Rapid monitoring of seagrass biomass using a simple linear modelling approach, in the field and from space

Lyons, Mitchell; Roelfsema, Christiaan M; Kovács, Éva; Samper‐Villarreal, Jimena; Saunders, Megan I.; Maxwell, Paul; Phinn, Stuart

doi:10.3354/meps11321

Cited by 31 publications

(13 citation statements)

References 34 publications

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“…Our model is therefore trained to predict the areas where seagrass of any species, biomass or cover level, could theoretically occur based on environmental conditions. Developing predictive models of the impact of sediment loads on seagrass which incorporate metrics other than presence vs. absence, such as species [ 51 ], cover [ 52 ], or biomass [ 53 ], are beyond the scope of this study, but are an important area of future research. Water clarity was assumed to be influenced by the distance to open ocean (source of clear water) and river mouths (source of turbid water), as they are likely to influence water clarity [ 54 , 55 ].…”

Section: Methodsmentioning

confidence: 99%

Increased sediment loads cause non-linear decreases in seagrass suitable habitat extent

et al. 2017

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Land-based activities, including deforestation, agriculture, and urbanisation, cause increased erosion, reduced inland and coastal water quality, and subsequent loss or degradation of downstream coastal marine ecosystems. Quantitative approaches to link sediment loads from catchments to metrics of downstream marine ecosystem state are required to calculate the cost effectiveness of taking conservation actions on land to benefits accrued in the ocean. Here we quantify the relationship between sediment loads derived from landscapes to habitat suitability of seagrass meadows in Moreton Bay, Queensland, Australia. We use the following approach: (1) a catchment hydrological model generates sediment loads; (2) a statistical model links sediment loads to water clarity at monthly time-steps; (3) a species distribution model (SDM) factors in water clarity, bathymetry, wave height, and substrate suitability to predict seagrass habitat suitability at monthly time-steps; and (4) a statistical model quantifies the effect of sediment loads on area of seagrass suitable habitat in a given year. The relationship between sediment loads and seagrass suitable habitat is non-linear: large increases in sediment have a disproportionately large negative impact on availability of seagrass suitable habitat. Varying the temporal scale of analysis (monthly vs. yearly), or varying the threshold value used to delineate predicted seagrass presence vs. absence, both affect the magnitude, but not the overall shape, of the relationship between sediment loads and seagrass suitable habitat area. Quantifying the link between sediment produced from catchments and extent of downstream marine ecosystems allows assessment of the relative costs and benefits of taking conservation actions on land or in the ocean, respectively, to marine ecosystems.

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Section: Methodsmentioning

confidence: 99%

Increased sediment loads cause non-linear decreases in seagrass suitable habitat extent

et al. 2017

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“…The previous section described categorical mapping approaches, however some studies have demonstrated quantitative mapping of a continuous benthic parameter such as live coral cover [92], seagrass standing crop [93] or seagrass biomass [94,95]. In these examples the analysis was based on the correlation between image bands (or combinations of bands) and field data on the parameter of interest.…”

Section: Quantitative Benthic Mappingmentioning

confidence: 99%

Remote Sensing of Coral Reefs for Monitoring and Management: A Review

Hedley¹,

et al. 2016

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Coral reefs are in decline worldwide and monitoring activities are important for assessing the impact of disturbance on reefs and tracking subsequent recovery or decline. Monitoring by field surveys provides accurate data but at highly localised scales and so is not cost-effective for reef scale monitoring at frequent time points. Remote sensing from satellites is an alternative and complementary approach. While remote sensing cannot provide the level of detail and accuracy at a single point than a field survey, the statistical power for inferring large scale patterns benefits in having complete areal coverage. This review considers the state of the art of coral reef remote sensing for the diverse range of objectives relevant for management, ranging from the composition of the reef: physical extent, benthic cover, bathymetry, rugosity; to environmental parameters: sea surface temperature, exposure, light, carbonate chemistry. In addition to updating previous reviews, here we also consider the capability to go beyond basic maps of habitats or environmental variables, to discuss concepts highly relevant to stakeholders, policy makers and public communication: such as biodiversity, environmental threat and ecosystem services. A clear conclusion of the review is that advances in both sensor technology and processing algorithms continue to drive forward remote sensing capability for coral reef mapping, particularly with respect to spatial resolution of maps, and synthesis across multiple data products. Both trends can be expected to continue.

