Improved seagrass mapping using linear spectral unmixing of aerial photographs

Uhrin, Amy V.; Townsend, Philip A.

doi:10.1016/j.ecss.2016.01.021

Cited by 23 publications

(16 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In shallow waters, various types of remote sensing such as aerial photography, high‐resolution satellite imagery, hyperspectral imagery, or LiDAR, are under development and have been tested to create baseline maps (Lathrop et al , ; Hill et al , ; Uhrin and Townsend, ). Conventional classification techniques are difficult to apply because of the inconsistency in spectral response of the bottom features of interest with changes in water depth and clarity (Wezernak and Lyzenga, ; Uhrin and Townsend, ).…”

Section: Discussionmentioning

confidence: 99%

“…Mapping by scuba is further challenged by accurately digitizing the maps. For remote sensing work, considerable efforts are involved in the ground‐truthing before and after image acquisition (Uhrin and Townsend, ).…”

Section: Discussionmentioning

confidence: 99%

“…Most of the methods mentioned above involve using boats/ships/AUVs entailing relatively high costs and limiting the mapping efforts to open (deeper) waters, possibly avoiding the shallower limits of the meadows being mapped. While mapping seagrasses is non‐trivial, mapping Halophila stipulacea meadows might be particularly challenging, as it is a small‐leaved low‐canopy seagrass species, rendering sonar‐based mapping methods (Komatsu et al , ; Sánchez‐Carnero et al , ) and potentially remote sensing (Uhrin and Townsend, ) impractical. Considerations that were taken into account for mapping of H .…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

A low cost field‐survey method for mapping seagrasses and their potential threats: an example from the northern Gulf of Aqaba, Red Sea

Winters

Edelist

Shem-Tov

et al. 2016

Aquatic Conservation

View full text Add to dashboard Cite

1. In the Gulf of Aqaba (GoA), coral reefs are considered the dominating ecosystem, while seagrass meadows, recognized worldwide as important ecosystems, have received little attention. Absence of comprehensive seagrass maps limits awareness, evaluations of associated ecosystem services, and implementation of conservation and management tools.2. Presented here are the first detailed maps of seagrass meadows along the Israeli coast of the northern GoA. Mapping was performed by snorkelling along transects perpendicular to the shore above meadows growing at 15-25 m. Measurements along these transects included position, meadow depth and visual estimations of seagrass cover. Shallow boundaries of meadows, parallel to shore, were recorded by GPS tracking. Supplementary work included drop-camera boat surveys to determine the position of the deeper edge of meadows. In addition, GIS layers were created that indicated shoreline infrastructures, near-shore human activities and potential pollution threats. Ecosystem services of seagrass meadows mapped were valuated using a benefit transfer approach.3. In total, 9.7 km of the 11 km shoreline were surveyed and 2830 data points collected. Seagrasses were growing along 7.5 km of the shoreline, with shallow (15-25 m) meadows found to cover an area of 707 000 m 2 and valued at more than US$ 2 000 000 yr -1 in associated ecosystem services. Pilot drop-camera surveys (additional 283 data points) indicated that meadows can extend down to 50 m in some places. Coastal uses and threats varied in character and location. A municipality runoff point and drainage canal located close to the largest meadow were identified as the main threats to local seagrasses. 4. These low-cost methods enhance our understanding of seagrass distribution in the northern GoA. They demonstrate a GIS-based tool for assessing how environmental changes might affect the cover and state of seagrasses, improving efforts to conserve seagrass, and have particular relevance to seagrass mapping in developing countries and/or island nations.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A low cost field‐survey method for mapping seagrasses and their potential threats: an example from the northern Gulf of Aqaba, Red Sea

Winters

Edelist

Shem-Tov

et al. 2016

Aquatic Conservation

View full text Add to dashboard Cite

show abstract

“…As a rule of thumb, if K > 0.80 the relationship between reference data and classification is considered strong. The value of K came out to be 0.72 to 0.98 which indicates positive strong results [13].…”

