Generalized Composite Mangrove Index for Mapping Mangroves Using Sentinel-2 Time Series Data

Abstract

Monitoring mangroves is critical to protect the coastal ecosystems. Some studies resorted to remote sensing for constructing mangrove indices (MIs). However, there are still some drawbacks in existing MIs. On the one hand, difficulty still persists in distinguishing mangroves from non-mangrove vegetation and non-vegetated areas at the same time. On the other hand, the existing MIs have not fully utilized the phenological trajectories, which can greatly help to distinguish mangroves from other land covers. To overcome these issues, we built a novel mangrove index, namely generalized composite mangrove index (GCMI) by compositing vegetation indices (VIs) and water indices (WIs) based on Sentinel-2 time series data. Firstly, to determine the optimal indices, a similarity trend distance (ST distance) measure was proposed based on Pearson correlation coefficient and dynamic time warping (DTW). Secondly, in order to optimize the weights of selected indices, a population reconstruction genetic algorithm (PRGA) was designed. Finally, mangroves were mapped by feeding the time series of GCMI into random forest (RF) classifier. Experiments conducted over three areas along the southern coast of China demonstrate that: 1) GCMI enhances the separability between mangroves and other land covers compared to the existing VIs, WIs, and MIs, with an averaged OA of 91.45%; 2) ST distance outperforms Euclidean distance, Cosine distance, Pearson correlation coefficient, and DTW in optimizing the weights of GCMI; and 3) PRGA greatly improves the probability of attaining global optimal result. The innovation lies in the presented GCMI considering both the vegetation trajectory information and water inundation using time series.

LinkNet-Spectral-Spatial-Temporal Transformer Based on Few-Shot Learning for Mangrove Loss Detection with Small Dataset

“…The field condition of the mangrove ecosystem is muddy [21] and is affected by tides [22] and dense forests [23], as well as other conditions. These are the biggest challenges for direct mangrove surveys in large areas [24]. In recent years, satellite imagery has become an important tool for monitoring mangrove ecosystems worldwide.…”

“…Sentinel-2 is an accessible passive remote-sensing system with global coverage that has 13 bands with varying spatial resolutions (10, 20, and 60 m). Several studies have effectively utilized machine-learning and deep-learning methods with Sentinel-2 data to understand the condition of mangroves [22,24,[27][28][29][30][39][40][41][42]. The FCN algorithm can extract more refined deep features and contribute to determining the spatial-spectral pattern of the images.…”

Spatio-Temporal Evolution Monitoring and Analysis of Tidal Flats in Beibu Gulf From 1987 to 2021 Using Multisource Remote Sensing

“…With improvements in the spectral, spatial, and temporal resolutions of remote sensing sensors, remote sensing technology is becoming increasingly suitable (WorldView, GeoEye-1, Beijing-3, et al) can be used for highprecision tidal flat extraction at the regional scale; however, the data coverage is limited and expensive, and the performance of the application is low in the large-scale range [20][21][22]. Currently, the Landsat and Sentinel series of opensource and easily accessible satellite images are often used to acquire large-scale tidal flats [1,2,15,[23][24][25].…”

Studying How Machine Learning Maps Mangroves in Moderate-Resolution Satellite Images

“…The presence of mangrove plants holds significant ecological and economic importance [5], including their role as coastline stabilizers, erosion mitigators, storm protectors, flood regulators, carbon absorbers, water quality maintainers, and breeding grounds for marine species and fauna [1], [6]. Hence, the conservation and wise use of the mangrove ecosystem have garnered global attention [7]. Mangrove mapping that is reliable and precise serves as the foundation and precursor for conservation and restoration efforts.…”

“…However, mangrove habitats are typically submerged by tidal seawater and are often distributed in hard-to-reach areas [1], [5]. This results in large-scale monitoring through field surveys becoming inefficient [7]. The advent of remote sensing technology, where objects can be observed from a distance without direct contact [1], coupled with geographic information systems (GIS), has become a crucial and convenient tool for regular mangrove ecosystem monitoring [8].…”