Dynamic Monitoring of Surface Water Area during 1989–2019 in the Hetao Plain Using Landsat Data in Google Earth Engine

Wang, Ruimeng; Xia, Haoming; Qin, Yaochen; Niu, Wenhui; Pan, Li; Li, Rumeng; Zhao, Xiaoyang; Bian, Xiqing; Fu, Pinde

doi:10.3390/w12113010

Cited by 36 publications

(28 citation statements)

References 72 publications

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“…All of the available Landsat images (7534 scenes) were used for the Hetao Plain in China for determining the long-term changes of the surface water area. Normalized Difference Water Index (NDWI) and Normalized Vegetation Index (NDVI) were applied to map the open-surface water from 1989 to 2019 in the GEE [47]. In the study conducted at the Tarim River Basin of China, 56284 Landsat scenes were used to determine the available surface area and generate a 30 m annual water frequency map from 1992 to 2019 [48].…”

Section: Criteriamentioning

confidence: 99%

Analysis of the Temporal Changes of Inland Ramsar Sites in Turkey Using Google Earth Engine

Dervisoglu

2021

IJGI

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Ramsar Convention (RC) is the first of modern intergovernmental agreement on the conscious use and conservation of natural resources. It provides a platform for contracting parties working together to develop the best available data, advice, and policy recommendations to increase awareness of the benefits of wetlands in nature and society. Turkey became a party of the RC in 1994, and in the years 1994 to 2013, 14 wetlands that reached the Ramsar criteria were recognized as Ramsar sites (RS). With this study, all inland RS in Turkey from 1985 to 2020 were examined, and changes in the water surface areas were evaluated on the GEE cloud computing platform using Landsat satellite images and the NDWI index. The closest meteorological station data to each RS were evaluated and associated with the surface area changes. The reasons for the changes in these areas, besides the meteorological effects, have been scrutinized using management plans and publications. As a result, inland wetlands decreased at different rates from 1985 to 2020, with a total loss of 31.38% and 21571.0 ha for the spring months. Since the designation dates of RS, the total amount of water surface area reduction was 27.35 %, constituting 17,758.90 ha.

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Section: Criteriamentioning

confidence: 99%

Analysis of the Temporal Changes of Inland Ramsar Sites in Turkey Using Google Earth Engine

Dervisoglu

2021

IJGI

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“…These problems have an impact on the modernization process of Chinese cities and towns, and have attracted the attention of relevant experts and researchers [18,19]. In recent years, with the advances of big data technology, more scholars use remote sensing big data to study urban built-up areas, vegetation, water, and other aspects [20][21][22]. In general, previous studies on urban shrinkage and expansion mainly rely on population data and statistical yearbooks [23,24].…”

Section: Introductionmentioning

confidence: 99%

Research on Large-Scale Urban Shrinkage and Expansion in the Yellow River Affected Area Using Night Light Data

Niu

Xia

Wang

et al. 2020

IJGI

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As the land use issue, caused by urban shrinkage in China, is becoming more and more prominent, research on urban shrinkage and expansion has become particularly challenging and urgent. Based on the points of interest (POI) data, this paper redefines the scope, quantity, and area of natural cities by using threshold methods, which accurately identify the shrinkage and expansion of cities in the Yellow River affected area using night light data in 2013 and 2018. The results show that: (1) there are 3130 natural cities (48,118.75 km2) in the Yellow River affected area, including 604 shrinking cities (8407.50 km2) and 2165 expanding cities (32,972.75 km2). (2) The spatial distributions of shrinking and expanding cities are quite different. The shrinking cities are mainly located in the upper Yellow River affected area, except for the administrative cities of Lanzhou and Yinchuan; the expanding cities are mainly distributed in the middle and lower Yellow River affected area, and the administrative cities of Lanzhou and Yinchuan. (3) Shrinking and expanding cities are typically smaller cities. The research results provide a quick data supported approach for regional urban planning and land use management, for when regional and central governments formulate the outlines of urban development monitoring and regional planning.

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“…A land surface water index (LSWI) is used to assist identification of the start and end growth stages of winter crops [8,30]. In this study, a modified normalized difference water index (mNDWI) and enhanced vegetation index (EVI) were also calculated to extract different land cover types [56][57][58][59]. The calculation formulas are as follows:…”

Section: Index Calculation and Time-series Processingmentioning

confidence: 99%

Mapping Winter Crops Using a Phenology Algorithm, Time-Series Sentinel-2 and Landsat-7/8 Images, and Google Earth Engine

et al. 2021

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With the increasing population and continuation of climate change, an adequate food supply is vital to economic development and social stability. Winter crops are important crop types in China. Changes in winter crops planting areas not only have a direct impact on China’s production and economy, but also potentially affects China’s food security. Therefore, it is necessary to obtain information on the planting of winter crops. In this study, we use the time series data of individual pixels, calculate the temporal statistics of spectral bands and the vegetation indices of optical data based on the phenological characteristics of specific vegetation or crops and record them in the time series data, and apply decision trees and rule-based algorithms to generate annual maps of winter crops. First, we constructed a dataset combining all the available images from Landsat 7/8 and Sentinel-2A/B. Second, we generated an annual map of land cover types to obtain the cropland mask in 2019. Third, we generated a time series of a single cropland pixel, and calculated the phenological indicators for classification by extracting the differences in phenological characteristics of different crops: these phenological indicators include SOS (start of season), SDP (start date of peak), EOS (end of season), GUS (green-up speed) and GSL (growing-season length). Finally, we identified winter crops in 2019 based on their phenological characteristics. The main advantages of the phenology-based algorithm proposed in this study include: (1) Combining multiple sensor data to construct a high spatiotemporal resolution image collection. (2) By analyzing the whole growth season of winter crops, the planting area of winter crops can be extracted more accurately, and (3) the phenological indicators of different periods are extracted, which is conducive to monitoring winter crop planting information and seasonal dynamics. The results show that the algorithm constructed in this study can accurately extract the planting area of winter crops, with user, producer, overall accuracies and Kappa coefficients of 96.61%, 94.13%, 94.56% and 0.89, respectively, indicating that the phenology-based algorithm is reliable for large area crop classification. This research will provide a point of reference for crop area extraction and monitoring.

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Dynamic Monitoring of Surface Water Area during 1989–2019 in the Hetao Plain Using Landsat Data in Google Earth Engine

Cited by 36 publications

References 72 publications

Analysis of the Temporal Changes of Inland Ramsar Sites in Turkey Using Google Earth Engine

Analysis of the Temporal Changes of Inland Ramsar Sites in Turkey Using Google Earth Engine

Research on Large-Scale Urban Shrinkage and Expansion in the Yellow River Affected Area Using Night Light Data

Mapping Winter Crops Using a Phenology Algorithm, Time-Series Sentinel-2 and Landsat-7/8 Images, and Google Earth Engine

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