Hydrogeomorphic indicators of wetland health inferred from multi-temporal remote sensing data for a new Ramsar site (Kaabar Tal), India

Singh, Manudeo; Sinha, Rajiv

doi:10.1016/j.ecolind.2021.107739

Cited by 26 publications

(9 citation statements)

References 65 publications

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“…These indices have been used extensively and have been useful in wetland related research 39 – 41 Moreover, some researchers believe that texture information can improve the accuracy of wetland information extraction, 42 so texture features are also used as classification features in this study. There are 13 texture metrics calculated and used as classification features in GEE according to the “glcmTexture” method.…”

Section: Methodsmentioning

confidence: 99%

Wetland mapping based on feature optimization and Google Earth Engine in Zhalong Nature Reserve

Huang

2022

J. Appl. Rem. Sens.

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Wetlands have very important and irreplaceable ecological service functions in protecting biodiversity and maintaining ecological balance. Due to the unique hydrological characteristics of wetlands, large-scale wetland mapping methods are usually time-consuming, labor intensive, and expensive. With the rise of big data and cloud computing platforms, there are opportunities for long time series and large-scale spatial data processing that improve largescale wetland mapping. In this study, the Sentinel-1, Sentinel-2, and digital elevation model derived from the Google Earth Engine were used to extract the wetland extent in the Zhalong Nature Reserve based on the Jeffries-Matusita (JM) distance for feature optimization and random forest. Research shows the following: (1) The JM distance for each feature showed that the optical features had the highest separation in all five features and (2) the classification after feature optimization performed best. User accuracy (UA) and producer accuracy (PA) for wetland reached 89.64% and 83.00%, respectively, and overall accuracy reached 90.76%. After feature optimization, PA and UA of wetland were each increased 4.26% and 1.07%, respectively, and the number of features was reduced from 46 to 28. The method used in this study is highly accurate in wetland mapping and can effectively avoid the problem of data redundancy.

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Section: Methodsmentioning

confidence: 99%

Wetland mapping based on feature optimization and Google Earth Engine in Zhalong Nature Reserve

Huang

2022

J. Appl. Rem. Sens.

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“…For restoration and management efforts, understanding the impact of geomorphic transformation of wetlands on ecological systems is important (Singh & Sinha, 2021b). For this, landscape matrices such as wetland area change, fragmentation, patch dynamics are frequently used by wetland hydrologists and ecologists (Allgeier et al, 2010; Jiang et al, 2014; Singh & Sinha, 2021a; Tomaselli et al, 2012). Various geomorphic transformations impact wetland biodiversity differently.…”

Section: Integrated Wetland Assessment: a Multi‐ And Cross‐scale Appr...mentioning

confidence: 99%

“…Restoration of wetlands is important but their continuous monitoring is also essential to evaluate the success of restoration efforts (Dorau et al, 2015). Remote‐sensing based wetland assessment and monitoring frameworks are easier to implement on large spatio‐temporal scales, and are relatively time‐efficient and cost‐effective compared to ground‐based approaches (Minotti et al, 2020; Ozesmi & Bauer, 2002; Singh & Sinha, 2021a), especially in large wetland systems (Singh & Sinha, 2021b). It is now a norm to exploit satellite remote‐sensing data for wetland assessments (e.g., Bwangoy et al, 2010; Evans et al, 2014; Jones, 2015; Marchetti et al, 2016; Sinha et al, 2017; Singh & Sinha, 2022; Waz & Creed, 2017; Yuan & Cohen, 2020), particularly for linking cross‐scale wetland structures, functions, and controls in large wetland systems and their surroundings (wetlandscapes).…”

Section: Introductionmentioning

confidence: 99%

Wetlandscape (dis)connectivity and fragmentation in a large wetland (Haiderpur) in west Ganga plains, India

Singh

Sinha

Mishra

et al. 2022

Earth Surf Processes Landf

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Wetlands are dynamic ecosystems that require continuous monitoring and assessment of degradation status to design strategies for their sustainable management.While hydrology provides the primary functional control for the wetland ecosystem, the loss of landscape connectivity influences wetland degradation in a major way as it leads to fragmentation. This article aims to integrate hydrogeomorphic and ecological concepts for the assessment of degradation status and its causal factors for a large wetland in the western Ganga plains, India, the Haiderpur, using a wetlandscape approach. We have used a remote-sensing-based approach, which offers a powerful tool for assessing and linking cross-scale structures, functions, and controls in a wetlandscape. The Haiderpur, a Ramsar site since December 2021, is an artificial wetland located on the right bank of the Ganga River wherein the inflows are controlled by a barrage constructed on the Ganga River apart from smaller tributaries flowing in from the north. A novel aspect of this work is the integration of river dynamics and its connectivity to the wetlandscape to understand the spatiotemporal variability in the waterspread area in the wetland. In this work, we have developed an integrated wetlandscape assessment approach by evaluating wetland's geomorphic and hydrological connectivity status for the period 1993-2019 (25 years) across three different spatial scalesregional, catchment, and wetland. We have highlighted the ecological implications of connectivity and patch dynamics for developing sustainable wetland management plans.

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“…The deterioration of ecosystem health is attributed to the expansion of human-altered land use (including farmland and built-up land) and the degradation of natural land cover (e.g., forest and grassland) [16]. For example, Singh et al [17] evaluated the wetland health of a Ramsar site (Kaabar Tal) in India using various landscape and remote sensing variables, such as Shannon's diversity index, patch density, and the normalized difference vegetation index. Socio-economic development including urbanization, population growth, and intensification of agriculture is the primary driving force for landscape and land use changes, and is also a fundamental factor responsible for wetland loss and degradation [18].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Multi-Metric Index for Health Assessment of the Poyang Lake Wetland

Yang,

Zhong,

Xia

et al. 2023

Remote Sensing

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The Poyang Lake wetland is home to many unique and threatened species. However, it has been severely degraded in recent decades due to the joint effects of human influence and climate change. Here we establish a wetland health index (WHI) for Poyang Lake, which considers five types of attributes (biological, water quality, sediment, land use and remote sensing, and socio-economic attributes) of the wetland to evaluate wetland conditions. Forty-nine variables across five categories were assembled as candidate metrics for the WHI through field surveys conducted in 2019 at 30 sample sites. Principal component analyses were performed to identify the most important variables in each of the five categories as the primary metrics of each index category (e.g., biological index). Eighteen variables were finally selected from the five categories to construct the WHI. The WHI scores varied from 0.34 to 0.80 at the 30 sample sites, with a mean of 0.55. The Poyang Lake wetland is generally in fair condition according to our WHI scores. Sample sites where connected rivers flow into the lake were assessed to be in a poor condition, highlighting the importance of reducing pollution input from rivers for wetland conservation. Scores of individual indices of the five categories were not highly correlated (0.29 ≤ pairwise Spearman’s r ≤ 0.69), suggesting that information provided by each index is different and might be complementary. The composite WHI as well as the individual category indices can provide comprehensive information on wetland conditions that would facilitate the development of more targeted and effective strategies for wetland management.

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Hydrogeomorphic indicators of wetland health inferred from multi-temporal remote sensing data for a new Ramsar site (Kaabar Tal), India

Cited by 26 publications

References 65 publications

Wetland mapping based on feature optimization and Google Earth Engine in Zhalong Nature Reserve

Wetland mapping based on feature optimization and Google Earth Engine in Zhalong Nature Reserve

Wetlandscape (dis)connectivity and fragmentation in a large wetland (Haiderpur) in west Ganga plains, India

A Comprehensive Multi-Metric Index for Health Assessment of the Poyang Lake Wetland

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