Benchmark for Automatic Clear-Cut Morphology Detection Methods
Derived from Airborne Lidar Data

J 2023

Benchmark for Automatic Clear-Cut Morphology Detection Methods Derived from Airborne Lidar Data

MELICHOVA, Zlatica, Stanislav PEKÁR and Peter SUROVY

Basic information

Original name

Benchmark for Automatic Clear-Cut Morphology Detection Methods Derived from Airborne Lidar Data

Authors

MELICHOVA, Zlatica (guarantor), Stanislav PEKÁR (703 Slovakia, belonging to the institution) and Peter SUROVY

Edition

Forests, MDPI, 2023, 1999-4907

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

40102 Forestry

Country of publisher

Switzerland

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 2.900 in 2022

RIV identification code

RIV/00216224:14310/23:00133014

Organization unit

Faculty of Science

DOI

http://dx.doi.org/10.3390/f14122408

UT WoS

001131325900001

Keywords in English

multitemporal laser scanning data; harvest detection; simplification polygons; clear-cut areas

Abstract

V originále

Forest harvest detection techniques have recently gained increased attention due to the varied results they provide. Correctly determining the acreage of clear-cut areas is crucial for carbon sequestration. Detecting clear-cut areas using airborne laser scanning (ALS) could be an accurate method for determining the extent of clear-cut areas and their subsequent map display in forest management plans. The shapes of ALS-detected clear-cut areas have uneven edges with protrusions that might not be readable when displayed correctly. Therefore, it is necessary to simplify these shapes for better comprehension. To simplify the shapes of ALS-scanned clear-cut areas, we tested four simplification algorithms using ArcGIS Pro 3.0.0 software: the retain critical points (Douglas-Peucker), retain critical bends (Wang-Muller), retain weighted effective areas (Zhou-Jones), and retain effective areas (Visvalingam-Whyatt) algorithms. Ground-truth data were obtained from clear-cut areas plotted in the forest management plan. Results showed that the Wang-Muller algorithm was the best of the four ALS algorithms at simplifying the shapes of detected clear-cut areas. Using the simplification algorithm reduced the time required to edit polygons to less than 1% of the time required for manual delineation.

Citovat

MELICHOVA, Zlatica, Stanislav PEKÁR and Peter SUROVY. Benchmark for Automatic Clear-Cut Morphology Detection Methods Derived from Airborne Lidar Data. Forests. MDPI, 2023, vol. 14, No 12, p. "2408", 14 pp. ISSN 1999-4907. Available from: https://dx.doi.org/10.3390/f14122408.

@article{2360479,
   author = {Melichova, Zlatica and Pekár, Stanislav and Surovy, Peter},
   article_number = {12},
   doi = {http://dx.doi.org/10.3390/f14122408},
   keywords = {multitemporal laser scanning data; harvest detection; simplification polygons; clear-cut areas},
   language = {eng},
   issn = {1999-4907},
   journal = {Forests},
   title = {Benchmark for Automatic Clear-Cut Morphology Detection Methods Derived from Airborne Lidar Data},
   url = {https://doi.org/10.3390/f14122408},
   volume = {14},
   year = {2023}
}

TY  - JOUR
ID  - 2360479
AU  - Melichova, Zlatica - Pekár, Stanislav - Surovy, Peter
PY  - 2023
TI  - Benchmark for Automatic Clear-Cut Morphology Detection Methods Derived from Airborne Lidar Data
JF  - Forests
VL  - 14
IS  - 12
SP  - "2408"
EP  - "2408"
PB  - MDPI
SN  - 19994907
KW  - multitemporal laser scanning data
KW  - harvest detection
KW  - simplification polygons
KW  - clear-cut areas
UR  - https://doi.org/10.3390/f14122408
N2  - Forest harvest detection techniques have recently gained increased attention due to the varied results they provide. Correctly determining the acreage of clear-cut areas is crucial for carbon sequestration. Detecting clear-cut areas using airborne laser scanning (ALS) could be an accurate method for determining the extent of clear-cut areas and their subsequent map display in forest management plans. The shapes of ALS-detected clear-cut areas have uneven edges with protrusions that might not be readable when displayed correctly. Therefore, it is necessary to simplify these shapes for better comprehension. To simplify the shapes of ALS-scanned clear-cut areas, we tested four simplification algorithms using ArcGIS Pro 3.0.0 software: the retain critical points (Douglas-Peucker), retain critical bends (Wang-Muller), retain weighted effective areas (Zhou-Jones), and retain effective areas (Visvalingam-Whyatt) algorithms. Ground-truth data were obtained from clear-cut areas plotted in the forest management plan. Results showed that the Wang-Muller algorithm was the best of the four ALS algorithms at simplifying the shapes of detected clear-cut areas. Using the simplification algorithm reduced the time required to edit polygons to less than 1% of the time required for manual delineation.
ER  -

MELICHOVA, Zlatica, Stanislav PEKÁR and Peter SUROVY. Benchmark for Automatic Clear-Cut Morphology Detection Methods Derived from Airborne Lidar Data. \textit{Forests}. MDPI, 2023, vol.~14, No~12, p.~''2408'', 14 pp. ISSN~1999-4907. Available from: https://dx.doi.org/10.3390/f14122408.

Detailed Information on Publication Record