Special Issue "GIS and RS in Ocean, Island and Coastal Zone"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Prof. Dr. Fenzhen Su
E-Mail Website
Guest Editor
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: high-dimension spatiotemporal big data; coastal zone remote sensing; integration and mining of dynamic geographic processes; geographic information services for environmental and resources management
Special Issues, Collections and Topics in MDPI journals
Prof. Cunjin Xue
E-Mail
Guest Editor
The Aerospace Information Research Institute, Chinese Academy of Sciences, China.
Interests: marine spatiotemporal data mining; marine GIS
Dr. Han Xiao
E-Mail
Guest Editor
Institute of Geographic Sciences and Natural Resources, Chinese Academy of Sciences, Beijing, China
Interests: coastal wetland remote sensing; spatial evolution processing of coastal wetland

Special Issue Information

Dear Colleagues,

Islands and coastal zones are ocean and land interaction zones, and have great economic, scientific and national-defense value. Due to the impacts of human activities (terrestrial pollutant discharge and urbanization) and the natural environment (global sea level rises and extreme weather), islands and coastal zones have become high-risk areas in the world. These areas 1) face rapid morphological changes, which need high-flexibility, time-efficient and high-resolution monitoring methods; 2) have both huge development space and high fragility, which calls for exploring a scientific carrying-capacity framework for resource utilization and spatial management; and 3) have multisource data support provided by spatiotemporal big data, but lack an analysis framework and platforms for their sustainable development. 

The innovations encouraged include but are not limited to:

1. The high-resolution, high-efficiency monitoring of island and coastal zones:

Coastline-type classification based on multi-source data;

Typical category monitoring (including coral reefs, mangroves, salt marshes, etc.);

Feature extraction (ships, fishing grounds, etc.).

2.The carrying-capacity evaluation of island and coastal zones:

Theories and methods for using big data in evaluating the carrying capacity of island and coastal zones;

The integrated application of multisource spatiotemporal big data including ecology, social economy, environment, disasters, etc.;

A carrying-capacity evaluation framework and scale effects;

The improved regional adaptability of evaluation indicators.

3. Applications for the sustainable development of island and coastal zones:

Integrated organization and management technology for spatial data (remote-sensing data and locations) and non-spatial data (text and pictures) of island and coastal zones;

A spatiotemporal big data mining model and analysis framework;

The future-scenario simulation of island and coastal zones. 

Prof. Fenzhen Su
Prof. Cunjin Xue
Dr. Han Xiao
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Island and coastal zones
  • High-efficiency monitoring
  • Carrying capacity
  • Spatiotemporal big data mining
  • Sustainable development

Published Papers (13 papers)

