Application of multisensoral remote sensing data in the mapping of alkaline fens Natura 2000 habitat

Ecological Indicators 70 (2016) 196–208

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Application of multisensoral remote sensing data in the mapping of alkaline fens Natura 2000 habitat Dominik Kopec´ a,∗ , Dorota Michalska-Hejduk a , Łukasz Sławik b , Tomasz Berezowski c , d c ´ ´ Marcin Borowski b , Stanisław Rosadzinski , Jarosław Chormanski a

Department of Geobotany and Plant Ecology, Faculty of Biology and Environmental Protection, University of Lodz, Banacha 1/3, 90-237 Łód´z, Poland MGGP Aero Ltd., Kaczkowskiego 6, 33-100 Tarnów, Poland c Department of Hydraulic Engineering, Warsaw University of Life Sciences, Nowoursynowska 166, 02-787 Warsaw, Poland d Department of Plant Ecology and Environmental Protection, Faculty of Biology, Adam Mickiewicz University in Pozna´ n, Umultowska 89, 61-614 Pozna´ n, Poland b

a r t i c l e

i n f o

Article history: Received 3 February 2016 Received in revised form 19 May 2016 Accepted 2 June 2016 Keywords: Fen meadow Eutrophic fens Supervised classification Thermal data ALS LST Conservation status 7230

a b s t r a c t The Biebrza River valley (NE Poland) is distinguished by largely intact, highly natural vegetation patterns and very good conservation status of wetland ecosystems. In 2013–2014, studies were conducted in the upper Biebrza River basin to develop a remote sensing method for alkaline fen classification – a protected Natura 2000 habitat (code 7230) – using remote sensing technologies. High resolution airborne true colour (RGB) and color infrared (CIR) orthophotomaps, the laser scanning point cloud and thermal day and night images were obtained in August 2013 in the Biebrza River basin. At the same time, botanical studies were conducted in this area using conventional phytosociological methods. The random forest classification method was used to distinguish patches (phytocoenoses) of alkaline fens in the study area. The developed method of wetland identification has an accuracy of 91.5%. The night land surface temperature (LST) appears to have the greatest indicator potential. The obtained inventory results were compared with the results of the traditional habitat 7230 mapping method, carried out in 2011–2013 by another team of authors, under the Management Plan (MP, 2014). The obtained results suggest that the developed method has a wide application in nature conservation. Remote sensing methods are alternatives to traditional methods and can be used to identify alkaline fens on a larger scale. © 2016 Elsevier Ltd. All rights reserved.

1. Introduction Remote sensing techniques are increasingly used in the identification and mapping of ecologically valuable plant communities (Corbane et al., 2013; Feilhauer et al., 2014; Harris et al., 2015; Spanhove et al., 2012; Stenzel et al., 2014). Identification of Natura 2000 habitats is particularly important to the EU Member States, and remote sensing can help in the implementation of this objective (Vanden Borre et al., 2011). In recent years, different methods were used in the identification of various Natura 2000 habitats e.g.: spectral mixture analysis (Delalieux et al., 2012; Mücher et al., 2013), a fuzzy classification system (Wagner-Lücker et al., 2013), mix of automated and supervised techniques (Alexandridis et al., 2009), pixel-based classification (Delalieux et al., 2012); machine learning and fuzzy categorization (Zlinszky et al., 2015); random forest

∗ Corresponding author. ´ E-mail address: [email protected] (D. Kopec). http://dx.doi.org/10.1016/j.ecolind.2016.06.001 1470-160X/© 2016 Elsevier Ltd. All rights reserved.

machine learning (Feilhauer et al., 2014; Zlinszky et al., 2014); and fuzzy classification with Support Vector Machines (Middleton et al., 2012). In most cases, hyperspectral data (Corbane et al., 2015; Delalieux et al., 2012; Middleton et al., 2012; Mücher et al., 2013) and laser scanning data (Zlinszky et al., 2012, 2015) were used in the identification process. Depending on the study, analyses were based either on satellite (Alexandridis et al., 2009) or high-resolution airborne data (Spanhove et al., 2012; Mücher et al., 2013). At present, in most cases, monitoring of Natura 2000 habitats is carried out through botanical fieldwork. It is, however, a very time-consuming method and therefore, often ineffective in the rapidly changing natural systems, particularly in ecosystems dependent on the water-level dynamics or extensive farming. For this reason, monitoring results obtained by traditional methods as the sole source of data are, in many cases, insufficient compared to modern remote sensing methods (EEA, 2015). Due to their advantages (repeatability, objectivity, verifiability), remote sensing methods are currently recommended by the European

