Estimation of carbon fluxes from eddy covariance data and satellite-derived vegetation indices in a karst grassland (Podgorski Kras, Slovenia)

Mestrado MEDfOR - Mediterranean Forestry and Natural Resources Management - Instituto Superior de AgronomiaThe Eddy covariance method is a widespread method used for measuring carbon fluxes between the atmosphere and the ecosystem. It provides a high temporal resolution of measurements, but it is restricted to an area around the tower called footprint, and other methods are usually used in combination with eddy covariance data in order to estimate carbon fluxes for larger areas. Spectral vegetation indices derived from increasingly available satellite data can be combined with eddy covariance data to estimate carbon fluxes outside of the tower footprint. Following that approach, the present study attempted to model carbon fluxes for a karst grassland in Slovenia. Three types of model were considered: (1) a linear relationship between NEE or GPP and each vegetation index, (2) a linear relationship between GPP and the product of a vegetation index with PAR, and (3) a simplified LUE model assuming a constant LUE. We compared the performance of several vegetation indices from two sources (Landsat and SPOT-Vegetation) as predictors of NEE and GPP, based on three accuracy metrics (R², RMSE and AIC). Two types of aggregation of flux data were explored, midday average fluxes and daily average fluxes. The Vapor Pressure Deficit was used to separate the growing season in two phases, a greening phase and a dry phase, which were considered separately in the modelling process, in addition to the growing season as a whole. The results showed that NDVI was the best predictor of GPP and NEE during the greening phase, whereas water related vegetation indices, namely LSWI and MNDWI were the best predictors during the dry phase, both for midday and daily aggregates. Model type 1 (linear relationship) was found to be the best in many cases. The best regression equations obtained were used to illustrate the mapping of GPP and NEE for the study areaN/

Empirical Approach for Modelling Tree Phenology in Mixed Forests Using Remote Sensing

Phenological events are good indicators of the effects of climate change, since phenological phases are sensitive to changes in environmental conditions. Although several national phenological networks monitor the phenology of different plant species, direct observations can only be conducted on individual trees, which cannot be easily extended over large and continuous areas. Remote sensing has often been applied to model phenology for large areas, focusing mostly on pure forests in which it is relatively easier to match vegetation indices with ground observations. In mixed forests, phenology modelling from remote sensing is often limited to land surface phenology, which consists of an overall phenology of all tree species present in a pixel. The potential of remote sensing for modelling the phenology of individual tree species in mixed forests remains underexplored. In this study, we applied the seasonal midpoint (SM) method with MODIS GPP to model the start of season (SOS) and the end of season (EOS) of six different tree species in Slovenian mixed forests. First, substitute locations were identified for each combination of observation station and plant species based on similar environmental conditions (aspect, slope, and altitude) and tree species of interest, and used to retrieve the remote sensing information used in the SM method after fitting the best of a Gaussian and two double logistic functions to each year of GPP time series. Then, the best thresholds were identified for SOS and EOS, and the results were validated using cross-validation. The results show clearly that the usual threshold of 0.5 is not best in most cases, especially for estimating the EOS. Despite the difficulty in modelling the phenology of different tree species in a mixed forest using remote sensing, it was possible to estimate SOS and EOS with moderate errors as low as <8 days (Fagus sylvatica and Tilia sp.) and <10 days (Fagus sylvatica and Populus tremula), respectively

Spatial Prediction of Soil Organic Carbon Stock in the Moroccan High Atlas Using Machine Learning

Soil organic carbon (SOC) is an essential component, which soil quality depends on. Thus, understanding the spatial distribution and controlling factors of SOC is paramount to achieving sustainable soil management. In this study, SOC prediction for the Ourika watershed in Morocco was done using four machine learning (ML) algorithms: Cubist, random forest (RF), support vector machine (SVM), and gradient boosting machine (GBM). A total of 420 soil samples were collected at three different depths (0-10 cm, 10-20 cm, and 20-30 cm) from which SOC concentration and bulk density (BD) were measured, and consequently SOC stock (SOCS) was determined. Modeling data included 88 variables incorporating environmental covariates, including soil properties, climate, topography, and remote sensing variables used as predictors. The results showed that RF (R-2 = 0.79, RMSE = 1.2%) and Cubist (R-2 = 0.77, RMSE = 1.2%) were the most accurate models for predicting SOC, while none of the models were satisfactory in predicting BD across the watershed. As with SOC, Cubist (R-2 = 0.86, RMSE = 11.62 t/ha) and RF (R-2 = 0.79, RMSE = 13.26 t/ha) exhibited the highest predictive power for SOCS. Land use/land cover (LU/LC) was the most critical factor in predicting SOC and SOCS, followed by soil properties and bioclimatic variables. Both combinations of bioclimatic-topographic variables and soil properties-remote sensing variables were shown to improve prediction performance. Our findings show that ML algorithms can be a viable tool for spatial modeling of SOC in mountainous Mediterranean regions, such as the study area

