Integrating field survey and orthophoto information to monitor coastal ...

Ecological Informatics 14 (2013) 48–52

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Integrating field survey and orthophoto information to monitor coastal habitats — A pilot study to develop methods and resolve key issues Anders Juel a,⁎, Rasmus Ejrnæs a, Jesper Fredshavn b, Geoff Groom a a b

Department of Bioscience, Aarhus University, Grenaavej 14, Kaloe, 8410 Roende, Denmark National Center for Environment and Energy, Aarhus University, Vejlsøvej 25, Postboks 314, 8600 Silkeborg, Denmark

a r t i c l e

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Article history: Received 27 January 2012 Accepted 30 November 2012 Available online 13 December 2012 Keywords: Habitat mapping Monitoring Coastal habitat Habitat structure Orthophoto imagery Object-based image analysis

a b s t r a c t Implementation of mapping and monitoring is necessary to supply sufficient information to guide an effective management of species, habitats and landscapes. Coastal ecosystems can be difficult to monitor effectively in the field due to spatially discontinuous and unpredictable processes such as encroachment, erosion and succession, while coverage of large extents is very expensive. Remote sensing-based monitoring provides an alternative, but satellite image data is often too expensive or too coarse in spatial resolution to detect fine-scale habitat structures. Using Danish coastal habitats as a case, a method is presented for monitoring habitat types and fine-scale structures, based on integration of field-acquired habitat characteristics with the habitat information interpreted from sub-meter RGB/NIR aerial imagery and digital elevation model data. Initial pilot studies show good correspondence between field-observed structure elements and structures delineated through object-based image analysis, while initial classifications results suggest possibilities of discriminating between different types of shrubs, herb communities and non-vegetated structures. © 2012 Elsevier B.V. All rights reserved.

1. Introduction The current biodiversity crisis invokes increasing demands for cost-effective mapping and monitoring of natural resources. The mapping and monitoring should be sufficiently informative as to guide the effective management of species, habitats and landscapes. On the other hand it also needs to be of utility at large geographical extents. In Europe the EU Habitat Directive demands of its member states, to carry out a surveillance of Annex I habitat types at a national level, in order to assess status and trends in distribution, area, structure and function. Field-based monitoring provides the best available data for many ecosystems, by supplying detailed spatial information and species records. Some habitats, though, are difficult to monitor effectively in the field due to spatially discontinuous and unpredictable processes such as flooding, encroachment, erosion and succession, while coverage of large extents is very costly. Further, even with strict rules for field mapping methods, inter-observer errors remain an issue (Stevens et al., 2004). There is therefore a need to develop monitoring methods, which can assess status and trends of habitat cover, structure and function, occurring at greater spatial and temporal scale. With advances in spatial resolution and quality of remote sensing (RS) data, and the possibilities offered by advanced object-based image analysis (OBIA) software, examples of RS-based habitat type ⁎ Corresponding author. Tel.: +45 87159014. E-mail addresses: [email protected] (A. Juel), [email protected] (R. Ejrnæs), [email protected] (J. Fredshavn), [email protected] (G. Groom). 1574-9541/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.ecoinf.2012.11.014

mappings have appeared, even at a national scale (Lucas et al., 2011). These examples offer a cost-effective alternative to field-based habitat mapping. We can divide primary RS data into satellite-based and aerial-based data, with the most commonly used being satellite data. The strengths of satellite data include high radiometrical resolution, with consistent values through regions, the inclusion of both near infrared (NIR) and shortwave infrared (SWIR) bands useful for vegetation indices, and the possibilities of acquiring multi-seasonal images of regions recorded over a short time span. Yet, for the purpose of identifying and delimiting detailed features within habitat types, most satellite imagery remains either too coarse in spatial resolution (e.g. from Landsat TM or SPOT) or too expensive (e.g. from IKONOS or Quickbird) for large and even medium sized areas (Klemas, 2008, 2011a). Aerial-based imagery has some advantages too, the major one being higher spatial resolution, the exclusion of atmospheric distortion, and in some cases, better availability and lower cost. The higher spatial resolution opens possibilities for mappings closer to field-derived mapping scales, making them intuitively understandable and possible to verify in the field. The use of aerial-based imagery has so far been limited by low radio metrical resolution of bands, low range in the spectral domain and variation in illumination depending on time of day, weather or variation in look angle (Lucas et al., 2007). The low sensor coverage implies that nationally covering data will usually be acquired over a long time scale, limiting the possibilities of acquiring multi-seasonal images (Lucas et al., 2007). The properties of aerial-based imagery, precludes analyses requiring a high level of spectral data fidelity (Groom et al., 2011) such as many

