stand level predictions were unbiased ( P > .05). Standard deviations of the differences between predicted and ground-truth values of mean height, dominant height, mean diameter, stem number, basal area, and volume were 0.61-1.17 m, 0.70-1.33 m, 1.37-1.61 cm, 16.9-22.2 $\%$ (128-400 ha 1), 8.6-11.7 $\%$ (2.33-2.54 m2 ha 1), and 11.4-14.2 $\%$ (18.3-31.9 m3 ha 1), respectively.

    
    

    @Article{naesset02,
    author = {Erik Naesset},
    title = {Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data},
    journal = rse,
    year = {2002},
    volume = {80},
    pages = {88-99},
    keyword = {},
    url = {http://www.sciencedirect.com/science/article/B6V6V-44SJGRK-8/1/cc10cdc259b3d87468ea32797d135fe5},
    abstract = {The mean tree height, dominant height, mean diameter, stem number, basal area, and timber volume of 144 georeferenced field sample plots were estimated from various canopy height and canopy density metrics derived by means of a small-footprint laser scanner over young and mature forest stands using regression analysis. The sample plots were distributed systematically throughout a 1000-ha study area, and the size of each plot was 200 m2. On the average, the distance between transmitted laser pulses was 0.9 m on the ground. The plots were divided into three strata according to age class and site quality. The stratum-specific regressions explained 82-95 $\%$ , 74-93 $\%$ , 39-78 $\%$ , 50-68 $\%$ , 69-89 $\%$ , and 80-93 $\%$ of the variability in ground-truth mean height, dominant height, mean diameter, stem number, basal area, and volume, respectively. A proposed practical two-stage procedure for prediction of corresponding characteristics of entire forest stands was tested. Sixty-one stands within the study area, with an average size of 1.6 ha each, were divided into 200 m2 regular grid cells. The six examined characteristics were predicted for each grid cell from the corresponding laser data utilizing the estimated regression equations. Average values for each stand was computed. Most stand level predictions were unbiased ( P > .05). Standard deviations of the differences between predicted and ground-truth values of mean height, dominant height, mean diameter, stem number, basal area, and volume were 0.61-1.17 m, 0.70-1.33 m, 1.37-1.61 cm, 16.9-22.2 $\%$ (128-400 ha 
     1), 8.6-11.7 $\%$ (2.33-2.54 m2 ha 
     1), and 11.4-14.2 $\%$ (18.3-31.9 m3 ha 
     1), respectively.},
    
    
    
    }
    




20. Erik Naesset and Tonje Oekland. Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve. rse, 79:105-115, 2002. [WWW ] Abstract:

    Tree height, the height from the ground surface to the tree crown, and the crown length as a proportion of tree height of individual trees were derived from various canopy height metrics measured by a small-footprint airborne laser scanner flown over a boreal forest reserve. The average spacing on the ground of the laser pulses ranged from 0.66 to 1.29 m. Ground-truth values were regressed against laser-derived canopy height metrics. The regressions explained 75 $\%$ , 53 $\%$ , and 51 $\%$ of the variability in ground-truth tree height, height to the crown, and relative crown length, respectively. Cross-validation of the regressions revealed standard deviations of the differences between predicted and ground-truth values of 3.15 m (17.6 $\%$ ), 2.19 m (39.1 $\%$ ), and 10.48 $\%$ (14.9 $\%$ of ground-truth mean), respectively. On 10 plots with size 50 m2 in the boreal forest reserve and on 27 plots with size 200 m2 in a managed spruce forest, mean tree height, average height from the ground surface to the crown, and average relative crown length were regressed against laser canopy height metrics. The coefficients of determination (R2) ranged from .47 to .91. Cross-validation revealed a precision of 1.49 m (7.6 $\%$ ), 1.24-1.52 m (20.9-23.3 $\%$ ), and 6.32- 7.11 $\%$ (8.8-10.9 $\%$ of ground-truth mean) for mean tree height, average height to the crown, and average relative crown length, respectively. At least, mean tree height can be determined more accurately from laser data than by current methods.

