A Review of Wetland Remote Sensing <s> Leaf Chemistry of Wetland Vegetation <s> ARTICLE I NFO Nutrient enrichment and eutrophication are major concerns in many estuarine and wetland ecosystems, and the need is urgent for fast, efficient, and synoptic ways to detect and monitor nutrients in wetlands and other coastal systems across multiple spatial and temporal scales. We integrated three approaches in a multi- disciplinary evaluation of the potential for using hyperspectral imaging as a tool to assess nutrient enrichment and vegetation responses in tidal wetlands. For hyperspectral imaging to be an effective tool, spectral signatures must vary in ways correlated with water nutrient content either directly, or indirectly via such proxies as vegetation responses to elevated nitrogen. Working in Elkhorn Slough, central California, where intensive farming practices generate considerable runoff of fertilizers and pesticides, we looked first for long- and short-term trends among temporally ephemeral point data for nutrients and other water quality characters collected monthly at 18 water sampling stations since 1988. Second, we assessed responses of the dominant wetland plant, Salicornia virginica (common pickleweed) to two fertilizer regimes in 0.25 m 2 <s> BIB001 </s> A Review of Wetland Remote Sensing <s> Leaf Chemistry of Wetland Vegetation <s> Mangrove photosynthetic activity and, consequently, physiological stress can be monitored indirectly using leaf chlorophyll-a (Chla) measurements. Recent studies have demonstrated the feasibility of mangrove leaf Chla content estimation from in situ hyperspectral vegetation indices (VI) but no such research has been conducted using data collected from contrasting seasons (i.e. dry and rainy). In this study, mangrove leaves were collected in a sub-tropical forest of the Mexican Pacific for Chla content determination and in situ hyperspectral measurements (450–1,000 nm). Specifically, we tested 35 VI to estimate Chla content based on a leaf sample of 360 collected from the same trees during both the dry and rainy seasons. The forest examined contained three species of mangrove (Rhizophora mangle, Avicennia germinans and Laguncularia racemosa) exhibiting various conditions of health (dwarf condition, tall and healthy). A principal component analysis, followed by linear regression analyses, were conducted in order to identify those VI that best predict mangrove leaf Chla content during the two seasons. The results indicate that VI derived from hyperspectral measurements collected during the dry season are better at estimating leaf Chla content than those collected during the rainy season. Among the 35 VI, the Vog1 (R740/R720) index was found to be the best predictor of mangrove leaf Chla content, resulting in R 2 values of 0.80 and 0.68 for the dry and rainy season respectively. These results would suggest that for identifying variation in mangrove forest stress (i.e. health) in sub-tropical regions, hyperspectral measurements should be carried out during the dry season. <s> BIB002 </s> A Review of Wetland Remote Sensing <s> Leaf Chemistry of Wetland Vegetation <s> Abstract The relationship between spectral reflectance and foliar chlorophyll (Chl) and nitrogen (N) was examined for 19 species over a six-month growing period at Mer Bleue, an ombrotrophic bog located near Ottawa, Ontario, Canada. The goal of this study was to model total Chl and N concentration at the landscape-scale from remotely sensed data utilizing a model insensitive to plant functional type (PFT), species and season. To date the relationship between spectral reflectance and foliar properties is poorly understood in peatlands owing to the scarcity of studies examining the spectral variability between mosses and vascular plants. A model that comprised a continuous wavelet transform coupled with a neural network was constructed to predict Chl and N content from selected wavelet features (coefficients) at both leaf and airborne image scales. The model was compared to thirteen common spectral indices used to determine vegetation properties in forest environments. The heterogeneity of the vascular plant/moss cover over small spatial scales ( 2 = 0.3. The final continuous wavelet model resulted in a noticeable improvement with R 2 values ranging from 0.8 to 0.9 (for both Chl and N content). We scaled up our predictive model from the leaf/capitulum level data to 40 cm spatial resolution 72-band airborne imagery (CASI-2 429.6–968.8 nm) to create surfaces of Chl and N content for the study area. <s> BIB003 </s> A Review of Wetland Remote Sensing <s> Leaf Chemistry of Wetland Vegetation <s> ABSTRACTIncreasing amounts of recycled water have been used in wetlands; as such, monitoring the growth status of wetland plants with remote sensing technology is of great significance to restore and reconstruct wetland added by recycled water. In this paper, Phragmites australis and Typha angustifolia were selected. Spectral reflectance was determined in the visible domain to avoid the influence of leaf water on the established model; chlorophyll and nitrogen contents were measured as data sources. Statistical analysis, including correlation analysis and stepwise regression analysis, was conducted to estimate chlorophyll and nitrogen contents with different derivative transform spectra, namely, first derivative reflectance spectrum (FDS), band depth (BD), continuum-removed derivative reflectance (CRDR), band depth ratio (BDR), and normalized band depth index (NBDI). After the selected bands were statistically analyzed, we found that the range of 550–750 nm, particularly 700–750 nm (red edge range), was t... <s> BIB004
Water quality in many wetlands has declined progressively over the past several decades because of the increasing usage of recycled water in wetlands and the inflow of nutrients from agricultural and urban areas. Wetland water eutrophication with high concentrations of chlorophyll occurred after pollution BIB001 . Siciliano et al. BIB001 first collected nutrients and other water quality data at 18 water sampling stations and then fertilized two sampling stations of them and measured the changes of the tissue composition of C (carbon), H (hydrogen), and N (nitrogen) and the spectral responses of leaves and canopy. They used HyMap hyperspectral imagery with 126 bands ranging from 440 to 2500 nm at 2.5-3.0 m spatial resolution to assess wetland ecosystem and found positive relationships between the N levels and plant responses for both plot scale and landscape scale. Significant correlations were also observed between water nutrients and the photochemical reflectance index (PRI) and derivative chlorophyll index (DCI). They concluded that hyperspectral data can be used to detect nutrient concentrations in wetlands. Nitrogen is one of the most important components in plant leaves and its change can be monitored using hyperspectral data. Zhang et al.  examined the relationship between leaf N concentration and laboratory hyperspectral data corresponding to two mangrove species under healthy, poor, and dwarf conditions. Two models, artificial neural network (ANN) and stepwise multiple linear regression (SMLR), were employed to predict N concentrations based on multiple wavebands; the former model predicted N concentration more accurately than the latter. Flores-De-Santiago et al. BIB002 tested 35 vegetation indices (VI) derived from in situ hyperspectral measurements (450-1000 nm) to estimate the leaf chlorophyll content based on 360 leaf samples during both the dry and rainy seasons. The results showed that VIs derived during the dry season obtained better estimates of lead chlorophyll than that of the rainy season and the index of R740/R720 was the best predictor of the mangrove leaf chlorophyll content. To model leaf chlorophyll and N concentration using remote sensing data, Kalacska et al. BIB003 examined the relationship between spectral reflectance and leaf chlorophyll and N concentration for 19 species in peatland during the growing season. A model that comprised a continuous wavelet transform and a neural network scalable from leaf to airborne hyperspectral imagery was proposed in their study and obtained satisfactory results (with a R 2 ranging from 0.8 to 0.9 for both chlorophyll and nitrogen). Guo and Guo BIB004 selected Phragmites australis and Typha angustifolia as targets and used correlation analysis and stepwise regression analysis applied to spectral reflectance in visible domain to estimate the chlorophyll and N contents in wetland. They found that the range of 700 to 750 nm was the most useful spectral range to estimate biochemical parameters. They also concluded that the difference of the data types and biological differences greatly influence the results.