A. Juel et al. / Ecological Informatics 14 (2013) 48–52

forms of pixel-based image analysis. However, much of the semantic information necessary to interpret images is not represented by single pixels (Brodsky et al., 2008), while the definitions of habitat types often rely on spatial context. A project using aerial-based imagery should therefore have the abilities of analyzing pixels in spatial context, as well as have the ability of relating information from different spatial levels. For this purpose OBIA is well suited by abilities of incorporating ecological relations and semantics into ruleset (i.e. stepwise OBIAanalysis algorithms) and having the possibilities of multi-scale analysis. OBIA also has parallels to manual image interpretation with its cognitive abilities and separation into image primitives. OBIA is especially suitable where the pixel size is far smaller than the feature to be mapped, e.g. patches of vegetation or substrate, as is the case when using aerialbased imagery (Groom et al., 2011). The advantage of an object-based approach, when using aerial imagery for habitat structure identification, is therefore evident. The application of RS-based monitoring is especially needed in coastal habitat types (Klemas, 2008, 2011b) with considerable spatial complexity and temporal variability. The natural dynamics in coastal habitat types are a prerequisite for the maintenance of their structure and biodiversity, but are regarded as threatened due to eutrophication (Remke et al., 2009a, 2009b), coastal engineering, climate change (Miller et al., 2010), the introduction of invasive species (Damgaard et al., 2011) and land use changes. Indicators of lost dynamics may be increasing area of closed vegetation cover, reed swamps, closed bush vegetation and the beginning formation of forest. Yet very little research on the implications of decreased habitat dynamics exists, and coastal zone management is usually done without thought to negate such effects (Baily and Nowell, 1996; Carboni et al., 2009). The coastal habitat types are further assessed to be particularly suited for remote sensing monitoring, since the coastal zone is largely held free from anthropogenic influences, with vegetation responding to variation in topography, hydrology and natural disturbances, as opposed to inland habitat types, where vegetation is often unpredictable without detailed knowledge of land use history (Dyer, 2010; Gilliam and Dick, 2010). In this study we develop a method for monitoring habitat structure based on aerial photographs using Danish coastal habitats as a case. With its 7300 km of coastline, Denmark contains major areas of coastal habitats, including a significant part of the European area of coastal dunes and salt marshes. Danish coasts are a challenging case as they span a gradient from extremely exposed to highly sheltered habitats, and therefore exhibit large variation in geomorphological processes involving erosion and sedimentation. The Danish National surveying program (NOVANA) provides fieldbased habitat type mappings in Natura-2000 sites (Fredshavn et al., 2011) and means to assess changes in species composition by detailed sampling in stationary, circular 5-m plots (Fredshavn et al., 2009). However, the Natura-2000 sites cover a minor and biased fraction of the habitats at national level, and no surveillance has yet been undertaken in the most dynamic habitats, including: embryonic dunes, white dunes, coastal lagoons, salt marshes (habitat types 1310 and 1320) and beaches. Here a strategy is outlined which integrates the characteristics of the habitat information, acquired from field survey, with the characteristics of habitat information that can be interpreted from sub-meter RGB/NIR aerial imagery and digital elevation model data. When the established relationships are applied to image data they provide a monitoring of habitat structure and dynamics in previously unmapped areas. The methodology is the subject of further development, but early results from test localities are shown and discussed in this paper. 2. Material and methods 2.1. Data In Denmark there has been a tradition for nation-wide aerial photography since the beginning of the 1990s, and since 2002 the data

