Potential for using remote sensing to estimate carbon fluxes across northern peatlands - A review

A Low-Cost Sensor Network for Monitoring Peatland

Peatlands across the world are vital carbon stores. However, human activities have caused the degradation of many sites, increasing their greenhouse gas emissions and vulnerability to wildfires. Comprehensive monitoring of peatlands is essential for their protection, tracking degradation and restoration, but current techniques are limited by cost, poor reliability and low spatial or temporal resolution. This paper covers the research, development, deployment and performance of a resilient and modular multi-purpose wireless sensor network as an alternative means of monitoring peatlands. The sensor network consists of four sensor nodes and a gateway and measures temperature, humidity, soil moisture, carbon dioxide and methane. The sensor nodes transmit measured data over LoRaWAN to The Things Network every 30 min. To increase the maximum possible deployment duration, a novel datastring encoder was implemented which reduced the transmitted datastring length by 23%. This system was deployed in a New Forest (Hampshire, UK) peatland site for two months and collected more than 7500 measurements. This deployment demonstrated that low-cost sensor networks have the potential to improve the temporal and spatial resolution of peatland emission monitoring beyond what is achievable with traditional monitoring techniques.

Detecting Spatial Patterns of Peatland Greenhouse Gas Sinks and Sources with Geospatial Environmental and Remote Sensing Data

Peatlands play a key role in the circulation of the main greenhouse gases (GHG) - methane (CH4), carbon dioxide (CO2), and nitrous oxide (N2O). Therefore, detecting the spatial pattern of GHG sinks and sources in peatlands is pivotal for guiding effective climate change mitigation in the land use sector. While geospatial environmental data, which provide detailed spatial information on ecosystems and land use, offer valuable insights into GHG sinks and sources, the potential of directly using remote sensing data from satellites remains largely unexplored. We predicted the spatial distribution of three major GHGs (CH4, CO2, and N2O) sinks and sources across Finland. Utilizing 143 field measurements, we compared the predictive capacity of three different data sets with MaxEnt machine-learning modeling: (1) geospatial environmental data including climate, topography and habitat variables, (2) remote sensing data (Sentinel-1 and Sentinel-2), and (3) a combination of both. The combined dataset yielded the highest accuracy with an average test area under the receiver operating characteristic curve (AUC) of 0.845 and AUC stability of 0.928. A slightly lower accuracy was achieved using only geospatial environmental data (test AUC 0.810, stability AUC 0.924). In contrast, using only remote sensing data resulted in reduced predictive accuracy (test AUC 0.763, stability AUC 0.927). Our results suggest that (1) reliable estimates of GHG sinks and sources cannot be produced with remote sensing data only and (2) integrating multiple data sources is recommended to achieve accurate and realistic predictions of GHG spatial patterns.

Upscaling methane fluxes from peatlands across a drainage gradient in Ireland using PlanetScope imagery and machine learning tools

Wetlands are one of the major contributors of methane (CH₄) emissions to the atmosphere and the intensity of emissions is driven by local environmental variables and spatial heterogeneity. Peatlands are a major wetland class and there are numerous studies that provide estimates of methane emissions at chamber or eddy covariance scales, but these are not often aggregated to the site/ecosystem scale. This study provides a robust approach to map dominant vegetation communities and to use these areas to upscale methane fluxes from chamber to site scale using a simple weighted-area approach. The proposed methodology was tested at three peatlands in Ireland over a duration of 2 years. The annual vegetation maps showed an accuracy ranging from 83 to 99% for near-natural to degraded sites respectively. The upscaled fluxes were highest (2.25 and 3.80 gC m^-2 y^-1) at the near-natural site and the rehabilitation (0.17 and 0.31 gC m^-2 y^-1), degraded (0.15 and 0.27 gC m^-2 y^-1) site emissions were close to net-zero throughout the study duration. Overall, the easy to implement methodology proposed in this study can be applied across various landuse types to assess the impact of peatland rehabilitation on methane emissions by mapping ecological change.

Terrestrial CO2 exchange diagnosis using a peatland-optimized vegetation photosynthesis and respiration model (VPRM) for the Hudson Bay Lowlands

Satellite-based light use efficiency (LUE) models have been widely used to estimate gross primary production in various terrestrial ecosystems such as forests and croplands, but northern peatlands have received less attention. In particular, the Hudson Bay Lowlands (HBL) which is a massive peatland-rich region in Canada has been largely ignored in previous LUE-based studies. These peatland ecosystems have accumulated large stocks of organic carbon over many millennia, and play a vital role in the global carbon cycle. In this study, we used the satellite data-driven Vegetation Photosynthesis and Respiration Model (VPRM) to examine the suitability of LUE models for carbon flux diagnosis in the HBL. VPRM was driven alternately with the satellite-derived enhanced vegetation index (EVI) and solar-induced chlorophyll fluorescence (SIF). The model parameter values were constrained by eddy covariance (EC) tower observations from the Churchill fen and Attawapiskat River bog sites. The main objectives of the study were to (i) investigate if site-specific parameter optimization improved NEE estimates, (ii) determine which satellite-based proxy of photosynthesis produced more reliable estimates of peatland net carbon exchange, and (iii) examine how LUE and other model parameters vary within and between the study sites. The results indicate that the VPRM mean diurnal and monthly estimates of NEE had significant strong agreements with EC tower fluxes at the two study sites. A comparison of the site-optimized VPRM against a generic peatland-optimized version of the model revealed that the site-optimized VPRM provided better estimates of NEE only during the calibration period at the Churchill fen. The diurnal and seasonal cycles of peatland carbon exchange were better captured by the SIF-driven VPRM, demonstrating that SIF is a more accurate proxy for photosynthesis compared to EVI. Our study suggests that satellite-based LUE models have the potential to be applied on a larger scale to the HBL region.

Impact of the Dates of Input Image Pairs on Spatio-Temporal Fusion for Time Series with Different Temporal Variation Patterns

Dense time series of remote sensing images with high spatio-temporal resolution are critical for monitoring land surface dynamics in heterogeneous landscapes. Spatio-temporal fusion is an effective solution to obtaining such time series images. Many spatio-temporal fusion methods have been developed for producing high spatial resolution images at frequent intervals by blending fine spatial images and coarse spatial resolution images. Previous studies have revealed that the accuracy of fused images depends not only on the fusion algorithm, but also on the input image pairs being used. However, the impact of input images dates on the fusion accuracy for time series with different temporal variation patterns remains unknown. In this paper, the impact of input image pairs on the fusion accuracy for monotonic linear change (MLC), monotonic non-linear change (MNLC), and non-monotonic change (NMC) time periods were evaluated, respectively, and the optimal selection strategies of input image dates for different situations were proposed. The 16-day composited NDVI time series (i.e., Collection 6 MODIS NDVI product) were used to present the temporal variation patterns of land surfaces in the study areas. To obtain sufficient observation dates to evaluate the impact of input image pairs on the spatio-temporal fusion accuracy, we utilized the Harmonized Landsat-8 Sentinel-2 (HLS) data. The ESTARFM was selected as the spatio-temporal fusion method for this study. The results show that the impact of input image date on the accuracy of spatio-temporal fusion varies with the temporal variation patterns of the time periods being fused. For the MLC period, the fusion accuracy at the prediction date (PD) is linearly correlated to the time interval between the change date (CD) of the input image and the PD, but the impact of the input image date on the fusion accuracy at the PD is not very significant. For the MNLC period, the fusion accuracy at the PD is non-linearly correlated to the time interval between the CD and the PD, the impact of the time interval between the CD and the PD on the fusion accuracy is more significant for the MNLC than for the MLC periods. Given the similar change of time intervals between the CD and the PD, the increments of R2 of fusion result for the MNLC is over ten times larger than those for the MLC. For the NMC period, a shorter time interval between the CD and the PD does not lead to higher fusion accuracies. On the contrary, it may lower the fusion accuracy. This study suggests that temporal variation patterns of the data must be taken into account when selecting optimal dates of input images in the fusion model.

