Diversity and Classification of Wetlands in International and National Perspectives

Chakraborty, Susanta Kumar; Sanyal, Poulomi; Ray, Ratnadeep

doi:10.1007/978-3-031-09253-4_3

Cited by 3 publications

(1 citation statement)

References 43 publications

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“…The importance of biodiversity and its critical role in the ecosystem is incomparable for Wetlands, which are among the most prolific ecosystems (Marion & Paillisson, 2003). However, they occupy a small portion of the earth but are essential and play a significant ecological function (Chakraborty et al, 2023). Wetlands provide socio‐economic benefits to society in addition to fishing and recreational activities (Islam et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Vegetation discrimination based on chlorophyll prediction in Marshy wetland using Unmanned Aerial Vehicles

Mohanty,

Pandey,

Singh

et al. 2024

Aquatic Conservation

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Wetlands are an integral part of our global ecosystems and play crucial roles in ecological functions such as carbon sequestration, flood mitigation, water purification, and recreational activities. The Ramsar Convention is the most significant wetland protection pact and is doing tremendous work in conserving wetlands worldwide. However, the wetlands area is still under threat due to anthropogenic activity. The current study utilized drone images, chlorophyll measurements and machine leaning to discriminate and map vegetation at marsh wetland area—the Ramsar site. The high‐resolution, multispectral imagery is acquired using a drone‐mounted MICAsense sensor. Eight spectral indices such as Normalized Difference Water Index (NDWI), Two‐Band Algorithm (2BDA), Normalized Difference Chlorophyll Index (NDCI), Normalized Difference Vegetation Index (NDVI) Enhanced Normalized Difference Vegetation Index (ENDVI), Green Normalized Difference Vegetation Index (GNDVI), and Normalised Difference RedEdge (NDRE) were calculated on the acquired imagery in order to discriminate the different vegetation covers such as floating aquatic vegetation (FAV), open water, and other vegetations types. These include the following: Eichhornia, Nymphea, Oleracea, Paspalam, and Oryza from agriculture land at the study site. Two models (viz., the Taylor plot and the Lek Profile methods) were employed to assess the sensitivity of the spectral indices for prediction of chlorophyll and vegetation discrimination. It is inferred from both methods that NDCI was most sensitive for chlorophyll prediction of vegetation followed by NGRDI/ ENDVI/ 2BDA and NDVI for chlorophyll prediction in wetland ecosystems. Further, three machine learning algorithms, support vector machine (SVM), random forest (RF), and gradient tree boost (GTB), were utilized for classification, and the performance accuracy of GTB was found to be the highest (0.893), followed by RF (0.851) and SVM (0.723). The GTB algorithm was applied over NDCI for vegetation discrimination. The study revealed that Eichhronia sp. is abundantly present at the study site; hence, strategic management plans should be carried out for the eradication of invasive species and proper management of wetland vegetation.

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Section: Introductionmentioning

confidence: 99%

Vegetation discrimination based on chlorophyll prediction in Marshy wetland using Unmanned Aerial Vehicles

Mohanty,

Pandey,

Singh

et al. 2024

Aquatic Conservation

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Wetlands contribution and linkage to support SDGs, its indicators and targets‐ A critical review

Mohanty,

Pandey,

Pandey

et al. 2024

Sustainable Development

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This study marks one of the pioneering efforts to compile comprehensive information on Ramsar sites globally. It delves into the significance of wetlands and the designation of Ramsar sites across various countries, incorporating a concise exploration of the utilization of Unmanned Aerial Vehicles (UAVs) for wetland monitoring and assessment. Additionally, the study conducts a comparative evaluation of Ramsar sites, analyzing their percentage area and overall coverage worldwide. Incorporating a Scientometric analysis utilizing the Scopus database, the study features a co‐occurrence map, thematic map, thematic evolution trend, and country collaboration map. Emphasizing the interconnection between wetlands and Sustainable Development Goals (SDGs), particularly SDG6 (Clean Water & Sanitation), SDG12 (Responsible Consumption & Production), SDG13 (Climate‐Action), SDG14 (Life Below Water) and SDG15 (Life on Land), the study delves into associated targets and indicators. Targets such as 6.1, 6.2, 6.3, 6.4, 6.5, 6a, 6b of SDG‐6, 12.1, 12.2, 12.4 of SDG‐12, and 13.2, 13.3 of SDG‐13 align with wetland management and conservation. Moreover, it affirms the role of wetlands in supporting targets 14.1, 14.2, 14.3, 14.4, 14.5, 14.6, 14a‐c of SDG‐14, and 15.1, 15.5, 15.6, 15.7, 15.8, and 15.8 of SDG‐15. Policies, regulations and management plans of different countries relevant for supporting and establishing relationship with SDGs are discussed in details. The study offers a detailed exploration of these targets, elucidating indicator types associated with each SDG target. By doing so, it provides valuable insights for future researchers and policymakers, underlining the indispensable contribution of wetlands to the direct and indirect fulfillment of SDGs 6,12,13,14,15 and 17.

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Spatio-temporal distribution profile of heavy metals in Kol wetland ecosystem in southern India

Varier,

Nisari,

George

et al. 2024

Environ Earth Sci

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Diversity and Classification of Wetlands in International and National Perspectives

Cited by 3 publications

References 43 publications

Vegetation discrimination based on chlorophyll prediction in Marshy wetland using Unmanned Aerial Vehicles

Vegetation discrimination based on chlorophyll prediction in Marshy wetland using Unmanned Aerial Vehicles

Wetlands contribution and linkage to support SDGs, its indicators and targets‐ A critical review

Spatio-temporal distribution profile of heavy metals in Kol wetland ecosystem in southern India

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