Section: ) Linear Spectral Unmixing (Lsu)mentioning

confidence: 93%

Automatic seagrass detection: A survey

Islam

Raza

Moniruzzamn

et al. 2017

2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA)

View full text Add to dashboard Cite

Seagrass is an important component of the marine ecosystem and plays a vital role in preserving the water quality. The traditional approaches for sea grass identification are either manual or semi-automated, resulting in costlier, time consuming and tedious solutions. There has been an increasing interest in the automatic identification of seagrasses and this article provides a survey of automatic classification techniques that are based on machine learning, fuzzy synthetic evaluation model and maximum likelihood classifier along with their performance. The article classifies the existing approaches on the basis of image types (i.e. aerial, satellite, and underwater digital), outlines the current challenges and provides future research directions.

show abstract

“…where X is the radii probability distribution matrix of pore types and e is the error vector that must be minimized to obtain the fraction vector f that best fits the total PSD Y. This type of problem can be solved in many ways [46][47][48][49]. In this research, we used the least square solution, and the fraction vector f can be computed by:…”

Section: Estimating the Proportion Of Different Pores Based On The Lementioning

confidence: 99%

Multi-Scale Quantitative Characterization of Pore Distribution Networks in Tight Sandstone by integrating FE-SEM, HPMI, and NMR with the Constrained Least Squares Algorithm

Zhou

Shi

et al. 2019

Energies

View full text Add to dashboard Cite

The goal of this study was to investigate the impacts of various sedimentary-diagenetic conditions on the macroscopic petrophysical parameters and microscopic pore structures of tight sandstones from the Lower Jurassic Badaowan Formation in the Southern Junggar Basin, China. Based on the traditional methods for establishing pore size distribution, including integrating the results of high-pressure mercury injection, nuclear magnetic resonance, and scanning electron microscopy, the constrained least squares algorithm was employed to automatically determine the porosity contributions of pore types with different origins. The results show that there are six genetic pore types: residual intergranular pores (RIPs), feldspar dissolution pores (FDPs), rock fragment dissolution pores (RFDPs), clay mineral intergranular pores (CIPs), intercrystalline pores of kaolinite (IPKs), and matrix pores (MPs). Four lithofacies were identified: the quartz cemented-dissolution facies (QCDF), carbonate cemented facies (CCF), authigenic clay mineral facies (ACMF), and matrix-caused tightly compacted facies (MCTF). Modified by limited dissolution, the QCDF with a high proportion of macropores (RIPs, FDPs, and RFDPs) exhibited a slightly higher porosity and considerably higher permeability than those of others. A large number of micropores (MPs, CIPs, and IPKs) in MCTF and ACMF led to slightly lower porosities but considerably lower permeabilities. Due to the tightly cemented carbonates in the CCF, its porosity reduced sharply, but the permeability of the CCF remained much higher those of the MCTF and ACMF. The results highlight that a high proportion of macropores with large radii and regular shapes provide more effective percolation paths than storage spaces. Nevertheless, micropores with small radii and complex pore structures have a limited contribution to flow capability. The fractal dimension analysis shows that a high proportion of MPs is the major reason for the heterogeneity in tight sandstones. The formation of larger macropores with smooth surfaces are more conductive for oil and gas accumulation.

show abstract

Improved seagrass mapping using linear spectral unmixing of aerial photographs

Cited by 23 publications

References 57 publications

A low cost field‐survey method for mapping seagrasses and their potential threats: an example from the northern Gulf of Aqaba, Red Sea

A low cost field‐survey method for mapping seagrasses and their potential threats: an example from the northern Gulf of Aqaba, Red Sea

Automatic seagrass detection: A survey

Multi-Scale Quantitative Characterization of Pore Distribution Networks in Tight Sandstone by integrating FE-SEM, HPMI, and NMR with the Constrained Least Squares Algorithm

Contact Info

Product

Resources

About