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acquisitions have been repeated on a biennial basis. Data have been captured with a ground sampling distance (GSD) of 80 cm in 1995 gradually reducing to 12.5 cm in 2008. Acquisition time has been post spring, late May till early July, and imagery is delivered orthorectified. The acquisitions have, besides RGB, included CIR/NIR data since 2010, thereby improving possibilities for using vegetation indices. This project deals with a present time classification, and therefore initially on the use of 2010 RGB and NIR images. Besides orthophoto imagery, a nationally covering LiDAR derived digital terrain model (DTM) and a digital surface model (DSM) from 2006 are used, with a GSD of 1.6 m and a vertical accuracy between 0.1 and 0.15 m. The DTM is used as an indication of the terrain and several indices of the terrain, e.g. slope and aspect while derivatives of the DSM are used to estimate vegetation height. Historical data, as well as future acquisitions of RGB/NIR and DTM's, will be incorporated at later stages of the project. 2.2. Overall strategy and approach The long term goal is to quantify spatio-temporal changes in structure and area of coastal habitat types. This paper focuses on the development of the overall approach and methods of present time mapping, which can be divided into 6 separate phases: 1) 2) 3) 4) 5) 6)

Selection of reference localities Initial segmentation Field-based labeling of structures and habitat types Construction of mapping library and extraction of key characteristics Application of segmentation and classification to unmapped extent Field-based validation of structure and habitat type classifications.

A key assumption of our approach is that habitat types in the coastal zone can be distinguished by firstly understanding the relationship of structural components within the habitat types, and key landscape components of the coastal zone. This understanding is obtained through an integrative approach, where samples of RGB/NIR images of coastal zone sites are segmented into primitive objects, which subsequently are labeled in the field into various structural categories, and on larger scale labeled in habitat type context. All data analysis is carried out in ArcGIS v. 10 and eCognition Developer v. 8. 2.3. Study area and field localities The terrestrial EU habitat types in the Danish coastal zone include dune systems (types 2100–2190), including coastal dunes with Juniperus spp. (type 2250), the salt marshes and salt meadows (types 1310–1330) and the vegetation on shingle or stony beaches (types 1210 and 1220), with a total of 14 habitat types constituting the main surveying target. The initial collection of reference data was based on a field and RS integrated analysis of selected field localities. 140 field localities, consisting of shore-inland transects, with a width of approximately 80 m, were selected to cover a minimum of 30 replicates of each of the 14 habitat types. The localities were selected stratified between habitat types and randomly between habitat type localities, both using an EU habitat type mapping and a mapping of generally protected salt marshes and coastal heathland. 2.4. Initial segmentation Using RGB/NIR image data, an initial multi-resolution segmentation was carried out in the reference localities using the eCognition Developer software v.8 (see Fig. 1). The segmentation parameters were adjusted to optimize the division of the habitat into primitive components, in extent as close as possible to real world objects (e.g. patches of sand, dwarf shrub, water, and characteristic plant species or plant communities),

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Fig. 1. Collection of reference data and input of data variables into a mapping library of the coastal zone. Spectral data are used to create an initial segmentation of localities which is following labeled into structure categories in the field. The inputs consist of spectral values, primitive object values, field labels of structures and habitat types and DTM data.

but with a minimum size of 1 m 2, which was considered to be the minimum mapable unit.

2.5. Field labeling In the field, the structure polygons obtained through the initial segmentation, were labeled into predefined structural classes based on %-coverage of categories 1–4 (1: >25%, 2: > 50%, 3: > 75%, 4: > 90). The structure polygons were validated, or corrected in extent, by dividing or merging in order to achieve the closest fit to real world objects. Additionally, on larger scale, a field-appointment of well-developed habitat types was carried out. At this stage of the project (January 2012) the first localities have been field-labeled, forming the basis for initial structure classification results.

2.6. Analysis of field data The acquired field information of structure polygons and habitat type references will be integrated into a mapping library containing information from 1) primitive objects, 2) field labeling of structures and habitat types, 3) supporting DTM data and 4) texture information from within objects (see Fig. 1): The data will be divided into four spatial levels which are analyzed to established relationships for structure and habitat identification and discrimination: -

Habitat series level (super level): Habitat type level: (super level) Structure polygon (main object level): Texture level (sub object level).

