Spatial-temporal dynamics of mangrove extent in Quang Ninh Province over 33 years (1987–2020): Implications toward mangrove management in Vietnam

Nguyen, Hai-Hoa; Nguyen, Cuong Trong; Vo, Nguyen Dai

doi:10.1016/j.rsma.2022.102212

Cited by 3 publications

(1 citation statement)

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“…When extracting mangroves in a large area, there is often a large time span between images due to missing data. During this period, mangroves are constantly affected by both natural and human factors, such as natural growth, disasters, human deforestation, invasive alien species, and artificial restoration [36,37], resulting in some differences between the extracted information on mangroves and the actual situation. For example, the changes in the mangroves in the two regions in different months are shown in Figure 6.…”

Section: Analysis Of Differences In Mangrove Extraction Resultsmentioning

confidence: 99%

Extraction the Spatial Distribution of Mangroves in the Same Month Based on Images Reconstructed with the FSDAF Model

You,

Deng,

Liu

et al. 2023

Forests

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Mangroves have extremely high economic and ecological value. Through remote sensing, the spatial distribution of and spatiotemporal changes in mangroves can be accurately obtained, providing data support for the sustainable development of coastal wetlands. However, due to the cloudy and rainy conditions in the growing areas of mangroves, there are relatively few high-quality image data available, resulting in a time difference between regional mosaic images, with a maximum difference of several months, which has a certain impact on accuracy when extracting the spatial distribution of mangroves in some regions. At present, most regional mangrove research has ignored the impact of the time difference between mosaic images, which not only leads to inaccurate monitoring results of mangroves’ spatial distribution and dynamic changes but also limits the frequency of monitoring of regional mangrove dynamic changes to an annual scale, making it difficult to achieve more refined time scales. Based on this, this study takes the coastal mangrove distribution area in China as the research area, uses Landsat 8 and MODIS images as basic data, reconstructs the January 2021 images of the research area based on the FSDAF model, and uses a random forest algorithm to extract the spatial distribution of mangrove forests and analyze the landscape pattern. The results showed that the fused image based on the FSDAF model was highly similar to the validation image, with an R value of 0.85, showing a significant positive correlation, indicating that the fused image could replace the original image for mangrove extraction in the same month. The overall accuracy of the spatial distribution extraction of mangroves based on the fused image was 89.97%. The high sample separation and spectral curve changes highly similar to the validation image indicate that the fused image can more accurately obtain the spatial distribution of mangroves. Compared to the original image, the fused image based on the FSDAF model is closer to the validation image, and the fused image can reflect the changes in mangroves in time series, thus achieving accurate acquisition of dynamic change information in a short time span. It provides data and methodological support for future monitoring of dynamic changes in large-scale mangroves. The total area of mangroves in China in January 2021 based on the fused image was 27,122.4 ha, of which Guangdong had the largest mangrove area, with 12,098.34 ha, while Macao had the smallest mangrove area of only 16.74 ha. At the same time, the mangroves in Guangdong and Guangxi had a high degree of fragmentation and were severely disturbed, requiring strengthened protection efforts, while the mangroves in Hong Kong, Zhejiang, and Macao had regular shapes, benefiting from local active artificial restoration.

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Section: Analysis Of Differences In Mangrove Extraction Resultsmentioning

confidence: 99%