Water Across Synthetic Aperture Radar Data (WASARD): SAR Water Body Classification for the Open Data Cube

The detection of inland water bodies from Synthetic Aperture Radar (SAR) data provides a great advantage over water detection with optical data, since SAR imaging is not impeded by cloud cover. Traditional methods of detecting water from SAR data involves using thresholding methods that can be labor intensive and imprecise. This paper describes Water Across Synthetic Aperture Radar Data (WASARD): a method of water detection from SAR data which automates and simplifies the thresholding process using machine learning on training data created from Geoscience Australias WOFS algorithm. Of the machine learning models tested, the Linear Support Vector Machine was determined to be optimal, with the option of training using solely the VH polarization or a combination of the VH and VV polarizations. WASARD was able to identify water in the target area with a correlation of 97% with WOFS. Sentinel-1, Open Data Cube, Earth Observations, Machine Learning, Water Detection 1. INTRODUCTION Water classification is an important function of Earth imaging satellites, as accurate remote classification of land and water can assist in land use analysis, flood prediction, climate change research, as well as a variety of agricultural applications [2]. The ability to identify bodies of water remotely via satellite is immensely cheaper than contracting surveys of the areas in question, meaning that an application that can accurately use satellite data towards this function can make valuable information available to nations which would not be able to afford it otherwise. Highly reliable applications for the remote detection of water currently exist for use with optical satellite data such as that provided by LANDSAT. One such application, Geoscience Australias Water Observations from Space (WOFS) has already been ported for use with the Open Data Cube [6]. However, water detection using optical data from Landsat is constrained by its relatively long revisit cycle of 16 days [5], and water detection using any optical data is constrained in that it lacks the ability to make accurate classifications through cloud cover [2]. The alternative solution which solves these problems is water detection using SAR data, which images the Earth using cloud-penetrating microwaves. Because of its advantages over optical data, much research has been done into water detection using SAR data. Traditionally, this has been done using the thresholding method, which involves picking a polarization band and labeling all pixels for which this bands value is below a certain threshold as containing water. The thresholding method works since water tends to return a much lower backscatter value to the satellite than land [1]. However, this method can be flawed since estimating the proper threshold is often imprecise, complicated, and labor intensive for the end user. Thresholding also tends to use data from only one SAR polarization, when a combination of polarizations can provide insight into whether water is present. [2] In order to alleviate these problems, this paper presents an application for the Open Data Cube to detect water from SAR data using support vector machine (SVM) classification. 2. PLATFORM WASARD is an application for the Open Data Cube, a mechanism which provides a simple yet efficient means of ingesting, storing, and retrieving remote sensing data. Data can be ingested and made analysis ready according to whatever specifications the researcher chooses, and easily resampled to artificially alter a scenes resolution. Currently WASARD supports water detection on scenes from ESAs Sentinel-1 and JAXAs ALOS. When testing WASARD, Sentinel-1 was most commonly used due to its relatively high spatial resolution and its rapid 6 day revisit cycle [5]. With minor alterations to the application's code, however, it could support data from other satellites. 3. METHODOLOGY Using supervised classification, WASARD compares SAR data to a dataset pre-classified by WOFS in order to train an SVM classifier. This classifier is then used to detect water in other SAR scenes outside the training set. Accuracy was measured according to the following metrics: Precision: a measure of what percentage of the points WASARD labels as water are truly water Recall: a measure of what percentage of the total water cover WASARD was able to identify. F1 Score: a harmonic average of the precision and recall scores Both precision and recall are calculated at the end of the training phase, when the trained classifier is compared to a testing dataset. Because the WOFS algorithms classifications are used as the truth values when training a WASARD classifier, when precision and recall are mentioned in this paper, they are always with respect to the values produced by WOFS on a similar scene of Landsat data, which themselves have a classification accuracy of 97% [6]. Visual representations of water identified by WASARD in this paper were produced using the function wasard_plot(), which is included in WASARD. 3.1 Algorithm Selection The machine learning model used by WASARD is the Linear Support Vector Machine (SVM). This model uses a supervised learning algorithm to develop a classifier, meaning it creates a vector which can be multiplied by the vector formed by the relevant data bands to determine whether a pixel in a SAR scene contains water. This classifier is trained by comparing data points from selected bands in a SAR scene to their respective labels, which in this case are water or not water as given by the WOFS algorithm. The SVM was selected over the Random Forest model, which outperformed the SVM in training speed, but had a greater classification time and lower accuracy, and the Multilayer Perceptron Artificial Neural Network, which had a slightly higher average accuracy than the SVM, but much greater training and classification times. Figure 1: Visual representation of the SVM Classifier. Each white point represents a pixel in a SAR scene. In Figure 1, the diagonal line separating pixels determined to be water from those determined not to be water represents the actual classification vector produced by the SVM. It is worth noting that once the model has been trained, classification of pixels is done in a similar manner as in the thresholding method. This is especially true if only one band was used to train the model. 3.1 Feature Selection Sentinel-1 collects data from two bands: the Vertical/Vertical polarization (VV) and the Vertical/Horizontal polarization (VH). When 100 SVM classifiers were created for each polarization individually, and for the combination of the two, the following results were achieved: Figure 2: Accuracy of classifiers trained using different polarization bands. Precision and Recall were measured with respect to the values produced by WOFS. Figure 2 demonstrates that using both the VV and VH bands trades slightly lower recall for significantly greater precision when compared with the VH band alone, and that using the VV band alone is inferior in both metrics. WASARD therefore defaults to using both the VV and VH bands, and includes the option to use solely the VH band. The VV polarizations lower precision compared to the VH polarization is in contrast to results from previous research and may merit further analysis [4]. 3.2 Training a Classifier The steps in training a classifier with WASARD are 1. Selecting two scenes (one SAR, one optical) with the same spatial extents, and acquired close to each other in time, with a preference that the scenes are taken on the same day. 2. Using the WOFS algorithm to produce an array of the detected water in the scene of optical data, to be used as the labels during supervised learning 3. Data points from the selected bands from the SAR acquisition are bundled together into an array with the corresponding labels gathered from WOFS. A random sample with an equal number of points labeled Water and Not Water is selected to be partitioned into a training and a testing dataset 4. Using Scikit-Learns LinearSVC object, the training dataset is used to produce a classifier, which is then tested against the testing dataset to determine its precision and recall The result is a wasard_classifier object, which has the following attributes: 1. f1, recall, and precision: 3 metrics used to determine the classifiers accuracy 2. Coefficient: Vector which the SVM uses to make its predictions. The classifier detects water when the dot product of the coefficient and the vector formed by the SAR bands is positive 3. Save(): allows a user to save a classifier to the disk in order to use it without retraining 4. wasard_classify(): Classifies an entire xarray of SAR data using the SVM classifier All of the above steps are performed automatically when the user creates a wasard_classifier object. 3.3 Classifying a Dataset Once the classifier has been created, it can be used to detect water in an xarray of SAR data using wasard_classify(). By taking the dot product of the classifiers coefficients and the vector formed by the selected bands of SAR data, an array of predictions is constructed. A classifier can effectively be used on the same spatial extents as the ones where it was trained, or on any area with a similar landscape. Whil

