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Advances in Geospatial Technologies in Monitoring Blue Carbon Ecosystems Using...
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Advances in Geospatial Technologies in Monitoring Blue Carbon Ecosystems Using Multiple Source Remote Sensing and GIS Data

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 18676

Special Issue Editors

Dr. Tien Dat Pham

E-Mail Website
Guest Editor
Department of Biological Sciences, Florida International University (FIU), Miami, FL 33199, USA
Interests: blue carbon ecosystems; Optical and SAR remote sensing; above-ground biomass retrievals; carbon stock estimation; machine learning; GIS; REDD+
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kunihiko Yoshino

E-Mail Website
Guest Editor
Department of Biological and Environmental Engineering Graduate School of Agricultural and Life Sciences, Faculty of Agriculture, the University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan
Interests: wetlands; blue carbon ecosystems; remote sensing; hyperspectral data; image processing and classification; GIS
Dr. Yudi Setiawan

E-Mail Website
Guest Editor
Department of Forest Resources Conservation and Ecotourism, Faculty of Forestry, Bogor Agricultural University, Kampus IPB Darmaga, Bogor 16880, Jawa Barat, Indonesia
Interests: wetlands; coastal vegetation; geospatial dynamics; environmental science; remote sensing; GIS

Special Issue Information

Dear Colleagues,

Blue carbon (BC) ecosystems consist of mangroves, seagrasses, and salt marshes, which play a crucial role worldwide by providing habitats for wildlife and a range of ecosystem services to coastal organisms. They play a key role in the global carbon cycle by reducing greenhouse gas emissions and mitigating the impacts of climate change. However, they have rapidly declined due to conversion to aquaculture, overexploitation, and removal for human settlements. Monitoring BC ecosystems remains challenging, as it requires accounting for seasonal dynamics in tidal fluctuation, changes in relative abundance of species, and complex community types. Remote sensing-based approaches have been proven to be suitable for effectively mapping and monitoring BC ecosystems. They have lower costs, higher accuracy, and easier repeatability and cover wider areas. However, they still have limitations caused by clouds and limited coverage of airborne datasets. Recent advances in geospatial technologies using multisensor data fusion and GIS-based approaches, integration of optical and synthetic aperture radar (SAR), LiDAR, and UAV data combined with advanced machine learning techniques in mapping and monitoring BC ecosystems can overcome these limitations, and encourage new approaches to develop more accurate mapping techniques.

In this context, this Special Issue encourages authors to share recent advances in geospatial technologies for monitoring BC ecosystems, such as their structures, species, biomass retrievals, and carbon stock estimation, using multisource remote sensing and GIS data combined with advanced techniques in geospatial technology, UAV photogrammetry, advanced artificial intelligence (AI) techniques (i.e., deep learning, machine learning, hybrid and ensemble techniques, transfer learning, tensor learning for classification and regression tasks, and high-performance computing) to extract knowledge from multisource earth observation data.

We kindly invite the scientific geospatial and remote sensing communities to contribute original research and review papers to this Special Issue addressing the following topics:

  1. Advanced technologies in remote sensing, GIS, UAV photogrammetry, and deep learning for monitoring BC ecosystems;
  2. Advanced machine learning techniques in estimating above-ground biomass (AGB) and BC stock estimation;
  3. Advanced techniques in multisource remote sensing data for biophysical parameter retrievals and carbon stock estimation;
  4. Advanced techniques in geospatial data in monitoring BC ecosystem changes;
  5. Advanced techniques in monitoring coastal aquatic species and structure;
  6. Multisensor data fusion techniques in monitoring BC communities;
  7. Real-world case studies with findings of clear interests to the geospatial technologies in monitoring BC communities.

Finally, authors are encouraged to share codes and data so that their studies can be easily reproduced and serve as a seed for future improvements.

Dr. Tien Dat Pham
Prof. Kunihiko Yoshino
Dr. Yudi Setiawan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly 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 1700 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

  • blue carbon ecosystems
  • mangroves
  • seagrass meadows
  • salt marshes
  • multi-sensor
  • data fusion
  • GIS
  • machine learning
  • biophysical retrievals
  • above-ground biomass
  • blue carbon stocks

Published Papers (4 papers)

