NDVI Based Change Detection in Sundarban Mangrove Forest Using Remote Sensing Data

Shimu, Sumona Akter; Aktar, Mouluda; Afjal, Masud Ibn; Nitu, Adiba Mahjabin; Uddin, Md. Palash; Mamun, Md. Al

doi:10.1109/eict48899.2019.9068819

Cited by 26 publications

(9 citation statements)

References 23 publications

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“…Satellite remote sensing images were used to conduct more in-depth research on pine disease identification, and the results showed that the spectral characteristics of pine trees before and after infection are quite different. This proves the feasibility of identifying pine wood nematodes via the relationships among transpiration rate, spectral response, and different vegetation indices [9,10]. Since then, an increasing number of studies have been conducted on the identification of forest disease by different types of remote sensing images [11,12].…”

Section: Introductionmentioning

confidence: 90%

Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms

Qin

Wang

et al. 2021

Remote Sensing

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Pine nematode is a highly contagious disease that causes great damage to the world’s pine forest resources. Timely and accurate identification of pine nematode disease can help to control it. At present, there are few research on pine nematode disease identification, and it is difficult to accurately identify and locate nematode disease in a single pine by existing methods. This paper proposes a new network, SCANet (spatial-context-attention network), to identify pine nematode disease based on unmanned aerial vehicle (UAV) multi-spectral remote sensing images. In this method, a spatial information retention module is designed to reduce the loss of spatial information; it preserves the shallow features of pine nematode disease and expands the receptive field to enhance the extraction of deep features through a context information module. SCANet reached an overall accuracy of 79% and a precision and recall of around 0.86, and 0.91, respectively. In addition, 55 disease points among 59 known disease points were identified, which is better than other methods (DeepLab V3+, DenseNet, and HRNet). This paper presents a fast, precise, and practical method for identifying nematode disease and provides reliable technical support for the surveillance and control of pine wood nematode disease.

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Section: Introductionmentioning

confidence: 90%

Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms

Qin

Wang

et al. 2021

Remote Sensing

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“…The Normalized Difference Vegetation Index (NDVI) vegetation index was applied to map mangrove density. NDVI is a mathematical combination between the red and near-infra-red (NIR) bands [10]. According to [8] and [10], the NDVI calculation formula is: The result of this process is visualized in a map of the vegetation density classification in the TWMK area and its surroundings.…”

Section: Methodsmentioning

confidence: 99%

“…NDVI is a mathematical combination between the red and near-infra-red (NIR) bands [10]. According to [8] and [10], the NDVI calculation formula is: The result of this process is visualized in a map of the vegetation density classification in the TWMK area and its surroundings. This map is then superimposed with a polygon map of the mangrove area in the TWMK area and its surroundings.…”

Section: Methodsmentioning

confidence: 99%

A Mangrove Density Mapping for Sustainable Utilization and Management in Klawalu Mangrove Tourism Park, Sorong City

Frans Pattiasina,

Tjolli,

Ellen Bless

et al. 2024

Jurnal Biotropika

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Data and information on resource utilization supported by vegetation mapping is the basis for developing a sustainable mangrove management strategy. This study aims to map the density of vegetation and identify the use of mangroves by the community in the Klawalu Mangrove Tourism Park (Taman Wisata Mangrove Klawalu-TWMK) Sorong City. The satellite imagery used is Sentinel 2A level 1C. Image pre-processing includes atmospheric correction, geometric correction and image cropping. Determination of the extent and distribution of mangrove vegetation is carried out by applying the color composite method and mangrove boundary delineation. Mangrove vegetation density mapping was carried out by applying the Normalized Difference Vegetation Index (NDVI) method. Identification of the species and intensity of mangrove utilization was carried out through observation and interviews with respondents who were determined purposively. Based on the results of the analysis, the total area of mangrove vegetation in TWMK and its surroundings which is mapped is 144.64 ha. Of this area, 69.01 ha (47.71%) is classified as very dense, 34.96 ha (24.17%) is classified as dense and 24.23 ha (16.75%) is classified as moderate, 12. 94 ha (8.95%) is classified as sparse, and 3.5 ha (2.42%) is classified as very sparse. The intensity of resource utilization from the mangrove ecosystem is quite high, especially the utilization of mangrove wood and coral mining. Awareness/education efforts need to be continued, as well as alternative livelihood solutions to reduce the heavy dependence on mangrove resources.

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“…Among the methods based on spectral vegetation indices, the normalized difference vegetation index (NDVI) has been widely applied [9]. Shimu et al [10] proposed a change Remote Sens. 2023, 15, 2412 2 of 20 detection technique using NDVI to quantify temporary changes in Landsat RS images of mangroves in the Sundarbans from 2011 to 2019.…”

Section: Introductionmentioning

confidence: 99%

Improvements in Forest Segmentation Accuracy Using a New Deep Learning Architecture and Data Augmentation Technique

Jia

Wei

2023

Remote Sensing

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Forests are critical to mitigating global climate change and regulating climate through their role in the global carbon and water cycles. Accurate monitoring of forest cover is, therefore, essential. Image segmentation networks based on convolutional neural networks have shown significant advantages in remote sensing image analysis with the development of deep learning. However, deep learning networks typically require a large amount of manual ground truth labels for training, and existing widely used image segmentation networks struggle to extract details from large-scale high resolution satellite imagery. Improving the accuracy of forest image segmentation remains a challenge. To reduce the cost of manual labelling, this paper proposed a data augmentation method that expands the training data by modifying the spatial distribution of forest remote sensing images. In addition, to improve the ability of the network to extract multi-scale detailed features and the feature information from the NIR band of satellite images, we proposed a high-resolution forest remote sensing image segmentation network by fusing multi-scale features based on double input. The experimental results using the Sanjiangyuan plateau forest dataset show that our method achieves an IoU of 90.19%, which outperforms prevalent image segmentation networks. These results demonstrate that the proposed approaches can extract forests from remote sensing images more effectively and accurately.

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NDVI Based Change Detection in Sundarban Mangrove Forest Using Remote Sensing Data

Cited by 26 publications

References 23 publications

Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms

Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms

A Mangrove Density Mapping for Sustainable Utilization and Management in Klawalu Mangrove Tourism Park, Sorong City

Improvements in Forest Segmentation Accuracy Using a New Deep Learning Architecture and Data Augmentation Technique

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