MLAs land cover mapping performance across varying geomorphology with Landsat OLI-8 and minimum human intervention
Introduction
With the explosion of population and prosperity of economic, the land cover around the world changed more rapidly in the second half of the twentieth century than at any other time in human history (MA, 2005). The land management and land policy of human play an important role in land use changes (Kuriqi et al., 2020). The land cover types and land cover usage have shifted substantially over recent decades, and these changes may have more significant effects on our globe than climate change (Change, 2000; Foley et al., 2005). In return, the land use change have impacts on environment and human well-being (Ali et al., 2020). Therefore, land cover monitoring in broad extent is necessary to identify the impacts of land cover change on our social as well as physical environmental conditions (Chen et al., 2016; Foody, 2002). The technology of remote sensing is the most useful method available for observing features on the earth over long time periods and wide spatial dimensions (Gross et al., 2013; Lillesand et al., 2015). The classification of land cover with remotely sensed data over large-scale is essential for the estimation of land cover changes considering the characteristics of Earth-observing satellites (Otukei and Blaschke, 2010; Rodriguez-Galiano et al., 2012).
Today, with the explosion of multi-source and multi-temporal remotely sensed data, the processing of huge volumes of satellite data are highly time-consuming which prevent the fully application of remote sensing (Rogan et al., 2008). There is high demand for accurate automated processing techniques for land cover mapping data (Lu and Weng, 2007). Scholars have proposed a number of classification approaches accordingly. The ISODATA, K-means, and minimum distance to means are common and traditional method in land classification. With the development of computer science, advanced techniques including machine learning algorithms (MLAs), for instance, decision tree (DT), random forest (RF), artificial neural network (ANN) and the support vector machine (SVM) sprung up to handle huge volumes of satellite data (Otukei and Blaschke, 2010). Compared with conventional parametric algorithms, MLAs have indeed presented a more accurate and efficient alternatives for land cover classification in managing large data volumes and complex landscapes (Cracknell and Reading, 2014; Rogan et al., 2008).
Unfortunately, MLAs come with several limitations in terms of land cover classification in broad extent (Rodriguez-Galiano et al., 2012). As a large area for land cover classification, very large data volumes and time-consuming data processing, integration and interpretation make minimal human intervention during land cover mapping highly desirable. Although the intervention with human is important to achieve a promising classification, the suitable parameters and “good” kernels of MLAs are hard to select for mappers (Rodriguez-Galiano et al., 2012). And a easily operated and readily automated MLAs, with very minimum possible human intervention, are required for the land cover mapping in a large area (Srivastava et al., 2012). The classification performance of various algorithms vary across different geomorphologies, and it remains unclear which classification algorithms are best suited to different types of land cover (Rogan and Miller, 2006; Shao and Lunetta, 2012). And how machine learning algorithms perform in terms of accuracy, computational complexity, and other indicators across diverse geomorphologies are yet unclear under the condition of minimum human intervention. Consequently, the selection of classification algorithm and identifying which classification algorithms achieve promising performance over different landscape have become particularly important over the large area with minimum human intervention. However, the knowledge of the performance comparison of MLAs in land cover mapping with minimum human intervention across diverse geomorphologies is rare.
In this study, we explored the performance of four MLAs (SVM, ANN, RF, and DT) for land cover mapping over three typical landscapes (plain, foothill, and mountain) in Hunan Province, China in 2017 with Landsat-8 OLI data. The classifications were conducted with minimal human intervention as the primary goal. These four MLAs were chosen as they are widely applied in land cover classification over large areas with remotely sensed data (Belgiu and Drăguţ, 2016; Mountrakis et al., 2011). First, we assessed the various algorithms' performance on different geomorphologies using the ROC curve and AUC value. We then compared their classification accuracy and spatial consistency indicators. Finally, we compared the distribution of accuracy and inconsistency of each algorithm along with altitude and slope. The main objectives of this study were to identify the performance of the four MLAs in land cover mapping with min human intervention across variable geomorphology. This study provide aids for mappers in selecting MLAs for land cover mapping in practice.
Section snippets
Study areas
Study area includes three typical regions in Hunan Province containing plain, foothill and mountain (Fig. 1). Hunan is one province of China that located in the south of the Yangtze River. The region is located in the zone of subtropical monsoon climate where winter is cold and snowy and summer is hot and dry. The annual average temperature in Hunan ranges from 16 °C to 18 °C, and its annual average rainfall varies from 1200 mm to 1700 mm (Ying et al., 2007). Because of the location in the
Methodology
The whole workflow of this study are described in the following (Fig. 2):
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Input data preparing. Dual-temporal (growing season and non-growing season) Landsat-8/OLI images and corresponding index across varying geomorphology were prepared, as well as the terrain. Then these input data were segmented with the multiresolution segmentation algorithm.
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Training and classification. Training and validation samples were acquired by stochastically selecting in Google Earth. The training samples were
Validation of machine learning algorithms performance
The ROC of each algorithm is shown in Fig. 4. In the mountain area (125040), all the MLAs effectively classified woodlands and paddyfields with AUC values greater than 0.9. The ANN did not classify dryland or wetland as well as other algorithms (Fig. 4D, J). The AUC values of RF, DT, SVM, and ANN on impervious surfaces were 0.822, 0.809, 0.778, and 0.749, respectively. RF and DT performed better than SVM or ANN on mountain areas as per the AUC values.
In the plains region (124040), the
Discussions
This study purposed to compare the performance of four general MLAs in land cover mapping across different geomorphologies with Landsat OLI-8 and minimal human intervention. We incorporated a suite of multi-temporal Landsat data and environmental variables. Same samples and variables were used during the classification with each MLA to guaranty the consistency in comparing the MLAs. The four classifiers indeed performed well across different geomorphologies with little human intervention. In
Conclusions
Land cover classification with satellite data is a promising approach to monitoring large-scale land surface information. During the land cover mapping in large area, the classification algorithms with minimal human intervention are desirable. The study compared the performance of the four mostly used MLAs (DT, RF, ANN, SVM) to identify which method perform the best in the condition of minimal human intervention varying with geomorphology. The result implied that RF is robust and highly
Declaration of Competing Interest
None.
Acknowledgements
The research has been supported from the Open Fund of Changsha University of Science & Technology (kfj190108), Scientific Research Foundation of Hunan Education Department (19C0043, 17B004) and the National Natural Science Foundation of China (No. 41671446). We express our respects to reviewers and editors.
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