A Review of Wetland Remote Sensing <s> Wetland Species Identification <s> A study was conducted to map the plant vigour gradient using hyperspectral imagery combined with field‐collected seasonal reflectance spectra of marsh species in a fragmented coastal wetland. Marsh surface types were identified by classifying a low‐tide hyperspectral image of New Jersey Meadowlands acquired by Airborne Imaging Spectroradiometer for Applications (AISA) in October 2000. Reflectance spectra of dominant marsh species and seasonal spectra of Phragmites australis from April to October in 2003 were measured in the field. The separability of field‐collected spectra of marsh species was tested and determined using non‐parametric U‐test. The spectra of Phragmites in greening‐up phases were used as the surrogate to determine the vigour gradient of common reed stands. The results demonstrate that the reflectance spectra of one healthy stand of Phragmites sampled across the growing season did provide equivalent signatures of pure physiognomic types, which can be used to determine the plant vigour grad... <s> BIB001 </s> A Review of Wetland Remote Sensing <s> Wetland Species Identification <s> As a first step in developing classification procedures for remotely acquired hyperspectral mapping of mangrove canopies, we conducted a laboratory study of mangrove leaf spectral reflectance at a study site on the Caribbean coast of Panama, where the mangrove forest canopy is dominated by Avicennia germinans, Laguncularia racemosa, and Rhizophora mangle. Using a high-resolution spectrometer, we measured the reflectance of leaves collected from replicate trees of three mangrove species growing in productive and physiologically stressful habitats. The reflectance data were analysed in the following ways. First, a one-way ANOVA was performed to identify bands that exhibited significant differences (P value<0.01) in the mean reflectance across tree species. The selected bands then formed the basis for a linear discriminant analysis (LDA) that classified the three types of mangrove leaves. The contribution of each narrow band to the classification was assessed by the absolute value of standardised coefficients associated with each discriminant function. Finally, to investigate the capability of hyperspectral data to diagnose the stress condition across the three mangrove species, four narrow band ratios (R 695/R 420, R 605/R 760, R 695/R 760, and R 710/R 760 where R 695 represents reflectance at wavelength of 695nm, and so on) were calculated and compared between stressed and non-stressed tree leaves using ANOVA. Results indicate a good discrimination was achieved with an average kappa value of 0.9. Wavebands at 780, 790, 800, 1480, 1530, and 1550 nm were identified as the most useful bands for mangrove species classification. At least one of the four reflectance ratio indices proved useful in detecting stress associated with any of the three mangrove species. Overall, hyperspectral data appear to have great potential for discriminating mangrove canopies of differing species composition and for detecting stress in mangrove vegetation. <s> BIB002 </s> A Review of Wetland Remote Sensing <s> Wetland Species Identification <s> Mangrove ecosystems dominate the coastal wetlands of tropical and subtropical regions throughout the world. They provide various ecological and economical ecosystem services contributing to coastal erosion protection, water filtration, provision of areas for fish and shrimp breeding, provision of building material and medicinal ingredients, and the attraction of tourists, amongst many other factors. At the same time, mangroves belong to the most threatened and vulnerable ecosystems worldwide and experienced a dramatic decline during the last half century. International programs, such as the Ramsar Convention on Wetlands or the Kyoto Protocol, underscore the importance of immediate protection measures and conservation activities to prevent the further loss of mangroves. In this context, remote sensing is the tool of choice to provide spatio-temporal information on mangrove ecosystem distribution, species differentiation, health status, and ongoing changes of mangrove populations. Such studies can be based on various sensors, ranging from aerial photography to high- and medium-resolution optical imagery and from hyperspectral data to active microwave (SAR) data. Remote-sensing techniques have demonstrated a high potential to detect, identify, map, and monitor mangrove conditions and changes during the last two decades, which is reflected by the large number of scientific papers published on this topic. To our knowledge, a recent review paper on the remote sensing of mangroves does not exist, although mangrove ecosystems have become the focus of attention in the context of current climate change and discussions of the services provided by these ecosystems. Also, climate change-related remote-sensing studies in coastal zones have increased drastically in recent years. The aim of this review paper is to provide a comprehensive overview and sound summary of all of the work undertaken, addressing the variety of remotely sensed data applied for mangrove ecosystem mapping, as well as the numerous methods and techniques used for data analyses, and to further discuss their potential and limitations. <s> BIB003 </s> A Review of Wetland Remote Sensing <s> Wetland Species Identification <s> Visual image interpretation and digital image classification have been used to map and monitor mangrove extent and composition for decades. The presence of a high-spatial resolution hyperspectral sensor can potentially improve our ability to differentiate mangrove species. However, little research has explored the use of pixel-based and object-based approaches on high-spatial hyperspectral datasets for this purpose. This study assessed the ability of CASI-2 data