scholarly journals Seagrass habitat suitability model for Redang Marine Park using multibeam echosounder data: Testing different spatial resolutions and analysis window sizes

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257761
Author(s):  
Muhammad Abdul Hakim Muhamad ◽  
Rozaimi Che Hasan ◽  
Najhan Md Said ◽  
Jillian Lean-Sim Ooi
Keyword(s):  
Spatial Resolution ◽  
Habitat Suitability ◽  
Prediction Models ◽  
Seagrass Habitat ◽  
Multibeam Echosounder ◽  
Marine Park ◽  
Marine Habitats ◽  
Fringing Reefs ◽  
Analysis Window ◽  
Resolution Dataset

Integrating Multibeam Echosounder (MBES) data (bathymetry and backscatter) and underwater video technology allows scientists to study marine habitats. However, use of such data in modeling suitable seagrass habitats in Malaysian coastal waters is still limited. This study tested multiple spatial resolutions (1 and 50 m) and analysis window sizes (3 × 3, 9 × 9, and 21 × 21 cells) probably suitable for seagrass-habitat relationships in Redang Marine Park, Terengganu, Malaysia. A maximum entropy algorithm was applied, using 12 bathymetric and backscatter predictors to develop a total of 6 seagrass habitat suitability models. The results indicated that both fine and coarse spatial resolution datasets could produce models with high accuracy (>90%). However, the models derived from the coarser resolution dataset displayed inconsistent habitat suitability maps for different analysis window sizes. In contrast, habitat models derived from the fine resolution dataset exhibited similar habitat distribution patterns for three different analysis window sizes. Bathymetry was found to be the most influential predictor in all the models. The backscatter predictors, such as angular range analysis inversion parameters (characterization and grain size), gray-level co-occurrence texture predictors, and backscatter intensity levels, were more important for coarse resolution models. Areas of highest habitat suitability for seagrass were predicted to be in shallower (<20 m) waters and scattered between fringing reefs (east to south). Some fragmented, highly suitable habitats were also identified in the shallower (<20 m) areas in the northwest of the prediction models and scattered between fringing reefs. This study highlighted the importance of investigating the suitable spatial resolution and analysis window size of predictors from MBES for modeling suitable seagrass habitats. The findings provide important insight on the use of remote acoustic sonar data to study and map seagrass distribution in Malaysia coastal water.

scholarly journals SEAGRASS HABITAT SUITABILITY MAP AT MERAMBONG SHOAL, JOHOR: A PRELIMINARY STUDY USING MULTIBEAM ECHOSOUNDER AND MAXENT MODELLING

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 463-470
Author(s):  
M. A. H. Muhamad ◽  
R. Che Hasan
Keyword(s):  
High Resolution ◽  
Habitat Suitability ◽  
Species Distribution Modelling ◽  
Seagrass Habitat ◽  
Habitat Suitability Model ◽  
Survey Area ◽  
Multibeam Echosounder ◽  
Suitability Map ◽  
Habitat Suitability Map ◽  
Northwest Region

Abstract. In recent years, there has been an increasing interest to use high-resolution multibeam dataset and Species Distribution Modelling (SDM) for seagrass habitat suitability model. This requires a specific variable derived from multibeam data and in-situ seagrass occurrence samples. The purpose of this study was (1) to derive variables from multibeam bathymetry data to be used in seagrass habitat suitability model, (2) to produce seagrass habitat suitability model using Maximum Entropy (MaxEnt), and (3) to quantify the contribution of each variable for predicting seagrass habitat suitability map. The study area was located at Merambong Shoal, covering an area of 0.04 km2, situated along Johor Strait. First, twelve (12) variables were derived from bathymetry data collected from multibeam echosounder using Benthic Terrain Modeller (BTM) tool. Secondly, all variables and seagrass occurrence samples were integrated in MaxEnt to produce seagrass habitat suitability map. The results showed that the Area Under Curve (AUC) values based on training and test data were 0.88 and 0.65, respectively. The northwest region of survey area indicated higher habitat suitability of seagrass, while the southeast region of survey area indicated lower suitability. Bathymetry mean found to be the most contributed variables among others. The spatial distribution of seagrass from modelling technique agreed with the previous studies and they are found to be distributed at depths ranging from 2.2 to 3.4 meters whilst less suitable with increasing of water depth. This study concludes that seagrass habitat suitability map with high-resolution pixel size (0.5 meter) can be produced at Merambong Shoal using acoustic data from multibeam echosounder coupled with MaxEnt and underwater video observations.

scholarly journals Housing Values and the Residential Settlement of Migrants: Zooming in on Neighbourhoods in Italian Provincial Capitals

2020 ◽  
Author(s):  
Sona Kalantaryan ◽  
Alfredo Alessandrini
Keyword(s):  
Housing Market ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Population Characteristics ◽  
Housing Values ◽  
Resident Population ◽  
Residential Units ◽  
The Relationship ◽  
Residential Settlement ◽  
Resolution Dataset

