Characterizing boreal peatland plant composition and species diversity with hyperspectral remote sensing

Peatlands, which account for approximately 15% of land surface across the arctic and boreal regions of the globe, are experiencing a range of ecological impacts as a result of climate change. Factors that include altered hydrology resulting from drought and permafrost thaw, rising temperatures, and elevated levels of atmospheric carbon dioxide have been shown to cause plant community compositional changes. Shifts in plant composition affect the productivity, species diversity, and carbon cycling of peatlands. We used hyperspectral remote sensing to characterize the response of boreal peatland plant composition and species diversity to warming, hydrologic change, and elevated CO2. Hyperspectral remote sensing techniques offer the ability to complete landscape-scale analyses of ecological responses to climate disturbance when paired with plot-level measurements that link ecosystem biophysical properties with spectral reflectance signatures. Working within two large ecosystem manipulation experiments, we examined climate controls on composition and diversity in two types of common boreal peatlands: a nutrient rich fen located at the Alaska Peatland Experiment (APEX) in central Alaska, and an ombrotrophic bog located in northern Minnesota at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment. We found a strong effect of plant functional cover on spectral reflectance characteristics. We also found a positive relationship between species diversity and spectral variation at the APEX field site, which is consistent with other recently published findings. Based on the results of our field study, we performed a supervised land cover classification analysis on an aerial hyperspectral dataset to map peatland plant functional types (PFTs) across an area encompassing a range of different plant communities. Our results underscore recent advances in the application of remote sensing measurements to ecological research, particularly in far northern ecosystems

Detecting peatland vegetation patterns with multi-temporal field spectroscopy

doi: 10.1080/15481603.2022.2152303Peatlands are one of the most significant terrestrial carbon pools, and the processes behind the carbon cycle in peatlands are strongly associated with different vegetation patterns. Handheld spectroradiometer data has been widely applied in ecological research, but there is a lack of studies on peatlands assessing how the temporal and spectral resolution affect the detectability of vegetation patterns. We collected field spectroscopy and vegetation inventory data at two northern boreal peatlands, Lompolojankka and Halssiaapa, between late May and August 2019. We conducted multivariate random forest regressions to examine the appropriate periods, benefits of multi-temporal data, and optimal spectral bandwidth and sampling interval for detecting plant communities and the two-dimensional (2D) %-cover, above-ground biomass (AGB) and leaf area index (LAI) of seven plant functional types (PFTs). In the best cross-site regression models for detecting plant community clusters (PCCs), R-2 was 42.6-48.0% (root mean square error (RMSE) 0.153-0.193), and for PFT 2D %-cover 53.9-69.8% (RMSE 8.2-17.6%), AGB 43.1-61.5% (RMSE 86.2-165.5 g/m(2)) and LAI 46.3-51.3% (RMSE 0.220-0.464 m(2)/m(2)). The multi-temporal data of the whole season increased R-2 by 13.7-24.6%-points and 10.2-33.0%-points for the PCC and PFT regressions, respectively. There was no single optimal temporal window for vegetation pattern detection for the two sites; in Lompolojankka the early growing season between late May and mid-June had the highest regression performance, while in Halssiaapa, the optimal period was during the peak season, from July to early August. In general, the spectral sampling interval between 1 to 10 nm yielded the best regression performance for most of the vegetation characteristics in Lompolojankka, whereas the optimal range extended to 20 nm in Halssiaapa. Our findings underscore the importance of fieldwork timing and the use of multi-temporal and hyperspectral data in detecting vegetation in spatially heterogeneous landscapes.Peer reviewe

A Multiscale Productivity Assessment of High Andean Peatlands across the Chilean Altiplano Using 31 Years of Landsat Imagery

