Ross Nelson

Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomass

The main objective of this study was to develop reliable processing and analysis techniques to facilitate the use of small-footprint lidar data for estimating tree crown diameter by measuring individual trees identifiable on the three-dimensional lidar surface. In addition, the study explored the importance of the lidar-derived crown diameter for estimating tree volume and biomass. The lidar dataset was acquired over deciduous, coniferous, and mixed stands of varying age classes and settings typical of the southeastern United States. For identifying individual trees, lidar processing techniques used data fusion with multispectral optical data and local filtering with both square and circular windows of variable size. The crown diameter was calculated as the average of two values measured along two perpendicular directions from the location of each tree top by fitting a fourth-degree polynomial on both profiles. The lidar-derived tree measurements were used with regression models and cross-validation to estimate plot level field-measured crown diameter. Linear regression was also used to compare plot level tree volume and biomass estimation with and without lidar-derived crown diameter measures from individual trees. Results for estimating crown diameter were similar for both pines and deciduous trees, with R2 values of 0.62‐0.63 for the dominant trees (root mean square error (RMSE) 1.36 to 1.41 m). Lidar-measured crown diameter improved R2 values for volume and biomass estimation by up to 0.25 for both pines and deciduous plots (RMSE improved by up to 8 m3/ha for volume and 7 Mg/ha for biomass). For the pine plots, average crown diameter alone explained 78% of the variance associated with biomass (RMSE 31.28 Mg/ha) and 83% of the variance for volume (RMSE 47.90 m3/ha).

Estimating forest biomass and volume using airborne laser data

Abstract An airborne, pulsed laser system was used to collect forest canopy height data over a southern pine forest in southwestern Georgia. The laser information, which consists of ranging (distance) data from aircraft to canopy and aircraft to ground, were used to try to predict ground-measured forest biomass and timber volume. One hundred thirteen 20-m laser segments on which forest mensuration data had been collected were analyzed. Two logarithmic equations were tested in conjunction with six laser canopy measurements to determine which model best described variation in the ground measurements. The best models explained between 53% and 65% of the variability noted in ground measurements of forest biomass (green weight) and volume. Biomass and volume estimates derived using laser data were very variable when compared with the corresponding ground measurements site by site. However, the test results indicate that these models predict mean total tree volume within 2.6% of the ground value, and mean biomass within 2.0% (based on 38 test plots). The results of this study showed that species stratification did not consistently improve regression relationships for four southern pine species.

Estimating plot-level tree heights with lidar : local filtering with a canopy-height based variable window size

In recent years, the use of airborne lidar technology to measure forest biophysical characteristics has been rapidly increasing. This paper discusses processing algorithms for deriving the terrain model and estimating tree heights by using a multiple return, high � / density, small-footprint lidar data set. The lidar data were acquired over deciduous, coniferous, and mixed stands of varying age classes and settings typical of the southeastern US. The specific objectives were: (1) to develop and test algorithms to estimate plot level tree height using lidar data, and (2) to investigate how ground measurements can help in the processing phase of lidar data for tree height assessment. The study area is located in the Piedmont physiographic province of Virginia, USA and includes a portion of the Appomattox-Buckingham State Forest (37825?N, 78841?W). Two lidar processing algorithms are discussed */the first based on single tree crown identification using a variable window size for local filtering, and the second based on the height of all laser pulses within the area covered by the ground truth data. Height estimates resulted from processing lidar data with both algorithms were compared to field measurements obtained with a plot design following the USDA Forest Service Forest Inventory and Analysis (FIA) field data layout. Linear regression was used to develop equations relating lidar-estimated parameters with field inventories for each of the FIA plots. As expected, the maximum height on each plot was predicted with the highest accuracy (R 2 values of 85 and 90%, for the first and the second algorithm, respectively). The variable window size algorithm performed better for predicting heights of dominant and co-dominant trees (R 2 values 84 � /85%), with a diameter at breast height (dbh) larger than 12.7 cm (5 in), when compared with the algorithm based on all laser