    
    

    @Article{naesset02b,
    author = {Erik Naesset and Tonje Oekland},
    title = {Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve},
    journal = rse,
    year = {2002},
    volume = {79},
    pages = {105-115},
    keyword = {},
    url = {http://www.sciencedirect.com/science/article/B6V6V-44MX8HM-C/1/13741566528f5f4b0c050c1f72b33061},
    abstract = {Tree height, the height from the ground surface to the tree crown, and the crown length as a proportion of tree height of individual trees were derived from various canopy height metrics measured by a small-footprint airborne laser scanner flown over a boreal forest reserve. The average spacing on the ground of the laser pulses ranged from 0.66 to 1.29 m. Ground-truth values were regressed against laser-derived canopy height metrics. The regressions explained 75 $\%$ , 53 $\%$ , and 51 $\%$ of the variability in ground-truth tree height, height to the crown, and relative crown length, respectively. Cross-validation of the regressions revealed standard deviations of the differences between predicted and ground-truth values of 3.15 m (17.6 $\%$ ), 2.19 m (39.1 $\%$ ), and 10.48 $\%$ (14.9 $\%$ of ground-truth mean), respectively. On 10 plots with size 50 m2 in the boreal forest reserve and on 27 plots with size 200 m2 in a managed spruce forest, mean tree height, average height from the ground surface to the crown, and average relative crown length were regressed against laser canopy height metrics. The coefficients of determination (R2) ranged from .47 to .91. Cross-validation revealed a precision of 1.49 m (7.6 $\%$ ), 1.24-1.52 m (20.9-23.3 $\%$ ), and 6.32- 7.11 $\%$ (8.8-10.9 $\%$ of ground-truth mean) for mean tree height, average height to the crown, and average relative crown length, respectively. At least, mean tree height can be determined more accurately from laser data than by current methods.},
    
    
    
    }
    




21. Anssi Pekkarinen. Image segment-based spectral features in the estimation of timber volume. rse, 82(2-3):349-359, 2002. [WWW ] Abstract:

    Plot- and stand-level errors associated with satellite image-based multisource forest inventory (MSFI) applications have been relatively high. The reasons suggested for that are related to the limited spatial resolution of the image material. The introduction of very high spatial resolution (VHR) images to MSFI applications should, therefore, diminish these errors. The use of VHR images is, however, problematic, because pixel-by-pixel analysis methods are no longer applicable. The paper presents an image segment-based approach to the determination of feature extraction and image analysis units. The study was carried out in Southern Finland and employed a spectrally averaged imaging spectrometer (AISA) image and field data gathered from sample plots. A two-phase segmentation method was applied and a large number of segment-based spectral features was extracted and used as input to a feature selection procedure. Forward selection based on an improvement of RMSE was applied. The performance of segment-based features (SF) was compared to that of reference features (RF) extracted from square-shaped windows. The estimation results revealed that even though the applied segmentation method succeeded well in the determination of units of feature extraction and image analysis, the differences between the performance of SF and RF were small and the plot-level estimation errors remained high. The study suggests that large estimation errors are due to the local nature of the field data and may be diminished using data that is representative at the segment level.

    
    

    @Article{pekkarinen02,
    author = {Anssi Pekkarinen},
    title = {Image segment-based spectral features in the estimation of timber volume },
    journal = rse,
    year = {2002},
    volume = {82},
    pages = {349-359},
    number = {2-3},
    url = {http://www.sciencedirect.com/science/article/B6V6V-46KR811-4/1/f84bb4538dfadf7834a97ecbb14d1bf1},
    keyword = {},
    abstract = {Plot- and stand-level errors associated with satellite image-based multisource forest inventory (MSFI) applications have been relatively high. The reasons suggested for that are related to the limited spatial resolution of the image material. The introduction of very high spatial resolution (VHR) images to MSFI applications should, therefore, diminish these errors. The use of VHR images is, however, problematic, because pixel-by-pixel analysis methods are no longer applicable. The paper presents an image segment-based approach to the determination of feature extraction and image analysis units. The study was carried out in Southern Finland and employed a spectrally averaged imaging spectrometer (AISA) image and field data gathered from sample plots. A two-phase segmentation method was applied and a large number of segment-based spectral features was extracted and used as input to a feature selection procedure. Forward selection based on an improvement of RMSE was applied. The performance of segment-based features (SF) was compared to that of reference features (RF) extracted from square-shaped windows. The estimation results revealed that even though the applied segmentation method succeeded well in the determination of units of feature extraction and image analysis, the differences between the performance of SF and RF were small and the plot-level estimation errors remained high. The study suggests that large estimation errors are due to the local nature of the field data and may be diminished using data that is representative at the segment level. },
    
    
    
    }
    




22. Asa Persson, Johan Holmgren, and Ulf Söderman. Detecting and Measuring Individual Trees Using an Airborne Laser Scanner. pers, 68(9):925-932, 2002. [WWW ] Abstract:

    High-resolution airborne laser scanner data offer the possibility to detect and measure individual trees. In this study, an algorithm which estimated position, height, and crown diameter of individual trees was validated with field measurements. Because all the trees in this study were measured on the ground with high accuracy, their positions could be linked with laser measurements, making validation on an individual tree basis possible. In total, 71 percent of the trees were correctly detected using laser scanner data. Because a large portion of the undetected trees had a small stem diameter, 91 percent of the total stem volume was detected. Height and crown diameter of detected trees could be estimated with a root-mean-square error (RMSE) of 0.63 m and 0.61 m, respectively. Stem diameter was estimated, using laser measured tree height and crown diameter, with an RMSE of 3.8 cm. Different laser beam diameters (0.26 to 3.68 m) were also tested, the smallest beam size showing a better detection rate in dense forest. However, estimates of tree height and crown diameter were not affected much by different beam size.

