Estimation of forest stand parameters using airborne LIDAR data

Öz

This study was carried out to examine the potential of LIDAR (Light Detection and Ranging) data in estimating some forest stand parameters. The stand parameters including Basal Area (BA), Number of Trees (N), Reineke's Density Index (RDI), and Mean Diameter at Breast Height (MDBH) were determined by field measurement at 30 sampling plots from the stands with different characteristics. The LIDAR metrics including the height percentiles (50th, 90th, 95th, and 99th) and the ratio of non-ground points (NGP) were calculated based on the LIDAR points that correspond to the sampling plots. Correlation analysis was conducted to investigate the relationships between the LIDAR metrics and stand parameters. There were no associations between the LIDAR metrics and the stand parameters, BA and RDI. On the other hand, statistically significant (p<0.01) correlations were determined between the LIDAR metrics and the stand parameters, N and MDBH (highest correlation coefficients (r) are 0.70 and 0.72, respectively). The same analyses were also carried out for the sampling plots (19 plots) taken from the pure conifer or conifer-dominated stands. A clear increase was observed in the correlation coefficients of the relations between the LIDAR metrics and the stand parameters. The highest correlation coefficients calculated for the N and MDBH were 0.82 and 0.84, respectively. In addition, statistically significant but weak relationships were found between the NGP and the stand parameters, BA and RDI when the 19 conifer plots were used (r is 0.46 and 0.58, respectively). Therefore, it may be concluded that if stands parameters are estimated with LIDAR data in complex forest ecosystems, the forest stands should be pre-stratified by definite criteria. The regression models developed by means of stepwise procedure explained 0.82% and 0.70% of the variation in N and MDBH, respectively. As a result, the N and MDBH can be predicted at plot level in conifer-dominated forest stands using airborne laser scanning data.

Anahtar Kelimeler

• Airborne laser scanning, Forest stand parameters, Forest stand structure, Forest inventory

Havasal LIDAR verileri kullanılarak meşcere parametrelerinin tahmin edilmesi

Öz

This study was carried out to examine the potential of LIDAR (Light Detection and Ranging) data in estimating some forest stand parameters. The stand parameters including Basal Area (BA), Number of Trees (N), Reineke's Density Index (RDI), and Mean Diameter at Breast Height (MDBH) were determined by field measurement at 30 sampling plots from the stands with different characteristics. The LIDAR metrics including the height percentiles (50th, 90th, 95th, and 99th) and the ratio of non-ground points (NGP) were calculated based on the LIDAR points that correspond to the sampling plots. Correlation analysis was conducted to investigate the relationships between the LIDAR metrics and stand parameters. There were no associations between the LIDAR metrics and the stand parameters, BA and RDI. On the other hand, statistically significant (p<0.01) correlations were determined between the LIDAR metrics and the stand parameters, N and MDBH (highest correlation coefficients (r) are 0.70 and 0.72, respectively). The same analyses were also carried out for the sampling plots (19 plots) taken from the pure conifer or conifer-dominated stands. A clear increase was observed in the correlation coefficients of the relations between the LIDAR metrics and the stand parameters. The highest correlation coefficients calculated for the N and MDBH were 0.82 and 0.84, respectively. In addition, statistically significant but weak relationships were found between the NGP and the stand parameters, BA and RDI when the 19 conifer plots were used (r is 0.46 and 0.58, respectively). Therefore, it may be concluded that if stands parameters are estimated with LIDAR data in complex forest ecosystems, the forest stands should be pre-stratified by definite criteria. The regression models developed by means of stepwise procedure explained 0.82% and 0.70% of the variation in N and MDBH, respectively. As a result, the N and MDBH can be predicted at plot level in conifer-dominated forest stands using airborne laser scanning data. Keywords: Airborne laser scanning, Forest stand parameters, Forest stand structure, Forest inventory

Anahtar Kelimeler

• Havadan lazer tarama, Meşcere parametreleri, Meşcere yapısı, Orman envanteri

Kaynakça

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