Mapping seagrass coverage and spatial patterns with high spatial resolution IKONOS imagery

International Journal of Applied Earth Observation and Geoinformation 54 (2017) 145–158

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Mapping seagrass coverage and spatial patterns with high spatial resolution IKONOS imagery Ruiliang Pu a,∗ , Susan Bell b a b

School of Geosciences, University of South Florida, 4202 E., Fowler Ave., NES 107, Tampa, FL 33620, USA Department of Integrative Biology, University of South Florida 4202 E. Fowler Ave., Tampa, FL, USA

a r t i c l e

i n f o

Article history: Received 10 June 2016 Received in revised form 13 August 2016 Accepted 24 September 2016 Keywords: Spatial pattern Ripley’s K function Water depth correction Submerged aquatic vegetation IKONOS Landsat TM Seagrass Florida

a b s t r a c t Seagrass habitats in subtidal coastal waters provide a variety of ecosystem functions and services and there is an increasing need to acquire information on spatial and temporal dynamics of this resource. Here, we explored the capability of IKONOS (IKO) data of high resolution (4 m) for mapping seagrass cover [submerged aquatic vegetation (%SAV) cover] along the mid-western coast of Florida, USA. We also compared seagrass maps produced with IKO data with that obtained using the Landsat TM sensor with lower resolution (30 m). Both IKO and TM data, collected in October 2009, were preprocessed to calculate water depth invariant bands to normalize the effect of varying depth on bottom spectra recorded by the two satellite sensors and further the textural information was extracted from IKO data. Our results demonstrate that the high resolution IKO sensor produced a higher accuracy than the TM sensor in a three-class % SAV cover classification. Of note is that the OA of %SAV cover mapping at our study area created with IKO data was 5–20% higher than that from other studies published. We also examined the spatial distribution of seagrass over a spatial range of 4–240 m using the Ripley’s K function [L(d)] and IKO data that represented four different grain sizes [4 m (one IKO pixel), 8 m (2 × 2 IKO pixels), 12 m (3 × 3 IKO pixels), and 16 m (4 × 4 IKO pixels)] from moderate-dense seagrass cover along a set of six transects. The Ripley’s K metric repeatedly indicated that seagrass cover representing 4 m × 4 m pixels displayed a dispersed (or slightly dispersed) pattern over distances of <4–8 m, and a random or slightly clustered pattern of cover over 9–240 m. The spatial pattern of seagrass cover created with the three additional grain sizes (i.e., 2 × 24 m IKO pixels, 3 × 34 m IKO pixels, and 4 × 4 m IKO pixels) show a dispersed (or slightly dispersed) pattern across 4–32 m and a random or slightly clustered pattern across 33–240 m. Given the first report on using satellite observations to quantify seagrass spatial patterns at a spatial scale from 4 m to 240 m, our novel analyses of moderate-dense SAV cover utilizing Ripley’s K function illustrate how data obtained from the IKO sensor revealed seagrass spatial information that would be undetected by the TM sensor with a 30 m pixel size. Use of the seagrass classification scheme here, along with data from the IKO sensor with enhanced resolution, offers an opportunity to synoptically record seagrass cover dynamics at both small and large spatial scales. © 2016 Elsevier B.V. All rights reserved.

1. Introduction Seagrass habitats provide a variety of ecosystem functions including supporting diverse flora and fauna, imparting stability to sediments, and supplying fauna with nursery sites. Thus, preservation of seagrass habitats is intimately related to the sustainability of overall coastal ecosystem function (e.g., Sagawa et al., 2010; Bell et al., 2006). Mapping the extent of seagrass habitats and characterizing the condition of the resource remain an important

∗ Corresponding author. E-mail address: [email protected] (R. Pu). http://dx.doi.org/10.1016/j.jag.2016.09.011 0303-2434/© 2016 Elsevier B.V. All rights reserved.

component of nearshore monitoring and management of ecosystems (e.g., Phinn et al., 2008; Fornes et al., 2006). If rapid mapping of seagrass habitats over a large areal extent is desirable, then remote sensing technology using aerial and satellite sensors has been demonstrated to be more cost-effective than using field surveys (Mumby et al., 1999). Current remote sensing methods for mapping seagrass resources in optically shallow coastal areas have evolved from visual interpretation of aerial photography (e.g., Fletcher et al., 2009; Meehan et al., 2005; Chauvaud et al., 1998) to semi-automated mapping from data sets of moderate resolution satellite images in association with field-survey and hydro-optical data (Luo et al., 2016; Hossain et al., 2015; Blakey et al., 2015; Pu et al., 2014, 2012; Roelfsema et al., 2009; Shapiro

