Modelling lidar-derived boreal forest canopy cover with SPOT 4 HRVIR data

Forest canopy cover (C) is needed in forest area monitoring and for many ecological models. Airborne scanning lidar sensors can produce fairly accurate C estimates even without field training data. However, optical satellite images are more cost-efficient for large area inventories. Our objective was to use airborne lidar data to obtain accurate estimates of C for a set of sample plots in a boreal forest and to generalize C for a large area using a satellite image. The normalized difference vegetation index (NDVI) and reduced simple ratio (RSR) were calculated from the satellite image and used as predictors in the regressions. RSR, which combines information from the red, near-infrared, and shortwave infrared bands, provided the best performance in terms of absolute root mean square error (RMSE) (7.3%) in the training data. NDVI produced a markedly larger RMSE (10.0%). However, in an independent validation data set, RMSE increased (13.0–17.1%) because the systematic sample of validation plots contained more variation than the training plots. Our results are better than those reported earlier, which is probably explained by more consistent C estimates derived from the lidar. Our approach provides an efficient method for creating C maps for large areas.     

Satellite-based mapping of Canadian boreal forest fires: Evaluation and comparison of algorithms

This paper evaluates annual fire maps that were produced from NOAA-14/AVHRR imagery using an algorithm described in a companion paper (Li et al., International Journal of Remote Sensing, 21, 3057-3069, 2000 (this issue)). Burned area masks covering the Canadian boreal forest were created by compositing the daily maps of fire hot spots over the summer and by examining Normalized Difference Vegetation Index (NDVI) changes after burning. Both masks were compared with fire polygons derived by Canadian fire agencies through aerial surveillance. It was found that the majority of fire events were captured by the satellite-based techniques, but burnt area was generally underestimated. The burn boundary formed by the fire pixels detected by satellite were in good agreement with the polygons boundaries within which, however, there were some fires missed by the satellite. The presence of clouds and low sampling frequency of satellite observation are the two major causes for the underestimation. While this problem is alleviated by taking advantage of NDVI changes, a simple combination of a hot spot technique with a NDVI method is not an ideal solution due to the introduction of new sources of uncertainty. In addition, the performance of the algorithm used in the International Geosphere-Biosphere Programme (IGBP) Data and Information System (IGBPDIS) for global fire detection was evaluated by comparing its results with ours and with the fire agency reports. It was found that the IGBP-DIS algorithm is capable of detecting the majority of fires over the boreal forest, but also includes many false fires over old burned scars created by fires taking place in previous years. A step-by-step comparison between the two algorithms revealed the causes of the problem and recommendations are made to rectify them.     

Satellite-based detection of Canadian boreal forest fires: Development and application of the algorithm

This study presents a comprehensive investigation of fires across the Canadian boreal forest zone by means of satellite-based remote sensing. A firedetection algorithm was designed to monitor fires using daily Advanced Very High Resolution Radiometer (AVHRR) images. It exploits information from multichannel AVHRR measurements to determine the locations of fires on satellite pixels of about 1 km2 under clear sky or thin smoke cloud conditions. Daily fire maps were obtained showing most of the active fires across Canada (except those obscured by thick clouds). This was achieved by first compositing AVHRR scenes acquired over Canada on a given day and then applying the fire-detection algorithm. For the fire seasons of 1994-1998, about 800 NOAA/AVHRR daily mosaics were processed. The results provide valuable nation-wide information on fire activities in terms of their locations, burned area, starting and ending dates, as well as development. The total burned area as detected by satellite across Canada is estimated to be approximately 3.9, 4.9, 1.3, 0.4 and 2.4 million hectares in 1994, 1995, 1996, 1997 and 1998, respectively. The peak month of burning varies considerably from one year to another between June and August, as does the spatial distribution of fires. In general, conifer forests appear to be more vulnerable to burning and fires tend to grow larger than in deciduous forests.     