Determining forest canopy characteristics using airborne laser data

Abstract A pulsed laser system was flown over a forested area in Pennsylvania which exhibited a wide range of canopy closure conditions. The lasing system acts as the ultraviolet light equivalent of radar, sensing not only the distance to the top of the forest canopy, but also the range to the forest floor. The data were analyzed to determine which components of the laser data could explain the variability in crown closure along the flight transect. Results indicated that canopy closure was most strongly related to the penetration capability of the laser pulse. Pulses were attenuated more quickly in a dense canopy. Hence the inability to find a strong ground return in the laser data after initially sensing the top of the canopy connoted dense canopy cover. Photogrammetrically acquired tree heights were compared to laser estimates; average heights differed by less than 1 m. The results indicated that the laser system may be used to remotely sense the vertical forest canopy profile. Elements of this profile are linearly related to crown closure and may be used to assess tree height.

Directional Reflectance Distributions of a Hardwood and Pine Forest Canopy

The directional reflectance distributions for both a hardwood and pine forest canopy at Beltsville, Maryland, were measured in June as a function of sun angle from a helicopter platform using a hand-held radiometer with AVHRR band 1 (0.58-0.68 ¿m) and band 2 (0.73-1.1 ¿m). Canopy characteristics were measured on the ground. The reflectance distributions are reported and compared to the scattering behavior of agricultural and natural grassland canopies. In addition, the three-dimensional radiative transfer model of Kimes was used to document the unique radiant transfers that take place in forest canopies due to their special geometric structure. Measurements and model simulations showed that the scattering behavior of relatively dense forest canopies is similar to the scattering behavior of agricultural crops and natural grasslands. Only in more sparse forest canopies with significant spacing between the tree crowns (or clumps of tree crowns) does the scattering behavior deviate from homogeneous agricultural and natural grassland canopies. This clumping of vegetation material has two effects on the radiant transfers within the canopy: A) it increases the probability of gap to the understory and/or soil layers that increases the influence of the scattering properties of these lower layers; and B) it increases the number of low transmitting clumps of vegetation within the scene causing increased backscatter and decreased forward scatter to occur relative to the homogeneous case. Both effects, referred to as phenomenon A and B, respectively, tend to increase backscatter relative to forward scatter.

Attributes of neural networks for extracting continuous vegetation variables from optical and radar measurements

Efficient algorithms that incorporate different types of spectral data and ancillary data are being developed to extract continuous vegetation variables. Inferring continuous variables implies that functional relationships must be found among the predicted variable(s), the remotely sensed data and the ancillary data. Neural networks have attributes which facilitate the extraction of vegetation variables. The advantages and power of neural networks for extracting continuous vegetation variables using optical and/or radar data and ancillary data are discussed and compared to traditional techniques. Studies that have made advances in this research area are reviewed and discussed. Neural networks can provide accurate initial models for extracting vegetation variables when an adequate amount of data is available. Networks provide a performance standard for evaluating existing physically based models. Many practical problems occur when inverting physically based models using traditional techniques and neural ne...

Measuring biomass and carbon in delaware using an airborne profiling LIDAR

A portable, inexpensive profiling light detection and ranging (LIDAR) system was used to inventory forests in Delaware, a small state (5205 km2) on the mid-Atlantic seaboard of the USA. Ground and airborne sampling procedures are described, and large-area inventory results are reported and compared to independent estimates. Systematic airborne LIDAR profiling measurements were used (1) to estimate forest merchantable volume, biomass and above-ground carbon statewide; and (2) to estimate impervious surface and open water area. Over 1300 km of laser profiling data acquired along parallel flight lines 4 km apart were analysed. Four explicitly linear models were considered to predict merchantable volume and total above-ground dry biomass. Merchantable volume estimates were within 21% of US Forest Service estimates at the county level and within 1% statewide. Total above-ground dry biomass estimates were within 22% of USFS estimates at the county level and within 16% statewide. LIDAR estimates of percentage impervious area surface for the three counties (Newcastle, Kent and Sussex) were 10.9, 3.4 and 2.8%, respectively, and 4.7% statewide. Comparable estimates developed using 30 m Enhanced Thematic Mapper digital data and mixture modelling were 8.8, 3.5 and 3.9%, respectively, and 4.9% statewide. Laser estimates of open water at the county and state level were comparable to 1997 Geographical Information System (GIS) estimates. Open water estimates based on laser transect data showed the three counties to have 3.0, 2.0 and 4.6% of their county area covered by water, and 3.5% of the state to be covered by open water. Comparable 1997 GIS estimates were 2.6, 2.1 and 4.8% (county), and 3.5% (state), respectively. The results of the study indicate that line intercept sampling techniques can be used in conjunction with a relatively inexpensive, portable airborne laser system to assess multiple resources regionally.

