Integrated Risk Assessment of Agricultural Drought Disasters in the Major Grain-Producing Areas of Jilin Province, China

Zhang, Jiawang; Wang, Jianguo; Chen, Shengbo; Wang, Mingchang; Tang, Siqi; Zhao, Wutao

doi:10.3390/land12010160

Cited by 8 publications

(5 citation statements)

References 42 publications

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“…Over the years, per capita grain sowing area constitutes more than 85% of the total arable land, and the annual grain out put is 16-25.1 million tons, making up approximately 4-5% of the country's total grain production capacity, which has reached 25 million tons. Over the years, per capita grain production, sales volume, and export volume of grain have been among the highest in China [33].…”

Section: Study Areamentioning

confidence: 99%

Improving Crop Mapping by Using Bidirectional Reflectance Distribution Function (BRDF) Signatures with Google Earth Engine

et al. 2023

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Recent studies have demonstrated the potential of using bidirectional reflectance distribution function (BRDF) signatures captured by multi-angle observation data to enhance land cover classification and retrieve vegetation architectures. Considering the diversity of crop architectures, we proposed that crop mapping precision may be enhanced by using BRDF signatures. We compared the accuracy of four supervised machine learning classifiers provided by the Google Earth Engine (GEE), namely random forest (RF), classification and regression trees (CART), support vector machine (SVM), and Naïve Bayes (NB), using the moderate resolution imaging spectroradiometer (MODIS) nadir BRDF-adjusted reflectance data (MCD43A4 V6) and BRDF and albedo model parameter data (MCD43A1 V6) as input. Our results indicated that using BRDF signatures leads to a moderate improvement in classification results in most cases, compared to using reflectance data from a single nadir observation direction. Specifically, the overall validation accuracy increased by up to 4.9%, and the validation kappa coefficients increased by up to 0.092. Furthermore, the classifiers were ranked in order of accuracy, from highest to lowest: RF, CART, SVM, and NB. Our study contributes to the development of crop mapping and the application of multi-angle observation satellites.

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Section: Study Areamentioning

confidence: 99%

Improving Crop Mapping by Using Bidirectional Reflectance Distribution Function (BRDF) Signatures with Google Earth Engine

et al. 2023

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“…The system dynamics model emphasizes the interaction, connection and dynamic evolution process between watershed ecosystems [28] . The research problem is described by a causality diagram consisting of interconnected feedback loops [29] .…”

Section: Threat Factors Analysis Of Watershed Ecological Security Bas...mentioning

confidence: 99%

Study on Ecological Security Evaluation Index System of the Yellow River Basin

Tian

Feng

et al. 2023

E3S Web of Conf.

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As an important energy base and ecological barrier in China, the ecological security of the Yellow River Basin has a very important strategic position in the pattern of high-quality development, but it is threatened by human activities and climate change. In order to evaluate the degree of ecological security in the Yellow River Basin and clarify the influencing factors of ecological security, this paper discusses the advantages and disadvantages of the existing ecological security assessment methods and models. On this basis, the system dynamics model is used to describe the process of ecological security threats, identify the key factors that threaten ecological security, and then sort out the ecological security assessment indicators. The ecological security assessment model composed of pressure(P), state(S), hidden dangers(D), response(R) and management (M) (PSDRM) indicators is constructed, and the ecological security assessment methods and processes are improved to provide theoretical guidance and technical support for improving the ecological security assessment of the basin.

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“…DU combined information diffusion models with machine learning to estimate typhoon storm surge disaster losses [9]. Zhang employed an information diffusion model to conduct comprehensive risk assessments of agricultural drought disasters in the main grain-producing areas of Jilin Province, China [10]. Wang utilized a generalized information diffusion model to evaluate and analyze post-earthquake road damage situations [11].…”

Section: Introductionmentioning

confidence: 99%

Interpolation of China’s Nearshore Sea Surface Temperature Based on Information Diffusion with Small Sample Sizes

Wang,

Shi,

et al. 2024

J. Phys.: Conf. Ser.

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Addressing the issue of data sparsity and gaps caused by missing values, this study employs an information diffusion approach to effectively spread information from sparse sample points to monitoring locations. By thoroughly extracting insights from a limited dataset, it achieves more precise interpolation outcomes. To validate the superiority of the information diffusion interpolation technique under conditions of sparse samples, we utilize sea surface temperature (SST) data from the offshore waters of China as a case study. We compare three interpolation methods: Kriging, Gaussian information diffusion, and asymmetric information diffusion. The calculations and comparisons of interpolation results are conducted across varying sample sizes. The findings indicate that in situations with relatively sparse samples, asymmetric information diffusion yields the most favorable results, with Kriging and Gaussian diffusion exhibiting comparable performance. In cases of extremely sparse samples, asymmetric information diffusion yields the lowest interpolation error, followed by Gaussian diffusion, while Kriging performs the least effectively. Thus, when confronted with sample sparsity, the application of the information diffusion interpolation method can yield notably improved results.

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Integrated Risk Assessment of Agricultural Drought Disasters in the Major Grain-Producing Areas of Jilin Province, China

Cited by 8 publications

References 42 publications

Improving Crop Mapping by Using Bidirectional Reflectance Distribution Function (BRDF) Signatures with Google Earth Engine

Improving Crop Mapping by Using Bidirectional Reflectance Distribution Function (BRDF) Signatures with Google Earth Engine

Study on Ecological Security Evaluation Index System of the Yellow River Basin

Interpolation of China’s Nearshore Sea Surface Temperature Based on Information Diffusion with Small Sample Sizes

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