scholarly journals Spatial–Temporal Variations of Total Nitrogen and Phosphorus in Poyang, Dongting and Taihu Lakes from Landsat-8 Data

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1704
Author(s):  
Wei Shang ◽  
Shuanggen Jin ◽  
Yang He ◽  
Yuanyuan Zhang ◽  
Jian Li
Keyword(s):  
Total Nitrogen ◽  
Taihu Lake ◽  
Poyang Lake ◽  
Social Economy ◽  
Temporal Variations ◽  
Google Earth ◽  
Dongting Lake ◽  
Landsat 8 ◽  
Monitoring Methods ◽  
Nitrogen And Phosphorus

Poyang Lake, Dongting Lake, and Taihu Lake are the largest freshwater lakes in the middle and lower reaches of the Yangtze River, China. In recent years, the eutrophication level of lakes has increased with the development of the social economy and caused many environmental and social problems. The concentrations of total nitrogen (TN) and total phosphorus (TP) are the key indicators of the degree of eutrophication, but the traditional ground monitoring methods are not capable of capturing such parameters in whole lakes with high spatial-temporal resolution. In this paper, empirical models are established and evaluated between the TN and TP and remote sensing spectral factors in the three lakes using Landsat 8 Operational Land Imager (OLI) satellite data and in-situ data. The results show that the inversion accuracy is higher than 75%. The TN and TP concentrations in the three lakes are inversed based on the Google Earth Engine (GEE) platform from 2014 to 2020 and their spatial-temporal variations are analyzed. The results show that the concentrations of TN and TP in Poyang Lake were decreased by 5.99% and 7.13% over 7 years, respectively, and the TN in Dongting Lake was decreased by 5.25% while the TP remained stable. The temporal changes in TN and TP concentrations displayed seasonal variations. A low concentration was observed in summer and high concentrations were in spring and winter. The average concentrations of TN and TP in Taihu Lake were higher than that of the other two lakes. The TP concentration was increased by 17.3% over 7 years, while the TN concentration remained almost stable. The variation in TN in Taihu Lake was the same as the growth cycle of algae, with higher value in spring and winter and lower value in summer, while the concentration of TP was lower in spring and winter and higher in summer. The spatial distribution of TN and TP concentrations in the three major lakes was significantly affected by human activities, and the concentrations of TN and TP were higher in areas near cities and agricultural activities.

Novel simulation of aqueous total nitrogen and phosphorus concentrations in Taihu Lake with machine learning

2022 ◽  
Vol 204 ◽  
pp. 111940
Author(s):  
Hao Lu ◽  
Liuyan Yang ◽  
Yifan Fan ◽  
Xin Qian ◽  
Tong Liu
Keyword(s):  
Machine Learning ◽  
Total Nitrogen ◽  
Taihu Lake ◽  
Nitrogen And Phosphorus

scholarly journals Differences of Regulative Flexibility between Hydrological Isolated and Connected Lakes in a Large Floodplain: Insight from Inundation Dynamics and Landscape Heterogeneity

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 991
Author(s):  
Jiakun Teng ◽  
Shaoxia Xia ◽  
Yu Liu ◽  
Peng Cui ◽  
Jiang Chen ◽  
...  
Keyword(s):  
Habitat Quality ◽  
Yangtze River ◽  
Vegetation Index ◽  
Poyang Lake ◽  
Wetland Management ◽  
Google Earth ◽  
Dongting Lake ◽  
Water Levels ◽  
Water Level Fluctuations ◽  
Jianghan Plain

The inundation areas of floodplains are crucial to wetland ecosystems, especially in supporting biodiversity. Accurately identifying the spatial and temporal patterns of inundation areas is important for understanding floodplain ecosystem processes. Here, lakes in the Yangtze River Floodplain were divided into two types according to hydrological conditions: the natural connected lakes (Dongting Lake and Poyang Lake) with natural water level fluctuations and the isolated lakes (lakes in Jianghan Plain) with stable water levels. We established a method to identify inundation areas using multi-sources remote sensing data based on the Google Earth Engine. The dynamics of inundation areas were determined, and the relative indices were calculated in common year (2017) and a drought year (2018). The differences between the connected lakes and the isolated lakes were analyzed, and impacts of hydrological fluctuations on inundation area and habitat quality were evaluated. The results show that lakes with natural hydrological fluctuations have a greater regulative flexibility, with both patch density (PD) and submerged elasticity index (SEI) values higher than that of isolated lakes. The trend of the vegetation index in the connected lakes and in the isolated lakes is also different. The mean EVI in Dongting Lake and Poyang Lake showed a U-shaped trend which is similar to the shape of the trend of PD. The trend of mean enhanced vegetation index (EVI) in the isolated lakes is the opposite and has a lower range of variation over a year. This study provides new indicators and rapid methods for habitat quality assessment in floodplains, as well as presenting scientific information useful for improving wetland management in the middle and lower Yangtze River.