The high Andean peatlands, locally known as "bofedales", are a unique type of wetland distributed across the high-elevation South American Altiplano plateau. This extensive peatland network stores significant amounts of carbon, regulates local and regional hydrological cycles, supports habitats for a variety of plant and animal species, and has provided critical water and forage resources for the livestock of the indigenous Aymara communities for thousands of years. Nevertheless, little is known about the productivity dynamics of the high Andean peatlands, particularly in the drier western Altiplano region bordering the Atacama desert. Here, we provide the first digital peatland inventory and multiscale productivity assessment for the entire western Altiplano (63,705 km(2)) using 31 years of Landsat data (about 9000 scenes) and a non-parametric approach for estimating phenological metrics. We identified 5665 peatland units, covering an area of 510 km(2), and evaluated the spatiotemporal productivity patterns at the regional, peatland polygon, and individual pixel scales. The regional assessment shows that the peatland areas and peatlands with higher productivity are concentrated towards the northern part of our study region, which is consistent with the Altiplano north-south aridity gradient. Regional patterns further reveal that the last seven years (2011-2017) have been the most productive period over the past three decades. While individual pixels show contrasting patterns of reductions and gains in local productivity during the most recent time period, most of the study area has experienced increases in annual productivity, supporting the regional results. Our novel database can be used not only to explore future research questions related to the social, biological, and hydrological influences on peatland productivity patterns, but also to provide technical support for the sustainable development of livestock practices and conservation and water management policy in the Altiplano region.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Detecting peatland vegetation patterns with multi-temporal field spectroscopy

Peatlands are one of the most significant terrestrial carbon pools, and the processes behind the carbon cycle in peatlands are strongly associated with different vegetation patterns. Handheld spectroradiometer data has been widely applied in ecological research, but there is a lack of studies on peatlands assessing how the temporal and spectral resolution affect the detectability of vegetation patterns. We collected field spectroscopy and vegetation inventory data at two northern boreal peatlands, Lompolojänkkä and Halssiaapa, between late May and August 2019. We conducted multivariate random forest regressions to examine the appropriate periods, benefits of multi-temporal data, and optimal spectral bandwidth and sampling interval for detecting plant communities and the two-dimensional (2D) %-cover, above-ground biomass (AGB) and leaf area index (LAI) of seven plant functional types (PFTs). In the best cross-site regression models for detecting plant community clusters (PCCs), R2 was 42.6–48.0% (root mean square error (RMSE) 0.153–0.193), and for PFT 2D %-cover 53.9–69.8% (RMSE 8.2–17.6%), AGB 43.1–61.5% (RMSE 86.2–165.5 g/m2) and LAI 46.3–51.3% (RMSE 0.220–0.464 m2/m2). The multi-temporal data of the whole season increased R2 by 13.7–24.6%-points and 10.2–33.0%-points for the PCC and PFT regressions, respectively. There was no single optimal temporal window for vegetation pattern detection for the two sites; in Lompolojänkkä the early growing season between late May and mid-June had the highest regression performance, while in Halssiaapa, the optimal period was during the peak season, from July to early August. In general, the spectral sampling interval between 1 to 10 nm yielded the best regression performance for most of the vegetation characteristics in Lompolojänkkä, whereas the optimal range extended to 20 nm in Halssiaapa. Our findings underscore the importance of fieldwork timing and the use of multi-temporal and hyperspectral data in detecting vegetation in spatially heterogeneous landscapes

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

Using Very High Resolution Remotely Piloted Aircraft Imagery to Map Peatland Vegetation Composition and Configuration Patterns within an Elevation Gradient

GIS has developed over the decades from theory to highly accurate scientific observation using satellites that provide high resolution imagery. Over the last decade drones have been introduced to the world of GIS and have been able to overcome some of the issues present in satellite and aerial imagery such as lower resolution for smaller objects and temporal constraints. My thesis aims to explore how accurately RPAS can identify vegetation communities classed by morphological structure when compared to ground based vegetation surveys in peatlands in Alberta. The wetland sites are situated across subregions that are currently not mapped by the Alberta Merged Wetland Inventory. Our research aims to answer the following questions: 1) assess at how differing image resolution (2 cm and 3 cm) influence the ability to identify morphologically functional classes within the RPAS imagery. 2) test the accuracy at which different morphological functional trait classes of vegetation could be digitized from remotely sensed imagery, highlight which classes had the highest and lowest accuracy and try to explain why. 3) Investigate if RPAS can be used to map out vegetation composition and configuration to replace ground based surveys. 4) Determine across an elevation gradient within the subregion groups (subalpine, montane and upper foothills) if there are any significant landscape metrics patterns that change across these subregions using elevation as a controlling variable. Flights were conducted with RPAS to collect imagery with a resolution of 2 cm and 3 cm then classified into digitized classes that represent the morphological structure of different vegetation across 18 peatland. 13 different features were classified in the 18 peatlands. All 18 peatland boundaries were delineated using slope, which removed classes such as roads, objects, culverts, and bridges. The delineated peatlands were then run through a landscape metric package in R to determine spatial patterns of vegetation at both landscape and class level. Landscape Metrics revealed composition and configuration characteristics that were significant when plotted against elevation for landscape level metrics. Replication of the results once accuracy has been increased using either higher resolution imagery or other sensors to determine validity of the results is needed