CO2Flux Model Assessment and Comparison between an Airborne Hyperspectral Sensor and Orbital Multispectral Imagery in Southern Amazonia

In environmental research, remote sensing techniques are mostly based on orbital data, which are characterized by limited acquisition and often poor spectral and spatial resolutions in relation to suborbital sensors. This reflects on carbon patterns, where orbital remote sensing bears devoted sensor systems for CO2 monitoring, even though carbon observations are performed with natural resources systems, such as Landsat, supported by spectral models such as CO2Flux adapted to multispectral imagery. Based on the considerations above, we have compared the CO2Flux model by using four different imagery systems (Landsat 8, PlanetScope, Sentinel-2, and AisaFenix) in the northern part of the state of Mato Grosso, southern Brazilian Amazonia. The study area covers three different land uses, which are primary tropical forest, bare soil, and pasture. After the atmospheric correction and radiometric calibration, the scenes were resampled to 30 m of spatial resolution, seeking for a parametrized comparison of CO2Flux, as well as NDVI (Normalized Difference Vegetation Index) and PRI (Photochemical Reflectance Index). The results obtained here suggest that PlanetScope, MSI/Sentinel-2, OLI/Landsat-8, and AisaFENIX can be similarly scaled, that is, the data variability along a heterogeneous scene in evergreen tropical forest is similar. We highlight that the spatial-temporal dynamics of rainfall seasonality relation to CO2 emission and uptake should be assessed in future research. Our results provide a better understanding on how the merge and/or combination of different airborne and orbital datasets that can provide reliable estimates of carbon emission and absorption within different terrestrial ecosystems in southern Amazonia.

Angolan highlands peatlands: Extent, age and growth dynamics

The Angolan highlands are hydrologically and ecologically important, supporting peatland deposits. Peatlands are carbon rich ecosystems and are the largest terrestrial carbon store. We present a first estimate of the extent of peatlands in the Angolan Highlands, using Google Earth Engine. Our conservative estimate of peatland coverage is 1634 km², 2.65% of a mapped area spanning approximately 61,590 km². This is a crucial first step in providing the peatland carbon inventory for the region and to facilitate conservation and management strategies. We include the peatland characteristics with respect to topographic data and common remote sensing indices of Normalised Difference Vegetation Index and Normalised Difference Water Index. The results suggest that Angolan Highlands peatland is highly variable in terms of elevation, slope, vegetation cover and standing water occurrence. Radiocarbon dating of riparian peatlands suggest two stages of peatland initiation: one about 7100 cal. yr BP, during the African humid period, and another from about 1100 cal. yr BP to present after the African humid period ended. The temporal control of riparian peat formation is river dynamics and the formation of terraces. Source lake peatland is slightly younger and has average maximum age of 890 cal. yr BP. The Angolan Highlands ecosystem and peatlands are possibly under strain from anthropogenic influence and climate change, making this peatland deposit a potential carbon emission source.

Assessing remote sensing as a tool to monitor hydrological stress in Irish catchments with Freshwater Pearl Mussel populations

The West Coast of Ireland hosts many of the few populations of Freshwater Peal Mussels (FPM) left in Europe. The decline of this keystone species is strongly related to deteriorating hydrological conditions, specifically to the threat of low flows during dry summers. Populations still capable of reproducing require a minimum discharge and flow velocity to support juvenile mussels, or else stress builds up and an entire generation may be lost. Monitoring environmental and hydrological conditions in small and remote FPM catchments is difficult due to the lack of infrastructure. Indices derived from remote sensing imagery can be used to assess hydrological variables at the catchment scale. Here, five indices are tested as possible surrogates for soil moisture and evapotranspiration, based on two relevant land-cover types: open peat habitats (OPH) and forestry. Selected indices are then assessed in their ability to reproduce seasonal patterns and in their response to a severe drought event. The moisture stress index (MSI) and normalized difference vegetation index (NDVI) were found to be the best surrogates for soil moisture and evapotranspiration respectively. Both indices showed seasonality patterns in the two land-cover types, although the variability of MSI was significantly higher. During the 2018 drought, MSI visibly increased only in OPH, while NDVI rose only for forestry. The results suggest that OPH enhances the long-term hydrological resilience of a catchment by conserving water in the peat substrate, while industrial forestry plantations exacerbate the pressure on water during drier periods. This has consequences for river discharge, freshwater biodiversity and specifically for FPM. Implementing these surrogates have the potential to identify land-use management strategies that reduce and even avert the effects of drought on FPM. Such strategies are increasingly necessary in a climate change context, as recurring summer droughts are expected in most of Europe.

Predicting forested wetland soil carbon using quantitative color sensor measurements in the region of northern Virginia, USA

Forested wetland soils within the Piedmont and Coastal Plain physiographic provinces of Northern Virginia (NOVA) were investigated to determine the utility of a handheld colorimeter, the Nix Pro Color Sensor ("Nix"), for predicting carbon contents (TC) and stocks (TC stocks) from on-site color measurements. Both the color variables recorded with each Nix scan ("Nix color variables"; n = 15) and carbon contents significantly differed between sites, with redder soils (higher a and h) at Piedmont sites, and higher TC at sites with darker soils (lower values of L, or lightness; p < 0.05). Nix-carbon correlation analysis revealed strong relationships between L (lightness), X (a virtual spectral variable), R (additive red), and K_K (black) and log-transformed TC (Ln[TC]; |r| = 0.70; p < 0.01 for all). Simple linear regressions were conducted to identify how well these four final Nix variables could predict soil carbon. Using all color measurements, about 50% of Ln(TC) variability could be explained by L, X, R, or K_K (p < 0.01), yet with higher predictive power obtained for Coastal Plain soils (0.55 < R² < 0.65; p < 0.01). Regression model strength was maximized between Ln(TC) and the four final Nix variables using simple linear regressions when color measurements observed at a specific depth were first averaged (0.66 < R² < 0.70; p < 0.01). While further study is warranted to investigate Nix applicability within various soil settings, these results demonstrate potential for the Nix and its soil color measurements to assist with rapid field-based assessments of soil carbon in forested wetlands.

