Mr. Dongying Zhang Profile

Dongying Zhang

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Proceedings Article | 23 January 2024 Paper

Exploring the source of GPM IMERG and GSMaP errors based on error component method over the Yellow River basin

Xiaohang Liu, Mengqiao He, Dongying Zhang

Proceedings Volume 12978, 129780R (2024) https://doi.org/10.1117/12.3020917

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Precise precipitation data is essential for effective hydrologic modeling, water resource management, and climate research. IMERG and GSMaP, which are high-resolution and wide-coverage satellite products, have great potential in monitoring precipitation. However, further research is required to understand the error characteristics of precipitation. This study focuses on the Yellow River Basin, results demonstrate that: (1) GPM IMERG and GSMaP display strong spatial patterns, with more precise detection in the east than the west. Hit bias dominates in the total bias, and each product overestimates the error. The GSMaP_Gauge product performs better in reducing hit bias and improving detection accuracy. (2) The Yellow River Basin is generally biased towards low-intensity precipitation events, with the GSMaP_Gauge product performing the best, followed by the IMERG_Final product with the smallest missed precipitation. (3) Altitude has a significant impact on the total bias and error components, mainly showing a negative correlation. As altitude increases, the total bias and error components gradually decrease. This study can facilitate the creation of quantitative bias models and the implementation of techniques to reduce bias in GPM IMERG and GSMaP by examining the structure and attributes of error components.

Proceedings Article | 19 January 2024 Paper

Monitoring the spatiotemporal changes in annual cropland area and evapotranspiration analysis in the Yellow River Basin from 2001 to 2021

Shuhui Guo, Mengqiao He, Dongying Zhang

Proceedings Volume 12980, 129802M (2024) https://doi.org/10.1117/12.3021089

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Crop area monitoring using remote sensing technology has played an important role in serving agricultural production, ensuring food security, and achieving sustainable water resources management. To obtain information on the distribution of major crops in the Yellow River Basin (YRB), this study utilized the MODIS time series remote sensing dataset from2001 to 2021, with wheat and maize as the main crops of interest. The study identified crop planting patterns and types using NDVI long-term time series data and a threshold method, and further analyzed and explored their spatiotemporal evolution patterns. Finally, the spatial characteristics of potential evapotranspiration in the YRB are analyzed. The results indicated that the overall classification error of wheat and maize planting areas in the YRB was small. There were significant spatiotemporal differences in crop planting structure in the YRB, with a gradual decline in wheat planting area and a continuous increase in maize planting area. The planting centers of both crops were shifting towards the northeast. The results will provide a rapid and robust method to be applied for wheat and maize planted area monitoring in other regions. This study also contributes to achieving multi-year dynamic monitoring of crop types and exploring the variation patterns of evapotranspiration in the YRB.

Proceedings Article | 30 August 2023 Paper

Land use type identification on the basis of drone imagery and object-oriented methods

Demao Sun, Mengqiao He, Dongying Zhang

Proceedings Volume 12797, 127971S (2023) https://doi.org/10.1117/12.3007579

KEYWORDS: Random forests, Image segmentation, Unmanned aerial vehicles, Image classification, Support vector machines, Machine learning, Remote sensing, Image processing algorithms and systems

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This paper takes part of Wulian County, Rizhao City, Shandong Province as the study area, and utilizes UAV image data for land use classification research. The object-oriented random forest (RF) algorithm, logistic regression (LR) algorithm and support vector machine (SVM) algorithm combined with spectral features, index features and texture features are used to classify the land use and compare the classification effects of different algorithms. The results show that the object-oriented random forest algorithm performs better than the other algorithms, achieving an overall accuracyof89.74% and a Kappa coefficient of 0.872. The combination of object-oriented and machine learning methods can be effective for land use classification, and the classification accuracy is also higher.

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