Estimating Forest Inventory Information for the Talladega National Forest Using Airborne Laser Scanning Systems

被引:0
作者
Lee, Taeyoon [1 ]
Vatandaslar, Can [1 ,2 ]
Merry, Krista [1 ]
Bettinger, Pete [1 ]
Peduzzi, Alicia [1 ]
Stober, Jonathan [3 ]
机构
[1] Univ Georgia, Warnell Sch Forestry & Nat Resources, Athens, GA 30602 USA
[2] Artvin Coruh Univ, Fac Forestry, TR-08000 Artvin, Turkiye
[3] Talladega Natl Forest, US Forest Serv, Heflin, AL 36264 USA
关键词
aerial imagery; airborne laser scanning; ALASSO; forest inventory; LiDAR; mixed pine-hardwood forest; ATTRIBUTES; FIELD;
D O I
10.3390/rs16162933
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Accurately assessing forest structure and maintaining up-to-date information about forest structure is crucial for various forest planning efforts, including the development of reliable forest plans and assessments of the sustainable management of natural resources. Field measurements traditionally applied to acquire forest inventory information (e.g., basal area, tree volume, and aboveground biomass) are labor intensive and time consuming. To address this limitation, remote sensing technology has been widely applied in modeling efforts to help estimate forest inventory information. Among various remotely sensed data, LiDAR can potentially help describe forest structure. This study was conducted to estimate and map forest inventory information across the Shoal Creek and Talladega Ranger Districts of the Talladega National Forest by employing ALS-derived data and aerial photography. The quality of the predictive models was evaluated to determine whether additional remotely sensed data can help improve forest structure estimates. Additionally, the quality of general predictive models was compared to that of species group models. This study confirms that quality level 2 LiDAR data were sufficient for developing adequate predictive models (R2adj. ranging between 0.71 and 0.82), when compared to the predictive models based on LiDAR and aerial imagery. Additionally, this study suggests that species group predictive models were of higher quality than general predictive models. Lastly, landscape level maps were created from the predictive models and these may be helpful to planners, forest managers, and landowners in their management efforts.
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页数:20
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