Comparing biophysical forest characteristics estimated from photogrammetric matching of aerial images and airborne laser scanning data

Recent development in aerial digital cameras and software facilitate the photogrammetric point cloud as a new data source in forest management planning. A total of 151 field training plots were distributed systematically within three predefined strata in a 852.6 ha study area located in the boreal forest in southeastern Norway. Stratum-specific regression models were fitted for six studied biophysical forest characteristics. The explanatory variables were various canopy height and canopy density metrics derived by means of photogrammetric matching of aerial images and small-footprint laser scanning. The ground sampling distance was 17 cm for the images and the airborne laser scanning (ALS) pulse density was 7.4 points m^–2. Resampled images were assessed to mimic acquisitions at higher flying altitudes. The digital terrain model derived from the ALS data was used to represent the ground surface. The results were evaluated using 63 independent test stands. When estimating height in young forest and mature forest on poor sites, the root mean square error (RMSE) values were slightly better using data from image matching compared to ALS. However, for all other combinations of biophysical forest characteristics and strata, better results were obtained using ALS data. In general, the best results were found using the highest image resolution.     

Using canopy heights from digital aerial photogrammetry to enable spatial transfer of forest attribute models: a case study in central Europe

This paper describes a workflow utilizing detailed canopy height information derived from digital airphotos combined with ground inventory information gathered in state-owned forests and regression modelling techniques to quantify forest-growing stocks in private woodlands, for which little information is generally available. Random forest models were trained to predict three different variables at the plot level: quadratic mean diameter of the 100 largest trees (d₁₀₀), basal area weighted mean height of the 100 largest trees (h₁₀₀), and gross volume (V). Two separate models were created – one for a spruce- and one for a beech-dominated test site. We examined the spatial portability of the models by using them to predict the aforementioned variables at actual inventory plots in nearby forests, in which simultaneous ground sampling took place. When data from the full set of available plots were used for training, the predictions for d₁₀₀, h₁₀₀, and V achieved out-of-bag model accuracies (scaled RMSEs) of 15.1%, 10.1%, and 35.3% for the spruce- and 15.9%, 9.7%, and 32.1% for the beech-dominated forest, respectively. The corresponding independent RMSEs for the nearby forests were 15.2%, 10.5%, and 33.6% for the spruce- and 15.5%, 8.9%, and 33.7% for the beech-dominated test site, respectively.     

Mapping site index and age by linking a time series of canopy height models with growth curves

Site index (SI) is one of the main measures of forest productivity in North America. For monospecific even-age stands, it is defined as the height of dominant trees at a given reference age or presented as an age–height curve. SI normally reflects the overall effect of all the environmental parameters that determine height growth locally. However, measuring SI can only be achieved though field observations and is, for this reason, limited to sample plots. In this study, we propose a new method for quantifying and mapping SI and age based on known age–height curves and time series of canopy height models (CHMs) produced using digital photogrammetry and lidar. Digital surface models (DSMs) are created by applying an automated stereo-matching algorithm to scanned aerial photographs. The canopy height is obtained by subtracting the lidar ground elevations from the DSM. Using aerial photographs covering the 1945–2003 interval and a recent lidar coverage, CHMs could be reconstructed retrospectively for a period of over 58 years. Regionally calibrated age–height curves were fitted to observations that were extracted cell-wise from the historical CHMs to estimate SI and age values for all undisturbed locations. Results demonstrate that SI and age of jack pine (Pinus banksiana [Lamb.]) stands can be quantified respectively with an average bias of 0.76 m (2.41 m root mean squared error, RMSE) and 1.86 years (7 years RMSE). The method can be used to produce quasi-continuous maps of SI and age and to estimate productivity in a spatially explicit way.     

A hierarchical approach coupled with coarse DEM information for improving the efficiency and accuracy of forest mapping over very rugged terrains

Forest mapping over mountainous terrains is difficult because of high relief. Although digital elevation models (DEMs) are often useful to improve mapping accuracy, high quality DEMs are seldom available over large areas, especially in developing countries. In this study, a hierarchical approach coupled with topographic information derived from coarse DEM was developed to improve the efficiency and accuracy of forest mapping over mountainous areas. The overall idea of increasing mapping accuracy over large mountainous areas is to reduce spectral variety over areas to be mapped. The approach consists of three major steps. The first step is to partition a large mountainous area into several small mapping zones. Forest mapping is then conducted in each zone independently. At the second step, forest areas are separated from non-forest areas through a semi-automatic binary classification procedure. At the third step, forested areas are then further classified into detailed forest types by coupling Landsat ETM+ imagery and two topographic variables derived from a coarse DEM (extracted from 1:250,000 digital elevation contour layer, which are readily available). This hierarchical approach was illustrated and evaluated through a case study in Northwest Yunnan, China, a very rugged terrain in the world. Forests and non-forests were separated accurately and efficiently (the overall accuracy is 0.97 and Khat value is 0.94 of whole area). It was found that the inclusion of the coarse topographic data improved the mapping accuracy significantly (overall accuracy from 0.74 to 0.84, from 0.76 to 0.89, from 0.78 to 0.84 in three test areas, respectively), and that the difference in accuracy between the use of coarse DEM data and the use of fine DEM data for the study area is not significant (overall accuracy from 0.84 to 0.86). The results indicate that the hierarchical approach, coupled with coarse DEM information, is effective in increasing the accuracy of forest mapping over very rugged terrains when high quality digital elevation models are not available.