    
    

    @Article{persson02,
    author = {Asa Persson and Johan Holmgren and Ulf Söderman},
    title = {Detecting and Measuring Individual Trees Using an Airborne Laser Scanner},
    journal = pers,
    year = {2002},
    volume = {68},
    pages = {925-932},
    number = {9},
    url = {http://www.asprs.org/asprs/publications/pe&rs/2002journal/september/abstracts.html#933},
    keyword = {},
    abstract = {High-resolution airborne laser scanner data offer the possibility to detect and measure individual trees. In this study, an algorithm which estimated position, height, and crown diameter of individual trees was validated with field measurements. Because all the trees in this study were measured on the ground with high accuracy, their positions could be linked with laser measurements, making validation on an individual tree basis possible. In total, 71 percent of the trees were correctly detected using laser scanner data. Because a large portion of the undetected trees had a small stem diameter, 91 percent of the total stem volume was detected. Height and crown diameter of detected trees could be estimated with a root-mean-square error (RMSE) of 0.63 m and 0.61 m, respectively. Stem diameter was estimated, using laser measured tree height and crown diameter, with an RMSE of 3.8 cm. Different laser beam diameters (0.26 to 3.68 m) were also tested, the smallest beam size showing a better detection rate in dense forest. However, estimates of tree height and crown diameter were not affected much by different beam size.},
    
    
    
    }
    




23. D. A. Pouliot, D. J. King, F. W. Bell, and D. G. Pitt. Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration. rse, 82(2-3):322-334, 2002. [WWW ] Keyword(s): Tree detection and delineation, Boreal forest regeneration, Local maximum and maximum rate of change analysis, Accuracy assessment. Abstract:

    Ensuring successful forest regeneration requires an effective monitoring program to collect information regarding the status of young crop trees and nearby competing vegetation. Current field-based assessment methodology provides the needed information, but is costly, and therefore assessment frequency is low. This often allows undesirable forest structures to develop that do not coincide with management objectives. Remote sensing techniques provide a potentially low-cost alternative to field-based assessment, but require the development of methods to easily and accurately extract the required information. Automated tree detection and delineation algorithms may be an effective means to accomplish this task. In this study, a tree detection¯delineation algorithm designed specifically for high-resolution digital imagery of 6-year-old trees is presented and rigorously evaluated. The algorithm is based on the analysis of local transects extending outward from a potential tree apex. The crown boundary is estimated using the point of maximum rate of change in the transect data and a rule base is applied to ensure that the point is contextually suitable. This transect approach is implemented in both the tree-detection and crown-delineation phases. The tree-detection algorithm refines the results of an initial local maximum filter by providing an outline for each detected tree and retaining only one local maximum value within this outline. The crown-delineation algorithm is similar to the detection algorithm, but applies a different rule set in creating a more detailed crown outline. Results show that the algorithm's tree-detection accuracy was better than that using commonly applied fixed-window local maximum filters; it achieved a best result of 91%. For the crown-delineation algorithm, measured diameters from delineated crowns were within 17.9% of field measurements of diameter at the crown base on an individual tree basis and within 3% when averaged for the study. Tests of image pixel spacings from 5 to 30 cm showed that tree-detection accuracy was stable except at the lowest (30-cm) resolution where errors were unacceptable. Delineated crown-diameter accuracy was more sensitive to image resolution, decreasing consistently and nonlinearly with increasing pixel spacing. These results highlight the need for very high resolution imagery in automated object-based analysis of forest regeneration.

