Spatiotemporal estimation of gross primary production for terrestrial wetlands using satellite and field data

Dąbrowska-Zielińska, K.; Misiura, Katarzyna; Malińska, Alicja; Gurdak, Radosław; Grzybowski, Patryk; Bartold, Maciej; Kluczek, Marcin

doi:10.1016/j.rsase.2022.100786

Cited by 11 publications

(6 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The estimated spatial changes in ChF from April to October 2022 are shown in Figur 8. The areas with non-wetland vegetation such as forests, arables, and meadows were ex cluded using classification from previous studies [79,80]. Most of the greening areas ar in the Biebrza Lower Basin, consistent with the previous studies [26,27,32].…”

Section: Spatial and Temporal Patterns Of Chlorophyll Fluorescencesupporting

confidence: 83%

A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands

Bartold

Kluczek

2023

Remote Sensing

View full text Add to dashboard Cite

Wetlands are a critical component of the landscape for climate mitigation, adaptation, biodiversity, and human health and prosperity. Keeping an eye on wetland vegetation is crucial due to it playing a major role in the planet’s carbon cycle and ecosystem management. By measuring the chlorophyll fluorescence (ChF) emitted by plants, we can get a precise understanding of the current state and photosynthetic activity. In this study, we applied the Extreme Gradient Boost (XGBoost) algorithm to map ChF in the Biebrza Valley, which has a unique ecosystem in Europe for peatlands, as well as highly diversified flora and fauna. Our results revealed the advantages of using a set of classifiers derived from EO Sentinel-2 (S-2) satellite image mosaics to accurately map the spatio-temporal distribution of ChF in a terrestrial landscape. The validation proved that the XGBoost algorithm is quite accurate in estimating ChF with a good determination of 0.71 and least bias of 0.012. The precision of chlorophyll fluorescence measurements is reliant upon determining the optimal S-2 satellite overpass time, which is influenced by the developmental stage of the plants at various points during the growing season. Finally, the model performance results indicated that biophysical factors are characterized by greenness- and leaf-pigment-related spectral indices. However, utilizing vegetation indices based on extended periods of remote sensing data that better capture land phenology features can improve the accuracy of mapping chlorophyll fluorescence.

show abstract

Section: Spatial and Temporal Patterns Of Chlorophyll Fluorescencesupporting

confidence: 83%

A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands

Bartold

Kluczek

2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Remote sensing: Finally, an emerging trend regarding models that include a remote sensing aspect was found in the past year (2022-2023): almost one in three (9/29) papers from the second literature search[MS5] included remote sensing data or was based on a remote-sensing classification model. Five of these papers (Ball et al, 2023;Dabrowska-Zielinska et al, 2022;Dadap et al, 2022;Jussila et al, 2023;and Puertas Orozco et al, 2023) presented their approach and results as distinctly remote-sensing oriented and were placed in their own "Remote sensing" category for post-hoc discussion. These take up the remaining 2.5% of the total database.…”

Section: Resultsmentioning

confidence: 99%

Reviews and Syntheses: Evaluating the Potential Application of Ecohydrological Models for Northern Peatland Restoration: A Scoping Review

Silva,

Healy,

Gill

2023

Preprint

View full text Add to dashboard Cite

Abstract. Peatland restoration and rehabilitation action has become more widely acknowledged as a necessary response to mitigating climate change risks and improving global carbon storage. Peatland ecosystems require restoration timespans on the order of decades and thus cannot be dependent upon the shorter-term monitoring often carried out in research projects. Hydrological assessments using geospatial tools provide the basis for planning restoration works as well as analysing associated environmental influences. “Restoration” encompasses applications to pre- and post-restoration scenarios for both bogs and fens, across a range of environmental impact fields. The aim of this scoping review is to identify, describe, and categorise current process-based modelling uses in peatlands in order to investigate the applicability and appropriateness of eco- and/or hydrological models for northern peatland restoration. Two literature searches were conducted using the Web of Science entire database in September 2022 and August 2023. Of the final 211 papers included in the review, models and their applications were categorised according to this review’s research interests in 7 distinct categories aggregating the papers’ research themes and model outputs. Restoration site context was added by identifying 234 unique study site locations from the full database which were catalogued and analysed against raster data for the Köppen-Geiger climate classification scheme. A majority of northern peatland sites were in temperate oceanic zones or humid continental zones experiencing snow. Over one in five models from the full database of papers was unnamed and likely single-use. The top three most-used of these models, based on the frequency of their use on distinct site locations, were LPJ, ecosys, and DigiBog, in that order. Key themes emerging from topics covered by papers in the database included: modelling restoration development from a bog growth perspective; the prioritisation of modelling GHG emissions dynamics as a part of policymaking; the importance of spatial connectivity within or alongside process-based models to represent heterogeneous systems; and the emerging prevalence of remote sensing and machine learning techniques to predict restoration progress with little physical site intervention. This review provides valuable context for the application of ecohydrological models in determining strategies for peatland restoration and evaluating post-intervention development over time.

show abstract

“…The first stage of the work involved field measurements and collecting satellite data of the area of interest. In the next step, the RESP and NEE models were developed, using the previously used soil moisture models (Dąbrowska-Zielińska et al 2018) and the gross primary production model (Dąbrowska-Zielińska et al 2022). It is clearly visible that the highest streams (above -18 µmol m-2 s-1) are obtained for areas with humidity above 80%.…”

Section: Discussionmentioning

confidence: 99%

Modelling Net Ecosystem Exchange in the Biebrza Wetlands using satellite and meteorological data

Dąbrowska-Zielińska

Misiura

Malińska

et al. 2022

Miscellanea Geographica

Self Cite

View full text Add to dashboard Cite

The CO2 flux on Biebrza Wetlands in Poland is analysed by ground measurements and satellite data. The main goal of this study was to determine the Net Ecosystem Exchange (NEE) for sedges, reeds and grasses using meteorological, soil and vegetation parameters and satellite indicators. The calculations are based on chamber measurements scattered over the main habitats and meteorological registrations, and compared to the 2015–2022 eddy covariance (EC) measurements. The NEE model, to get its distribution throughout the day, was elaborated using modelled gross primary production GPP and modelled respiration RESP. The model of ecosystem respiration was developed applying air temperature, soil temperature, soil moisture and NDVI. The satellite vegetation index NDVI, surface and air difference temperature (TS-TA), and NDII proved to be good predictors for the daily mean GPP. The data from the EC tower was used to compare the results from modelled NEE and CO2 flux.

show abstract

Spatiotemporal estimation of gross primary production for terrestrial wetlands using satellite and field data

Cited by 11 publications

References 37 publications

A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands

A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands

Reviews and Syntheses: Evaluating the Potential Application of Ecohydrological Models for Northern Peatland Restoration: A Scoping Review

Modelling Net Ecosystem Exchange in the Biebrza Wetlands using satellite and meteorological data

Contact Info

Product

Resources

About