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Article
Global Fisheries Responses to Culture, Policy and COVID-19 from 2017 to 2020
Remote Sens. 2021, 13(22), 4507; https://doi.org/10.3390/rs13224507 - 09 Nov 2021
Viewed by 436
Abstract
Global Fishing Watch (GFW) provides global open-source data collected via automated monitoring of vessels to help with sustainable management of fisheries. Limited previous global fishing effort analyses, based on Automatic Identification System (AIS) data (2017–2020), suggest economic and environmental factors have less influence [...] Read more.
Global Fishing Watch (GFW) provides global open-source data collected via automated monitoring of vessels to help with sustainable management of fisheries. Limited previous global fishing effort analyses, based on Automatic Identification System (AIS) data (2017–2020), suggest economic and environmental factors have less influence on fisheries than cultural and political events, such as holidays and closures, respectively. As such, restrictions from COVID-19 during 2020 provided an unprecedented opportunity to explore added impacts from COVID-19 restrictions on fishing effort. We analyzed global fishing effort and fishing gear changes (2017–2019) for policy and cultural impacts, and then compared impacts of COVID-19 lockdowns across several countries (i.e., China, Spain, the US, and Japan) in 2020. Our findings showed global fishing effort increased from 2017 to 2019 but decreased by 5.2% in 2020. We found policy had a greater impact on monthly global fishing effort than culture, with Chinese longlines decreasing annually. During the lockdown in 2020, trawling activities dropped sharply, particularly in the coastal areas of China and Spain. Although Japan did not implement an official lockdown, its fishing effort in the coastal areas also decreased sharply. In contrast, fishing in the Gulf of Mexico, not subject to lockdown, reduced its scope of fishing activities, but fishing effort was higher. Our study demonstrates, by including the dimensions of policy and culture in fisheries, that large data may materially assist decision-makers to understand factors influencing fisheries’ efforts, and encourage further marine interdisciplinary research. We recommend the lack of data for small-scale Southeast Asian fisheries be addressed to enable future studies of fishing drivers and impacts in this region. Full article
(This article belongs to the Special Issue GIS and RS in Ocean, Island and Coastal Zone)
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Article
Research of the Dual-Band Log-Linear Analysis Model Based on Physics for Bathymetry without In-Situ Depth Data in the South China Sea
Remote Sens. 2021, 13(21), 4331; https://doi.org/10.3390/rs13214331 - 28 Oct 2021
Viewed by 366
Abstract
The current widely used bathymetric inversion model based on multispectral satellite imagery mostly relies on in-situ depth data for establishing a liner/non-linear relationship between water depth and pixel reflectance. This paper evaluates the performance of a dual-band log-linear analysis model based on physics [...] Read more.
The current widely used bathymetric inversion model based on multispectral satellite imagery mostly relies on in-situ depth data for establishing a liner/non-linear relationship between water depth and pixel reflectance. This paper evaluates the performance of a dual-band log-linear analysis model based on physics (P-DLA) for bathymetry without in-situ depth data. This is done using WorldView-2 images of blue and green bands. Further, the pixel sampling principles for solving the four key parameters of the model are summarized. Firstly, this paper elaborates on the physical mechanism of the P-DLA model. All unknown parameters of the P-DLA model are solved by different types of sampling pixels extracted from multispectral images for bathymetric measurements. Ganquan Island and Zhaoshu Island, where accuracy evaluation is performed for the bathymetric results of the P-DLA model with in-situ depth data, were selected to be processed using the method to evaluate its performance. The root mean square errors (RMSEs) of the Ganquan Island and Zhaoshu Island results are 1.69 m and 1.74 m with the mean relative error (MREs) of 14.8% and 18.3%, respectively. Meanwhile, the bathymetric inversion is performed with in-situ depth data using the traditional dual-band log-linear regression model (DLR). The results show that the accuracy of the P-DLA model bathymetry without in-situ depth data is roughly equal to that of the DLR model water depth inversion based on in-situ depth data. The results indicate that the P-DLA model can still obtain relatively ideal bathymetric results despite not having actual bathymetric data in the model training. It also demonstrates underwater microscopic features and changes in the islands and reefs. Full article
(This article belongs to the Special Issue GIS and RS in Ocean, Island and Coastal Zone)
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Article
Monitoring Coastline Changes of the Malay Islands Based on Google Earth Engine and Dense Time-Series Remote Sensing Images
Remote Sens. 2021, 13(19), 3842; https://doi.org/10.3390/rs13193842 - 25 Sep 2021
Cited by 1 | Viewed by 605
Abstract
The use of remote sensing to monitor coastlines with wide distributions and dynamic changes is significant for coastal environmental monitoring and resource management. However, most current remote sensing information extraction of coastlines is based on the instantaneous waterline, which is obtained by single-period [...] Read more.