Characterizing Boreal Peatland Plant Composition and Species Diversity with Hyperspectral Remote Sensing

Peatlands, which account for approximately 15% of land surface across the arctic and boreal regions of the globe, are experiencing a range of ecological impacts as a result of climate change. Factors that include altered hydrology resulting from drought and permafrost thaw, rising temperatures, and elevated levels of atmospheric carbon dioxide have been shown to cause plant community compositional changes. Shifts in plant composition affect the productivity, species diversity, and carbon cycling of peatlands. We used hyperspectral remote sensing to characterize the response of boreal peatland plant composition and species diversity to warming, hydrologic change, and elevated CO2. Hyperspectral remote sensing techniques offer the ability to complete landscape-scale analyses of ecological responses to climate disturbance when paired with plot-level measurements that link ecosystem biophysical properties with spectral reflectance signatures. Working within two large ecosystem manipulation experiments, we examined climate controls on composition and diversity in two types of common boreal peatlands: a nutrient rich fen located at the Alaska Peatland Experiment (APEX) in central Alaska, and an ombrotrophic bog located in northern Minnesota at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment. We found a strong effect of plant functional cover on spectral reflectance characteristics. We also found a positive relationship between species diversity and spectral variation at the APEX field site, which is consistent with other recently published findings. Based on the results of our field study, we performed a supervised land cover classification analysis on an aerial hyperspectral dataset to map peatland plant functional types (PFTs) across an area encompassing a range of different plant communities. Our results underscore recent advances in the application of remote sensing measurements to ecological research, particularly in far northern ecosystems

Modelling peatland water table depth using remotely sensed satellite data

Peatlands are carbon-rich wetland ecosystems and represent the largest terrestrial carbon store. Although they are natural carbon sinks, damage, drainage and extraction over past decades have turned peatlands into a global carbon source. To tackle this nearly irreversible loss, peatland conservation and restoration projects on global and national levels have been increasing in numbers. High water table depth (WTD) is a highly important factor that influences peatland condition, resilience and ability to accumulate carbon. Given the extent of peatlands, a regular physical collection of data in situ, looking forward, would be impractical and difficult to accomplish, and the development of a remote sensing methods for peatland WTD monitoring would be highly beneficial. The accessibility to satellite data along with advancements in sensors, both in variety - optical, microwave, thermal, and their resolutions - spatial, spectral, and temporal, has greatly increased in the last decade. Combined with advances in image processing using cloud computing and machine learning, it has made it easier to access and process remotely sensed data. Synthetic aperture radar (SAR), with its ability to provide data regardless of the weather, has emerged as an important source of data for environmental applications. This project aimed to advance the usage of remotely sensed SAR data to predict peatland water table depth. First, a unique high resolution laboratory study was completed confirming SAR backscatter sensitivity to changes in peatland soil moisture and water table depth. This was followed by a case study for the Forsinard Flows area, where Sentinel-1 SAR data were used to build and test three models of different complexity for WTD prediction. The random forest model was found to be the most suited with an overall good temporal fit, highest correlation scores and lowest RMSE values. The model was later tested on a wider Peatland ACTION dataset, reaching an even higher score, affirming its applicability to peatlands in various conditions (near natural, degraded and undergoing restoration). In the final section of the thesis, up to twenty year-long time series of remote sensing data were analysed to investigate trends and change points in peatland restoration areas. The trends found using lower resolution satellite data from MODIS gave mixed results and would only be indicative of very abrupt changes, such as tree felling. The trends from the modelled WTD series based on Sentinel-1 data were indicative of positive trajectories towards higher WTD, following restoration. The results from this thesis suggest that remotely sensed data can be informative about changes in the WTD and overall peatland condition, can be used to look at seasonal change, and can be indicative of restoration progress and response to droughts. Recent studies have shown a close link between greenhouse gasses and peatland WTD, therefore, if methods of predicting WTD based on remotely sensed data are developed further, they ultimately could be used as a proxy for greenhouse gas emission reporting