Monitoring of mangrove dynamic change in Beibu Gulf of Guangxi based on reconstructed time series images

Qixu You; Weixi Deng; Xu Tang; Yao Liu; Peng Lei; Jianjun Chen; Haotian You

doi:10.1016/j.scitotenv.2024.170395

Monitoring of mangrove dynamic change in Beibu Gulf of Guangxi based on reconstructed time series images

Sci Total Environ. 2024 Mar 20:917:170395. doi: 10.1016/j.scitotenv.2024.170395. Epub 2024 Feb 1.

Authors

Qixu You¹, Weixi Deng², Xu Tang¹, Yao Liu¹, Peng Lei³, Jianjun Chen⁴, Haotian You⁵

Affiliations

¹ College of Geomatics and Geoinformation, Guilin University of Technology, No. 12 Jian'gan Road, Guilin 541006, China.
² Key Laboratory of Digital Cartography and Land Information Application, Third Institute of Geographic Information Mapping, Ministry of Natural Resources, Chengdu 610000, China.
³ The School of Hydraulic Engineering, Guangxi Vocational College of Water Resources and Electric Power, No. 99 Chang'yi Road, Nanning 530023, China.
⁴ College of Geomatics and Geoinformation, Guilin University of Technology, No. 12 Jian'gan Road, Guilin 541006, China; Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, No. 12 Jian'gan Road, Guilin 541004, China.
⁵ College of Geomatics and Geoinformation, Guilin University of Technology, No. 12 Jian'gan Road, Guilin 541006, China; Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, No. 12 Jian'gan Road, Guilin 541004, China. Electronic address: youht@glut.edu.cn.

PMID: 38307277
DOI: 10.1016/j.scitotenv.2024.170395

Abstract

In the mangrove growth area, the availability of high-quality optical images is limited throughout the year due to cloud cover, precipitation, and sensor revisiting cycles. In the worst-case scenario, severe conditions may lead to the unavailability of, causing variations in monitoring times for mangroves across different years. This significantly impacts the accuracy of long-term sequence monitoring of mangrove dynamics. To monitor long-term dynamic changes in mangrove spatial distribution, area, and ecology we reconstructed comprehensive time series images from 2000 to 2020 based on Landsat, Sentinel-2, and moderate-resolution imaging spectroradiometer (MODIS) images. We employed neighborhood-similar pixel interpolator (NSPI) strip filling, Fmask and temporal NSPI cloud-removal and filling, and FSDAF model to monitor the long-term dynamic changes in mangrove spatial distribution, area, and ecology. All three methods effectively reconstructed the images, with the FSDAF model exhibiting the greatest accuracy. The reconstructed images suggested that the mangroves demonstrated an overall growth trend from 2000 to 2020, with an increase from 3796.74 ha to 7676.89 ha, an increase of approximately 3880.15 ha over 20 years. Despite this growth, the number of patches gradually increased, the degree of fragmentation consistently worsened, and the landscape shape gradually became irregular. The study area demonstrated pronounced overall heterogeneity, with a gradually increase in the degree of dispersion, indicating evident overall instability. Additionally, the centroid of the mangroves moved towards the ocean, which complicated their growth environment and posed a serious threat to their growth and recovery. Anthropogenic disturbance is the main factor driving changes in mangrove areas. Driving factors that affected the change in mangrove areas were ranked as follows: GDP > highway mileage > population density > precipitation > humidity > wind speed > sunshine > temperature. The results of this study provide comprehensive data for the protection and restoration of mangroves.

Keywords: FSDAF spatiotemporal fusion model; Multidimensional ecological analysis; NSPI stripe filling; Spatial distribution; Temporal NSPI cloud-removal and filling.