for mangrove species mapping using pixel-based and object-based approaches at the mouth of the Brisbane River area, southeast Queensland, Australia. Three mapping techniques used in this study: spectral angle mapper (SAM) and linear spectral unmixing (LSU) for the pixel-based approaches, and multi-scale segmentation for the object-based image analysis (OBIA). The endmembers for the pixel-based approach were collected based on existing vegetation community map. Nine targeted classes were mapped in the study area from each approach, including three mangrove species: Avicennia marina, Rhizophora stylosa, and Ceriops australis. The mapping results showed that SAM produced accurate class polygons with only few unclassified pixels (overall accuracy 69%, Kappa 0.57), the LSU resulted in a patchy polygon pattern with many unclassified pixels (overall accuracy 56%, Kappa 0.41), and the object-based mapping produced the most accurate results (overall accuracy 76%, Kappa 0.67). Our results demonstrated that the object-based approach, which combined a rule-based and nearest-neighbor classification method, was the best classifier to map mangrove species and its adjacent environments. <s> BIB004 </s> A Review of Wetland Remote Sensing <s> Wetland Species Identification <s> The aim of the present work is to unveil the potential of some of the unexplored remote sensing techniques for mangrove studies. The paper deals with the classification of an Earth Observing–1 Hyperion image of the mangrove area of Bhitarkanika National Park, Odisha, India into mangrove floristic composition classes. Out of 196 calibrated bands of the image, 56 were found to be highly uncorrelated and contained maximum information; therefore, these 56 bands were used for classification. Amongst the three full–pixel classifiers tested in the investigation, Support Vector Machine produced the best results in terms of training pixel accuracy with overall precision of 96.85 %, in comparison to about 70–72.0 % for the other two classifiers. A total of five mangrove classes were obtained – pure or dominant class of Heritiera fomes, mixed class of H. fomes, mixed Excoecaria agallocha with Avicennia officinalis, mixed class of fringing Sonneratia apetala and class comprising of mangrove associates with salt resistant grasses. Post–classification field data also established the same. Pure or dominant classes of H. fomes occupied more than 50 % of the total mangrove vegetation in the forest blocks of the National Park. Spectral profile matching of image pixels with that of in–situ collected canopy reflectance profile revealed good match for H. fomes (pure or dominant stands). Red–edge index, which was a preferred criterion for matching was notably correlated in case of H. fomes and E. agallocha. The outcomes indicated the efficacy of hyperspectral canopy reflectance library for such kind of work. It is hoped that the methodology presented in this paper will prove to be useful and may be followed for producing mangrove floristic maps at finer levels. <s> BIB005
Hyperspectral sensors provide tens to hundreds of narrow bands, which provide detailed wetland vegetation information. They greatly improve the wetland vegetation type identification BIB003 . Mangrove forest is one of the most important ecosystems because of its role in protecting coastal environment. Mangrove species identification provides basic information on wetland inventory and vegetation community changes. Wang and Sousa BIB002 measured the spectral reflectance of mangrove leaves in the laboratory using a high-resolution spectrometer and concluded that the most useful bands for mangrove species identification were the bands at 780, 790, 800, 1480, 1530, and 1550 nm. Kamal and Phinn BIB004 used a CASI-2 hyperspectral dataset with 30 bands and 4 m spatial resolution to map mangrove species at the mouth of Brisbane River in Australia. The results from pixel-based (spectral angle mapper, SAM, and linear spectral unmixing, LSU) and object-based methods were compared and it was concluded that the object-based approach is the best method for mangrove species identification. Kumar et al. BIB005 used an Earth Observing-1 Hyperion image with 196 bands ranging from 357 to 2576 nm to identify the floristic mangrove composition in Bhitarkanika National Park, India, and determined five mangrove classes with satisfactory accuracy. Hyperspectral data ranging from 400 to 2500 nm were used to discriminate 46 vegetation species in tropical wetlands by Prospere et al.  . Subsequent to comparisons with other classification methods, the authors found that the generalized linear model fitted with elastic net penalties (GLMnet) was the best method to discern the plant types in wetlands. Species invasion is a serious type of ecosystem disturbance, which decreases biodiversity and threatens the resident species with extinction. To manage the invasive species, the amount and distribution of them must be understood, which is a challenge using traditional optical sensors. Hyperspectral images provide sufficient spectral detail to resolve this challenge. Artigas and Yang BIB001 used Airborne Imaging Spectroradiometer for Applications (AISA) to map the marsh species in New Jersey Meadowlands of the United States and concluded that Phragmites have a similar reflectance across the growing season, which might be used to map their distribution. Andrew and Ustin  used HyMap imagery with 128 bands and 3 m spatial resolution to map the invasive plants of Lepidium latifolium in three wetlands of California. Classification and regression tree models (CART) and mixture-tuned match filter (MTMF) and spectral physiological indexes were used in their study; satisfactory results were obtained at two wetland sites, with~90% accuracy; however, the results for other sites are characterized by poor accuracy. The reasons for success and failure were analyzed.