Neural network application for cloud detection in SPOT VEGETATION images

SPOT VEGETATION is a recent sensor at 1 km resolution for land surface studies. Cloud detection based on this sensor is complicated by the absence of a thermal band. An artificial neural network was thus trained for the cloud detection on atmospherically corrected S1 daily data and on top of the atmosphere reflectance P data, from the SPOT VEGETATION system. It consists of a multi‐layer perceptron with one hidden sigmoid layer, trained with the Levenberg–Marquardt back‐propagation algorithm and generalized by the Bayesian regularization. Two neural networks allowed optimal cloud detections to be obtained. The first used all four bands of S1 data with 13 hidden nodes, and the second employed all four bands of P data with 11 hidden nodes. The multiple‐layer perceptrons lead to a cloud detection accuracy of 98.0% and 97.6% for S1 and P data, respectively, when trained to map three predefined values that classify cloud, water and land. The network was further evaluated using three SPOT VEGETATION images taken at different dates. The network detected not only bright thick clouds but also thin or less bright clouds. The analysis demonstrated the superior classification of the network over the standard cloud masks provided with the data.     

Using SPOT-4 HRVIR and VEGETATION sensors to assess impact of tropical forest fires in Roraima,Brazil

Due to the El Niño phenomenon, the 1997-1998 dry season in Roraima (Brazil, Amazonia) was particularly pronounced. Consequently, vegetation fires spread widely and were monitored by many satellites in real time. Satellite images are currently being used to monitor vegetation fires either globally for climate studies or more regionally for impact assessment. After reviewing different satellite data used for impact assessment, this paper focuses on the contribution of SPOT-4's imagery provided by high resolution HRVIR and coarse resolution VEGETATION sensors. These sensors are described with emphasis on those characteristics of potential benefit for forest mapping and fire detection. Early images of Roraima from SPOT-4 are analysed and interpreted to delineate the areas already damaged by fire. VEGETATION images provide a first estimate of damaged areas on a regional scale and an indication of the main ecosystems affected. SPOT HRVIR is used to establish a much more precise classification of various ecosystems. Each vegetation class is associated with a biomass density. From the known burned areas, an estimate of burned biomass during the 1998 dry season is computed, as well as total carbon release. On an intensive study site of 20 400 km 2, 3060 km 2 of savannahs and crops and 6980 km 2 of forest have been burned; the corresponding carbon release is estimated as 210 000 t for croplands and savannahs and 23 M t for the evergreen seasonal forest. The estimated burnt surface areas derived from VEGETATION are then cross-validated with HRVIR and thus an attempt is made to extrapolate the burned biomass with the help of VEGETATION on a regional scale.     

Siberian silkmoth outbreak pattern analysis based on SPOT VEGETATION data

The spatial pattern of Siberian silkmoth outbreak in south Siberian mountains was analysed based on SPOT VEGETATION data. A digital elevation model (DEM) was also used to relate outbreak area dynamics with topographic elements (elevation, azimuth and slope steepness). To avoid bias of spatial pattern data, areas with a given damage category and with given azimuth, slope steepness and elevation were referenced to the areas with similar parameters within the entire study area. The outbreak began between the elevations of ～430–480 m and on south‐west slopes with steepness <5°; these conditions appear to be the most favourable pest habitat. As the pest searched for food it moved up and down slope, resulting in an elevation distribution split within a range of ～390–540 m and slope steepness up to 15°. In the final phase the azimuth distribution of damaged stands became even, showing that pests at this phase settle in non‐optimal habitat. The final outbreak area was ～20 000 ha, which is in good agreement with on‐ground data. The correlation between the initial phase of infestation and topographic features can be used to prioritize pest monitoring. Data obtained show that the SPOT VEGETATION sensor is applicable for monitoring taiga landscapes vulnerable to Siberian silkmoth outbreaks.     

Monitoring snow-cover dynamics in Northern Fennoscandia with SPOT VEGETATION images

In order to monitor snow-cover dynamics in the Tana River Basin in Northern Fennoscandia, SPOT VEGETATION (VGT) images of the snowmelt seasons of 1998 and 1999 were used to identify snow-covered areas, employing an algorithm that was originally developed for data from the Moderate Resolution Imaging Spectroradiometer (MODIS). This algorithm is based on the Normalized Difference Snow Index (NDSI), which usually is calculated from the green and mid-infrared bands. In the absence of a green band, the applicability of this algorithm to VGT data from the red and mid-infrared bands was tested by comparing NDSI values with a corresponding Landsat Thematic Mapper (TM) image. The best agreement was found with slightly lower threshold values for the NDSI. Comparison of the snow-cover estimates also allowed testing of the performance of the NDSI-based algorithm in partially snow-free conditions. By applying the algorithm to ten-day syntheses of VGT images, the moment of snow disappearance could be registered for each 1×1?km pixel in the study area. The results were largely consistent with observations at meteorological stations in the area, confirming the effectiveness of VGT images and the algorithm employed in monitoring snow-cover depletion patterns.     

