Retrieving plant functional traits through time series analysis of satellite observations using machine learning methods

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F23%3A00572902" target="_blank" >RIV/86652079:_____/23:00572902 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14310/23:00131110 RIV/00216208:11310/23:10468045

Výsledek na webu

<a href="https://www.tandfonline.com/doi/full/10.1080/01431161.2023.2216847?scroll=top&needAccess=true&role=tab" target="_blank" >https://www.tandfonline.com/doi/full/10.1080/01431161.2023.2216847?scroll=top&needAccess=true&role=tab</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/01431161.2023.2216847" target="_blank" >10.1080/01431161.2023.2216847</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Retrieving plant functional traits through time series analysis of satellite observations using machine learning methods

Popis výsledku v původním jazyce

Plant functional traits (e.g. leaf pigment and water contents, specific leaf area) serve as important indicators of plant condition, both within a given vegetation season and between years. Remote sensing-based methods allow for non-destructive and repeatable monitoring of the Earth's surface and thus offer an efficient way to map and monitor these traits. In our study, we used a large database of ground survey data sampled at several contrasting phenological phases of vegetation to develop and compare different machine learning models trained to estimate selected plant functional traits at two different sites: mixed floodplain forest at Lanzhot and beech forest at Stitna, both in the Czech Republic. Empirical models were trained as predictors using 1) Sentinel-2 satellite data (a data set with higher spatial and spectral resolution), and 2) Harmonized Landsat Sentinel-2 (HLS) product (a data set with higher temporal resolution). The most successfully retrieved traits were chlorophyll and carotenoid content (R-2 = 0.78 and 0.65, respectively). Although models trained with Sentinel-2 predictors proved to be slightly better in terms of validation statistics compared to HLS predictors, the HLS product may be preferable for some applications requiring analysis at a high frequency. The best-performing machine learning algorithm, canonical correlation forest, was then applied per pixel to all cloud-free images from the HLS product at both study sites for the years 2019-2021. This allowed us to create a time series of plant functional traits useful for observing differences between the two sites, as well as between growing seasons, and also to observe patterns of spatial behaviour using map outputs.

Název v anglickém jazyce

Retrieving plant functional traits through time series analysis of satellite observations using machine learning methods

Popis výsledku anglicky

Plant functional traits (e.g. leaf pigment and water contents, specific leaf area) serve as important indicators of plant condition, both within a given vegetation season and between years. Remote sensing-based methods allow for non-destructive and repeatable monitoring of the Earth's surface and thus offer an efficient way to map and monitor these traits. In our study, we used a large database of ground survey data sampled at several contrasting phenological phases of vegetation to develop and compare different machine learning models trained to estimate selected plant functional traits at two different sites: mixed floodplain forest at Lanzhot and beech forest at Stitna, both in the Czech Republic. Empirical models were trained as predictors using 1) Sentinel-2 satellite data (a data set with higher spatial and spectral resolution), and 2) Harmonized Landsat Sentinel-2 (HLS) product (a data set with higher temporal resolution). The most successfully retrieved traits were chlorophyll and carotenoid content (R-2 = 0.78 and 0.65, respectively). Although models trained with Sentinel-2 predictors proved to be slightly better in terms of validation statistics compared to HLS predictors, the HLS product may be preferable for some applications requiring analysis at a high frequency. The best-performing machine learning algorithm, canonical correlation forest, was then applied per pixel to all cloud-free images from the HLS product at both study sites for the years 2019-2021. This allowed us to create a time series of plant functional traits useful for observing differences between the two sites, as well as between growing seasons, and also to observe patterns of spatial behaviour using map outputs.

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

<a href="/cs/project/LTAUSA18154" target="_blank" >LTAUSA18154: Hodnocení funkce ekosystémů na základě sledování kvantitativních parametrů vegetace z dat dálkového průzkumu Země vysokého prostorového, spektrálního a časového rozlišení</a><br>

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

International Journal of Remote Sensing

ISSN

0143-1161

e-ISSN

1366-5901

Svazek periodika

44

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

23

Strana od-do

3083-3105

Kód UT WoS článku

000997638600001

EID výsledku v databázi Scopus

2-s2.0-85162070165