Abstract This study looks at the relationship between housing values (prices and rents) and the residential settlement of migrants in different neighbourhoods in Italian provincial capitals. We exploit here the high spatial resolution dataset on the settlement of migrants developed within the Data for Integration (D4I) project. The D4I information on resident population characteristics was merged with a dataset on housing values for civilian and economic residential units using boundaries defined by local housing market characteristics. The results suggest that: (1) more diverse neighbourhoods are also those with relatively lower housing values; (2) the relationship between housing values and the concentration of migrants is non-linear; and (3) the sign and significance of the association varies significantly depending on the origin of migrants.

scholarly journals Biogeography of Cephalopods in the Southern Ocean Using Habitat Suitability Prediction Models

Ecosystems ◽  
2015 ◽  
Vol 19 (2) ◽  
pp. 220-247 ◽  
Author(s):  
José C. Xavier ◽  
Ben Raymond ◽  
Daniel C. Jones ◽  
Huw Griffiths
Keyword(s):  
Southern Ocean ◽  
Habitat Suitability ◽  
Prediction Models

Decadal biomass and area changes in a multi-species meadow in Singapore: application of multi-resolution satellite imagery

2018 ◽  
Vol 61 (3) ◽  
pp. 289-304
Author(s):  
James F. Bramante ◽  
Suryati M. Ali ◽  
Alan D. Ziegler ◽  
Tsai M. Sin
Keyword(s):  
Spatial Resolution ◽  
Satellite Images ◽  
Average Rate ◽  
Seagrass Habitat ◽  
Seagrass Bed ◽  
Environmental Pressures ◽  
Global Trends ◽  
Very High Spatial Resolution ◽  
Satellite Sensors ◽  
Very High

Abstract Due to the dearth of information regarding current and changing health of seagrass habitat in the Indo-Pacific region, prior research into global trends of seagrass habitat health has included little data from this region, even though it contains the highest abundance and species diversity of seagrass globally. This study evaluates the suitability of four satellite sensors [Worldview-2 (WV2), Advanced Land Imager (ALI), Enhanced Thematic Mapper+ (ETM+), Operational Land Imager (OLI)] for determining trends in seagrass habitat extent over the past decade in Singapore’s largest seagrass meadow, and thus contributes incrementally to the data available for regional or global analyses of seagrass habitat health. Using all four sensors, we find that seagrass bed extent at Pulau Semakau, Singapore, declined 37% from 2001 to 2015 at an average rate of 3.9% year−1. Using very high spatial resolution satellite images, we calculate that, although bed extent decreased 18% from April 2011 to June 2013, median meadow biomass increased, indicating that complex meadow dynamics may be mediating seagrass response to anthropogenic and environmental pressures. From a technological perspective, we find that, despite their lower spatial resolution, freely available satellite images can be used to measure the extent of a narrow, multi-species seagrass bed and to determine decadal trends reliably.

Modeling benthic solar exposure (UV and visible) in dynamic coastal systems to better inform seagrass habitat suitability

2021 ◽  
pp. 151481
Author(s):  
Olivia Cronin-Golomb ◽  
Joshua P. Harringmeyer ◽  
Matthew W. Weiser ◽  
Xiaohui Zhu ◽  
Nilotpal Ghosh ◽  
...  
Keyword(s):  
Habitat Suitability ◽  
Seagrass Habitat ◽  
Solar Exposure ◽  
Coastal Systems

scholarly journals SEABED TOPOGRAPHY MAPPING IN SANGIHE TALAUD WATERS USING MULTIBEAM ECHOSOUNDER

Oseanika ◽  
2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Dwi Haryanto ◽  
Muhamad Irfan ◽  
Taufan Wiguna ◽  
Hendra Kurnia Febriawan
Keyword(s):  
High Resolution ◽  
Multibeam Echosounder ◽  
The North ◽  
North Sulawesi ◽  
Marine Habitats ◽  
Seabed Topography ◽  
High Resolution Bathymetry ◽  
New Discovery