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and Rohmann, 2006; Gullström et al., 2006; Schweizer et al., 2005; Pasqualini et al., 2005; Dekker et al., 2005). Recent improvements in spatial resolution in satellite multispectral remote sensing (e.g., IKONOS and WorldView-2) have raised interest in the possible use of these methods to map and monitor benthic habitats (e.g., Garcia et al., 2015; Noiraksar et al., 2014; Lyons et al., 2011; Fornes et al., 2006; Mishra et al., 2006; Mumby and Edwards, 2002) and a number of studies have reported that such high resolution data have an improved capability for mapping seagrass habitats compared to moderate resolution satellite data. For example, Mumby and Edwards (2002) compared IKONOS (IKO) classification results in Caribbean coastal areas with different airborne and spaceborne sensors’ data. They concluded that although IKO data were unable to discriminate habitats in a complex (13 classes) classification scheme, the IKO data were adequate for four-class coarse habitat classification (coral, macroalgae, seagrass, and sand). They found that the IKO sensor with its textural information provides an acceptable accuracy (75% overall accuracy), which exceeds that obtained with a moderate resolution sensor like Landsat TM. Phinn et al. (2008) also compared multi-sensor data of Landsat TM, QuickBird-2 and hyperspectral airborne CASI-2 images to map seagrass species identity, cover and biomass in shallow waters. Their results demonstrated that mapping of seagrass cover (four seagrass cover classes), species and biomass to high accuracy levels ( >80%) was not possible across all image types. However, for each parameter mapped, airborne hyperspectral data produced the highest overall accuracies of the three sensors examined, followed by QuickBird-2. Additionally, classification of tropical coral reef environments (including various combinations of sediments, carbonate pavement, seagrass, algae, and corals) indicated that data from IKO and QuickBird sensors outperformed those from Landsat ETM+ (Benfield et al., 2007; Andréfouët et al., 2003). Thus, there is apparent agreement that comparatively high accuracies of seagrass mapping can be obtained using data from high resolution multispectral sensors. Since 2000, examples of studies using the high resolution satellite data to map seagrass extent have been reported. For instance, a mapping study of Posidonia oceanica from IKO data by Fornes et al. (2006) utilized an automated classification of four classes: sand, rock, P. oceanica and unclassified pixels and their protocols led to an overall accuracy of 84%. Wang et al. (2007) using QuickBird-2 data to map seagrass into three cover classes [high-density (50–100%), low-density (5–50%) and unvegetated (<5%) cover], achieved an overall accuracy of 75%. Recently, Knudby and Nordlund (2011) tested the utility of IKO satellite imagery for mapping distribution and biomass of multiple seagrass species in a 4.1 km2 area. When they mapped seagrass into either a dense or sparse class, an overall accuracy of 77.7% was obtained. As reviewed above, previous studies demonstrated the high resolution sensors’ improvement for mapping seagrass habitats compared to moderate resolution satellite sensors. Results from these studies reaffirm that the small “pixel size” and enhanced spatial resolution lead to improved mapping accuracy of seagrass resources and other benthic biota or sediments. One study used textural information extracted from IKO data in a mapping effort of coastal habitats, which provided information on relationship among neighbor pixels (Mumby and Edwards, 2002). Thus, data collected from high resolution satellite sensors can be used not only for seagrass mapping but also for exploring the spatial structure and distribution patterns of seagrass taxa. A variety of techniques are available for use in extracting spatial information from thematic maps. Such spatial information may be extracted from high resolution remote sensing image data. As a complement to field-based data, spatial information derived from remotely sensed imagery may offer insight into spatial patterns that change over time thereby suggesting follow-up investigations

on community processes and gap dynamics. Ripley’s K function is frequently used in terrestrial settings for characterizing patterns of spatial distribution of targets in points or patches. Specifically, Ripley’s K function is used to compare a given point (or patch) distribution at different scales with a random distribution (i.e., the point distribution under investigation is tested against the null hypothesis that the points (or patches) are distributed randomly and independently). The Ripley’s K function has been used for analyzing spatial vegetation patterns over a landscape (Koukoulas and Blackburn, 2005; Haase, 1995), spatial patterns of invasive species (Deckers et al., 2005; Call and Nilsen, 2003; Suzuki et al., 2003) and forest mortality (Hatala et al., 2010; Kelly and Meentemeyer, 2002; He and Duncan, 2000). Other researchers have used Ripley’s K function to test hypotheses about spatial aggregation of resources (e.g., Melles et al., 2009). For seagrasses we expect that spatial patterns of cover will likely change across a set of spatial scales of observation (see Fonseca et al., 2002; Robbins and Bell, 2000; Fonseca and Bell, 1998). To date, some of the most extensive information on seagrass pattern and spatial scale has come from aerial photography or towed videos (see above studies) but no comparable spatial information has been extracted from high resolution satellite images and analyzed to characterize spatial structure. The overall purpose of this study was to explore the potential of the enhanced high resolution of IKO data, which allow their textural information to be added for classification, for improving mapping accuracy of submerged aquatic vegetation (SAV: here defined as all seagrasses plus infrequently encountered rhizophytic algae; %SAV: percentage of SAV cover) in coastal areas along the mid-western coast of Florida, USA. Of special interest was a comparison of data provided by TM and IKO sensors and a spatial pattern analysis of seagrass habitat with IKO data overlapped on a TM image. Specifically, in this study, we: (1) compared the capability of the two satellite sensors (TM and IKO) with different spatial resolutions for mapping three%SAV cover classes and (2) applied Ripley’s K function to investigate the spatial distribution of SAV based upon a classification produced from IKO data representing different resolution and grain sizes. 2. Materials and methods 2.1. Study area and data sets 2.1.1. Study area The study area (center: 28◦ 03 36 N, 82◦ 48 45 W), approximately 105 km2 , is located along the northwest coastline of Pinellas County (Fig. 1), Florida, USA. This area is characterized by extensive development of subtropical seagrass meadows in shallow, relatively clear waters (Meyer and Levy, 2008). The substrate consists of unconsolidated soft sediments including a range of muddy to shelly sands with occasional hard bottom areas. The water depth ranges from 0 to 4 m (mean low water, MLW) with the majority of seagrass habitats found between 0.5 and 3 m. Three seagrass species are numerically dominant: Syringodium filiforme, Thalassia testudinum, and Halodule wrightii. Occasionally Halophila engelmanni occurs sparsely within the seagrass beds. In addition, a variety of marine rhizophytic algae, mixed with seagrass, was observed in selected locations. 2.1.2. Data sets Satellite imagery Data from two satellite sensors, Landsat TM and GeoEye IKONOS (IKO), were acquired on the same day, October 1, 2009. Three TM visible bands (TM1-3) and three IKO visible bands (IKO1-3) were used in this analysis. Both sensors’ visible bands have similar wavelengths. The spatial resolutions of TM and IKO visible bands are 30 m and 4 m (multispectral), respectively.