Modeling forest canopy heights: The effects of canopy shape

Abstract Three-dimensional models that represent the top-of-canopy forest height structure were developed to simulate airborne laser profiling responses along forested transects. The simulator which produced these 3-D models constructed individual tree crowns according to a trees total height, height to first branch, crown diameter, and crown shape (cone, parabola, ellipse, sphere, or a random assortment of these shapes), and then inserted these crowns into a fixed-area plot using mapped stand (x,y) coordinates. This two-dimensional array of forest canopy heights was randomly transected to simulate measurements made by an airborne ranging laser. These simulated laser measurements were regressed with ground reference measures to develop predictive linear relationships. The assumed crown shape had a significant impact on 1) simulated laser measurements of height and 2) estimates of basal area, woody volume, and above-ground dry biomass derived via simulation. As canopy shape progressed from a conic form to a more spheric structure, average canopy height, canopy profile area, and canopy volume increased, canopy height variation decreased, and coefficients of variability were stable or decreased. In Costa Rican tropical forests, simulated laser measurements of average height, canopy profile area, and canopy volume increased 8–10% when a parabolic rather than a conic shape was assumed. An elliptic canopy was 16–18% taller, on average, than a conic canopy, and a spheric canopy was 23–25% taller. The effect of these height increases and height variability changes can profoundly affect basal area, volume, and biomass estimates, but the degree to which these estimates are affected is study-area-dependent. Since canopy shape may significantly affect such estimates, canopy shapes should be noted when field data are collected for purposes of height simulation. If canopy shapes are not noted and are unknown, an assumption of an elliptical shape is suggested in order to mitigate potentially large errors which may be incurred using a generic assumption of a cone or sphere.

The effects of spatial resolution on the classification of Thematic Mapper data

Abstract Actual and degraded LANDSAT-4 Thematic Mapper (TM) data were analysed to examine the effect of spatial resolution on the performance of a per pixel, maximum-likelihood classification algorithm. Analysis of variance (ANOVA) and a balanced, three-factor, eight-treatment, fixed-effects model were used to investigate the interactions between spatial resolution and two other TM refinements, spectral band configuration and data quantization. The goal was to evaluate quantitatively the effects of these attributes on classification accuracies obtained with all pixels (pure pixels plus mixed pixels) and on accuracies obtained with pure pixels alone. A comparison of results from these separate analyses supported previous explanations of the effects of increasing spatial resolution. First, the difficulty in classifying mixed pixels was demonstrated by an average 21 per cent decrease in percentage accuracy from the pure-pixel case to the pure-plus-mixed-pixel case for the eight ANOVA treatments. In the pure-...

Identifying deforestation in Brazil using multiresolution satellite data

abstract MSS, LAC, GAC and GOES data were used to delineate the extent of deforestation in Rondonia, Brazil, in order to identify those satellite data sources appropriate for monitoring deforestation on a continental/subcontinental scale. These data were processed to differentiate forest from non-forest (cleared, colonized areas) using two different classification procedures. The first procedure utilizes all available spectral bands of data in conjunction with a maximum likelihood classifier to discriminate cleared areas from primary forest. The technique is called probability thresholding. The second employs the red and nearinfrared spectral data to calculate a vegetation index which is subsequently thresholded from forest/non-forest delineation. Ground reference data were not available; the 80m (spatial resolution) MSS digital data products served as the reference data source. The 1·1 km LAC, 4 km GAC and 0·9 km GOES (visible band) images were compared with the MSS imagery. Areal comparisons indicated t...