    
    

    @Article{pouliot02,
    author = {D. A. Pouliot and D. J. King and F. W. Bell and D. G. Pitt},
    title = {Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration},
    journal = rse,
    year = {2002},
    volume = {82},
    pages = {322-334},
    number = {2-3},
    url = {http://www.sciencedirect.com/science/article/B6V6V-460M924-3/1/8d4821aa9d98eac63a74c4180433d35f},
    keyword = {Tree detection and delineation, Boreal forest regeneration, Local maximum and maximum rate of change analysis, Accuracy assessment},
    abstract = {Ensuring successful forest regeneration requires an effective monitoring program to collect information regarding the status of young crop trees and nearby competing vegetation. Current field-based assessment methodology provides the needed information, but is costly, and therefore assessment frequency is low. This often allows undesirable forest structures to develop that do not coincide with management objectives. Remote sensing techniques provide a potentially low-cost alternative to field-based assessment, but require the development of methods to easily and accurately extract the required information. Automated tree detection and delineation algorithms may be an effective means to accomplish this task. In this study, a tree detection¯delineation algorithm designed specifically for high-resolution digital imagery of 6-year-old trees is presented and rigorously evaluated. The algorithm is based on the analysis of local transects extending outward from a potential tree apex. The crown boundary is estimated using the point of maximum rate of change in the transect data and a rule base is applied to ensure that the point is contextually suitable. This transect approach is implemented in both the tree-detection and crown-delineation phases. The tree-detection algorithm refines the results of an initial local maximum filter by providing an outline for each detected tree and retaining only one local maximum value within this outline. The crown-delineation algorithm is similar to the detection algorithm, but applies a different rule set in creating a more detailed crown outline. Results show that the algorithm's tree-detection accuracy was better than that using commonly applied fixed-window local maximum filters; it achieved a best result of 91%. For the crown-delineation algorithm, measured diameters from delineated crowns were within 17.9% of field measurements of diameter at the crown base on an individual tree basis and within 3% when averaged for the study. Tests of image pixel spacings from 5 to 30 cm showed that tree-detection accuracy was stable except at the lowest (30-cm) resolution where errors were unacceptable. Delineated crown-diameter accuracy was more sensitive to image resolution, decreasing consistently and nonlinearly with increasing pixel spacing. These results highlight the need for very high resolution imagery in automated object-based analysis of forest regeneration. },
    
    
    
    }
    




24. David Riano, Erich Meier Britta Allgoewer, and Emilio Chuvieco. GENERATION OF VEGETATION HEIGHT, VEGETATION COVER AND CROWN BULK DENSITY FROM AIRBORNE LASER SCANNING DATA. 2002. Keyword(s): Airborne laser scanning, fire behaviour modelling, vegetation height, vegetation cover, crown bulk density. Abstract:

    Vegetation height, vegetation cover and crown bulk density, that are critical for fire behavior modeling, were produced from airborne laser scanning (LIDAR). High-density laser scanning data, 1.5 m across track and 0.11 m along track, were provided by Toposys, a German company specialized in LIDAR data. Raw data containing x, y and z (above sea level) coordinates were used to produce an algorithm for estimating these forest parameters. The algorithm was based on a cluster analysis used to discriminate crown base height. As a result two groups of heights were obtained, identifying both tree and understory parameters. Validation of the method is being carried out.

    
    

    @Article{riano02,
    author = {David Riano and Erich Meier Britta Allgoewer and Emilio Chuvieco},
    title = {GENERATION OF VEGETATION HEIGHT, VEGETATION COVER AND CROWN BULK DENSITY FROM AIRBORNE LASER SCANNING DATA},
    journal = {},
    year = {2002},
    volume = {},
    pages = {},
    number = {},
    url = {},
    keyword = {Airborne laser scanning; fire behaviour modelling; vegetation height; vegetation cover; crown bulk density},
    abstract = {Vegetation height, vegetation cover and crown bulk density, that are critical for fire behavior modeling, were produced from airborne laser scanning (LIDAR). High-density laser scanning data, 1.5 m across track and 0.11 m along track, were provided by Toposys, a German company specialized in LIDAR data. Raw data containing x, y and z (above sea level) coordinates were used to produce an algorithm for estimating these forest parameters. The algorithm was based on a cluster analysis used to discriminate crown base height. As a result two groups of heights were obtained, identifying both tree and understory parameters. Validation of the method is being carried out.},
    
    
    
    }
    




25. K. Todd, F. Csillag, and P. Atkinson. Three-dimensional mapping of light transmittance and foliage distribution using LIDAR. Canadian Journal of Remote Sensing, 29:544-555, 2002.
    

    @article{RefWorks:842,
    author={K. Todd and F. Csillag and P. Atkinson},
    year={2002},
    title={Three-dimensional mapping of light transmittance and foliage distribution using LIDAR},
    journal={Canadian Journal of Remote Sensing},
    volume={29},
    pages={544-555} 
    }
    




26. Charles Vorosmarty, Larry Hinzman, Bruce Peterson, David Bromwich, Lawrence Hamilton, James Morison, Vladimir Romanovsky, Matthew Sturm, and Robert Webb. Arctic-CHAMP: A Program to Study Arctic Hydrology and its Role in Global Change. EOS, 83(22):241-249, May 2002. Note: NOTE: This artical has a figure (Fig. 1) with a caption that described the major modalities of water transport. It gave a very precise description of low-land floodplain flows and of confined river channel flows. This lets me sound much more specific and knowledgable when describing the hydrologic applications relelvant to my DEM research.
    