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Research

30 pages, 8810 KiB  
Article
Assessing Potential Climatic and Human Pressures in Indonesian Coastal Ecosystems Using a Spatial Data-Driven Approach
by Adam Irwansyah Fauzi, Anjar Dimara Sakti, Balqis Falah Robbani, Mita Ristiyani, Rahiska Tisa Agustin, Emi Yati, Muhammad Ulin Nuha, Nova Anika, Raden Putra, Diyanti Isnani Siregar, Budhi Agung Prasetyo, Atriyon Julzarika and Ketut Wikantika
ISPRS Int. J. Geo-Inf. 2021, 10(11), 778; https://doi.org/10.3390/ijgi10110778 - 15 Nov 2021
Cited by 12 | Viewed by 4317
Abstract
Blue carbon ecosystems are key for successful global climate change mitigation; however, they are one of the most threatened ecosystems on Earth. Thus, this study mapped the climatic and human pressures on the blue carbon ecosystems in Indonesia using multi-source spatial datasets. Data [...] Read more.
Blue carbon ecosystems are key for successful global climate change mitigation; however, they are one of the most threatened ecosystems on Earth. Thus, this study mapped the climatic and human pressures on the blue carbon ecosystems in Indonesia using multi-source spatial datasets. Data on moderate resolution imaging spectroradiometer (MODIS) ocean color standard mapped images, VIIRS (visible, infrared imaging radiometer suite) boat detection (VBD), global artificial impervious area (GAIA), MODIS surface reflectance (MOD09GA), MODIS land surface temperature (MOD11A2), and MODIS vegetation indices (MOD13A2) were combined using remote sensing and spatial analysis techniques to identify potential stresses. La Niña and El Niño phenomena caused sea surface temperature deviations to reach −0.5 to +1.2 °C. In contrast, chlorophyll-a deviations reached 22,121 to +0.5 mg m−3. Regarding fishing activities, most areas were under exploitation and relatively sustained. Concerning land activities, mangrove deforestation occurred in 560.69 km2 of the area during 2007–2016, as confirmed by a decrease of 84.9% in risk-screening environmental indicators. Overall, the potential pressures on Indonesia’s blue carbon ecosystems are varied geographically. The framework of this study can be efficiently adopted to support coastal and small islands zonation planning, conservation prioritization, and marine fisheries enhancement. Full article
(This article belongs to the Special Issue Advances in Geospatial Technologies in Monitoring Blue Carbon Ecosystems Using Multiple Source Remote Sensing and GIS Data)
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21 pages, 6103 KiB  
Article
Detecting Multi-Decadal Changes in Seagrass Cover in Tauranga Harbour, New Zealand, Using Landsat Imagery and Boosting Ensemble Classification Techniques
by Nam-Thang Ha, Merilyn Manley-Harris, Tien-Dat Pham and Ian Hawes
ISPRS Int. J. Geo-Inf. 2021, 10(6), 371; https://doi.org/10.3390/ijgi10060371 - 31 May 2021
Cited by 17 | Viewed by 3800
Abstract
Seagrass provides a wide range of essential ecosystem services, supports climate change mitigation, and contributes to blue carbon sequestration. This resource, however, is undergoing significant declines across the globe, and there is an urgent need to develop change detection techniques appropriate to the [...] Read more.
Seagrass provides a wide range of essential ecosystem services, supports climate change mitigation, and contributes to blue carbon sequestration. This resource, however, is undergoing significant declines across the globe, and there is an urgent need to develop change detection techniques appropriate to the scale of loss and applicable to the complex coastal marine environment. Our work aimed to develop remote-sensing-based techniques for detection of changes between 1990 and 2019 in the area of seagrass meadows in Tauranga Harbour, New Zealand. Four state-of-the-art machine-learning models, Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boost (XGB), and CatBoost (CB), were evaluated for classification of seagrass cover (presence/absence) in a Landsat 8 image from 2019, using near-concurrent Ground-Truth Points (GTPs). We then used the most accurate one of these models, CB, with historic Landsat imagery supported by classified aerial photographs for an estimation of change in cover over time. The CB model produced the highest accuracies (precision, recall, F1 scores of 0.94, 0.96, and 0.95 respectively). We were able to use Landsat imagery to document the trajectory and spatial distribution of an approximately 50% reduction in seagrass area from 2237 ha to 1184 ha between the years 1990–2019. Our illustration of change detection of seagrass in Tauranga Harbour suggests that machine-learning techniques, coupled with historic satellite imagery, offers potential for evaluation of historic as well as ongoing seagrass dynamics. Full article
(This article belongs to the Special Issue Advances in Geospatial Technologies in Monitoring Blue Carbon Ecosystems Using Multiple Source Remote Sensing and GIS Data)
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17 pages, 4019 KiB  
Article