Scale Dependence of Sex-Specific Movement in a Small-Bodied Stream Fish

Animal movement at localised scales is often modulated by competing pressures such as avoiding predators while acquiring resources and mates. The relative magnitude of these trade‐offs may affect males and females differently, often resulting in sex‐specific differences in movement. Sex‐biases in movement have been linked to mating systems (e.g. monogamy or polygamy) in birds and mammals; however, this relationship has received less attention among fishes. Using passive integrated transponder tags and a series of stationary antennas, we evaluated the movement dynamics of a small‐bodied, sexually dimorphic stream fish Fundulus olivaceus over a 30‐day period in a fourth‐order tributary to the Pascagoula River in Mississippi (U.S.A.). We documented dissimilar sex‐specific movement behaviours at different spatial scales that were likely to be facilitated by differential resource demands and competitive pressures. Females exhibited an increased propensity to engage in longer, exploratory moves (\u3e30 m); whereas most males remained active within an established territory, making few long‐distance longitudinal movements. Local activity levels (proportion of individuals moving) were positively related to density (manipulated during the study), and density was found to affect the magnitude of sex‐specific movement. In contrast to females, males increased local activity and movement distance at the reduced density, presumably to expand territory size or mate‐searching behaviours, suggesting local mate competition may suppress the movement distance of males. Despite some evidence substantiating a relationship between movement and mating system, our results suggest that the documented sex‐specific differences may be related to traits that co‐evolve with mating systems, rather than the mating system per se. Our findings also highlight the importance of spatial scale when evaluating patterns of sex‐biased movement tendencies

