Krzysztof Stereńczak

Comparison of various algorithms for DTM interpolation from LIDAR data in dense mountain forests

Abstract The main aim of the work presented here has been to evaluate which combination of filtering and interpolation algorithms can offer the best DTM accuracy in conditions involving very dense forest in mountainous areas. The study area was in the Sudety Mountains of southwestern Poland, close to the Czech border. For each filtration, almost 100 DTMs were generated, with final analysis confined to just 6 most-accurate models. The results show that slope and particularly undergrowth vegetation are the most important factors influencing DTM accuracy in dense mountain forests. However, all methods of interpolation are capable of reaching very similar levels of error after proper calibration.

What do we expect from forests? The European view of public demands

The paper constitutes an overview of the hitherto prevailing knowledge of the factors which influence the attractiveness of forests. What is more, it shows, in a cross-sectoral manner, the study methods and general preferences of people in the context of recreational use of forests. 109 papers published in the years 2000-2016 have been analyzed. In the work, five main issues were discussed, which constitute the study subject i.e.: a) the preferred forest type and function; b) expenses incurred by people to reach a forest (time and distance); c) the societys demand for technical infrastructure and forest management; d) factors disturbing the recreation in forest areas; e) reasons and frequency of visits to forests for recreation purposes. The results indicate that the following have an impact on the perception of forests: tree stand factors (age, species composition, etc.), social factors (age, material status, interests, etc.), and factors related to human activity (the extent of forest operations, noise, littering, etc.). Based on the literature, it is possible to indicate a model forest, which in view of respondents, is described as the one that is preferred for recreation purposes. The model differs depending on the analyzed part of Europe.

Parameter optimization of image classification techniques to delineate crowns of coppice trees on UltraCam-D aerial imagery in woodlands

Abstract Estimating the optimal parameters of some classification techniques becomes their negative aspect as it affects their performance for a given dataset and reduces classification accuracy. It was aimed to optimize the combination of effective parameters of support vector machine (SVM), artificial neural network (ANN), and object-based image analysis (OBIA) classification techniques by the Taguchi method. The optimized techniques were applied to delineate crowns of Persian oak coppice trees on UltraCam-D very high spatial resolution aerial imagery in Zagros semiarid woodlands, Iran. The imagery was classified and the maps were assessed by receiver operating characteristic curve and other performance metrics. The results showed that Taguchi is a robust approach to optimize the combination of effective parameters in these image classification techniques. The area under curve (AUC) showed that the optimized OBIA could well discriminate tree crowns on the imagery ( AUC = 0.897 ), while SVM and ANN yielded slightly less AUC performances of 0.819 and 0.850, respectively. The indices of accuracy (0.999) and precision (0.999) and performance metrics of specificity (0.999) and sensitivity (0.999) in the optimized OBIA were higher than with other techniques. The optimization of effective parameters of image classification techniques by the Taguchi method, thus, provided encouraging results to discriminate the crowns of Persian oak coppice trees on UltraCam-D aerial imagery in Zagros semiarid woodlands.

Testing and evaluating different LiDAR-derived canopy height model generation methods for tree height estimation

Abstract The LiDAR-derived Canopy height model (CHM), due to its wide applications in forestry, is important for foresters. Before a CHM can be considered a valuable, objective source of information about a canopy surface, it must be properly interpolated and preprocessed, which may sometimes be challenging, especially in case of multilayer and multispecies forest. This study tested and evaluated the impact of CHM interpolation methods on the accuracy of estimating tree height, which is one of the most important trees and stands feature. Tree heights calculated from 5 CHMs (1. raw CHM; 2. pit-free CHM; 3. spike-free CHM; 4. Smoothed CHM (with a median filter and with a Gaussian filter) were compared to heights measurements in the field. It was also tested whether applying linear regression can improve the accuracy of tree height estimations based on LiDAR-derived CHMs. The obtained results indicate that the method of generating CHMs influences the accuracy of tree height estimations. The mean differences between the means of field heights and LiDAR-derived heights (for each CHM separately and the 99th percentile) were statistically significant. The most accurate results were obtained with the spike-free CHM (RMSE calculated for all trees was 1.42 m (5.80%)). The smallest errors were observed for conifers–the RMSEs obtained for the spike-free CHM were 1.07 m (3.75%) and 1.18 m (4.57%) for spruce and pine, respectively. The use of linear regression improved the accuracy of tree height estimations from LiDAR data (especially for the CHMs filtered with Gaussian and median filters).