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Environmental Agency as suitable methods for Natura 2000 Habitats monitoring (EEA, 2014). Nonetheless, they must be used in combination with the traditional fieldwork methods (EEA, 2014; Pettorelli et al., 2014). Studies incorporating various remote sensing data are currently conducted in several types of ecosystems (e.g. Mitchell et al., 2015; Onojeghuo and Blackburn, 2011). As it follows from dozens of studies, the use of combined data leads to an increased quality of habitat identification or determination of biophysical parameters by an average of 20%, as compared to using only one data source (Torabzadeh et al., 2014). 1.1. The objective The main objective of this study was to check whether it is possible to identify alkaline fens with a varied conservation status by remote sensing methods, based mainly on LiDAR, land surface temperature (LST) data. We have also tried to answer the following question: Are the habitat patches with favourable conservation status (FV) different in physical parameters from patches with unfavourable-inadequate and unfavourable-bad conservation status (U1, U2)? 1.2. Why alkaline fens? Alkaline fens are one of the most valuable habitats in Europe, both because of their high biodiversity, sensitivity to changes in the water conditions (e.g. Hájek et al., 2006), rarity and the tendency of decline (Raeymaekers, 2000). Alkaline fens are defined by Natura ˇ 2000 guidelines (Sefferova Stanova et al., 2008) as “mires of small sedge and brown moss communities on permanently waterlogged soils, with a calcareous water supply, minimum fluctuations in the water level and peaty substrate”. In Europe, they occur in most biogeographical regions, in 23 EU Member States. The largest area of alkaline fens (more than 60% of their total area) is located in the Boreal and Continental biogeographical regions, with 30% of the ˇ total area located in Poland and Estonia (Sefferova Stanova et al., 2008). According to the Manual of European Union Habitats (European Commission, 2007), alkaline fens are generally characterised by: a “bryophyte” carpet formed by Campylium stellatum, Drepanocladus cossonii, Cratoneuron commutatum, Caliergonella cuspidata, Ctenidium molluscum, Fissidens adianthoides, Bryum pseudotriquetrum and others, a grass-like growth of Schoenus nigricans, S. ferrugineus, Eriophorum latifolium, Carex davalliana, C. flava, C. lepidocarpa, C. hostiana, C. panicea, Juncus subnodulosus, Trichophorum cespitosum, Eleocharis quinqueflora, and a very rich herbaceous flora, including Tofieldia calyculata (in northern Europe Tofieldia pusilla), Dactylorhiza incarnata, D. traunsteinerioides, D. russowii, D. majalis ssp. brevifolia, D. cruenta, Liparis loeselii, Herminium monorchis, Epipactis palustris, Pinguicula vulgaris, Pedicularis sceptrum-carolinum, Primula farinosa, Swertia perennis (European Commission, 2007). In addition, due to geographical differences, each Member State has different, local species characteristic of a given habitat. In the case of Poland, such a list was compiled by Koczur (2010). Alkaline fens have been selectively drained in the past and have become very rare (Holden et al., 2004). The abundance of species occurring on alkaline fens implies that these habitats have a high conservation priority (Raeymaekers, 2000). To preserve the ecological value of alkaline fens, the European environmental policies have introduced a formal protection of fen meadows under Council Directive 92/43/EEC on the Conservation of Natural Habitats and of Wild Fauna and Flora (European Commission, 1992). Many alkaline fen remnants are protected as Special Areas of Conservation, contributing to the Natura 2000 network (European Commission, 1992).

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In the past, valleys of many small rivers in the late-glacial landscape of the Central-European Lowland were filled with large, flow-through mires (Holden et al., 2004). At present, the largest preserved mire fragments occur in Poland in the Biebrza River valley (Herbichowa and Wołejko, 2004) and in the Rospuda River valley ´ (Jabłonska et al., 2014). The Biebrza valley is an excellent area to study alkaline fens preserved over a large area with a varied conservation status (Jarzombkowski, 2010; Koczur, 2010; Kotowski et al., 2013). Differences in the conservation status are mainly associated with secondary succession, which leads to the development of tall herbaceous vegetation, willow bushes and finally−climax vegetation, i.e. wetland woods with the dominance of Alnus glutinosa or Betula pubescens (Kotowski et al., 2013). There are many different plant communities developed on alkaline fens in the Biebrza River valley. They are varied both in terms of appearance, species composition and partly ecological characteristics. They represent over 20 phytosociological units (Wołejko et al., 2005). There is no clear dominant in their species composition. Their well-developed and preserved phytocoenoses have a high species richness, no dominant species and a two-layer structure. The higher layer is built of perennials with the dominance of monocotyledons (sedges and grasses), e.g. Carex lepidocarpa, Carex panicea, Carex flava, and the lower layer – of bryophytes, which on average occur with a denser cover compared to perennials. The latter consists mainly of Bryopsida, e.g. Drepanocladus intermedius, Campylium stellatum, Helodium blandowii, Paludella squarrosa, but often also peat mosses (e.g. Sphagnum teres, Sphagnum warnstorfii) (Herbichowa and Wołejko, 2004; Koczur, 2010). The existence of early habitat succession stages seems to be a key factor in the survival of species-rich alkaline fens. At many sites, this demand is met through mowing or grazing. The above-mentioned species are dependent on the constant hydrological regime. When temporary changes in the water regime occur, such as flooding or lowering of the water level, species do not flower and can survive in a vegetative stage. If changes are permanent, the species disappear after 3 or 4 years. The number of individuals decreases after even a short-term flooding (Jersakova and Kindlmann, 2004).

2. Materials and methods 2.1. Study area The research was conducted over an area of 72.71 km2 in the upper Biebrza River basin. The study area is protected as a Natura 2000 site (code PLH200008) and as Biebrza National Park (Fig. 1). The Biebrza River catchment is located in the subcontinental/subboreal climate zone with an average annual temperature of 6.8◦C. The average annual precipitation ranges from 550 to 700 mm/yr, and the evapotranspiration – from 460 to 480 mm/yr (Kossowska-Cezak, 1984). The topographic elevation of the Biebrza River basin varies between 110 and 130 m a.s.l., while the adjacent morainic plateau and the outwash plain vary between 130 and 180 m. The valley is completely covered with peat deposits ˙ of 3–7 m thickness, partly underlain by calcareous gyttja (Zurek, 1984). In general, the Biebrza River flows westwards in the upper basin through the wide valley using fragments of the ice-marginal valley. The highest discharge recorded by the gauge in Sztabin was 96 m3 /s, the lowest one – 0.32 m3 /s, the mean value from 1951 to 1995 was 4.86 m3 /s (Byczkowski and Fal, 2004). The type and condition of marsh ecosystems in this area largely depend on hydrological recharge. In the upper basin, the groundwater discharge dominates, with a significant influence of the inundation phenomenon during short spring-thaw flood periods occurring every year and occasional summer storm rain flash floods. The hydrochemistry of the Biebrza mire water is governed

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Fig. 1. Location of the study area with alkaline fens acc. to Management Plan (MP, 2014).