Fine-scale genetic population structure of an understory rainforest

Abstract We studied five populations of a rainforest understory insectivorous bird (Myrmeciza exsul, chestnutbacked antbird) in a fragmented landscape in northeastern Costa Rica in order to test hypotheses about the influence of forest fragmentation on population genetic structure using 16 microsatellite loci. Bayesian assignment approaches-perhaps the most conservative analyses we performed-consistently grouped the sites into two distinct groups, with all individuals from the smallest and most isolated population clustering separately from the other four sites. Additional analyses revealed (1) overall significant genetic structure; (2) a pattern of population differentiation consistent with a hypothesis of isolation by resistance (landscape connectivity), but not distance; and (3) relatively short dispersal distances indicated by elevated mean pairwise relatedness in several of the sites. Our results are somewhat surprising given the small geographic distances between sites (11-34 km) and the short time (*60 years) since wide-spread deforestation in this landscape. We suspect fine-scale genetic structure may occur in many resident tropical bird species, and in the case of the chestnut-backed antbird it appears that anthropogenic habitat fragmentation has important population genetic implications. It appears that chestnut-backed antbirds may persist in fragmented landscapes in the absence of significant migration among patches, but mechanisms that allow this species to persist when many other similar species do not are not well understood

Stability, Persistence and Habitat Associations of the Pearl Darter \u3ci\u3ePercina aurora\u3c/i\u3e in the Pascagoula River System, Southeastern USA

The southeastern United States represents one of the richest collections of aquatic biodiversity worldwide; however, many of these taxa are under an increasing threat of imperilment, local extirpation, or extinction. The pearl darter Percina aurora is a small-bodied freshwater fish endemic to the Pearl and Pascagoula river systems of Mississippi and Louisiana (USA). The last collected specimen from the Pearl River drainage was taken in 1973, and it now appears that populations in this system are likely extirpated. This reduced the historical range of this species by approximately 50%, ultimately resulting in federal protection under the US Endangered Species Act in 2017. To better understand the current distribution and general biology of extant populations, we analyzed data collected from a series of surveys conducted in the Pascagoula River drainage from 2000 to 2016. Pearl darters were captured at relatively low abundance (2.4 ± 4.0 ind. per collection) from 57% of 308 collections. We identified strong relationships between local habitat variables and occurrence and catch-per-unit-effort (CPUE) of pearl darters. Pearl darters were frequently encountered and in greater abundance in depositional areas characterized by low-velocity habitats and finer substrates. Patterns of occurrence and CPUE were spatiotemporally variable across years; however, repeated collections from a subset of localities collected across a decade or more indicated long-term persistence and stability, suggesting population resilience throughout the Pascagoula River drainage

Documentation of a Gulf Sturgeon Spawning Site on the Yellow River, Alabama, USA

The Gulf Sturgeon Recovery Plan (USFWS, GSMFC and NMFS 1995) stressed the need to provide maximum protection to Gulf sturgeon spawning habitat. The approach employed by various Gulf sturgeon researchers, including ourselves, to document spawning has been to identify potential spawning habitat on the basis of physical characteristics and/or tracking data, collect eggs, and then raise the eggs in the laboratory until the point where the larval fish can be identified (e.g., Marchant and Shutters 1996, Sulak and Clugston 1998, 1999). However, collecting eggs in any appreciable number is usually difficult, and these eggs may not always be viable upon return to the laboratory. Molecular methods provide an alternative means of identifying the species represented by an egg. Notable examples related to sturgeon conservation include cases where molecular markers were used to verify the sources of commercially available caviar (DeSalle and Birstein 1996, Birstein et al. 1999). Parauka and Giorgianni (2002) reported that potential Gulf sturgeon spawning habitat is present in the Yellow River; however, efforts to document spawning by the collection of eggs or larvae have been unsuccessful in the past. Herein, we report on the first successful collection of eggs from a potential spawning site on the Yellow River and the verification of their identity as Gulf sturgeon by using molecular methods

Environmental Conditions of 2 River Drainages Into the Northern Gulf of Mexico During Successful Hatching of Alabama Shad (\u3ci\u3eAlosa alabamae\u3c/i\u3e)