Delineation of homogeneous forest patches using combination of field measurements and LiDAR point clouds as a reliable reference for evaluation of low resolution global satellite data

BackgroundsThere are many satellite systems acquiring environmental data on the world. Acquired global remote sensing datasets require ground reference data in order to calibrate them and assess their quality. Regarding calibration and validation of these datasets with broad geographical extents, it is essential to register zones which might be considered as Homogeneous Patches (HPs). Such patches enable an optimal calibration of satellite data/sensors, and what is more important is an analysis of components which significantly influence electro-magnetic signals registered by satellite sensors.MethodsWe proposed two structurally different methods to identify HPs: predefined thresholding-based one (static one), and statistical thresholding-based technique (dynamic one). In the first method, 3 different thresholds were used: 5%, 10%, and 20%. Next, it was aimed to assess how delineated HPs were spatially matched to satellite data with coarse spatial resolution. Selected cell sizes were 25, 50, 100, 250, and 500 m. The number of particular grid cells which almost entirely fell into registered HPs was counted (leaving 2% cell area tolerance level). This procedure was executed separately for each variant and selected structural variables, as well as for their intersection parts.ResultsThe results of this investigation revealed that ALS data might have the potential in the identification of HPs of forest stands. We showed that different ALS based variables and thresholds of HPs definition influenced areas which can be treated as similar and homogeneous. We proved that integration of more than one structural variable limits size of the HPs, in contrast, visual interpretation revealed that inside such patches vegetation structure is more constant.ConclusionsWe concluded that ALS data can be used as a potential source of data to “enlarge” small ground sample plots and to be used for evaluation and calibration of remotely sensed datasets provided by global systems with coarse spatial resolutions.

The Role of Topography in the Distribution and Intensity of Damage Caused by Deer in Polish Mountain Forests.

The increase in the deer population observed in recent decades has strongly impacted forest regeneration and the forest itself. The reduction in the quality of raw wood material, as a consequence of deer-mediated damage, constitutes a significant burden on forest owners. The basis for the commencement of preventive actions in this setting is the understanding of the populations and behaviors of deer in their natural environment. Although multiple studies have been carried out regarding this subject, only a few suggested topography as an important factor that may influence the distribution and intensity of deer-mediated damage. The detailed terrain models based on LiDAR data as well as the data on damage caused by deer from the State Forests database enabled thorough analyses of the distribution and intensity of damage in relation to land form in this study. These analyses were performed on three mountain regions in Poland: the Western Sudety Mountains, the Eastern Sudety Mountains, and the Beskidy Mountains. Even though these three regions are located several dozen to several hundred kilometers apart from each other, not all evaluated factors appeared common among them, and therefore, these regions have been analyzed separately. The obtained results indicated that the forest damage caused by deer increased with increasing altitude above 1000 m ASL. However, much larger areas of damage by deer were observed at elevations ranging from 401 to 1000 m ASL than at elevations below 400 m ASL. Moreover, the locations of damage (forest thickets and old stands) indicated that red deer is the species that exerts the strongest pressure on forest ecosystems. Our results show the importance of deer foraging behavior to the structure of the environment.