by three principal sources of water: (1) precipitation, including snowmelt, (2) groundwater seepage to the surface, and (3) river ´ floods (Chormanski et al., 2011; Keizer et al., 2014), which could be observed on a microscale in the area selected for this research. Anibas et al. (2012) indicated a predominant groundwater recharge measured as upward water fluxes along the upper Biebrza River reaches. In general, the vegetation zoning and productivity are related to the dominance of these water types in the mires and floodplains. In the floodplain zone, nutrient availability and productivity are governed by the river, which provided the vegetation with nutrients dissolved in the river water or attached to the loam and silt sediment. The relationship between nutrient availability, productivity, and species composition outside the floodplain zone in the upper basin is more complicated and its dynamics appear to be related to the succession and management (Wassen et al., 2006). The described area together with the Rospuda valley is believed to be one of the most valuable complexes of soligenous fens in Europe, including the protected and valuable Natura 2000 habitat ˇ – Alkaline fens (code 7230) (Sefferova Stanova et al., 2008). Fens occurring in the study area are one of the best preserved and most similar to natural systems, hence their high nature conservation value (Jarzombkowski, 2010). In the studied fragment of the Biebrza

valley, habitat 7230 covers currently 8.49 km2 (Fig. 1) (MP, 2014). Phytocoenoses of the surveyed fens in the upper Biebrza basin cover relatively large areas and are characterised by varying conservation status. Despite the implemented conservation measures, however, the area of moss-sedge vegetation is gradually decreasing in the valley due to the progressive expansion of trees (mainly Betula pubescens and Alnus glutinosa) and shrubs (mainly Salix cinerea) as well as desiccation of the habitat (Piórkowski, 2005). In some places, the encroachment of the common reed is also observed. Active protection measures have been implemented in the Biebrza River valley for many years: manual removal of trees and larger shrubs as well as large-scale mowing with the use of snowcats (Kotowski et al., 2013). 2.2. Methods The new remote sensing method for the identification of alkaline fens is described in four stages: Acquisition of remote sensing data (Step A), Preparation of data for analysis (Step B), Segmentation of data (Step C), Classification with validation (Step D). Step A. Data acquisition Step A1. Acquisition of remote sensing data

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Remote sensing data were acquired from three sensors located on a fixed-wing aircraft: a laser scanner – RIEGL LMS-Q680i (Riegle Laser Measurement Systems GmbH, Horn, Austria), a large-format camera – Vexcel UltraCam Eagle (Microsoft Photogrammetry Division, Graz, Austria) and a thermal camera−IGI DigiTherm 640 (Ingenieur-Gesellschaft für Interfaces GmBH, Kreuztal, Germany). Data were collected in the period from 28 July 2013 to 4 August 2013. The aerial images with a resolution of 0.1 m, 70% longitudinal overlap and 40% latitudinal overlap, in the range of R, G, B, NIR, were obtained on the 28th of July 2013. Thermal images, which recorded a spectral range from 7.5 ␮m to 14 ␮m (night temperature – NT and day temperature – DT), with a resolution of 1 m and an accuracy of +/−1.5 K, were obtained on the 3rd of August 2013 (between 9:00–11:00 p.m. NT) and the 4th of August 2013 (between 8:30 a.m. and 10:30 a.m. DT). The point cloud was obtained with a density of 4 pt/m2 and a strip overlap of >55%, a laser pulse repetition rate of 200 kHz and a scan angle of 60◦ . The flight mission was carried out at a height of 500 m ALG (Above Ground Level), therefore the footprint achieve was 0.25 m (laser beam divergence <=0.5 mrad) Step A2. Acquisition of botanical data The botanical fieldwork was conducted simultaneously with the remote sensing data acquisition, and consisted of the collection of detailed point (site) information on the distribution of alkaline fens. A GPS receiver Topcon GRS-1 with the D-GPS mode (<0.5 m accuracy) was used in the field, applying a correction signal available from the Polish TPI NET Pro system. A total of 686 data points were collected regarding the occurrence of alkaline fens, out of which 345 randomly selected points were used to train the classifier in Step D2 (157 for Alkaline fen and 188 for other non-forest wetland vegetation), while the remaining 341 points (170 for Alkaline fen and 171 for other non-forest wetland vegetation) were used to validate the classification results in Step D3. All points were located in habitat patches not smaller than 500 m2 and at least 10 m from the edge of a given patch. Relevés with an area of 25 m2 were made at 44 validation points. The relevés were made for the purpose of phytosociological verification and documentation of the obtained results. The research was performed using the Braun-Blanquet method applying the modified 9-point cover-abundance scale (Barkman et al., 1964). Assessment of the habitat conservation status was carried out in accordance with the valid Polish methodology for habitat 7230 monitoring (Koczur, 2010). In accordance with this method, indicators based on the number and cover-abundance values of characteristic and native expansive species (both herbaceous plants as well as young trees and shrubs) were used to assess the conservation status of habitat patches. Quantitative data derived from relevés were used in calculations, and the degrees of cover were converted into average percentage cover according to the following rule (van der Maarel, 1979): r = 0.1; + = 1; 1 = 2.5; 2m = 4.25; 2a = 7.5; 2b = 20; 3 = 37.5; 4 = 62.5; 5 = 87.5. Step B. Data preparation for analysis The objective of the next Step was to process and convert the source remote sensing data into input layers (Table 1) required for segmentation (Step C) and classification (Step D). Step B1. From aerial photographs Aerial photographs were subjected to the process of aerial triangulation, which was performed in the Image Station Automatic Triangulation software (ISAT ver. 2013). The accuracy of internal alignment of the block of aerial photographs was ca. 4.6 cm (sigma value in the ground units, 2.4 ␮m in image units), and the accuracy gained on the Ground Control Points was 4 cm for XY and 2 cm for Z value – this is the accuracy of fitting the block of aerial photographs to the ground. DTM based on the ALS data was used in the orthorectification of imagery. An orthophotomap with a resolution of 10 cm was generated in natural colours RGB and unnatural colours CIR (Fig. 2G) using the INPHO software OrthoVista. Aerial