In recent years, the Alabama shad (Alosa alabamae) has experienced dramatic declines and extirpations from portions of its native range. Habitat degradation and barriers to migration are considered contributing factors to contraction in the distributional range this species. To identify conditions during successful spawning, river temperatures and discharges in 2 drainages of the northern Gulf of Mexico (the Apalachicola and Pascagoula rivers) were characterized during successful hatching “windows.” Sampling during 2005–2009 yielded 400 juvenile Alabama shad of which 261 were aged from counts of rings on sagittal otoliths. Results from logistic regression revealed that successful spawning coincided with increases in temperature within a specific range (9.4–21.5°C) and with an average drainage-dependent discharge volume (625.6 m³/s in the Apalachicola River and \u3e400.7 m³/s in the Pascagoula River). Timing of successful hatching windows differed between drainages but not between years within each drainage. Documenting and identifying the river conditions during successful reproduction provide important information on how to manage rivers to aid in the recovery of this species of conservation concern

First Record of the Invasive Asian Fish Tapeworm \u3ci\u3eBothriocephalus acheilognathi\u3c/i\u3e in Honduras, Central America

This paper provides the first report of the invasive Asian fish tapeworm, Bothriocephalus acheilognathi Yamaguti, 1934, in Honduras. The cestode was found in Profundulus portillorum (Cyprinodontiformes: Profundulidae), which represents a new host record, and which is a member of a genus faced with a variety of conservation challenges, now potentially complicated by the presence of this pathogenic cestode. Nearly complete sequence data from the ITS-1 5.8S and ITS-2 regions corroborate the determination based on morphological characteristics. Several species of carp were introduced to Honduras for aquaculture purposes in the early 1980s and the presence of the Asian fish tapeworm in Honduras may be related to these introductions. In addition, this report documents the currently known geographical distribution of this parasite in Central America, first recorded from Panamá and now from Honduras

Evolution of a Sexually Dimorphic Trait In a Broadly Distributed Topminnow (\u3ci\u3eFundulus olivaceus\u3c/i\u3e)

Understanding the interaction between sexual and natural selection within variable environments is crucial to our understanding of evolutionary processes. The handicap principle predicts females will prefer males with exaggerated traits provided those traits are indicators of male quality to ensure direct or indirect female benefits. Spatial variability in ecological factors is expected to alter the balance between sexual and natural selection that defines the evolution of such traits. Male and female blackspotted topminnows (Fundulidae: Fundulus olivaceus) display prominent black dorsolateral spots that are variable in number across its broad range. We investigated variability in spot phenotypes at 117 sites across 13 river systems and asked if the trait was sexually dimorphic and positively correlated with measures of fitness (condition and gonadosomatic index [GSI]). Laboratory and mesocosm experiments assessed femalemate choice and predation pressure on spot phenotypes. Environmental and community data collected at sampling locations were used to assess predictive models of spot density at the individual, site, and river system level. Greater number of spots was positively correlated with measures of fitness in males. Males with more spots were preferred by females and suffered greater mortality due to predation. Water clarity (turbidity) was the best predictor of spot density on the drainage scale, indicating that sexual and natural selection for the trait may be mediated by local light environments

Evolution of a Sexually Dimorphic Trait in a Broadly Distributed Topminnow (Fundulus Olivaceus)

Understanding the interaction between sexual and natural selection within variable environments is crucial to our understanding of evolutionary processes. The handicap principle predicts females will prefer males with exaggerated traits provided those traits are indicators of male quality to ensure direct or indirect female benefits. Spatial variability in ecological factors is expected to alter the balance between sexual and natural selection that defines the evolution of such traits. Male and female blackspotted topminnows (Fundulidae: Fundulus olivaceus) display prominent black dorsolateral spots that are variable in number across its broad range. We investigated variability in spot phenotypes at 117 sites across 13 river systems and asked if the trait was sexually dimorphic and positively correlated with measures of fitness (condition and gonadosomatic index [GSI]). Laboratory and mesocosm experiments assessed female mate choice and predation pressure on spot phenotypes. Environmental and community data collected at sampling locations were used to assess predictive models of spot density at the individual, site, and river system level. Greater number of spots was positively correlated with measures of fitness in males. Males with more spots were preferred by females and suffered greater mortality due to predation. Water clarity (turbidity) was the best predictor of spot density on the drainage scale, indicating that sexual and natural selection for the trait may be mediated by local light environments