The use of remote sensing to monitor coastlines with wide distributions and dynamic changes is significant for coastal environmental monitoring and resource management. However, most current remote sensing information extraction of coastlines is based on the instantaneous waterline, which is obtained by single-period imagery. The lack of a unified standard is not conducive to the dynamic change monitoring of a changeable coastline. The tidal range observation correction method can be used to correct coastline observation to a unified climax line, but it is difficult to apply on a large scale because of the distribution of observation sites. Therefore, we proposed a coastline extraction method based on the remote sensing big data platform Google Earth Engine and dense time-series remote sensing images. Through the instantaneous coastline probability calculation system, the coastline information could be extracted without the tidal range observation data to achieve a unified tide level standard. We took the Malay Islands as the experimental area and analyzed the consistency between the extraction results and the existing high-precision coastline thematic products of the same period to achieve authenticity verification. Our results showed that the coastline data deviated 10 m in proportion to a reach of 40% and deviated 50 m within a reach of 89%. The overall accuracy was kept within 100 m. In addition, we extracted 96 additional islands that have not been included in public data. The obtained multi-phase coastlines showed the spatial distribution of the changing hot regions of the Malay Islands’ coastline, which greatly supported our analysis of the reasons for the expansion and retreat of the coastline in this region. These research results showed that the big data platform and intensive time-series method have considerable potential in large-scale monitoring of coastline dynamic change and island reef change monitoring. Full article
(This article belongs to the Special Issue GIS and RS in Ocean, Island and Coastal Zone)
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Article
Long-Term Changes in the Land–Ocean Ecological Environment in Small Island Countries in the South Pacific: A Fiji Vision
Remote Sens. 2021, 13(18), 3740; https://doi.org/10.3390/rs13183740 - 18 Sep 2021
Viewed by 782
Abstract
Small island countries in the South Pacific are ecologically fragile areas, vulnerable to climate change, and the long-term ecological changes in the sea and land have an important impact on their sustainable development. This study takes Fiji, a typical small island country in [...] Read more.
Small island countries in the South Pacific are ecologically fragile areas, vulnerable to climate change, and the long-term ecological changes in the sea and land have an important impact on their sustainable development. This study takes Fiji, a typical small island country in the South Pacific, as an example, to analyze the change and connection of marine and terrestrial ecosystem environments based on 30 years of multi-source, satellite, remote-sensing data. From 1991 to 2019, according to the change in forest area in Fiji, three stages were delineated: first was a period of stability, then a decrease, and then a recovery in recent years. From 1991 to 2002, Fiji’s vegetation accounted for 73% of the total area; sea environment surrounding the islands, such as sea level height and sea surface temperature, were relatively low, with high water transparency. From 2002 to 2014, with the development of forestry and tourism, vegetation decreased by 6.89% and bare land increased, which changes the runoff erosion in the drainage basin; correspondingly, the chlorophyll a concentration in three major estuaries was found to be slightly increased with low water transparency. Meanwhile, coupled with the rising sea temperature, the area of Fiji’s coral reefs shrank significantly, with 51.13% of the total loss of coral reefs occurring in the Vanua Levu, where bare land and runoff were more distributed in its drainage basin. From 2014 to 2019, Fiji’s vegetation and coral reef areas recovered from the former stage; affected by short-term climate oscillations such as El Niño-Southern Oscillation (ENSO), the sea surface temperature showed a significant abnormal drop and the water transparency decreased. In the past 30 years (1993–2018), the sea level rise rate around Fiji reached 4 mm/year, and the temperature increased by 0.3 °C, which threatens the coastal ecosystem environment, including coral reefs and mangrove; inappropriate land-use change would worsen the situation in these ecologically fragile areas. Full article
(This article belongs to the Special Issue GIS and RS in Ocean, Island and Coastal Zone)
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Article
Can the Structure Similarity of Training Patches Affect the Sea Surface Temperature Deep Learning Super-Resolution?
Remote Sens. 2021, 13(18), 3568; https://doi.org/10.3390/rs13183568 - 08 Sep 2021
Viewed by 439
Abstract
Meso- and fine-scale sea surface temperature (SST) is an essential parameter in oceanographic research. Remote sensing is an efficient way to acquire global SST. However, single infrared-based and microwave-based satellite-derived SST cannot obtain complete coverage and high-resolution SST simultaneously. Deep learning super-resolution (SR) [...] Read more.
