UAV-Borne Imagery Can Supplement Airborne Lidar in the Precise Description of Dynamically Changing Shrubland Woody Vegetation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F22%3A91131" target="_blank" >RIV/60460709:41330/22:91131 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/2072-4292/14/9/2287" target="_blank" >https://www.mdpi.com/2072-4292/14/9/2287</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/rs14092287" target="_blank" >10.3390/rs14092287</a>

Jazyk výsledku

angličtina

Název v původním jazyce

UAV-Borne Imagery Can Supplement Airborne Lidar in the Precise Description of Dynamically Changing Shrubland Woody Vegetation

Popis výsledku v původním jazyce

Airborne laser scanning (ALS) is increasingly used for detailed vegetation structure mapping, however, there are many local scale applications where it is economically ineffective or unfeasible from the temporal perspective. Unmanned aerial vehicles (UAVs) or airborne imagery (AImg) appear to be promising alternatives, but only a few studies have examined this assumption outside economically exploited areas (forests, orchards, etc.). The main aim of this study was to compare the usability of normalized digital surface models (nDSMs) photogrammetrically derived from UAV borne and airborne imagery to those derived from low and high density ALS scanning for the precise local scale modelling of woody vegetation structures (the number and height of trees or shrubs) across six dynamically changing shrubland sites. The success of the detection of woody plant tops was initially almost 100 for UAV based models, however, deeper analysis revealed that this was due to the fact that omission and commission errors were approximately equal and the real accuracy was approx. 70% for UAV based models compared to 95,8 for the high density ALS model. The percentage mean absolute errors of shrub or tree heights derived from UAV data ranged between 12,2 and 23,7, and AImg height accuracy was relatively lower. Combining UAV borne or AImg based digital surface models (DSM) with ALS based digital terrain models (DTMs) significantly improved the nDSM height accuracy but failed to significantly improve the detection of the number of individual shrubs or trees. The height accuracy and detection success using low or high density ALS did not differ. Therefore, we conclude that UAV borne imagery has the potential to replace custom ALS in specific local scale applications, especially at dynamically changing sites where repeated ALS is costly, and the combination of such data with (albeit outdated and sparse) ALS based digital terrain models can further improve the success of the use of such data.

Název v anglickém jazyce

UAV-Borne Imagery Can Supplement Airborne Lidar in the Precise Description of Dynamically Changing Shrubland Woody Vegetation

Popis výsledku anglicky

Airborne laser scanning (ALS) is increasingly used for detailed vegetation structure mapping, however, there are many local scale applications where it is economically ineffective or unfeasible from the temporal perspective. Unmanned aerial vehicles (UAVs) or airborne imagery (AImg) appear to be promising alternatives, but only a few studies have examined this assumption outside economically exploited areas (forests, orchards, etc.). The main aim of this study was to compare the usability of normalized digital surface models (nDSMs) photogrammetrically derived from UAV borne and airborne imagery to those derived from low and high density ALS scanning for the precise local scale modelling of woody vegetation structures (the number and height of trees or shrubs) across six dynamically changing shrubland sites. The success of the detection of woody plant tops was initially almost 100 for UAV based models, however, deeper analysis revealed that this was due to the fact that omission and commission errors were approximately equal and the real accuracy was approx. 70% for UAV based models compared to 95,8 for the high density ALS model. The percentage mean absolute errors of shrub or tree heights derived from UAV data ranged between 12,2 and 23,7, and AImg height accuracy was relatively lower. Combining UAV borne or AImg based digital surface models (DSM) with ALS based digital terrain models (DTMs) significantly improved the nDSM height accuracy but failed to significantly improve the detection of the number of individual shrubs or trees. The height accuracy and detection success using low or high density ALS did not differ. Therefore, we conclude that UAV borne imagery has the potential to replace custom ALS in specific local scale applications, especially at dynamically changing sites where repeated ALS is costly, and the combination of such data with (albeit outdated and sparse) ALS based digital terrain models can further improve the success of the use of such data.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10511 - Environmental sciences (social aspects to be 5.7)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Remote Sensing

ISSN

2072-4292

e-ISSN

2072-4292

Svazek periodika

14

Číslo periodika v rámci svazku

9

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

18

Strana od-do

2287-2304

Kód UT WoS článku

000794455600001

EID výsledku v databázi Scopus

2-s2.0-85132588403