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Fig. 1. Location of the study area with a total of 105 km2 , St Joseph Sound and Clearwater Harbor, Pinellas County, Florida, USA. The blown up color composite images (RGB vs. TM bands 3,2, and 1) show locations (middle) of 57 transect samples in Fall 2009, indicated by a red symbol and nearby %SAV cover number, locations (middle) of six transects (X-profiles) in red used for detailed examination (see below), and distributions (left) of validation patches for three-class seagrass cover classification (in yellow dots). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

SAV map Additional information on seagrass distribution from the Clearwater/St. Joseph Sound study site was available from the Southwest Florida Water Management District (SWFWMD) via a map constructed through visually interpreting 1:24,000 natural color aerial photography flown in January 2010 (see SWFWMD, 2015). The map represented a data set closest in time to the acquisition date (Oct. 1, 2009) of the two sensors’ imagery, TM and IKO. SWFWMD categorized seagrass beds in the study areas into three classes: Low/No SAV (SAV < 25%), Patchy (SAV 25–75%), and Continuous (SAV > 75%). According to specifications, a 90% accuracy rating was required for mapping the SAV map (SWFWMD, 2015). Hereafter, the seagrass map is referred to as SAV Map 2010. The SAV Map 2010 along with data from 57 field surveys (below) was used to create a validation data set. Field data collection Field data on SAV cover from the study area were collected at 57 locations during September and October, 2009. Locations were chosen utilizing a spatially stratified random sampling procedure such that they were spatially allocated to different areas of (1) Continuous, (2) “Patchy” seagrass beds and (3) areas delineated as “No SAV”. These categories of seagrass cover/distribution were selected to form SAV maps produced by aerial photographic interpretation conducted by the SWFWMD. At each chosen location a 30 m long transect was established and the start and end points of a sampling transect were recorded with GPS measurements. Quadrats (0.5 m2 ) were positioned along each tran-

sect at 5 m increments. The following information was collected or measured from each quadrat: shoot density and percent cover (%SAV cover) for any SAV species present, substrate type, and water depth (m). Mean transect values of %SAV cover were then used to determine training areas (for both three-class and two-class SAV cover classifications)/test areas (for the three-class SAV cover classification only) for%SAV cover classifications with satellite image data at 30 m (TM) and 4 m (IKO) resolutions. 2.2. Methods 2.2.1. Image preprocessing Geometric accuracy for the two sensors, assessed with GPS measurements (Rino 530 HCx GARMIN GPS with accuracy <3 m) collected from ground control points located at bridges, features along shoreline and island chains within and surrounding the study area was determined to be less than one half of a pixel. Additionally, the image geometric error caused by the variation in depth across the study area, ranging from 0 to 4 m (MLW), was very slight. For the radiometric correction, we first used ENVI4.5 image processing software (Exelis, 2015) with built-in calibration coefficients and a separate metadata file for the TM and IKO sensors to convert image digital numbers to at-sensor radiance values. Then we used a radiometrically corrected approach to convert at-sensor radiance to at-water surface radiance by deducting atmospheric path radi-

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Table 1 Information on depth invariant bands calculated from the three TM and IKO visible bands. TM

IKO

Depth invariant band

Definition

Depth invariant band

Definition

TM12 TM13 TM23

ln(TM1)−0.291* ln(TM2) ln(TM1)−0.112* ln(TM3) ln(TM2)−0.521* ln(TM3)

IKO12 IKO13 IKO23

ln(IKO1)−0.721* ln(IKO2) ln(IKO1) − 0.223* ln(IKO3) ln(IKO2) − 0.302* ln(IKO3)

Note: TM1, IKO1 = blue, 450–520 nm; TM2, IKO2 = green, 520–600 nm; TM3, IKO3 = red, 630–690 nm.

ance. This approach is similar to the dark pixel subtraction approach (Schowengerdt, 2007; Teillet and Fedosejevs, 1995) (see SM 2 from Pu et al., 2012 for the detailed description to the approach). All three visible bands in at-water surface radiance for the two sensors were used to calculate depth-invariant bands (Lyzenga, 1978, 1981) to normalize the effect of water depth on the radiance from benthic substrates. Three depth-invariant bands, TM12, TM13, and TM23 were calculated from three TM visible bands and three depth-invariant bands, IKO12, IKO13, and IKO23 were calculated from three IKO visible bands (see Table 1). The detailed calculation method of depth-invariant bands was introduced in Pu et al. (2012). The depth-invariant bands were directly used in the twoand three-class%SAV cover classifications (described below). The effectiveness of utilizing the depth-invariant model to normalize water depth variation in spectral analyses was evident from previous work (see SM 4 from Pu et al., 2012) reporting a comparison of coefficients of variation (CVs) of spectra extracted from three regions of interest (ROIs) with variable water depths over the same sand bottom for different visible bands and corresponding depthinvariant bands. Specifically, the mean CV of depth-invariant bands among three ROIs for both TM and Advanced Land Imager (ALI) sensors was approximately 8% of the mean CV of visible bands in radiance for the same two sensors (i.e., 92% of the mean CV of visible bands in radiance among the three ROIs for the two sensors was reduced).

2.2.2. Textural feature extraction and selection To use high resolution spatial information of the IKO sensor efficiently, textural features were extracted from IKO imagery. A total of 13 textural features were selected and extracted from each of the three depth-invariant bands, IKO12, IKO13, and IKO23 in this analysis, comprising five 1st-order grey level statistical textural features and eight 2nd-order grey level statistical textural features [see the 13 textural feature definitions in Table 3 by Pu and Cheng (2015)]. The selection of the 13 textural features was based on literature review and their potential for mapping forest LAI and seagrass cover from high resolution multispectral data, which has been demonstrated in many existing studies (e.g., Kraus et al., 2009; Mumby and Edwards, 2002; Zhou et al., 2014; Pu and Cheng, 2015). The 1st-order statistical textural features are derived from the pixel values in a moving window with different window sizes, but the 2nd-order statistical textural features are calculated from the spatial-dependency gray-level co-occurrence matrices (GLCM) describing the probability of each pair of pixel values co-occurring when considering both window size and direction (Haralick et al., 1973). To reduce redundancy and data dimensionality without losing significant useful spatial information, Jeffries-Matusita (J-M) and Transformed Divergence (TD) separability measures (Richards, 2013) were adopted. In this study, given the three depth-invariant bands and approximately 10% (selected) of total textural features (39), a group of three or five textural features was selected based on their individual J-M values (selecting three or five features with the first three (or five) highest J-M values with also considering their higher TD values).