A Training Sample Migration Method for Wetland Mapping and Monitoring Using Sentinel Data in Google Earth Engine

Wetlands are one of the most important ecosystems due to their critical services to both humans and the environment. Therefore, wetland mapping and monitoring are essential for their conservation. In this regard, remote sensing offers efficient solutions due to the availability of cost-efficient archived images over different spatial scales. However, a lack of sufficient consistent training samples at different times is a significant limitation of multi-temporal wetland monitoring. In this study, a new training sample migration method was developed to identify unchanged training samples to be used in wetland classification and change analyses over the International Shadegan Wetland (ISW) areas of southwestern Iran. To this end, we first produced the wetland map of a reference year (2020), for which we had training samples, by combining Sentinel-1 and Sentinel-2 images and the Random Forest (RF) classifier in Google Earth Engine (GEE). The Overall Accuracy (OA) and Kappa coefficient (KC) of this reference map were 97.93% and 0.97, respectively. Then, an automatic change detection method was developed to migrate unchanged training samples from the reference year to the target years of 2018, 2019, and 2021. Within the proposed method, three indices of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and the mean Standard Deviation (SD) of the spectral bands, along with two similarity measures of the Euclidean Distance (ED) and Spectral Angle Distance (SAD), were computed for each pair of reference–target years. The optimum threshold for unchanged samples was also derived using a histogram thresholding approach, which led to selecting the samples that were most likely unchanged based on the highest OA and KC for classifying the test dataset. The proposed migration sample method resulted in high OAs of 95.89%, 96.83%, and 97.06% and KCs of 0.95, 0.96, and 0.96 for the target years of 2018, 2019, and 2021, respectively. Finally, the migrated samples were used to generate the wetland map for the target years. Overall, our proposed method showed high potential for wetland mapping and monitoring when no training samples existed for a target year.

Predicting Soil Respiration from Plant Productivity (NDVI) in a Sub-Arctic Tundra Ecosystem

Soils represent the largest store of carbon in the biosphere with soils at high latitudes containing twice as much carbon (C) than the atmosphere. High latitude tundra vegetation communities show increases in the relative abundance and cover of deciduous shrubs which may influence net ecosystem exchange of CO2 from this C-rich ecosystem. Monitoring soil respiration (Rs) as a crucial component of the ecosystem carbon balance at regional scales is difficult given the remoteness of these ecosystems and the intensiveness of measurements that is required. Here we use direct measurements of Rs from contrasting tundra plant communities combined with direct measurements of aboveground plant productivity via Normalised Difference Vegetation Index (NDVI) to predict soil respiration across four key vegetation communities in a tundra ecosystem. Soil respiration exhibited a nonlinear relationship with NDVI (y = 0.202e3.508 x, p < 0.001). Our results further suggest that NDVI and soil temperature can help predict Rs if vegetation type is taken into consideration. We observed, however, that NDVI is not a relevant explanatory variable in the estimation of SOC in a single-study analysis.

Towards a microbial process-based understanding of the resilience of peatland ecosystem service provisioning - A research agenda

Peatlands are wetland ecosystems with great significance as natural habitats and as major global carbon stores. They have been subject to widespread exploitation and degradation with resulting losses in characteristic biota and ecosystem functions such as climate regulation. More recently, large-scale programmes have been established to restore peatland ecosystems and the various services they provide to society. Despite significant progress in peatland science and restoration practice, we lack a process-based understanding of how soil microbiota influence peatland functioning and mediate the resilience and recovery of ecosystem services, to perturbations associated with land use and climate change. We argue that there is a need to: in the short-term, characterise peatland microbial communities across a range of spatial and temporal scales and develop an improved understanding of the links between peatland habitat, ecological functions and microbial processes; in the medium term, define what a successfully restored 'target' peatland microbiome looks like for key carbon cycle related ecosystem services and develop microbial-based monitoring tools for assessing restoration needs; and in the longer term, to use this knowledge to influence restoration practices and assess progress on the trajectory towards 'intact' peatland status. Rapid advances in genetic characterisation of the structure and functions of microbial communities offer the potential for transformative progress in these areas, but the scale and speed of methodological and conceptual advances in studying ecosystem functions is a challenge for peatland scientists. Advances in this area require multidisciplinary collaborations between peatland scientists, data scientists and microbiologists and ultimately, collaboration with the modelling community. Developing a process-based understanding of the resilience and recovery of peatlands to perturbations, such as climate extremes, fires, and drainage, will be key to meeting climate targets and delivering ecosystem services cost effectively.

Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data

Peatlands play an important role in the global carbon cycle as they contain a large soil carbon stock. However, current climate change could potentially shift peatlands from being carbon sinks to carbon sources. Remote sensing methods provide an opportunity to monitor carbon dioxide (CO2) exchange in peatland ecosystems at large scales under these changing conditions. In this study, we developed empirical models of the CO2 balance (net ecosystem exchange, NEE), gross primary production (GPP), and ecosystem respiration (ER) that could be used for upscaling CO2 fluxes with remotely sensed data. Two to three years of eddy covariance (EC) data from five peatlands in Sweden and Finland were compared to modelled NEE, GPP and ER based on vegetation indices from 10 m resolution Sentinel-2 MSI and land surface temperature from 1 km resolution MODIS data. To ensure a precise match between the EC data and the Sentinel-2 observations, a footprint model was applied to derive footprint-weighted daily means of the vegetation indices. Average model parameters for all sites were acquired with a leave-one-out-cross-validation procedure. Both the GPP and the ER models gave high agreement with the EC-derived fluxes (R2 = 0.70 and 0.56, NRMSE = 14% and 15%, respectively). The performance of the NEE model was weaker (average R2 = 0.36 and NRMSE = 13%). Our findings demonstrate that using optical and thermal satellite sensor data is a feasible method for upscaling the GPP and ER of northern boreal peatlands, although further studies are needed to investigate the sources of the unexplained spatial and temporal variation of the CO2 fluxes.

A Hybrid Deep Learning-Based Spatiotemporal Fusion Method for Combining Satellite Images with Different Resolutions

Spatiotemporal fusion (STF) is considered a feasible and cost-effective way to deal with the trade-off between the spatial and temporal resolution of satellite sensors, and to generate satellite images with high spatial and high temporal resolutions. This is achieved by fusing two types of satellite images, i.e., images with fine temporal but rough spatial resolution, and images with fine spatial but rough temporal resolution. Numerous STF methods have been proposed, however, it is still a challenge to predict both abrupt landcover change, and phenological change, accurately. Meanwhile, robustness to radiation differences between multi-source satellite images is crucial for the effective application of STF methods. Aiming to solve the abovementioned problems, in this paper we propose a hybrid deep learning-based STF method (HDLSFM). The method formulates a hybrid framework for robust fusion with phenological and landcover change information with minimal input requirements, and in which a nonlinear deep learning-based relative radiometric normalization, a deep learning-based superresolution, and a linear-based fusion are combined to address radiation differences between different types of satellite images, landcover, and phenological change prediction. Four comparative experiments using three popular STF methods, i.e., spatial and temporal adaptive reflectance fusion model (STARFM), flexible spatiotemporal data fusion (FSDAF), and Fit-FC, as benchmarks demonstrated the effectiveness of the HDLSFM in predicting phenological and landcover change. Meanwhile, HDLSFM is robust for radiation differences between different types of satellite images and the time interval between the prediction and base dates, which ensures its effectiveness in the generation of fused time-series data.

