Vegetation changes in Yellow River Delta wetlands from 2018 to 2020 using PIE-Engine and short time series Sentinel-2 images

Dong, Chen; Wang, Zhiyong; Ning, Xi-Jing; Li, Zhenjin; Zhang, Long; Liu, Xiaotong

doi:10.3389/fmars.2022.977050

Cited by 14 publications

(7 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since some areas of the study area are inaccessible, it is necessary to use the method of visual interpretation to obtain the sample data. Sample selection is based on Google Earth images, Landsat series images, and other scholars' research results [11][12] . The total number of samples in this experiment is 945, and the number of training samples and validation samples for each location is shown in Table 1.…”

Section: Sample Datamentioning

confidence: 99%

Monitoring wetland evolution in the Yellow River delta from 1990 to 2020 based on long time series landsat data

liu,

Wang,

Zhang

2023

Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023)

View full text Add to dashboard Cite

Section: Sample Datamentioning

confidence: 99%

Monitoring wetland evolution in the Yellow River delta from 1990 to 2020 based on long time series landsat data

liu,

Wang,

Zhang

2023

Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023)

View full text Add to dashboard Cite

“…PA and SA share remarkable morphological and color similarities, which create signi cant challenges for accurate crosstemporal supervised classi cation (Chang et al 2022). However, there exist discernible differences in the phenology of these saltmarshes, providing an opportunity for differentiation.…”

Section: Phenology Identi Cationmentioning

confidence: 99%

“…Prior evidences indicate that PA's growth spans April to October, peaking between late July and early August, whereas SA's growth lags behind PA in the YRD wetland by about a month (Chang et al 2022; C. . Therefore, we synthesized remote sensing imagery for three timeframes in 2021: April to June, June to October, and October to November, and displayed in a false-color composite of near-infrared, infrared, and green bands (Fig.…”

Section: Phenology Identi Cationmentioning

confidence: 99%

“…Wang et al 2021a). Considering the signi cant advantages of the Sentinel-2 dataset in terms of resolution (up to 10m), it enables the possibility of long-term and high spatiotemporal resolution wetland mapping (Chang et al 2022;Zhou, 2022;Dong et al 2016), and can deepen our comprehension of the multiple hydrological driving forces on the regime shifts of estuarine wetland evolution (Goldberg et al 2020; Murray et al 2019).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spatio-temporal changes and hydrological forces of wetland landscape pattern in the Yellow River Delta during 1986-2022

Qiu,

Liu,

Chen

et al. 2023

Preprint

View full text Add to dashboard Cite

Context: Estuarine wetlands provide valuable ecosystem services but have undergone continuous change under the pressures of climate change and anthropogenic disturbance. The Yellow River Delta (YRD) wetland, which is one of the most vigorous delta areas in the world, has undergone dramatic changes. However, the long-term and high-resolution typical salt marsh vegetation evolution processes and hydrological drivers remain unclear. Objectives (1) Generate annual mapping of salt marsh vegetation in the YRD wetland from 1986 to 2022. (2) Analyze the trends of wetland patch area and landscape pattern, and explain the hydrological drivers of landscape pattern evolution. Methods Combining Landsat 5‒8 and Sentinel-2 images, vegetation phenology, remote sensing indices, and Random Forest supervised classification to map the typical salt marsh vegetation. We applied piecewise linear regression model to analyze YRD wetland changes, and stepwise multiple linear regression was used for assessing the impact of hydrological factors on landscape pattern. Results We identified three stages of landscape pattern evolution with 1997 and 2009 as turning points, including the rapid expansion stage, gradual decline stage, and bioinvasion stage. In the first phase, the wetland area was expanded by 70.45%, while the typical salt marsh vegetation, Phragmites australis area was reduced by 25%. In the second phase, the wetland was reduced by 21.33% and the Phragmites australis area was reduced by 15.96%. The third stage, Spartina alterniflora demonstrated an unstoppable trend of rapid expansion, with an area increase of 68 times relative to 2009, expanding at an average rate of 344 hm2 per year. Conclusions Areas of wetland, tidal flat, and Phragmites australis are significantly influenced by cumulative sediment and cumulative runoff, which total explain 61.5%, 75.7% and 63.8% of their variations, respectively. Wetland and tidal flat areas increased with the cumulative sediment, while cumulative runoff had a weak negative effect. As for Phragmites australis, cumulative runoff had a positive effect, whereas cumulative sediment had a negative effect. Water resources regulation measures should be taken to prevent the degradation of wetland ecosystems, and intervention measures can be implemented during the seedling stage to control the invasion of Spartina alterniflora.

