Ordination and hyperspectral remote sensing approach to classify peatland biotopes along soil moisture and fertility gradients

Middleton, Maarit; Närhi, Paavo; Arkimaa, Hilkka; Hyvönen, Eija; Kuosmanen, V.; Treitz, Paul; Sutinen, Raimo

doi:10.1016/j.rse.2012.06.010

Cited by 40 publications

(41 citation statements)

References 55 publications

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“…Therefore, it is important that in addition to forbs and shrubs, graminoids, mosses and lichens should also be identified at the species level when carrying out floristic analyses in patterned peatlands. This result is in line with earlier findings concerning peatland vegetation mapping that show how different moss and graminoid species locate differently on ordination axes (Harris et al., ; Middleton et al., ). However, in other types of environments with fragmented vegetation patterns but higher forb and shrub species richness, such as tundra, an extended PFT approach can be more valuable (Mikola et al., ; Virtanen & Ek, ).…”

Section: Discussionsupporting

confidence: 92%

Comparing ultra‐high spatial resolution remote‐sensing methods in mapping peatland vegetation

Räsänen

Juutinen

Tuittila

et al. 2019

J Vegetation Science

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Questions How to map floristic variation in a patterned fen in an ecologically meaningfully way? Can plant communities be delineated with species data generalized into plant functional types? What are the benefits and drawbacks of the two selected remote‐sensing approaches in mapping vegetation patterns, namely: (a) regression models of floristically defined fuzzy plant community clusters and (b) classification of predefined habitat types that combine vegetation and land cover information? Location Treeless 0.4 km2 mesotrophic string–flark fen in Kaamanen, northern Finland. Methods We delineated plant community clusters with fuzzy c‐means clustering based on two different inventories of plant species and functional type distribution. We used multiple optical remote‐sensing data sets, digital elevation models and vegetation height models derived from drone, aerial and satellite platforms from ultra‐high to very high spatial resolution (0.05–3 m) in an object‐based approach. We mapped spatial patterns for fuzzy and crisp plant community clusters using boosted regression trees, and fuzzy and crisp habitat types using supervised random forest classification. Results Clusters delineated with species‐specific data or plant functional type data produced comparable results. However, species‐specific data for graminoids and mosses improved the accuracy of clustering in the case of flarks and string margins. Mapping accuracy was higher for habitat types (overall accuracy 0.72) than for fuzzy plant community clusters (R2 values between 0.27 and 0.67). Conclusions For ecologically meaningful mapping of a patterned fen vegetation, plant functional types provide enough information. However, if the aim is to capture floristic variation in vegetation as realistically as possible, species‐specific data should be used. Maps of plant community clusters and habitat types complement each other. While fuzzy plant communities appear to be floristically most accurate, crisp habitat types are easiest to interpret and apply to different landscape and biogeochemical cycle analyses and modeling.

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Section: Discussionsupporting

confidence: 92%

Comparing ultra‐high spatial resolution remote‐sensing methods in mapping peatland vegetation

Räsänen

Juutinen

Tuittila

et al. 2019

J Vegetation Science

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“…Furthermore, the method outlined in this study does hold advantages over other constrained ordination approaches that have been used to map floristic gradients across peatlands. This is because constrained ordinations, where the ordination axes are forced to be linear combinations of a number of explanatory variables, often require ancillary environmental variables (e.g., moisture and pH), to be measured in situ (Middleton et al, 2012) or use spectral data (Thomas et al, 2002), which can lead to overfitting due to collinearity if the number of spectral bands approaches the number of sampled plots.…”

Section: Discussionmentioning

confidence: 99%

“…This combined ordination-regression approach has been used to map relatively homogenous landscapes such as grasslands (Schmidtlein & Sassin, 2004) but there are limited studies that utilise this approach for mapping species composition in heterogeneous landscapes (Feilhauer et al, 2011). Few have employed ordination approaches for specifically mapping peatland vegetation (Middleton et al, 2012;Thomas et al, 2002) but those that have often combine traditional ordination techniques (e.g., correspondence analysis or canonical correspondence analysis) with supervised classification (e.g., maximum likelihood classification or support vector machines). There are very few studies that investigate the potential of ordination-regression methods for continuous mapping of peatland floristic composition (Schmidtlein et al, 2007) and none that have used this approach for mapping peatland plant functional types (PFTs).…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral remote sensing of peatland floristic gradients

