Integration of ground-based and remote sensing data with deep learning algorithms for mapping habitats in Natura 2000 protected oak forests

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F25%3A00141108" target="_blank" >RIV/00216224:14310/25:00141108 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S1439179125000064#:~:text=This%20study%20aims%20to%20combine%20ground-based%20and%20remote,habitats%20and%20communities%20within%20the%20Natura%202000%20network" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1439179125000064#:~:text=This%20study%20aims%20to%20combine%20ground-based%20and%20remote,habitats%20and%20communities%20within%20the%20Natura%202000%20network</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.baae.2025.01.006" target="_blank" >10.1016/j.baae.2025.01.006</a>

Result language

angličtina

Original language name

Integration of ground-based and remote sensing data with deep learning algorithms for mapping habitats in Natura 2000 protected oak forests

Original language description

Landscape changes caused by climate change require new methods for forest research, analysis, mapping, and monitoring. This study aims to combine ground-based and remote sensing data utilising deep learning techniques to map protected forest habitats and communities within the Natura 2000 network. The study also seeks to evaluate the accuracy of this approach, specifically in oak-dominated forests, as well as identify the optimal time period within a year for effective habitat identification. Using the specialised software NaturaSat, automated segmentations were performed based on the coordinates of phytosociological releves and forest strands defined in database. Oak-dominated forest habitats were differentiated solely through multispectral data obtained from Sentinel-2 satellites. A dataset was selected for the training of a deep learning algorithm called the Natural Numerical Network on the basis of the analysis results. This algorithm aims to create a prediction map of habitats dominated by Quercus cerris, which is also known as the relevancy map. Through the utilisation of the Natural Numerical Network, a training accuracy of 95.24% was achieved. Field validation, which was conducted at randomly generated locations within the relevancy map, yielded an accuracy of 98.33%. The most distinguishing differences in band characteristics between the two oak-dominated habitats were observed during the autumn months. This study presents a framework that integrates terrestrial and remote sensing data. This method can serve as a basis for mapping forest habitats and observing changes related to climate change. Moreover, it contributes to the documentation of nature conservation and the mapping of landscapes.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10511 - Environmental sciences (social aspects to be 5.7)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

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

Name of the periodical

Basic and Applied Ecology

ISSN

1439-1791

e-ISSN

—

Volume of the periodical

83

Issue of the periodical within the volume

March 2025

Country of publishing house

DE - GERMANY

Number of pages

11

Pages from-to

136-146

UT code for WoS article

001426265600001

EID of the result in the Scopus database

2-s2.0-85217508276