Mapping of crop types in 1989, 1999, 2009 and 2019 to assess major land cover trends of the Udon Thani Province, Thailand

Som-ard, Jaturong; Immitzer, Markus; Vuolo, Francesco; Ninsawat, Sarawut; Atzberger, Clement

doi:10.1016/j.compag.2022.107083

Cited by 14 publications

(18 citation statements)

References 70 publications

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“…According to AOI climatic conditions and agronomic practices, sugarcane primarily occurs during the dry season (from January to March) and the rainy season (from September to November). The harvesting period typically takes place from mid-December to March, aligning with the local sugarcane growth cycle [29]. Moreover, within AOI, the arrangement of sugarcane fields involves an inter-row spacing ranging from 0.2 to 0.4 m, while the row spacing typically falls within the range from 1.2 to 1.5 m.…”

Section: Field Data Collectionmentioning

confidence: 99%

“…Starting from the processed S2 images, several vegetation indices (VIs) were calculated. VIs have been recognized as important features for characterizing crop phenological dynam-ics, especially for mapping and monitoring sugarcane health and AGB [21,29]. Therefore, VIs involved in this study are the normalized difference vegetation index (NDVI) [54], green normalized difference vegetation index (GNDVI) [55], normalized difference water index (NDWI) [56], enhanced vegetation index (EVI) [57], normalized difference infrared index (NDII) [58], soil-adjusted vegetation index (SAVI) [59], leaf area index (LAI) [60], greenness index (GI) [61], moisture stress index (MSI) [62], ratio vegetation index (RVI) [63], green ratio vegetation index (GRVI) [64], and normalized difference red/green redness index (RI) [65].…”

Section: Sentinel-2 Datamentioning

confidence: 99%

“…Starting from 2015, the Copernicus S1 and S2 satellites have provided images with high-temporal (5-12 days) and geometric resolution (ground sample distance equal to 10 m), making them highly valuable for tracking rapid crop phenological dynamics [17,[28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimating Sugarcane Aboveground Biomass and Carbon Stock Using the Combined Time Series of Sentinel Data with Machine Learning Algorithms

Suwanlee,

Pinasu,

Som-ard

et al. 2024

Remote Sensing

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Accurately mapping crop aboveground biomass (AGB) in a timely manner is crucial for promoting sustainable agricultural practices and effective climate change mitigation actions. To address this challenge, the integration of satellite-based Earth Observation (EO) data with advanced machine learning algorithms offers promising prospects to monitor land and crop phenology over time. However, achieving accurate AGB maps in small crop fields and complex landscapes is still an ongoing challenge. In this study, the AGB was estimated for small sugarcane fields (<1 ha) located in the Kumphawapi district of Udon Thani province, Thailand. Specifically, in order to explore, estimate, and map sugarcane AGB and carbon stock for the 2018 and 2021 years, ground measurements and time series of Sentinel-1 (S1) and Sentinel-2 (S2) data were used and random forest regression (RFR) and support vector regression (SVR) applied. Subsequently, optimized predictive models used to generate large-scale maps were adapted. The RFR models demonstrated high efficiency and consistency when compared to the SVR models for the two years considered. Specifically, the resulting AGB maps displayed noteworthy accuracy, with the coefficient of determination (R2) as 0.85 and 0.86 with a root mean square error (RMSE) of 8.84 and 9.61 t/ha for the years 2018 and 2021, respectively. In addition, mapping sugarcane AGB and carbon stock across a large scale showed high spatial variability within fields for both base years. These results exhibited a high potential for effectively depicting the spatial distribution of AGB densities. Finally, it was shown how these highly accurate maps can support, as valuable tools, sustainable agricultural practices, government policy, and decision-making processes.

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Section: Field Data Collectionmentioning

confidence: 99%

Section: Sentinel-2 Datamentioning

confidence: 99%

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Estimating Sugarcane Aboveground Biomass and Carbon Stock Using the Combined Time Series of Sentinel Data with Machine Learning Algorithms

Suwanlee,

Pinasu,

Som-ard

et al. 2024

Remote Sensing

Self Cite

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“…Historically, one of the most influential causes of land cover changes is urbanisation [ 1 , 2 ]. Temporal observation of land cover changes is necessary for advanced crop [ 3 ] and disaster management (e.g., forest fires [ 4 ] and landslides [ 5 ]). Different land cover types have a heterogeneous regional impact on climate change [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Graph Neural Network-Based Method of Spatiotemporal Land Cover Mapping Using Satellite Imagery

Kavran

Mongus

Žalik

et al. 2023

Sensors

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Multispectral satellite imagery offers a new perspective for spatial modelling, change detection and land cover classification. The increased demand for accurate classification of geographically diverse regions led to advances in object-based methods. A novel spatiotemporal method is presented for object-based land cover classification of satellite imagery using a Graph Neural Network. This paper introduces innovative representation of sequential satellite images as a directed graph by connecting segmented land region through time. The method’s novel modular node classification pipeline utilises the Convolutional Neural Network as a multispectral image feature extraction network, and the Graph Neural Network as a node classification model. To evaluate the performance of the proposed method, we utilised EfficientNetV2-S for feature extraction and the GraphSAGE algorithm with Long Short-Term Memory aggregation for node classification. This innovative application on Sentinel-2 L2A imagery produced complete 4-year intermonthly land cover classification maps for two regions: Graz in Austria, and the region of Portorož, Izola and Koper in Slovenia. The regions were classified with Corine Land Cover classes. In the level 2 classification of the Graz region, the method outperformed the state-of-the-art UNet model, achieving an average F1-score of 0.841 and an accuracy of 0.831, as opposed to UNet’s 0.824 and 0.818, respectively. Similarly, the method demonstrated superior performance over UNet in both regions under the level 1 classification, which contains fewer classes. Individual classes have been classified with accuracies up to 99.17%.

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“…The combination of Landsat and Sentinel-2 sensors has also become an important data option for assessing the changes in land cover in different countries around the world, such as Germany, Russia, and Poland [61]. Nasiri and Som-ard et al determined the spectral time index extracted from the satellite time series through the synthesis method of Landsat 8 and Sentinel 2, and generated a land cover map [62,63]. The results showed that Landsat 8 had greater advantages over Sentinel 2 in the monitoring of forests, herbaceous vegetation, and water; the former was more accurate [64].…”

Section: Introductionmentioning

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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Mapping of crop types in 1989, 1999, 2009 and 2019 to assess major land cover trends of the Udon Thani Province, Thailand

Cited by 14 publications

References 70 publications

Estimating Sugarcane Aboveground Biomass and Carbon Stock Using the Combined Time Series of Sentinel Data with Machine Learning Algorithms

Estimating Sugarcane Aboveground Biomass and Carbon Stock Using the Combined Time Series of Sentinel Data with Machine Learning Algorithms

Graph Neural Network-Based Method of Spatiotemporal Land Cover Mapping Using Satellite Imagery

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

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