Classification Methods for Mapping Mangrove Extents and Drivers of Change in Thanh Hoa Province, Vietnam during 2005-2018

Mangrove forests have been globally recognised as their vital functions in preventing coastal erosion, mitigating effects of wave actions and protecting coastal habitats and adjacent shoreline land-uses from extreme coastal events. However, these functions are under severe threats due to the rapid growth of population, intensive shrimp farming and the increased intensity of severe storms in Hau Loc and Nga Son districts, Thanh Hoa province. This research was conducted to monitor spatial-temporal changes in mangrove extents using Landsat and Sentinel imageries from 2005 to 2018. Unsupervised and supervised classification methods and vegetation indices were tested to select the most suitable classification method for study sites, then to quantify mangrove extents and their changes in selected years. The findings show that supervised classification was the most suitable in study sites compared to vegetation indices and unsupervised classification. Mangrove forest extents increased by 7.5 %, 38.6 %, and 47.8 % during periods of 2005 - 2010, 2010 - 2015 and 2015 - 2018, respectively. An increase of mangrove extents resulted from national programs of mangrove rehabilitation and restoration during 2005- 2018, increased by 278.0 ha (123.0 %)

Monitoring temporal changes in coastal mangroves to understand the impacts of climate change : Red Sea, Egypt

Funding Information: The authors would like to thank Dr Samira R Mansour for help in reviewing the paper. The present paper is not funded from any agency or organization, all work completed in the Suez Canal University, Ismailia Egypt. Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB). Publisher Copyright: © 2023, The Author(s).Peer reviewedPublisher PD

Mangrove forest mapping through remote sensing imagery: study case for Buenaventura, Colombia

[EN] Mangroves are plant communities of high ecological and economic importance for coastal regions. This investigation provides a methodology for mapping Mangrove forests through remote sensing images in a semidetail scale (1:25,000) in a sector of the municipality of Buenaventura, Colombia. A Sentinel 2 image and 2017 highresolution ortophotomosaic of the municipality were used for the mangrove cartography, using QGIS software, spectral analysis was performed and supervised classification was established using Maximum Likelihood algorithm. Results shown that mangrove is the most representative cover in the study area whit 7,264.21 ha in total extension (59.21% of total area), the development classification got a thematic accuracy of 80% and 0.70 in Kappa index. The used methodology can be used as an academic and research reference for mangrove semi-detail mapping in the world.[ES] Los manglares son comunidades vegetales de alta importancia ecología y económica para las regiones costeras. La presente investigación proporciona un método para determinar la cartografía de bosques manglar mediante imágenes de sensores remotos a escala 1:25.000 en un sector del municipio de Buenaventura, Colombia; para la cartografía de bosques de manglar se empleó una imagen satelital Sentinel 2 y una ortofotografía de alta resolución del año 2017; usando el software libre QGIS, se realizó los análisis espectrales, se estableció una clasificación supervisada mediante el algoritmo de máxima verosimilitud. Los resultados obtenidos muestran que la cobertura de manglar es la de mayor representatividad en el área de estudio con una extensión total de 7.264,21 ha (59,21% del área total), la clasificación desarrollada presentó una exactitud temática global de 80% e índice de Kappa de 0,70. El método empleado sirve como un referente sobre la cartografía de bosques de manglar en el mundo.Perea-Ardila, MA.; Oviedo-Barrero, F.; Leal-Villamil, J. (2019). Cartografía de bosques de manglar mediante imágenes de sensores remotos: estudio de caso Buenaventura, Colombia. Revista de Teledetección. (53):73-86. https://doi.org/10.4995/raet.2019.11684SWORD73865

Mapping Mangrove Forests Based on Multi-Tidal High-Resolution Satellite Imagery

Mangrove forests, which are essential for stabilizing coastal ecosystems, have been suffering from a dramatic decline over the past several decades. Mapping mangrove forests using satellite imagery is an efficient way to provide key data for mangrove forest conservation. Since mangrove forests are periodically submerged by tides, current methods of mapping mangrove forests, which are normally based on single-date, remote-sensing imagery, often underestimate the spatial distribution of mangrove forests, especially when the images used were recorded during high-tide periods. In this paper, we propose a new method of mapping mangrove forests based on multi-tide, high-resolution satellite imagery. In the proposed method, a submerged mangrove recognition index (SMRI), which is based on the differential spectral signature of mangroves under high and low tides from multi-tide, high-resolution satellite imagery, is designed to identify submerged mangrove forests. The proposed method applies the SMRI values, together with textural features extracted from high-resolution imagery and geographical features of mangrove forests, to an object-based support vector machine (SVM) to map mangrove forests. The proposed method was evaluated via a case study with GF-1 images (high-resolution satellites launched by China) in Yulin City, Guangxi Zhuang Autonomous Region of China. The results show that our proposed method achieves satisfactory performance, with a kappa coefficient of 0.86 and an overall accuracy of 94%, which is better than results obtained from object-based SVMs that use only single-date, remote sensing imagery