Analysis of the causes of wetland landscape patterns and hydrological connectivity changes in Momoge National Nature Reserve based on the Google Earth Engine Platform

Cui, Geng; Liu, Yan; Tong, Shuo

doi:10.1007/s12517-021-06568-8

Cited by 30 publications

(12 citation statements)

References 32 publications

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“…Such approaches may prove valuable in identifying waterbodies, which may subsequently be interrogated to determine more detailed wetland characteristics such as class, form, and type. Other decision tree methods have also been used to map wetland classes, flora, and fauna, including: Classification Tree (CT) Analysis [282][283][284][285], Gradient Boosting (GB) [106,[286][287][288], and Classification And Regression Tree (CART) [6,[289][290][291]. Baker et al [106] noted GB to be preferable to CT approaches for mapping wetland, non-wetland, and riparian land cover classes; however, Tulbure et al [282] obtained an overall accuracy of 96% when classifying water bodies from other land cover types.…”

Section: Decision Treesmentioning

confidence: 99%

Remote Sensing of Wetlands in the Prairie Pothole Region of North America

Montgomery

Mahoney

Brisco³

et al. 2021

Remote Sensing

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The Prairie Pothole Region (PPR) of North America is an extremely important habitat for a diverse range of wetland ecosystems that provide a wealth of socio-economic value. This paper describes the ecological characteristics and importance of PPR wetlands and the use of remote sensing for mapping and monitoring applications. While there are comprehensive reviews for wetland remote sensing in recent publications, there is no comprehensive review about the use of remote sensing in the PPR. First, the PPR is described, including the wetland classification systems that have been used, the water regimes that control the surface water and water levels, and the soil and vegetation characteristics of the region. The tools and techniques that have been used in the PPR for analyses of geospatial data for wetland applications are described. Field observations for ground truth data are critical for good validation and accuracy assessment of the many products that are produced. Wetland classification approaches are reviewed, including Decision Trees, Machine Learning, and object versus pixel-based approaches. A comprehensive description of the remote sensing systems and data that have been employed by various studies in the PPR is provided. A wide range of data can be used for various applications, including passive optical data like aerial photographs or satellite-based, Earth-observation data. Both airborne and spaceborne lidar studies are described. A detailed description of Synthetic Aperture RADAR (SAR) data and research are provided. The state of the art is the use of multi-source data to achieve higher accuracies and hybrid approaches. Digital Surface Models are also being incorporated in geospatial analyses to separate forest and shrub and emergent systems based on vegetation height. Remote sensing provides a cost-effective mechanism for mapping and monitoring PPR wetlands, especially with the logistical difficulties and cost of field-based methods. The wetland characteristics of the PPR dictate the need for high resolution in both time and space, which is increasingly possible with the numerous and increasing remote sensing systems available and the trend to open-source data and tools. The fusion of multi-source remote sensing data via state-of-the-art machine learning is recommended for wetland applications in the PPR. The use of such data promotes flexibility for sensor addition, subtraction, or substitution as a function of application needs and potential cost restrictions. This is important in the PPR because of the challenges related to the highly dynamic nature of this unique region.

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Section: Decision Treesmentioning

confidence: 99%

Remote Sensing of Wetlands in the Prairie Pothole Region of North America

Montgomery

Mahoney

Brisco³

et al. 2021

Remote Sensing

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“…Increasing the edge area ratio after growing fragmentation exerts extra pressure on wetland habitat and ecology (Mukherjee and Pal, 2021). This trend of morphological wetland scape transformation is commonly found across the world (Shen et al, 2019;Cui et al, 2021;Das et al, 2021;.…”

Section: Identifying Associated Modification In Some Specific Casesmentioning

confidence: 99%

“…Some people residing near wetland areas also reported that after shallowing wetlands, inconsistence water appearance in wetland, people received an opportunity to convert the wetland into perennial agriculture land. Since the flood plain wetlands are fed by rain and floodwater, the dynamics of the flood of a river are strongly linked with riparian flood plain (Fritz et al, 2018;Vidon et al, 2019;Alafifi and Rosenberg, 2020;Cui et al, 2021). Lowering of floodwater level in Atreyee river during monsoon season was identified as a prime reason behind the reduction of the active flood plain.…”

