-
S. Adlen,
E. E. Brodsky,
T. Oki,
A. J. Ridley,
L. Sanchez,
C. Simionato,
K. Yoshizawa,
and U. Shamir.
New Report Charts Course for Future of Geosciences.
Eos,
85(3),
2004.
@article{RefWorks:787,
author={S. Adlen and E. E. Brodsky and T. Oki and A. J. Ridley and L. Sanchez and C. Simionato and K. Yoshizawa and U. Shamir},
year={2004},
title={New Report Charts Course for Future of Geosciences},
journal={Eos},
volume={85},
number={3}
}
-
Xuexia Chen,
Lee Vierling,
Eric Rowell,
and Thomas DeFelice.
Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest.
rse,
91(1):14-26,
2004.
Keyword(s): IKONOS,
Landsat 7 ETM+,
Lidar,
Effective LAI,
Linear spectral mixture analysis,
Endmembers,
EVI,
NDVI,
Pinus ponderosa,
Monodominant forest.
Abstract: |
Structural and functional analyses of ecosystems benefit when high accuracy vegetation coverages can be derived over large areas. In this study, we utilize IKONOS, Landsat 7 ETM+, and airborne scanning light detection and ranging (lidar) to quantify coniferous forest and understory grass coverages in a ponderosa pine (Pinus ponderosa) dominated ecosystem in the Black Hills of South Dakota. Linear spectral mixture analyses of IKONOS and ETM+ data were used to isolate spectral endmembers (bare soil, understory grass, and tree/shade) and calculate their subpixel fractional coverages. We then compared these endmember cover estimates to similar cover estimates derived from lidar data and field measures. The IKONOS-derived tree/shade fraction was significantly correlated with the field-measured canopy effective leaf area index (LAIe) (r2=0.55, p<0.001) and with the lidar-derived estimate of tree occurrence (r2=0.79, p<0.001). The enhanced vegetation index (EVI) calculated from IKONOS imagery showed a negative correlation with the field measured tree canopy effective LAI and lidar tree cover response (r2=0.30, r=-0.55 and r2=0.41, r=-0.64, respectively; p<0.001) and further analyses indicate a strong linear relationship between EVI and the IKONOS-derived grass fraction (r2=0.99, p<0.001). We also found that using EVI resulted in better agreement with the subpixel vegetation fractions in this ecosystem than using normalized difference of vegetation index (NDVI). Coarsening the IKONOS data to 30 m resolution imagery revealed a stronger relationship with lidar tree measures (r2=0.77, p<0.001) than at 4 m resolution (r2=0.58, p<0.001). Unmixed tree/shade fractions derived from 30 m resolution ETM+ imagery also showed a significant correlation with the lidar data (r2=0.66, p<0.001). These results demonstrate the power of using high resolution lidar data to validate spectral unmixing results of satellite imagery, and indicate that IKONOS data and Landsat 7 ETM+ data both can serve to make the important distinction between tree/shade coverage and exposed understory grass coverage during peak summertime greenness in a ponderosa pine forest ecosystem. |
@Article{chen04,
author = {Xuexia Chen and Lee Vierling and Eric Rowell and Thomas DeFelice},
title = {Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest},
journal = rse,
year = {2004},
volume = {91},
pages = {14-26},
number = {1},
url = {},
keyword = {IKONOS, Landsat 7 ETM+, Lidar, Effective LAI, Linear spectral mixture analysis, Endmembers, EVI, NDVI, Pinus ponderosa, Monodominant forest },
abstract = {Structural and functional analyses of ecosystems benefit when high accuracy vegetation coverages can be derived over large areas. In this study, we utilize IKONOS, Landsat 7 ETM+, and airborne scanning light detection and ranging (lidar) to quantify coniferous forest and understory grass coverages in a ponderosa pine (Pinus ponderosa) dominated ecosystem in the Black Hills of South Dakota. Linear spectral mixture analyses of IKONOS and ETM+ data were used to isolate spectral endmembers (bare soil, understory grass, and tree/shade) and calculate their subpixel fractional coverages. We then compared these endmember cover estimates to similar cover estimates derived from lidar data and field measures. The IKONOS-derived tree/shade fraction was significantly correlated with the field-measured canopy effective leaf area index (LAIe) (r2=0.55, p<0.001) and with the lidar-derived estimate of tree occurrence (r2=0.79, p<0.001). The