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photographs were used to compute NDVI according to the standard method (Rouse et al., 1973) (Fig. 2B). Step B2. LAI index The LAI index (Fig. 2A) was calculated based on the regression between LAI (ground point measurements) measured within one week from the aerial overpass and NDVI (derived from aerial photographs). The regression model was derived from 221 LAI measurements in non-forest areas of the study area. The equation took the following form: LAI = 0.212 × e3.304×NDVI with R2 = 0.38 (Fig. 3). We did not validate this model because LAI was not a crucial part of this research. Our intention was to obtain the best fit of NDVI with the measured LAI as an input for classification. LAI from such a model does not provide any additional information compared to NDVI. However, this model allowed us to re-project the NDVI data to LAI so it might be easier for the classifier to use it as a variable and certainly easier for a user to interpret the results. Step B3. From LST data (Land Surface Temperature) Two orthophotomaps presenting the distribution of day (DT) (Fig. 2E) and night (NT) (Fig. 2F) temperature were prepared (resolution of 1 m). Aerial triangulation of thermal images was carried out using the software ImageStation Automatic Triangulation (ISAT) and ground control points measured on the RGB orthophotomap. The internal accuracy of an aligned block was ca. 2.6 cm in field units, the fitting into the photogrammetric matrix was 2.4 cm for XY. The process of orthorectification and mosaicking of thermal images was performed in the software ERDAS Imagine. Next, the temperature amplitude (TA) (Fig. 2D) was calculated as a difference between values of DT and NT orthophotomap pixels according to the formula: TA = DT − NT. Step B4. From the ALS data (Airborne Laser Scanning) The ALS point cloud was spatially calibrated in the software (RiProcess 1.6.4) by fitting into the geodetic coordinate system using the GPS/INS data and through the scanning series alignment process. The accuracy of this process was assessed at the level of 1 sigma = 0.05 cm. This was followed by the point cloud classification using automatic filter routines and interactively, performed with the TerraScan package TerraSolid software (version 14 https://www.terrasolid.com/download/tscan.pdf). Each point of the point cloud was assigned to the corresponding land-cover class in accordance with the ASPRS standard (1-processed, unclassified; 2-soil; 3-low vegetation; 4-medium vegetation; 5-high vegetation; 6-buildings and civil engineering works; 7-noise), separating e.g. anthropogenic objects, soil and vegetation cover. The TerraSolid software was used to produce: • the Digital TerrainModel (DTM) by creating a raster with a 0.5 × 0.5 m cell size based on the triangular model created from the cloud of class 2-soil points (Fig. 2H). • the Digital Surface Model of Canopy (DSMofC) by creating a raster with a 0.5 m square based on the triangular model created from the cloud of points (the first reflection) of selected classes: 2-soil, 3-low vegetation, 4-medium vegetation, 5-high vegetation. • the Canopy Height Model (CHM) layer (which describes a standard vegetation model) was generated based on the differences between DTM and DSMofC analysed in the GlobalMapper software (Fig. 2C). Furthermore, following the analysis of the point cloud, vertical distribution of the point cloud was calculated (expressed in voxels) for classes 3, 4 and 5. A system of voxels with the following dimensions was used in the horizontal XY plane of 2 × 2 m, and in the vertical Z plane – 0.2 m. For each voxel, the number of reflections was calculated, i.e. ALS points contained in a given voxel. Calculations on voxels were carried out up to a height of 3.80 m above the ground level. In total, one voxel column with a side of 2 × 2 m consists of 19 layers of voxels, each with a height of 20 cm. The next

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Fig. 2. Information layers involved in Step C & Step D ((A) Leaf Area Index; (B) Normalized Difference Vegetation Index; (C) Canopy Height Model; (D) Daily Temperature Amplitude; (E) Day Temperature; (F) Night Temperature; (G) CIR Orthophotomap; (H) Digital Terrain Model).

step consisted in switching to a raster format of data and the number of points recorded in voxels assume a form of continuous raster layers with a side of 2 × 2 m. As a result, the raster file consisting of 19 channels was created, one for each voxel level.

The last stage involved automatic ISODATA (Tou and Gonzalez, 1974) classification (using the Erdas Imagine software) of 19 channels from the raster file with the initial value set at 32. The results of classification were a single-channel file (bin’s classification) with

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Table 1 Parameters defining the characteristics of a segment included in the analyses (Step C & Step D). Source remote sensing data

Layers

Step

Parameters defining the characteristics of a segment included in the analyses

Thermal data

Daytime temperature distribution (DT) Night-time temperature distribution (NT) Disparity between the daytime (DT) and night-time (NT) temperature distribution

D

Mean Day Temperature (MDT)

D

Mean Night Temperature (MNT) Mean Daily Temperature Amplitude (MTA)

ALS data

Aerial images

D

Bin

C, D

Bin

C, D

Bin

C, D

Bin

C, D

Bin

C, D

CHM

C, D

CHM

C, D

CHM

C, D

CHM

C, D

CHM

C, D

Orthophotomap CIR NDVI

C D

LAI

D

Segments geometry parameters

Fig. 3. Regression model used to estimate LAI with NDVI data. The line represents the regression fit: LAI = 0.212 × e3.304×NDVI with r2 = 0.38 and the points indicate LAI values from ground measurements and corresponding NDVI from aerial photographs.