2.7. Application to unmapped extent The mapping procedure in the entire coastal zone is carried out by repeating the segmentation procedure, forming the structure polygons as the primary source of information. The identification and separation of structures and habitats will be carried out by rule-based classification based on the parameters extracted from the mapping library. The classification will take advantage of all four initially used levels of analysis if parameters from all levels are found to improve the discrimination of either structures or habitat types. The statistical analysis will use the maximum likelihood methods, which has the advantage of supplying the probability of membership to a certain habitat type. Hierarchical classification of structures and habitat types will be applied by initially classifying easily recognizable types (derived from mapping library), and thereafter classifying surrounding area, based on the natural shore-inland sequence and connectivity relationship. To identify and delimit the habitat types, the habitat type-structure relationships, from the mapping library will be used to locate core-habitat type areas. From these core areas, a region growing procedure will be applied, using habitat type specific limitations and context relations to adjacent habitat types. 2.8. Validation The final classification of structures and habitat types needs to be field verified using a method similar to the initial field labeling. If the classification of certain habitat types or structures proves to have low classification accuracy, ruleset parameters can be adjusted and re-validated. Obtaining accuracy close to 100% of specific features is not completely necessary since this has the potential to increase variance in classification results and compromise the classification of


other features. The accuracy of habitat classifications can instead be used as a supplementary input to the estimation of habitat type area on a national level. 2.9. The pilot project Since field references have been collected from only four transects, there are currently insufficient data to create the full mapping library. As an initial test of the possibilities of discriminating between structures, and as a source for indices and context rules, structure classification was performed in a field labeled locality using the nearest neighbor classification algorithm. The used test locality consisted of a dune system, intervened with salt marsh, with herb, shrub and high canopy vegetation, and man-made structures. The pilot test classification was based on the use of RGB, NIR and DTM values and indices at object level, and simple context rules of adjacency and distance. No texture or habitat series level information was included at this stage. 3. Preliminary findings and discussion 3.1. Reference data collection The reference data collected from the test localities has been suitable for providing fine-scale information needed for structure classification. The delineated structures from the image segmentation have also proven to have good correspondence with the structural units observed during field observation. However, since the structural contents are not discrete features, there will always be structures with varying cover within and between structure polygons. Such inconsistencies will somewhat be addressed through the texture analyses, where small scale spectral variations can be investigated and potentially lead to the division or merging of classes. Nevertheless, some small individual trees and shrubs are not captured in the image segmentation, but this can be considered as a trade-off in the configuration of the reference segmentation, which both has to detect and delineate structural objects, while also delivering objects of a size possible to locate and label in the field. 3.2. Initial test classification results Although the pilot study is limited to a small spatial extent, the nearest neighbor classification in the field locality shows the potential of fine scale delineation and discrimination between different structural elements, such as different types of shrubs, different herb communities, water and sand, on the basis of a small range of data and context rules. In some cases misclassification occurs between similar classes such as between a road and sparse herb vegetation, between a road and other manmade structures or between different types of shrub vegetation. In some of these instances this should not be seen as a failure of the method since there in reality is a gradual transition between classes, such as in the case of a dirt track with sparse vegetation. Also misclassifications within certain clusters of structures (e.g. different manmade structures) are of minor importance. The classification can be considerably more refined after feature extraction from the mapping library and the configuration of additional indices. Further the classification will be improved by application of the habitat series and texture relationship, which, respectively, will place objects in a spatially wider context and define their internal spectral complexity. In some cases the classes, constructed by the field worker, were merged together into one where considered to be more detailed than the level of interest. This is for example the case in the salt marsh area where several structure classes, characterized by different dominant species, were merged into the class ‘reed swamp’. The construction of the mapping library thus provides the ability to investigate if consistent characteristics of such dominating species and subtypes can be applied for discrimination.

