关键词: Landsat 影像, 地表特征, 土地覆盖分类, 集成学习, 土地覆盖变化模拟

Abstract:

【Objective】This study aims to exert the respective advantages of individual base classifiers, different ensemble learning strategies were tested in the present study to improve the final land cover classification accuracy. On this basis, different land cover change simulation models were implemented and compared to obtain more accurate land cover projection results to provide references for formulating decisions of rational development and utilization of land resources in the future.【Method】Based on the Landsat Thematic Mapper (TM)/Operational Land Imager (OLI) images acquired in 2000, 2010 and 2020 covering Jiangning District of Nanjing City, the classification scheme, including water body, built-up land, forest land, grass land, crop land and unused land, was defined by referring to relevant industry standards and the actual conditions of the study area. The land cover classification performances of five base classifiers, including the Maximum Likelihood Classifier, Mahalanobis Distance Classifier, Minimum Distance Classifier, Neural Network, and Support Vector Machine(SVM) Classifiers, were implemented and quantitatively evaluated, followed by an integrated combination of the five individual classification results using the random forest algorithm and Dempster-Shafer (D-S) evidence theory. The final integrated classifications in 2000, 2010 and 2020 with higher overall accuracy were created after comparing the respective ensemble performances of the two algorithms. Based on the optimal integrated land cover classification maps in 2000 and 2010, the cellular automata (CA)-Markov model, PLUS model, and artificial neural network (ANN)-CA model were used to predict the land cover pattern of 2020 in the study area, and the prediction results of different models were compared with the real integrated classification results in 2020 to derive a spatial agreement index, determine the best model for land cover change simulation, and generate the projected land cover pattern of 2030 in Jiangning District.【Result】An independent validation showed that for a single base classifier, SVM algorithm achieved the best classification effect in 2000, with an overall accuracy at 88.75% and a Kappa coefficient of 0.77. In 2010, the neural network algorithm obtained the best classification effect, with an overall accuracy of 88.75% and a Kappa coefficient of 0.83. In 2020, the maximum likelihood algorithm had the best classification effect, with an overall accuracy of 82.75% and a Kappa coefficient of 0.74. Regarding the two ensemble methods, the random forest algorithm achieved the best integrated classification effect in 2000, with an overall accuracy of 91.25% and a Kappa coefficient of 0.85. The evidence theory process achieved the best integrated classification effect in 2010, with an overall accuracy and Kappa coefficient of 90.80% and 0.86, respectively. Moreover, the random forest algorithm achieved the best integrated classification effect in 2020, with an overall accuracy and Kappa coefficient of 93.75% and 0.91, respectively. Regarding land cover prediction, the PLUS model obtained a spatial agreement measure of 98.54%, outperforming the CA-Markov and ANN-CA models.According to the optimal prediction results in 2030 made by the PLUS model, there was a slight expansion of built-up land, but the extent was limited. The areal reduction in cropland was observed but within a controllable range. The changes in forest, grassland, and unused land were slight, while the water body was decreased.【Conclusion】Overall, the present study demonstrated that proper assemble learning strategies produce more accurate land cover classifications in contrast to a single base classifier. In addition, a suitable land cover change prediction or simulation model that adapts to local land use status and policy regulations effectively predicts the future land cover classification pattern. Thus, these strategies provide more reliable methods and data support for the development of land use and urban management decisions.

Key words: Landsat imagery, surface features, land cover classification, assemble learning, land cover change simulation