Catchment characteristics control boreal mire nutrient regime and vegetation patterns over ~5000 years of landscape development

Vegetation holds the key to many properties that make natural mires unique, such as surface microtopography, high biodiversity values, effective carbon sequestration and regulation of water and nutrient fluxes across the landscape. Despite this, landscape controls behind mire vegetation patterns have previously been poorly described at large spatial scales, which limits the understanding of basic drivers underpinning mire ecosystem services. We studied catchment controls on mire nutrient regimes and vegetation patterns using a geographically constrained natural mire chronosequence along the isostatically rising coastline in Northern Sweden. By comparing mires of different ages, we can partition vegetation patterns caused by long-term mire succession

The Kulbacksliden Research Infrastructure: a unique setting for northern peatland studies

Boreal peatlands represent a biogeochemically unique and diverse environment in high-latitude landscape. They represent a long-term globally significant sink for carbon dioxide and a source of methane, hence playing an important role in regulating the global climate. There is an increasing interest in deciphering peatland biogeochemical processes to improve our understanding of how anthropogenic and climate change effects regulate the peatland biogeochemistry and greenhouse gas balances. At present, most studies investigating land-atmosphere exchanges of peatland ecosystems are commonly based on single-tower setups, which require the assumption of homogeneous conditions during upscaling to the landscape. However, the spatial organization of peatland complexes might feature large heterogeneity due to its varying underlying topography and vegetation composition. Little is known about how well single site studies represent the spatial variations of biogeochemical processes across entire peatland complexes. The recently established Kulbacksliden Research Infrastructure (KRI) includes five peatland study sites located less than 3 km apart, thus providing a unique opportunity to explore the spatial variation in ecosystem-scale processes across a typical boreal peatland complex. All KRI sites are equipped with eddy covariance flux towers combined with installations for detailed monitoring of biotic and abiotic variables, as well as catchment-scale hydrology and hydrochemistry. Here, we review studies that were conducted in the Kulbacksliden area and provide a description of the site characteristics as well as the instrumentation available at the KRI. We highlight the value of long-term infrastructures with ecosystem-scale and replicated experimental sites to advance our understanding of peatland biogeochemistry, hydrology, ecology, and its feedbacks on the environment and climate system

Empirical vs. light-use efficiency modelling for estimating carbon fluxes in a mid-succession ecosystem developed on abandoned karst grassland

Karst systems represent an important carbon sink worldwide. However, several phenomena such as the CO2 degassing and the exchange of cave air return a considerable amount of CO2 to the atmosphere. It is therefore of paramount importance to understand the contribution of the ecosystem to the carbon budget of karst areas. In this study conducted in a mid-succession ecosystem developed on abandoned karst grassland, two types of model were assessed, estimating the gross primary production (GPP) or the net ecosystem exchange (NEE) based on seven years of eddy covariance data (2013%2019): (1) a quadratic vegetation index-based empirical model with five alternative vegetation indices as proxies of GPP and NEE, and (2) the vegetation photosynthesis model (VPM) which is a light use efficiency model to estimate only GPP. The Enhanced Vegetation Index (EVI) was the best proxy for NEE whereas SAVI performed very similarly to EVI in the case of GPP in the empirical model setting. The empirical model performed better than the VPM model which tended to underestimate GPP. Therefore, for this ecosystem, we suggest the use of the empirical model provided that the quadratic relationship observed persists. However, the VPM model would be a good alternative under a changing climate, as it is rooted in the understanding of the photosynthesis process, if the scalars it involves could be improved to better estimate GPP

Carbon flux and environmental parameters data from an eddy covariance tower in a mid-succession ecosystem developed on abandoned karst grassland in Slovenia (2012-2019)

This data set was used to estimate carbon fluxes by comparing eddy covariance tower (Long = 13.916701, Lat = 45.543491) measurements with vegetation indices based estimates

Estimation of carbon fluxes from Eddy covariance data and satellite-derived vegetation indices in a Karst grassland (Podgorski Kras, Slovenia)