enhanced vegetation index (EVI) calculated from IKONOS imagery showed a negative correlation with the field measured tree canopy effective LAI and lidar tree cover response (r2=0.30, r=-0.55 and r2=0.41, r=-0.64, respectively; p<0.001) and further analyses indicate a strong linear relationship between EVI and the IKONOS-derived grass fraction (r2=0.99, p<0.001). We also found that using EVI resulted in better agreement with the subpixel vegetation fractions in this ecosystem than using normalized difference of vegetation index (NDVI). Coarsening the IKONOS data to 30 m resolution imagery revealed a stronger relationship with lidar tree measures (r2=0.77, p<0.001) than at 4 m resolution (r2=0.58, p<0.001). Unmixed tree/shade fractions derived from 30 m resolution ETM+ imagery also showed a significant correlation with the lidar data (r2=0.66, p<0.001). These results demonstrate the power of using high resolution lidar data to validate spectral unmixing results of satellite imagery, and indicate that IKONOS data and Landsat 7 ETM+ data both can serve to make the important distinction between tree/shade coverage and exposed understory grass coverage during peak summertime greenness in a ponderosa pine forest ecosystem.},
}
-
Matthew L. Clark,
David B. Clark,
and Dar A. Roberts.
Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape.
rse,
91(1):68-89,
2004.
[WWW
] Keyword(s): Tropical environment,
Airborne laser scanner,
Lidar methods,
Digital terrain models,
Geostatistics,
Kriging,
Tree height estimation,
Digital canopy model.
Abstract: |
Meso-scale digital terrain models (DTMs) and canopy-height estimates, or digital canopy models (DCMs), are two lidar products that have immense potential for research in tropical rain forest (TRF) ecology and management. In this study, we used a small-footprint lidar sensor (airborne laser scanner, ALS) to estimate sub-canopy elevation and canopy height in an evergreen tropical rain forest. A fully automated, local-minima algorithm was developed to separate lidar ground returns from overlying vegetation returns. We then assessed inverse distance weighted (IDW) and ordinary kriging (OK) geostatistical techniques for the interpolation of a sub-canopy DTM. OK was determined to be a superior interpolation scheme because it smoothed fine-scale variance created by spurious understory heights in the ground-point dataset. The final DTM had a linear correlation of 1.00 and a root-mean-square error (RMSE) of 2.29 m when compared against 3859 well-distributed ground-survey points. In old-growth forests, RMS error on steep slopes was 0.67 m greater than on flat slopes. On flatter slopes, variation in vegetation complexity associated with land use caused highly significant differences in DTM error distribution across the landscape. The highest DTM accuracy observed in this study was 0.58-m RMSE, under flat, open-canopy areas with relatively smooth surfaces. Lidar ground retrieval was complicated by dense, multi-layered evergreen canopy in old-growth forests, causing DTM overestimation that increased RMS error to 1.95 m. A DCM was calculated from the original lidar surface and the interpolated DTM. Individual and plot-scale heights were estimated from DCM metrics and compared to field data measured using similar spatial supports and metrics. For old-growth forest emergent trees and isolated pasture trees greater than 20 m tall, individual tree heights were underestimated and had 3.67- and 2.33-m mean absolute error (MAE), respectively. Linear-regression models explained 51$\%$ (4.15-m RMSE) and 95$\%$ (2.41-m RMSE) of the variance, respectively. It was determined that improved elevation and field-height estimation in pastures explained why individual pasture trees could be estimated more accurately than old-growth trees. Mean height of tree stems in 32 young agroforestry plantation plots (0.38 to 18.53 m tall) was estimated with a mean absolute error of 0.90 m (r2=0.97; 1.08-m model RMSE) using the mean of lidar returns in the plot. As in other small-footprint lidar studies, plot mean height was underestimated; however, our plot-scale results have stronger linear models for tropical, leaf-on hardwood trees than has been previously reported for temperate-zone conifer and deciduous hardwoods. |
