Coral reef habitats mapping of Spermonde Archipelago using remote sensing compared with in situ survey of fish abundance

2019 ◽

pp. 1-15

Author(s):

Aramita Livia Ardis ◽

Mega Laksmini Syamsudin ◽

Herman Hamdani ◽

Lantun Paradhita Dewanti

Keyword(s):

Remote Sensing ◽

Coral Reef ◽

Carrying Capacity ◽

National Park ◽

Coral Cover ◽

Sea Surface ◽

Remote Sensing Technology ◽

Sensing Technology ◽

Coral Reef Ecosystems

Karimunjawa is one of the main destinations that present underwater beauty that is quite popular. But due to increased tourism activities provide economic benefits but also have a negative impact on coral reef ecosystems so that prudent and sustainable management is needed, these characteristics are felt capable of being helped by remote sensing technology. The purpose of this research is to analyze the coral reef zoning for the development of ecotourism segmentation and the carrying capacity of coral reef ecosystems and to map the condition of coral reef ecosystems in the Karimunjawa National Park area through remote sensing technology. The method used in data collection uses a survey method which is divided into 2 types in-situ conducted on 19th April 2019 to 2nd May 2019 and ex-situ taken for 4 years for coral cover and 1 year for sea surface temperature. By using quantitative descriptive analysis, land suitability results are obtained based on the land suitability index approach and the percentage of coral cover in determining the mapping of ecotourism segmentation areas. The results of this research show that through in-situ approach, data collection in three stations on Sintok and Menjangan Kecil Islands has good coral cover while Cemara Besar is damaged. The appropriate Tourism Conformity Index value is on Menjangan Kecil Island while the other two stations are not so that the carrying capacity calculation is only done on the appropriate and very appropriate island. Inversely proportional through the analysis of the Scenic Beauty Estimation value, Cemara Besar Island which shows a high value while on the Menjangan Kecil Island the lowest. Spatial analysis shows that the fluctuation in sea surface temperature during one year is not too significant and is still limited to the optimum temperature range for coral growth so that it does not affect the conditions causing damage to coral reefs, called bleaching. Looking at the distribution of coral reefs via satellite, over the past 4 years shows an increase in dead coral cover leaving 6,752,802 m2 in 2019.

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The next step in shallow coral reef monitoring: Combining remote sensing and in situ approaches

Marine Pollution Bulletin ◽

10.1016/j.marpolbul.2010.07.033 ◽

2010 ◽

Vol 60 (11) ◽

pp. 1956-1968 ◽

Cited By ~ 22

Author(s):

Julie Scopélitis ◽

Serge Andréfouët ◽

Stuart Phinn ◽

Lara Arroyo ◽

Mayeul Dalleau ◽

...

Keyword(s):

Remote Sensing ◽

Coral Reef ◽

Reef Monitoring

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Mapping the change of coral reefs using remote sensing and in situ measurements: a case study in Pangkajene and Kepulauan Regency, Spermonde Archipelago, Indonesia

Journal of Oceanography ◽

10.1007/s10872-017-0422-4 ◽

2017 ◽

Vol 73 (5) ◽

pp. 623-645 ◽

Cited By ~ 11

Author(s):

La Ode Muhammad Yasir Haya ◽

Masahiko Fujii

Keyword(s):

Remote Sensing ◽

Coral Reefs ◽

In Situ Measurements ◽

Spermonde Archipelago

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Using Remote Sensing and in situ Measurements for Efficient Mapping and Optimal Sampling of Coral Reefs

Frontiers in Marine Science ◽

10.3389/fmars.2021.689489 ◽

2021 ◽

Vol 8 ◽

Author(s):

Alberto Candela ◽

Kevin Edelson ◽

Michelle M. Gierach ◽

David R. Thompson ◽

Gail Woodward ◽

...

Keyword(s):

Remote Sensing ◽

Coral Reef ◽

Coral Reefs ◽

Decision Theory ◽

Sample Selection ◽

Global Scale ◽

Global Mapping ◽

And Performance ◽

Reef Condition

Coral reefs are of undeniable importance to the environment, yet little is known of them on a global scale. Assessments rely on laborious, local in-water surveys. In recent years remote sensing has been useful on larger scales for certain aspects of reef science such as benthic functional type discrimination. However, remote sensing only gives indirect information about reef condition. Only through combination of remote sensing and in situ data can we achieve coverage to understand reef condition and monitor worldwide condition. This work presents an approach to global mapping of coral reef condition that intelligently selects local, in situ measurements that refine the accuracy and resolution of global remote sensing. To this end, we apply new techniques in remote sensing analysis, probabilistic modeling for coral reef mapping, and decision theory for sample selection. Our strategy represents a fundamental change in how we study coral reefs and assess their condition on a global scale. We demonstrate feasibility and performance of our approach in a proof of concept using spaceborne remote sensing together with high-quality airborne data from the NASA Earth Venture Suborbital-2 (EVS-2) Coral Reef Airborne Laboratory (CORAL) mission as a proxy for in situ samples. Results indicate that our method is capable of extrapolating in situ features and refining information from remote sensing with increasing accuracy. Furthermore, the results confirm that decision theory is a powerful tool for sample selection.

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Hyperspectral Remote Sensing of the Coastal Ocean: Adaptive Sampling and Forecasting of In situ Optical Properties

10.21236/ada624963 ◽

2001 ◽

Author(s):

Oscar Schofield ◽

Scott Glenn ◽

Dale Haidvogel ◽

Frederick Grassle

Keyword(s):

Remote Sensing ◽

Optical Properties ◽

Adaptive Sampling ◽

Coastal Ocean ◽

Hyperspectral Remote Sensing

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Deep Learning for Monitoring Agricultural Drought in South Asia Using Remote Sensing Data

Remote Sensing ◽

10.3390/rs13091715 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1715

Author(s):

Foyez Ahmed Prodhan ◽

Jiahua Zhang ◽

Fengmei Yao ◽

Lamei Shi ◽

Til Prasad Pangali Sharma ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

South Asia ◽

Model Performance ◽

Remote Sensing Data ◽

Agricultural Drought ◽

Gradient Boosting ◽

Complex Nature ◽

Sensing Data

Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R2 ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring.

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