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Balenović Ivan, PhD.

Accuracy Assessment of Digital Terrain Models of Lowland Pedunculate Oak Forests Derived from Airborne Laser Scanning and Photogrammetry

volume: 39, issue: 1

Digital terrain models (DTMs) present important data source for different applications in
environmental disciplines including forestry. At regional level, DTMs are commonly created
using airborne digital photogrammetry or airborne laser scanning (ALS) technology. This
study aims to evaluate the vertical accuracy of DTMs of different spatial resolutions derived
from high-density ALS data and existing photogrammetric (PHM) data in the dense lowland
even-aged pedunculate oak forests located in the Pokupsko basin in Central Croatia. As expected,
the assessment of DTMs’ vertical accuracy using 22 ground checkpoints shows higher
accuracy for ALS-derived than for PHM-derived DTMs. Concerning the different resolutions
of ALS-derived (0.5 m, 1 m, 2 m, 5 m) and PHM-derived DTMs (0.5 m, 1 m, 2 m, 5 m,
8 m) compared in this research, the ALS-derived DTM with the finest resolution of 0.5 m
shows the highest accuracy. The root mean square error (RMSE) and mean error (ME) values
for ALS-derived DTMs range from 0.14 m to 0.15 m and from 0.09 to 0.12 m, respectively,
and the values decrease with decreasing spatial resolution. For the PHM-derived DTMs, the
RMSE and ME values are almost identical regardless of resolution and they are 0.35 m and
0.17 m, respectively. The findings suggest that the 8 m spatial resolution is optimal for a
given photogrammetric data, and no finer than 8 m spatial resolution is required. This research
also reveals that the national digital photogrammetric data in the study area contain certain
errors (outliers) specific to the terrain type, which could considerably affect the DTM accuracy.
Thus, preliminary evaluation of photogrammetric data should be done to eliminate possible
outliers prior to the DTM generation in lowland forests with flat terrain. In the absence
of ALS data, the finding in this research could be of interests to countries, which still rely on
similar photogrammetric data for DTM generation.

Testing the Applicability of the Official Croatian DTM for Normalization of UAV-based DSMs and Plot-level Tree Height Estimations in Lowland Forests

volume: 40, issue: 1

The Airborne Laser Scanning (ALS) technology has been implemented in operational forest
inventories in a number of countries. At the same time, as a cost-effective alternative to ALS,
Digital Aerial Photogrammetry (PHM), based on aerial images, has been widely used for the
past 10 years. Recently, PHM based on Unmanned Aerial Vehicle (UAV) has attracted great
attention as well. Compared to ALS, PHM is unable to penetrate the forest canopy and, ultimately,
to derive an accurate Digital Terrain Model (DTM), which is necessary to normalize
point clouds or Digital Surface Models (DSMs). Many countries worldwide, including Croatia,
still rely on PHM, as they do not have complete DTM coverage by ALS (DTMALS). The
aim of this study is to investigate if the official Croatian DTM generated from PHM (DTMPHM)
can be used for data normalization of UAV-based Digital Surface Model (DSMUAV) and estimating
plot-level mean tree height (HL) in lowland pedunculate oak forests. For that purpose,
HL estimated from DSMUAV normalized with DTMPHM and with DTMALS were generated and
compared as well as validated against field measurements. Additionally, elevation errors in
DTMPHM were detected and eliminated, and the improvement by using corrected DTMPHM
(DTMPHMc) was evaluated. Small, almost negligible variations in the results of the leave-oneout
cross-validation were observed between HL estimated using proposed methods. Compared
to field data, the relative root mean square error (RMSE%) values of HL estimated from DSMUAV
normalized with DTMALS, DTMPHM, and DTMPHMc were 5.10%, 5.14%, and 5.16%, respectively.
The results revealed that in the absence of DTMALS, the existing official Croatian DTM
could be readily used in remote sensing based forest inventory of lowland forest areas. It can
be noted that DTMPHMc did not improve the accuracy of HL estimates because the gross errors
mainly occurred outside of the study plots. However, since the existence of the gross errors in
Croatian DTMPHM has been confirmed by several studies, it is recommended to detect and
eliminate them prior to using the DTMPHM in forest inventory.

Hand-Held Personal Laser Scanning – Current Status and Perspectives for Forest Inventory Application

volume: 42, issue:

The emergence of hand-held Personal Laser Scanning (H-PLS) systems in recent years resulted in initial research on the possibility of its application in forest inventory, primarily for the estimation of the main tree attributes (e.g. tree detection, stem position, DBH, tree height, etc.). Research knowledge acquired so far can help to direct further research and eventually include H-PLS into operational forest inventory in the future. The main aims of this review are:

Þ   to present the current state of the art for H-PLS systems

Þ   briefly describe the fundamental concept and methods for H-PLS application in forest inventory

Þ   provide an overview of the results of previous studies

Þ   emphasize pros and cons for H-PLS application in forest inventory in relation to conventional field measurements and other similar laser scanning systems

Þ   highlight the main issues that should be covered by further H-PLS-based forest inventory studies.

Application of UAS for Monitoring of Forest Ecosystems – A Review of Experience and Knowledge

volume: 43, issue:

In the last couple of years, there have been a great number of articles that cover and emphasize the advantages and possibilities that UAS (Unmanned Air System) offers in forest ecosystem research. In the available research, alongside UAS, the importance of developing sensors that are designed to be used with UAV (Unamnned Air Vehicle), a flight programming software and UAS collected data processing software have been pointed out. With the widespread use of high-precision sensors and accompanying software in forestry, it is possible to obtain accurate data in a short time that replaces long-term manpower in the field with equal or in some cases, such as windthrow calculation or wildlife counting, greater accuracy. The former practice of manual imagery processing is being partly replaced with automated approaches. The paper analyses studies that deal with some form of application of UAS in forestry, e.g. forest inventory, forest operations, ecological monitoring, forest pests and forest fires, and wildlife monitoring. In the forest inventory, a large number of studies deal with the possibilities of applying UAS in mapping vegetation and individual trees, morphological research of individual parts of trees, surface analysis, etc. The use of remote and proximal sensing technologies in forest engineering has mainly been focused on defining surface roughness and topology, road geometry, planning and maintenance, ground-based and cable-based harvesting and soil characteristics and displacement. Wildfire monitoring already relies heavily on the use of UAS and thermal cameras in operations, and it is similar to the mapping of windthrow or directions of the spread of certain insects important for forestry. In wildlife research, numerous studies deal with abundance research of individual terrestrial birds and mammals using UAS thermal imagery. With some drawbacks such as wildlife disturbance or limited UAV range, common to most of the processed studies are positive attitudes regarding the application of UAS in forestry sensing and monitoring, which is slowly becoming a common operative practice, with the scientists’ focus being on developing automated approaches in UAS imagery processing. Reducing the error by improving the technological characteristics of the sensors will in the long run reduce the number of people required to collect data important for forestry, reduce risks and in some cases increase accuracy.

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Web of Science Impact factor (2021): 2.542
Five-years impact factor: 2.443

Quartile: Q2 - Forestry

Subject area

Agricultural and Biological Sciences

Category/Quartile

Forestry/Q1