Extracting Canopy Surface Texture from Airborne Laser Scanning Data for the Supervised and Unsupervised Prediction of Area-Based Forest Characteristics

Abstract

Abstract:Area-based analyses of airborne laser scanning (ALS) data are an established approach to obtain wall-to-wall predictions of forest characteristics for vast areas. The analyses of sparse data in particular are based on the height value distributions, which do not produce optimal information on the horizontal forest structure. We evaluated the complementary potential of features quantifying the textural variation of ALS-based canopy height models (CHMs) for both supervised (linear regression) and unsupervised (k-Means clustering) analyses. Based on a comprehensive literature review, we identified a total of four texture analysis methods that produced rotation-invariant features of different order and scale. The CHMs and the textural features were derived from practical sparse-density, leaf-off ALS data originally acquired for ground elevation modeling. The features were extracted from a circular window of 254 m 2 and related with boreal forest characteristics observed from altogether 155 field sample plots. Features based on gray-level histograms, distribution of forest patches, and gray-level co-occurrence matrices were related with plot volume, basal area, and mean diameter with coefficients of determination (R 2 ) of up to 0.63-0.70, whereas features that measured the uniformity of local binary patterns of the CHMs performed poorer. Overall, the textural features compared favorably with benchmark features based on the point data, indicating that the textural features contain additional information useful for the prediction of forest characteristics. Due to the developed processing routines for raster data, the CHM features may potentially be extracted with a lower computational burden, which promotes their use for applications such as pre-stratification or guiding the field plot sampling based solely on ALS data.

Forest Site and Type Variability in ALS-Based Forest Resource Inventory Attribute Predictions over Three Ontario Forest Sites

“…Our results demonstrate that FRI attribute predictions improved at all three sites in terms of RMSE, bias, and R 2 , albeit to varying degrees, when texture and/or intensity metrics were integrated with normalized height metrics derived from ALS data. These findings support previous studies (e.g., [33,35,37,38]). Ozdemir and Donoghue [33] found that the CHM-based texture metrics improved predictions of height, DBH, and crown variability over using point-based ALS metrics alone in their Figure 6.…”

“…Ozdemir and Donoghue [31] showed that texture metrics generated from ALS-derived canopy height models (CHM) explained tree size diversity (tree height and QMD diversity). Niemi and Vauhkonen [33] found that texture metrics improved predictions of VOL and BA in Finland's boreal forest and helped cluster forest stands into groups with increasing maturity and stocking characteristics. Hence, texture appears to capture upper canopy variability which can be indicative of stand development stage [33].…”

“…Niemi and Vauhkonen [33] found that texture metrics improved predictions of VOL and BA in Finland's boreal forest and helped cluster forest stands into groups with increasing maturity and stocking characteristics. Hence, texture appears to capture upper canopy variability which can be indicative of stand development stage [33]. Intensity metrics are another set of ALS metrics that, until recently, have generally been excluded from ALS metric sets for the prediction of FRI attributes because of the radiometric heterogeneities in uncalibrated intensity values due to system-and environment-induced distortions [34].…”

“…Based on the utility of texture metrics derived from optical imagery for forestry applications (e.g., [27,28]), texture metrics have also been developed from ALS-based canopy height models (CHM) [29][30][31][32]. These texture metrics allow for a more detailed characterization of horizontal forest structure or upper canopy variability, i.e., in the prediction of species [29], growing stock attributes [31,33], and the spatial arrangement of trees [30,32]. Ozdemir and Donoghue [31] showed that texture metrics generated from ALS-derived canopy height models (CHM) explained tree size diversity (tree height and QMD diversity).…”

A Novel Approach for Coarse-to-Fine Windthrown Tree Extraction Based on Unmanned Aerial Vehicle Images

“…Although areas with concentrated trees were identified, due to the effects of resolution and obstruction, it was difficult to achieve extraction of individual trees via optical remote sensing images and standard aerial photographs. LiDAR, with its ability to penetrate vegetation and forest canopies, proved to be an effective tool for extraction of individual fallen trees, thereby overcoming the problem of obstruction [9][10][11][12][13]. The focus of current research is the identification and extraction of individual fallen trees using airborne laser scanning.…”