scholarly journals The use of singlebeam echo-sounder depth data to produce demersal fish distribution models that are comparable to models produced using multibeam echo-sounder depth

2021 ◽  
Author(s):  
Marcela Montserrat Landero Figueroa ◽  
Miles Parsons ◽  
Benjamin Saunders ◽  
Ben Radford ◽  
Chandra Salgado-Kent ◽  
...  
Keyword(s):  
High Resolution ◽  
Demersal Fish ◽  
Important Variable ◽  
Fish Distribution ◽  
Slope Aspect ◽  
Distribution Models ◽  
Explanatory Variables ◽  
Continuous Map ◽  
Spatial Coverage ◽  
Echo Sounder

Seafloor characteristics can help in the prediction of fish distribution, which is required for fisheries and conservation management. Despite this, only 5-10% of the world’s seafloor has been mapped at high resolution as it is a time-consuming and expensive process. Multibeam echo-sounders (MBES) can produce high-resolution bathymetry and a broad swath coverage of the seafloor, but require greater financial and technical resources for operation and data analysis than singlebeam echo-sounders (SBES). In contrast, SBES provide comparatively limited spatial coverage, as only a single measurement is made from directly under the vessel. Thus, producing a continuous map requires interpolation to fill gaps between transects. This study assesses the performance of demersal fish species distribution models by comparing those derived from interpolated SBES data with full-coverage MBES distribution models. A Random Forest classifier was used to model the distribution of Abalistes stellatus, Gymnocranius grandoculis, Lagocephalus sceleratus, Loxodon macrorhinus, Pristipomoides multidens and Pristipomoides typus, with depth and depth derivatives (slope, aspect, standard deviation of depth, terrain ruggedness index, mean curvature and topographic position index) as explanatory variables. The results indicated that distribution models for A. stellatus, G. grandoculis, L. sceleratus, and L. macrorhinus performed poorly for MBES and SBES data with Area Under the Receiver Operator Curves (AUC) below 0.7. Consequently, the distribution of these species could not be predicted by seafloor characteristics produced from either echo-sounder type. Distribution models for P. multidens and P. typus performed well for MBES and the SBES data with an AUC above 0.8. Depth was the most important variable explaining the distribution of P. multidens and P. typus in both MBES and SBES models. While further research is needed, this study shows that in resource-limited scenarios, SBES can produce comparable results to MBES for use in demersal fish management and conservation.

scholarly journals The use of singlebeam echo‐sounder depth data to produce demersal fish distribution models that are comparable to models produced using multibeam echo‐sounder depth

2021 ◽  
Author(s):  
Marcela Montserrat Landero Figueroa ◽  
Miles J. G. Parsons ◽  
Benjamin J. Saunders ◽  
Ben Radford ◽  
Chandra Salgado‐Kent ◽  
...  
Keyword(s):  
Demersal Fish ◽  
Fish Distribution ◽  
Distribution Models ◽  
Depth Data ◽  
Echo Sounder

scholarly journals Testing the Improvement of Coral Reef Associated Fish Distribution Models Based on Multibeam Bathymetry by Adding Seafloor Backscatter Data

2021 ◽  
Vol 8 ◽  
Author(s):  
Marcela Montserrat Landero Figueroa ◽  
Miles J. G. Parsons ◽  
Benjamin J. Saunders ◽  
Ben Radford ◽  
Iain M. Parnum
Keyword(s):  
Strong Relationship ◽  
Demersal Fish ◽  
Important Variable ◽  
Acoustic Energy ◽  
Fish Distribution ◽  
Distribution Models ◽  
Seafloor Morphology ◽  
Seafloor Backscatter ◽  
Using Data ◽  
Lagocephalus Sceleratus

Demersal fishes constitute an essential component of the continental shelf ecosystem, and a significant element of fisheries catch around the world. However, collecting distribution and abundance data of demersal fish, necessary for their conservation and management, is usually expensive and logistically complex. The increasing availability of seafloor mapping technologies has led to the opportunity to exploit the strong relationship demersal fish exhibit with seafloor morphology to model their distribution. Multibeam echo-sounder (MBES) systems are a standard method to map seafloor morphology. The amount of acoustic energy reflected by the seafloor (backscatter) is used to estimate specific characteristics of the seafloor, including acoustic hardness and roughness. MBES data including bathymetry and depth derivatives were used to model the distribution of Abalistes stellatus, Gymnocranius grandoculis, Lagocephalus sceleratus, Lethrinus miniatus, Loxodon macrorhinus, Lutjanus sebae, and Scomberomorus queenslandicus. The possible improvement of model accuracy by adding the seafloor backscatter was tested in three different areas of the Ningaloo Marine Park off the west coast of Australia. For the majority of species, depth was a primary variable explaining their distribution in the three study sites. Backscatter was identified to be an important variable in the models, but did not necessarily lead to a significant improvement in the demersal fish distribution models’ accuracy. Possible reasons for this include: the depth and derivatives were capturing the significant changes in the habitat, or the acoustic data collected with a high-frequency MBES were not capturing accurately relevant seafloor characteristics associated with the species distribution. The improvement in the accuracy of the models for certain species using data already available is an encouraging result, which can have a direct impact in our ability to monitor these species.