    @article{RefWorks:750,
    author={Charles Vorosmarty and Larry Hinzman and Bruce Peterson and David Bromwich and Lawrence Hamilton and James Morison and Vladimir Romanovsky and Matthew Sturm and Robert Webb},
    year={2002},
    month={May},
    title={Arctic-CHAMP: A Program to Study Arctic Hydrology and its Role in Global Change},
    journal={EOS},
    volume={83},
    number={22},
    pages={241-249},
    note={NOTE: This artical has a figure (Fig. 1) with a caption that described the major modalities of water transport. It gave a very precise description of low-land floodplain flows and of confined river channel flows. This lets me sound much more specific and knowledgable when describing the hydrologic applications relelvant to my DEM research.} 
    }
    

1. Hans-Erik Andersen, Stephen E. Reutebuch, and Gerard F. Schreuder. Bayesian Object Recognition for the Analysis of Complex Forest Scenes in Airborne Laser Scanner Data. In ISPRS Commission III, Symposium 2002 September 9 - 13, 2002, Graz, Austria, pages A-035 ff (7 pages), 2002. [WWW ] Keyword(s): Forestry, laser scanning, LIDAR, object, recognition, remote sensing, statistics. Abstract:

   Bayesian object recognition is applied to the analysis of complex forest object configurations measured in high-density airborne laser scanning (LIDAR) data. With the emergence of high-resolution active remote sensing technologies, highly detailed, spatially explicit forest measurement information can be extracted through the application of statistical object recognition algorithms. A Bayesian approach to object recognition incorporates a probabilistic model of the active sensing process and places a prior probability model on object configurations. LIDAR sensing geometry is explicitly modelled in the domain of scan space, a three- dimensional analogue to two-dimensional image space. Prior models for object configurations take the form of Markov marked point processes, where pair-wise object interactions depend upon object attributes. Inferences are based upon the posterior distribution of the object configuration given the observed LIDAR. Given the complexity of the posterior distribution, inferences are based upon dependent samples generated via Markov chain Monte Carlo simulation. This algorithm was applied to a 0.21 ha area within Capitol State Forest, WA, USA. Algorithm-based estimates are compared to photogrammetric crown measurements and field inventory data.

   
   

   @InProceedings{andersen02,
   author = {Hans-Erik Andersen and Stephen E. Reutebuch and Gerard F. Schreuder},
   title = {Bayesian Object Recognition for the Analysis of Complex Forest Scenes in Airborne Laser Scanner Data},
   year = {2002},
   booktitle = {ISPRS Commission III, Symposium 2002 September 9 - 13, 2002, Graz, Austria},
   pages = {A-035 ff (7 pages)},
   number = {},
   url = {http://www.isprs.org/commission3/proceedings/papers/paper041.pdf},
   keyword = {Forestry; laser scanning; LIDAR; object; recognition; remote sensing; statistics},
   abstract = {Bayesian object recognition is applied to the analysis of complex forest object configurations measured in high-density airborne laser scanning (LIDAR) data. With the emergence of high-resolution active remote sensing technologies, highly detailed, spatially explicit forest measurement information can be extracted through the application of statistical object recognition algorithms. A Bayesian approach to object recognition incorporates a probabilistic model of the active sensing process and places a prior probability model on object configurations. LIDAR sensing geometry is explicitly modelled in the domain of scan space, a three- dimensional analogue to two-dimensional image space. Prior models for object configurations take the form of Markov marked point processes, where pair-wise object interactions depend upon object attributes. Inferences are based upon the posterior distribution of the object configuration given the observed LIDAR. Given the complexity of the posterior distribution, inferences are based upon dependent samples generated via Markov chain Monte Carlo simulation. This algorithm was applied to a 0.21 ha area within Capitol State Forest, WA, USA. Algorithm-based estimates are compared to photogrammetric crown measurements and field inventory data.},
   
   
   
   }
   




2. Helen Burman. Laser Strip Adjustment for Data Calibration and Verification. In ISPRS Commission III, Symposium 2002 September 9 - 13, 2002, Graz, Austria, pages A-067 ff (6 pages), 2002. [WWW ] Keyword(s): Laser strips, Least-Squares, Adjustment, Matching, GPS, INS. Abstract:

   Laser scanning is dependent on georeferencing by satellite positioning and inertial navigation to give orientation of each laser shot. Orientation errors are at the same time one of the main contributors to the laser data error budget. Satellite positioning error like atmospheric delay, cycle slips and loss-of-lock together with drifts in accelerometers and gyros in the inertial system results in orientation errors which often are of a systematic nature. Some errors can be corrected for by making overlapping laser strips coincide and by making laser strips coincide with ground truth. In this purpose a laser strip adjustment program, TerraMatch, was developed. This paper presents the mathematical model used, the main features of the program and results from practical tests.