Meso- and fine-scale sea surface temperature (SST) is an essential parameter in oceanographic research. Remote sensing is an efficient way to acquire global SST. However, single infrared-based and microwave-based satellite-derived SST cannot obtain complete coverage and high-resolution SST simultaneously. Deep learning super-resolution (SR) techniques have exhibited the ability to enhance spatial resolution, offering the potential to reconstruct the details of SST fields. Current SR research focuses mainly on improving the structure of the SR model instead of training dataset selection. Different from generating the low-resolution images by downscaling the corresponding high-resolution images, the high- and low-resolution SST are derived from different sensors. Hence, the structure similarity of training patches may affect the SR model training and, consequently, the SST reconstruction. In this study, we first discuss the influence of training dataset selection on SST SR performance, showing that the training dataset determined by the structure similarity index (SSIM) of 0.6 can result in higher reconstruction accuracy and better image quality. In addition, in the practical stage, the spatial similarity between the low-resolution input and the objective high-resolution output is a key factor for SST SR. Moreover, the training dataset obtained from the actual AMSR2 and MODIS SST images is more suitable for SST SR because of the skin and sub-skin temperature difference. Finally, the SST reconstruction accuracies obtained from different SR models are relatively consistent, yet the differences in reconstructed image quality are rather significant. Full article
(This article belongs to the Special Issue GIS and RS in Ocean, Island and Coastal Zone)
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Article
LSTM-Based Remote Sensing Inversion of Largescale Sand Wave Topography of the Taiwan Banks
Remote Sens. 2021, 13(16), 3313; https://doi.org/10.3390/rs13163313 - 21 Aug 2021
Viewed by 538
Abstract
Shallow underwater topography has important practical applications in fisheries, navigation, and pipeline laying. Traditional multibeam bathymetry is limited by the high cost of largescale topographic surveys in large, shallow sand wave areas. Remote sensing inversion methods to detect shallow sand wave topography in [...] Read more.
Shallow underwater topography has important practical applications in fisheries, navigation, and pipeline laying. Traditional multibeam bathymetry is limited by the high cost of largescale topographic surveys in large, shallow sand wave areas. Remote sensing inversion methods to detect shallow sand wave topography in Taiwan rely heavily on measured water depth data. To address these problems, this study proposes a largescale remote sensing inversion model of sand wave topography based on long short-term memory network machine learning. Using multi-angle sun glitter remote sensing to obtain sea surface roughness (SSR) information and by learning and training SSR and its corresponding water depth information, the sand wave topography of a largescale shallow sea sand wave region is extracted. The accuracy of the model is validated through its application to a 774 km2 area in the sand wave topography of the Taiwan Banks. The model obtains a root mean square error of 3.31–3.67 m, indicating that the method has good generalization capability and can achieve a largescale topographic understanding of shallow sand waves with some training on measured bathymetry data. Sand wave topography is widely present in tidal environments; our method has low requirements for ground data, with high application value. Full article
(This article belongs to the Special Issue GIS and RS in Ocean, Island and Coastal Zone)
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Article
Impact of Port Construction on the Spatial Pattern of Land Use in Coastal Zones Based on CLDI and LUT Models: A Case Study of Qingdao and Yantai
Remote Sens. 2021, 13(16), 3110; https://doi.org/10.3390/rs13163110 - 06 Aug 2021
Viewed by 471
Abstract
Ports are an important type of land use in coastal cities, and the development of ports has a significant influence on the spatial pattern of land use in port cities. However, the research focusing on economic indicators hardly reflects the process of changes [...] Read more.
Ports are an important type of land use in coastal cities, and the development of ports has a significant influence on the spatial pattern of land use in port cities. However, the research focusing on economic indicators hardly reflects the process of changes in the spatial distribution of land development in coastal port cities. This paper introduces a spatial association rule method to establish a coastline and land development intensity (CLDI) model and land use transfer (LUT) model in the vertical direction of coastal zones to mine the association rules between shoreline change and land development intensity along the sea–land gradient in the Qingdao and Yantai coastal zones and to explore the important land development sequence patterns. The results showed that, in the early stage of regional development, the land development intensity decreased from sea to land. In the later stage, as the industry transferred to nearby towns, the land units with extremely strong and strong levels started to move to the end or middle of the sequence. With the improvement of the urban construction level, the simple LUT pattern sequence that increased building land through the occupation of cultivated land and forestland was replaced gradually by complex sequences with multiple components. The relationship between land development and distance from the port showed that the