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“…Sebagai tahap awal untuk mengestimasi karbon tersimpan menggunakan citra satelit, diperlukan model hubungan antara tutupan jenis lamun dengan karbon tersimpannya. Lyons et al (2015), sudah mulai menggunakan pendekatan model linear untuk menganalisis hubungan antara penutupan dan biomassa lamun, namun belum sampai mengestimasi simpanan karbonnya. Oleh karena itu penelitian ini dilakukan untuk mendapatkan model hubungan antara simpanan karbon dengan persen penutupan pada 6 jenis lamun.…”

Section: Pendahuluanunclassified

Hubungan Antara Persen Penutupan dan Simpanan Karbon Lamun

Mashoreng

Selamat

Amri

et al. 2018

jaki

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Padang lamun merupakan salah satu ekosistem pesisir yang berperan sebagai penyimpan karbon yang cukup penting. Selama ini estimasi karbon tersimpan pada komunitas lamun masih dilakukan menggunakan metode pencuplikan secara langsung. Namun untuk kepentingan survey pada kawasan yang luas, cara tersebut membutuhkan waktu, tenaga dan biaya yang cukup besar. Oleh karena itu diperlukan metode untuk mengestimasi karbon tersimpan lamun dengan memanfaatkan citra satelit sehingga dapat dilakukan secara cepat, mudah dan murah. Sebagai tahap awal untuk mengestimasi karbon tersimpan menggunakan citra satelit, diperlukan model hubungan antara tutupan jenis lamun dengan karbon tersimpannya sebagaimana yang dilakukan pada penelitian ini. Penelitian dilakukan pada bulan September-Oktober 2016 di Pulau Barranglompo Makassar. Penelitian diawali dengan mengambil biomassa 6 jenis lamun yaitu Enhalus acoroides, Thalassia hemprichii, Cymodocea rotundata, Halodule uninervis, Halophila ovalis dan Syringodium isoetifolium pada area seluas 25cm x 25 cm. Pengambilan biomassa setiap jenis lamun dilakukan untuk masing-masing jenis dengan 10 tingkatan persentase tutuapn lamun. sebanyak 10 kali pada persen tutupan jenis lamun yang berbeda-beda, mulai dari tutupan rendah sampai tutupan tertinggi yang ditemukan di lapangan. Penentuan penutupan lamun dilakukan dengan cara visual pada plot berukuran 50cm x 50cm. Selanjutnya dilakukan analisis karbon organik jaringan lamun (daun, rhizoma, akar dan seludang) masing-masing jenis, dengan ulangan 5 kali. Hasil perkalian antara biomassa lamun dengan kandungan karbonnya merupakan karbon tersimpan lamun tersebut. Hubungan antara persen tutupan jenis lamun dan karbon tersimpan dianalisis menggunakan regresi polynomial. Hasil penelitian menunjukkan bahwa pada semua jenis lamun yang diamati, hubungan antara persen tutupan dengan simpan karbonnya mempunyai hubungan positif yang kuat. Koefisien determinasi, r2 berkisar 0,7413-0,9838 untuk simpanan karbon bagian bawah dan 0,8017-0,9683 untuk simpanan karbon bagian atas.

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Rapid monitoring of seagrass biomass using a simple linear modelling approach, in the field and from space

Abstract: Resale or republication not permitted without written consent of the publisherUsing in situ measurements (red triangles), we modelled above-ground biomass in over 20 000 benthic photos (inset) and a time-series of seagrass maps (main image).

Cited by 31 publications

References 34 publications

Increased sediment loads cause non-linear decreases in seagrass suitable habitat extent

Increased sediment loads cause non-linear decreases in seagrass suitable habitat extent

Remote Sensing of Coral Reefs for Monitoring and Management: A Review

Hubungan Antara Persen Penutupan dan Simpanan Karbon Lamun

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