Mapping gap fraction, LAI and defoliation using various ALS penetration variables

Four alternative airborne laser scanning (ALS) canopy penetration variables were compared for their suitability for mapping of gap fraction, leaf area index and disturbances in a Scots pine forest. The variables were based on either echo counting or intensity, and on either first or first and last echoes. ALS data and field-measured gap fraction and effective leaf area index (LAIe) were gathered before and after a severe insect defoliation by pine sawflies. LAIe is a commonly used form of leaf area index that is mathematically derived from gap fraction, and includes the areas of foliage, branches and trunks, and which is not corrected for the clumping of foliage. The ALS penetration variables were almost equally strongly related to field-measured gap fraction and LAIe. The estimated slopes in the LAIe models varied from 0.94 to 2.71, and had coefficient of determination R 2 values of 0.92–0.94. They were strongly correlated to each other (R 2 values of 0.95–0.98) and agreed fairly well for temporal changes of LAIe during the summer and the insect defoliation (R 2 values of 0.82–0.95). Counting of first and last echoes produced penetration rates close to the gap fraction, and this penetration variable was able to penetrate tree crowns. Ground-only echoes represented mostly between-tree gaps, and canopy-first-ground-last pulses represented mostly within-canopy gaps. However, the penetration variables based on first and last echoes suffered from the problem that a second echo might be impaired both in low and in tall canopies. In low canopies, two adjacent echoes from the same pulse would be too close in time to be separated by the sensor, while in tall canopies the pulse might apparently be fragmented down through the canopy. The intensity-based penetration variables needed to be supplemented with reflectance values, or at least the ratio between reflectance of the canopy and the ground, and this ratio was estimated from the data. The study demonstrated that one might be able to distinguish between disturbance types, e.g. between defoliation and cutting, by comparing alternative ALS penetration variables. Insect defoliation was dominated by an increase in within-canopy gaps and, correspondingly, the fraction of partly penetrating canopy-first-ground-last pulses. Tree removals from cutting were dominated by increases in between-tree gaps and the corresponding fraction of ground-only pulses.

[1]  S. Running,et al.  The Earth Observing System and Forest Management , 2000 .

[2]  M. Rautiainen,et al.  Hot spot reflectance signatures of common boreal lichens , 2005 .

[3]  D. Baldocchi,et al.  Estimation of leaf area index in open-canopy ponderosa pine forests at different successional stages and management regimes in Oregon , 2001 .

[4]  P. Ciais,et al.  Old-growth forests as global carbon sinks , 2008, Nature.

[5]  Sylvain G. Leblanc,et al.  A practical scheme for correcting multiple scattering effects on optical LAI measurements , 2001 .

[6]  K. Itten,et al.  Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction , 2006 .

[7]  T. O. Kvålseth.  Cautionary Note about R 2 , 1985 .

[8]  S. Solberg.  Comparing discrete echoes counts and intensity sums from ALS for estimating forest LAI and gap fraction , 2008 .

[9]  Lars Eklundh,et al.  Mapping insect defoliation in Scots pine with MODIS time-series data , 2009 .

[10]  P. Stenberg,et al.  A method to account for shoot scale clumping in coniferous canopy reflectance models , 2003 .

[11]  Svein Solberg,et al.  Assessment of defoliation during a pine sawfly outbreak: Calibration of airborne laser scanning data with hemispherical photography , 2007 .

[12]  N. Coops,et al.  Using airborne and ground-based ranging lidar to measure canopy structure in Australian forests , 2003 .

[13]  Tiit Nilson,et al.  Estimating canopy cover in Scots pine stands , 2005 .

[14]  Erik Næsset,et al.  Mapping defoliation during a severe insect attack on Scots pine using airborne laser scanning , 2006 .

[15]  W. Cohen,et al.  Lidar Remote Sensing of the Canopy Structure and Biophysical Properties of Douglas-Fir Western Hemlock Forests , 1999 .

[16]  S. T. Gower,et al.  Leaf area index of boreal forests: theory, techniques, and measurements , 1997 .

[17]  A. Taylor,et al.  Widespread Increase of Tree Mortality Rates in the Western United States , 2009, Science.

[18]  E. Næsset,et al.  Single Tree Segmentation Using Airborne Laser Scanner Data in a Structurally Heterogeneous Spruce Forest , 2006 .

[19]  C. Hopkinson,et al.  Testing LiDAR models of fractional cover across multiple forest ecozones , 2009 .

[20]  P. Stenberg,et al.  Performance of the LAI-2000 plant canopy analyzer in estimating leaf area index of some Scots pine stands. , 1994, Tree physiology.

[21]  W. Kurz,et al.  Mountain pine beetle and forest carbon feedback to climate change , 2008, Nature.

[22]  M. Rombach,et al.  Description and applications of the multipolarized dual band OrbiSAR-1 InSAR sensor , 2003, 2003 Proceedings of the International Conference on Radar (IEEE Cat. No.03EX695).