areas with strong land development intensity gradually moved from coastal to inland areas over time. Port shipping has a profound influence on port city land use patterns. Industrial transfer drives the development of surrounding towns during the metaphase. This trend was used to build a second port to realize the division of transportation capacity, as the old port’s carrying capacity tended to become saturated. This paper revealed the general changes in the important land use patterns in port areas through a comparative study of the Qingdao and Yantai port areas and the differences among different geographical locations and development processes. This study provides a reference for the rational planning of coastal zone spatial layouts and provides a model basis for the analysis of the spatial structure of coastal zones. This information can be used to coordinate the relationship between ports and cities and promote the sustainable development of coastal zones. Full article
(This article belongs to the Special Issue GIS and RS in Ocean, Island and Coastal Zone)
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Article
The Key Reason of False Positive Misclassification for Accurate Large-Area Mangrove Classifications
Remote Sens. 2021, 13(15), 2909; https://doi.org/10.3390/rs13152909 - 24 Jul 2021
Viewed by 553
Abstract
Accurate large-area mangrove classification is a challenging task due to the complexity of mangroves, such as abundant species within the mangrove category, and various appearances resulting from a large latitudinal span and varied habitats. Existing studies have improved mangrove classifications by introducing time [...] Read more.
Accurate large-area mangrove classification is a challenging task due to the complexity of mangroves, such as abundant species within the mangrove category, and various appearances resulting from a large latitudinal span and varied habitats. Existing studies have improved mangrove classifications by introducing time series images, constructing new indices sensitive to mangroves, and correcting classifications by empirical constraints and visual inspections. However, false positive misclassifications are still prevalent in current classification results before corrections, and the key reason for false positive misclassification in large-area mangrove classifications is unknown. To address this knowledge gap, a hypothesis that an inadequate classification scheme (i.e., the choice of categories) is the key reason for such false positive misclassification is proposed in this paper. To validate this hypothesis, new categories considering non-mangrove vegetation near water (i.e., within one pixel from water bodies) were introduced, which is inclined to be misclassified as mangroves, into a normally-used standard classification scheme, so as to form a new scheme. In controlled conditions, two experiments were conducted. The first experiment using the same total features to derive direct mangrove classification results in China for the year 2018 on the Google Earth Engine with the standard scheme and the new scheme respectively. The second experiment used the optimal features to balance the probability of a selected feature to be effective for the scheme. A comparison shows that the inclusion of the new categories reduced the false positive pixels with a rate of 71.3% in the first experiment, and a rate of 66.3% in the second experiment. Local characteristics of false positive pixels within 1 × 1 km cells, and direct classification results in two selected subset areas were also analyzed for quantitative and qualitative validation. All the validation results from the two experiments support the finding that the hypothesis is true. The validated hypothesis can be easily applied to other studies to alleviate the prevalence of false positive misclassifications. Full article
(This article belongs to the Special Issue GIS and RS in Ocean, Island and Coastal Zone)
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Article
Spatially Modeling the Synergistic Impacts of Global Warming and Sea-Level Rise on Coral Reefs in the South China Sea
Remote Sens. 2021, 13(13), 2626; https://doi.org/10.3390/rs13132626 - 04 Jul 2021
Viewed by 1036
Abstract
Global warming and sea-level rise (SLR) induced by rising atmospheric CO2 concentrations can cause coral bleaching, death, and submergence of the world’s coral reefs. Adopting the GIS and RS methods, we modeled how these two stressors combine to influence the future growth [...] Read more.
Global warming and sea-level rise (SLR) induced by rising atmospheric CO2 concentrations can cause coral bleaching, death, and submergence of the world’s coral reefs. Adopting the GIS and RS methods, we modeled how these two stressors combine to influence the future growth of the atolls and table reefs of three archipelagoes in the South China Sea (SCS), based on geomorphic and ecological zones. A large-scale survey of the coral communities in Xisha Islands in 2014, Dongsha Islands in 2014–2016 and Nansha Islands in 2007 provided zone-specific process datasets on the range of reef accretion rates. Sea surface temperature and extreme (minimum and maximum) SLR data above 1985–2005 levels by 2100 in the SCS were derived from the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) models forced with the Representative Concentration Pathways (RCPs). Our model projected that: (1) the Xisha Islands and Dongsha Islands may have a better growth status, because the reef flat biotic sparse zone may be recolonized with hard coral and become a biotic dense zone; (2) the southern Nansha Islands reefs have a risk of stopping growing due to their earlier annual bleaching years. The increasing of water depths of these reefs is stronger in the RCP with more emissions. Our approach offers insights into the best-case and worst-case impacts of two global environmental pressures on potential future reef growth under a changing climate. Full article