scholarly journals Spatial and Temporal Analysis of Surface Urban Heat Island and Thermal Comfort Using Landsat Satellite Images between 1989 and 2019: A Case Study in Tehran

2021 ◽  
Vol 13 (21) ◽  
pp. 4469
Author(s):  
Faezeh Najafzadeh ◽  
Ali Mohammadzadeh ◽  
Arsalan Ghorbanian ◽  
Sadegh Jamali
Keyword(s):  
Thermal Comfort ◽  
Urban Heat Island ◽  
Temporal Variations ◽  
Google Earth ◽  
Landsat 8 ◽  
Heat Island ◽  
Urban Heat ◽  
Surface Urban Heat Island ◽  
Spatio Temporal ◽  
The City

Mapping and monitoring the spatio-temporal variations of the Surface Urban Heat Island (SUHI) and thermal comfort of metropolitan areas are vital to obtaining the necessary information about the environmental conditions and promoting sustainable cities. As the most populated city of Iran, Tehran has experienced considerable population growth and Land Cover/Land Use (LULC) changes in the last decades, which resulted in several adverse environmental issues. In this study, 68 Landsat-5 and Landsat-8 images, collected from the Google Earth Engine (GEE), were employed to map and monitor the spatio-temporal variations of LULC, SUHI, and thermal comfort of Tehran between 1989 and 2019. In this regard, planar fitting and Gaussian Surface Model (GSM) approaches were employed to map SUHIs and derive the relevant statistical values. Likewise, the thermal comfort of the city was investigated by the Urban Thermal Field Variance Index (UTFVI). The results indicated that the SUHI intensities have generally increased throughout the city by an average value of about 2.02 °C in the past three decades. The most common reasons for this unfavorable increase were the loss of vegetation cover (i.e., 34.72%) and massive urban expansions (i.e., 53.33%). Additionally, the intra-annual investigations in 2019 revealed that summer and winter, with respectively 8.28 °C and 4.37 °C, had the highest and lowest SUHI magnitudes. Furthermore, the decadal UTFVI maps revealed notable thermal comfort degradation of Tehran, by which in 2019, approximately 52.35% of the city was identified as the region with the worst environmental condition, of which 59.94% was related to human residents. Additionally, the relationships between various air pollutants and SUHI intensities were appraised, suggesting positive relationships (i.e., ranging between 0.23 and 0.43) that can be used for establishing possible two-way mitigations strategies. This study provided analyses of spatio-temporal monitoring of SUHI and UTFVI throughout Tehran that urban managers and policymakers can consider for adaption and sustainable development.

scholarly journals Long-Term Spatial and Temporal Monitoring of Cyanobacteria Blooms Using MODIS on Google Earth Engine: A Case Study in Taihu Lake

2019 ◽  
Vol 11 (19) ◽  
pp. 2269 ◽  
Author(s):  
Tianxia Jia ◽  
Xueqi Zhang ◽  
Rencai Dong
Keyword(s):  
Remote Sensing ◽  
Taihu Lake ◽  
Management Strategies ◽  
Google Earth ◽  
Monitoring Methods ◽  
Cyanobacteria Bloom ◽  
Cyanobacteria Blooms ◽  
Routine Monitoring ◽  
Google Earth Engine