Section: Identifying Associated Modification In Some Specific Casesmentioning

confidence: 99%

Impact of river flow modification on wetland hydrological and morphological characters

Saha

Pal

Sarda

2022

Preprint

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A good number of researchers investigated the impact of flow modification on hydrological, ecological, geomorphological conditions in a river. A few works also focused on hydrological modification on wetland with some parameters but as far the knowledge is concerned, linking river flow modification to wetland hydrological and morphological transformation following an integrated modeling approach is almost absent. The current study aimed to explore the degree of hydrological alteration in the river and its effect on downstream riparian wetlands adopting advanced modeling approaches. After damming maximally 67 to 95% hydrological alteration was recorded in respect to maximum, minimum, and average discharges. Wavelet transformation analysis figured out a strong power spectrum after 2012 (damming year). Due to attenuation of flow, the active inundation area was reduced by 66.29%. After damming, 524.03 km2 (48.97% to total pre-dam wetland) was completely obliterated. Hydrological strength (HS) modeling also reported areas under high HS was declined by 14% after post-dam condition. WSS and HS matrix, a new approach, used to feature wetland coupling inundation connectivity and current hydrological state. HS under critical and stress wetland hydrological security zones deteriorated in the post-dam period. The morphological transformation was also well recognized showing an increase of area under the patch, edge, and decrease of the area under large core area. All these findings established a good linkage between river flow modification and wetland transformation and it provided a good clue for managing wetland.

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“…These changes caused ecological problems, such as a reduction in biodiversity and water resources, a degradation in water quality, and a loss of wetland functions in LBYD [27]. It is critical to explore the reasons driving wetland degradation [29,30]. Additionally, it is thus necessary to analyze the factors affecting wetland changes and design policies for mitigating further degradation in LBYD.…”

Section: Introductionmentioning

confidence: 99%

“…Given this context of climate change and intensifying human activities, what was the main driving reason for the degradation of LBYD? [29,30] In order to understand this problem, the main research contents were: (1) to characterize the processes involved in wetland fragmentation during the years 1980-2017;…”

Section: Introductionmentioning

confidence: 99%

Landscape Pattern Evolution Processes and the Driving Forces in the Wetlands of Lake Baiyangdian

Zhao¹,

Gong²,

Zeng³

et al. 2021

Sustainability

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The spatiotemporal features of land use changes and the evolution process of landscape pattern from 1980 to 2017 were investigated using historical satellite images from a Landsat Thematic Mapper (TM) for 1980, 1990, 2000, 2005, 2010 and 2017 in the wetlands of Lake Baiyangdian in the North China Plain (NCP). Landscape pattern indices were used to quantify landscape changes in wetlands, and a redundancy analysis (RDA) was conducted to analyze the driving forces and quantitatively explain the effects of human activities and natural changes on wetland fragmentation. The results showed that the total wetland area was 234.4 km2 in 1980 but it decreased by 8.1% at an average decrease rate of 0.5 km2 per year. The dominant transition between land use types was from natural wetlands to artificial wetlands, and wetland conversion to dry land and residential land. The RDA results suggested that agricultural activities and total population were the main driving factors affecting wetland landscape. Additionally, climate change provided a potentially favorable environment for agricultural development, due to the increased temperatures and decreased wind speeds. Additionally, governmental policy changes and dam construction also played the roles in land use changes.

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Analysis of the causes of wetland landscape patterns and hydrological connectivity changes in Momoge National Nature Reserve based on the Google Earth Engine Platform

Cited by 30 publications

References 32 publications

Remote Sensing of Wetlands in the Prairie Pothole Region of North America

Remote Sensing of Wetlands in the Prairie Pothole Region of North America

Impact of river flow modification on wetland hydrological and morphological characters

Landscape Pattern Evolution Processes and the Driving Forces in the Wetlands of Lake Baiyangdian

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