Spatial–Temporal Dynamics of Wetland Vegetation Related to Water Level Fluctuations in Poyang Lake, China

Tan, Zhiqiang; Jiang, Jiahu

doi:10.3390/w8090397

Cited by 30 publications

(16 citation statements)

References 49 publications

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“…Accurate estimating for vegetation biomass is the basis of studying material circulation, energy flow, and bio-productivity. This allows a better understanding of the spatial temporal dynamics of wetland ecosystems and provides basic information for evaluating wetland ecological status and managing wetland natural resources sustainably [9][10][11], especially in floodplain wetlands where seasonal water level altering often result in potential variations in vegetation growth and distribution [12]. Therefore, the rapid and efficient biomass estimation of wetland vegetation, especially aboveground biomass (AGB), at a regional scale is required [13].…”

Section: Introductionmentioning

confidence: 99%

Estimating Aboveground Biomass Using Sentinel-2 MSI Data and Ensemble Algorithms for Grassland in the Shengjin Lake Wetland, China

Zhou

2021

Remote Sensing

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Wetland vegetation aboveground biomass (AGB) directly indicates wetland ecosystem health and is critical for water purification, carbon cycle, and biodiversity conservation. Accurate AGB estimation is essential for the monitoring and supervision of ecosystems, especially in seasonal floodplain wetlands. This paper explored the capability of spectral and texture features from the Sentinel-2 Multispectral Instrument (MSI) for modeling grassland AGB using random forest (RF) and extreme gradient boosting (XGBoost) algorithms in Shengjin Lake wetland (a Ramsar site). We use five-fold cross-validation to verify the model effectiveness. The results indicated that the RF and XGBoost models had a robust and efficient performance (with root mean square error (RMSE) of 126.571 g·m−2 and R2 of 0.844 for RF, RMSE of 112.425 g·m−2 and R2 of 0.869 for XGBoost), and the XGBoost models, by contrast, performed better. Both traditional and red-edge vegetation indices (VIs) obtained satisfactory results of AGB estimation (RMSE = 127.936 g·m−2, RMSE = 125.879 g·m−2 in XGBoost models, respectively), with the red-edge VIs contributed more to the AGB models. Moreover, we selected eight gray-level co-occurrence matrix (GLCM) textures calculated by four processing window sizes using the mean value of four offsets, and further analyzed the results of three analysis sets. Textures derived from traditional and red-edge bands using a 7 × 7 window size performed better in biomass estimation. This finding suggested that textures derived from the traditional bands were as important as the red-edge bands. The introduction of textures moderately improved the accuracy of modeling AGB, whereas the use of textures alo ne was not satisfactory. This research demonstrated that using the Sentinel-2 MSI and the two ensemble algorithms is an effective method for long-term dynamic monitoring and assessment of grass AGB in seasonal floodplain wetlands, which can support sustainable management and carbon accounting of wetland ecosystems.

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Section: Introductionmentioning

confidence: 99%

Estimating Aboveground Biomass Using Sentinel-2 MSI Data and Ensemble Algorithms for Grassland in the Shengjin Lake Wetland, China

Zhou

2021

Remote Sensing

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“…Water regime changes may strongly affect the wetland microbial composition as well as functional activity in terms of biogeochemical cycling and energy flux 5 . Altered hydrological and physicochemical characteristics of the waters and soils have been documented to affect the plant, animal, and microbial communities 6 – 9 . Many studies have described shifts in the soil microbial community composition or community functional activity, but most did not investigate changes in the community composition and function together 10 – 13 ; thus, it remains unclear to what extent community structure determines a functional profile in the different environments 14 , 15 .…”

Section: Introductionmentioning

confidence: 99%

Bacterial and Fungal Community Composition and Functional Activity Associated with Lake Wetland Water Level Gradients

et al. 2018

Sci Rep

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The water regime is often the primary force driving the evolution of freshwater lakes, but how soil microbes responded to this process is far from understood. This study sampled wetland soils from a shallow lake that experienced water regime changes, Poyang Lake of China, to explore the features of bacterial and fungal community in response to water level changes. The soil physicochemical properties, T-RFLP based community structures and soil activities (including basal respiration, microbial biomass and enzymes) were all determined. Soil microbial eco-function was captured by testing the carbon metabolism with Biolog-Ecoplate. The results showed remarkable influence of the water level gradients on the soil physicochemical properties, microbial community structures and soil activities. However, the carbon utilization profile exhibited weak connections with the environmental variables and microbial community structures (p > 0.05). The microbial activities were significantly correlated with both bacterial and fungal community structures. Our results also emphasized the ascendant role of the deterministic process in the assemblages of microbial community structures and functions in wetland. In conclusion, this study revealed the discrepancy between community structures and ecofunctions in response to water level gradients, and a relatively stable eco-function helped to maintain the ecosystem function of wetland from a long-term perspective.

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“…Using time-series of both MODIS and Landsat, the hydrological influence on the distribution and transition of wetland cover was studied [29]. Tan and Jiang [30], Wan et al [31], You et al [32], Zhang et al [33] had also analyzed the relationship between wetland vegetation and water level for Lake Poyang and Dongting. However, Lake Poyang and Dongting are the only two lakes that are not dammed in the Yangtze river basin.…”

Section: Introductionmentioning

confidence: 99%

Long Term Aquatic Vegetation Dynamics in Longgan Lake Using Landsat Time Series and Their Responses to Water Level Fluctuation

Tan

Xing

Yang

et al. 2020

Water

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Aquatic vegetation in shallow freshwater lakes are severely degraded worldwide, even though they are essential for inland ecosystem services. Detailed information about the long term variability of aquatic plants can help investigate the potential driving mechanisms and help mitigate the degradation. In this paper, based on Google Earth Engine cloud-computing platform, we made use of a 33-year (1987–2019) retrospective archive of moderate resolution Landsat TM, ETM+ and OLI satellite images to estimate the extent changes in aquatic vegetation in Longgan Lake from Middle Yangtze River Basin in China using the modified enhanced vegetation index, including emerged, floating-leaved and floating macrophytes. The analysis of the long term dynamics of aquatic vegetation showed that aquatic vegetation were mainly distributed in the western part of the lake, where lake bottom elevation ranged from 11 to 12 m, with average water depth of less than 1 m in spring. The vegetation area variation for the 33-year period were divided into six stages. In years with heavy precipitation, the vegetation area decreased sharply. In the following years, the area normally restored. Aquatic vegetation area had a significant negative correlation with the spring water level and summer water level. The results showed that aquatic vegetation was negatively affected when water depth exceeded 2.5 m in May and 5 m in summer. It is recommended that water depth remain close to 1 m in spring and close to 3 m in summer for aquatic vegetation growth. Our study provide quantitative evidence that water-level fluctuations drive vegetation changes in Longgan Lake, and present a basis for sustainable lake restoration and management.

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Spatial–Temporal Dynamics of Wetland Vegetation Related to Water Level Fluctuations in Poyang Lake, China

Cited by 30 publications

References 49 publications

Estimating Aboveground Biomass Using Sentinel-2 MSI Data and Ensemble Algorithms for Grassland in the Shengjin Lake Wetland, China

Estimating Aboveground Biomass Using Sentinel-2 MSI Data and Ensemble Algorithms for Grassland in the Shengjin Lake Wetland, China

Bacterial and Fungal Community Composition and Functional Activity Associated with Lake Wetland Water Level Gradients

Long Term Aquatic Vegetation Dynamics in Longgan Lake Using Landsat Time Series and Their Responses to Water Level Fluctuation

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