Forest attributes estimation using aerial laser scanner and TM data

Aim of study: The aim of this study was performance of four non-parametric algorithms including the k-NN, SVR, RF and ANN to estimate forest volume and basal area attributes using combination of Aerial Laser Scanner and Landsat-TM data.Area of study: Data in small part of a mixed managed forest in the Waldkirch region, Germany.Material and methods: The volume/ha and basal area/ha in the 411 circular plots were estimated based on DBH and height of trees using volume functions of study area. The low density ALS raw data as first and last pulses were prepared and automatically classified into vegetation and ground returns to generate two fine resolution digital terrain and surface models after noise removing. Plot-based height and density metrics were extracted from ALS data and used both separated and combined with orthorectified and processed TM bands. The algorithms implemented with different options including k-NN with different distance measures, SVR with the best regularized parameters for four kernel types, RF with regularized decision tree parameters and ANN with different types of networks. The algorithm performances were validated using computing absolute and percentage RMSe and bias on unused test samples.Main results: Results showed that among four methods, SVR using the RBF kernel could better estimate volume/ha with lower RMSe and bias (156.02 m3 ha–1 and 0.48, respectively) compared to others. In basal area/ha, k-NN could generate results with similar RMSe (11.79 m3 ha–1) but unbiased (0.03) compared to SVR with RMSe of 11.55 m3 ha–1 but slightly biased (–1.04).Research highlights: Results exposed that combining Lidar with TM data could improve estimations compared to using only Lidar or TM data.Key words: forest attributes estimation; ALS; TM; non-parametric algorithms.</p

INVISTIGATION ON CANOPY HEIGHT AND DENSITY DIFFERENTIATIONS IN THE MANAGED AND UNMANAGED FOREST STANDS USING LIDAR DATA (CASE STUDY: SHASTKALATEH FOREST, GORGAN)

Forest management plans are interesting to keep the forest stand natural composite and structure after silvicultural and management treatments. In order to investigate on stand differences made by management treatments, comparing of these stands with unmanaged stands as natural forests is necessary. Aerial laser scanners are providing suitable 3D information to map the horizontal and vertical characteristics of forest structures. In this study, different of canopy height and canopy cover variances between managed and unmanaged forest stands as well as in two dominant forest types were investigated using Lidar data in Dr. Bahramnia forest, Northern Iran. The in-situ information was gathered from 308 circular plots by a random systematic sampling designs. The low lidar cloud point data were used to generate accurate DEM and DSM models and plot-based height statistics metrics and canopy cover characteristics. The significant analyses were done by independent T-test between two stands in same dominant forest types. Results showed that there are no significant differences between canopy cover mean in two stands as well as forest types. Result of statistically analysis on height characteristics showed that there are a decreasing the forest height and its variance in the managed forest compared to unmanaged stands. In addition, there is a significant difference between maximum, range, and mean heights of two stands in 99 percent confidence level. However, there is no significant difference between standard deviation and canopy height variance of managed and unmanged stands. These results showd that accomplished management treatments and cuttings could lead to reducing of height variances and converting multi-layers stands to two or single layers. Results are also showed that the canopy cover densities in the managed forest stands are changing from high dense cover to dense cover