Quantitative Assessment and Driving Force Analysis of Mangrove Forest Changes in China from 1985 to 2018 by Integrating Optical and Radar Imagery
by Yuhan Zheng and Wataru Takeuchi
ISPRS Int. J. Geo-Inf. 2020, 9(9), 513; https://doi.org/10.3390/ijgi9090513 - 25 Aug 2020
Cited by 13 | Viewed by 3030
Abstract
Mangrove ecosystems are valuable, yet vulnerable, and therefore they have been an important subject of protection and restoration in China. Reliable information on long-term China mangrove dynamics is lacking but vital to analyze the driving forces and evaluate the efforts of mangrove conversation. [...] Read more.
Mangrove ecosystems are valuable, yet vulnerable, and therefore they have been an important subject of protection and restoration in China. Reliable information on long-term China mangrove dynamics is lacking but vital to analyze the driving forces and evaluate the efforts of mangrove conversation. This study aims to quantify the conversions among mangroves and other land covers with high accuracy. The updated mangrove base map for 2018 was produced by integrating Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar 2 (PALSAR-2) yearly mosaics and Landsat imagery with an overall accuracy of 95.23 ± 6.02%. Then, a novel approach combining map-to-image and image-to-image methods was proposed to detect the changed pixels in mangrove forests from 1985 to 2018. The mangrove base map was adopted to mask the images from other years. To determine the changed pixels, the differencing values in the masked area between two images were calculated and compared with the corresponding thresholds. Based on the changed pixels, the possible driving forces were analyzed and associated with socioeconomic development. The resultant mangrove dynamics demonstrated that mangrove forests in China experienced a tendency of loss first and recovery later during the past 30 years. Most mangrove gains came from aquaculture and mudflat, whilst losses were due to the built-up construction and aquaculture reclamation. These conversions indicated that mangrove deforestations were mainly due to human-induced destruction, while the recoveries were strongly associated with conservation and restoration actions. Full article
(This article belongs to the Special Issue Advances in Geospatial Technologies in Monitoring Blue Carbon Ecosystems Using Multiple Source Remote Sensing and GIS Data)
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27 pages, 8587 KiB  
Article
Mapping Submerged Aquatic Vegetation along the Central Vietnamese Coast Using Multi-Source Remote Sensing
by Tran Ngoc Khanh Ni, Hoang Cong Tin, Vo Trong Thach, Cédric Jamet and Izuru Saizen
ISPRS Int. J. Geo-Inf. 2020, 9(6), 395; https://doi.org/10.3390/ijgi9060395 - 16 Jun 2020
Cited by 7 | Viewed by 5337
Abstract
Submerged aquatic vegetation (SAV) in the Khanh Hoa (Vietnam) coastal area plays an important role in coastal communities and the marine ecosystem. However, SAV distribution varies widely, in terms of depth and substrate types, making it difficult to monitor using in-situ measurement. Remote [...] Read more.
Submerged aquatic vegetation (SAV) in the Khanh Hoa (Vietnam) coastal area plays an important role in coastal communities and the marine ecosystem. However, SAV distribution varies widely, in terms of depth and substrate types, making it difficult to monitor using in-situ measurement. Remote sensing can help address this issue. High spatial resolution satellites, with more bands and higher radiometric sensitivity, have been launched recently, including the Vietnamese Natural Resources, Environment, and Disaster Monitoring Satellite (VNREDSat-1) (V1) sensor from Vietnam, launched in 2013. The objective of the study described here was to establish SAV distribution maps for South-Central Vietnam, particularly in the Khanh Hoa coastal area, using Sentinel-2 (S2), Landsat-8, and V1 imagery, and then to assess any changes to SAV over the last ten years, using selected historical data. The satellite top-of-atmosphere signals were initially converted to radiance, and then corrected for atmospheric effects. This treated signal was then used to classify Khanh Hoa coastal water substrates, and these classifications were evaluated using 101 in-situ measurements, collected in 2017 and 2018. The results showed that the three satellites could provide high accuracy, with Kappa coefficients above 0.84, with V1 achieving over 0.87. Our results showed that, from 2008 to 2018, SAV acreage in Khanh Hoa was reduced by 74.2%, while gains in new areas compensated for less than half of these losses. This is the first study to show the potential for using V1 and S2 data to assess the distribution status of SAV in Vietnam, and its outcomes will contribute to the conservation of SAV beds, and to the sustainable exploitation of aquatic resources in the Khanh Hoa coastal area. Full article
(This article belongs to the Special Issue Advances in Geospatial Technologies in Monitoring Blue Carbon Ecosystems Using Multiple Source Remote Sensing and GIS Data)
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