@Article{clark04,
author = {Matthew L. Clark and David B. Clark and Dar A. Roberts},
title = {Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape},
journal = rse,
year = {2004},
volume = {91},
pages = {68-89},
number = {1},
url = {http://www.sciencedirect.com/science/article/B6V6V-4C6KW12-1/2/cccb9dc34f259771e334172aa9a9f036},
keyword = {Tropical environment, Airborne laser scanner, Lidar methods, Digital terrain models, Geostatistics, Kriging, Tree height estimation, Digital canopy model},
abstract = {Meso-scale digital terrain models (DTMs) and canopy-height estimates, or digital canopy models (DCMs), are two lidar products that have immense potential for research in tropical rain forest (TRF) ecology and management. In this study, we used a small-footprint lidar sensor (airborne laser scanner, ALS) to estimate sub-canopy elevation and canopy height in an evergreen tropical rain forest. A fully automated, local-minima algorithm was developed to separate lidar ground returns from overlying vegetation returns. We then assessed inverse distance weighted (IDW) and ordinary kriging (OK) geostatistical techniques for the interpolation of a sub-canopy DTM. OK was determined to be a superior interpolation scheme because it smoothed fine-scale variance created by spurious understory heights in the ground-point dataset. The final DTM had a linear correlation of 1.00 and a root-mean-square error (RMSE) of 2.29 m when compared against 3859 well-distributed ground-survey points. In old-growth forests, RMS error on steep slopes was 0.67 m greater than on flat slopes. On flatter slopes, variation in vegetation complexity associated with land use caused highly significant differences in DTM error distribution across the landscape. The highest DTM accuracy observed in this study was 0.58-m RMSE, under flat, open-canopy areas with relatively smooth surfaces. Lidar ground retrieval was complicated by dense, multi-layered evergreen canopy in old-growth forests, causing DTM overestimation that increased RMS error to 1.95 m. A DCM was calculated from the original lidar surface and the interpolated DTM. Individual and plot-scale heights were estimated from DCM metrics and compared to field data measured using similar spatial supports and metrics. For old-growth forest emergent trees and isolated pasture trees greater than 20 m tall, individual tree heights were underestimated and had 3.67- and 2.33-m mean absolute error (MAE), respectively. Linear-regression models explained 51$\%$ (4.15-m RMSE) and 95$\%$ (2.41-m RMSE) of the variance, respectively. It was determined that improved elevation and field-height estimation in pastures explained why individual pasture trees could be estimated more accurately than old-growth trees. Mean height of tree stems in 32 young agroforestry plantation plots (0.38 to 18.53 m tall) was estimated with a mean absolute error of 0.90 m (r2=0.97; 1.08-m model RMSE) using the mean of lidar returns in the plot. As in other small-footprint lidar studies, plot mean height was underestimated; however, our plot-scale results have stronger linear models for tropical, leaf-on hardwood trees than has been previously reported for temperate-zone conifer and deciduous hardwoods.},
}
-
M. A. Hassan and R. D. Woodsmith.
Bed load transport in an obstruction-formed pool in a forest, gravelbed stream.
Geomorphology,
58(1-4):203-221,
2004.
@article{RefWorks:808,
author={M. A. Hassan and R. D. Woodsmith},
year={2004},
title={Bed load transport in an obstruction-formed pool in a forest, gravelbed stream},
journal={Geomorphology},
volume={58},
number={1-4},
pages={203-221}
}
-
C. D. Holder.
Rainfall interception and fog precipitation in a tropical montane cloud forest of Guatemala.
Forest Ecology and Management,
190(2-3,22):373-384,
2004.
@article{RefWorks:810,
author={C. D. Holder},
year={2004},
title={Rainfall interception and fog precipitation in a tropical montane cloud forest of Guatemala},
journal={Forest Ecology and Management},
volume={190},
number={2-3,22},
pages={373-384}
}
-
Johan Holmgren and Asa Persson.
Identifying species of individual trees using airborne laser scanner.
rse,
90(4):415-423,
2004.
Keyword(s): Laser,
Tree detection,
Species classification,
Crown base height.