2.2.3. Three-class %SAV cover classification Determination of the training areas for a three-class% cover classification of%SAV was based upon two sets of information: field observations from 57 transects described in Section 2.1.2 and clustered results by using ISODATA (an unsupervised procedure) with data from TM and IKO sensors. The transect observations within clustered homogeneous patches (created by ISODATA) provide an indicator of different levels of%SAV cover that could be grouped into “cover classes” and the clustered results present spectrally homogeneous patches so that ROIs can be determined for training areas for subsequent supervised classification (Pu et al., 2012). All ROIs delineated for training areas were located so as to surround transects within clustered homogeneous patches and these composed approximately the top half of the ROI. Table 2 outlines definitions of%SAV cover classes used in the three-class SAV cover classification which we adopted and provides detailed information on training and validation areas for each sensor. To validate the classification results created with the training data determined using the above approach, we delineated a set of patches each the size of approximately 7–10 TM pixels near the 57 transect locations on SAV Map 2010. A validation patch was designated if (1) in close proximity to the validation patch, values for an SAV observation from a transect and its corresponding SAV Map 2010 mapping value were of the same SAV cover class; and (2) the patch did not cover any pixels already used for training samples. Since the 57 transects were distributed using a spatially stratified random sampling procedure, the validation dataset might be considered to have a similar random sampling property as the set of transects. As a result, a total of 41 validation patches were created (see Fig. 1). The classification criteria of%SAV cover were similar to those used by Meyer and Pu (2012). Using the two sensors’ images, two supervised methods: MLC (maximum likelihood classifier) and SVM (support vector machine) algorithms, in ENVI5.1 (Exelis, 2015) were applied to classify seagrass habitats across the study area. SVM separates the classes with a decision surface that maximizes the margin between the classes, and using a pairwise classification strategy for multiclass classification (Exelis, 2015). In this study, the SVM classifier with four types of kernels: linear, polynomial, radial basis function (RBF), and sigmoid (Burges, 1998; Li et al., 2014) was tested to compare its performance with MLC. To evaluate and compare the capability of data from the two sensors to map the three-class seagrass habitats, the standard accuracy indicators [overall accuracy (OA), and Producer’s and User’s accuracy] (Congalton and Mead, 1983; Congalton et al., 1983) were adopted. All accuracy indicators were calculated from an independent validation set of samples (Table 2) for evaluating the three-class SAV cover classification maps with TM and IKO sensors’ data. Further, a detailed comparison of three-class%SAV cover classification results from TM and IKO corresponding to locations on six transects (X-profiles identified in Fig. 1), along with TM and IKO blue band radiance values for the corresponding transects, was performed. Only the blue band was used in our analyses because this band usually has the highest reflected radiance over clear water surfaces compared to the other visible bands (Jensen, 2007).

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Table 2 Information on classification of submerged aquatic vegetation (SAV) classes including numbers of training and validation pixels. Class

Description

TM

IKO

Training

Continuous Patchy No SAV Total areas/pixels

SAV cover percentage >75% SAV cover percentage 25–75% SAV cover percentage <25%

Validation

n n   w(dij ) i=1 j=1

n2

,

for i = / j

Pixels

Areas

Pixels

Areas

Pixels

Areas

Pixels

18 13 8 39

1605 1053 1263 3921

25 9 7 41

199 101 67 367

17 11 8 36

57720 35656 33753 127129

25 9 7 41

5207 2448 1853 9508

(1)

where A is the area of the plot; d is the radius of the search circle centered on event i; n is the number of events in the plot, and w(dij ) is a weight that (if there is no boundary correction) is 1 when the distance dij between point i and point j is less than or equal to d and 0 otherwise. When edge correction is applied, the w(dij ) is modified slightly by multiplying a conditional weight coefficient [see Eq. (8) in Dixon, 2002]. The expected value of K(d) for a random Poisson distribution is d2 and deviations from this expectation indicate scales of clustering and dispersion (Dixon, 2002; Kiskowski et al., 2009). The K(d) can be normalized so that its expected value is d (linear) (Dixon, 2002; Besag, 1977): L(d) =



K(d)/

Validation

Areas

2.2.4. Analysis of seagrass spatial distribution patterns To investigate the spatial structure of seagrass habitat we used the Ripley’s K function in ArcGIS [ESRI ArcGIS Desktop 10.1, Spatial Statistics Tools, Analyzing Patterns with Multi-Distance Spatial Cluster Analysis (Ripley’s K Function)] to analyze the spatial patterns of patchy seagrass habitat in point data. Ripley’s K function, K(d) (Ripley, 1976, 1981) was used to test for deviations from CSR (Complete Spatial Randomness) for single point patterns and used to examine the two-class classification map created with IKO image. Ripley’s K function is described as: K(d) = A

Training

(2)

Ripley’s K function is typically used to compare a given point distribution with a random distribution; i.e., the point distribution under investigation is tested against the null hypothesis that the points are distributed randomly and independently. For example, a positive value of (L(d) − d) indicates clustering over that spatial scale whereas a negative value indicates dispersion (Kiskowski et al., 2009). Because the analysis required a binary classification of cover, to investigate the spatial structure and distribution of seagrass habitats assigned to the “patchy ‘ category from the three-class cover classification, two cover classes were erected. A threshold of 25% SAV cover was used to distinguish locations as moderate/dense SAV versus% SAV cover < 25% as “No’ SAV (Meyer and Pu, 2012; Tomasko et al., 2005). Using this criterion, training data determined with methods described in Section 2.2.3 and IKO data (three depth-invariant bands only) (Table 1), a pixel-based, twoclass map was created. The 0.5 m2 field observations along the 57 transects were used to validate the accuracy of the two-class cover classification through placing the field-based observations from the 399 quadrat observations along the 57 field transects onto the two-class SAV cover classification map and evaluating agreement between the two sets of data. The pixel- based map was then converted to a point shape file (one point represents one pixel). We then used the Ripley’s K equation to quantify spatial patterns of patchy seagrass habitat based upon measurements at 4-m resolution (grain size, 1 × 1 IKO 4 m pixel = 4 × 4 m2 ) both within individual TM pixels (30 m) and across multiple TM pixels. The Rip-

