Characterizing global satellite-based indicators for coastal vulnerability to erosion management as exemplified by a regional level analysis from Northeast Brazil

Queiroz, Heithor Alexandre de Araújo; Gonçalves, Rodrigo Mikosz; Mishra, Manoranjan

doi:10.1016/j.scitotenv.2021.152849

Cited by 8 publications

(5 citation statements)

References 121 publications

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“…For example, the slower pre-nesting behavior is highly sensitive to threats such as coastal fishing and beach development, which brings with it a myriad of threats such as light pollution, introduced predators, vehicle traffic, and beach armoring (van de Me et al, 2012;Kamrowski et al, 2013;Aguilera et al, 2019;Nelson Sella and Fuentes, 2019;Colman et al, 2020;Lira et al, 2023;Sella et al, 2023). Moreover, the coastline of Northeastern Brazil, being in the corner of the South American continent, is particularly susceptible to beach erosion (Siqueira et al, 2021;Queiroz et al, 2022), which could be aggravated by climate change (Fuentes et al, 2010;Fuentes et al, 2012;Patrıćio et al, 2021). Our study site has a narrow band of beach surrounded by cliffs, and suitable habitat for nesting is being squeezed with sea level rise (see Mazaris et al, 2009).…”

Section: Discussionmentioning

confidence: 99%

Decoding the internesting movements of marine turtles using a fine-scale behavioral state approach

Santos,

Cullen,

Vieira

et al. 2023

Front. Ecol. Evol.

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IntroductionAn understanding of animal behavior is critical to determine their ecological role and to inform conservation efforts. However, observing hidden behaviors can be challenging, especially for animals that spend most of their time underwater. Animal-borne devices are valuable tools to estimate hidden behavioral states.MethodsWe investigated the fine-scale behavior of internesting hawksbill turtles using the mixed-membership method for movement (M4) which integrated dive variables with spatial components and estimated latent behavioral states.ResultsFive latent behavioral states were identified: 1) pre-nesting, 2) transit, 3) quiescence, and 4) area restricted search within and 5) near the residence of turtles. The last three states associated with a residency period, showed lower activity levels. Notably, when compared to other behaviors the pre-nesting exhibited shallower and remarkably long dives of up to 292 minutes. We noted high fidelity to residence core areas and nesting beaches, within and between nesting seasons, with residence areas decreasing within a season.DiscussionThe latent behaviors identified provide the most detailed breakdown of turtle movement behaviors during the internesting period to date, providing valuable insights into their ecology and behavior. This information can inform marine turtle conservation and management efforts since utilization distributions of individual behavioral states can be used to determine spatially-explicit susceptibility of turtles to various threats based on their behavior. The analyses of utilization distribution revealed a minimal overlap with existing marine protected areas (0.4%), and we show how a new proposal would expand protection to 30%. In short, this study provides valuable guidance for conservation and management of internesting marine turtles at a fine spatiotemporal resolution and can be used to enhance national action plans for endangered species, including the expansion of existing Marine Protected Areas. By flexibly incorporating biologically informative parameters, this approach can be used to study behavior outside of the hawksbill breeding season or even beyond this species.

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Section: Discussionmentioning

confidence: 99%

Decoding the internesting movements of marine turtles using a fine-scale behavioral state approach

Santos,

Cullen,

Vieira

et al. 2023

Front. Ecol. Evol.

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“…Teknologi penginderaan jauh mampu memberikan gambaran mengenai kondisi morfologi pesisir dengan resolusi spasial dan temporal yang memadai (Hendy M.A. Marfai et al, 2018;Queiroz et al, 2022).…”

Section: Pendahuluanunclassified

Data Pesawat Udara Tanpa Awak Untuk Pendukung Analisis Dinamika Pesisir dan Erosi Pantai

Fatchurohman,

Indrawati,

Ningtiyas

et al. 2023

SPATIAL

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Kawasan pesisir menjadi salah satu area yang paling rentan terhadap ancaman bencana akibat percepatan perubahan iklim. Salah satu ancaman yang hampir terjadi di seluruh Kawasan pesisir di dunia adalah erosi pantai. Kawasan Pesisir Kabupaten Bantul, DIY menjadi salah satu area yang terdampak cukup parah oleh erosi pantai. Penelitian ini bertujuan untuk (1) Mengetahui proses dinamika pesisir di Kawasan Pantai Pandansimo ; (2) Memetakan tingkat kecepatan abrasi di Pantai Pandansimo, dan (3) Mengetahui penyebab utama dari proses erosi pantai yang terjadi. Citra satelit resolusi tinggi Pleiades dengan resolusi spasial 50cm digunakan untuk mengetahui perubahan garis pantasi secara temporal. Selain itu, citra satelit yang diperoleh dari Google Earth juga digunakan untuk analisis dari tahun 2010-2021. Perubahan garis pantai dihitung menggunakan software DSAS. Data morfologi detail didapatkan dari hasil akuisisi foto udara menggunakan pesawat tanpa awak multirotor. Berdasarkan hasil analisi diketahui bahwa proses dinamika pesisir didominasi oleh proses erosi pantai. Tercatat nilai kemunduran garis pantai rerata sebesar 2.46 meter/tahun. Erosi pantai yang terjadi disebabkan oleh beberapa faktor hidrodinamik dan morfodinamik seperti peningkatan kejadian gelombang ekstrem, berkurangnya suplai sedimen fluvial, dan fenomena perubahan iklim.

