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:  Combination with Other Sensors 
1. Abstract of Cited Paper (BIB001):  This study integrates data from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Light Detection and Ranging (LiDAR) to map the spatial configuration of coastal wetland landscapes. We test data sources in their ability to capture marsh features and sources are combined to improve wetland characterizations. The complex ecosystem characteristics of the Wells Maine National Estuarine Research Reserve marshes and surrounding areas provide an ideal study site. The results of this study suggest that ASTER visible, near and shortwave infra red spectral bands combined with LiDAR last return signals provide accurate wetland cover maps categorized in broad cover classes. LiDAR contributions are important in areas of elevation and structure variability where multispectral data are unable to distinguish fine scale variations in vegetation and water signals. ASTER's cost effectiveness and spectral range offer a data-rich alternative to expensive high spatial resolution imagery typically used ... 
2. Abstract of Cited Paper (BIB002):  The distribution of mangroves and other tropical and subtropical vegetation in the Greater Everglades Ecosystem is largely dependent on subtle variations in elevation, with mangroves occupying the lowest elevations. Combining a digital terrain model (DTM) derived from last-return light detection and ranging (LiDAR) data with IKONOS multispectral imagery in a maximum likelihood supervised classification resulted in a 7.1% increase in overall classification accuracy among seven classes (red mangrove, black mangrove, tropical hardwood hammock, coastal rock barren vegetation, mudflat, sand/rock and asphalt) compared with using the multispectral imagery alone, and the classification accuracy was improved for all four spectrally similar vegetation classes. A digital canopy model (DCM) was created by subtracting the digital terrain model from a digital surface model derived from LiDAR first returns. The DCM-recorded heights well correlated with mangrove canopy heights measured in the field but were systematicall... 
3. Abstract of Cited Paper (BIB003):  Inventorying and monitoring are imperative to management of vulnerable coastal wetlands. Multi-date and multi-sensor remote sensing offer new capabilities to wetland programmes such as the US National Wetland Inventory. This pilot study focuses on swamp forests and pocosins, marshes, shrub–scrub and invasive Phragmites australis. Combinations of spaceborne multi-date Synthetic Aperture Radar (SAR) imagery and airborne light detection and ranging (LiDAR) elevation (bare earth elevation and vegetation height) were evaluated. Multi-date SAR data (horizontal-horizontal and horizontal-vertical dual polarizations) highlighted physiognomic dynamics, with LiDAR vegetation canopy discerning selected classes. The highest overall accuracy used SAR, LiDAR canopy and digital elevation model (DEM) data (81% κ = 0.744), but not significantly different from the SAR-only classification (81% κ = 0.742). Both classifications exceeded the data combination using SAR data and DEM (66% κ = 0.521) and SAR data with vegetation ca... 
4. Abstract of Cited Paper (BIB004):  Abstract This paper presents a new approach for mapping wetland inundation change using Landsat and LiDAR intensity data. In this approach, LiDAR data were used to derive highly accurate reference subpixel inundation percentage (SIP) maps at the 30-m resolution. The reference SIP maps were then used to establish statistical relationships between SIP and Landsat data. Inundation changes were mapped by applying the derived relationships to Landsat images acquired in different years. This approach was applied to the upper Choptank River sub-watershed to map wetland inundation for average (2005 and 2007), dry (2009), and wet (2010) years. The derived SIP maps revealed large changes in wetland inundation among dry, average, and wet years. Total areas of near complete inundation (SIP > 75%) and high inundation (SIP between 50% and 75%) in the wet year of 2010 were about five and three times of those in the dry year of 2009, respectively. The wet year also had more medium inundated areas (SIP between 25% and 50%) than the average and dry years, but low inundated areas (SIP 
5. Abstract of Cited Paper (BIB005):  tAlthough wetlands play a key role in controlling flooding and nonpoint source pollution, sequesteringcarbon and providing an abundance of ecological services, the inventory and characterization of wetlandhabitats are most often limited to small areas. This explains why the understanding of their ecologicalfunctioning is still insufficient for a reliable functional assessment on areas larger than a few hectares.While LiDAR data and multispectral Earth Observation (EO) images are often used separately to mapwetland habitats, their combined use is currently being assessed for different habitat types. The aim ofthis study is to evaluate the combination of multispectral and multiseasonal imagery and LiDAR data toprecisely map the distribution of wetland habitats. The image classification was performed combiningan object-based approach and decision-tree modeling. Four multispectral images with high (SPOT-5)and very high spatial resolution (Quickbird, KOMPSAT-2, aerial photographs) were classified separately.Another classification was then applied integrating summer and winter multispectral image data andthree layers derived from LiDAR data: vegetation height, microtopography and intensity return. Thecomparison of classification results shows that some habitats are better identified on the winter image andothers on the summer image (overall accuracies = 58.5 and 57.6%). They also point out that classificationaccuracy is highly improved (overall accuracy = 86.5%) when combining LiDAR data and multispectralimages. Moreover, this study highlights the advantage of integrating vegetation height, microtopographyand intensity parameters in the classification process. This article demonstrates that information providedby the synergetic use of multispectral images and LiDAR data can help in wetland functional assessment© 2014 Elsevier B.V. All rights reserved.

Literature Review:
As mentioned above, many scholars have mapped and monitored wetlands using optical remote sensing images, radar data, and LiDAR data separately and obtained satisfactory results. The combination of these three types of remote sensing data was also assessed for different wetland types. Pavri et al. BIB002 combined ASTER and LiDAR data to map the structure of coastal wetlands and obtained accurate results. LiDAR plays an irreplaceable role in the determination of the elevation and structure of landscapes, whereas other techniques have difficulties to identify vegetation and water. Huang et al. BIB005 monitored wetland inundation using Landsat and LiDAR data. In their paper, LiDAR data were first used to calculate the inundation ratio of a 30 m Landsat image; subsequently, the relationship between the inundation ratio and the surface reflectance was determined based on Landsat data. At last, they built an effective inundation model based on Landsat data. Elevation is a key factor affecting the spatial distribution of mangroves. The use of a digital terrain model (DTM) derived from LiDAR data may increase the mangrove map accuracy. Chadwick BIB003 found that the overall accuracy increased by 7.1% when combination of IKONOS and DTM data was used compared with using the IKONOS data alone. Rapinel et al. BIB006 compared the wetland classification accuracy by using LiDAR data in combination with multi-spectral and multi-seasonal images and high spatial resolution images alone and also found that the accuracy clearly increased when using the combination of methods. Chust et al. BIB001 used the combination of LiDAR data with multi-spectral images for mapping wetland habitats and came to similar conclusions. Allen et al. BIB004 reported that multi-date SAR and the combination of multi-date SAR and multi-date LiDAR both obtained a classification with higher accuracy; no clearly difference was found between the two approaches.