Assessing the extent of agriculture/pasture and secondary succession forest in the Brazilian Legal Amazon using SPOT VEGETATION data

There has been growing concern about land use/land cover change in tropical regions, as there is evidence of its influence on the observed increase in atmospheric carbon dioxide concentration and consequent climatic changes. Mapping of deforestation by the Brazil's National Space Research Institute (INPE) in areas of primary tropical forest using satellite data indicates a value of 587,727 km² up to the year 2000. Although most of the efforts have been concentrated in mapping primary tropical forest deforestation, there is also evidence of large-scale deforestation in the cerrado savanna, the second most important biome in the region.The main purpose of this work was to assess the extent of agriculture/pasture and secondary succession forest in the Brazilian Legal Amazon (BLA) in 2000, using a set of multitemporal images from the 1-km SPOT-4 VEGETATION (VGT) sensor. Additionally, we discriminated primary tropical forest, cerrado savanna, and natural/artificial waterbodies. Four classification algorithms were tested: quadratic discriminant analysis (QDA), simple classification trees (SCT), probability-bagging classification trees (PBCT), and k-nearest neighbors (K-NN). The agriculture/pasture class is a surrogate for those areas cleared of its original vegetation cover in the past, acting as a source of carbon. On the contrary, the secondary succession forest class behaves as a sink of carbon.We used a time series of 12 monthly composite images of the year 2000, derived from the SPOT-4 VGT sensor. A set of 19 Landsat scenes was used to select training and testing data. A 10-fold cross validation procedure rated PBCT as the best classification algorithm, with an overall sample accuracy of 0.92. High omission and commission errors occurred in the secondary succession forest class, due to confusion with agriculture/pasture and primary tropical forest classes. However, the PBCT algorithm generated the lower misclassification error in this class. Besides, this algorithm yields information about class membership probability, with ∼80% of the pixels with class membership probability greater or equal than 0.8. The estimated total area of agriculture/pasture and secondary succession forest in 2000 in the BLA was 966 × 10³ and 140 × 10³ km², respectively. Comparison with an existing land cover map indicates that agriculture/pasture occurred primarily in areas previously occupied by primary tropical forest (46%) and cerrado savanna (33%), and also in transition forest (19%), and other vegetation types (2%). This further confirms the existing evidence of extensive cerrado savanna conversion.This study also concludes that SPOT-4 VGT data are adequate for discriminating several major land cover types in tropical regions. Agriculture/pasture was mapped with errors of about 5%. Very high classification errors were associated with secondary succession forest, suggesting that a different methodology/sensor has to be used to address this difficult land cover class (namely with the inclusion of ancillary data). For the other classes, we consider that accurate maps can be derived from SPOT-4 VGT data with errors lower than 20% for the cerrado savanna, and errors lower than 10% for the other land cover classes. These estimates may be useful to evaluate impacts of land use/land cover change on the carbon and water cycles, biotic diversity, and soil degradation.     

基于图像型森林火灾无线远程监控系统

针对目前我国森林火情远程监控中长时间不间断监测、运行成本过高等问题,提出了一种基于Linux系统和AT91RM9200处理器为平台的森林火灾监控系统。嵌入式处理器通过安装在森林监测点摄像头获取图像信息,采用图像增强、颜色聚类等图像处理手段,提取图像的颜色信息及其闪烁频率特性,运用火情识别算法判断视频图像帧是否含有火灾火焰,如果系统判断火灾发生,将火情信息通过无线网络传送到监控中心,交由上位机的监测人员做进一步处理。     

Satellite-based forest fire detection for fire control in boreal forests

Forest fires in large sparsely populated areas in the boreal forest zone are difficult to detect by ground based means. Satellites can be a viable source of information to augment air-borne reconnaissance. The Advanced Very High Resolution Radiometer (AVHRR) sensor aboard the National Oceanic and Atmospheric Administration (NOAA) satellites has been used to detect and map fires in the past mainly in the tropics and mainly for environmental monitoring purposes. This article describes real-time forest fire detection where the aim is to inform local fire authorities on the fire. The fire detection is based on the 3.7 mu m channel of the NOAA AVHRR sensor. In the fire detection algorithm, imaging geometry is taken into account in addition to the data from the near-infrared and thermal infrared channels. In an experiment in summer 1995, 16 fires were detected in Finland. One was a forest fire, 11 were prescribed burnings and 4 false alarms. Three of the false alarms were due to steel factories. We conclude that satellite-based fire detection for fire control is feasible in the boreal forest zone if the continuous supply of frequent middle-infrared data can be guaranteed in the future.     