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“…Os índices de vegetação têm sido utilizados para monitorar a cobertura vegetal da Terra tanto em escalas globais como locais (Miura et al, 2001). Através de imagens de satélite temporais é possível observar a variabilidade espacial dos índices de vegetação, entre eles, o mais difundido é o NDVI, conforme exemplos citados pelos autores (Alphan, 2013;Maglione, 2014;Gonçalves et al, 2019;Araujo et al, 2019;Queiroz et al, 2022).…”

Section: Introductionunclassified

O uso do sensoriamento remoto para identificar através de índices a linha de vegetação

Amancio

Gonçalves

Holanda

et al. 2023

Rev. Bras. Geog. Fis.

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A linha de vegetação é uma feição presente na geomorfologia costeira. Pode ser considerada como um indicativo da posição espacial da linha de costa. Existem diversas formas para mapear a linha de vegetação, cada qual com sua respectiva resolução espacial. Nesta contribuição foram extraídas a linha de vegetação por diferentes métodos, entre eles o uso de índices de vegetação tais como o VARI (Visual Atmospheric Resistance Index) obtidos pelo mosaico de ortofotos digitais a partir do RPAS (Remotely Piloted Aircraft System); o NDVI (Normalized Difference Vegetation Index) através de imagens de satélites artificiais oriundas do Landsat 8, Sentinel 2 e Planet e o método de posicionamento relativo com GNSS (Global Navigation Satellite Systems). Os objetivos são: (i) identificar a linha de vegetação a partir do VARI obtido por RPAS e (ii) a partir do NDVI utilizando o Landsat 8, Sentinel 2 e Planet e (iii) comparar as respectivas acurácias encontradas tendo como valor verdadeiro a linha de vegetação obtida por GNSS. Os resultados obtidos foram representados em ordem de acurácia seguidas das respectivas médias em metros e desvio padrão: Landsat 8 (-13,30 ±4,99), Sentinel 2 (2,08 ±2,92), Planet (0,10 ±1,39) e VARI (-0,11 ±1,15). Tais resultados evidenciaram a aplicabilidade e limitações de cada método em função da resolução espacial e qualidade posicional para a extração da linha de vegetação.Palavras-chave: Linha de Vegetação; Visual Atmospheric Resistance Index (VARI); Normalized Difference Vegetation Index (NDVI); Remotely Piloted Aircraft System (RPAS); Global Navigation Satellite Systems (GNSS).The remote sensing usage to identify through indices the vegetation lineA B S T R A C TThe vegetation line is a feature present in coastal geomorphology. It can be considered as an indication of the shoreline spatial position. There are several approaches to map the vegetation line, considering its respective spatial resolution. In this contribution, the vegetation line was extracted by different methods, including the use of vegetation indices such as the VARI (Visual Atmospheric Resistance Index) obtained by the mosaic of digital orthophotos from RPAS (Remotely Piloted Aircraft System); the NDVI (Normalized Difference Vegetation Index) through artificial satellite images from Landsat 8, Sentinel 2 and Planet and the relative positioning method with GNSS (Global Navigation Satellite Systems). The aims are: (i) to identify the vegetation line from VARI obtained by RPAS and from NDVI using Landsat 8, Sentinel 2 and Planet and (ii) to compare the respective accuracies found having as true value the vegetation line obtained by GNSS. The results obtained were represented in order of accuracy followed by the respective averages in meters and standard deviation: Landsat 8 (-13.30 ±4.99), Sentinel 2 (2.08 ±2.92), Planet (0.10 ±1.39) and VARI (-0.11 ±1.15). These results showed the applicability and limitations of each method in terms of spatial resolution and positional quality for extracting the vegetation line.Keywords: Vegetation Line; Visual Atmospheric Resistance Index (VARI); Normalized Difference

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Characterizing global satellite-based indicators for coastal vulnerability to erosion management as exemplified by a regional level analysis from Northeast Brazil

Cited by 8 publications

References 121 publications

Decoding the internesting movements of marine turtles using a fine-scale behavioral state approach

Decoding the internesting movements of marine turtles using a fine-scale behavioral state approach

Data Pesawat Udara Tanpa Awak Untuk Pendukung Analisis Dinamika Pesisir dan Erosi Pantai

O uso do sensoriamento remoto para identificar através de índices a linha de vegetação

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