As cyanobacteria blooms occur in many types of inland water, routine monitoring that is fast and accurate is important for environment and drinking water protection. Compared to field investigations, satellite remote sensing is an efficient and effective method for monitoring cyanobacteria blooms. However, conventional remote sensing monitoring methods are labor intensive and time consuming, especially when processing long-term images. In this study, we embedded related processing procedures in Google Earth Engine, developed an operational cyanobacteria bloom monitoring workflow. Using this workflow, we measured the spatiotemporal patterns of cyanobacteria blooms in China’s Taihu Lake from 2000 to 2018. The results show that cyanobacteria bloom patterns in Taihu Lake have significant spatial and temporal differentiation: the interannual coverage of cyanobacteria blooms had two peaks, and the condition was moderate before 2006, peaked in 2007, declined rapidly after 2008, remained moderate and stable until 2015, and then reached another peak around 2017; bays and northwest lake areas had heavier cyanobacteria blooms than open lake areas; most cyanobacteria blooms primarily occurred in April, worsened in July and August, then improved after October. Our analysis of the relationship between cyanobacteria bloom characteristics and environmental driving factors indicates that: from both monthly and interannual perspectives, meteorological factors are positively correlated with cyanobacteria bloom characteristics, but as for nutrient loadings, they are only positively correlated with cyanobacteria bloom characteristics from an interannual perspective. We believe reducing total phosphorous, together with restoring macrophyte ecosystem, would be the necessary long-term management strategies for Taihu Lake. Our workflow provides an automatic and rapid approach for the long-term monitoring of cyanobacteria blooms, which can improve the automation and efficiency of routine environmental management of Taihu Lake and may be applied to other similar inland waters.

scholarly journals Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park

2021 ◽  
Vol 13 (12) ◽  
pp. 2299
Author(s):  
Andrea Tassi ◽  
Daniela Gigante ◽  
Giuseppe Modica ◽  
Luciano Di Martino ◽  
Marco Vizzari
Keyword(s):  
Land Cover ◽  
National Park ◽  
Time Window ◽  
Central Italy ◽  
Google Earth ◽  
Data Cube ◽  
Landsat 8 ◽  
Change Analysis ◽  
Object Based ◽  
Google Earth Engine

With the general objective of producing a 2018–2020 Land Use/Land Cover (LULC) map of the Maiella National Park (central Italy), useful for a future long-term LULC change analysis, this research aimed to develop a Landsat 8 (L8) data composition and classification process using Google Earth Engine (GEE). In this process, we compared two pixel-based (PB) and two object-based (OB) approaches, assessing the advantages of integrating the textural information in the PB approach. Moreover, we tested the possibility of using the L8 panchromatic band to improve the segmentation step and the object’s textural analysis of the OB approach and produce a 15-m resolution LULC map. After selecting the best time window of the year to compose the base data cube, we applied a cloud-filtering and a topography-correction process on the 32 available L8 surface reflectance images. On this basis, we calculated five spectral indices, some of them on an interannual basis, to account for vegetation seasonality. We added an elevation, an aspect, a slope layer, and the 2018 CORINE Land Cover classification layer to improve the available information. We applied the Gray-Level Co-Occurrence Matrix (GLCM) algorithm to calculate the image’s textural information and, in the OB approaches, the Simple Non-Iterative Clustering (SNIC) algorithm for the image segmentation step. We performed an initial RF optimization process finding the optimal number of decision trees through out-of-bag error analysis. We randomly distributed 1200 ground truth points and used 70% to train the RF classifier and 30% for the validation phase. This subdivision was randomly and recursively redefined to evaluate the performance of the tested approaches more robustly. The OB approaches performed better than the PB ones when using the 15 m L8 panchromatic band, while the addition of textural information did not improve the PB approach. Using the panchromatic band within an OB approach, we produced a detailed, 15-m resolution LULC map of the study area.

scholarly journals Retrieval and Evaluation of Chlorophyll-a Concentration in Reservoirs with Main Water Supply Function in Beijing, China, Based on Landsat Satellite Images

2021 ◽  
Vol 18 (9) ◽  
pp. 4419
Author(s):  
Yuequn Lai ◽  
Jing Zhang ◽  
Yongyu Song ◽  
Zhaoning Gong
Keyword(s):  
Remote Sensing ◽  
Water Supply ◽  
Chlorophyll A ◽  
Phosphorus Concentration ◽  
Landsat 8 ◽  
Supply Function ◽  
Estimation Model ◽  
Nitrogen And Phosphorus ◽  
Chlorophyll A Concentration ◽  
Guanting Reservoir

Remote sensing retrieval is an important technology for studying water eutrophication. In this study, Guanting Reservoir with the main water supply function of Beijing was selected as the research object. Based on the measured data in 2016, 2017, and 2019, and Landsat-8 remote sensing images, the concentration and distribution of chlorophyll-a in the Guanting Reservoir were inversed. We analyzed the changes in chlorophyll-a concentration of the reservoir in Beijing and the reasons and effects. Although the concentration of chlorophyll-a in the Guanting Reservoir decreased gradually, it may still increase. The amount and stability of water storage, chlorophyll-a concentration of the supply water, and nitrogen and phosphorus concentration change are important factors affecting the chlorophyll-a concentration of the reservoir. We also found a strong correlation between the pixel values of adjacent reservoirs in the same image, so the chlorophyll-a estimation model can be applied to each other.