(This article belongs to the Special Issue GIS and RS in Ocean, Island and Coastal Zone)
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Article
Changes in Ecosystems and Ecosystem Services in the Guangdong-Hong Kong-Macao Greater Bay Area since the Reform and Opening Up in China
Remote Sens. 2021, 13(9), 1611; https://doi.org/10.3390/rs13091611 - 21 Apr 2021
Cited by 3 | Viewed by 610
Abstract
Ecosystem services provide important support for the sustainable development of humans; these services are provided by various ecosystems, but they have been severely influenced by anthropogenic activities globally in the past several decades. To respond to the Sustainable Development Goals of the United [...] Read more.
Ecosystem services provide important support for the sustainable development of humans; these services are provided by various ecosystems, but they have been severely influenced by anthropogenic activities globally in the past several decades. To respond to the Sustainable Development Goals of the United Nations, this study investigated the changes in ecosystem structure and estimated the associated ecosystem services value (ESV) since China’s reform and opening-up policy in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), one of the most developed and populous areas of China. Our results showed that dramatic changes in ecosystem structure occurred in the GBA, characterized by unpresented construction land sprawl (an average of 148 km2/yr) and extensive farmland loss (an average of 111 km2/yr). The change size and rate of ecosystems from 2000 to 2010 was the biggest and fastest, followed by that from 1990 to 2000. The ESV of the study area showed an overall decreasing trend, declining from 464 billion yuan to 346 billion yuan. The ESV supported by forest ecosystems and water body ecosystems made dominant contributions to the total ESV, ranging from 92% to 95%. Strong spatial heterogeneity of the ESV of the GBA might be noted throughout the study period, with lower values in the central region and higher values in the surrounding region. To realize sustainable development in the GBA; this study strongly suggests that local governments, and the public, scientifically use various ecosystems and their services, focusing on vigorously protecting ecosystems with high and important ESVs, such as water body, wetland, forest, and farmland ecosystems. Full article
(This article belongs to the Special Issue GIS and RS in Ocean, Island and Coastal Zone)
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Article
Offshore Hydrocarbon Exploitation Observations from VIIRS NTL Images: Analyzing the Intensity Changes and Development Trends in the South China Sea from 2012 to 2019
Remote Sens. 2021, 13(5), 946; https://doi.org/10.3390/rs13050946 - 03 Mar 2021
Viewed by 594
Abstract
The South China Sea is rich in hydrocarbon resources and has been exploited for decades by countries around it. However, little is known about the hydrocarbon exploitation (HE) activities in the South China Sea in recent years, especially its intensity changes and development [...] Read more.
The South China Sea is rich in hydrocarbon resources and has been exploited for decades by countries around it. However, little is known about the hydrocarbon exploitation (HE) activities in the South China Sea in recent years, especially its intensity changes and development trends. Here, a long-time series of monthly Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light (NTL)images were applied to observe and analyze the HE dynamics in the South China Sea from 2012 to 2019. A target recognition method combining feature increment strategy and random forest model was proposed to obtain the spatial distribution of offshore HE targets, with an average comprehensive precision of 94.44%. Then, a spatio-temporal statistical analysis was carried out on the intensity changes and development trends of HE activities. The results showed that: (1) From 2012 to 2019, the quantity of HE targets in the South China Sea has increased from 215 to 310, from rapid to stable increasing taking 2014 as a turning point. (2) The distribution density of HE targets increases year by year, with the maximum density reaching 59/ 10,000 Km2, and with the most significant increase in the new hydrocarbon-bearing fields close to the deep-sea. (3) The quantity of HE targets shallower than -300m has been increasing with years, but showing a decreasing proportion trend, falling from 96.7% in 2012 to 94.2% of the total in 2019. (4) After 2015, the exploitation core of most hydrocarbon-bearing basins began to shift from shallow-sea to deep-sea, with gradually increasing exploitation depth, among which the maximum depth reaching −1580 m. Against the background of the changes in international crude oil prices and the vigorous development of deep-sea HE, this research provides important information and methodological references for the formulation and analysis of offshore hydrocarbon resource exploitation strategies. Full article
(This article belongs to the Special Issue GIS and RS in Ocean, Island and Coastal Zone)
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Other

