Stand Density Extraction and Analysis of Plantations Based on QuickBird and Worldview-2 Images

Stand density is one of the important forest structure parameters. QuickBird and Worldview-2 high spatial resolution remote sensing images were compared. The Jiangle state-owned forest farm in Fujian Province was the experimental area. A spectral local maximum filtering method was used to extract the number of individual trees in a mountain forest. Nonlinear quadratic polynomial regression models were established for the number of local maximum points and the actual stand density. The near-infrared II waveband of Worldview-2 imagery was used to extract the pure Chinese fir stands with an accuracy of 72.5%, R-2 = 0.502, and RMSE = 35.77 and the Masson pine stands with an accuracy of 78.35%, R-2 = 0.754, RMSE = 41.46, while the accuracy of all unclassified stands was only 0.2907. The results showed that the classification of tree species can improve the accuracy of modeling. The quadratic polynomial model using Worldview-2 image data achieved better results. Stand density in the Jiangle state-owned forest farm was extracted using the NIR II band after fusion of Worldview-2. A stand density planning map was constructed using the best models applied to the different forest types. The method of combining high-spatial resolution imagery and sampling plots to estimate stand density was proven to be feasible. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.