Aboveground biomass estimates of tropical mangrove forest using Sentinel-1 SAR coherence data - The superiority of deep learning over a semi-empirical model

Ghosh, Sanatan; Behera, Mukunda Dev

doi:10.1016/j.cageo.2021.104737

Cited by 39 publications

(12 citation statements)

References 57 publications

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“…TensorFlow (Google Inc., Menlo Park, CA, USA) is the most extensively used and popular low-level framework [30]. Keras is a well-known advanced DL framework with TensorFlow as its backend engine [50].…”

Section: Kerasmentioning

confidence: 99%

“…DL models can use multiple successive hidden layers, and the learning process involves determining the parameter values of each layer. In recent years, various geospatial analytic projects have used DL algorithms [30,31]. A study [32] proposed a deep-learning-based water-body-extraction model that exploited sentinel-1 data and flood-related geospatial datasets.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Aboveground Carbon Density of Forests Using Deep Learning and Multisource Remote Sensing

et al. 2022

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Forests are crucial in carbon sequestration and oxygen release. An accurate assessment of forest carbon storage is meaningful for Chinese cities to achieve carbon peak and carbon neutrality. For an accurate estimation of regional-scale forest aboveground carbon density, this study applied a Sentinel-2 multispectral instrument (MSI), Advanced Land Observing Satellite 2 (ALOS-2) L-band, and Sentinel-1 C-band synthetic aperture radar (SAR) to estimate and map the forest carbon density. Considering the forest field-inventory data of eastern China from 2018 as an experimental sample, we explored the potential of the deep-learning algorithms convolutional neural network (CNN) and Keras. The results showed that vegetation indices from Sentinel-2, backscatter and texture characters from ALOS-2, and coherence from Sentinel-1 were principal contributors to the forest carbon-density estimation. Furthermore, the CNN model was found to perform better than traditional models. Results of forest carbon-density estimation validated the improvements effectively by combining the optical and radar data. Compared with traditional regression methods, deep learning has a higher potential for accurately estimating forest carbon density using multisource remote-sensing data.

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Section: Kerasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of Aboveground Carbon Density of Forests Using Deep Learning and Multisource Remote Sensing

et al. 2022

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“…Remote sensing is a science and art that studies an object and to obtain information about a phenomenon through the process of analyzing data using a tool without direct contact with the object being analyzed. This technology has developed rapidly and currently uses satellites to record data and process it to get information [14].…”

Section: Land Cover Analysis Of Nipa Palm Using Unmanned Aerial Vehic...mentioning

confidence: 99%

Potential analysis of Nipa Palm (Nypa Fruticans) in Singkil

Ibrahim

Julianti

Supriana

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Nipa palm (Nypa fruticans) is the plant that grouped in mangrove forest ecosystems which has a great economic potential when developed with a good strategy. The potential of nipa palm in Singkil has not been mapped properly, so that the objective of this research was to analyze the potential of nipa palm in Singkil. Data in this research were obtained directly from observations in the form of ground checks at several research locations and aerial photo data taken directly in the field using UAV vehicles. The results of data obtained through observation and documentation are then processed before being used to validate the results of image data processing. The use of UAV data provides a solution for mapping and monitoring nipa palm with high accuracy as well as a solution to the limited availability of nipa palm data in Singkil. Nipa palm was found spread along the estuary of the Lae Singkil River with total area 768.08 Ha. In the aerial photo taken in Kampong Pulosarok covering an area of 37.01 Ha, it was found that the land covering of nipa palm was 20.12 Ha or 54%. Whereas, in the aerial photo taken in Kampong Kilangan covering an area of 34.41 ha, it was found that the land covering of nipa palm was 22.16 ha or 64%. From the data, it can conclude that the nipa palm potential in Singkil is large.

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“…Remote sensing technology allows monitoring of mangrove forests to be faster, more efficient, and with a broader scope than with conventional methods [86]. Remote sensing has been used to identify attributes such as biomass [87][88][89], carbon stocks [90,91], species [92][93][94][95], and mangrove health [96][97][98].…”

Section: Mangrove Extentmentioning

confidence: 99%

Mangrove Forest Landcover Changes in Coastal Vietnam: A Case Study from 1973 to 2020 in Thanh Hoa and Nghe An Provinces

Nguyen

Hardy

Le³

et al. 2021

Forests

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Mangrove forests can ameliorate the impacts of typhoons and storms, but their extent is threatened by coastal development. The northern coast of Vietnam is especially vulnerable as typhoons frequently hit it during the monsoon season. However, temporal change information in mangrove cover distribution in this region is incomplete. Therefore, this study was undertaken to detect change in the spatial distribution of mangroves in Thanh Hoa and Nghe An provinces and identify reasons for the cover change. Landsat satellite images from 1973 to 2020 were analyzed using the NDVI method combined with visual interpretation to detect mangrove area change. Six LULC classes were categorized: mangrove forest, other forests, aquaculture, other land use, mudflat, and water. The mangrove cover in Nghe An province was estimated to be 66.5 ha in 1973 and increased to 323.0 ha in 2020. Mangrove cover in Thanh Hoa province was 366.1 ha in 1973, decreased to 61.7 ha in 1995, and rose to 791.1 ha in 2020. Aquaculture was the main reason for the loss of mangroves in both provinces. Overall, the percentage of mangrove loss from aquaculture was 42.5% for Nghe An province and 60.1% for Thanh Hoa province. Mangrove restoration efforts have contributed significantly to mangrove cover, with more than 1300 ha being planted by 2020. This study reveals that improving mangrove restoration success remains a challenge for these provinces, and further refinement of engineering techniques is needed to improve restoration outcomes.

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Aboveground biomass estimates of tropical mangrove forest using Sentinel-1 SAR coherence data - The superiority of deep learning over a semi-empirical model

Cited by 39 publications

References 57 publications

Estimation of Aboveground Carbon Density of Forests Using Deep Learning and Multisource Remote Sensing

Estimation of Aboveground Carbon Density of Forests Using Deep Learning and Multisource Remote Sensing

Potential analysis of Nipa Palm (Nypa Fruticans) in Singkil

Mangrove Forest Landcover Changes in Coastal Vietnam: A Case Study from 1973 to 2020 in Thanh Hoa and Nghe An Provinces

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