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For Review Only View metadata, citation and similar papers at core.ac.uk brought to you by CORE Page 4 of 13 JSTARS-2018-00674 provided by Bournemouth University Research1 Online 1 2 3 On bird species diversity and remote sensing – utilizing lidar 4 5 and hyperspectral data to assess the role of vegetation 6 7 structure and foliage characteristics as drivers of avian 8 diversity 9 10 11 Markus Melin, Ross A. Hill, Paul E. Bellamy and Shelley A. Hinsley 12 13 14 habitats have been conducted with various remote sensing 15 Abstract — Avian diversity has long been used as a surrogate techniques in growing numbers [1-4]. While the topics of 16 for overall diversity. In forest ecosystems, it has been assumed these studies range from individual species and habitats to 17 that vegetation structure, composition and condition have a global issues, the backbone is always in linking the significant impact on avian diversity. Today, these features can descriptions of the habitat provided by remote sensing to the 18 be assessed via remote sensing. This study examined how 19 structure metrics from lidar data andFor narrowband Review indices from ecological Only question in hand. The extent to which this is 20 hyperspectral data relate with avian diversity. This was assessed successful is then partly dependent on the capabilities of the 21 in four deciduous-dominated woods with differing age and chosen remote sensing method. 22 structure set in an agricultural matrix in eastern England. The A topic where remote sensing has been widely utilized is 23 woods were delineated into cells within which metrics of avian the assessment of plant and animal diversity. From the diversity and remote sensing based predictors were calculated. kingdom of Animalia, birds have been the most widely 24 Best subset regression was used to obtain best lidar models, 25 hyperspectral models and finally, the best models combining examined group of species in this context due to widely 26 variables from both datasets. The aims were not only to examine existing, long-term monitoring programs and because their 27 the drivers of avian diversity, but to assess the capabilities of the diversity is known to correlate with overall biodiversity [5-6]. 28 two remote sensing techniques for the task. The amount of Based on this, it has been taken that remote sensing methods 29 understorey vegetation was the best single predictor, followed by should be able to capture and describe whatever in the habitat Foliage Height Diversity, reflectance at 830 nm, Anthocyanin drives avian diversity. To this end, many papers have noted 30 Reflectance Index 1 and Vogelmann Red Edge Index 2. This 31 showed the significance of the full vertical profile of vegetation, the importance of three-dimensional vegetation structure as a 32 the condition of the upper canopy, and potentially tree species determinant of avian diversity [7-11]. However, it has also 33 composition. The results thus agree with the role that vegetation been noted that a second powerful driver of avian diversity 34 structure, condition and floristics are assumed to have for relates to the floristic characteristics of the habitat [12-14]. 35 diversity. However, the inclusion of hyperspectral data resulted Both of these features can be accurately assessed with remote in such minor improvements to models that its collection for 36 sensing, and in the past this has been done most often with these purposes should be assessed critically. airborne laser scanning [15] and multi- or hyperspectral 37 38 Index Terms — diversity, forest, structure, floristics, lidar, imaging [16]. 39 hyperspectral, ALS, bird, habitat The usefulness of lidar in ecological studies is well 40 established and has been reviewed focusing on habitat 41 I. INTRODUCTION assessment [3], animal ecology [4], and biodiversity [17]. A 42 N recent research, remote sensing and ecology have, to a key aspect is the capability of lidar to describe the three- 43 I large extent, become inseparable. Studies assessing dimensional structure of vegetation in the layer under the top 44 behavior and habitat use of wildlife or the status of wildlife canopy [18-20]. Recent publications, have reported positive 45 relationships between lidar-based metrics of understorey 46 vegetation, in particular, and avian diversity and occurrence The corresponding author was funded by Finnish Cultural Foundation’s 47 personal grant (Suomen Kulttuurirahasto – www.skr.fi/en) applied via the [21-23]. 