32 values corresponding to a combination of clustered values of 19 channels. Step C. Segmentation of data The process of segmentation was conducted on the basis of a 2 × 2 m pixel, using the Trimble software eCognition. The task was based on selected parameters of scale (50), shape (0.1) and compactness (0.9) of segments. A three-stage segmentation method was adopted. The first stage involved segmentation based only on the CIR orthophotomap. During the second stage, segments derived from the first stage were divided into smaller, homogeneous (in

D

Dominant fraction of bins’ classification with the “zero” value ignored in the calculation (MAJ F CL 0) Dominant fraction of bins’ classification (MAJ F CL) Dominant fraction of bins’ classification with the “zero” value ignored in the calculation (MAJ CL 0) Dominant fraction of bins’ classification (MAJ CL) Diversity of bins’ classification (DIV CL) Mean vegetation height (Mean H) Standard deviation of vegetation height (SD H) Maximum height of vegetation (Max H) Minimum height of vegetation (Min H) Range of vegetation height Max H–Min H (Range H) – Mean value of Normalized Difference Vegetation Index (Mean NDVI) Mean Leaf Area Index (Mean LAI) Area, Circularity, Perimeter, Compactness

terms of vegetation height) segments, based on CHM. At the third stage, based on the information derived from bin’s classification, segments from the second stage were divided into smaller, homogeneous (in terms of vertical vegetation structure) segments. As a result of the segmentation process, the study area was divided into ca. 20,000 unique polygons, homogeneous in terms of selected physical characteristics of the vegetation. The accuracy of the adopted parameters was verified at each stage of the process by visual examination of the homogeneity of segments on the CIR orthophotomap and CHM. Step D. Classification of habitat 7230 based on the segmentation results Step D1. Delimitation of the study area boundaries Classification was conducted only within the limits of non-forest vegetation in the Biebrza valley, which develops on the organic substrate. Boundaries separating the organic deposits from the mineral ones were determined on the basis of DTM and the network of major watercourses (the Biebrza and its tributaries). In the upper Biebrza basin, the Biebrza River flows through a narrow and deep valley, and hence the organic deposit layer of the floodplain is located at a sharp boundary relative to mineral deposits forming the slopes of the valley and consequently, this boundary strictly corresponds to the land elevation ranging from 2.5 m to 3.5 m above the water surface of the river network in the valley. For this reason, the authors’ method of boundary delimitation con-

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sists in the determination of the altitude buffer from the ordinate axis of watercourses in the range of variation: 2.5 m from the ordinate of the Biebrza’s tributaries to 3.5 m from the ordinate of the Biebrza river-bed, adopted as zero. The thus delimited boundary was verified manually by a geomorphologist, based on the DTM hillshade, the analysis of edges, slopes the vigour of plants on the CIR orthophotomap. The boundary between forest and non-forest ecosystems was delimited based on the vegetation CHM. Forest vegetation segments were defined as those where plants higher than 3 m occur with the cover above 50%. Other segments were assigned to non-forest ecosystems, the area of which defined the area of the conducted analyses. Step D2. Classification For each of the distinguished segments (polygons), 19 parameters were calculated, which describe physical characteristics of homogeneous vegetation patches in a multidimensional way (Table 1). Classification was the next step of the analyses. The supervised classification involved two classes – “alkaline fen” and “other nonforest vegetation” – and was conducted using the random forest algorithm (Breiman, 2001) in the three following steps. In the first step, all 19 previously calculated parameters of the objects were used as an input variable for general classification. The outcome of the general classification was the parameters’ importance measure (i.e. mean decrease in accuracy; Breiman and Cutler, 2014) given by the random forest algorithm. The mean decrease in the accuracy is obtained for the overall classification and for each input variable (i.e. object’s parameter) used in the classification. The mean decrease in the accuracy values was further used to rank the input variables in decreasing order of importance for the habitat 7230 classification. The higher the value of the mean accuracy decrease the higher the variable importance for the classification. Step D3. Importance of variables In the second step, we obtained error statistics for the classification using different sets of input variables. This was achieved by multiple classification runs with one variable subsequently added at a time based on the importance rank, i.e. starting with only one, the most important variable and finishing with all 19 variables. In order to achieve representative error statistics, we repeated the classification 50 times for each variable set. At this step we computed a classification error based on the random forest built-in bootstrap estimation, called out-of-bag (OOB) error [%], rather than on the independent validation set used in the section “Step D2”. The OOB error is an unbiased estimator of the test set error (Breiman, 2001), thus any further cross-validation was not required. The outcome of this step was the information about the optimal number of the most important variables that minimize the random forest classification error. Step D4. Prediction and validation In the last step, we used the optimal variables’ set to conduct the final classification of habitat 7230 in the objects. The classification quality was assessed with the OOB error and with a classification accuracy measure [%] calculated using the independent validation set (N = 341): at = oc /ot × 100 where oc is the number of correctly classified objects and the ot is the total number of objects in the validation set. Classification accuracy for each class was assessed by producer accuracy [%]: ap = occ /otc × 100 and user accuracy [%]: au = occ /oco × 100

Fig. 4. OOB error obtained from the multiple random forest classification runs with the increasing number of the most important variables. The ranked list of variables’ importance is presented in Table 2.

where occ is the number of correctly classified objects for a given class, otc is the number of objects from a given class in the validation set, and oco is the number of objects classified as a given class in the validation set. The producer accuracy estimates the probability that a given class in the study area is classified correctly, whilst the user accuracy estimates the probability that an object classified into a given class indeed belongs to a given class. Another accuracy measure used was the Kappa index of agreement –  [−] (Cohen, 1960). This frequently used measure allows to quantify the agreement between the categorical data. The perfect  = 1 and  < 0 indicates no agreement between the data. We also calculated the 95% confidence intervals (CI) for the at and  estimates.

3. Results 3.1. Classification of habitat 7230 based on the segmentation results The importance of 19 parameters obtained from the initial random forest classification is presented in Table 2. The importance rank of variables differs between the two classes, but the general pattern of high importance of Mean Night Temperature (MNT) and the Majority of bin’s classification (MAJ F CL 0) and vegetation structure-related variables (Mean vegetation height (Min H), Mean LAI, Max vegetation height (Max H) and the Range of vegetation height (Range H)) and low importance of geometrical parameters (Circularity, Perimeter and Compactness) of the objects are similar in both classes and in the overall values. Some parameters, e.g. standard deviation of vegetation height (SD H) and mean NDVI were of high importance to the alkaline fen Natura 2000 habitat, while less important in the case of other wetland communities (=non alkaline fen). The most important parameter for habitat 7320 is MNT. However, based on the multiple random forest classification runs presented in Fig. 4, the subsequent seven most important parameters (MAJ F CL 0, Majority of bin’s classification (MAJ CL), SD H, Mean H, Mean LAI, Max H and Range H) effectively reduce the classification error. Inclusion of any further variables did not decrease the error (in some cases even increased; Fig. 4).

