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Mangroves, vital salt-tolerant coastal forests, confer numerous societal benefits. However, mapping and monitoring these dense, coastal ecosystems is challenging due to limited field access. Leveraging recent hyperspectral remote sensing advancements, this study addresses this challenge. Hyperspectral imaging's narrow contiguous bands are potent for characterizing vegetation structure. The research centers on evaluating hyperspectral remote sensing potential, identifying suitable vegetation indices (VI) for the study area, and employing a multistage Decision Tree (DT) by stacking Vis at each stage to extract distinctive mangrove features. Mangrove attributes like unique leaf greenness, high canopy moisture, coastal-induced temperature moderation, and distinct shortwave infrared absorption contrast them from terrestrial vegetation. Various spectral Vegetation Indices (VI) capture these features by utilizing hyperspectral image bands across wavelengths. The proposed study employs Decision Tree (DT) classifier to optimize feature extraction for mangrove mapping by employing VI-based decision functions to discriminate mangroves from non-mangrove classes. Focused on the Marine National Park (MNP) in Jamnagar, Gulf of Kutch, the study exploits AVIRIS-NG hyperspectral data. It employs the Atmospherically Resistant Vegetation Index (ARVI) for vegetation separation, the Normalized Difference Infrared Index (NDII) for elevated canopy moisture analysis, and Shortwave Infrared Absorption Depth (SIAD) to assess unique shortwave infrared absorption in mangroves. Moreover, it integrates the Mangrove Vegetation Index (MVI) and Normalized Difference Mangrove Index (NDMI) for mangrove recognition. Stacking these indices in a DT remarkably boosts classification accuracy, yielding a peak overall accuracy (OA) of 94.71% and a kappa value (k) of 0.86. Published data and reports corroborate these results. This milestone furnishes preliminary insights into mangrove presence, vital for remote sensing-driven mapping and monitoring within challenging field-access environments. Furthermore, these findings pave the way for species-level discrimination via spectral matching and Machine Learning (ML) algorithms. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Mangroves are coastal evergreen, salt-tolerant forest ecosystems that play a vital role in biodiversity conservation. Effective mapping and monitoring of mangrove ecosystems are essential for informed management and scientific decision-making. However, the dense and often inaccessible nature of mangrove forests makes traditional field surveys and ground truth collection challenging. Hyperspectral remote sensing offers a valuable solution by enabling species-level discrimination in mangroves through the extraction of vegetation structure and composition from imagery using diverse classification approaches. This study focuses on the discrimination of mangrove species in the mangrove forest areas of Marine National Park (MNP), Jamnagar, Gulf of Kutch, Lothian Island, Sundarbans, West Bengal. The proposed approach utilizes the Modified Spectral Angle Mapper (Modified SAM) algorithm with AVIRIS-NG hyperspectral data. This algorithm addresses the limitations of the traditional Spectral Angle Mapper (SAM) algorithm, which fails to account for the spatial structure and information of mangrove species. Additionally, the proposed algorithm reduces the reliance on a large number of training samples and complex classifiers, which are insufficient for extracting mangrove characteristics due to computational inefficiencies and limited generalization capabilities. By incorporating both spatial and spectral information, the Modified SAM algorithm enhances performance and classification accuracy, even in mixed mangrove pixel regions. This is achieved through the utilization of various distance and similarity measures, including angle change resulting from shape alterations in mangrove spectra, angle change due to the magnitude of mangrove spectra changes, and distance change between two mangrove spectra. These measures facilitate the identification of subtle differences between mangrove spectra. The results demonstrate a significant improvement in classification accuracy compared to the traditional SAM algorithm, particularly in mixed mangrove pixel regions. The achieved overall accuracy (OA) is 93.48% for the Lothian Islands, Sundarbans, West Bengal, and 94.32% for the Marine National Park (MNP), Jamnagar, Gulf of Kutch. The proposed Modified SAM algorithm, in conjunction with AVIRIS-NG hyperspectral data, offers an effective approach for reliable and efficient classification and discrimination of mangrove species. The algorithm's enhanced performance and classification accuracy have significant implications for mapping, monitoring, and managing mangrove ecosystems, supporting conservation efforts, and facilitating informed decision-making for sustainable mangrove ecosystems. [ABSTRACT FROM AUTHOR]

• Mapping of LULC change in the mangrove forest of Gulf of Khambhat, Gujarat. • Identification of mangrove area affected by land-use and climate change activities. • Deforestation of mangroves as a result of land conversion to non-mangrove areas. The most important biological ecosystems on the earth are mangroves, palms, shrubs, and trees that have adjusted to the challenging environments of high salinity, warm air and temperatures, severe tides, murky, sediment-hampered waterways, and oxygen-depleted soils make up mangroves that are mostly found in the intertidal zone. It is a sanctuary for a variety of aquatic animals and provide protection to coastal land. It acts as "Kalpvruksh" for human being and aquatic culture. Despite the importance of the mangrove ecosystem, the area of mangroves has been declining for several decades. The multispectral data and image processing techniques are frequently used to map and monitor mangrove changes. In this study, the changes of mangroves were accomplished along the Ghogha coast, Bhavnagar district of Gujarat coast, India using the Landsat 8 OLI and Sentinel–2A satellite data for 2016 and 2018, respectively. The image analysis and change detection studies were carried out using QGIS (3.10.14) and ArcMap (10.7.1) software. Using high-resolution satellite data and the Normalized Difference Vegetation Index (NDVI), this research attempts to determine the geographical extent of mangroves. The result shows that the total mangrove cover decreased from 2016 to 2018 and Sentinel-2A images shows a better performance in comparison to Landsat 8 OLI, because of its higher spatial resolution. [ABSTRACT FROM AUTHOR]

The C-band SAR on-board EOS-04 mission provides unique opportunities to characterize forest vegetation through its sensitiveness to vegetation structure and allweather imaging capability over regions of perpetual cloud cover. The present study has brought out the applications of EOS-04 data for estimation of aboveground biomass (AGB) of tropical deciduous forests and scrublands, mapping of forest cover and delineation of mangroves vegetation. The study suggested that EOS04 data can be used for mapping AGB of tropical scrublands and low density forests of AGB ≤ 80 t/ha. The overall RMSE for all vegetation with AGB ≤ 80 t/ha was 15.3 t/ha (R² – 0.49). It was shown that the integration of EOS-04 and Sentinel-2 data improved AGB estimates across biomass ranges of 0–245 t/ha (RMSE – 21.60 t/ha and 0.81). EOS-04 data was also found to be useful for the delineation of mangroves and forest vegetation using machine-learning algorithms. The study supports operational use of EOS-04 data for estimation of AGB over low biomass tropical forests and scrublands. [ABSTRACT FROM AUTHOR]