You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Review of Wetland Remote Sensing 
Chapter Title:  Other Themes 
1. Abstract of Cited Paper (BIB001):  Mangroves are an assemblage of salt-tolerant woody hydrophytes that are the foundational species along tropical and subtropical coastlines, estuaries, lagoons and rivers. Once covering 75% of the world's tropical and subtropical coastlines, mangrove forests have been declining rapidly throughout the world in the last few decades. Monitoring and modelling mangrove forest growth are critical for their conservation. Salinity is one of the primary limiting factors for mangrove forest growth. In this letter, we report the potential of using hyperspectral remotely sensed data collected in the field for monitoring the photosynthesis rate of red (Rhizophora mangle) and white (Laguncularia racemosa) mangroves with regard to salinity gradient. Using photochemical reflectance index (PRI) as proxy for photosynthetic rate, PRI for both species are strongly related to salinity gradient, indicating the potential of monitoring mangrove forest growth on a regional scale using hyperspectral remote sensing. 
2. Abstract of Cited Paper (BIB002):  Peatlands are important terrestrial carbon stores. Restoration of degraded peatlands to restore ecosystem services is a major area of conservation effort. Monitoring is crucial to judge the success of this restoration. Remote sensing is a potential tool to provide landscape-scale information on the habitat condition. Using an empirical modelling approach, this paper aims to use airborne hyperspectral image data with ground vegetation survey data to model vegetation abundance for a degraded upland blanket bog in the United Kingdom (UK), which is undergoing restoration. A predictive model for vegetation abundance of Plant Functional Types (PFT) was produced using a Partial Least Squares Regression (PLSR) and applied to the whole restoration site. A sensitivity test on the relationships between spectral data and vegetation abundance at PFT and single species level confirmed that PFT was the correct scale for analysis. The PLSR modelling allows selection of variables based upon the weighted regression coefficient of the individual spectral bands, showing which bands have the most influence on the model. These results suggest that the SWIR has less value for monitoring peatland vegetation from hyperspectral images than initially predicted. RMSE values for the validation data range between 10% and 16% cover, indicating that the models can be used as an operational tool, considering the subjective nature of existing vegetation survey results. These predicted coverage images are the first quantitative landscape scale monitoring results to be produced for the site. High resolution hyperspectral mapping of PFTs has the potential to assess recovery of peatland systems at landscape scale for the first time.

Literature Review:
Mangroves are the most important plants along coastlines, estuaries, lagoons, and rivers because of their salt-tolerance. However, the higher salt content will also affect the mangrove health. Song et al. BIB002 used the photochemical reflectance index (PRI) derived from hyperspectral data collected in the field as proxy for the photosynthetic rate to monitor the growth of red (Rhizophora mangle) and white (Laguncularia racemosa) mangroves under different salinity and determined strong correlations for both species. Wong and Fung  used hyperspectral imagery from EO-1 Hyperion and radar data from Environmental Satellite (ENVISAT)/Advanced Synthetic Aperture Radar (ASAR) supplemented with in situ field surveys to explore their capability in monitoring the biophysical conditions of mangrove stands through LAI estimation. They concluded that radar data alone cannot effectively map the mangrove LAI; however, the combination of hyperspectral and radar data leads to an improvement. Ong et al. BIB001 monitored the anthropogenic iron oxide dust on the vegetation using airborne hyperspectral data and found that the dust can be observed within 2 km from the source and that rainfall reduces the dust load. Cole et al. BIB003 modeled the vegetation abundance of peatland using airborne hyperspectral data and field survey data. They proposed an empirical model to predict the vegetation abundance of plant functional types (PFTs) using partial least squares regression (PLSR) and obtained satisfactory results.