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Technical Note
Integrating Multiple Datasets and Machine Learning Algorithms for Satellite-Based Bathymetry in Seaports
Remote Sens. 2021, 13(21), 4328; https://doi.org/10.3390/rs13214328 - 28 Oct 2021
Viewed by 339
Abstract
Water depth estimation in seaports is essential for effective port management. This paper presents an empirical approach for water depth determination from satellite imagery through the integration of multiple datasets and machine learning algorithms. The implementation details of the proposed approach are provided [...] Read more.
Water depth estimation in seaports is essential for effective port management. This paper presents an empirical approach for water depth determination from satellite imagery through the integration of multiple datasets and machine learning algorithms. The implementation details of the proposed approach are provided and compared against different existing machine learning algorithms with a single training set. For a single training set and a single machine learning method, our analysis shows that the proposed depth estimation method provides a better root-mean-square error (RMSE) and a higher coefficient of determination (R2) under turbid water conditions, with overall RMSE and R2 improvements of 1 cm and 0.7, respectively. The developed method may be employed in monitoring dredging activities, especially in areas with polluted water, mud and/or a high sediment content. Full article
(This article belongs to the Special Issue GIS and RS in Ocean, Island and Coastal Zone)
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Technical Note
An Adaptive Piecewise Harmonic Analysis Method for Reconstructing Multi-Year Sea Surface Chlorophyll-A Time Series
Remote Sens. 2021, 13(14), 2727; https://doi.org/10.3390/rs13142727 - 11 Jul 2021
Viewed by 613
Abstract
High-quality remotely sensed satellite data series are important for many ecological and environmental applications. Unfortunately, irregular spatiotemporal samples, frequent image gaps and inevitable observational biases can greatly hinder their application. As one of the most effective gap filling and noise reduction approaches, the [...] Read more.
High-quality remotely sensed satellite data series are important for many ecological and environmental applications. Unfortunately, irregular spatiotemporal samples, frequent image gaps and inevitable observational biases can greatly hinder their application. As one of the most effective gap filling and noise reduction approaches, the harmonic analysis of time series (HANTS) method has been widely used to reconstruct geographical variables; however, when applied on multi-year time series over large spatial areas, the optimal harmonic formulas are generally varied in different locations or change across different years. The question of how to choose the optimal harmonic formula is still unanswered due to the deficiency of appropriate criteria. In this study, an adaptive piecewise harmonic analysis method (AP-HA) is proposed to reconstruct multi-year seasonal data series. The method introduces a cross-validation scheme to adaptively determine the optimal harmonic model and employs an iterative piecewise scheme to better track the local traits. Whenapplied to the satellite-derived sea surface chlorophyll-a time series over the Bohai and Yellow Seas of China, the AP-HA obtains reliable reconstruction results and outperforms the conventional HANTS methods, achieving improved accuracy. Due to its generic approach to filling missing observations and tracking detailed traits, the AP-HA method has a wide range of applications for other seasonal geographical variables. Full article
(This article belongs to the Special Issue GIS and RS in Ocean, Island and Coastal Zone)
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