Abstract
Earth Observation (EO) data plays a major role in supporting surveying compliance of several multilateral environmental treaties, such as UN-REDD+ (United Nations Reducing Emissions from Deforestation and Degradation). In this context, land cover maps of remote sensing data are the most commonly used EO products and development of adequate classification strategies is an ongoing research topic. However, the availability of meaningful multispectral data sets can be limited due to cloud cover, particularly in the tropics. In such regions, the use of SAR systems (Synthetic Aperture Radar), which are nearly independent form weather conditions, is particularly promising. With an ever-growing number of SAR satellites, as well as the increasing accessibility of SAR data, potentials for multi-frequency remote sensing are becoming numerous. In our study, we evaluate the synergistic contribution of multitemporal L-, C-, and X-band data to tropical land cover mapping. We compare classification outcomes of ALOS-2, RADARSAT-2, and TerraSAR-X datasets for a study site in the Brazilian Amazon using a wrapper approach. After preprocessing and calculation of GLCM texture (Grey Level Co-Occurence), the wrapper utilizes Random Forest classifications to estimate scene importance. Comparing the contribution of different wavelengths, ALOS-2 data perform best in terms of overall classification accuracy, while the classification of TerraSAR-X data yields higher accuracies when compared to the results achieved by RADARSAT-2. Moreover, the wrapper underlines potentials of multi-frequency classification as integration of multi-frequency images is always preferred over multi-temporal, mono-frequent composites. We conclude that, despite distinct advantages of certain sensors, for land cover classification, multi-sensoral integration is beneficial.
Highlights
Land Use and Land Cover Change (LUCC) is a main contributor to many acute environmental problems, constituting a loss of biological diversity [1], intensifying the emission of greenhouse gases [2], and affecting the climate [3,4]
Comparing the contribution of different wavelengths, ALOS-2 data perform best in terms of overall classification accuracy, while the classification of TerraSAR-X data yields higher accuracies when compared to the results achieved by RADARSAT-2
Comparing all mono-temporal, single-sensor results, i.e., the results achieved in the first wrapper iteration, it can be assessed that the AL2 data yields the highest accuracies, even when the weakest AL2 classification (AL2-Mar, 59.60%) performs better than the best non-AL2 dataset (TSX-Mar, 57.53%)
Summary
Land Use and Land Cover Change (LUCC) is a main contributor to many acute environmental problems, constituting a loss of biological diversity [1], intensifying the emission of greenhouse gases [2], and affecting the climate [3,4]. It is a major driver of global environmental change [5]. Change detection is closely linked to land cover mapping. While methods exist to directly detect gradients within remote sensing data [7,8], many applications are based on the comparison of land cover products at different points in time [9,10,11]
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