ley’s K analysis was applied to a selected six sets of 8 × 8 TM pixels along each of the six X-profiles (Fig. 1). These sets of 8 × 8 30 m resolution TM pixels were used to evaluate whether it was possible to resolve finer scale spatial patterns with 4-m IKO pixels that would otherwise be undetected using TM pixels. To test effects of different grain sizes on spatial patterns of seagrass landscape, we also used three additional grain sizes (spatial resolutions) (2 × 24 m IKO pixels = 8 × 8 m2 ; 3 × 3 4-m IKO pixels = 12 × 12 m2 ; 4 × 4 m IKO pixels = 16 × 16 m2 ) to describe spatial patterns of seagrass landscape. We aggregated 2 × 2, 3 × 3, and 4 × 4 IKO pixels units and assigned the aggregated feature (i.e., moderate/dense SAV or No SAV) to the most dominant class in the aggregated unit by averaging class codes in the two-class map (code 1 for class: moderate/dense SAV and code 2 for class: No SAV). We acknowledge that the use of different grain sizes may result in different spatial process patterns for some landscape metrics (Wu et al., 2002). For example, when Wu et al. (2002) tested different grain sizes or spatial resolutions, ranging from 1 × 1 to 100 × 100 original Landsat TM pixels to characterize land use/land cover (LULC) spatial distribution, they demonstrated different LULC spatial patterns with varying grain sizes.

3. Results and analysis 3.1. Seagrass classification maps In this study, referring to Pu and Cheng (2015) and considering IKO spatial resolution, we chose a 3 × 3 window size for extracting the five 1st-order statistical textural features and a 5 × 5 window ◦ size and 90 direction for calculating the eight 2nd-order statistical textural features. To reduce redundancy and data dimensionality without losing significant useful spatial information, a set of five 2nd order statistical textural features was selected from a total of 39 (13 features × 3 bands) textural features, which could account for 93.09% [1.846 (J-M value from the five selected textural features)/1.983 (J-M value from all 39 textural features) × 100%] of total of three-class separability based on J-M and TD separability measures. The selected five textural features included three (K1MEA, K1ENT, and K1ASM) extracted from IKO12, which are Mean, Entropy, and Angular second moment, respectively, and two (K3MEA and K3ASM) extracted from IKO23, which are Mean and Angular second moment, respectively. The remaining 34 (39–5) textural features were not used in this study. Representations of%SAV cover across the study area based upon data provided by the two sensors are presented in Fig. 2. Classification results for percent SAV cover presented in Fig. 2 were created with the MLC classifier and three TM depth-invariant bands (Fig. 2a), the MLC classifier and three IKO depth-invariant bands and five selected textural features (Fig. 2b), and the SVM classifier and three IKO depth-invariant bands and five selected textural features (Fig. 2c). A comparison of the spatial representations indicates general agreement of cover classification between the two sensors with the exception of the two small areas highlighted in Fig. 2. The depth-invariant model used to normalize water depth

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Fig. 2. Results of classification of three-class%SAV cover created using the two sensors TM and IKO data. Classification results were created with (a) MLC classifier and three TM depth-invariant bands, (b) MLC classifier and three IKO depth-invariant bands and five selected textural features, and (c) SVM classifier and three IKO depth-invariant bands and five selected textural features. The locations of six transects (X-profiles in Fig. 4 and Supplementary Fig. 3) in yellow are also shown in (a). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Table 3 Accuracies calculated from validation samples by using the MLC and SVM classifiers with the two sensors’ (TM and IKO) data. Class

TM

IKO

MLC

Continuous Patchy No SAV Average OA (%) Kappa Variance

SVM

MLC

SVM

P.’s a. (%)

U.’s a. (%)

P.’s a. (%)

U.’s a. (%)

P.’s a. (%)

U.’s a. (%)

P.’s a. (%)

U.’s a. (%)

78.89 87.00 92.54 86.14 83.61 0.7394 0.001179

98.74 68.50 77.50 81.58

71.86 72.28 86.57 76.90 74.66 0.5984 0.001820

91.67 54.89 74.36 73.64

85.85 89.34 90.72 88.64 87.69 0.8010 0.000035

99.29 73.41 82.93 85.21

87.00 92.36 93.74 91.03 89.69 0.8329 0.000030

99.49 76.59 86.72 87.60

Note: P.’s a. = Producer’s accuracy, U.’s a. = User’s accuracy.

variation in spectral analyses (Pu et al., 2012) was effective. The three-class%SAV cover classification results were evaluated with overall accuracy (OA), Producer’s and User’s accuracies, and Kappa values, calculated from validation samples (Table 2). From Table 3, the comparison of the three classification results based upon accuracy indicators (OAs, etc.) revealed that IKO (both MLC and SVM)

derived results were consistently better than those corresponding TM (both MLC and SVM) derived classification results in mapping %SAV cover except No SAV created by IKO MLC compared to that created by TM MLC. Classification accuracies created between using TM and IKO data with the same classifiers were significantly different [alpha = 0.05 (one tail), Table 4]. These were true for results

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Table 4 Z-statistic tests (Z1-Test) calculated from Kappa-Variance of seagrass classification results (Table 3), generated from validation samples (Table 2) between TM and IKO sensors. Two-sample difference of proportions tests (Z2-Test) calculated from seagrass classification results (OA from Table 3) and validation samples (Table 2) between TM and IKO sensors. p-value

IKO MLC Z1-Test

TM

MLC

Z1-Test Z2-Test Z1-Test Z2-Test

SVM

SVM Z2-Test

Z1-Test

Z2-Test

0.038* 0.010** 0.000** 0.000**

|K1 −K2 |

Z-statistic test: Z1 = √

V1 +V2

where, k1 and k2 are kappa vappa values calculated from IKO and TM data sets, respectively, and v1 and v2 are corresponding variances. |P −P | Two-sample difference of proportions test: Z2 = ␴p1 −p2 1

2

where, P1 and P2 are proportions (accuracies) of samples 1 and 2, respectively is standard error of the difference of proportions. * Difference between classification accuracies is significant (alpha = 0.05) (one tail). ** Difference between classification accuracies is significant (alpha = 0.01) (one tail).

