Airborne laser scanning (ALS) technology allows accurate information about the forest environment to be obtained. The high precision of ALS allows the detection of individual trees. An individual tree is composed of many elements and requires relatively complex algorithms. Factors that determine and affect the accuracy of calculating the number of trees can be split into the following groups: biological factors, technical factors related to the flight and data acquisition parameters, technical factors related to data processing, problems of results verification. The article synthesises the main problems arising during the development of methods for detection of individual trees and acquisition of their characteristics in a managed forest in Central Europe.
This paper describes a method of determining the stocking density and volume of forest stands based on airborne laser-scanning data. The aim of this study was to determine the relationship between ground-based measurements of standing volume and tree-density, and those acquired based on the Crown Height Model (CHM) interpolated from airborne laser scanning data. Data were collected from 34 sample plots of two sizes for the CHM analysis: 500 m2 (radius 12.61 m) and 1963.5 m2 (radius of 25.0 m): Trees for sampling were selected using two methods, those whose “centroid” was fully within the sample plot (the tree was considered to be within the sample plots if the centroid of the crown was inside the circle) and those at the “border” (the tree was included in the sample plot if, at least, one part of the contour of the crown was inside the circle). There was a strong relationship (R2 = 0.86) between standing volume measured in sample plots on the ground and the indices produced by the crown elevation model at the locations where the ground-based measurements were performed.
Two change detection techniques (NDVI differencing and post-classification analysis) were compared, in order to detect canopy cover changes in forests on the area of twelve forest districts in the Sudety and West Beskidy Mountains in Poland, using 2012 and 2013 Black-Bridge satellite images. Although the classification accuracy of the respective images was high (about 95%), the accuracy of the difference in bi-temporal images was much worse because of the short time between the dates of images and the imperfection of the algorithm calculating the unclear boundary between the forest and no-forest areas. NDVI differencing method and thresholding brought much better overall results, although roads, clouds and fogs caused much problem performing pseudo-changes.
The GNSS (Global Navigation Satellite System) receivers are commonly used in forest management in order to determine objects coordinates, area or length assessment and many other tasks which need accurate positioning. Unfortunately, the forest structure strongly limits access to satellite signals, which makes the positioning accuracy much weak comparing to the open areas. The main reason for this issue is the multipath phenomenon of satellite signal. It causes radio waves reflections from surrounding obstacles so the signal do not reach directly to the GNSS receiver’s antenna. Around 50% of error in GNSS positioning in the forest is because of multipath effect. In this research study, an attempt was made to quantify the forest stand features that may influence the multipath variability. The ground truth data was collected in six Forest Districts located in different part of Poland. The total amount of data was processed for over 2,700 study inventory plots with performed GNSS measurements. On every plot over 25 forest metrics were calculated and over 25 minutes of raw GNSS observations (1500 epochs) were captured. The main goal of this study was to find the way of multipath quantification and search the relationship between multipath variability and forest structure. It was reported that forest stand merchantable volume is the most important factor which influence the multipath phenomenon. Even though the similar geodetic class GNSS receivers were used it was observed significant difference of multipath values in similar conditions.
The main goal of this research is to shed further light on the sensitivity of the vegetation indices to spatial changes of stand parameters. The analysis was done within mountain forests in the Sudetes and the Beskids in southern Poland. Some 1327 stands were analysed with more than 70 percent of spruce contribution in the species composition. The response of selected vegetation indices was verified in relation to the alterations of spruce participation, stand height, volume, stand density and diameter. The following indices were analysed: Normalized Difference Vegetation Index, Normalized Difference Red Edge Index, Green Normalized Difference Vegetation Index and Wide Dynamic Range Vegetation Index. Indices were calculated based on the Rapid Eye (Black Bridge) images. All the analysed stand characteristics influence the values of vegetation indices. In general: mean height, diameter at breast height, volume and spruce participation are the most negatively correlated with the indices. Density is a variable that, in general, cannot directly be used for indices correction, because it is hard to find any stable trend. NDRE is the most stable index for the analysis of stand characteristics.