Shrimp pond effluent dominates foliar nitrogen in disturbed mangroves as mapped using hyperspectral imagery

Effects of shrimp pond effluent on functional traits and functional diversity of mangroves in Zhangjiang Estuary

In recent years, the scale of shrimp ponds has rapidly increased adjacent to mangrove forests. Discharge of shrimp pond effluent has led to degradation of the surrounding environment and reduction of biodiversity in the estuary. But it remains poorly understood how shrimp pond effluent affects functional traits and functional diversity of mangroves. We sampled roots, stems and leaves of Kandelia obovata and other mangrove plants, as well as sediments and pore water from shrimp pond effluent polluted area (P) and clean area (control area, C) in Zhangjiang Estuary in southeast coast of China. Twenty plant functional traits and six functional diversity indices were analyzed to explore the effects of shrimp pond effluent on individual plants and mangrove communities. The results showed that the discharge of shrimp pond effluent significantly affected the nutrient content in soils and pore water, for example, sediment NH₄⁺ and NO₃^- concentration increased from 0.26 ± 0.06 to 0.77 ± 0.29 mg/g and from 0.05 ± 0.03 to 0.16 ± 0.05 mg/g, respectively, when comparing the C and P site. Furthermore, some mangrove plant functional traits such as plant height, diameter at breast height, canopy thickness and specific leaf area were significantly increased by the effluent discharge. Functional diversity in the polluted area reduced as a whole compared to the control area. In particular, ammonium and nitrate nitrogen input is the main reason to induce the changes of plant functional traits and functional diversity. Besides, the community structure changed from functional differentiation to functional convergence after shrimp pond effluent discharge. In addition, the long-term shrimp pond effluent discharge may lead to the ecological strategy shift of K. obovata, while different organs may adopt different ways of nutrient uptake and growth strategies in the face of effluent disturbance. In conclusion, pollution from shrimp pond does affect the functional traits of mangrove plants and functional diversity of mangrove community. These results provide strong evidence to assess the impact of effluent discharges on mangrove plants and provide theoretical basis for conservation and sustainable development of mangroves.

Remote sensing techniques: mapping and monitoring of mangrove ecosystem—a review

Mangrove forests are considered to be the most productive ecosystem yet vanishing rapidly over the world. They are mostly found in the intertidal zone and sheltered by the seacoast. Mangroves have potential socio-economic benefits such as protecting the shoreline from storm and soil erosion, flood and flow control, acting as a carbon sink, provides a fertile breeding ground for marine species and fauna. It also acts as a source of income by providing various forest products. Restoration and conservation of mangrove forests remain a big challenge due to the large and inaccessible areas covered by mangroves forests which makes field assessment difficult and time-consuming. Remote sensing along with various digital image classification approaches seem to be promising in providing better and accurate results in mapping and monitoring the mangroves ecosystem. This review paper aims to provide a comprehensive summary of the work undertaken, and addresses various remote sensing techniques applied for mapping and monitoring of the mangrove ecosystem, and summarize their potential and limitation. For that various digital image classification techniques are analyzed and compared based on the type of image used with its spectral resolution, spatial resolution, and other related image features along with the accuracy of the classification to derive specific class information related to mangroves. The digital image classification techniques used for mangrove mapping and monitoring in various studies can be classified into pixel-based, object-based, and knowledge-based classifiers. The various satellite image data analyzed are ranged from light detection and ranging (LiDAR), hyperspectral and multispectral optical imagery, synthetic aperture radar (SAR), and aerial imagery. Supervised state of the art machine learning/deep machine learning algorithms which use both pixel-based and object-based approaches and can be combined with the knowledge-based approach are widely used for classification purpose, due to the recent development and evolution in these techniques. There is a huge future scope to study the performance of these classification techniques in combination with various high spatial and spectral resolution optical imageries, SAR and LiDAR, and also with multi-sensor, multiresolution, and temporal data.

Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges

The mangrove ecosystem plays a vital role in the global carbon cycle, by reducing greenhouse gas emissions and mitigating the impacts of climate change. However, mangroves have been lost worldwide, resulting in substantial carbon stock losses. Additionally, some aspects of the mangrove ecosystem remain poorly characterized compared to other forest ecosystems due to practical difficulties in measuring and monitoring mangrove biomass and their carbon stocks. Without a quantitative method for effectively monitoring biophysical parameters and carbon stocks in mangroves, robust policies and actions for sustainably conserving mangroves in the context of climate change mitigation and adaptation are more difficult. In this context, remote sensing provides an important tool for monitoring mangroves and identifying attributes such as species, biomass, and carbon stocks. A wide range of studies is based on optical imagery (aerial photography, multispectral, and hyperspectral) and synthetic aperture radar (SAR) data. Remote sensing approaches have been proven effective for mapping mangrove species, estimating their biomass, and assessing changes in their extent. This review provides an overview of the techniques that are currently being used to map various attributes of mangroves, summarizes the studies that have been undertaken since 2010 on a variety of remote sensing applications for monitoring mangroves, and addresses the limitations of these studies. We see several key future directions for the potential use of remote sensing techniques combined with machine learning techniques for mapping mangrove areas and species, and evaluating their biomass and carbon stocks.

Hyperspectral Estimation of the Chlorophyll Content in Short-Term and Long-Term Restorations of Mangrove in Quanzhou Bay Estuary, China

The chlorophyll content can indicate the general health of vegetation, and can be estimated from hyperspectral data. The aim of this study is to estimate the chlorophyll content of mangroves at different stages of restoration in a coastal wetland in Quanzhou, China, using proximal hyperspectral remote sensing techniques. We determine the hyperspectral reflectance of leaves from two mangrove species, Kandelia candel and Aegiceras corniculatum, from short-term and long-term restoration areas with a portable spectroradiometer. We also measure the leaf chlorophyll content (SPAD value). We use partial-least-squares stepwise regression to determine the relationships between the spectral reflectance and the chlorophyll content of the leaves, and establish two models, a full-wave-band spectrum model and a red-edge position regression model, to estimate the chlorophyll content of the mangroves. The coefficients of determination for the red-edge position model and the full-wave-band model exceed 0.72 and 0.82, respectively. The inverted chlorophyll contents are estimated more accurately for the long-term restoration mangroves than for the short-term restoration mangroves. Our results indicate that hyperspectral data can be used to estimate the chlorophyll content of mangroves at different stages of restoration, and could possibly be adapted to estimate biochemical constituents in leaves.

Hyperspectral imaging for mapping of total nitrogen spatial distribution in pepper plant

Visible/near-infrared (Vis/NIR) hyperspectral imaging was employed to determine the spatial distribution of total nitrogen in pepper plant. Hyperspectral images of samples (leaves, stems, and roots of pepper plants) were acquired and their total nitrogen contents (TNCs) were measured using Dumas combustion method. Mean spectra of all samples were extracted from regions of interest (ROIs) in hyperspectral images. Random frog (RF) algorithm was implemented to select important wavelengths which carried effective information for predicting the TNCs in leaf, stem, root, and whole-plant (leaf-stem-root), respectively. Based on full spectra and the selected important wavelengths, the quantitative relationships between spectral data and the corresponding TNCs in organs (leaf, stem, and root) and whole-plant (leaf-stem-root) were separately developed using partial least-squares regression (PLSR). As a result, the PLSR model built by the important wavelengths for predicting TNCs in whole-plant (leaf-stem-root) offered a promising result of correlation coefficient (R) for prediction (RP = 0.876) and root mean square error (RMSE) for prediction (RMSEP = 0.426%). Finally, the TNC of each pixel within ROI of the sample was estimated to generate the spatial distribution map of TNC in pepper plant. The achievements of the research indicated that hyperspectral imaging is promising and presents a powerful potential to determine nitrogen contents spatial distribution in pepper plant.

Monitoring plant response to phenanthrene using the red edge of canopy hyperspectral reflectance

To investigate the mechanisms and potential for the remote sensing of phenanthrene-induced vegetation stress, we measured field canopy spectra, and associated plant and soil parameters in the field controlled experiment in the Yellow River Delta of China. Two widely distributed plant communities, separately dominated by reed (Phragmites australis) and glaucous seepweed (Suaeda salsa), were treated with different doses of phenanthrene. The canopy spectral changes of plant community resulted from the decreases of biomass and foliar projective coverage, while leaf photosynthetic pigment concentrations showed no significance difference among treatments. The spectral response to phenanthrene included a flattened red edge, with decreased first derivative of reflectance. The red edge slope and area consistently responded to phenanthrene, showing a strong relationship with aboveground biomass, coverage and canopy pigments density. These results suggest the potential of remote sensing and the importance of field validation to correctly interpret the causes of the spectral changes.