from both the Z-Statistic Test (Z1-Test) comparing the data derived Kappa-Variance values calculated from validation samples and the Two-Sample Difference of Proportions Test (Z2-Test) (McGrew and Monroe, 2000) utilizing data derived OAs and validation sample sizes. In general, use of data from the IKO sensor and selected textural information resulted in a more accurate SAV cover map than that produced with data from the TM sensor, which could be due to IKO sensor’s high spatial resolution. A total of 342 IKO 4 m pixels in the two-class SAV cover classification map were labeled as the same SAV cover class as that observed from the corresponding 0.5-m2 quadrats resulting in a correct SAV cover mapping accuracy of 85.71% (=342/399 × 100%). Given the approach (Section 2.2.3) used to determine training samples (the Continuous and Patchy training samples in Table 2 merged into moderate to dense SAV and No SAV training samples in Table 2) for the two-class SAV cover classification, a relatively lower SAV mapping accuracy for the two-class cover classification (compared to those in Table 3) might be expected because the validation result for the two-class cover classification was derived from individual 0.5-m2 quadrat observations whereas the training MLC/SVM classifiers samples represented the average class code of the seven 0.5-m2 quadrat observations for a single transect. Additionally, the training data determined in Section 2.2.3 were likely to be favorable to classify a pixel correctly as ROIs were delineated from a clustered homogeneous patch that encompassed a transect. To further investigate the spatial distributional patterns of seagrass cover assessed by IKO (4 m) but located within individual TM (30 m) pixels, one patch of four TM pixels was extracted from each of the six transects (X-profiles) (locations marked along the X-profile of Fig. 4 and Supplementary Fig. 3) and the corresponding 225 IKO (4-m) pixels (one TM pixel contains 56.25 IKO 4-m pixels) were also retrieved. Examination of the maps of TM and IKO pixels for identical patches (Fig. 3) revealed both agreement and disagreement between classifications of the areal dimensions contained within the set of four TM patches. For example, within a TM pixel designated as ‘No SAV’, pixels of the Patchy class were recorded with IKO (see Fig. 3c). Likewise, within a TM pixel designated as Patchy, pixels of the Continuous class were recorded with IKO data. These figures aptly illustrate the additional information on seagrass cover classification that may be gleaned from improved spatial resolution with IKO data.

3.2. Spatial patterns of SAV distribution The absolute radiance from the TM blue band at all X-profiles was consistently higher than that from the IKO blue band, as evident from plots of all six X-profiles (see two in Fig. 4 and the other four

in Supplementary Fig. 3). These results are likely associated with the different off-nadir view angles (␪) used by the two sensors that may impact the recording of absolute radiance over the study area. Based on metadata, IKO had an off-nadir view angle of 18.25◦ while the view angle of TM sensor was always close to a nadir (vertical) direction. A close to nadir view angle would be expected to receive more reflected radiance over a target (pixel) than that from an offnadir view direction (L␭cos , where, L␭ is a reflected radiance over a pixel at a nadir view direction); this is consistent with our results. Note that both sensors’ data were acquired at a similar time (1052 h for TM vs. 1127 h for IKO) on the same day (October 1, 2009) so it was not necessary to account for seasonal or diurnal effects. Although the magnitude of blue band radiance differed between both TM and IKO sensors, data from both sensors displayed consistent trends in pattern of radiance values along a given transect (the X-profile). Classification of% SAV cover using these radiance values when plotted along transects (X profiles) for each sensor (Fig. 4) illustrates the effects of differing spatial resolution on designation of cover category for individual pixels. The variation of radiance among neighboring and nearby pixels gathered from IKO sensor data is much greater than that from the TM sensor with the lower spatial resolution. The Ripley’s K function [L(d)] analysis method was applied to the first two X-profiles (Fig. 4) with the reduce-analysis-area edge effect correction and the user-provided-study-area-feature-class parameters (ESRI ArcGIS Desktop 10.1, Spatial Statistics Tools) to produce the corresponding L(d) graphs presented in Figs. 5 and 6. These X-profiles contained areas of both no and moderate/dense SAV cover levels. As indicated in the plots of 4-m SAV IKOpixels/points placed within a larger arrangement of 8 × 8 TM-pixels of 30 m resolution [see Figs. 5 (a1) and 6 (a1)], Ripley K values representing random or slightly clustered patterns (∼8–240 m) of moderate/dense SAV pixels were most commonly observed [Figs. 5 (a2) and 6 (a2)] across the majority of the distances examined. However, most graphs [Figs. 5 (a2) and 6 (a2)] representing Ripley’s K function were consistent with the pixels of moderate/dense SAV detected using the IKO sensor displaying a “slightly dispersed or dispersed” distribution when examined within two (≤8 m) IKO–4 m pixels [Figs. 5 (a2) and 6 (a2)]. Compared to the spatial patterns derived from the 1 × 14 m IKO pixel, the spatial patterns across different scales in the plots created with the three additional grain sizes (i.e., 2 × 2 4-m IKO pixels, 3 × 3 4-m IKO pixels, and 4 × 44 m IKO pixels) are slightly different across 4–32 m but similar across 33–240 m (see Table 5 for the detailed summary of spatial patterns at different spatial scales and grain sizes from Figs. 5 and 6). We were unable to produce plots created with the 3 × 3 4-m IKO pixels and 4 × 44 m IKO pixels grain

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Fig. 3. Spatial distribution and SAV class of seagrass cover in IKO pixels within a corresponding set of four TM pixels extracted from the 6 transects (X-profiles) in Fig. 1. The locations from which the SAV classes [Continuous (=Con.), Patchy, and NoSAV] were extracted from Fig. 4 for corresponding Fig. 3 (a, b) and Supplementary Fig. 3 for corresponding Fig. 3 (c–f) are shown. For each grouping of four TM pixels the number of pixels within each SAV class for the two different sensors is reported.

Table 5 Summary of spatial patterns at different spatial scales and grain sizes from Figs. 5 and 6. Figure #

plot

5

(a2) 4mX4m (b2) 8mX8m (a2) 4mX4m (b2) 8mX8m (c2) 12mX12m (d2) 16mX16m

6

Spatial scale 4–8 m

9–12 m

13–16 m

17–32 m

33–240 m

Slightly D or R Slightly D or R D D D D or slightly D

R or slightly C R R or slightly C D or slightly D D or slightly D D or slightly D

R or slightly C R R or slightly C R or slightly C R or slightly C D or slightly D

R or slightly C R R or slightly C R or slightly C R or slightly C R or slightly C

R or slightly C R R or slightly C R or slightly C R or slightly C R or slightly C

Note: D = dispersed pattern, R = random pattern, and C = clustered pattern.

sizes for Fig. 5 because no pixels met our operational definition of “moderate/dense” SAV cover level after pixels were combined. In considering Ripley’s K values across the four different scales in the patched area in the study area, the slightly dispersed or dispersed patterns (4–32 m) and the random or slightly clustered patterns (33–240 m) of moderate/dense SAV pixels were most commonly observed across the majority of the distances examined. Additional plots of Ripley’s K values for X profiles (Supplementary Figs. 4–7) generally also support these patterns.