Factors influencing the accuracy of ground-based tree-height measurements for major European tree species

Tree height is one of the most important forest characteristics and is one of the crucial measurements taken for either practical or scientific reasons. However, the accuracy of a tree-height measurement may vary in relation to many factors. The work described here thus sought to evaluate the accuracy of ground-based tree-height measurements for major forest-forming tree species of the temperate and boreal zones. The focus was on the importance of factors affecting accuracy of the measurements in question at larger geographical scales. In line with the above research goals, data were gathered from 299 stands throughout Poland and heights of 2388 sample trees of eight species, growing in different stands and site conditions, were measured; heights were then compared with measured lengths of felled trees as a reference. In total, 10 variables to determine factors that may influence ground-based tree-height measurement accuracy were used. We merged them into 4 groups: measurements, topography, stand and biometric-related factors. Results showed that biometric and topographic factors had the greatest relative influence on the accuracy of measurements of tree height. Tree length and species, followed by the slope of the terrain, tree age, and height above sea level were the most important factors found to affect accuracy. In most of the cases studied the terrestrial tree-height measurements were underestimated when set against definitive measurements of length. This was true for all species studied except oak, for which height measurements were typically overestimated. Notwithstanding the broad geographical scope of the work, the particular device used and the team factor were only found to have a marginal influence on measurement accuracy.

Development of a robust canopy height model derived from ALS point clouds for predicting individual crown attributes at the species level

ABSTRACT Tree crown attributes are important parameters during the assessment and monitoring of forest ecosystems. Canopy height models (CHMs) derived from airborne laser scanning (ALS) data have proved to be a reliable source for extracting different biophysical characteristics of single trees and at stand level. However, ALS-derived tree measurements (e.g., mean crown diameter) can be negatively affected by pits that appear in the CHMs. Thus, we propose a novel method for generating pit-free CHMs from ALS point clouds for estimating crown attributes (i.e., area and mean diameter) at the species level. The method automatically calculates a threshold for a pixel based on the range of height values within neighbouring pixels; if the pixel falls below the threshold then it is recognized as a pitted pixel. The pit is then filled with the median of the values of the neighbouring pixels. Manually delineated individual tree crowns (ITC) of four deciduous and two coniferous species on Colour Infrared (CIR) stereo images were used as a reference in the analysis. In addition, a variety of different algorithms for constructing CHMs were compared to investigate the performance of different CHMs in similar forest conditions. Comparisons between the estimated and observed crown area (R2 = 0.95, RMSE% = 19.12% for all individuals) and mean diameter (R2 = 0.92, RMSE% = 12.16% for all individuals) revealed that ITC attributes were correctly estimated by segmentation of the pit-free CHM proposed in this study. The goodness of matching and geometry revealed that the delineated crowns correctly matched up to the reference data and had identical geometry in approximately 70% of cases. The results showed that the proposed method produced a CHM that estimates crown attributes more accurately than the other investigated CHMs. Furthermore, the findings suggest that the proposed algorithm used to fill pits with the median of height observed in surrounding pixels significantly improve the accuracy of the results the species level due to a higher correlation between the estimated and observed crown attributes. Based on these results, we concluded that the proposed pit filling method is capable of providing an automatic and objective solution for constructing pit-free CHMs for assessing individual crown attributes of mixed forest stands.

“Laser Discoverers” – Web‐based User‐generated Content in Heritage Detection in Poland

This article presents the results and potential of using volunteered geographic information (VGI) in heritage detection. Research was completed under the project entitled “Laser Discoverers – non-invasive examination and documentation of archeological and historical objects in the Swie R tokrzyskie Voivodeship”, carried out as a part of the Ministry of Science and Higher Education program entitled “The Paths of Copernicus”. Within the project, strong emphasis was placed on promotional and awareness-raising activities, to involve as many voluntary users as possible. Project participants had at their disposal a web application, which provided access to a digital terrain model (DTM) where they identified possible heritage objects. All samples of data were additionally available in eight variants of sunshine, based on the simulation of sunlight from eight directions and at a constant angle. In total, 5,989 elementary areas with dimensions of 100 3 100 m were used for the project. After conducting a field inventory, Internet users together with specialists were able to recognize several thousands of potential archaeological and historic objects. During the project, approximately 10% of those features were verified through non-invasive (field survey) work, with 75% success.