Monitoring herbaceous fuel moisture content with SPOT VEGETATION time-series for fire risk prediction in savanna ecosystems

This paper evaluated the capacity of SPOT VEGETATION time-series to monitor herbaceous fuel moisture content (FMC) in order to improve fire risk assessment in the savanna ecosystem of Kruger National Park in South Africa. In situ herbaceous FMC data were used to assess the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Vegetation Dryness Index (VDI), Improved VDI (IVDI), and Accumulated Relative NDVI Decrement (ARND) during the dry season. The effect of increasing amounts of dead vegetation on the monitoring capacity of derived indices was studied by sampling mixed live and dead FMC. The IVDI was proposed as an improvement of the VDI to monitor herbaceous FMC during the dry season. The IVDI is derived by replacing NDVI with the integrated Relative Vegetation Index (iRVI), as an approximation of yearly herbaceous biomass, when analyzing the 2-dimensional space with NDWI. It was shown that the iRVI offered more information than the NDVI in combination with NDWI to monitor FMC. The VDI and IVDI exhibited a significant relation to FMC with R² of 0.25 and 0.73, respectively. The NDWI, however, correlated best with FMC (R² = 0.75), while the correlation of ARND and FMC was weaker (R² = 0.60) than that found for NDVI, NDWI, and IVDI. The use of in situ herbaceous FMC consequently indicated that NDWI is appropriate as spatio-temporal information source of herbaceous FMC variation which can be used to optimize fire risk and behavior assessment for fire management in savanna ecosystems.     

Assimilation of SPOT/VEGETATION NDVI data into a sahelian vegetation dynamics model

This paper presents a method to monitor the dynamics of herbaceous vegetation in the Sahel. The approach is based on the assimilation of Normalized Difference Vegetation Index (NDVI) data acquired by the VEGETATION instrument on board SPOT 4/5 into a simple sahelian vegetation dynamics model. The study region is located in the Gourma region of Mali. The vegetation dynamics model is coupled with a radiative transfer model (the SAIL model). First, it is checked that the coupled models allow for a realistic simulation of the seasonal and interannual variability of NDVI over three sampling sites from 1999 to 2004. The data assimilation scheme relies on a parameter identification technique based on an Evolution Strategies algorithm. The simulated above-ground herbage mass resulting from NDVI assimilation is then compared to ground measurements performed over 13 study sites during the period 1999-2004. The assimilation scheme performs well with 404 kg DM/ha of average error (n = 126 points) and a correlation coefficient of r = 0.80 (to be compared to the 463 kg DM/ha and r = 0.60 of the model performance without data assimilation). Finally, the sensitivity of the herbage mass model estimates to the quality of the meteorological forcing (rainfall and net radiation) is analyzed thanks to a stochastic approach.     

Mathematical simulation of forest fires using multiprocessor computers

The results of numerical simulation for the three-phase model for forest fires using a multi-processor computer are described. An analysis of the efficiency of applied parallel algorithms is made.     

Multi-temporal JERS SAR data in boreal forest biomass mapping

Multi-temporal JERS SAR data were studied for forest biomass mapping. The study site was located in South-eastern Finland in Ruokolahti. Pre-processing of JERS SAR data included ortho-rectification and radiometric normalization of topographic effects.In single-date regression analysis between backscatter amplitude and stem volume, summer scenes from July to October produced correlation coefficients (r) between 0.63 and 0.81. Backscatter level and the slope of the (linear) regression line were stable from scene to scene. Winter scenes acquired in very cold and dry winter conditions had a very low correlation. One winter scene acquired in conditions where snow is not completely frozen produced a correlation coefficient similar to summer scenes.Multivariate regression analysis with a 6-date JERS SAR dataset produced correlation coefficient of 0.85. A combined JERS-optical regression analysis improved the correlation coefficient to 0.89 and also alleviated the saturation, which affects both SAR and optical data.The stability of the regression results in summer scenes suggests that a simple constant model could be used in wide-area forest biomass mapping if accuracy requirements are low and if biomass estimates are aggregated to large areal units.     