Fusion and Correction of Multi-Source Land Cover Products Based on Spatial Detection and Uncertainty Reasoning Methods in Central Asia

Land cover products are an indispensable data source in land surface process research, and their accuracy directly affects the reliability of related research. Due to the differences in factors such as satellite sensors, the temporal–spatial resolution of remote sensing images, and landcover interpretation technologies, various recently released land cover products are inconsistent, and their accuracy is usually insufficient to meet application requirements. This study, therefore, established a fusion and correction method for multi-source landcover products by combining them with landcover statistics from the Food and Agriculture Organization of the United Nations (FAO), introducing a spatial consistency discrimination technique, and applying an improved Dempster-Shafer evidence fusion method. The five countries in Central Asia were used for a method application and verification assessment. The nine products selected (CCI-LC, CGLS, FROM-GLC, GLCNMO, MCD12Q, GFSAD30, PALSAR, GSWD, and GHS-BUILT) were consistent in time and covered the study area. Based on the interpretation of 1437 high-definition image verification areas, the overall accuracy of the fusion landcover result was 85.32%, and the kappa coefficient was 0.80, which was better than that of the existing comprehensive products. The spatial consistency fusion method had the advantage of an improved statistical fitting, with an overall similarity statistic of 0.999. The improved Dempster-Shafer evidence theory fusion method had an accuracy that was 4.86% higher than the spatial consistency method, and the kappa coefficient increased by 0.07. Combining these two methods improved the consistency of the multi-source data fusion and correction method established in this paper and will also provide more reliable basic data for future research in Central Asia.

The potential of CO2 satellite monitoring for climate governance: A review

Good-quality CO₂ emission data are fundamental for effective climate policy and governance. Data manipulation should be deterred, while developing countries are generally weaker than developed countries in compiling bottom-up CO₂ emission inventories due to less adequate data collection capacity. This paper assesses the capabilities of CO₂ satellites as objective, independent, potentially low-cost and external data sources for monitoring energy-related anthropogenic CO₂ emissions at regional/national, megacity and point-source geographical scales. After overviewing all major CO₂ satellites, SCIAMACHY, GOSAT and OCO-2 are focused on due to their wider research applications and higher CO₂ sensitivity in total column measurements that include near surface emissions. Nighttime light satellite data for proxy CO₂ monitoring are also brought into comparison to distinguish the importance of direct CO₂ satellite monitoring. Studies are reviewed from the perspectives of spatial and temporal capability and accuracy to comprehend the current statuses of applications, assess the strengths and weaknesses of research methods, investigate major challenges and propose suggestions for future progress. We conclude that CO₂ satellite monitoring can strengthen the data foundation for implementing international climate treaties and domestic climate policies.

Analyzing Daily Estimation of Forest Gross Primary Production Based on Harmonized Landsat-8 and Sentinel-2 Product Using SCOPE Process-Based Model

Vegetation top-of-canopy reflectance contains valuable information for estimating vegetation biochemical and structural properties, and canopy photosynthesis (gross primary production (GPP)). Satellite images allow studying temporal variations in vegetation properties and photosynthesis. The National Aeronautics and Space Administration (NASA) has produced a harmonized Landsat-8 and Sentinel-2 (HLS) data set to improve temporal coverage. In this study, we aimed to explore the potential and investigate the information content of the HLS data set using the Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model to retrieve the temporal variations in vegetation properties, followed by the GPP simulations during the 2016 growing season of an evergreen Norway spruce dominated forest stand. We optimized the optical radiative transfer routine of the SCOPE model to retrieve vegetation properties such as leaf area index and leaf chlorophyll, water, and dry matter contents. The results indicated percentage differences less than 30% between the retrieved and measured vegetation properties. Additionally, we compared the retrievals from HLS data with those from hyperspectral airborne data for the same site, showing that HLS data preserve a considerable amount of information about the vegetation properties. Time series of vegetation properties, retrieved from HLS data, served as the SCOPE inputs for the time series of GPP simulations. The SCOPE model reproduced the temporal cycle of local flux tower measurements of GPP, as indicated by the high Nash–Sutcliffe efficiency value (>0.5). However, GPP simulations did not significantly change when we ran the SCOPE model with constant vegetation properties during the growing season. This might be attributed to the low variability in the vegetation properties of the evergreen forest stand within a vegetation season. We further observed that the temporal variation in maximum carboxylation capacity had a pronounced effect on GPP simulations. We focused on an evergreen forest stand. Further studies should investigate the potential of HLS data across different forest types, such as deciduous stand.

Herbivore Impacts on Carbon Cycling in Boreal Forests

Large herbivores can have substantial effects on carbon (C) cycling, yet these animals are often overlooked in C budgets. Zoogeochemical effects may be particularly important in boreal forests, where diverse human activities are facilitating the expansion of large herbivore populations. Here, we argue that considering trophic dynamics is necessary to understand spatiotemporal variability in boreal forest C budgets. We propose a research agenda to scale local studies to landscape extents to measure the zoogeochemical impacts of large herbivores on boreal forest C cycling. Distributed networks of exclosure experiments, empirical studies across gradients in large herbivore abundance, multiscale models using herbivore distribution data, and remote sensing paired with empirical data will provide comprehensive accounting of C source-sink dynamics in boreal forests.

Long Term Interferometric Temporal Coherence and DInSAR Phase in Northern Peatlands

Peatlands of northern temperate and cold climates are significant pools of stored carbon. Understanding seasonal dynamics of peatland surface height and volume, often referred to as mire breathing or oscillation, is the key to improve spatial models of material flow and gas exchange. The monitoring of this type of dynamics over large areas is only feasible by remote sensing instruments. The objective of this study is to examine the applicability of Sentinel-1 synthetic aperture radar interferometry (InSAR) to characterize seasonal dynamics of peatland surface height and water table (WT) over open raised bog areas in Endla mire complex in central Estonia, characteristic for northern temperate bogs. Our results show that InSAR temporal coherence, sufficient for differential InSAR (DInSAR), is preserved in the open bog over more than six months of temporal baseline. Moreover, the coherence which is lost in a dry summer, make a recovery in autumn correlate with WT dynamics. The relationship between the coherence from a single master image and the corresponding WT difference is described by the second degree polynomial regression model (Root Mean Squared Error RMSE = 0.041 for coherence magnitude). It is also demonstrated that DInSAR phase is connected to bog surface dynamics and reveals differences between bogs and for ecotopes within a bog. These findings suggest that InSAR long term temporal coherence could be used to describe seasonal bog WT dynamics and differentiate between mire types and ecotopes within a bog. Moreover, DInSAR analysis has the potential to characterize seasonal mire surface oscillation which may be important for assessing the capacity of water storage or restoration success in northern temperate bogs.