   
   

   @InProceedings{burman02,
   author = {Helen Burman },
   title = {Laser Strip Adjustment for Data Calibration and Verification},
   year = {2002},
   booktitle = {ISPRS Commission III, Symposium 2002 September 9 - 13, 2002, Graz, Austria},
   pages = {A-067 ff (6 pages)},
   number = {},
   url = {http://www.isprs.org/commission3/proceedings/papers/paper169.pdf},
   keyword = {Laser strips; Least-Squares; Adjustment; Matching; GPS; INS},
   abstract = {Laser scanning is dependent on georeferencing by satellite positioning and inertial navigation to give orientation of each laser shot. Orientation errors are at the same time one of the main contributors to the laser data error budget. Satellite positioning error like atmospheric delay, cycle slips and loss-of-lock together with drifts in accelerometers and gyros in the inertial system results in orientation errors which often are of a systematic nature. Some errors can be corrected for by making overlapping laser strips coincide and by making laser strips coincide with ground truth. In this purpose a laser strip adjustment program, TerraMatch, was developed. This paper presents the mathematical model used, the main features of the program and results from practical tests. },
   
   
   
   }
   




3. Sagi Filin. Surface Clustering from Airborne Laser Scanning Data Sagi Filin. In ISPRS Commission III, Symposium 2002 September 9 - 13, 2002, Graz, Austria, pages A-119 ff (6 pages), 2002. [WWW ] Keyword(s): Clustering, Laser altimetry, Surface classification, Surface reconstruction, Data segmentation. Abstract:

   This paper presents an algorithm for the extraction of surface clusters from airborne laser data. Surface structure analysis is fundamental to almost any application involving LIDAR data, yet most algorithms focus only on identifying planar segments. The proposed algorithm is more general insofar as it aims at extracting surface segments that exhibit an homogeneous behavior, without restriction to one specific pattern. The algorithm adopts a data clustering methodology for this purpose, which offers a very general and flexible way to identify homogeneous patterns in the data.

   
   

   @InProceedings{filin02,
   author = {Sagi Filin},
   title = {Surface Clustering from Airborne Laser Scanning Data Sagi Filin},
   year = {2002},
   booktitle = {ISPRS Commission III, Symposium 2002 September 9 - 13, 2002, Graz, Austria},
   pages = {A-119 ff (6 pages)},
   number = {},
   url = {http://www.isprs.org/commission3/proceedings/papers/paper120.pdf},
   keyword = {Clustering; Laser altimetry; Surface classification; Surface reconstruction; Data segmentation},
   abstract = {This paper presents an algorithm for the extraction of surface clusters from airborne laser data. Surface structure analysis is fundamental to almost any application involving LIDAR data, yet most algorithms focus only on identifying planar segments. The proposed algorithm is more general insofar as it aims at extracting surface segments that exhibit an homogeneous behavior, without restriction to one specific pattern. The algorithm adopts a data clustering methodology for this purpose, which offers a very general and flexible way to identify homogeneous patterns in the data.},
   
   
   
   }
   




4. A. Minagawa, K. Uda, and N. Tagawa. Region extraction based on belief propagation for gaussian model. In Anonymous, editor, , volume 2, pages 507-510, 2002.
   

   @inproceedings{RefWorks:829,
   author={A. Minagawa and K. Uda and N. Tagawa},
   editor={Anonymous },
   year={2002},
   title={Region extraction based on belief propagation for gaussian model},
   volume={2},
   pages={507-510} 
   }
   




5. Ulla Pyysalo and Hannu Hyyppae. Reconstructing Tree Crowns from Laser Scanner Data for Feature Extraction. In ISPRS Commission III, Symposium 2002 September 9 - 13, 2002, Graz, Austria, pages B-218 ff (4 pages), 2002. [WWW ] Keyword(s): Reconstruction, Feature Extraction, Laser Scanning, Tree Crown, Crown Profile. Abstract:

   The objective of this study was to carry out reconstruction of single tree crowns from laser scanner data to use the obtained vector model for feature extraction. The reconstruction was implemented in several stages. First, pulses which have reflected from each tree were marked off from the original point cloud. Ground points were then separated from all points using digital terrain model and analysing the histogram of terrain height values. In the next stage canopy was described with vector polygons, and the location of the trunk was estimated. With respect to the location of the trunk, tree points were transferred from (xyz)-co-ordinate system to the polarco- ordinate system (a,r,h), and features were estimated from the vector model. Evaluation of the reconstruction was performed choosing a test area and processing 50 single trees, and comparing results to the field measurements. In the study it was found that dense laser scanner data detail describes the upper canopy of forest and therefore is suitable for tree height information extraction. The lower crown was found less detail measured with laser scanner and parameters extracted from that part were less accurate, but trend setting. Obtained distance profile seemed to give tendency for the tree specie.

   
   

   @InProceedings{pyysalo02,
   author = {Ulla Pyysalo and Hannu Hyyppae},
   title = {Reconstructing Tree Crowns from Laser Scanner Data for Feature Extraction},
   year = {2002},
   booktitle = {ISPRS Commission III, Symposium 2002 September 9 - 13, 2002, Graz, Austria},
   pages = {B-218 ff (4 pages)},
   number = {},
   url = {http://www.isprs.org/commission3/proceedings/papers/paper004.pdf},
   keyword = {Reconstruction; Feature Extraction; Laser Scanning; Tree Crown;Crown Profile},
   abstract = {The objective of this study was to carry out reconstruction of single tree crowns from laser scanner data to use the obtained vector model for feature extraction. The reconstruction was implemented in several stages. First, pulses which have reflected from each tree were marked off from the original point cloud. Ground points were then separated from all points using digital terrain model and analysing the histogram of terrain height values. In the next stage canopy was described with vector polygons, and the location of the trunk was estimated. With respect to the location of the trunk, tree points were transferred from (xyz)-co-ordinate system to the polarco- ordinate system (a,r,h), and features were estimated from the vector model. Evaluation of the reconstruction was performed choosing a test area and processing 50 single trees, and comparing results to the field measurements. In the study it was found that dense laser scanner data detail describes the upper canopy of forest and therefore is suitable for tree height information extraction. The lower crown was found less detail measured with laser scanner and parameters extracted from that part were less accurate, but trend setting. Obtained distance profile seemed to give tendency for the tree specie. },
   
   
   
   }
   




6. Marco Roggero. Object Segmentation with Region Growing and Principal Component Analysis. In ISPRS Commission III, Symposium 2002 September 9 - 13, 2002, Graz, Austria, pages A-289 ff (6 pages), 2002. [WWW ] Keyword(s): Principal Component Analysis, Tensorization, Region Growing, Discrete Geometry, Laser Scanning. Abstract:

   The paper considers the problem of object segmentation and shape recognition in discrete noisy data. Two different algorithms combine region growing techniques with principal component analysis. The proposed algorithms are applied to a data set from airborne laser scanners.

   
   

   @InProceedings{roggero02a,
   author = {Marco Roggero},
   title = {Object Segmentation with Region Growing and Principal Component Analysis},
   year = {2002},
   booktitle = {ISPRS Commission III, Symposium 2002 September 9 - 13, 2002, Graz, Austria},
   pages = {A-289 ff (6 pages)},
   number = {},
   url = {http://www.isprs.org/commission3/proceedings/papers/paper076.pdf},
   keyword = {Principal Component Analysis; Tensorization; Region Growing; Discrete Geometry; Laser Scanning},
   abstract = {The paper considers the problem of object segmentation and shape recognition in discrete noisy data. Two different algorithms combine region growing techniques with principal component analysis. The proposed algorithms are applied to a data set from airborne laser scanners.},
   
   
   
   }
   




7. Mathias Schardt, Michaela Ziegler, Andreas Wimmer, Roland Wack, and Juha Hyyppae. Assessment of Forest Parameters by Means of Laser Scanning. In ISPRS Commission III, Symposium 2002 September 9 - 13, 2002, Graz, Austria, pages A-302 ff (8 pages), 2002. [WWW ] Keyword(s): Forest Inventory, Laser Scanning Data, Segmentation. Abstract:

   This paper deals with forest inventory methods based on laser scanning and satellite remote sensing. It will be demonstrated to what extent forest inventories can benefit from the synergistic use of both sensor types. The forest inventory parameters to be assessed are: tree height, timber volume, tree species, tree age, stand boundary, and basal area. The results presented are derived from the ÒHIGHSCANÓ project (Assessing forest stand attributes by integrated use of high-resolution satellite imagery and laser scanner) which is coordinated by the Helsinki University of Technology and financed by the EU, DG XII. Developments have been carried out in close co-operation with forest management authorities, in particular with private forest owners. In this paper the results derived from the Austrian test sites will be presented.