Classification of boreal forest cover types using SAR images

Mapping forest cover types in the boreal ecosystem is important for understanding the processes governing the interaction of the surface with the atmosphere. In this paper, we report the results of the land-cover classification of the SAR (synthetic aperture radar) data acquired during the Boreal Ecosystem Atmospheric Study's intensive field campaigns over the southern study area near Prince Albert, Canada. A Bayesian maximum a posteriori classifier was applied on the national Aeronautics and Space Administration/Jet Propulsion Laboratory airborne SAR images covering the region during the peak of the growing season in July 1994. The approach is supervised in the sense that a combination of field data and existing land-cover maps are used to develop training areas for the desired classes. The images acquired were first radiometrically and absolutely calibrated, the incidence angle effect in airborne images was corrected to an acceptable accuracy, and the images were used in a mosaic form and geocoded and georeferenced with an existing land-cover map for validation purposes. The results show that SAR images can be classified into dominant forest types such as jack pine, black spruce, trembling aspen, clearing, open water, and three categories of mixed strands with better than 90% accuracy. The unispecies stands such as jack pine and black spruce are separated with 98% accuracy, but the accuracy of mixed coniferous and deciduous stands suffers from confusing factors such as varying species composition, surface moisture, and understory effects. To satisfy the requirements of process models, the number of cover types was reduced from eight to five general classes of conifer wet, conifer dry, mixed deciduous, disturbed, and open water. Reduction of classes improved the overall accuracy of the classification over the entire region from 77% to 92%.     

The use of SPOT VEGETATION data in a classification tree approach for burnt area mapping in Australian savanna

An algorithm to map burnt areas has been developed for SPOT VEGETATION (VGT) data in Australian woodland savannas. A time series of daily VGT images (15 May to 15 July 1999) was composited into 10-day periods by applying a minimum value criterion to the near-infrared band (0.78-0.89 @m). The algorithm was developed using a classification tree methodology that was confirmed as a powerful means of image classification. This methodology allowed the identification of three classes of burnt surfaces that appear to be differentiated by the proportion of the pixel that is burnt, the intensity of the fire and the density of the tree layer. The performance of the algorithm was assessed by classification of one VGT composite image (31 May-9 June) using, as representative of the ground truth, burnt areas extracted from two Landsat TM scenes (9 June). We randomly extracted 30 windows (each of ～14 km by 14 km) for which we compared the percentage of area burnt as derived from TM and VGT. The estimated mean absolute deviation in the percentage of the area burnt in each window is - 6.3%. In the area common to the two datasets a total amount of 6473 km 2 was estimated to be burnt in the VGT classification against 7536 km 2 that was burnt according to TM images. The accuracy of the classification was found to vary with the vegetation type being the most accurate estimate in low woodland with an underestimation error of 8.6%. These results show that VGT could be a very useful sensor for burnt area mapping over large woodland areas, although the low spatial resolution and the lack of a thermal band can be a limitation in certain conditions (e.g. understorey burns). The same methodology will be applied to map burnt areas for the entire Australian continent.     

Mean Compositing,an alternative strategy for producing temporal syntheses. Concepts and performance assessment for SPOT VEGETATION time series

Various compositing criteria have been proposed to produce cloud‐free images from optical time series. However, they often favour specific atmospheric and geometric conditions, which may cause serious inconsistencies in the syntheses. Algorithms including BRDF normalization minimize variations induced by the anisotropy of the target. However, their operational implementation faces some issues. This study proposes to avoid these issues by using a new strategy based on a statistical approach, i.e. Mean Compositing, and by comparing it with three existing techniques. A quantitative evaluation methodology with statistical tests on reflectance and texture values as well as visual comparisons were applied to numerous SPOT VEGETATION time series. The performance criterion was to best mimic the information content of a single cloud‐free near‐nadir view image. Moreover a quantitative approach was used to assess the temporal consistency of the syntheses. The results showed that the proposed strategy combined with an efficient quality control produces images with greater spatial consistency than currently available VEGETATION products but produces slightly more uneven time series than the most advanced compositing algorithm.     