Integrating eddy fluxes and remote sensing products in a rotational grazing native tallgrass prairie pasture

Eddy covariance (EC) systems provide integrated fluxes within their footprint areas. Spatial heterogeneity of up-scaled areas and spatio-temporal mismatches between EC footprint and remote sensing pixels jeopardize the performance of most satellite-based models. To examine the impact of spatial resolution of satellite products on up-scaling of fluxes, we compared the relationships between measured eddy fluxes and enhanced vegetation index (EVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) at 500 and 250 m spatial resolutions, Visible Infrared Imaging Radiometer Suite (VIIRS) at 500 m spatial resolution, and Landsat at 30 m spatial resolution but integrated at the paddock-scale. The experiment was conducted over a grazed native tallgrass prairie pasture, which was divided into nine paddocks for rotational grazing. The EVI data from all satellites showed consistency in detecting vegetation phenology. Seasonality of EC-measured fluxes corresponded well with remotely-sensed vegetation phenology. Approximately 80% of contribution to eddy fluxes came from within 80 m upwind distance of the 2.7 m tall EC tower. As a result, the major contributing area for the measured fluxes was mostly limited to the paddock containing the EC tower. Different timings and duration of grazing caused some heterogeneity among paddocks within the pasture. The EVI of different spatial scales showed strong relationships with CO₂ fluxes. However, Landsat-derived EVI integrated for the paddock containing the EC tower showed substantially stronger relationships with CO₂ fluxes than did MODIS and VIIRS-derived EVI integrated for multiple paddocks, most likely due to similar spatial resolutions of remote sensing and EC observations. Results illustrate that satellite products of fine-scale spatial resolution that are comparable to EC footprints can help improve the performance of satellite-based models for modeling or up-scaling of eddy fluxes, especially in heterogeneous ecosystems.

A Novel Deep Learning-Based Spatiotemporal Fusion Method for Combining Satellite Images with Different Resolutions Using a Two-Stream Convolutional Neural Network

Spatiotemporal fusion is considered a feasible and cost-effective way to solve the trade-off between the spatial and temporal resolution of satellite sensors. Recently proposed learning-based spatiotemporal fusion methods can address the prediction of both phenological and land-cover change. In this paper, we propose a novel deep learning-based spatiotemporal data fusion method that uses a two-stream convolutional neural network. The method combines both forward and backward prediction to generate a target fine image, where temporal change-based and a spatial information-based mapping are simultaneously formed, addressing the prediction of both phenological and land-cover changes with better generalization ability and robustness. Comparative experimental results for the test datasets with phenological and land-cover changes verified the effectiveness of our method. Compared to existing learning-based spatiotemporal fusion methods, our method is more effective in predicting phenological change and directly reconstructing the prediction with complete spatial details without the need for auxiliary modulation.

Review of Top-of-Canopy Sun-Induced Fluorescence (SIF) Studies from Ground, UAV, Airborne to Spaceborne Observations

Remote sensing (RS) of sun-induced fluorescence (SIF) has emerged as a promising indicator of photosynthetic activity and related stress from the leaf to the ecosystem level. The implementation of modern RS technology on SIF is highly motivated by the direct link of SIF to the core of photosynthetic machinery. In the last few decades, a lot of studies have been conducted on SIF measurement techniques, retrieval algorithms, modeling, application, validation, and radiative transfer processes, incorporating different RS observations (i.e., ground, unmanned aerial vehicle (UAV), airborne, and spaceborne). These studies have made a significant contribution to the enrichment of SIF science over time. However, to realize the potential of SIF and to explore its full spectrum using different RS observations, a complete document of existing SIF studies is needed. Considering this gap, we have performed a detailed review of current SIF studies from the ground, UAV, airborne, and spaceborne observations. In this review, we have discussed the in-depth interpretation of each SIF study using four RS platforms. The limitations and challenges of SIF studies have also been discussed to motivate future research and subsequently overcome them. This detailed review of SIF studies will help, support, and inspire the researchers and application-based users to consider SIF science with confidence.

Investigating Spatial and Vertical Patterns of Wetland Soil Organic Carbon Concentrations in China’s Western Songnen Plain by Comparing Different Algorithms

Investigating the spatial and vertical patterns of wetland soil organic carbon concentration (SOCc) is important for understanding the regional carbon cycle and managing the wetland ecosystem. By integrating 160 wetland soil profile samples and environmental variables from climatic, topographical, and remote sensing data, we spatially predicted the SOCc of wetlands in China’s Western Songnen Plain by comparing four algorithms: random forest (RF), support vector machine (SVM) for regression, inverse distance weighted (IDW), and ordinary kriging (OK). The predicted results of the SOCc from the different algorithms were validated against independent testing samples according to the mean error, root mean squared error, and correlation coefficient. The results show that the measured SOCc values at depths of 0–30, 30–60, and 60–100 cm were 15.28, 7.57, and 5.22 g·kg−1, respectively. An assessment revealed that the RF algorithm was most accurate for predicting SOCc; its correlation coefficients at the different depths were 0.82, 0.59, and 0.51, respectively. The attribute importance from the RF indicates that environmental variables have various effects on the SOCc at different depths. The land surface temperature and land surface water index had a stronger influence on the spatial distribution of SOCc at the depths of 0–30 and 30–60 cm, whereas topographic factors, such as altitude, had a stronger influence within 60–100 cm. The predicted SOCc of each vertical depth increased gradually from south to north in the study area. This research provides an important case study for predicting SOCc, including selecting factors and algorithms, and helps with understanding the carbon cycles of regional wetlands.

Assessment of five satellite-derived LAI datasets for GPP estimations through ecosystem models

Ecosystem models have been widely used for obtaining gross primary productivity (GPP) estimations at multiple scales. Leaf area index (LAI) is a critical variable in these models for describing the vegetation canopy structure and predicting vegetation-atmosphere interactions. However, the uncertainties in LAI datasets and the effects of their representation on simulated GPP remain unclear, especially over complex terrain. Here, five most popular datasets, namely the Long-term Global Mapping (GLOBMAP) LAI, Global LAnd Surface Satellite (GLASS) LAI, Geoland2 version 1 (GEOV1) LAI, Global Inventory Monitoring and Modeling System (GIMMS) LAI, and Moderate Resolution Imaging Spectroradiometer (MODIS) LAI, were selected to examine the influences of LAI representation on GPP estimations at 95 eddy covariance (EC) sites. The GPP estimations from the Boreal Ecosystem Productivity Simulator (BEPS) model and the Eddy Covariance Light Use Efficiency (EC-LUE) model were evaluated against EC GPP to assess the performances of LAI datasets. Results showed that MODIS LAI had stronger linear correlations with GLASS and GEOV1 than GIMMS and GLOMAP at the study sites. The GPP estimations from GLASS LAI had a better agreement with EC GPP than those from other four LAI datasets at forest sites, while the GPP estimations from GEOVI LAI matched best with EC GPP at grass sites. Additionally, the GPP estimations from GLASS and GEOVI LAI presented better performances than the other three LAI datasets at crop sites. Besides, the results also showed that complex terrain had larger discrepancies of LAI and GPP estimations, and flat terrain presented better performances of LAI datasets in GPP estimations. Moreover, the simulated GPP from BEPS was more sensitive to LAI than those from EC - LUE, suggesting that LAI datasets can also lead to different uncertainties in GPP estimations from different model structures. Our study highlights that the satellite-derived LAI datasets can cause uncertainties in GPP estimations through ecosystem models.