4. Discussion The overall accuracy of our three-class %SAV cover mapping result created with IKO data (Table 3) was 5–20% higher than that reported in previously published studies (see the 2nd and 3rd paragraph in Introduction). In our study, TM and IKO sensors produced higher accuracy for classification of the three-class%SAV cover compared to previous investigations of seagrass mapping that used similar classification schemes (e.g., a three-class %SAV

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Fig. 4. (a, b) Radiance from TM (30 m resolution, TM1 blue band) and IKO (4 m resolution, IKO1 blue band) sensors and results of three-class %SAV cover classes from TM and IKO along the X-profiles [see classification results in Figs. Fig. 2(a) and (b)]. Both radiance and cover classification are plotted versus pixel number along the first two (the most north two) of six selected transects (X-profiles) indicated in Fig. 1. see Supplementary Fig. 3 (a–d) for both radiance and cover classes plotted versus pixel number along the remaining four selected transects (X-profiles) indicated in Fig. 1. The six transect profiles are presented sequentially from the top to the bottom of the image. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

cover scheme) and either moderate-spatial resolution (TM/ETM+, 30 m resolution) or high resolution (IKO/QuickBird, 2.5–5 m resolution) data. Without a study that directly evaluates different methods of data collection and classification methods, it is difficult to specifically identify the factors leading to increased accuracy in our study compared to earlier reports. However we expect that our improved seagrass mapping (Table 3) may be the result of our using the depth-invariant model (for both sensors) to normalize the effect of water depth on seagrass habitat classification, which has been shown to increase seagrass classification accuracy (e.g., Pu et al., 2012), and spatial/textural information (IKO sensor only), which was demonstrated to be useful for quantifying seagrass (e.g., Mumby and Edwards, 2002). Additionally, our mapping was confined to areas of relatively clear and shallow water and benthic substrates, which might confuse seagrass classification, (e.g., Knudby and Nordlund, 2008) were absent from our site. The high resolution IKO sensor produced a higher accuracy (OA and Kappa) than the TM sensor for both classifiers derived

results, and the accuracy differences between the two sensors were significant at alpha = 0.05. The higher accuracy derived from the IKO sensor for both classifiers’ results, compared to that from TM sensor, resulted from the additional spatial (textural) information extracted from the high resolution IKO data. Use of spectral information alone (i.e., the three depth-invariant bands) from IKO data led to only a slight improvement (alpha = 0.10) in SAV classification across a large spatial extent compared to the classification produced from TM data (OA = 86.42% for IKO versus 83.61% for TM with MLC). It is interesting that, the derived results generated by the IKO SVM classifier was better than that by that generated by the IKO MLC (both SVM and MLC using the three depth-invariant bands and five selected textural features as input). However, for data obtained using the TM sensor, an opposite set of results was recorded and situation was inverse. This might be explained from the three properties of SVMs for remote sensing image classification (Li et al., 2014; Bruzzone et al., 2007; Burges 1998). (1) SVM can handle data in high dimensionality efficiently. In our case, SVM

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Fig. 5. Spatial distribution of pixels (points) with either moderate−dense (black square; ≥25%) or no (white square; <25%) SAV cover extracted from IKO data [4 m classification result (a1) and averaged (class codes in the two-class map) to 8 m result (b1)]. Data are not available for averaged to 12 m and 16 m results. Corresponding results for [L(d)] Ripley’s K function are presented in (a2, b2). The plots represent data from a set of 8 × 8 TM pixels and the corresponding 60 × 60 IKO pixels extracted from the first X-profile (see their corresponding locations in Fig. 4). The two dashed lines outline 99% confidence limits of the [L(d)] values. The three red boxes at low left corner (a1 and b1) are the software function features without the research meanings. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

working is more efficiently for IKO data than TM data because of IKO data with 8-D (three depth-invariant bands plus five textural features) compared to TM data with 3-D (three depth-invariant

bands only). (2) SVM deals with noisy samples in a robust way. From statistical learning theory, SVM often yields good classification results from complex and noisy data (Exelis, 2015) for IKO data

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Fig. 6. Spatial distribution of pixels (points) with either moderate−dense (black square; ≥ 25%) or no (white square; <25%) SAV cover extracted from IKO data [4 m classification result (a1) and averaged (class codes in the two-class map) to 8 m, 12 m, and 16 m results (b1, c1, d1)]. Corresponding results for [L(d)] Ripley’s K function are presented in (a2, b2, c2, d2). The plots represent data from a set of 8 × 8 TM pixels and the corresponding 60 × 60 IKO pixels extracted from the second X-profile (see their corresponding locations in Fig. 4). The two dashed lines outline 99% confidence limits of the [L(d)] values. The three red boxes at low left corner (a1 through d1) are the software function features without the research meanings. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

with textural features etc. are relatively complex compared to TM data (only using three depth-invariant bands). (3) SVM makes use of only those most characteristic samples as the support vectors

in construction of the classification models. For this property, the more training samples, the more effective support vectors can be found. So the more training samples from IKO data have led to more