Abstract: |
Individual trees can be detected using high-density airborne laser scanner data. Also, variables characterizing the detected trees such as tree height, crown area, and crown base height can be measured. The Scandinavian boreal forest mainly consists of Norway spruce (Picea abies L. Karst.), Scots pine (Pinus sylvestris L.), and deciduous trees. It is possible to separate coniferous from deciduous trees using near-infrared images, but pine and spruce give similar spectral signals. Airborne laser scanning, measuring structure and shape of tree crowns could be used for discriminating between spruce and pine. The aim of this study was to test classification of Scots pine versus Norway spruce on an individual tree level using features extracted from airborne laser scanning data. Field measurements were used for training and validation of the classification. The position of all trees on 12 rectangular plots (50?20 m2) were measured in field and tree species was recorded. The dominating species (>80$\%$) was Norway spruce for six of the plots and Scots pine for six plots. The field-measured trees were automatically linked to the laser-measured trees. The laser-detected trees on each plot were classified into species classes using all laser-detected trees on the other plots as training data. The portion correctly classified trees on all plots was 95$\%$. Crown base height estimations of individual trees were also evaluated (r=0.84). The classification results in this study demonstrate the ability to discriminate between pine and spruce using laser data. This method could be applied in an operational context. In the first step, a segmentation of individual tree crowns is performed using laser data. In the second step, tree species classification is performed based on the segments. Methods could be developed in the future that combine laser data with digital near-infrared photographs for classification with the three classes: Norway spruce, Scots pine, and deciduous trees. |
@Article{holmgren,
author = {Johan Holmgren and Asa Persson},
title = {Identifying species of individual trees using airborne laser scanner},
journal = rse,
year = {2004},
volume = {90},
pages = {415-423},
number = {4},
url = {},
keyword = {Laser,Tree detection,Species classification,Crown base height},
abstract = {Individual trees can be detected using high-density airborne laser scanner data. Also, variables characterizing the detected trees such as tree height, crown area, and crown base height can be measured. The Scandinavian boreal forest mainly consists of Norway spruce (Picea abies L. Karst.), Scots pine (Pinus sylvestris L.), and deciduous trees. It is possible to separate coniferous from deciduous trees using near-infrared images, but pine and spruce give similar spectral signals. Airborne laser scanning, measuring structure and shape of tree crowns could be used for discriminating between spruce and pine. The aim of this study was to test classification of Scots pine versus Norway spruce on an individual tree level using features extracted from airborne laser scanning data. Field measurements were used for training and validation of the classification. The position of all trees on 12 rectangular plots (50?20 m2) were measured in field and tree species was recorded. The dominating species (>80$\%$) was Norway spruce for six of the plots and Scots pine for six plots. The field-measured trees were automatically linked to the laser-measured trees. The laser-detected trees on each plot were classified into species classes using all laser-detected trees on the other plots as training data. The portion correctly classified trees on all plots was 95$\%$. Crown base height estimations of individual trees were also evaluated (r=0.84). The classification results in this study demonstrate the ability to discriminate between pine and spruce using laser data. This method could be applied in an operational context. In the first step, a segmentation of individual tree crowns is performed using laser data. In the second step, tree species classification is performed based on the segments. Methods could be developed in the future that combine laser data with digital near-infrared photographs for classification with the three classes: Norway spruce, Scots pine, and deciduous trees.},
}
-
H. Lee and K. C. Slatton.
Measuring Forest Gap Morphology with Airborne Laser Swath Mapping.
IEEE Geoscience and Remote Sensing Letters,
2004.
Note: Note: (in preparation).
@article{RefWorks:817,
author={H. Lee and K. C. Slatton},
year={2004},
title={Measuring Forest Gap Morphology with Airborne Laser Swath Mapping},
journal={IEEE Geoscience and Remote Sensing Letters},
note={note: (in preparation)}
}
-
B. J. Luzum,
K. C. Slatton,
and R. L. Shrestha.
Analysis of Spatial and Temporal Stability of Airborne Laser Swath Mapping Data in a Novel Feature Space.
IEEE Trans. on Geoscience and Remote Sensing,
2004.
Note: Note: (submitted).
@article{RefWorks:821,
author={B. J. Luzum and K. C. Slatton and R. L. Shrestha},
year={2004},
title={Analysis of Spatial and Temporal Stability of Airborne Laser Swath Mapping Data in a Novel Feature Space},
journal={IEEE Trans. on Geoscience and Remote Sensing},
note={note: (submitted)}
}
-
B. J. Luzum,
K. C. Slatton,
and R. L. Shrestha.
