Land-cover mapping using Random Forest classification and incorporating NDVI time-series and texture: a case study of central Shandong

Jin, Yuhao; Liu, Xiaoping; Chen, Yimin; Liang, Xun

doi:10.1080/01431161.2018.1490976

Cited by 133 publications

(87 citation statements)

References 40 publications

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“…The overall accuracy was utilized to assess the accuracy of land use mapping using CART model. The overall accuracy is the proportion of total correctly classified pixels compared to the total number of pixels in the map [62,63]. The overall accuracy was calculated based on an error matrix generated by comparing reference data with classification results [64].…”

Section: Mapping Land Use Using the Cart Modelmentioning

confidence: 99%

Mapping Cropland Abandonment in Mountainous Areas Using an Annual Land-Use Trajectory Approach

Song

2019

Sustainability

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In recent years, with the unceasing acceleration of China's urbanization and rapid development of the country's economy, cropland abandonment has become an ongoing issue, especially in mountainous areas. Mapping abandoned cropland using remote sensing technology is still challenging due to the difficulties in distinguishing abandoned cropland from fallowed land. In addition, there are few credible approaches to map timing and recultivation of abandoned cropland. In this context, this research developed an annual land-use trajectory approach to map different types of abandoned cropland based on freely available Landsat and HJ-1 satellite images with a spatial resolution of 30 m. Based on this method, five types of cropland change, including one type of fallowed land and two types of abandoned land, were identified in a mountainous town of China (Zhongduo). In addition, the timing and recultivation of abandoned cropland were assessed. The accuracy of the abandonment map was assessed as high as 86.67%. It was found that the percentages of spontaneous abandoned, induced abandoned, fallowed, stable, and lost cropland in Zhongduo were 23.16%, 8.40%, 13.69%, 27.84%, and 26.91% from 2012 to 2017, respectively. The average time for spontaneously abandoned cropland was 3.45 years. Furthermore, 50.54% of the spontaneously abandoned cropland and 12.91% of induced abandoned cropland were recultivated due to fluctuating prices of agricultural products and materials, and the implemented land transfer policy.

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Section: Mapping Land Use Using the Cart Modelmentioning

confidence: 99%

Mapping Cropland Abandonment in Mountainous Areas Using an Annual Land-Use Trajectory Approach

Song

2019

Sustainability

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“…The built-up class attained higher PA values than UA values, which means that the XGB method correctly identified more ground truth pixels as bare soil, but the commission error was higher than the omission error. Including the Grey Level Co-occurrence Matrix (GLCM) texture variables increases classification results (Jin et al, 2018), especially for various built-up classes (Zakeri et al, 2017). Low vegetation class was mostly misclassified to the forest class, and viceversa.…”

Section: Land-cover Classification On a Single-date S1 And S2 Imagerymentioning

confidence: 99%

Integration of Multitemporal Sentinel-1 and Sentinel-2 Imagery for Land-Cover Classification Using Machine Learning Methods

Dobrinić

Medak

Gašparović

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Using space-borne remote sensing data is widely used for land-cover classification (LCC) due to its ability to provide a big amount of data with a regular temporal revisit time. In recent years, optical and synthetic aperture radar (SAR) imagery have become available for free, and their integration in time series have improved LCC. This research evaluates the classification accuracy using multitemporal (MT) Sentinel-1 (S1) and Sentinel-2 (S2) imagery. Pixel-based LCC is made for S1 and S2 imagery, and for a combination of both datasets with Random Forest (RF) and Extreme Gradient Boosting (XGBoost; XGB). The extent of the study area, is located in the south-east of France, in Lyon. Regardless of LCC using single-date or MT data, the highest classification results were achieved with integrated S1 and S2 imagery and XGB method, whereas overall accuracy (OA) and Kappa coefficient (Kappa) increased from 85.51% to 91.09%, and from 0.81 to 0.88, respectively. Furthermore, the integration of MT imagery significantly improved the classification of urban areas and reduced misclassification between forest and low vegetation. In this paper, in terms of the pixel-based classification, XGB produced slightly better results than RF, and outperformed it in terms of computational time. This research improved LCC with integration of radar and optical MT imagery, which can be useful for areas hampered by a frequent cloud cover. Future work should use the aforementioned data for specific applications in remote sensing, as well as evaluate the classification performance with different approaches, such as neural networks or deep learning.

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“…Random Forest is one of the popular ensemble decision tree classifiers in remote sensing. Many researchers have demonstrated that the performance of RF is better than traditional single tree learning [72][73][74]. The advantages of RF include being less sensitive to overtraining and noises, the ability to generate variable importance for eliminating less important features in order to reduce dimension and computing time, as well as being unresponsive to overtraining [72,75].…”

Section: Pixel-based Techniques For Land Cover Classificationmentioning

confidence: 99%

“…Many researchers have demonstrated that the performance of RF is better than traditional single tree learning [72][73][74]. The advantages of RF include being less sensitive to overtraining and noises, the ability to generate variable importance for eliminating less important features in order to reduce dimension and computing time, as well as being unresponsive to overtraining [72,75]. Nonetheless, RF tended to be insensitive to mislabeled training [76], delicate to spatial autocorrelation [77,78], and failed to deal with imbalance training [78].…”

Section: Pixel-based Techniques For Land Cover Classificationmentioning

confidence: 99%

Combining Binary and Post-Classification Change Analysis of Augmented ALOS Backscatter for Identifying Subtle Land Cover Changes

2019

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This research aims to detect subtle changes by combining binary change analysis, the Iteratively Reweighted Multivariate Alteration Detection (IRMAD), over dual polarimetric Advanced Land Observing Satellite (ALOS) backscatter with augmented data for post-classification change analysis. The accuracy of change detection was iteratively evaluated based on thresholds composed of mean and a range constant of standard deviation. Four datasets were examined for post-classification change analysis including the dual polarimetric backscatter as the benchmark and its augmented data with indices, entropy alpha decomposition and selected texture features. Variable importance was then evaluated to build a best subset model employing seven classifiers, including Bagged Classification and Regression Tree (CAB), Extreme Learning Machine Neural Network (ENN), Bagged Multivariate Adaptive Regression Spline (MAB), Regularised Random Forest (RFG), Original Random Forest (RFO), Support Vector Machine (SVM), and Extreme Gradient Boosting Tree (XGB). The best accuracy was 98.8%, which resulted from thresholding MAD variate-2 with constants at 1.7. The highest improvement of classification accuracy was obtained by amending the grey level co-occurrence matrix (GLCM) texture. The identification of variable importance (VI) confirmed that selected GLCM textures (mean and variance of HH or HV) were equally superior, while the contribution of index and decomposition were negligible. The best model produced similar classification accuracy at about 90% for both years 2007 and 2010. Tree-based algorithms including RFO, RFG and XGB were more robust than SVM and ENN. Subtle changes indicated by binary change analysis were somewhat hidden in post-classification analysis. Reclassification by combining all important variables and adding five classes to include subtle changes assisted by Google Earth yielded an accuracy of 82%.

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Land-cover mapping using Random Forest classification and incorporating NDVI time-series and texture: a case study of central Shandong

Cited by 133 publications

References 40 publications

Mapping Cropland Abandonment in Mountainous Areas Using an Annual Land-Use Trajectory Approach

Mapping Cropland Abandonment in Mountainous Areas Using an Annual Land-Use Trajectory Approach

Integration of Multitemporal Sentinel-1 and Sentinel-2 Imagery for Land-Cover Classification Using Machine Learning Methods

Combining Binary and Post-Classification Change Analysis of Augmented ALOS Backscatter for Identifying Subtle Land Cover Changes

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