Land‐cover classification in the Brazilian Amazon with the integration of Landsat ETM+ and Radarsat data

Abstract

Land-cover classification with remotely sensed data in moist tropical regions is a challenge due to the complex biophysical conditions. This paper explores techniques to improve land-cover classification accuracy through a comparative analysis of different combinations of spectral signatures and textures from Landsat Enhanced Thematic Mapper Plus (ETM + ) and Radarsat data. A wavelet-merging technique was used to integrate Landsat ETM + multispectral and panchromatic data or Radarsat data. Grey-level co-occurrence matrix (GLCM) textures based on Landsat ETM + panchromatic or Radarsat data and different sizes of moving windows were examined. A maximum-likelihood classifier was used to implement image classification for different combinations. This research indicates the important role of textures in improving land-cover classification accuracies in Amazonian environments. The incorporation of data fusion and textures increases classification accuracy by approximately 5.8-6.9% compared to Landsat ETM + data, but data fusion of Landsat ETM + multispectral and panchromatic data or Radarsat data cannot effectively improve land-cover classification accuracies.

A new approach for finding an appropriate combination of texture parameters for classification

“…To analyse the significance of few parameters needed in texture feature extraction like spatial resolution, spectral band and size of the moving window, inter-pixel distance and quantization level of the image, the study by Marceau et al (1990) showed that the size of the window was found to be the most important parameter affecting classification accuracy. The grey level co-occurrence matrix (GLCM) textures based on Landsat ETM þ panchromatic and different sizes of moving windows have also been investigated by Lu et al (2007); their findings support that not all texture measures can improve classification performance and for the same texture measure, selecting the appropriate window size and spectral band is crucial. Chen et al (2004) analysed the effects of texture window size on classification accuracy and concluded that the texture was more effective for improving the classification accuracy of land use classes at finer resolution levels.…”

Optical and radar data integration for land use and land cover mapping in the Brazilian Amazon

“…Accurate mapping of the land use and land cover (LULC) classes provides the basis for many applications and research subjects, such as environmental analysis and modeling (Laurila et al 2010), global and regional climate change (Renzullo et al 2008), and multitemporal analysis (Mcnairn et al 2009), among others. Therefore, as the result of several efforts, progress has been made in improving the methods for extracting information from different data types to increase the discriminability of the classes and, consequently, the accuracy of the LULC mapping (Dutra et al 2002;Lu, Batistella, and Moran 2007;Santos and Messina 2008;Ban, Hu, and Rangel 2010;Walker et al 2010), leading to better classification results. Under many circumstances, the use of a single type of sensor (optical or radar) does not provide sufficient information about the phenomena and/or objects under study.…”

Habitat Mapping and Change Assessment of Coastal Environments: An Examination of WorldView-2, QuickBird, and IKONOS Satellite Imagery and Airborne LiDAR for Mapping Barrier Island Habitats

“…LiDAR images vary in brightness because smooth surfaces (e.g., sand) tend to reflect more light back to the sensor than rough surfaces (e.g., marsh), which tend to scatter more light. Lu et al's [28] study indicated that fusing satellite imagery with texture data can improve classification accuracy.…”