scholarly journals Easy-to-use spatial Random Forest-based downscaling-calibration method for producing high resolution and accurate precipitation data

2021 ◽  
Author(s):  
Chuanfa Chen ◽  
Baojian Hu ◽  
Yanyan Li
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Spatial Autocorrelation ◽  
Land Surface ◽  
Calibration Method ◽  
Rain Gauge ◽  
Important Variable ◽  
Kriging Interpolation ◽  
Slope Aspect ◽  
Precipitation Data

Abstract. High resolution and accurate precipitation data is significantly important for numerous hydrological applications. To enhance the spatial resolution and accuracy of satellite-based precipitation products, an easy-to-use downscaling-calibration method based on spatial Random Forest (SRF) is proposed in this paper, where the spatial autocorrelation between precipitation measurements is taken into account. The proposed method consists of two main stages. Firstly, the satellite-based precipitation was downscaled by SRF with the incorporation of some high-resolution covariates including latitude, longitude, DEM, NDVI, terrain slope, aspect, relief, and land surface temperatures. Then, the downscaled precipitation was calibrated by SRF with rain gauge observations and the aforementioned high-resolution variables. The monthly Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) located in Sichuan province, China from 2015 to 2019 was processed using our method and its results were compared with those of some classical methods including geographically weighted regression (GWR), artificial neural network (ANN), random forest (RF), kriging interpolation only on gauge measurements, bilinear interpolation-based downscaling and then SRF-based calibration (Bi-SRF), and SRF-based downscaling and then geographical difference analysis (GDA)-based calibration (SRF-GDA). Results show that: (1) the proposed method outperforms the other methods as well as the original IMERG; (2) the monthly-based SRF estimation is slightly more accurate than the annual-based SRF fraction disaggregation method; (3) SRF-based downscaling and calibration preforms better than bilinear downscaling (Bi-SRF) and GDA-based calibration (SRF-GDA); (4) kriging seems more accurate than GWR and ANN in terms of quantitative accuracy measures, whereas its precipitation map cannot capture the detailed spatial precipitation patterns; and (5) among the predictors for calibration, the precipitation interpolated by kriging on the gauge measurements is the most important variable, indicating the significance for the inclusion of spatial autocorrelation information in gauge measurements.

scholarly journals Transition models for count data: a flexible alternative to fixed distribution models

2021 ◽  
Author(s):  
Moritz Berger ◽  
Gerhard Tutz
Keyword(s):  
Count Data ◽  
Regression Models ◽  
Negative Binomial ◽  
Real Data ◽  
Distribution Models ◽  
Explanatory Variables ◽  
Excess Zeros ◽  
Proposed Model ◽  
Transition Models ◽  
Fixed Distribution

AbstractA flexible semiparametric class of models is introduced that offers an alternative to classical regression models for count data as the Poisson and Negative Binomial model, as well as to more general models accounting for excess zeros that are also based on fixed distributional assumptions. The model allows that the data itself determine the distribution of the response variable, but, in its basic form, uses a parametric term that specifies the effect of explanatory variables. In addition, an extended version is considered, in which the effects of covariates are specified nonparametrically. The proposed model and traditional models are compared in simulations and by utilizing several real data applications from the area of health and social science.

