Multi-sensor airborne and satellite data for upscaling tree number information in a structurally complex eucalypt forest

Abstract

Detailed information on the number and density of trees is important for conservation and sustainable use of forest resources. In this respect, remote sensing technology is a reliable tool for deriving timely and fine-scale information on forest inventory attributes. However, to better predict and understand the functioning of the forest, fine-scale measures of tree number and density must be extrapolated to the forest plot or stand levels through upscaling. In this study, we compared and combined three sources of remotely sensed data, including low point density airborne laser scans (ALS), synthetic aperture radar (SAR) and very-high resolution WorldView-2 imagery to upscale the total number of trees to the plot level in a structurally complex eucalypt forest using random forest regression. We used information on number of trees previously derived from high point density ALS as training data for a random forest regressor and field inventory data for validation. Overall, our modelled estimates resulted in significant fits (p <  0.05) with goodness-of-fit (R2) of 0.61, but systematically underestimated tree numbers. The ALS predictor variables (e.g. canopy cover and height) were the best for estimating tree numbers (R2 = 0.48, nRMSE = 61%), as compared to WorldView-2 and SAR predictor variables (R2 < 0.35). Overall, the combined use of WorldView-2, ALS and SAR predictors for estimating tree numbers showed substantial improvement in R2 of up to 0.13 as compared to their individual use. Our findings demonstrate the potential of using low point density ALS, SAR and WorldView-2 imagery to upscale high point density ALS derived tree numbers at the plot level in a structurally complex eucalypt forest.

Yuri Shendryk

PhD Student

Yuri currently works for Dendra Systems which uses data processing to monitor and restore ecosystems. You can read more about this work here.

Mark Broich

Postdoctoral Research Fellow

Played a central research role on the Geospatial Analysis for Environmental Change team and had a key role in building the GAEC lab.

Mirela G. Tulbure

Professor

I am an Associate Professor with the Center for Geospatial Analytics at North Carolina State University (NCSU).