Identification and Analysis of Airborne Laser Swath Mapping Data in a Novel Feature Space.
IEEE Geoscience and Remote Sensing Letters,
2004.
Note: Note: (in press).
@article{RefWorks:822,
author={B. J. Luzum and K. C. Slatton and R. L. Shrestha},
year={2004},
title={Identification and Analysis of Airborne Laser Swath Mapping Data in a Novel Feature Space},
journal={IEEE Geoscience and Remote Sensing Letters},
note={note: (in press)}
}
-
B. J. Luzum,
K. C. Slatton,
and R. L. Shrestha.
Information-Theoretic Data Segmentation of Airborne Laser Swath Mapping Data.
IEEE Trans. on Geoscience and Remote Sensing,
2004.
Note: Note: (submitted).
@article{RefWorks:820,
author={B. J. Luzum and K. C. Slatton and R. L. Shrestha},
year={2004},
title={Information-Theoretic Data Segmentation of Airborne Laser Swath Mapping Data},
journal={IEEE Trans. on Geoscience and Remote Sensing},
note={note: (submitted)}
}
-
M. Maltamo,
K. Eerikaeinen,
J. Pitkaenen,
J. Hyyppae,
and M. Vehmas.
Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions.
rse,
90(3):319-330,
2004.
[WWW
] Keyword(s): Inventory,
Lidar,
pdf,
Segmentation,
Truncation point,
Weibull.
Abstract: |
Laser scanners of small footprint diameter and high sampling density provide possibility to obtain accurate height information on the forest canopy. When applying tree crown segmentation methods, individual single trees can be recognised and tree height as well as crown area can be detected. Detection of suppressed trees from a height model based on laser scanning is difficult; however, it is possible to predict these trees by using theoretical distribution functions. In this study, two different methods are used to predict small trees. In the first method, the parameter prediction method is utilised with the complete Weibull distribution, the parameters of which are predicted with separate parameter prediction models; thus, small trees are determined from the predicted tree height distribution. In the second method, the two-parameter left-truncated Weibull distribution is fitted to the detected tree height distribution. The results are presented by using timber volume and stem density as predicted stand characteristics. The results showed that the root mean square error (RMSE) for the timber volume is about 25$\%$ when using only information obtained from laser scanning, whereas the RMSE for the number of stems per ha is about 75$\%$. Predictions for both characteristics are also highly biased and the underestimates are 24$\%$ and 62$\%$, respectively. The use of the parameter prediction method to describe small trees improved the accuracy considerably; the RMSE figures for estimates of timber volume and number of stems are 16.0$\%$ and 49.2$\%$, respectively. The bias for the estimates is also decreased to 6.3$\%$ for timber volume and 8.2$\%$ for the number of stems. When a left-truncated height distribution is used to predict the heights of the missing small trees, the RMSEs for the estimates of timber volume and number of stems are 22.5$\%$ and 72.7$\%$, respectively. In the case of the timber volume, the reliability figures for both the original laser scanning-based estimates and for the estimates that also contain small trees are comparable to those obtained by conventional compartment-wise Finnish field inventories. |
@Article{maltamo04,
author = {M. Maltamo and K. Eerikaeinen and J. Pitkaenen and J. Hyyppae and M. Vehmas},
title = {Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions},
journal = rse,
year = {2004},
volume = {90},
pages = {319-330},
number = {3},
url = {http://www.sciencedirect.com/science/article/B6V6V-4BSVGD0-1/2/988bdbc998926880c6fa8be98fd7e1ef},
keyword = {Inventory, Lidar; pdf, Segmentation, Truncation point, Weibull},
abstract = {Laser scanners of small footprint diameter and high sampling density provide possibility to obtain accurate height information on the forest canopy. When applying tree crown segmentation methods, individual single trees can be recognised and tree height as well as crown area can be detected. Detection of suppressed trees from a height model based on laser scanning is difficult; however, it is possible to predict these trees by using theoretical distribution functions. In this study, two different methods are used to predict small trees. In the first method, the parameter prediction method is utilised with the complete Weibull distribution, the parameters of which are predicted with separate parameter prediction models; thus, small trees are determined from the predicted tree height distribution. In the second method, the two-parameter left-truncated Weibull distribution is fitted to the detected tree height distribution. The results are presented by using timber volume and stem density as predicted stand characteristics. The results showed that the root mean square error (RMSE) for the timber volume is about 25$\%$ when using only information obtained from laser scanning, whereas the RMSE for the number of stems per ha is about 75$\%$. Predictions for both characteristics are also highly biased and the underestimates are 24$\%$ and 62$\%$, respectively. The use of the parameter prediction method to describe small trees improved the accuracy considerably; the RMSE figures for estimates of timber volume and number of stems are 16.0$\%$ and 49.2$\%$, respectively. The bias for the estimates is also decreased to 6.3$\%$ for timber volume and 8.2$\%$ for the number of stems. When a left-truncated height distribution is used to predict the heights of the missing small trees, the RMSEs for the estimates of timber volume and number of stems are 22.5$\%$ and 72.7$\%$, respectively. In the case of the timber volume, the reliability figures for both the original laser scanning-based estimates and for the estimates that also contain small trees are comparable to those obtained by conventional compartment-wise Finnish field inventories.},
