A Land Cover Change Detection Method Combing Spectral Values and Class Probabilities

Xing, Huaqiao; Zhu, Lin; Niu, Jingge; Chen, Bingyao; Wang, Wei; Hou, Dongyang

doi:10.1109/access.2021.3087206

Cited by 13 publications

(3 citation statements)

References 42 publications

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“…The revisit cycle is shortened to 5 days when both satellites are working simultaneously. In mid-latitude regions with better observation angles, the satellite revisit period can be reduced to 2 to 3 days, which is very favorable for constructing long-time sequence images [40,41].…”

Section: Satellite Datamentioning

confidence: 99%

Winter Wheat Mapping in Shandong Province of China with Multi-Temporal Sentinel-2 Images

Feng,

Chen,

Liu

et al. 2024

Applied Sciences

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Wheat plays an important role in China’s and the world’s food supply, and it is closely related to economy, culture and life. The spatial distribution of wheat is of great significance to the rational planning of wheat cultivation areas and the improvement of wheat yield and quality. The current rapid development of remote sensing technology has greatly improved the efficiency of traditional agricultural surveys. The extraction of crop planting structure based on remote sensing images and technology is a popular topic in many researches. In response to the shortcomings of traditional methods, this research proposed a method based on the fusion of the pixel-based and object-oriented methods to map the spatial distribution of winter wheat. This method was experimented and achieved good results within Shandong Province. The resulting spatial distribution map of winter wheat has an overall accuracy of 92.2% with a kappa coefficient of 0.84. The comparison with the actual situation shows that the accuracy of the actual recognition of winter wheat is higher and better than the traditional pixel-based classification method. On this basis, the spatial pattern of winter wheat in Shandong was analyzed, and it was found that the topographic undulations had a great influence on the spatial distribution of wheat. This study vividly demonstrates the advantages and possibilities of combining pixel-based and object-oriented approaches through experiments, and also provides a reference for the next related research. Moreover, the winter wheat map of Shandong produced in this research is important for yield assessment, crop planting structure adjustment and the rational use of land resources.

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Section: Satellite Datamentioning

confidence: 99%

Winter Wheat Mapping in Shandong Province of China with Multi-Temporal Sentinel-2 Images

Feng,

Chen,

Liu

et al. 2024

Applied Sciences

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“…By comparing the degree of difference in index, the degree of difference in spectral value, and the degree of difference in spectral shape between the remote sensing image of the base year and the remote sensing image of the previous year, it is determined whether the sample area of the base year and the previous year has changed. In this regard, the degree of difference in indices is calculated using absolute distances, while the degree of difference in spectral magnitudes and the degree of difference in spectral shapes are calculated by the change vector analysis (CVA) [34], [35] and spectral angle mapper (SAM) methods, respectively. On this basis, the OTSU method is used to determine whether the corresponding sample point has changed.…”

Section: A Migration Of Sample Points Based On Index and Spectralmentioning

confidence: 99%

MLC30: A New 30 m Land Cover Dataset for Myanmar From 1990 to 2020 Using Training Sample Migration Framework

Xing,

Zhu,

Zhang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Myanmar has experienced rapid socio-economic developments in recent decades, which have a greater impact on land cover change. Accurate long time series land cover datasets for Myanmar can be of great help in environmental protection and natural resource management. However, there are relatively few existing studies on long time series land cover datasets in Myanmar, and the acquisition of training samples within different time series is a big challenge. Therefore, this study used Google Earth Engine and Landsat imagery to produce a land cover dataset for every two years from 1990 to 2020 using a training sample migration framework. First, the differences in index change, spectral value change, and spectral shape change were used to determine whether the sample points had changed between the base year and the previous year, and then a small number of samples were manually selected. Second, the spectral features, index information, and texture information of the remote sensing images and the objectoriented segmentation method were used to obtain object-oriented multidimensional features. Finally, the random forest method was employed to train the samples of the previous year to obtain the land cover data of the previous year. The results of the study show that the average overall precision of the land cover classification results for Myanmar for 1990-2020 is 0.83 and Kappa is 0.79. In addition, the land cover classification results for Myanmar of 1990-2020 are significantly better than those of Globeland30-2020, FROM-GLC, and Dynamic World land cover, and comparing with these products showed good agreement.

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“…The implications of these changes on ecosystems at various scales are a growing concern [3], [4], [5], [6], [7]. Historically, human activities have dramatically transformed landscapes, particularly in tropical regions [8], [9], [10]. While proper planning is crucial for sustainable agricultural development, uncontrolled changes have led to reduced wetlands and lost natural vegetation, exacerbated by population pressures and poor land management, contributing to widespread land degradation [11], [12], [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Land-Use Land-Cover Change Detection Using Geospatial Techniques in Zalingei, Central Darfur, Sudan

Ahmed,

Kheiry,

Yasin

2023

J. Inf. Syst. Informatics

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Using remote sensing for land use and land cover (LULC) is essential for systems that help people make decisions because it gives valuable information about space and time. A study was conducted in Zalingei, Sudan, to analyze the changes in LULC over 30 years from 1991 to 2021 using multi-temporal Landsat images. Thematic Mapper (TM) and Operational Land Imager (OLI) were classified using the supervised classification method. The pictures were divided into four groups based on how the land was used: residential areas, bodies of water, vegetation cover, and bare land. Results showed that the residential area increased by 20.74% while the water body increased by 2.32%. However, the vegetation cover decreased by 0.7%, and bare land decreased by 22.37%. The changes were caused by people, which shows how vital good land management practices and involvement from the local community are for reducing LULC change. So, to reduce LULC change in the study area, proper land management practices and active participation from the local community are needed. The study concluded that remote sensing technology is an effective tool for assessing and mapping land use and land cover changes and providing valuable information for decision support systems.

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A Land Cover Change Detection Method Combing Spectral Values and Class Probabilities

Cited by 13 publications

References 42 publications

Winter Wheat Mapping in Shandong Province of China with Multi-Temporal Sentinel-2 Images

Winter Wheat Mapping in Shandong Province of China with Multi-Temporal Sentinel-2 Images

MLC30: A New 30 m Land Cover Dataset for Myanmar From 1990 to 2020 Using Training Sample Migration Framework

Land-Use Land-Cover Change Detection Using Geospatial Techniques in Zalingei, Central Darfur, Sudan

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