A model of gross primary productivity based on satellite data suggests formerly afforested peatlands undergoing restoration regain full photosynthesis capacity after five to ten years

Peatlands are an important terrestrial carbon store, but disturbance has resulted in the degradation of many peatland ecosystems and caused them to act as a net carbon source. Restoration work is being undertaken but monitoring the success of these schemes can be difficult and costly using traditional field-based methods. A landscape-scale alternative is to use satellite data to assess the condition of peatlands and to estimate gaseous carbon fluxes. In this study we used Moderate Resolution Imaging Spectroradiometer (MODIS) products to model Gross Primary Productivity (GPP) over peatland sites at various stages of restoration. We found that the MOD17A2H GPP product overestimates GPP modelled from data collected by eddy covariance towers situated at two ex-forestry sites undergoing restoration towards blanket bog at the Forsinard Flows RSPB reserve, Scotland, UK (one full year of data), and a near-natural Atlantic blanket bog site in Glencar, Ireland (ten-year data series). We calibrated a Temperature and Greenness (TG) model for the Forsinard sites and found it to be more accurate than the MODIS GPP product at local scale. We also found that inclusion of a wetness factor using the Normalised Difference Water Index (NDWI) improved inter-annual accuracy of the model. This TGWa (annual Temperature, Greenness and Wetness) model was then applied to six control sites comprising near-natural bog across the reserve, and to six sites on which restoration began between 1998 and 2006. GPP from 2005 to 2016 was estimated for each site using the model. The resulting modelled trends are positive at all six restored sites, increasing by approximately 5.5 g C/m²/yr every year since restoration began in the Forsinard Flows reserve. The results suggest that peatland sites undergoing restoration at Forsinard Flows reach the carbon assimilation potential of near-natural bog sites between 5 and 10 years after restoration was begun.

Contrasting Performance of the Remotely-Derived GPP Products over Different Climate Zones across China

Precise quantification of terrestrial gross primary production (GPP) has been recognized as one of the most important components in understanding the carbon balance between the biosphere and the atmosphere. In recent years, although many large-scale GPP estimates from satellite data and ecosystem models have been generated, few attempts have been made to compare the different GPP products at national scales, particularly for various climate zones. In this study, two of the most widely-used GPP datasets were systematically compared over the eight climate zones across China’s terrestrial ecosystems from 2001 to 2015, which included the moderate resolution imaging spectroradiometer (MODIS) GPP and the breathing Earth system simulator (BESS) GPP products. Additionally, the coarse (0.05o) GPP estimates from the vegetation photosynthesis model (VPM) at the same time scale were used for auxiliary analysis with the two products. Both MODIS and BESS products exhibited a decreasing trend from the southeast region to the northwest inland. The largest GPP was found in the tropical humid region with 5.49 g C m−2 d−1 and 5.07 g C m−2 d−1 for MODIS and BESS, respectively, while the lowest GPP was distributed in the warm temperate arid region, midtemperate semiarid region and plateau zone. Meanwhile, the work confirmed that all these GPP products showed apparent seasonality with the peaks in the summertime. However, large differences were found in the interannual variations across the three GPP products over different climate regions. Generally, the BESS GPP agreed better than the MODIS GPP when compared to the seasonal and interannual variations of VPM GPP. Furthermore, the spatial correlation analysis between terrestrial GPP and the climatic factors, including temperature and precipitation, indicated that natural rainfall dominated the variability in GPP of Northern China, such as the midtemperate semiarid region, while temperature was a key controlling factor in the Southern China and the Tibet Plateau area.

Hyplant-Derived Sun-Induced Fluorescence—A New Opportunity to Disentangle Complex Vegetation Signals from Diverse Vegetation Types

Hyperspectral remote sensing (RS) provides unique possibilities to monitor peatland vegetation traits and their temporal dynamics at a fine spatial scale. Peatlands provide a vital contribution to ecosystem services by their massive carbon storage and wide heterogeneity. However, monitoring, understanding, and disentangling the diverse vegetation traits from a heterogeneous landscape using complex RS signal is challenging, due to its wide biodiversity and distinctive plant species composition. In this work, we aim to demonstrate, for the first time, the large heterogeneity of peatland vegetation traits using well-established vegetation indices (VIs) and Sun-Induced Fluorescence (SIF) for describing the spatial heterogeneity of the signals which may correspond to spatial diversity of biochemical and structural traits. SIF originates from the initial reactions in photosystems and is emitted at wavelengths between 650–780 nm, with the first peak at around 687 nm and the second peak around 760 nm. We used the first HyPlant airborne data set recorded over a heterogeneous peatland area and its surrounding ecosystems (i.e., forest, grassland) in Poland. We deployed a comparative analysis of SIF and VIs obtained from differently managed and natural vegetation ecosystems, as well as from diverse small-scale peatland plant communities. Furthermore, spatial relationships between SIF and VIs from large-scale vegetation ecosystems to small-scale peatland plant communities were examined. Apart from signal variations, we observed a positive correlation between SIF and greenness-sensitive VIs, whereas a negative correlation between SIF and a VI sensitive to photosynthesis was observed for large-scale vegetation ecosystems. In general, higher values of SIF were associated with higher biomass of vascular plants (associated with higher Leaf Area Index (LAI)). SIF signals, especially SIF760, were strongly associated with the functional diversity of the peatland vegetation. At the peatland area, higher values of SIF760 were associated with plant communities of high perennials, whereas, lower values of SIF760 indicated peatland patches dominated by Sphagnum. In general, SIF760 reflected the productivity gradient on the fen peatland, from Sphagnum-dominated patches with the lowest SIF and fAPAR values indicating lowest productivity to the Carex-dominated patches with the highest SIF and fAPAR values indicating highest productivity.

Evaluating and comparing remote sensing terrestrial GPP models for their response to climate variability and CO2 trends

Remote sensing (RS)-based models play an important role in estimating and monitoring terrestrial ecosystem gross primary productivity (GPP). Several RS-based GPP models have been developed using different criteria, yet the sensitivities to environmental factors vary among models; thus, a comparison of model sensitivity is necessary for analyzing and interpreting results and for choosing suitable models. In this study, we globally evaluated and compared the sensitivities of 14 RS-based models (2 process-, 4 vegetation-index-, 5 light-use-efficiency, and 3 machine-learning-based models) and benchmarked them against GPP responses to climatic factors measured at flux sites and to elevated CO₂ concentrations measured at free-air CO₂ enrichment experiment sites. The results demonstrated that the models with relatively high sensitivity to increasing atmospheric CO₂ concentrations showed a higher increasing GPP trend. The fundamental difference in the CO₂ effect in the models' algorithm either considers the effect of CO₂ through changes in greenness indices (nine models) or introduces the influences on photosynthesis (three models). The overall effects of temperature and radiation, in terms of both magnitude and sign, vary among the models, while the models respond relatively consistently to variations in precipitation. Spatially, larger differences among model sensitivity to climatic factors occur in the tropics; at high latitudes, models have a consistent and obvious positive response to variations in temperature and radiation, and precipitation significantly enhances the GPP in mid-latitudes. Compared with the results calculated by flux-site measurements, the model performance differed substantially among different sites. However, the sensitivities of most models are basically within the confidence interval of the flux-site results. In general, the comparison revealed that models differed substantially in the effect of environmental regulations, particularly CO₂ fertilization and water stress, on GPP, and none of the models performed consistently better across the different ecosystems and under the various external conditions.