48 Foundation’s Post Doc Pool (http://www.postdocpooli.fi/?lang=en). Bird data For assessing the floristic component of a habitat (dominant 49 collection was supported by the Wildlife Trust for Bedfordshire, tree, shrub and grass species, etc.), multi- and hyperspectral 50 Cambridgeshire and Northamptonshire. Airborne lidar and hyperspectral data data analyses are the dominant remote sensing methods. Their were acquired by Natural Environment Research Council’s (NERC) Airborne 51 Research Facility (NERC ARF). Atmospheric correction of the hyperspectral usefulness in vegetation studies relates to their capability to 52 imagery was carried out by Dr Dan Clewley at the Data Analysis Node of address biophysical characteristics of vegetation, such as Leaf 53 Plymouth Marine Laboratory. (Corresponding author: Markus Melin). Area Index, chlorophyll content, water content and Markus Melin and Ross Hill are at Department of Life and Environmental 54 Sciences, Bournemouth University, BH12 5BB Poole, United Kingdom. (e- concentration of nutrients in leaves, to name a few [16]. In 55 mails: [email protected] (currently); [email protected]). Paul forest contexts, hyperspectral data have been used to 56 Bellamy is at the RSPB Centre for Conservation Science, SG19 2DL discriminate tree species from one another [24-25], to estimate Bedfordshire, United Kingdom (e-mail: [email protected]) and 57 Shelley Hinsley is at the Centre for Ecology and Hydrology, Wallingford canopy reflectance [26], nutrient content [27], chlorophyll 58 OX10 8BB Oxfordshire, United Kingdom (e-mail: [email protected]). 59 60 Page 5 of 13 JSTARS-2018-00674 2 1 content [28] and moisture [29]. These features, in turn, B. Bird data collection 2 translate themselves into descriptions about the composition Each wood was visited four times from late March to the 3 and health of the canopy and therefore about its quality as a beginning of July in 2014. Visits started shortly after dawn 4 habitat. and avoided weather conditions likely to depress bird activity 5 Comparative studies between lidar and multispectral 6 (i.e. rain and strong winds). Birds were recorded using a spot remote sensing methods in assessing avian diversity have been mapping technique based on the method used in the Common 7 conducted [30-33]. These have found that lidar based forest 8 Birds Census of the British Trust for Ornithology [34]. Each structure measures were better at explaining variation in avian wood was searched systematically using a route designed to 9 diversity. However, we are not aware of studies assessing 10 encounter all breeding territories and the surveys were done by wildlife or avian diversity with hyperspectral data, or 11 expert bird ecologists. comparing its performance against the most widely used 12 All birds seen or heard were recorded on a map of the wood method for assessing the structural component of vegetation, and the locations were later digitized. Individuals were 13 i.e. lidar. recorded only once and in cases where the same bird was 14 In this paper we integrate airborne lidar and hyperspectral suspected to be observed twice, the second observation was 15 data with data of avian diversity. The aims are to compare the omitted. A complete list of the species included in the analysis 16 capabilities of both datasets in identifying the drivers of and the number of observations across the four field visits in 17 diversity, and to gain insight about which features of 2014 is given in Table I. 18 vegetation structure, condition and potentially composition 19 TABLE I (when estimated from remote sensingFor sources) Review are most Only 20 LIST OF BIRD SPECIES INCLUDED IN THE ANALYSIS important for determining avian diversity. Number of 21 Species Latin name observations 22 II. MATERIALS Blackbird Turdus merula 75 23 Blackcap Sylvia atricapilla 75 24 A. Study area Blue tit Cyanistes caeruleus 239 25 The study area comprised four woods located in Bullfinch Pyrrhula pyrrhula 27 Chaffinch Fringilla coelebs 104 26 Cambridgeshire, eastern England, a landscape dominated by Chiffchaff Phylloscopus collybita 39 27 intensive arable agriculture (52°25'13.5" N, 0°12'34.0" W). Crow Corvus corone 11 28 The four woods were: Riddy Wood (9.4 ha), Lady’s Wood Coal tit Periparus ater 40 Dunnock Prunella modularis 20 29 (8.4 ha), Raveley Wood (7.2 ha) and Gamsey Wood (4.9 ha) Goldfinch Carduelis carduelis 16 30 (Figure 1). Garden warbler Sylvia borin 5 31 The four woods are broadly similar in plant species Great tit Parus major 147 Great-spotted 32 Dendrocopos major 38 composition, the dominant tree species being Common Ash woodpecker 33 (Fraxinus excelsior), English Oak (Quercus robur), Field Green Picus viridis 22 34 Maple (Acer campestre) and elm (Ulmus spp.). The elm tends woodpecker 35 Jay Garrulus glandarius 11 to occur in discrete patches within each wood while the other Lesser Sylvia curruca 2 36 three species are well mixed. The main shrub species are whitethroat 37 Common Hazel (Corylus avellana), hawthorns (Crataegus Long-tailed tit Aegithalos caudatus 54 38 Magpie Pica pica 17 spp.) and Blackthorn (Prunus spinosa); they are well mixed Marsh tit Poecile palustris 23 39 and common throughout the woods, reaching heights from one Nuthatch Sitta europaea 3 40 Robin Erithacus rubecula 127 41 Song thrush Turdus philomelos 9 42 Stock dove Columba oenas 37 Treecreeper Certhia familiaris 82 43 Whitethroat Sylvia communis 6 44 Wren Troglodytes troglodytes 160 45 Yellowhammer Emberiza citrinella 4 TOTAL 1393 46 47 48 49 C.
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