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3.3. Data use Although the advantages of satellite imagery are often reported to out-weigh those of aerial imagery (Lucas et al., 2011) the conservation output requirements of the mapping work described here favors the use of aerial imagery. The small extent of Denmark has made it economically feasible to acquire national coverage aerial photography, which has formed basis for numerous spatio-temporal analysis (Groom et al., 2006), though usually carried out by manual classification rather than automated (Nygaard et al., 2011). By comparison, national coverage of satellite-based imagery is spatially coarse and historically limited. The small area of Denmark (43,094 km2) has also had the effect that country-wide aerial photograph coverage has been obtained within a short time frame (45 days, 22nd May to 12. July), thereby reducing seasonal variability effects in the image data. Classification accuracy could markedly be improved if seasonal vegetation changes could be analyzed through multi-seasonal imagery e.g. as used by Lucas et al. (2007, 2011). Although the biennial orthophoto acquisitions are the only national coverage data of its kind in Denmark, pre-flush orthophotos are at present acquired on a regional basis with coverage of one of four regions every year. These acquisitions could become an important data source and be applied by creating region-covering subprojects with temporally offset analysis between each of the four regions. With the respective strengths and weaknesses of satellite and aerial imagery outlined, it is apparent that combining information from both sources could further enhance the possibilities of achieving an accurate mapping. Satellite imagery could be applied to delimit the coastal zone, separating it from inland natural and intensive agricultural areas. Further, depending on the available data quality and number of acquisitions, satellite imagery could be used either for a direct classification of certain habitat types, or to guide the classification of the occurring habitat series. Either way, satellite imagery would also improve structure classification by placing structure elements into the context of a certain habitat type or habitat series. Using satellite imagery with a very high spatial resolution might also be able to supplement the classification of larger structural elements. However it is unlikely that acquiring such data on a regular basis is possible and the associated costs are likely to prohibit such data acquisition. Since the current national DTM and DSM are derived from LiDAR data acquired in 2006, topography and vegetation structure can have changed markedly since the acquisition. The use of the elevation models should therefore only be applied to large scale analysis of habitat types and series, and not small scale structures, since the cover of these may have changed dramatically. For more accurate structure classifications a new LiDAR acquisition with a high point density is therefore of vital importance.

3.4. Target specific OBIA projects In Denmark the combination of orthophotos and OBIA has been applied in several research projects, typically aiming at narrower target features, than general habitat or land cover mappings. The framework has been applied to capture the loss of small water bodies (Groom et al., 2011) and shrub encroachment and general change in heathland (Groom, unpublished). These projects illustrate the possibilities of how multi-temporal orthophoto imagery can be analyzed. Even though a habitat type mapping is a form of land cover mapping, it has a very specific management application and includes very specific habitat class definitions, i.e. that datasets are interrogated for unique combinations of features. Therefore certain elements of more target specific approaches should be integrated in habitat mapping to capture such distinguishing specific features. It is generally recognized that there is no “one size fits all solution” when it comes to OBIA methods, and it is therefore relevant to look at how generic experiences and methods

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from targeted OBIA uses can be incorporated into a more general habitat type mapping. 4. Conclusion These initial studies suggest a clear spatial correspondence between structural units observed in the field and those delineated through image segmentations. Test classifications show promising abilities of discriminating between different structural contents, such as different types of shrubs and herb communities. However, large scale data collection, and subsequent construction of discrimination rules, is needed to secure the robustness over larger spatial extents. In the long term, the goal of monitoring coastal habitats and their structures is to develop a change detection analysis which can provide unique opportunities of understanding of the dynamic processes and the successional development in the coastal zone. If correlated with species and environmental data this will not only provide the best possible knowledge for habitat conservation, but also have the prospects of answering much greater fundamental questions within biodiversity, succession and ecosystem dynamics. Acknowledgments The authors would like to thank the 15th of June Foundation for financial support and the Danish Nature Agency, Ministry of the Environment, for comments on field methods and for gathering the initial field reference data. References Baily, B., Nowell, D., 1996. Techniques for monitoring coastal change: a review and case study. Ocean Coastal Management 32, 85–95. Brodsky, A., Luo, J., Nash, H., 2008. CoReJava: learning functions expressed as objectoriented programs. Seventh International Conference on Machine Learning and Applications, Proceedings, pp. 368–375. Carboni, M., Carranza, M.L., Acosta, A., 2009. Assessing conservation status on coastal dunes: a multiscale approach. Landscape and Urban Planning 91, 17–25.

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