The potential for modelling peatland habitat condition in Scotland using long-term MODIS data

Globally, peatlands provide an important sink of carbon in their near natural state but potentially act as a source of gaseous and dissolved carbon emission if not in good condition. There is a pressing need to remotely identify peatland sites requiring improvement and to monitor progress following restoration. A medium resolution model was developed based on a training dataset of peatland habitat condition and environmental covariates, such as morphological features, against information derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), covering Scotland (UK). The initial, unrestricted, model provided the probability of a site being in favourable condition. Receiver operator characteristics (ROC) curves for restricted training data, limited to those located on a peat soil map, resulted in an accuracy of 0.915. The kappa statistic was 0.8151, suggesting good model fit. The derived map of predicted peatland condition at the suggested 0.56 threshold was corroborated by data from other sources, including known restoration sites, areas under known non-peatland land cover and previous vegetation survey data mapped onto inferred condition categories. The resulting locations of the areas of peatland modelled to be in favourable ecological condition were largely confined to the North and West of the country, which not only coincides with prior land use intensity but with published predictions of future retraction of the bioclimatic space for peatlands. The model is limited by a lack of spatially appropriate ground observations, and a lack of verification of peat depth at training site locations, hence future efforts to remotely assess peatland condition will require more appropriate ground-based monitoring. If appropriate ground-based observations could be collected, using remote sensing could be considered a cost-efficient means to provide data on changes in peatland habitat condition.

Biogeochemistry of soil organic matter in agroecosystems & environmental implications

The biogeochemistry of soil organic matter (SOM), as a highly complex and dynamic soil property, is of vital importance for the health and ecological functioning of ecosystems, including managed and natural ones. Dominantly composed of carbon (C), SOM functions in global C cycling, including C sequestration and emission (e.g. soil respiration). Mediterranean agroecosystems especially, due to favourable climate conditions for mineralisation of SOM, are expected to go through enhanced SOM decomposition (i.e. C emission) under the ongoing global warming and related climatic change and variability (frequent heat waves, fires and extreme water disturbances). The relatively stable (humified) SOM components, especially in the organically-enriched topsoil layers, due to their specific physical chemistry (strongly charged interface) may have a significant role in biogeochemistry of charged (in)organic nutrients and/or contaminants such as toxic metal ions and persistent organic pollutants. The recent studies show that some natural vulnerabilities of Mediterranean regions (such as high risk of the erosion-driven processes) can increase movement of some hazardous pedospheric constituents (e.g. pesticides) to water bodies and/or into the air, thus influencing the whole ecosystem health. A majority of recent surveys confirm depletion of SOM and spatially variable distribution of metal contamination in the Mediterranean topsoils. Using the advanced geochemical prediction approaches in combination with the relevant soil databases, we characterised organo-mineral and organo-metal complexation and its effect on speciation and sorption of trace metals in karstified Mediterranean agroecosystems. Metal biogeochemistry was found to vary markedly under relatively constant pedosphere conditions, depending on organo-mineral soil components and pH, which may significantly impact metal mobility/availability in the soil-plant continuum. The knowledge of the SOM spatial distribution and dynamics and its interactions with other pedovariables is essential for sustainable management of SOM and control of contaminant mobility to avoid degradation processes in (agro)ecosystems.

A data science challenge for converting airborne remote sensing data into ecological information

Ecology has reached the point where data science competitions, in which multiple groups solve the same problem using the same data by different methods, will be productive for advancing quantitative methods for tasks such as species identification from remote sensing images. We ran a competition to help improve three tasks that are central to converting images into information on individual trees: (1) crown segmentation, for identifying the location and size of individual trees; (2) alignment, to match ground truthed trees with remote sensing; and (3) species classification of individual trees. Six teams (composed of 16 individual participants) submitted predictions for one or more tasks. The crown segmentation task proved to be the most challenging, with the highest-performing algorithm yielding only 34% overlap between remotely sensed crowns and the ground truthed trees. However, most algorithms performed better on large trees. For the alignment task, an algorithm based on minimizing the difference, in terms of both position and tree size, between ground truthed and remotely sensed crowns yielded a perfect alignment. In hindsight, this task was over simplified by only including targeted trees instead of all possible remotely sensed crowns. Several algorithms performed well for species classification, with the highest-performing algorithm correctly classifying 92% of individuals and performing well on both common and rare species. Comparisons of results across algorithms provided a number of insights for improving the overall accuracy in extracting ecological information from remote sensing. Our experience suggests that this kind of competition can benefit methods development in ecology and biology more broadly.

Information Needs of Next-Generation Forest Carbon Models: Opportunities for Remote Sensing Science

Forests are integral to the global carbon cycle, and as a result, the accurate estimation of forest structure, biomass, and carbon are key research priorities for remote sensing science. However, estimating and understanding forest carbon and its spatiotemporal variations requires diverse knowledge from multiple research domains, none of which currently offer a complete understanding of forest carbon dynamics. New large-area forest information products derived from remotely sensed data provide unprecedented spatial and temporal information about our forests, which is information that is currently underutilized in forest carbon models. Our goal in this communication is to articulate the information needs of next-generation forest carbon models in order to enable the remote sensing community to realize the best and most useful application of its science, and perhaps also inspire increased collaboration across these research fields. While remote sensing science currently provides important contributions to large-scale forest carbon models, more coordinated efforts to integrate remotely sensed data into carbon models can aid in alleviating some of the main limitations of these models; namely, low sample sizes and poor spatial representation of field data, incomplete population sampling (i.e., managed forests exclusively), and an inadequate understanding of the processes that influence forest carbon accumulation and fluxes across spatiotemporal scales. By articulating the information needs of next-generation forest carbon models, we hope to bridge the knowledge gap between remote sensing experts and forest carbon modelers, and enable advances in large-area forest carbon modeling that will ultimately improve estimates of carbon stocks and fluxes.

Do High-Voltage Power Transmission Lines Affect Forest Landscape and Vegetation Growth: Evidence from a Case for Southeastern of China

The rapid growth of the network of high-voltage power transmission lines (HVPTLs) is inevitably covering more forest domains. However, no direct quantitative measurements have been reported of the effects of HVPTLs on vegetation growth. Thus, the impacts of HVPTLs on vegetation growth are uncertain. Taking one of the areas with the highest forest coverage in China as an example, the upper reaches of the Minjiang River in Fujian Province, we quantitatively analyzed the effect of HVPTLs on forest landscape fragmentation and vegetation growth using Landsat imageries and forest inventory datasets. The results revealed that 0.9% of the forests became edge habitats assuming a 150 m depth-of-edge-influence by HVPTLs, and the forest plantations were the most exposed to HVPTLs among all the forest landscape types. Habitat fragmentation was the main consequence of HVPTL installation, which can be reduced by an increase in the patch density and a decrease in the mean patch area (MA), largest patch index (LPI), and effective mesh size (MESH). In all the landscape types, the forest plantation and the non-forest land were most affected by HVPTLs, with the LPI values decreasing by 44.1 and 20.8%, respectively. The values of MESH decreased by 44.2 and 32.2%, respectively. We found an obvious increasing trend in the values of the normalized difference vegetation index (NDVI) in 2016 and NDVI growth during the period of 2007 to 2016 with an increase in the distance from HVPTL. The turning points of stability were 60 to 90 meters for HVPTL corridors and 90 to 150 meters for HVPTL pylons, which indicates that the pylons have a much greater impact on NDVI and its growth than the lines. Our research provides valuable suggestions for vegetation protection, restoration, and wildfire management after the construction of HVPTLs.