Very high resolution environmental predictors in species distribution models

2013 ◽  
Vol 38 (1) ◽  
pp. 79-96 ◽  
Author(s):  
Jean-Nicolas Pradervand ◽  
Anne Dubuis ◽  
Loïc Pellissier ◽  
Antoine Guisan ◽  
Christophe Randin
Keyword(s):  
High Resolution ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Environmental Data ◽  
Swiss Alps ◽  
Fine Scale ◽  
Distribution Models ◽  
Model Quality ◽  
Vegetation Height ◽  
Very High

Recent advances in remote sensing technologies have facilitated the generation of very high resolution (VHR) environmental data. Exploratory studies suggested that, if used in species distribution models (SDMs), these data should enable modelling species’ micro-habitats and allow improving predictions for fine-scale biodiversity management. In the present study, we tested the influence, in SDMs, of predictors derived from a VHR digital elevation model (DEM) by comparing the predictive power of models for 239 plant species and their assemblages fitted at six different resolutions in the Swiss Alps. We also tested whether changes of the model quality for a species is related to its functional and ecological characteristics. Refining the resolution only contributed to slight improvement of the models for more than half of the examined species, with the best results obtained at 5 m, but no significant improvement was observed, on average, across all species. Contrary to our expectations, we could not consistently correlate the changes in model performance with species characteristics such as vegetation height. Temperature, the most important variable in the SDMs across the different resolutions, did not contribute any substantial improvement. Our results suggest that improving resolution of topographic data only is not sufficient to improve SDM predictions – and therefore local management – compared to previously used resolutions (here 25 and 100 m). More effort should be dedicated now to conduct finer-scale in-situ environmental measurements (e.g. for temperature, moisture, snow) to obtain improved environmental measurements for fine-scale species mapping and management.

scholarly journals Validation of Sea Surface Temperature from GCOM-C Satellite Using iQuam Datasets and MUR-SST in Indonesian Waters

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Bambang Sukresno ◽  
Dinarika Jatisworo ◽  
Rizki Hanintyo
Keyword(s):  
High Resolution ◽  
Error Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Important Variable ◽  
Sea Surface ◽  
Analysis Method ◽  
Validation Process ◽  
Indonesian Seas

Sea surface temperature (SST) is an important variable in oceanography. One of the SST data can be obtained from the Global Observation Mission-Climate (GCOM-C) satellite. Therefore, this data needs to be validated before being applied in various fields. This study aimed to validate SST data from the GCOM-C satellite in the Indonesian Seas. Validation was performed using the data of Multi-sensor Ultra-high Resolution sea surface temperature (MUR-SST) and in situ sea surface temperature Quality Monitor (iQuam). The data used are the daily GCOM-C SST dataset from January to December 2018, as well as the daily dataset from MUR-SST and iQuam in the same period. The validation process was carried out using the three-way error analysis method. The results showed that the accuracy of the GCOM-C SST was 0.37oC.

scholarly journals Using LiDAR-derived vegetation metrics for high-resolution, species distribution models for conservation planning

Ecosphere ◽  
2013 ◽  
Vol 4 (3) ◽  
pp. art42 ◽  
Author(s):  
S. L. Farrell ◽  
B. A. Collier ◽  
K. L. Skow ◽  
A. M. Long ◽  
A. J. Campomizzi ◽  
...  
Keyword(s):  
High Resolution ◽  
Species Distribution ◽  
Conservation Planning ◽  
Species Distribution Models ◽  
Distribution Models

Exploring microphysical properties of marine boundary layer clouds through network analysis

2021 ◽  
Author(s):  
Lucile Ricard ◽  
Athanasios Nenes ◽  
Jakob Runge ◽  
Paraskevi Georgakaki
Keyword(s):  
Boundary Layer ◽  
High Resolution ◽  
Network Analysis ◽  
Marine Boundary Layer ◽  
Large Eddy Simulations ◽  
Dynamical Structure ◽  
Boundary Layer Clouds ◽  
Microphysical Properties ◽  
Spatial Coverage ◽  
Large Eddy

<p>Aerosol-cloud interactions remain the largest uncertainty in assessments of anthropogenic climate forcing, while the complexity of these interactions require methods that enable abstractions and simplifications that allow their improved treatment in climate models. Marine boundary layer clouds are an important component of the climate system as their large albedo and spatial coverage strongly affect the planetary radiative balance. High resolution simulations of clouds provide an unprecedented understanding of the structure and behavior of these clouds in the marine atmosphere, but the amount of data is often too large and complex to be useful in climate simulations. Data reduction and inference methods provide a way that to reduce the complexity and dimensionality of datasets generated from high-resolution Large Eddy Simulations.</p><p>In this study we use network analysis, (the δ-Maps method) to study the complex interaction between liquid water, droplet number and vertical velocity in Large Eddy Simulations of Marine Boundary Layer clouds. δ-Maps identifies domains that are spatially contiguous and possibly overlapping and characterizes their connections and temporal interactions. The objective is to better understand microphysical properties of marine boundary layer clouds, and how they are impacted by the variability in aerosols. Here we will capture the dynamical structure of the cloud fields predicted by the MIMICA Large Eddy Simulation (LES) model. The networks inferred from the different simulation fields are compared between them (intra-comparisons) using perturbations in initial conditions and aerosol, using a set of four metrics. The networks are then evaluated for their differences, quantifying how much variability is inherent in the LES simulations versus the robust changes induced by the aerosol fields. </p>

scholarly journals Predicting Microhabitat Suitability for an Endangered Small Mammal Using Sentinel-2 Data

