Abstract
Lidar point clouds have been frequently used in forest inventories. The higher point density has provided better representation of trees in forest plantations. So we developed a new approach to fill this gap in the integrated crop-livestock-forest system, the sampling forest inventory, which uses the principles of individual tree detection applied under different plot arrangements. We use a UAV-lidar system (GatorEye) to scan an integrated crop-livestock-forest system with Eucalyptus benthamii seed forest plantations. On the high density UAV-lidar point cloud (>1400 pts. m2), we perform a comparison of two forest inventory approaches: Sampling Forest Inventory (SFI) with circular (1380 m2 and 2300 m2) and linear (15 trees and 25 trees) plots and Individual Tree Detection (ITD). The parametric population values came from the approach with measurements taken in the field, called forest inventory (FI). Basal area and volume estimates were performed considering the field heights and the heights measured in the LiDAR point clouds. We performed a comparison of the variables number of trees, basal area, and volume per hectare. The variables by scenarios were submitted to analysis of variance to verify if the averages are considered different or equivalent. The RMSE (%) were calculated to explain the deviation between the measured volume (filed) and estimated volume (LiDAR) values of these variables. Additionally, we calculated rRMSE, Standard error, AIC, R2, Bias, and residual charts. The basal area values ranged from 7.40 m2 ha−1 (C1380) to 8.14 m2 ha−1 281 (C2300), about −5.9% less than the real value (8.65 m2 ha−1). The C2300 scenario was the only one whose confidence interval (CI) limits included the basal area real. For the total stand volume, the ITD scenario was the one that presented the closer values (689.29 m3) to the real total value (683.88 m3) with the real value positioned in the CI. Our findings indicate that for the stand conditions under study, the SFI approach (C2300) that considers an area of 2300 m2 is adequate to generate estimates at the same level as the ITD approach. Thus, our study should be able to assist in the selection of an optimal plot size to generate estimates with minimized errors and gain in processing time.
Highlights
Forest plantation area is constantly increasing across the globe, and the goods and services provided by these forests are becoming tremendously diverse [1]
We explore the following questions: can we improve/alter the traditional approach of forest inventory with sampling techniques in plots to techniques that use more remote sensing technology, in this case considering point clouds of Light Detection and Ranging (LiDAR) data to measure individual tree variables as samples that would be multiplied by the number of trees obtained by parametric counting in point clouds? Could this change in approach provide better accuracy in the estimates generated? To answer these questions, several variables such as the number of trees, basal area, and stand volume were evaluated
All scenarios performed statistically similar for individual tree volume IF, Individual Tree Detection (ITD), C2300, L25, C1380 and linear plots contained 15 (L15)
Summary
Forest plantation area is constantly increasing across the globe, and the goods and services provided by these forests are becoming tremendously diverse [1]. In this context, Brazilian forest plantations stand out with a prominent position, in which more than 10 million ha supply industries of pulp and paper, fuelwood, and solid wood products [2,3]. Fast and reliable information regarding stand structure and wood availability is crucial to ensure industrial demand [4]. Eucalyptus trees established in crop-live-stock-forest systems are usually planted in rows, presenting lower stand density and diverse canopy structures. In [10], the authors highlighted the main technologies for remote sensing-assisted forest inventories, and Light Detection and Ranging (LiDAR) was listed as a major player
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