show abstract

“…The robust performance and versatility of PIE-Engine have already been demonstrated in various fields of geology and remote sensing. Notable applications include the extraction of vegetation changes in the Yellow River Delta wetlands [49], the fine classification and mapping of wetlands [50], and the monitoring of the area of large inland lakes [51]. In addition, the remote sensing cloud computing platform helps researchers to easily access high-performance computing resources by providing a convenient environment for algorithm development and data interaction [52].…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Impact and Importance of Natural, Economic, and Mining Activities on Environmental Quality Using the PIE-Engine Cloud Platform: A Case Study of Seven Typical Mining Cities in China

Zeng,

Dai,

et al. 2024

Sustainability

View full text Add to dashboard Cite

The environmental quality of a mining city has a direct impact on regional sustainable development and has become a key indicator for assessing the effectiveness of national environmental policies. However, against the backdrop of accelerated urbanization, increased demand for resource development, and the promotion of the concept of ecological civilization, mining cities are faced with the major challenge of balancing economic development and ecological environmental protection. This study aims to deeply investigate the spatial and temporal variations of environmental quality and its driving mechanisms of mineral resource-based cities. This study utilizes the wide coverage and multitemporal capabilities of MODIS optical and thermal infrared remote sensing data. It innovatively develops the remote sensing ecological index (RSEI) algorithm on the PIE-Engine cloud platform to quickly obtain the RSEI, which reflects the quality of the ecological environment. The spatial and temporal evolution characteristics of the environmental quality in seven typical mining cities in China from 2001 to 2022 were analyzed. Combined with the vector mine surface data, the spatial and temporal variability of the impacts of mining activities on the ecological environment were quantitatively separated and explored. In particular, the characteristics of mining cities were taken into account by creating buffer zones and zoning statistics to analyze the response relationship between RSEI and these factors, including the distance to the mining area and the percentage of the mining area. In addition, the drivers and impacts of RSEI in 2019 were analyzed through Pearson correlation coefficients pixel by pixel with 10 factors, including natural, economic, and mining. Regression modeling of RSEI in 2019 was performed using the random forest (RF) model, and these drivers were ranked in order of importance through random forest factor importance assessment. The results showed that (1) the ecological quality of mining cities changed significantly during the study period, and the negative impacts of mining activities on the ecological environment were significant. (2) The areas with low RSEI values were closely related to the mining areas and cities. (3) The RSEI in the mining areas of mining cities was generally lower than the average level of the cities. The RSEI gradually increased as the distance to the mine site increased. (4) The increase in the size of the mine area initially exacerbates the impact on the ecological environment, but the impact is weakened beyond a certain threshold. (5) The distance to the mining area is the most important factor affecting the quality of the ecological environment, followed by DEM, GDP, and precipitation. This study is of great importance for advancing sustainable development in mining cities and formulating sustainable strategies.

show abstract

Vegetation changes in Yellow River Delta wetlands from 2018 to 2020 using PIE-Engine and short time series Sentinel-2 images

Cited by 14 publications

References 52 publications

Monitoring wetland evolution in the Yellow River delta from 1990 to 2020 based on long time series landsat data

Monitoring wetland evolution in the Yellow River delta from 1990 to 2020 based on long time series landsat data

Spatio-temporal changes and hydrological forces of wetland landscape pattern in the Yellow River Delta during 1986-2022

Quantifying the Impact and Importance of Natural, Economic, and Mining Activities on Environmental Quality Using the PIE-Engine Cloud Platform: A Case Study of Seven Typical Mining Cities in China

Contact Info

Product

Resources

About