Classification of land use in Hetao Irrigation District of Inner Mongolia using feature optimal decision trees

Up-to-date classification of land use types has become a critical component in current strategies to manage natural resources and the regional environment. Alternatively, remote sensing has also been widely used over the past 20 years as an effective tool for spatial data acquisition, particularly for the sustainable management of natural resources and economical perspective to the land use and land cover changes. However, the land use classification using remote sensing is subjected to the characteristics of dispersion and fragmentation in the Hetao irrigation district of northwest China in recent years. This study aims to quantify the effects of duration and characteristic variables on the recognition accuracy of remote sensing for land use types. A decision-tree model was also established to classify the land use types using the integrated band reflectance, spectral index, and texture feature of different periods based on Landsat time-series image data. The model was finally verified by the measured data and Google Earth images from the quantitative structure and spatial layout. The specific procedure was as follows. Firstly, the characteristic variables were extracted from the Landsat time-series images of different periods, including the features of band, spectra, and texture. Principal Component Analysis (PCA) was selected to extract the feature factors. Only a few independent variables were selected from multiple variables or factors, aiming to fully reflect the information of more original indexes. Secondly, seven schemes were constructed using the characteristic factors, including three single-category schemes (Scheme 1 to 3), and four combined-category schemes (Scheme 4 to 7). Finally, a classification model of land use was constructed and then verified in different periods via the decision tree. The results showed that: 1) The highest repetition rate was found in the Green and Ent (entropy) with other factors in different months. The correlation between Normalized Differential Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) was much higher to be selected in future research. 2) The combined feature variables greatly improved the accuracy of classification, where the average overall accuracy and Kappa coefficient increased by 6.72% and 0.09, respectively, compared with the single feature variable. 3) There were some effects of different recognition periods on the accuracy of the model. The accuracy of the classification model in the band, spectral index, and texture feature using remote sensing images in August was better than that of other periods, where the misclassification was reduced on the spatial layout of unused and residential land. Specifically, the overall accuracy, Kappa coefficient, producer accuracy, and user accuracy were 80.23%, 0.74%, 80.95%, and 86.26%, respectively. Correspondingly, the best identification period was August in the study area, followed by September. 4) The optimal remote-sensing model was utilized to identify the agricultural land, forest, grassland, wasteland, water bodies, and build-up land under the optimal recognition period and combination, where the high accuracies were achieved: 96.83%, 73.33%, 70.00%, 65.52%, 100.00%, and 80.00%, respectively. In addition, the user accuracies were 76.62%, 100.00%, 82.35%, 82.61%, 100.00%, and 80.00%, respectively. In a word, the feature optimal decision-tree model under the optimal identification period significantly reduced the amount of data and the difficulty of model application, particularly suitable for the long-time and spatial changes of land use types. The finding can provide promising technical support to effectively improve the accuracy of land use classification in modern resource management.