A novel index for assessment of riparian strip efficiency in agricultural landscapes using high spatial resolution satellite imagery

Riparian strips are used worldwide to protect riverbanks and water quality in agricultural zones because of their numerous environmental benefits. A metric called Riparian Strip Quality Index, which is based on the percentage area of riparian vegetation, is used to evaluate their ecological condition. This index measures the potential capacity of riparian strips to filter sediments, retain pollutants, and provide shelter for terrestrial and aquatic species. This research aims to improve this metric by integrating the ability of riparian strips to intercept surface runoff, which is the major cause of water pollution and erosion in productive areas. In Canada and the Nordic countries, rapid surface drainage from snow melt and spring rains is often practiced to avoid production delays and losses. This reduces the efficiency of riparian buffer strips by promoting soil erosion due to concentrated runoff. A new proposed metric called Riparian Strip Efficiency Index (RSEI), incorporates not only land cover information, but topographic and hydrologic variables to model the intensity and spatial distribution of runoff streamflow, and the capability of riparian strips to retain sediments and pollutants. The research is performed over the La Chevrotière River Basin in the Portneuf municipality in Québec (Canada) using hydrological modeling, land cover and topographic data extracted from very high spatial resolution WorldView-2 imagery as a unique source of inputs. The results show that RSEI provides a better characterization of the ecosystem services of riparian strips in terms of pollutants filtration and prevention of soil erosion in agricultural areas. RSEI will allow a better management of agricultural practices such as drainage and land leveling. Further, it will provide to land managers information to monitor environmental changes and to prioritize intervention areas, which ultimately targets to ensure optimal allocation of private or public funds toward the most inefficient and threatened riparian strips.

Assessing hyperspectral indices for tracing chlorophyll fluorescence parameters in deciduous forests

Chlorophyll fluorescence can be used to quantify the efficiency of photochemistry and heat dissipation. While several instruments such as Pulse-Amplitude-Modulation (PAM) fluorometers are available for taking direct measurements of parameters related to chlorophyll fluorescence, large-scale instantaneous ecosystem monitoring remains difficult. Several hyperspectral indices have been claimed to be closely related to some chlorophyll fluorescence parameters (e.g. photosystem II quantum yield (Yield), qP, NPQ), which may pave a way for efficient large-scale monitoring of fluorescence parameters. In this study, we have examined 30 published hyperspectral indices for their possible use in tracing chlorophyll fluorescence parameters. The comparison is based on a series of unique datasets with synchronous measurements of reflected hyperspectra and seven fluorescence parameters (i.e., F_m, F₀, F_s, F_m', Yield, qP and NPQ) from leaves of Fagus crenata and other six broadleaf species sampled in Mt. Naeba, Japan. Among them, the first dataset is composed of seasonal canopy field measurements of Fagus crenata leaves, while the second is composed of field measurements of other deciduous species including Lindera umbellate, Clethra barbinervis, Viburnum furcatum, Eleutherococcus sciadophylloides, Quercus crispula and Acer japonicum. Furthermore, an additional dataset composed of data resulting from various controlled experiments using inhibitors has been applied for improving physiological interpretations of indices. Results revealed that PRI had higher coefficients of determination and lower root mean square errors than other indices evaluated with a set of chlorophyll fluorescence parameters. However, this pattern was seen only for beech leaves and performed poorly across other species. As a result, no specific indices that are currently available are recommended for tracing fluorescence parameters.

Determining Subarctic Peatland Vegetation Using an Unmanned Aerial System (UAS)

Rising global temperatures tied to increases in greenhouse gas emissions are impacting high latitude regions, leading to changes in vegetation composition and feedbacks to climate through increased methane (CH4) emissions. In subarctic peatlands, permafrost collapse has led to shifts in vegetation species on landscape scales with high spatial heterogeneity. Our goal was to provide a baseline for vegetation distribution related to permafrost collapse and changes in biogeochemical processes. We collected unmanned aerial system (UAS) imagery at Stordalen Mire, Abisko, Sweden to classify vegetation cover types. A series of digital image processing routines were used to generate texture attributes within the image for the purpose of characterizing vegetative cover types. An artificial neural network (ANN) was developed to classify the image. The ANN used all texture variables and color bands (three spectral bands and six metrics) to generate a probability map for each of the eight cover classes. We used the highest probability for a class at each pixel to designate the cover type in the final map. Our overall misclassification rate was 32%, while omission and commission error by class ranged from 0% to 50%. We found that within our area of interest, cover classes most indicative of underlying permafrost (hummock and tall shrub) comprised 43.9% percent of the landscape. Our effort showed the capability of an ANN applied to UAS high-resolution imagery to develop a classification that focuses on vegetation types associated with permafrost status and therefore potentially changes in greenhouse gas exchange. We also used a method to examine the multiple probabilities representing cover class prediction at the pixel level to examine model confusion. UAS image collection can be inexpensive and a repeatable avenue to determine vegetation change at high latitudes, which can further be used to estimate and scale corresponding changes in CH4 emissions.

Peatland vegetation composition and phenology drive the seasonal trajectory of maximum gross primary production

Gross primary production (GPP) is a key driver of the peatland carbon cycle. Although many studies have explored the apparent GPP under natural light conditions, knowledge of the maximum GPP at light-saturation (GPP_max) and its spatio-temporal variation is limited. This information, however, is crucial since GPP_max essentially constrains the upper boundary for apparent GPP. Using chamber measurements combined with an external light source across experimental plots where vegetation composition was altered through long-term (20-year) nitrogen addition and artificial warming, we could quantify GPP_max in-situ and disentangle its biotic and abiotic controls in a boreal peatland. We found large spatial and temporal variations in the magnitudes of GPP_max which were related to vegetation species composition and phenology rather than abiotic factors. Specifically, we identified vegetation phenology as the main driver of the seasonal GPP_max trajectory. Abiotic anomalies (i.e. in air temperature and water table level), however, caused species-specific divergence between the trajectories of GPP_max and plant development. Our study demonstrates that photosynthetically active biomass constrains the potential peatland photosynthesis while abiotic factors act as secondary modifiers. This further calls for a better representation of species-specific vegetation phenology in process-based peatland models to improve predictions of global change impacts on the peatland carbon cycle.

Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions

Satellite time series with high spatial resolution is critical for monitoring land surface dynamics in heterogeneous landscapes. Although remote sensing technologies have experienced rapid development in recent years, data acquired from a single satellite sensor are often unable to satisfy our demand. As a result, integrated use of data from different sensors has become increasingly popular in the past decade. Many spatiotemporal data fusion methods have been developed to produce synthesized images with both high spatial and temporal resolutions from two types of satellite images, frequent coarse-resolution images, and sparse fine-resolution images. These methods were designed based on different principles and strategies, and therefore show different strengths and limitations. This diversity brings difficulties for users to choose an appropriate method for their specific applications and data sets. To this end, this review paper investigates literature on current spatiotemporal data fusion methods, categorizes existing methods, discusses the principal laws underlying these methods, summarizes their potential applications, and proposes possible directions for future studies in this field.