Contrasting Dynamics of Littoral and Riparian Reed Stands within a Wetland Complex of Lake Cerknica

Ojdanič, Nik; Zelnik, Igor; Holcar, Matej; Gaberščik, Alenka; Golob, Aleksandra

doi:10.3390/plants12051006

Cited by 3 publications

(4 citation statements)

References 51 publications

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“…Similarly, Micklin [48] believed that irrigated water withdrawal was the dominant factor causing the recession of the Aral Sea from 1911 to 2010. Nik et al [22] proved that more constant and moderate conditions at the riparian site benefited the growth and productivity of the common reed, we also found the annual in the lakeside area of the Aral Sea was significantly affected by the water level and lake surface area, showing a significant positive correlation. Therefore, the shrinkage of the Aral Sea caused by human factors is an important factor affecting the growth of Aral Sea vegetation.…”

Section: Discussionsupporting

confidence: 73%

“…The Normalized Difference Vegetation Index (NDVI) shows light absorbance and reflectance. For each pixel, we then calculated the NDVI using the following equation [22]:…”

Section: Normalized Difference Vegetation Index (Ndvi) and Maximum Va...mentioning

confidence: 99%

“…NDVI has various uses, including biomass estimation [18], plant productivity monitoring [19], plant stress detection [20], and leaf water potential estimation [21]. Nik et al [22] created an NDVI time series extending to the contrasting dynamics of littoral and riparian reed stands within a wetland complex of Lake Cerknica. Li et al [23] use the NDVI time series to find the monotonic trend of vegetation in China.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of NDVI Trends and Driving Factors in the Buffer Zone of the Aral Sea

Cui

Zheng

et al. 2023

Water

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A buffer zone can be used to analyze the influence of the lake on the surrounding spatial elements, which is of great significance for discussing the problems of lake retreat, vegetation degradation, and overall environmental deterioration in the Aral Sea. Taking the 3 km buffer zone of the Aral Sea as the research area, the spatiotemporal variation characteristics and main influencing factors of the Normalized Difference Vegetation Index (NDVI) in the Aral Sea research area were studied using remote sensing over 31 years (1987, 1997, 1992, 2007, 2010, 2014, 2015, 2017, and 2018). The results showed that the vegetation growth in the Aral Sea buffer zone deteriorates with the retreat of the lake; the vegetation of the small Aral Sea began to recover due to the stable water volume and salt content of the lake; vegetation began to grow in the west coast of the West Aral Sea; the shrinkage of the Aral Sea caused by human activities is an important factor affecting the growth of the vegetation. This study provides a reference for the restoration and reconstruction of regional vegetation.

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Section: Discussionsupporting

confidence: 73%

“…The Normalized Difference Vegetation Index (NDVI) shows light absorbance and reflectance. For each pixel, we then calculated the NDVI using the following equation [22]:…”

Section: Normalized Difference Vegetation Index (Ndvi) and Maximum Va...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analysis of NDVI Trends and Driving Factors in the Buffer Zone of the Aral Sea

Cui

Zheng

et al. 2023

Water

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“…Changing trends can be quantified using NDVI time series, but this estimation can significantly differ on the base of the (1) analysed satellite dataset, (2) the spatiotemporal resolution of the used satellite products, and (3) the applied statistical method [23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Urban and Peri-Urban Vegetation Monitoring Using Satellite MODIS NDVI Time Series, Singular Spectrum Analysis, and Fisher–Shannon Statistical Method

Telesca

Lovallo²,

Cardettini

et al. 2023

Sustainability

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The purpose of this work was to evaluate the potential of Singular Spectrum Analysis (SSA) and the Fisher–Shannon method to analyse NDVI MODIS time series and to capture and estimate inner vegetation anomalies in forest covers. In particular, the Fisher–Shannon method allows to calculate two quantities, the Fisher Information Measure (FIM) and the Shannon entropy power (SEP), which are used to characterise the complexity of a time series in terms of organisation/disorder. Pilot sites located both in urban (Milano, Torino, and Roma) and peri-urban areas (Appia Park, Castel Porziano, and Castel Volturno) were selected. Among the six sites, Roma, Castel Porziano, and Castel Volturno are affected by the parasite Toumeyella parvicornis. The time series was analysed using the products available in Google Earth Engine. To explore and characterise long-term vegetation dynamics, the time series was analysed using a multistep processing chain based on the (i) normalisation of the satellite time series, (ii) removal of seasonality and any other periodical cycles using SSA, (iii) analysis of the de-trended data using the Fisher–Shannon statistical method, and (iv) validation through comparison with independent data and ancillary information. Our findings point out to a clear discrimination between healthy and unhealthy sites, being the first (Milano, Torino, Appia) characterised by a larger FIM (lower SEP) and the second (Roma, Castel Porziano, Castel Volturno) by a lower FIM (larger SEP). The results of the investigations showed that the use of the SSA and Fisher–Shannon statistical methods coupled with the NDVI time series of the MODIS satellite made it possible to effectively identify and characterise subtle but physically significant signals veiled by seasonality and annual cycles.

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Water Dynamics Analysis in Karst Flood Areas Using Sentinel-1 Time Series

Breznik,

Oštir,

Ivačič

et al. 2023

Remote Sensing

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Studying karst water dynamics is challenging because of the often unknown underground flows. Therefore, studies of visible karst waters receive considerable research emphasis. Researchers are turning to various data sources, including remote sensing imagery, to study them. This research paper presents an assessment of a water bodies dataset, automatically detected from Sentinel-1 imagery, for karst flood research. Statistical and visual analyses were conducted to assess the reliability and effectiveness of the dataset. Spearman’s correlation coefficients were employed for statistical analysis to determine the degree of correlation between the areas of water bodies dataset and official water level data. Visual analyses involved the creation of heat maps based on the identified water areas, which were then compared to official flood maps, and the preparation of an analysis of historical flood events or results of hydrological and hydraulic modelling. Additionally, vegetation maps were produced to identify areas that lacked detection and complemented the heat maps. Statistical assessment showed a strong correlation (≥0.6) between the dataset and official water level data in smaller flood-prone areas with less complex inflow. Visual analyses using heat maps and vegetation maps effectively identified frequently flooded areas but had limitations in areas with dense vegetation. Comparisons with flood maps showed an important value of the dataset as an additional source of information for karst flood studies. This assessment highlights the dataset’s potential in combination with other data sources and modelling approaches.

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Contrasting Dynamics of Littoral and Riparian Reed Stands within a Wetland Complex of Lake Cerknica

Cited by 3 publications

References 51 publications

Analysis of NDVI Trends and Driving Factors in the Buffer Zone of the Aral Sea

Analysis of NDVI Trends and Driving Factors in the Buffer Zone of the Aral Sea

Urban and Peri-Urban Vegetation Monitoring Using Satellite MODIS NDVI Time Series, Singular Spectrum Analysis, and Fisher–Shannon Statistical Method

Water Dynamics Analysis in Karst Flood Areas Using Sentinel-1 Time Series

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