Spatiotemporal graph-based analysis of land cover evolution using remote sensing time series data

Zou, Xinyu; Liu, Xiangnan; Liu, Meiling; Tian, Lingwen; Zhu, Lihong; Zhang, Qian

doi:10.1080/13658816.2023.2168006

Cited by 3 publications

(2 citation statements)

References 89 publications

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“…Spatial analysis is the quantitative study of geospatial phenomena, and it is the core of remote sensing and GIS. By using spatial analysis, we can describe the evolutionary process and spatiotemporal association of land cover adequately [105,106]. Meanwhile, the transfer matrix can define the important processes of changes in land cover [107]; we can obtain the temporal and spatial changes in different land cover types and understand the overall status of regional ecosystem service functions from land cover change.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Conflict Analysis and Prediction of Long Time Series Land Cover Changes in the Black Soil Region of Northeast China Using Remote Sensing and GIS

Ding

Jiang

Tan

et al. 2023

IJGI

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Using remote sensing and GIS techniques to monitor long time series land cover changes is of great significance to understanding the impact of human activities on spatiotemporal conflicts and changes in cropland and forest ecosystems in the black soil region of Northeast China. Spatial analysis and dynamic degree were used to analyze the evolutionary process and spatiotemporal association of land cover from 1990 to 2020; the transfer matrix was used to analyze and reveal dynamic conversions of land cover from 1990 to 2000, 2000 to 2010, and 2010 to 2020; and the GM (1,1) model was used to forecast the changes in land cover by 2025 based on historical data. The results indicated that the dominance of forest and cropland did not change from 1990 to 2020, and the average area of forest and cropland was 512,713 km2 and 486,322 km2, respectively. The mutual conversion between cropland, forest, grassland, and bare areas was the most frequent. The area of cropland converted into forest and grassland was 14,167 km2 and 25,217 km2, respectively, and the area of forest and grassland converted into cropland was 27,682 km2 and 23,764 km2, respectively, from 1990 to 2000. A similar law of land cover change was also presented from 2000 to 2020. In addition, the predicted values of cropland, forest, grassland, shrubland, wetland, water bodies, impervious surfaces, and bare areas were 466,942 km2, 499,950 km2, 231,524 km2, 1329 km2, 11,775 km2, 18,453 km2, 30,549 km2, and 189,973 km2, respectively, by 2025. The maximum and minimum residuals between the predicted and actual values were 6241 km2 and −156 km2 from 1990 to 2020. The evaluation results of the GM (1,1) model showed that all of the evaluation indices were within an acceptable range, and that the posteriori error ratio and class ratio dispersion were both less than 0.25. Through comparison with other studies, this study is not only able to provide some experience for further analyzing the spatial and temporal changes in land cover and its future prediction but also provide a basis for comprehensive management in Northeast China.

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Section: Discussionmentioning

confidence: 99%

Spatiotemporal Conflict Analysis and Prediction of Long Time Series Land Cover Changes in the Black Soil Region of Northeast China Using Remote Sensing and GIS

Ding

Jiang

Tan

et al. 2023

IJGI

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“…The analysis of land use change using temporal spatial data is highly valuable, particularly for identifying areas experiencing land use changes (Nuraeni et al, 2017;Zou et al, 2023). Nearest Neighbor Analysis (NNA) is a method employed to examine the distance to the nearest neighbor within a random pattern of points (Riadhi et al, 2020;Ofem & Ufot-Akpabio, 2023).…”

Section: Introductionmentioning

confidence: 99%

Utilizing Google Earth Data to Assess Spatial-Temporal Land Use Changes around Jatibarang Reservoir, Semarang City

Handayani

Wibowo

2023

Idn. J. Earth Sci.

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This study aims to assess the spatial-temporal land use changes around Jatibarang Reservoir in Semarang City, Indonesia City from 2012 to 2022, utilizing Google Earth data. The Jatibarang Reservoir and its surrounding areas have been subject to rapid urbanization and land use transformations in recent years, leading to significant environmental and socio-economic implications. The classification accuracy of seven land use categories was validated, demonstrating a high level of accuracy. The analysis reveals a dominance of agricultural land use, with notable shifts observed in forest cover, settlements, and reservoir areas. Population growth, economic development, and policy interventions are identified as the main drivers behind these land use changes. The spatial analysis using Nearest Neighbor Analysis (NNA) indicates a scattered distribution pattern of land use changes, primarily concentrated in the western part of the reservoir area. These land use changes have significant social, economic, and environmental implications, impacting the sustainability of the reservoir and surrounding areas. The study highlights the need for improved data quality, rigorous validation methods, and spatial modeling approaches in future research. By understanding the dynamics of land use changes and their implications, policymakers and stakeholders can make informed decisions for sustainable land management and environmental conservation in similar contexts.

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Integrating Local-Global Structural Interaction Using Siamese Graph Neural Network for Urban Land Use Change Detection From VHR Satellite Images

Lou,

Li,

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Spatiotemporal graph-based analysis of land cover evolution using remote sensing time series data

Cited by 3 publications

References 89 publications

Spatiotemporal Conflict Analysis and Prediction of Long Time Series Land Cover Changes in the Black Soil Region of Northeast China Using Remote Sensing and GIS

Spatiotemporal Conflict Analysis and Prediction of Long Time Series Land Cover Changes in the Black Soil Region of Northeast China Using Remote Sensing and GIS

Utilizing Google Earth Data to Assess Spatial-Temporal Land Use Changes around Jatibarang Reservoir, Semarang City

Integrating Local-Global Structural Interaction Using Siamese Graph Neural Network for Urban Land Use Change Detection From VHR Satellite Images

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