Jeffries Matusita based mixed-measure for improved spectral matching in hyperspectral image analysis

Mangrove swamps and forests are an essential interface of the coastal zone that provide various ecological and economic services contributing to coastal protection and carbon credits. The ever-changing land use along the coastal tract, especially saltpans and agricultural activities in the mangrove habitats, contribute to the reduction of mangrove swamp sprawl and degrade the mangrove forest. Remote sensing techniques are routinely used to provide spatial-temporal information on mangrove ecosystem distribution, species identification, health status, and population. By adopting supervised classification techniques using the capabilities of indices such as the Normalized Difference Water Index (NDWI) and the Normalized Difference Vegetation Index (NDVI), we attempt to map the spatio-temporal variations of the Muthupet Lagoon regions. The increase of land use changes in the vicinity of the Muthupet Lagoon drastically decrease the freshwater flow and create significant impacts on the mangrove habitat. The study described herein documented degradation of 40.3 ha of dense mangroves from 2013 to 2016, 135.5 ha from 2008 to 2013, and 166 ha from 1999 to 2008 due to high salinity, coastal erosion, and the intrusion of saltpans, aquaculture farmlands, and other human activities. The area of sparse mangroves increased by 38.2 ha between 2013 and 2016, by 42.7 ha from 2008 to 2013, and by 191.3 ha from 1999 to 2008 due to prominent restoration activities.

Utility of Landsat Data for Assessing Mangrove Degradation in Muthupet Lagoon, South India

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

Discriminating trees species from the relationship between spectral reflectance and chlorophyll contents of mangrove forest in Malaysia

. This paper proposes a novel hyperspectral matching algorithm by integrating the stochastic Jeffries-Matusita measure (JM) and the deterministic Spectral Angle Mapper (SAM), to accurately map the species and the associated landcover types of the mangroves of east coast of India using hyperspectral satellite images. The JM-SAM algorithm signifies the combination of a qualitative distance measure (JM) and a quantitative angle measure (SAM). The spectral capabilities of both the measures are orthogonally projected using the tangent and sine functions to result in the combined algorithm. The developed JM-SAM algorithm is implemented to discriminate the mangrove species and the landcover classes of Pichavaram (Tamil Nadu), Muthupet (Tamil Nadu) and Bhitarkanika (Odisha) mangrove forests along the Eastern Indian coast using the Hyperion image dat asets that contain 242 bands. The developed algorithm is extended in a supervised framework for accurate classification of the Hyperion image. The pixel-level matching performance of the developed algorithm is assessed by the Relative Spectral Discriminatory Probability (RSDPB) and Relative Spectral Discriminatory Entropy (RSDE) measures. From the values of RSDPB and RSDE, it is inferred that hybrid JM-SAM matching measure results in improved discriminability of the mangrove species and the associated landcover types than the individual SAM and JM algorithms. This performance is reflected in the classification accuracies of species and landcover map of Pichavaram mangrove ecosystem. Thus, the JM-SAM (TAN) matching algorithm yielded an accuracy better than SAM and JM measures at an average difference of 13.49 %, 7.21 % respectively, followed by JM-SAM (SIN) at 12.06%, 5.78% respectively. Similarly, in the case of Muthupet, JM-SAM (TAN) yielded an increased accuracy than SAM and JM measures at an average difference of 12.5 %, 9.72 % respectively, followed by JM-SAM (SIN) at 8.34 %, 5.55% respectively. For Bhitarkanika, the combined JM-SAM (TAN) and (SIN) measures improved the performance of individual SAM by (16.1 %, 15%) and of JM by (10.3%, 9.2%) respectively.

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Jeffries Matusita-Spectral Angle Mapper (JM-SAM) spectral matching for species level mapping at Bhitarkanika, Muthupet and Pichavaram mangroves

In the Muthupettai mangrove environment, spectral properties of six mangrove species viz. Avicennia marina, Aegiceras corniculatum, Excoecaria agallocha, Acanthus ilicifolius, Suaeda monoica and S. maritima was studied using Multi band Ground Truth Radiometer (Model-041). The study found that the chlorophyll concentration of different mangrove leaves varies between 0.05 and 0.36 mg g -1 , registering the minimum in S. maritima and maximum in E. agallocha. Interestingly species with higher chlorophyll concentration showed lower reflectance values alteast in the spectral bands 1 and 2. E. agallocha registered 0.36 mg g -1 of chlorophyll while it recorded only 2.18 and 2.43% reflectance where as S. maritima recorded 3.16 and 3.27% of reflectance in bands 1 and 2. This indicates chlorophyll concentration is one of major factors responsible in determining the reflectance pattern of the pant communities. The spectral properties of mangroves were largely differed with that of the water and soil samples collected from the same locations, these results favourd the utilization of remotely sensed data for depicting various water and soil quality parameters from that of mangrove species in the mangrove environment. This study also found that the difference in reflectance of mangroves at canopy level is not only influenced by the chlorophyll content of species but also by the prevailing environmental condition and background reflectance of soil and water as well

Ajithkumar Tt

Papers

Spectral reflectance properties of mangrove species of the Muthupettai mangrove environment, Tamil Nadu