2020 ◽  
Vol 12 (3) ◽  
pp. 562 ◽  
Author(s):  
Francesco Valerio ◽  
Eduardo Ferreira ◽  
Sérgio Godinho ◽  
Ricardo Pita ◽  
António Mira ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Landscape Heterogeneity ◽  
Remote Sensing Data ◽  
Predictive Distribution ◽  
Distribution Models ◽  
Explanatory Variables ◽  
Habitat Features ◽  
Main Challenge ◽  
Cabrera Vole ◽  
Sentinel 2

Accurate mapping is a main challenge for endangered small-sized terrestrial species. Freely available spatio-temporal data at high resolution from multispectral satellite offer excellent opportunities for improving predictive distribution models of such species based on fine-scale habitat features, thus making it easier to achieve comprehensive biodiversity conservation goals. However, there are still few examples showing the utility of remote-sensing-based products in mapping microhabitat suitability for small species of conservation concern. Here, we address this issue using Sentinel-2 sensor-derived habitat variables, used in combination with more commonly used explanatory variables (e.g., topography), to predict the distribution of the endangered Cabrera vole (Microtus cabrerae) in agrosilvopastorial systems. Based on vole surveys conducted in two different seasons over a ~176,000 ha landscape in Southern Portugal, we assessed the significance of each predictor in explaining Cabrera vole occurrence using the Boruta algorithm, a novel Random forest variant for dealing with high dimensionality of explanatory variables. Overall, results showed a strong contribution of Sentinel-2-derived variables for predicting microhabitat suitability of Cabrera voles. In particular, we found that photosynthetic activity (NDI45), specific spectral signal (SWIR1), and landscape heterogeneity (Rao’s Q) were good proxies of Cabrera voles’ microhabitat, mostly during temporally greener and wetter conditions. In addition to remote-sensing-based variables, the presence of road verges was also an important driver of voles’ distribution, highlighting their potential role as refuges and/or corridors. Overall, our study supports the use of remote-sensing data to predict microhabitat suitability for endangered small-sized species in marginal areas that potentially hold most of the biodiversity found in human-dominated landscapes. We believe our approach can be widely applied to other species, for which detailed habitat mapping over large spatial extents is difficult to obtain using traditional descriptors. This would certainly contribute to improving conservation planning, thereby contributing to global conservation efforts in landscapes that are managed for multiple purposes.

scholarly journals Bag of Geomorphological Words: A Framework for Integrating Terrain Features and Semantics to Support Landform Object Recognition from High-Resolution Digital Elevation Models

2020 ◽  
Vol 9 (11) ◽  
pp. 620
Author(s):  
Xiran Zhou ◽  
Xiao Xie ◽  
Yong Xue ◽  
Bing Xue ◽  
Kai Qin ◽  
...  
Keyword(s):  
Object Recognition ◽  
High Resolution ◽  
Feature Vector ◽  
Dimensional Space ◽  
Digital Elevation Models ◽  
Three Dimensional ◽  
Slope Aspect ◽  
Digital Elevation ◽  
Great Possibility ◽  
High Level

High-resolution digital elevation models (DEMs) and its derivatives (e.g., curvature, slope, aspect) offer a great possibility of representing the details of Earth’s surface in three-dimensional space. Previous research investigations concerning geomorphological variables and region-level features alone cannot precisely characterize the main structure of landforms. However, these geomorphological variables are not sufficient to represent a complex landform object’s whole structure from a high-resolution DEM. Moreover, the amount of the DEM dataset is limited, including the landform object. Considering the challenges above, this paper reports an integrated model called the bag of geomorphological words (BoGW), enabling automatic landform recognition via integrating point and linear geomorphological variables, region-based features (e.g., shape, texture), and high-level landform descriptions. First, BoGW semantically characterizes the composition of geomorphological variables and meaningful parcels of each type of landform. Based on a landform’s semantics, the proposed method then integrates geomorphological variables and region-level features (e.g., shape, texture) to create the feature vector for the landform. Finally, BoGW classifies a region derived from high-resolution DEM into a predefined type of landform by the feature vector. The experimental results on crater and cirque detection indicated that the proposed BoGW could support landform object recognition from high-resolution DEMs.

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