Snow cover monitoring in the Northern Patagonia Icefield using MODIS satellite images (2000–2006)

The snow cover of the Northern Patagonia Icefield (NPI) was monitored after applying the Normalized Difference Snow Index (NDSI) and the Red/NIR band ratio to 134 Moderate Resolution Imaging Spectroradiometer (MODIS) images captured between 2000 and 2006. The final results show that the snow cover extent of the NPI fluctuates a lot in winter, in addition to its seasonal behaviour. The minimum snow cover extent of the period (3600 km²) was observed in March 2000 and the maximum (11,623 km²) in August 2001. We found that temperature accounts for approximately 76% of the variation of the snow cover extent over the entire icefield. We also show two different regimes of winter snow cover fluctuations corresponding to the eastern and the western sides of the icefield. The seasonality of the snow cover on the western side was determined by temperature rather than precipitation, while on the east side the seasonality of the snow cover was influenced by the seasonal behaviour of both temperature and precipitation. This difference can be explained by the two distinct climates: coastal and continental. The fluctuations in the winter snow cover extent were more pronounced and less controlled by temperature on the western side than on the eastern side of the icefield. Snow cover extent was correlated with temperature R² = 0.75 and R² = 0.74 for the western and eastern sides, respectively. Since limited meteorological data are available in this region, our investigation confirmed that the change in snow cover is an interesting climatic indicator over the NPI providing important insights in mass balance comprehension. Since snow and ice were distinguished snow cover fluctuations can be associated to fluctuations in the snow accumulation area of the NPI. In addition, days with minimum snow covers of summer season can be associated to the period in which Equilibrium Line Altitude (ELA) is the highest.     

Improving MODIS Spatial Resolution for Snow Mapping Using Wavelet Fusion and ARSIS Concept

We propose to fuse the high spatial content of two 250-m spectral bands of the moderate resolution imaging spectroradiometer (MODIS) into its five 500-m bands using wavelet-based multiresolution analysis. Our objective was to test the effectiveness of this technique to increase the accuracy of snow mapping in mountainous environments. To assess the performance of this approach, we took advantage of the simultaneity between the advanced spaceborne thermal emission and reflection radiometer (ASTER) and MODIS sensors. With a 15-m spatial resolution, the ASTER sensor provided reference snow maps, which were then compared to MODIS-derived snow maps. The benefit of the method was assessed through the investigation of various metrics, which showed an improvement from 3% to 20%. Therefore, the enhanced snow map is of great benefit for environmental and hydrological applications in steep terrain.     

An intelligent geospatial processing unit for image classification based on geographic vector agents (GVAs)

Spatial modeling methods usually use pixels and image objects as fundamental processing units to address real‐world objects, geo‐objects, in image space. To do this, both pixel‐based and object‐based approaches typically employ a linear two‐staged workflow of segmentation and classification. Pixel‐based methods segment a classified image to address geo‐objects in image space. In contrast, object‐based approaches classify a segmented image to identify geo‐objects from raster datasets. These methods lack the ability to simultaneously integrate the geometry and theme of geo‐objects in image space. This article explores Geographical Vector Agents (GVAs) as an automated and intelligent processing unit to directly address real‐world objects in the process of remote sensing image classification. The GVA is a distinct type of geographic automata characterized by elastic geometry, dynamic internal structure, neighborhoods and their respective rules. We test this concept by modeling a set of objects on a subset IKONOS image and LiDAR DSM datasets without the setting parameters (e.g. scale, shape information), usually applied in conventional Geographic Object‐Based Image Analysis (GEOBIA) approaches. The results show that the GVA approach achieves more than 3.5% improvement for correctness, 2% improvement for quality, although no significant improvement for completeness to GEOBIA, thus demonstrating the competitive performance of GVAs classification.