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Table 2 Variables ranked based on the overall mean decrease in the accuracy importance obtained with the initial random forest classification, i.e. with all variables (objects’ parameters). Values of a mean decrease in accuracy are also provided for the individual classes. Variable

Importance rank

MNT MAJ F CL 0 MAJ CL SD H Mean H Mean LAI Max H Range H MTA MAJ CL 0 Mean NDVI MDT DIV CL Area Min H Circularity Perimeter Compactness MAJ F CL

Mean decrease in accuracy

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Other non-forest wetlands

Alkaline fen

Overall

18.2 15.1 15.4 6.9 11.6 9.8 8.7 10.2 9.2 11.7 3.2 3.8 3.6 4.7 4.2 4.0 3.7 3.7 3.5

23.0 24.5 18.6 19.4 16.8 15.5 16.2 16.1 11.9 10.3 12.6 10.2 6.7 6.8 7.3 5.0 5.5 5.3 2.5

27.2 25.6 21.5 20.7 19.6 18.0 17.1 17.1 14.2 14.2 11.6 11.4 8.4 8.1 7.5 6.4 6.4 6.2 4.5

Fig. 5. Result of alkaline fens classification in the upper Biebrza River basin, presented on the background of the CIR orthophotomap.

Table 3 Confusion matrix of the classification results calculated with the validation set. Classification result

Alkaline fen Other non-forest vegetation ap

Validation set

au

Alkaline fen

Other non-forest vegetation

154 174 90.1%

124 158 92.9%

The final classification result for habitat 7230 is presented in Fig. 5. Habitat 7230 occurs mostly in the central part of the study area in the valley parts, not adjacent to the river. The total area of the habitat is 7.88 km2 , i.e. 11% of the study area The overall classification accuracy at was 91.5% (88.0–94.2% CI 95%) with the OOB error of 10.3%. The ␬ was 0.83 (0.77–0.89 CI 95%) indicating almost perfect agreement. The errors of individual classes are presented in Table 3. Assuming the number of validation samples is

92.8% 90.3% at = 91.5%

large enough for accuracy measures (at , au and ap ), showing similar, high values, we can state that the results are not biased towards any class and that the overall accuracy well reflects the final map quality. A clearly visible classification error is Natura 2000 habitat 7230 in the objects surrounded by irrigated meadows in the southern and north-eastern part of the study area. These erroneous commitments occur only at the location of drainage ditches where vegetation is not managed. The total area of these commitments

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is 0.04 km2 , i.e. 0.5% of the area of the objects classified as habitat 7230. 3.2. Phytosociological description of the selected alkaline fen patches Validation of the Natura 2000 alkaline fen habitat map carried out at stage D3 indicates that 91.5% phytocoenoses were correctly identified. They represented, for instance, the following plant associations: Campylio-Caricetum dioicae, Sphagno warnstorfianiEriophoretum latifoliae, Menyantho trifoliatae-Spagnetum teretis. Vascular plant species typical of the habitat (Koczur, 2010) were represented mostly by (the number of plots in which a species occurs over the total number of plots is given in brackets): Carex lepidocarpa (43%), Carex panicea (50%), Dactylorhiza incarnata (53%), Epipactis palustris (60%) and Menyanthes trifoliata (93%); less frequently by: Carex dioica (3%), Eriophorum latifolium (15%), Liparis loeseli (5%), Parnassia palustris (20%), Pedicularis palustris (10%) and Triglochin palustris (8%). Bryophytes typical of the habitat were usually represented by: Helodium blandowii (28%), Limprichtia cossonii (35%), Tomentypnum nitens (38%), less frequently by: Bryum pseudotriquetrum (20%), Campylium stellatum var. stellatum (18%), Fissidens adianthoides (10%), Hamatocaulis vernicosus (18%), Paludella squarrosa (10%), Scorpidium scorpioides (3%), Sphagnum teres (13%), Sphagnum warnstorfii (5%). Analysis of the conservation status of those 40 habitat patches distinguished by remote sensing methods, performed based on the specific structure and functions’ indicators (Koczur, 2010), showed that their conservation status varies: 19 patches were preserved at favourable status (FV) and 21 patches at unfavourable – inadequate status (U1) or unfavourable – bad status (U2) (U1 and U2 were combined together under the “U” category). “Expansion of shrubs and undergrowth of trees” was the primary indicator determining the classification of habitat patches into a specific conservation status. The conducted comparison of the average value of physical parameters: Mean H, NDVI, LAI, MNT, MDT, and MTA between habitat patches with FV and U status showed that they varied considerably. Habitat patches preserved at favourable status are characterised by statistically significantly lower average vegetation height (mean 0.19), NDVI (mean 0.58) and LAI (mean 2.11) as well as a higher daily temperature range (mean 7.64) (Fig. 6). 4. Discussion 4.1. Comparison with other remote sensing methods of wetland identification—anything new? Species occurring on wetlands are particularly sensitive to changes in the habitat conditions (moisture content, temperature and nutrients, i.e. trophic conditions, nutrients regimes) (Foster et al., 1988; Minkkinen et al., 2002). Even small changes in the habitat may result in the wetland flora changes (Buttler et al., 2015). Consequently, the continuous monitoring of wetlands is crucial for their protection and early detection of potential changes (Miller et al., 2016). As a response to this demand, attempts have been undertaken to develop a remote sensing method for wetland identification (Feilhauer et al., 2014; Harris et al., 2006, 2015; Middleton et al., 2012). These analyses are based on high resolution data obtained from airborne platforms and focus on multispectral or hyperspectral data analysis. The accuracy of wetland identification (Natura 2000 habitats with codes 7110, 7120, 7140, 7230) is varied and ranges from 71% for studies conducted in southern Germany (Feilhauer et al., 2014) to 96% for eutrophic fens in the south-western Finnish Lapland (Middleton et al., 2012). The accuracy of the method described in this paper, based on a multisensoral