Harris

Charnock

Lucas

2015

Remote Sensing of Environment

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Previous studies have shown that the floristic composition of northern peatlands provides important information regarding ecosystem processes and their responses to environmental change. Remote sensing is the most expeditious method of obtaining floristic information at landscape and regional scales, but the spatial complexity of many northern peatlands and the spectral similarity of a number of peatland vegetation species is such that the success of traditional methods of vegetation classification is often limited. Here, we assessed whether ordination and regression analyses may be a useful alternative method for mapping peatland plant communities from remote sensing data. We used isometric feature mapping (Isomap) to describe the community structure of the peatland vegetation and related the identified continuous floristic gradients to hyperspectral imagery (AISA Eagle) using partial least squares regression (PLSR). We performed the same analysis at two hierarchical levels of species aggregation in order to map continuous gradients in the composition of both species and plant functional types (PFTs), the latter of which is the most widely used level of aggregation in northern ecosystems. Isomap was able to transfer 82% and more than 96% of the observed ground-based observations to the ordination space for plots characterised by species and PFT; respectively. The modelled floristic gradients showed good agreement with ground-based species and PFT observations although the strength of the agreement was proportional to the amount of floristic variation explained by each ordination axis (r2 val =0.74, 0.45 and 0.30 for the first three ordination axes and r2 val =0.68 and 0.66 for the first two ordination axes; for species and PFT floristic gradients respectively). We also found that how a PFT is defined has an important influence on the success with which it can be mapped. The resultant mapped floristic gradients enabled visualisation of homogeneous vegetation stands, heterogeneous mixtures of different key species and PFTs, and the presence of continuous and abrupt floristic transitions, without the need for unique spectral signatures or the collection of data characterising ancillary environmental variables.authorsversionPeer reviewe

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“…Remote sensing techniques in general have shown potential for peatland monitoring, but most previous studies have focused on the use of relatively coarse spatial resolution imagery that often resulted in limited discrimination of cover types or biophysical characteristics [3]. Alternative techniques such as data fusion between high spatial resolution imagery and LiDAR [3], classification of pan-sharpened multispectral imagery [4], analysis of airborne hyperspectral imagery [5] and object based classification of aerial photography [6] have reduced the thematic uncertainty in peatland classifications. Nevertheless, for the detection of short durational phenomena such as the flowering events of some peatland species, rapid deployment and high temporal resolution data may be required.…”

Section: Introductionmentioning

confidence: 99%

“…The objective of our study was to determine the areal coverage of E. vaginatum from remote controlled rotorcraft videography and estimate its contribution to the total bog flux of CH 4 . Several studies have focused on overall classifications of bogs [2,[4][5][6]20,21]. Here we focus only on the detection and classification of E. vaginatum.…”

Section: Introductionmentioning

confidence: 99%

Videographic Analysis of Eriophorum Vaginatum Spatial Coverage in an Ombotrophic Bog

et al. 2013

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Abstract:The use of Remotely Piloted Aircraft Systems (RPAS) as well as newer automated unmanned aerial vehicles is becoming a standard method in remote sensing studies requiring high spatial resolution (<1 m) and very precise temporal data to capture phenological events. In this study we use a low cost rotorcraft to map Eriophorum vaginatum at Mer Bleue, an ombrotrophic bog located east of Ottawa, ON, Canada. We focus on E. vaginatum because this sedge plays an important role in methane (CH 4 ) gas exchange in peatlands. Using the remote controlled rotorcraft we were able to record, process, and mosaic 11.1 hectares of 4.5 cm spatial resolution imagery extracted from individual frames of video recordings (post georegistration RMSE 4.90 ± 4.95 cm). Our results, based on a supervised classification (96% accuracy) of the red, green, blue image planes, indicate a total tussock cover of 2,417 m 2 . Because the basal area of the plant is more relevant for calculating its contribution to the CH 4 flux, the tussock area was related to the basal area from field data (R 2 = 0.88, p < 0.0001). Our final results indicate a total basal area of 1,786 ± 62.8 m 2 . Based on temporal measurements of CH 4 flux from the peatland as a whole that vary over the growing season, we estimate the E. vaginatum contribution to range from 3.0% to 17.3% of that total. Overall, our low cost approach was an effective non-destructive way to derive E. vaginatum coverage and estimate CH 4 exchange over the growing season. OPEN ACCESSRemote Sens. 2013, 5 6502

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Ordination and hyperspectral remote sensing approach to classify peatland biotopes along soil moisture and fertility gradients

Cited by 40 publications

References 55 publications

Comparing ultra‐high spatial resolution remote‐sensing methods in mapping peatland vegetation

Comparing ultra‐high spatial resolution remote‐sensing methods in mapping peatland vegetation

Hyperspectral remote sensing of peatland floristic gradients

Videographic Analysis of Eriophorum Vaginatum Spatial Coverage in an Ombotrophic Bog

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