Search Results (5)

In recent times, environmental stewardship of mangroves has provided the impetus to protect and restore these ecosystems for their inherent ability to protect coastal regions from climate change, sequester carbon dioxide as rich blue carbon, and support human well-being through a multitude of ecosystem services. Participatory stakeholder assessment, as a part of the present study, integrated local stakeholder perspectives in assessing drivers of mangrove loss in Bhitarkanika and Mahanadi delta, Odisha, providing empirical evidence through a mixed-method approach. The use of a Likert scale provided the methodology to develop a single composite variable as the best measure of central tendency. In total, 27.5% of the respondents were locals and were living close to the study area for generations, whereas the other 72.5% represented researchers, academics, and forest department officials. Stakeholder responses at the ground level indicated that Bhitarkanika and Mahanadi delta were facing increased frequency of extreme climatic events followed, by aquaculture and other land-use changes, which can be considered potential drivers causing mangrove loss. Co-development of future scenarios by integrating concerns of all the stakeholders emerged as a potential solution to effectively address the trade-offs arising from local anthropogenic interferences, as well as large-scale developmental activities. This study highlights the need for convergence of multi-disciplinary knowledge from diverse stakeholder groups, including traditional and indigenous knowledge, for the purpose of developing accurate plausible alternative scenarios. Interactive governance and incentivization approaches, along with alternative livelihood opportunities, are proposed as the means to improve conservation and restoration in the region based on the present study. Understanding of the coupled socio-ecological system and its relevance is found to be critical to improve bi-directional linkages of ecosystem health and human well-being. Full article

Although studies on species-level classification and mapping using multisource data and machine learning approaches are plenty, the use of data with ideal placement of central wavelength and bandwidth at appropriate spatial resolution, for the classification of mangrove species is underreported. The species composition of a mangrove forest has been estimated utilising the red-edge spectral bands and chlorophyll absorption information from AVIRIS-NG and Sentinel-2 data. In this study, three dominant species, Heritiera fomes, Excoecaria agallocha and Avicennia officinalis, have been classified using the random forest (RF) model for a mangrove forest in Bhitarkanika Wildlife Sanctuary, India. Various combinations of reflectance/backscatter bands and vegetation indices derived from Sentinel-2, AVIRIS-NG, and Sentinel-1 were used for species-level discrimination and mapping. The RF model showed maximum accuracy using Sentinel-2, followed by the AVIRIS-NG, in discriminating three dominant species and two mixed compositions. This study indicates the potential of Sentinel-2 data for discriminating various mangrove species owing to the appropriate placement of central wavelength and bandwidth in Sentinel-2 at ≥10 m spatial resolution. The variable importance plots proved that species-level classification could be attempted using red edge and chlorophyll absorption information. This study has wider applicability in other mangrove forests around the world. Full article

Canopy height serves as a good indicator of forest carbon content. Remote sensing-based direct estimations of canopy height are usually based on Light Detection and Ranging (LiDAR) or Synthetic Aperture Radar (SAR) interferometric data. LiDAR data is scarcely available for the Indian tropics, while Interferometric SAR data from commercial satellites are costly. High temporal decorrelation makes freely available Sentinel-1 interferometric data mostly unsuitable for tropical forests. Alternatively, other remote sensing and biophysical parameters have shown good correlation with forest canopy height. The study objective was to establish and validate a methodology by which forest canopy height can be estimated from SAR and optical remote sensing data using machine learning models i.e., Random Forest (RF) and Symbolic Regression (SR). Here, we analysed the potential of Sentinel-1 interferometric coherence and Sentinel-2 biophysical parameters to propose a new method for estimating canopy height in the study site of the Bhitarkanika wildlife sanctuary, which has mangrove forests. The results showed that interferometric coherence, and biophysical variables (Leaf Area Index (LAI) and Fraction of Vegetation Cover (FVC)) have reasonable correlation with canopy height. The RF model showed a Root Mean Squared Error (RMSE) of 1.57 m and R² value of 0.60 between observed and predicted canopy heights; whereas, the SR model through genetic programming demonstrated better RMSE and R² values of 1.48 and 0.62 m, respectively. The SR also established an interpretable model, which is not possible via any other machine learning algorithms. The FVC was found to be an essential variable for predicting forest canopy height. The canopy height maps correlated with ICESat-2 estimated canopy height, albeit modestly. The study demonstrated the effectiveness of Sentinel series data and the machine learning models in predicting canopy height. Therefore, in the absence of commercial and rare data sources, the methodology demonstrated here offers a plausible alternative for forest canopy height estimation. Full article

Mangrove forest coastal ecosystems contain significant amount of carbon stocks and contribute to approximately 15% of the total carbon sequestered in ocean sediments. The present study aims at exploring the ability of Earth Observation EO-1 Hyperion hyperspectral sensor in estimating aboveground carbon stocks in mangrove forests. Bhitarkanika mangrove forest has been used as case study, where field measurements of the biomass and carbon were acquired simultaneously with the satellite data. The spatial distribution of most dominant mangrove species was identified using the Spectral Angle Mapper (SAM) classifier, which was implemented using the spectral profiles extracted from the hyperspectral data. SAM performed well, identifying the total area that each of the major species covers (overall kappa = 0.81). From the hyperspectral images, the NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) were applied to assess the carbon stocks of the various species using machine learning (Linear, Polynomial, Logarithmic, Radial Basis Function (RBF), and Sigmoidal Function) models. NDVI and EVI is generated using covariance matrix based band selection algorithm. All the five machine learning models were tested between the carbon measured in the field sampling and the carbon estimated by the vegetation indices NDVI and EVI was satisfactory (Pearson correlation coefficient, R, of 86.98% for EVI and of 84.1% for NDVI), with the RBF model showing the best results in comparison to other models. As such, the aboveground carbon stocks for species-wise mangrove for the study area was estimated. Our study findings confirm that hyperspectral images such as those from Hyperion can be used to perform species-wise mangrove analysis and assess the carbon stocks with satisfactory accuracy. Full article

Aquaculture is the farming of fish, crustaceans, molluscus, aquatic plants, algae, and other aquatic organisms. Aquaculture farming in coastal areas of India plays key role in the economy which contributes 1.1% of GDP. In Odisha, the aquaculture system exports 26% of its products to foreign countries. Artificial neural networks have a feature of pattern recognition, which uses a training dataset to identify patterns of any feature from satellite images. The term pattern recognition considers a wide range of information and processing problems of great practical significance. This study was ckarried out in two coastal districts, namely, Bhadrak and Kendrapada in Odisha state. Landsat-8 satellite data (OLI sensor) were used, and training sites were generated. The pattern recognition features of the neural network were used to extract aquaculture features from satellite images. We analyzed the areas that were converted to aquaculture from 2002 to 2017 using the neural network classification. There was a two-fold increase in aquaculture activities from 2002 to 2017 in the two coastal districts. The increases in aquaculture activities indicated that aquaculture plays an important role in the socio-economic developmental of coastal people. Full article