scholarly journals Patterns, Trends and Drivers of Water Transparency in Sri Lanka Using Landsat 8 Observations and Google Earth Engine

2021 ◽  
Vol 13 (11) ◽  
pp. 2193
Author(s):  
Deepakrishna Somasundaram ◽  
Fangfang Zhang ◽  
Sisira Ediriweera ◽  
Shenglei Wang ◽  
Ziyao Yin ◽  
...  
Keyword(s):  
Sri Lanka ◽  
Human Activity ◽  
Google Earth ◽  
Water Transparency ◽  
Conservation Strategies ◽  
Landsat 8 ◽  
Essential Information ◽  
Annual Variations ◽  
North East ◽  
South West Monsoon

Addressing inland water transparency and driver effects to ensure the sustainability and provision of good quality water in Sri Lanka has been a timely prerequisite, especially under the Sustainable Development Goals 2030 agenda. Natural and anthropogenic changes lead to significant variations in water quality in the country. Therefore, an urgent need has emerged to understand the variability, spatiotemporal patterns, changing trends and impact of drivers on transparency, which are unclear to date. This study used all available Landsat 8 images from 2013 to 2020 and a quasi-analytical approach to assess the spatiotemporal Secchi disk depth (ZSD) variability of 550 reservoirs and its relationship with natural (precipitation, wind and temperature) and anthropogenic (human activity and population density) drivers. ZSD varied from 9.68 cm to 199.47 with an average of 64.71 cm and 93% of reservoirs had transparency below 100 cm. Overall, slightly increasing trends were shown in the annual mean ZSD. Notable intra-annual variations were also indicating the highest and lowest ZSD during the north-east monsoon and south-west monsoon, respectively. The highest ZSD was found in wet zone reservoirs, while dry zone showed the least. All of the drivers were significantly affecting the water transparency in the entire island. The combined impact of natural factors on ZSD changes was more significant (77.70%) than anthropogenic variables, whereas, specifically, human activity accounted for the highest variability across all climatic zones. The findings of this study provide the first comprehensive estimation of the ZSD of entire reservoirs and driver contribution and also provides essential information for future sustainable water management and conservation strategies.

scholarly journals The Effect of Bacillus licheniformis MH48 on Control of Foliar Fungal Diseases and Growth Promotion of Camellia oleifera Seedlings in the Coastal Reclaimed Land of Korea

Pathogens ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 6 ◽  
Author(s):  
Sang-Jae Won ◽  
Jun-Hyeok Kwon ◽  
Dong-Hyun Kim ◽  
Young-Sang Ahn
Keyword(s):  
Total Nitrogen ◽  
Bacillus Licheniformis ◽  
Growth Promotion ◽  
Dry Weight ◽  
Fungal Diseases ◽  
Camellia Oleifera ◽  
Bacterial Inoculation ◽  
Lytic Enzymes ◽  
Nitrogen And Phosphorus ◽  
Reclaimed Land

This study investigated the control of foliar fungal diseases and growth promotion of Camellia oleifera seedlings in coastal reclaimed land through the use of Bacillus licheniformis MH48. B. licheniformis MH48 can produce lytic enzymes chitinase and β-1,3-glucanase that can inhibit foliar pathogens by 37.4 to 50.5%. Nevertheless, foliar diseases appeared in the seedlings with bacterial inoculation, and their survival rate decreased because they were unable to withstand salt stress. However, B. licheniformis MH48 significantly increased the total nitrogen and phosphorus contents in the soils through fixing atmospheric nitrogen and solubilizing phosphorus. The growth of seedlings with bacterial inoculation increased, particularly in root dry weight, by 7.42 g plant−1, which was 1.7-fold greater than that of the control. B. licheniformis MH48 produces the phytohormone auxin, which potentially stimulates seedling root growth. C. oleifera seedlings significantly increased in total nitrogen content to 317.57 mg plant−1 and total phosphorus content to 46.86 mg plant−1. Our results revealed the effectiveness of B. licheniformis MH48 not only in the control of foliar fungal diseases but also in the growth promotion of C. oleifera seedlings in coastal lands.

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