The application of multibeam echosounder for seabed topography has been developing rapidly. Multibeam echosounder is a very efficient way to get a wide seabed topography coverage for each ping, so it can produce high-resolution seabed topography maps. These maps can be used as a reference for further investigation or exploration, for example geological studies, marine habitats and others. RV Baruna Jaya IV is operated by Laboratory for Marine Survey Technology – BPPT that have been hull mounted equipped a Germany technology multibeam echosunder Seabeam 1050D system. The Seabeam 1050D allow to sweep measuring the seabed topography using 126 beams simultaneously from port to starboard sites. R.V. Baruna Jaya IV and the Okeanos Explorer of NOAA have been conducted joint Indonesia - U.S. Expedition to Sangihe Talaud waters (INDEX SATAL) in the north area of the North Sulawesi Province during July - August, 2010. Seabed topography of less than 2000 metres were recorded by Seabeam 1050D system, the area of larger depths of 2000 metres to 6000 metres recorded by Simrad EM-302 from Okeanos Explorer. The seabed topography in Sangihe Talaud waters has a varied seabed topography. The new discovery that showed on the map is a 1600 m height of seamount, risen up from the depth of 2300m to 710m. Others geological seabed can be identified according to high resolution bathymetry map resulted from this study.Keywords: multibeam echosounder, seabed topography, seamount, Sangihe Talaud

scholarly journals Long-Term and High-Resolution Global Time Series of Brightness Temperature from Copula-Based Fusion of SMAP Enhanced and SMOS Data

2018 ◽  
Vol 10 (11) ◽  
pp. 1842 ◽  
Author(s):  
Christof Lorenz ◽  
Carsten Montzka ◽  
Thomas Jagdhuber ◽  
Patrick Laux ◽  
Harald Kunstmann
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
High Resolution ◽  
Brightness Temperature ◽  
Spatial Resolution ◽  
Prediction Models ◽  
Weather Prediction ◽  
Brightness Temperatures ◽  
Climate Analysis

Long and consistent soil moisture time series at adequate spatial resolution are key to foster the application of soil moisture observations and remotely-sensed products in climate and numerical weather prediction models. The two L-band soil moisture satellite missions SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) are able to provide soil moisture estimates on global scales and in kilometer accuracy. However, the SMOS data record has an appropriate length of 7.5 years since late 2009, but with a coarse resolution of ∼25 km only. In contrast, a spatially-enhanced SMAP product is available at a higher resolution of 9 km, but for a shorter time period (since March 2015 only). Being the fundamental observable from passive microwave sensors, reliable brightness temperatures (Tbs) are a mandatory precondition for satellite-based soil moisture products. We therefore develop, evaluate and apply a copula-based data fusion approach for combining SMAP Enhanced (SMAP_E) and SMOS brightness Temperature (Tb) data. The approach exploits both linear and non-linear dependencies between the two satellite-based Tb products and allows one to generate conditional SMAP_E-like random samples during the pre-SMAP period. Our resulting global Copula-combined SMOS-SMAP_E (CoSMOP) Tbs are statistically consistent with SMAP_E brightness temperatures, have a spatial resolution of 9 km and cover the period from 2010 to 2018. A comparison with Service Soil Climate Analysis Network (SCAN)-sites over the Contiguous United States (CONUS) domain shows that the approach successfully reduces the average RMSE of the original SMOS data by 15%. At certain locations, improvements of 40% and more can be observed. Moreover, the median NSE can be enhanced from zero to almost 0.5. Hence, CoSMOP, which will be made freely available to the public, provides a first step towards a global, long-term, high-resolution and multi-sensor brightness temperature product, and thereby, also soil moisture.

scholarly journals Sentinel-2 and Landsat-8 Multi-Temporal Series to Estimate Topsoil Properties on Croplands

2021 ◽  
Vol 13 (17) ◽  
pp. 3345
Author(s):  
Fabio Castaldi
Keyword(s):  
Time Series ◽  
Soil Properties ◽  
Spatial Resolution ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Visible Region ◽  
Bare Soil ◽  
Landsat 8 ◽  
Economic Perspectives ◽  
Sentinel 2

The spatial and temporal monitoring of soil organic carbon (SOC), and other soil properties related to soil erosion, is extremely important, both from the environmental and economic perspectives. Sentinel-2 (S2) and Landsat-8 (L8) time series increase the probability to observe bare soil fields in croplands, and thus, monitor soil properties over large regions. In this regard, this work suggests an automated pixel-based approach to select only pure soil pixels in S2 and L8 time series, and to make a synthetic bare soil image (SBSI). The SBSIs and the soil properties measured in the framework of the European LUCAS survey were used to calibrate SOC, clay, and CaCO3 prediction models. The results highlight a high correlation between laboratory soil spectra and the SBSIs median spectra, especially for the SBSI obtained by a three-year S2 collection, which provides satisfactory results in terms of SOC prediction accuracy (RPD: 1.74). The comparison between S2 and L8 results demonstrated the higher capability of the S2 sensor in terms of SOC prediction accuracy, mainly due to the greater spatial resolution of the bands in the visible region. Whereas, neither S2 nor L8 could accurately predict the clay and CaCO3 content. This is because of the low spectral and spatial resolution of their SWIR bands that prevent the exploitation of the narrow spectral features related to these two soil attributes. The results of this study prove that large S2 time series can estimate and monitor SOC in croplands using an automated pixel-based approach that selects pure soil pixels and retrieves reliable synthetic soil spectra.

Sign in / Sign up

Export Citation Format

Share Document