}
-
J. McKean and J. Roering.
Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry.
Geomorphology,
57(3-4):331-351,
2004.
@article{RefWorks:825,
author={J. McKean and J. Roering},
year={2004},
title={Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry},
journal={Geomorphology},
volume={57},
number={3-4},
pages={331-351}
}
-
Miina Rautiainen,
Pauline Stenberg,
Tiit Nilson,
and Andres Kuusk.
The effect of crown shape on the reflectance of coniferous stands.
rse,
89(1):41-52,
2004.
[WWW
] Keyword(s): Scots pine,
Norway spruce,
Leaf area index,
Reflectance model.
Abstract: |
The Kuus-Nilson forest reflectance model was used to study the effect of crown shape on the reflectance of Scots pine and Norway spruce stands. In the first part of the study, we examined spruce and pine stands with an age range of 20Ð100 years and compared their simulated hemisphericalÐdirectional reflectance factors (HDRFs) at nadir in red (661 nm), NIR (838 nm) and MIR (1677 nm) when crowns were modeled as ellipsoids or cones. In all the cases, when a stand was modeled with conical crowns, it had a smaller reflectance factor than the same stand with ellipsoidal crowns. To analyze the sensitivity of HDRF on crown shape, in the second part of the study we simulated the angular distributions of HDRF of two pine stands with different leaf area index (LAI) and canopy closure values at 661 nm assuming four different crown shapes (cone, cylinder, ellipsoid, and cylinder bottom, cone top) and separated the components forming the HDRF. Considerable difference in the HDRF between the four crown shapes was observed: The larger the crown volume, the higher the canopy reflectance at similar LAI and canopy closure. A comparison of the two stands revealed that in denser stands (with a higher canopy closure) single scattering from tree crowns was responsible for the difference in HDRF between the different crown shapes, whereas in stands with a smaller canopy closure the single scattering from ground dominated the HDRF. Finally, the role of crown shape for the retrieval of LAI by inversion from remotely sensed data is discussed. |
@article{rautiainen04,
author = {Miina Rautiainen and Pauline Stenberg and Tiit Nilson and Andres Kuusk},
title = {The effect of crown shape on the reflectance of coniferous stands},
journal = rse,
year = {2004},
volume = {89},
pages = {41-52},
number = {1},
url = {http://www.sciencedirect.com/science/article/B6V6V-4B3K43F-2/2/2f040f207133105bb5b8104a66d83e2f},
keyword = {Scots pine, Norway spruce, Leaf area index, Reflectance model},
abstract = {The Kuus-Nilson forest reflectance model was used to study the effect of crown shape on the reflectance of Scots pine and Norway spruce stands. In the first part of the study, we examined spruce and pine stands with an age range of 20Ð100 years and compared their simulated hemisphericalÐdirectional reflectance factors (HDRFs) at nadir in red (661 nm), NIR (838 nm) and MIR (1677 nm) when crowns were modeled as ellipsoids or cones. In all the cases, when a stand was modeled with conical crowns, it had a smaller reflectance factor than the same stand with ellipsoidal crowns. To analyze the sensitivity of HDRF on crown shape, in the second part of the study we simulated the angular distributions of HDRF of two pine stands with different leaf area index (LAI) and canopy closure values at 661 nm assuming four different crown shapes (cone, cylinder, ellipsoid, and cylinder bottom, cone top) and separated the components forming the HDRF. Considerable difference in the HDRF between the four crown shapes was observed: The larger the crown volume, the higher the canopy reflectance at similar LAI and canopy closure. A comparison of the two stands revealed that in denser stands (with a higher canopy closure) single scattering from tree crowns was responsible for the difference in HDRF between the different crown shapes, whereas in stands with a smaller canopy closure the single scattering from ground dominated the HDRF. Finally, the role of crown shape for the retrieval of LAI by inversion from remotely sensed data is discussed.},