   
   

   @InProceedings{schardtetal02,
   author = {Mathias Schardt and Michaela Ziegler and Andreas Wimmer and Roland Wack and Juha Hyyppae},
   title = {Assessment of Forest Parameters by Means of Laser Scanning},
   year = {2002},
   booktitle = {ISPRS Commission III, Symposium 2002 September 9 - 13, 2002, Graz, Austria},
   pages = {A-302 ff (8 pages)},
   number = {},
   url = {http://www.isprs.org/commission3/proceedings/papers/paper176.pdf},
   keyword = {Forest Inventory; Laser Scanning Data; Segmentation},
   abstract = {This paper deals with forest inventory methods based on laser scanning and satellite remote sensing. It will be demonstrated to what extent forest inventories can benefit from the synergistic use of both sensor types. The forest inventory parameters to be assessed are: tree height, timber volume, tree species, tree age, stand boundary, and basal area. The results presented are derived from the ÒHIGHSCANÓ project (Assessing forest stand attributes by integrated use of high-resolution satellite imagery and laser scanner) which is coordinated by the Helsinki University of Technology and financed by the EU, DG XII. Developments have been carried out in close co-operation with forest management authorities, in particular with private forest owners. In this paper the results derived from the Austrian test sites will be presented. },
   
   
   
   }
   




8. George Vosselman. On the Estimation of Planimetric Offsets in Laser Altimetry Data. In ISPRS Commission III, Symposium 2002 September 9 - 13, 2002, Graz, Austria, pages A-375 ff (6 pages), 2002. [WWW ] Keyword(s): Laser altimetry, strip adjustment, error estimation, least squares matching. Abstract:

   Offsets between overlapping strips of laser altimetry data serve as the input for strip adjustment procedures that estimate and eliminate systematic errors in laser altimetry datasets. For a three-dimensional strip adjustment offsets are to be measured in three dimensions. Height offsets can be determined straightforward by comparing the heights of horizontal planes. Planimetric offsets are more difficult to determine. This paper shows that the usage of standard least squares matching algorithms on height data as well as on reflectance data may lead to significant biases in the estimation of planimetric offsets. For height data, a model based estimation of linear features is proposed since the number of locations in strip overlaps that are suitable for the estimation of offsets in three dimensions may not be sufficient to estimate all error parameters of a strip adjustment. To improve both the offset estimation and the offset variance estimation using reflectance data an edge response function is introduced. This function takes into account the difference in size of a laser beam's footprint and the distance between successive laser points.

   
   

   @InProceedings{vosselman02b,
   author = {George Vosselman},
   title = {On the Estimation of Planimetric Offsets in Laser Altimetry Data},
   year = {2002},
   booktitle = {ISPRS Commission III, Symposium 2002 September 9 - 13, 2002, Graz, Austria},
   pages = {A-375 ff (6 pages)},
   number = {},
   url = {http://www.isprs.org/commission3/proceedings/papers/paper007.pdf},
   keyword = {Laser altimetry; strip adjustment; error estimation; least squares matching},
   abstract = {Offsets between overlapping strips of laser altimetry data serve as the input for strip adjustment procedures that estimate and eliminate systematic errors in laser altimetry datasets. For a three-dimensional strip adjustment offsets are to be measured in three dimensions. Height offsets can be determined straightforward by comparing the heights of horizontal planes. Planimetric offsets are more difficult to determine. This paper shows that the usage of standard least squares matching algorithms on height data as well as on reflectance data may lead to significant biases in the estimation of planimetric offsets. For height data, a model based estimation of linear features is proposed since the number of locations in strip overlaps that are suitable for the estimation of offsets in three dimensions may not be sufficient to estimate all error parameters of a strip adjustment. To improve both the offset estimation and the offset variance estimation using reflectance data an edge response function is introduced. This function takes into account the difference in size of a laser beam's footprint and the distance between successive laser points. },
   
   
   
   }
   




9. C. A. Weed, M. M. Crawford, A. L. Neuenschwander, and R. Gutierrez. Classification of LIDAR data using a lower envelope follower and gradient based operator. In Anonymous, editor, , pages 1384-1386, 2002.
   

   @inproceedings{RefWorks:845,
   author={C. A. Weed and M. M. Crawford and A. L. Neuenschwander and R. Gutierrez},
   editor={Anonymous },
   year={2002},
   title={Classification of LIDAR data using a lower envelope follower and gradient based operator},
   pages={1384-1386} 
   }
   

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