The Eddy Covariance method (EC) is widely used for measuring carbon (C) and energy fluxes at high frequency between the atmosphere and the ecosystem, but has some methodological limitations and a spatial restriction to an area, called a footprint. Remotely sensed information is usually used in combination with eddy covariance data in order to estimate C fluxes over larger areas. In fact, spectral vegetation indices derived from available satellite data can be combined with EC measurements to estimate C fluxes outside of the tower footprint. Following this approach, the present study aimed to model C fluxes for a karst grassland in Slovenia. Three types of model were considered: (1) a linear relationship between Net Ecosystem Exchange (NEE) or Gross Primary Production (GPP) and each vegetation index; (2) a linear relationship between GPP and the product of a vegetation index with PAR (Photosynthetically Active Radiation); and (3) a simplified LUE (Light Use-Efficiency) model assuming a constant LUE. We compared the performance of several vegetation indices derived from two remote platforms (Landsat and Proba-V) as predictors of NEE and GPP, based on three accuracy metrics, the coefficient of determination (R2), the Root Mean Square Error (RMSE) and the Akaike Information Criterion (AIC). Two types of aggregation of flux data were explored: midday average and daily average fluxes. The vapor pressure deficit (VPD) was used to separate the growing season into two phases, a wet and a dry phase, which were considered separately in the modelling process, in addition to the growing season as a whole. The results showed that NDVI is the best predictor of GPP and NEE during the wet phase, whereas water-related vegetation indices, namely LSWI and MNDWI, were the best predictors during the dry phase, both for midday and daily aggregates. Model 1 (linear relationship) was found to be the best in many cases. The best regression equations obtained were used to map GPP and NEE for the whole study area. Digital maps obtained can practically contribute, in a cost-effective way to the management of karst grasslands

Estimation of carbon fluxes from eddy covariance data and satellite-derived vegetation indices in a karst grassland (Podgorski Kras, Slovenia)

The Eddy Covariance method (EC) is widely used for measuring carbon (C) and energy fluxes at high frequency between the atmosphere and the ecosystem, but has some methodological limitations and a spatial restriction to an area, called a footprint. Remotely sensed information is usually used in combination with eddy covariance data in order to estimate C fluxes over larger areas. In fact, spectral vegetation indices derived from available satellite data can be combined with EC measurements to estimate C fluxes outside of the tower footprint. Following this approach, the present study aimed to model C fluxes for a karst grassland in Slovenia. Three types of model were considered: (1) a linear relationship between Net Ecosystem Exchange (NEE) or Gross Primary Production (GPP) and each vegetation index(2) a linear relationship between GPP and the product of a vegetation index with PAR (Photosynthetically Active Radiation)and (3) a simplified LUE (Light Use-Efficiency) model assuming a constant LUE. We compared the performance of several vegetation indices derived from two remote platforms (Landsat and Proba-V) as predictors of NEE and GPP, based on three accuracy metrics, the coefficient of determination (R$^2$), the Root Mean Square Error (RMSE) and the Akaike Information Criterion (AIC). Two types of aggregation of flux data were explored: midday average and daily average fluxes. The vapor pressure deficit (VPD) was used to separate the growing season into two phases, a wet and a dry phase, which were considered separately in the modelling process, in addition to the growing season as a whole. The results showed that NDVI is the best predictor of GPP and NEE during the wet phase, whereas water-related vegetation indices, namely LSWI and MNDWI, were the best predictors during the dry phase, both for midday and daily aggregates. Model 1 (linear relationship) was found to be the best in many cases. The best regression equations obtained were used to map GPP and NEE for the whole study area. Digital maps obtained can practically contribute, in a cost-effective way to the management of karst grasslands

Catchment characteristics control boreal mire nutrient regime and vegetation patterns over ~5000 years of landscape development

Vegetation holds the key to many properties that make natural mires unique, such as surface microtopography, high biodiversity values, effective carbon sequestration and regulation of water and nutrient fluxes across the landscape. Despite this, landscape controls behind mire vegetation patterns have previously been poorly described at large spatial scales, which limits the understanding of basic drivers underpinning mire ecosystem services. We studied catchment controls on mire nutrient regimes and vegetation patterns using a geographically constrained natural mire chronosequence along the isostatically rising coastline in Northern Sweden. By comparing mires of different ages, we can partition vegetation patterns caused by long-term mire succession (&lt;5000 years) and present-day vegetation responses to catchment eco-hydrological settings. We used the remote sensing based normalized difference vegetation index (NDVI) to describe mire vegetation and combined peat physicochemical measures with catchment properties to identify the most important factors that determine mire NDVI. We found strong evidence that mire NDVI depends on nutrient inputs from the catchment area or underlying mineral soil, especially concerning phosphorus and potassium concentrations. Steep mire and catchment slopes, dry conditions and large catchment areas relative to mire areas were associated with higher NDVI. We also found long-term successional patterns, with lower NDVI in older mires. Importantly, the NDVI should be used to describe mire vegetation patterns in open mires if the focus is on surface vegetation, since the canopy cover in tree-covered mires completely dominated the NDVI signal. With our study approach, we can quantitatively describe the connection between landscape properties and mire nutrient regime. Our results confirm that mire vegetation responds to the upslope catchment area, but importantly, also suggest that mire and catchment aging can override the role of catchment influence. This effect was clear across mires of all ages, but was strongest in younger mires