analysis, was 91.5% (Fig. 4). The innovative solution in the proposed method of alkaline fens’ (code 7230) identification consists in the departing from the hyperspectral data analysis in favour of acquisition and analysis of three data sets – ALS, LST and CIR and RGB aerial photographs (Table 1 and Fig. 2). Our results obtained with this method support the findings derived from studies conducted in forest ecosystems (Torabzadeh et al., 2014) regarding the high efficiency of methods involving the use of multiple sensors. It has been determined that LST data from the night collection (NT) (Table 2) have the greatest indicator potential among different types of source data. LST with much lower resolution (derived from the MODIS satellite) was previously used to analyse the vegetation, and its importance as an indicator was proven in the vegetation identification (Nemani and Running, 1997; Roberts et al., 2015), analysis of land cover change detection in the Arctic permafrost landscape (Muster et al., 2015) and analysis of spatial changes in urban areas (Nguyen et al., 2015). So far, the LST has not been used for the identification of Natura 2000 habitats, including mires. Therefore, the presented pioneering results represent the basis for further development of LST analyses as a source of data on Natura 2000 habitats and their diversity. Nonetheless, the presented studies are only a single analysis based on data from a geographically limited region. And the demonstrated implementation and indicator function of LST may change during the year and depend on momentary weather conditions (Roberts et al., 2015). The obtained results indicate that well-preserved alkaline fens are characterised by a relatively low LST at night and a wide daily atmospheric temperature range. These indices were of crucial diagnostic importance in the performed classification. This probably results from the fact that plant communities of alkaline fens develop in places with an increased supply of relatively cold ground waters (Vitt, 1994). During a hot day, the LST increases up to a level similar to other non-forest wetland communities. However, because of the constant groundwater seepage, it quickly drops at night. This proved to be of great diagnostic significance. Also data from ALS were diagnostically important. This is due, however, to the fact that the vertical and horizontal structure of vegetation on the alkaline fen habitat has many individual features distinguishing them from e.g. reeds and rushes or wet meadows, which are characterised by the dominance of short perennials and a dense cover of the moss layer. In addition, there are no clear dominants in most patches of this Natura 2000 habitat. The average vegetation height in the well-preserved patches is only ca. 0.3 cm (Fig. 6). Data coming from the RGB camera were of much lesser diagnostic importance. The LAI calculated based on RGB data proved to be less important in the identification of habitat 7230 (Table 2). Therefore, it seems reasonable to develop methods of alkaline fen habitat identification based primarily on ALS and thermal data. 4.2. Applicability and limitations of the methodology The Natura 2000 network currently covers over 18% of Europe in the form of over 26,000 sites spread across all 28 Member States, representing the world’s largest system of protected areas (European Commission, 2010). Implementation of the Habitats Directive imposes on the Member States not only the obligation to provide information on the distribution and the area of natural habitats but also their continuous monitoring (European Commission, 1992). Reporting on the conservation status of habitats based on 6-year cycles should include changes in the habitat area as well as qualitative changes occurring in protected habitat patches (Vanden Borre et al., 2011). A constant inflow of up-to-date data is difficult to obtain with the traditional fieldwork methods (Harris et al., 2015). This particularly applies to large areas with difficult access and high dynamics of vegetation changes (e.g.

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Fig. 6. Mean values of Mean H, MTA, Mean NDVI, Mean LAI for the conservation status FV and U of the habitat referred to as alkaline fens (cod 7230). Statistical significance of differences between means was checked by Student’s t-test at p < 0.05.

ecosystems dependent on the groundwater regime or extensive management) (Vanden Borre et al., 2011). Considering the above arguments, the remote sensing methods should become the main tool used in the implementation of environmental policy criteria, especially in relation to areas difficult for direct penetration, e.g. wetlands (Harris et al., 2015; Mayer and Lopez, 2011). In most European countries, including Poland (Mróz, 2010–2012), obliged by the Habitats Directive to monitor the conservation status of habitats, standards for the evaluation of habitats were developed, based on such indicators as vertical and horizontal structure of a plant community, the presence of typical and alien species (particularly plant species), abiotic factors or current disturbances (Table 4). Some of the indicators can be easily evaluated by remote sensing methods e.g. the land-use intensity or encroachment of woody vegetation on pasture and meadow communities (e.g. Franke et al., 2012). Nevertheless, many indicators are challenging, especially those that rely on the presence of particular species or their groups (Corbane et al., 2015). Therefore, it is very important to find alternative indicators – which could serve as proxies (Vanden Borre et al., 2011) – suitable for remote sensing evaluation and sufficiently correlated with indicators assessed through traditional field studies. For example, Mean H and Mean LAI proved to be such good indicators, reflecting the habitat con-

servation status based on the indicator “expansion of shrubs and undergrowth of trees” used in traditional methods of monitoring (Fig. 6). The presented calibration of remote sensing indices was based on the mean ranges determined for individual indices. However, there are no close correlations between the conservation status indices and the number of patches with the FV or U status. Further research should aimed at defining more precise boundaries between individual conservation statuses, e.g. trough determining the possibility of distinguishing the conservation status U1. It is also important to develop indicators for the evaluation of habitat conservation status in such a way that they are unified, objectified and easy to repeat across the Natura 2000 network (Corbane et al., 2015). Remote sensing methods used in the evaluation of conservation status of alkaline fens in the Biebrza River valley seem to be objective and correspond with the results obtained by the fieldwork. Furthermore, they proved to be far more accurate with regard to the identification of habitat patches, particularly in places with no direct access. Compared with the results of alkaline fens’ inventory presented in the Management Plan (MP, 2014), remote sensing methods contributed both to the clarification of boundaries of large inventoried fen patches, the identification of many small patches in addition to those listed in

