Abstract
Abstract. Crop mapping and time series analysis of agronomic cycles are critical for monitoring land use and land management practices, and analysing the issues of agro-environmental impacts and climate change. Multi-temporal Landsat data can be used to analyse decadal changes in cropping patterns at field level, owing to its medium spatial resolution and historical availability. This study attempts to develop robust remote sensing techniques, applicable across a large geographic extent, for state-wide mapping of cropping history in Queensland, Australia. In this context, traditional pixel-based classification was analysed in comparison with image object-based classification using advanced supervised machine-learning algorithms such as Support Vector Machine (SVM). For the Darling Downs region of southern Queensland we gathered a set of Landsat TM images from the 2010–2011 cropping season. Landsat data, along with the vegetation index images, were subjected to multiresolution segmentation to obtain polygon objects. Object-based methods enabled the analysis of aggregated sets of pixels, and exploited shape-related and textural variation, as well as spectral characteristics. SVM models were chosen after examining three shape-based parameters, twenty-three textural parameters and ten spectral parameters of the objects. We found that the object-based methods were superior to the pixel-based methods for classifying 4 major landuse/land cover classes, considering the complexities of within field spectral heterogeneity and spectral mixing. Comparative analysis clearly revealed that higher overall classification accuracy (95%) was observed in the object-based SVM compared with that of traditional pixel-based classification (89%) using maximum likelihood classifier (MLC). Object-based classification also resulted speckle-free images. Further, object-based SVM models were used to classify different broadacre crop types for summer and winter seasons. The influence of different shape, textural and spectral variables, and their weights on crop-mapping accuracy, was also examined. Temporal change in the spectral characteristics, specifically through vegetation indices derived from multi-temporal Landsat data, was found to be the most critical information that affects the accuracy of classification. However, use of these variables was constrained by the data availability and cloud cover.
Highlights
Land management practices have significant impacts on the condition of land and water and the profitability and sustainability of agriculture
The maximum likelihood classifier (MLC) was applied on the same dataset and the results clearly revealed that Support Vector Machine (SVM) techniques produced superior classification accuracy, and generated a neater and speckle-free image (Figure 5)
This is well supported by several other studies (Castillejo-González et al, 2009; Peña-Barragán et al, 2011). This investigation further combined the superiority of objectbased data with a powerful non-parametric SVM classifier (Boser et al, 1992; Dixon and Candade, 2007; Huang et al, 2002) to perform automated large-area broadacre crop mapping
Summary
Land management practices have significant impacts on the condition of land and water and the profitability and sustainability of agriculture. Crop mapping and time series analysis of agronomic cycles are critical for monitoring landuse and land management practices, and analysing the issues of agro-environmental impacts and climate change. Developments in remote sensing techniques offer a powerful and cost effective means for land use/land cover mapping, by virtue of their synoptic coverage and their ability to collect data at different spatial, spectral, radiometric and temporal resolutions. Multi-temporal Landsat data can be used to analyse decadal changes in cropping patterns at paddock level, owing to its medium spatial resolution and historical availability. Utilisation of time series satellite data was proved to be essential for high accuracy of crop classification (Barbosa et al, 1996; Serra and Pons, 2008; Simonneaux et al, 2008)
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