Monitoring Spatiotemporal Changes of Impervious Surfaces in Beijing City Using Random Forest Algorithm and Textural Features

Dong, Xuegang; Meng, Zhiguo; Wang, Yongzhi; Zhang, Yuanzhi; Sun, Haoteng; Wang, Qingshuai

doi:10.3390/rs13010153

Cited by 20 publications

(13 citation statements)

References 42 publications

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“…e algorithm can be seen from the 1970s and 1980s. e most commonly used algorithms are the ID3 logging algorithm, the C4.5 logging algorithm, and the push logging algorithm [8].…”

Section: Decision Treementioning

confidence: 99%

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Rapid Identification of Tobacco Mildew Based on Random Forest Algorithm

Jiang

Zhang

Huang

et al. 2022

Scientific Programming

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In order to further improve the identification efficiency of tobacco mildew, a rapid identification model of tobacco mildew based on random forest algorithm was proposed in this study. In order to ensure the feasibility and pertinence of the model study, this study takes redried leaf tobacco as the research object, selects high-temperature and high-humidity environment as the experimental conditions, and obtains the sample data of the degree of tobacco mildew under different experimental conditions. At the same time, this paper constructs a rapid identification model of tobacco mildew with the help of random forest algorithm. Through the model experimental results, it is found that the accuracy of the model for the rapid identification of training samples can reach 93.82%, while the accuracy of independent testing is 94.84%. The experimental results fully reflect the availability and efficiency of the random forest algorithm model in the rapid identification of tobacco mildew.

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“…e algorithm can be seen from the 1970s and 1980s. e most commonly used algorithms are the ID3 logging algorithm, the C4.5 logging algorithm, and the push logging algorithm [8].…”

Section: Decision Treementioning

confidence: 99%

“…For the training sample set (8), N is the total number of samples, the object in X has an M-dimensional feature vector, and Y includes F different categories of information.…”

Section: Random Forest Modelmentioning

confidence: 99%

Rapid Identification of Tobacco Mildew Based on Random Forest Algorithm

Jiang

Zhang

Huang

et al. 2022

Scientific Programming

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“…Related studies have shown that the addition of texture features can improve accuracy of ISA mapping [43,44]. Referring to the research of Szantoi et al [45], this study selected the following six second-order texture features with minimal correlation for classification to calculate including Entropy (ENT), Angular Second Moment (ASM), Dissimilarity (DIS), Homogeneity (HOM), Mean (MEAN), and Variance (VAR) [43]. Their calculation formulas are as follows.…”

Section: Textural Featuresmentioning

confidence: 99%

Large-Scale Impervious Surface Area Mapping and Pattern Evolution of the Yellow River Delta Using Sentinel-1/2 on the GEE

Liu

Zhang

et al. 2022

Remote Sensing

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The ecological environment of Yellow River Delta High-efficiency Ecological Economic Zone (YRDHEEZ) is adjacent to the Bohai Sea. The unique geographical location makes it highly sensitive to anthropogenic disturbances. As an important land surface biophysical parameter, the impervious surface area (ISA) can characterize the level of urbanization and measure the intensity of human activities, and hence, the timely understanding of ISA dynamic changes is of great significance to protect the ecological safety of the YRDHEEZ. Based on the multi-source and multi-modal Sentinel-1/2 remotely sensed data provided by Google Earth Engine (GEE) cloud computing platform, this study developed a novel approach for the extraction of time-series ISA in the YRDHEEZ through a combination of random forest algorithm and numerous representative features extracted from Sentinel-1/2. Subsequently, we revealed the pattern of the ISA spatial-temporal evolution in this region over the past five years. The results demonstrated that the proposed method has good performance with an average overall accuracy of 94.84% and an average kappa coefficient of 0.9393, which verified the feasibility of the proposed method for large-scale ISA mapping with 10m. Spatial-temporal evolution analysis revealed that the ISA of the YRDHEEZ decreased from 5211.39 km2 in 2018 to 5147.02 km2 in 2022 with an average rate of −16.09 km2/year in the last 5 years, suggesting that the ISA of YRDHEEZ has decreased while its overall pattern was not significantly changed over time. The presented workflow can provide a reference for large-scale ISA mapping and its evolution analysis, especially in regions on estuarine deltas.

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“…All images were acquired in winter and spring (between January and April) (as shown in Table 1). When only Landsat spectral features are used for wetland classification, different objects may have the same spectral features, so only spectral features cannot distinguish wetland types [39,40]. In order to improve the accuracy of wetland recognition, we use two image enhancement methods: principal component analysis (PCA) and minimum noise fraction rotation (MNF).…”

Section: Datasetsmentioning

confidence: 99%

Satellite-Based Monitoring of Coastal Wetlands in Yancheng, Jiangsu Province, China

Chen

Feng

Wang

et al. 2022

JMSE

Self Cite

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The dynamic process of the wetland can reflect its impact on the environment, and finding a balance point supporting harmonious coexistence between man and nature has become an issue of increasing concern. On the basis of previous studies that have focused on local coastal wetlands, the temporal and spatial changes and driving forces of wetlands in the Yancheng coastal area from 1991 to 2021 were analyzed over a larger area. According to the study findings: (1) The results of the study of the Yancheng coastal wetland with a larger scope differed significantly from findings resulting from a study of coastal wetland only. This difference was mainly reflected in the relatively stable situation of wetland ecology as a whole, while the changes in local surface features were more significant. (2) Natural wetlands were transformed into artificial wetlands and non-wetland types, and artificial wetlands were transformed into non-wetland types; additionally, reverse transformations and internal transformations of surface features also took place. For instance, the saltpan was transformed into mudflats (86.26 km2), and some mudflats into herbaceous vegetation (193.47 km2). (3) When analyzing the impact intensity of human activities on the Yancheng wetland, it was found that this factor has experienced a process of first rising and then falling. The index was 0.650, 0.653, 0.664, 0.661, and 0.641 in 1991, 2000, 2008, 2016, and 2021, respectively. (4) Lastly, an analysis of factors driving wetland change revealed that human factors were the most critical reasons for wetland landscape change. Our work can play a reference and inspiration role in the monitoring and protection of similar coastal wetlands.

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Monitoring Spatiotemporal Changes of Impervious Surfaces in Beijing City Using Random Forest Algorithm and Textural Features

Cited by 20 publications

References 42 publications

Rapid Identification of Tobacco Mildew Based on Random Forest Algorithm

Rapid Identification of Tobacco Mildew Based on Random Forest Algorithm

Large-Scale Impervious Surface Area Mapping and Pattern Evolution of the Yellow River Delta Using Sentinel-1/2 on the GEE

Satellite-Based Monitoring of Coastal Wetlands in Yancheng, Jiangsu Province, China

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