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Table 4 Indices of conservation status and their calibration (acc. to Koczur, 2010) collated with remote sensing parameters resulting from the analysis (Fig. 6). Index Indices currently used in the field (acc. to Koczur, 2010) Characteristic species Dominant species

Invasive alien species Native expansive species of herbaceous plants Expansion of shrubs and undergrowth of canopy trees Cover and species structure of mosses and liverworts

Proposal of remote sensing indices H [m] MTA [◦ C] NDVI LAI

FV

U

≥8 per 2000 m2 Species characteristic of the habitat dominate or there is no single dominant, instead several characteristic species dominate None or single (up to 5% cover)

≤7 per 2000 m2 Lack of clear dominants, characteristic species do not dominate or species not characteristic of the habitat dominate Cover by invasive species 5% and more

None or single (up to 5% cover) None or single (up to 5% cover)

Cover by expansive species 5% and more Cover by tree and shrub species 5% and more

Total moss cover is 50% and more, bryophytes characteristic of the habitat and peat mosses tolerant to alkaline conditions (e.g. Sphagnum teres) account for 70% or more of the moss layer

Total moss cover below or above 50%, while peat mosses tolerant to alkaline conditions (e.g. Sphagnum teres) account for 70% of the moss layer

A low value indicates favourable conservation status. The limit value for FV/U is ca. 0.30 m. Daily atmospheric temperature range >7.4 ◦ C may indicate well-preserved alkaline fens. A value below 0.6 is a diagnostic feature of alkaline fens. A low value of the index is related to a large area of the last year’s necromass. Well-preserved fens are characterised by low values of LAI (<2.2), which is associated with attributes of NDVI and H indices.

Fig. 7. Alkaline fen in the Upper Biebrza valley acc. to Management Plan (MP, 2014) and identified by remote sensing methods, presented on the background of the CIR orthophotomap.

the protection plan as well as the indication of areas wrongly classified as alkaline fens due to generalization (Fig. 7). Of the total area of 8.49 km2 included in the protection plan, 3.03 km2 , i.e. more than 30%, was not classified as alkaline fens by remote sensing methods. At the same time, 3.31 km2 of other patches were classified as alkaline fens. Over 30% of the inventoried habitat was described more precisely compared to the previous results, which is very important in the context of habitat loss by an EU Member State obliged by the Habitats Directive to protect this habitat and the necessity to determine the causes thereof (Vanden Borre et al., 2011). On the other hand, one of the limitations of remote sensing methods, compared to field mapping, is time restriction in the case of fens used for agricultural purposes, i.e. remote sensing must be carried out before the first mowing. Only 50% (0.26 km2 ) of the alkaline fen patches mown before the data acquisition (0.55 km2 ) were iden-

tified as alkaline fens using the remote sensing methods, which is only 3% of the total area of fens reported in the study. The high costs of airborne data are obviously the disadvantage of the applied methods when used for monitoring of small areas compared to traditional field methods supported only by an aerial photo of a given area. These costs, however, will decrease due to the progressive miniaturization of sensors and consequently, the possibility of installing cameras on UAV planes (Schwarzbach et al., 2009).

5. Conclusions The results of the research show that alkaline fens can be spatially mapped using remote sensing methods. The developed method of identifying the Natura 2000 habitat Alkaline fens has

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been successfully tested in the Biebrza River valley and the results obtained provide the basis for its application both in Poland and Europe. The advantages of this method include the objectivity, repeatability, accuracy and expeditiousness possible to maintain throughout the inventoried area (Corbane et al., 2015; Harris et al., 2015). This is particularly important in a large and inaccessible terrain (Vanden Borre et al., 2011). The objectivity and repeatability of the method make it possible to use it as a tool in monitoring changes in the plant species composition (Luft et al., 2014; Middleton et al., 2012; Mücher et al., 2013) resulting from e.g. climate changes (Gray et al., 2013) or human activity (Kotowski et al., 2013). It has been determined that LST data from the night images NT have the greatest indication potential among different types of source data. So far, the LST has not been used for identification of Natura 2000 habitats, including fens. The presented pioneering results represent the basis for further development of LST analyses as a source of data on Natura 2000 habitats and their diversity. Future studies should focus on testing whether the developed method can be implemented in the identification of other nonforest Natura 2000 habitats and whether the developed method can be successfully used for the alkaline fen habitat in other areas in Europe. It is necessary to develop methods capable of determining the conservation status of habitat patches in accordance with the adopted scale of habitat assessment FV, U1, U2. The approach presented in this paper seems to be very useful in habitats mapping, while LST data seems to be very promising in the research on the vegetation health, especially in wetland ecosystems and habitats. These problems are the main objectives of the HabitatARS project. Authors’ contribution D. Kopec´ is the author of the research concept, prepared the manuscript, conducted the field research and analysed the botanical data; D. Michalska-Hejduk prepared the manuscript, conducted the field research, made phytosociological descriptions and analyses of botanical data, reviewed the manuscript; Ł. Sławik is the initiator of the study and responsible for airborne data acquisition and the author of two subchapters; T. Berezowski is the author of the classification and its concept, he also reviewed the manuscript and authored two subchapters; M. Borowski conducted the image segmentation and the data pre-processing; S. ´ ´ identified of bryophytes; J. Chormanski is the coRosadzinski author of one subchapter. Acknowledgments We would like to thank two anonymous reviewers for many significant remarks. The research was partly supported by the project HabitatARS (BIOSTRATEG2/297915/3/NCBR/2016). The innovative approach supporting monitoring of non-forest Natura 2000 habitats, using remote sensing methods financed by The National Centre for Research and Development. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.ecolind.2016. 06.001. References Alexandridis, T.K., Lazaridou, E., Tsirika, A., Zalidis, G.C., 2009. Using Earth Observation to update a Natura 2000 habitat. J. Environ. Manage. 90, 2243–2251. ´ Anibas, C., Verbeiren, B., Buis, K., Chormanski, J., De Doncker, L., Okruszko, T., Meire, P., Batelaan, O., 2012. A hierarchical approach on groundwater-surface

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