Monitoring elevation variations in leaf phenology of deciduous broadleaf forests from SPOT/VEGETATION time-series

In mountain forest ecosystems where elevation gradients are prominent, temperature gradient-based phenological variability can be high. However, there are few studies that assess the capability of remote sensing observations to monitor ecosystem phenology along elevation gradients, despite their relevance under climate change. We investigated the potential of medium resolution remotely sensed data to monitor the elevation variations in the seasonal dynamics of a temperate deciduous broadleaf forested ecosystem. Further, we explored the impact of elevation on the onset of spring leafing. This study was based on the analysis of multi-annual time-series of VEGETATION data acquired over the French Pyrenees Mountain Region (FPMR), in conjunction with simultaneous ground-based observations of leaf phenology made for two dominant tree species in the region (oak and beech). The seasonal variations in the perpendicular vegetation index (PVI) were analyzed during a five-year period (2002 to 2006). The five years of data were averaged into a one sole year in order to fill the numerous large spatio-temporal gaps due to cloud and snow presence - frequent in mountains - without altering the temporal resolution. Since a VEGETATION pixel (1 km²) includes several types of land cover, the broadleaf forest-specific seasonal dynamics of PVI was reconstructed pixel-by-pixel using a temporal unmixing method based on a non-parametric statistical approach. The spatial pattern of the seasonal response of PVI was clearly consistent with the relief. Nevertheless the elevational or geographic range of tree species, which differ in their phenology sensitivity to temperature, also has a significant impact on this pattern. The reduction in the growing season length with elevation was clearly observable from the delay in the increase of PVI in spring and from the advance of its decrease in the fall. The elevation variations in leaf flushing timing were estimated from the temporal change in PVI in spring over the study area. They were found to be consistent with those measured in situ (R2 > 0.95). It was deduced that, over FPMR, the mean delay of leaf flushing timing for every 100 m increase in elevation was estimated be approximately 2.3 days. The expected estimation error of satellite-based leaf unfolding date for a given elevation was approximately 2 days. This accuracy can be considered as satisfactory since it would allow us to detect changes in leafing timing of deciduous broadleaf forests with a magnitude equivalent to that due to an elevation variation of 100 m (2.3 days on average), or in other words, to that caused by a variation in the mean annual air temperature of 0.5 °C. Although averaging the VEGETATION data over five years led to a loss of interannual information, it was found to be a robust approach to characterise the elevation variations in spring leafing and its long-term trends.     

Combining AVHRR and ATSR satellite sensor data for operational boreal forest fire detection

The potential to combine data from two different satellite systems was studied to increase fire detection sensitivity and image acquisition frequency in real-time fire detection and fire control. A fully automatic fire detection algorithm was applied to all scenes that were acquired using both satellite systems. Local fire authorities were notified about each detected fire in their territory using real-time fire reports that were sent by telefax. The average time from the start of National Oceanic and Atmospheric Administration (NOAA), Advanced Very High Resolution Radiometer (AVHRR) image acquisition until the sending of a telefax fire report was 25 min. During the straw-burning season in April 2000, the Along Track Scanning Radiometer (ATSR) instrument detected twice as many fires as the AVHRR per unit image area. The main reason for this may be the average resolution cell of the ATSR, which is half the size of that of the AVHRR in terms of area. The response from fire authorities was used to estimate the number of correct alerts and false alarms. A false alarm rate of 12% and 7% was obtained in the fire seasons of 1999 and 2000, respectively.     

LIDAR-based geometric reconstruction of boreal type forest stands at single tree level for forest and wildland fire management

Vegetation structure is an important parameter in fire risk assessment and fire behavior modeling. We present a new approach deriving the structure of the upper canopy by segmenting single trees from small footprint LIDAR data and deducing their geometric properties. The accuracy of the LIDAR data is evaluated using six geometric reference targets, with the standard deviation of the LIDAR returns on the targets being as low as 0.06 m. The segmentation is carried out by using cluster analysis on the LIDAR raw data in all three coordinate dimensions. From the segmented clusters, tree position, tree height, and crown diameter are derived and compared with field measurements. A robust linear regression of 917 tree height measurements yields a slope of 0.96 with an offset of 1 m and the adjusted R² resulting at 0.92. However, crown diameter is not well matched by the field measurements, with R² being as low as 0.2, which is most certainly due to random errors in the field measurements. Finally, a geometric reconstruction of the forest scene using a paraboloid model is carried out using values of tree position, tree height, crown diameter, and crown base height.