Unveiling the Potential of Drone-Borne Optical Imagery in Forest Ecology: A Study on the Recognition and Mapping of Two Evergreen Coniferous Species

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985939%3A_____%2F23%3A00576527" target="_blank" >RIV/67985939:_____/23:00576527 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60460709:41320/23:97168 RIV/60076658:12310/23:43906663

Výsledek na webu

<a href="https://doi.org/10.3390/rs15184394" target="_blank" >https://doi.org/10.3390/rs15184394</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/rs15184394" target="_blank" >10.3390/rs15184394</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Unveiling the Potential of Drone-Borne Optical Imagery in Forest Ecology: A Study on the Recognition and Mapping of Two Evergreen Coniferous Species

Popis výsledku v původním jazyce

For tasks involving tree crown recognition for counting or mapping multiple tree species, dedicated neural networks designed for object detection and counting, such as the YOLOv8 model, are more suitable and reliable. Although more complex image segmentation algorithms can also yield satisfactory results for mapping, their accuracy may be lower, and the learning process may be longer and computationally intensive. Instance segmentation neural networks are primarily recommended for tasks involving the assessment of separate tree crowns, with results requiring careful expert validation.nWe stress to carefully consider the specific research task and the complexity of object classification when selecting segmentation methods. More complex tasks, such as differentiating between visually similar tree species, may necessitate additional strategies or modifications to existing segmentation algorithms to enhance accuracy. The continuous development of robust and accurate segmentation methods for such intricate tasks is an ongoing focus of research in the fields of remote sensing and computer vision.nSolving practical problems related to tree recognition requires a multi-step process that involves collaboration among experts with different skills and experiences. It is essential to adopt biology- and landscape-oriented approaches when applying remote sensing methods, which requires proficiency not only in remote sensing and deep learning techniques but also in understanding the biological aspects of forest ecosystems. This approach will not only aid in collecting primary remote data but will also significantly enhance the quality of the final recognition results.

Název v anglickém jazyce

Unveiling the Potential of Drone-Borne Optical Imagery in Forest Ecology: A Study on the Recognition and Mapping of Two Evergreen Coniferous Species

Popis výsledku anglicky

For tasks involving tree crown recognition for counting or mapping multiple tree species, dedicated neural networks designed for object detection and counting, such as the YOLOv8 model, are more suitable and reliable. Although more complex image segmentation algorithms can also yield satisfactory results for mapping, their accuracy may be lower, and the learning process may be longer and computationally intensive. Instance segmentation neural networks are primarily recommended for tasks involving the assessment of separate tree crowns, with results requiring careful expert validation.nWe stress to carefully consider the specific research task and the complexity of object classification when selecting segmentation methods. More complex tasks, such as differentiating between visually similar tree species, may necessitate additional strategies or modifications to existing segmentation algorithms to enhance accuracy. The continuous development of robust and accurate segmentation methods for such intricate tasks is an ongoing focus of research in the fields of remote sensing and computer vision.nSolving practical problems related to tree recognition requires a multi-step process that involves collaboration among experts with different skills and experiences. It is essential to adopt biology- and landscape-oriented approaches when applying remote sensing methods, which requires proficiency not only in remote sensing and deep learning techniques but also in understanding the biological aspects of forest ecosystems. This approach will not only aid in collecting primary remote data but will also significantly enhance the quality of the final recognition results.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10618 - Ecology

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

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

15

Číslo periodika v rámci svazku

18

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

16

Strana od-do

4394

Kód UT WoS článku

001074095800001

EID výsledku v databázi Scopus

2-s2.0-85172930063