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effective support vectors to be found than the less training samples from TM data. Overall, the IKO sensor more successfully characterized smaller spatial scale heterogeneity of seagrass cover than the TM sensor, a result also demonstrated by the pixel by pixel classification results presented in Figs. 3 and 4. Such findings may be linked to the overall higher number of classified pixels produced with IKO images (Fig. 3) and a seagrass spatial pattern muted by analyses conducted at 30-m resolution. Notably, an increase in observations of all three%SAV cover classes created from the IKO sensor compared to that recorded from a corresponding TM sensor (Fig. 4) was recorded in our study. From a practical point of view, the IKO sensor may be very appropriate for defining benthic habitat types in areas where seagrass beds are of limited extent and that require high resolution imagery be utilized. Our results from a subtropical seagrass bed align with those by Mumby and Edwards (2002) who also argued that data from the IKO sensor may be comparable and cost-effective for mapping seagrass areas < 500 km2 and monitoring dynamics of patches < 10 m2 . Moreover, Peneva et al. (2008) suggested that seagrass mapping via satellite would require sensors that had resolution of “3 m or better” given the dimensions of patches formed by the seagrass, Halodule wrightii, within the Northern Gulf of Mexico, that they observed using a HyMap airborne hyperspectral system. Per the Ripley’s function [L(d)] outputs (Figs. 5 and 6) created from the IKO data, several issues need to be considered. (1) When continuous pixel areas are converted to points with a fixed interval/distance between two neighbor points, this process might be prone to creating a “dispersed” pattern at a small scale (e.g., 1–2 4 m IKO pixels) so interpretation of spatial structure of seagrass over 0–8 m needs to be interpreted cautiously. (2) The boundary correction effect on the Ripley’s K-function [L(d)] outputs was significant, especially for a small scale pattern. For example, it might be possible that using the simulate-outer-boundary-values method in this study to correct the boundary effect might lead to a small scale pattern as “slightly dispersed” or “dispersed” pattern because this method creates points outside the study area (i.e., plot area here) boundary that mirror those found inside the boundary in order to correct for underestimates near the edges. Therefore, in this study, we utilized the reduce-analysis-area method for correcting the edge effect, and we believe that the results produced by using the edge effect correction method are a better representation of spatial patterns over small scales than those obtained by using the simulate-outer-boundary-values edge correction method because the former uses actual points within plot area (not simulated points from the latter). (3) The Ripley’s K-function statistic is very sensitive to the size of the study area (McGrew and Monroe, 2000). In this study, we chose a user-provided-study-area-featureclass to run the Ripley’s K-function such that all L(d) outputs from Figs. 5 and 6 (and Supplementary Figs. 4–7 also) at different spatial scales were associated with the user-specified study area (i.e., plot area here). Earlier studies have reported on the spatial structure of seagrass landscapes using high resolution aerial photography or towed videos (see Introduction), and we show here that information on seagrass spatial pattern (from 4 to 32 m) can also be described from high resolution satellite images. Based on our knowledge, this is the first report on using satellite observations to quantify seagrass spatial patterns at a spatial scale from 4 m to 240 m. Compared to the spatial patterns derived from the 1 × 14 m IKO pixel, the spatial patterns across different scales in the plots created with the three additional grain sizes (i.e., 8 m, 12 m and 16 m image scales) are slightly different across 4–32 m but similar across 33–240 m (see Table 5 for the detailed summary of spatial patterns at different spatial scales and grain sizes from Figs. 5 and 6). This means that the 4–16 m image scales might have a similar spatial pattern for the

patchy seagrass areas in the study area. Such findings from the Ripley’s K outputs differ from similar analyses conducted on terrestrial landscapes by Wu et al. (2002) who found LULC spatial patterns to differ with varying grain sizes. This might be explained as patchy seagrass sizes in this study were relatively small (e.g. <250 m), while patches up to 3000 m were recorded in the study by Wu et al. (2002). While our study did not investigate the underlying factors responsible for the different spatial patterns of seagrass cover at 4–32 m compared to larger dimensions, a number of suggestions have been offered in other reports. For example, van der Heide et al. (2010) and Fonseca et al. (1998) proposed that different seagrass spatial patterns reflected the spatial scale over which physical processes, such as currents, waves, light, and sediment transport, operate. Fonseca et al. (2002) documented that spatial scale dependence of seagrass cover at some sites was evident at a distance of 100 m. However, factors influencing the large scale spatial structure in seagrass taxa remain poorly studied. For instance, the seagrass clustering pattern [e.g., Supplementary Fig. 7 (b2)] may be related to clonal growth patterns displayed at a large scale (e.g., >32 m). At a smaller spatial scale (1–10 s of meters), seagrass production of vertical short shoots at regular intervals along a horizontal rhizome may underlie the formation of a dispersed set of seagrass patches over 4–32 m [see Duarte et al. (2006)]. The close coupling of spatial patterning of seagrass patches to biological characteristics of asexual seagrass growth for Halodule wrightii, a dominant seagrass species in our study area, has also been supported by investigations of seagrass patch expansion and rhizome extension by Jensen and Bell (2001). Additionally, Holmes et al. (2007) reported that the emergence of species patterns in seagrass landscapes was influenced by differences in clonal growth among seagrass species. Our study illustrates how IKO data and Ripley’s K can be used to characterize seagrass spatial patterns at both the small and large scales and contribute to our understanding of these patterns.

5. Conclusions Data from remote sensors not only can contribute to improving capabilities to monitor seagrass resources over large areal extents, but can also help characterize spatial patterns across different scales. Using water depth normalized data (and also textural information extracted from IKO sensor) collected from both Landsat TM and IKO sensors on the same day, a three-class%SAV cover classification was improved compared to those previously reported. The high resolution IKO sensor has produced a higher accuracy of%SAV cover classification than the TM sensor, and the overall accuracy of our three-class%SAV cover mapping result created with IKO data was 5–20% higher than that reported in literature. Previous studies have reported on the spatial structure of seagrass landscapes using high resolution aerial photography or towed videos, and we show here that information on seagrass spatial pattern (from 4–240 m) can also be described from high resolution IKO satellite images. Analysis of seagrass cover using Ripley’s K function [L(d)] revealed dispersed spatial patterns of seagrass at a spatial scale of <4–32 m; random or clustered patterns were recorded at distances >32 m. Based upon our knowledge, this is the first report on using satellite observations to quantify seagrass spatial patterns at a spatial scale from 4 m to 240 m. Thus, while the spatial information extracted from high resolution IKO sensor can improve SAV classification compared with Landsat TM data, our novel findings also demonstrate how IKO provided insight into seagrass spatial patterns not discernable with Landsat TM. Data from an IKO sensor may be very appropriate for defining seagrass cover levels in areas where seagrass beds are of limited extent and high resolution imagery is required.

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