}
-
K. C. Slatton and M. Crawford.
Multiscale Fusion of INSAR DEMs for Hydrologic Modeling.
IEEE Transactions on Geoscience and Remote Sensing,
2004.
Note: Note: (in preparation).
@article{RefWorks:839,
author={K. C. Slatton and M. Crawford},
year={2004},
title={Multiscale Fusion of INSAR DEMs for Hydrologic Modeling},
journal={IEEE Transactions on Geoscience and Remote Sensing},
note={note: (in preparation)}
}
-
Xiaowei Yu,
Juha Hyypae andHarri Kaartinen,
and Matti Maltamo.
Automatic detection of harvested trees and determination of forest growth using airborne laser scanning.
rse,
90(4):451-462,
2004.
[WWW
] Keyword(s): Trees,
Forest,
Laser.
Abstract: |
This paper demonstrates the applicability of small footprint, high sampling density airborne laser scanners for boreal forest change detection, i.e. the estimation of forest growth and monitoring of harvested trees. Two laser acquisitions were carried out on a test site using a Toposys-1 laser scanner. Three-dimensional canopy height models were calculated for both data sets using raster-based algorithms. Object-oriented algorithms were developed for detecting harvested and fallen trees, and for measuring forest growth at plot and stand levels. Out of 83 field-checked harvested trees, 61 could be automatically and correctly detected. All mature harvested trees were detected; it was mainly the smaller trees that were not. Forest growth was demonstrated at plot and stand levels using an object-oriented tree-to-tree matching algorithm and statistical analysis. The precision of the estimated growth, based on field checking or statistical analysis, was about 5 cm at stand level and about 10Ð15 cm at plot level. The authors expect that the methods may be feasible in large area forest inventories that make use of permanent sample plots. Together with methods for detecting individual sample trees, the methods described may be used to replace a large number of permanent plots with laser scanning techniques. |
@Article{yu04,
author = {Xiaowei Yu and Juha Hyypae andHarri Kaartinen and Matti Maltamo},
title = {Automatic detection of harvested trees and determination of forest growth using airborne laser scanning},
journal = rse,
year = {2004},
volume = {90},
pages = {451-462},
number = {4},
url = {http://www.sciencedirect.com/science/article/B6V6V-4CC223J-1/2/df27b0d1e1a0d27df0669f0a814ee7e5},
keyword = {Trees, Forest, Laser},
abstract = {This paper demonstrates the applicability of small footprint, high sampling density airborne laser scanners for boreal forest change detection, i.e. the estimation of forest growth and monitoring of harvested trees. Two laser acquisitions were carried out on a test site using a Toposys-1 laser scanner. Three-dimensional canopy height models were calculated for both data sets using raster-based algorithms. Object-oriented algorithms were developed for detecting harvested and fallen trees, and for measuring forest growth at plot and stand levels. Out of 83 field-checked harvested trees, 61 could be automatically and correctly detected. All mature harvested trees were detected; it was mainly the smaller trees that were not. Forest growth was demonstrated at plot and stand levels using an object-oriented tree-to-tree matching algorithm and statistical analysis. The precision of the estimated growth, based on field checking or statistical analysis, was about 5 cm at stand level and about 10Ð15 cm at plot level. The authors expect that the methods may be feasible in large area forest inventories that make use of permanent sample plots. Together with methods for detecting individual sample trees, the methods described may be used to replace a large number of permanent plots with laser scanning techniques.},
}