Uncertainty in Remote Sensing and GIS

Super-resolution Land Cover Mapping from Remotely Sensed Imagery using a Hopfield Neural Network Part 2

Simulated Remotely Sensed Imagery In this section, simulated remotely sensed imagery from two sensors, Landsat TM and SPOT HRV, are used to enable refinement of the technique and clear demonstration of the workings of the network. The use of simulated imagery avoids the uncertainty inherent in real imagery caused by the sensor’s PSF, atmospheric and […]

Super-resolution Land Cover Mapping from Remotely Sensed Imagery using a Hopfield Neural Network Part 3

Discussion The high accuracy shown for the results in Figures 6.4(c) and 6.5(c) indicates that the Hopfield neural network has considerable potential for the accurate mapping of land cover class proportions within pixels and, consequently reducing the uncertainty of the soft classification it was derived from. Spatial resolution and accuracy increases over hard classification also […]

Uncertainty in Land Cover Mapping from Remotely Sensed Data using Textural Algorithm and Artificial Neural Networks Part 1

Introduction Textural information is increasingly recognized as being useful in the classification of remotely sensed imagery. Many algorithms for the quantification of texture have been proposed, such as second-order statistics derived from the grey level cooccurrence matrix (Haralick et al., 1973) and geostatistical measures of spatial variability (Miranda et al., 1992, 1996; Ramstein and Raffy, […]

Uncertainty in Land Cover Mapping from Remotely Sensed Data using Textural Algorithm and Artificial Neural Networks Part 2

Spatial distribution of the studied land cover classes The complexity of the boundaries in the study area was analysed from the land cover representation produced from the photointerpretation (ground data). Data layers with buffers (corridors) along boundaries were constructed using 3 x 3, 5 x 5 and 7 x 7 windows. Nearly half of the […]

Remote Monitoring of the Impact of ENSO-related Drought on Sabah Rainforest using NOAA AVHRR Middle Infrared Reflectance: Exploring Emissivity Uncertainty Part 1

Introduction It is known that the terrestrial biosphere exhibits variation in its properties over a range of both spatial and temporal scales (Van Gardingen et al., 1997; Slaymaker and Spencer, 1998; Trudgill, 2001) and this variability is both naturally and anthro-pogenically driven. This variability has direct implications for human society, as well as the Earth […]

Remote Monitoring of the Impact of ENSO-related Drought on Sabah Rainforest using NOAA AVHRR Middle Infrared Reflectance: Exploring Emissivity Uncertainty Part 2

Uncertainty Associated with the Use of MIR Reflectance As with the use of the NOAA AVHRR NDVI there are several uncertainties with using NOAA AVHRR MIR reflectance. In particular, there are uncertainties associated with the method of deriving MIR reflectance (Nerry et al., 1998; Roger and Vermote, 1998) from the total radiant energy measured in […]

Land Cover Map 2000 and Meta-data at the Land Parcel Level

Introduction If available, the retention and provision of uncertainty information within large, especially national level, data sets is often limited to a single estimate for the whole data set. The uncertainty within the data set is not confined to the whole data set level, and is derived from uncertainty in the individual ‘sampling units’ or […]

Analysing Uncertainty Propagation in GIS: Why is it not that Simple?

Introduction To introduce the problem addressed in this topic, let us look at the following example. Figure 10.1a shows a Digital Elevation Model (DEM) of a 2 x 2.5km area in the Vorarlberg region in the Austrian Alps. Figure 10.1b shows the corresponding slope map, computed with a second-order finite difference method (Skidmore, 1989). As […]

Managing Uncertainty in a Geospatial Model of Biodiversity Part 1

Introduction The boreal forest spans the northern hemisphere through Canada, Russia and Alaska and plays an important role in the societies which depend on it. In addition to its vital role in Earth-atmosphere interactions, the boreal forest is of great economic importance as a renewable natural resource. It provides valuable and numerous pulp, paper and […]

Managing Uncertainty in a Geospatial Model of Biodiversity Part 2

Distance from ridgeline The distance from ridgeline (DFR) variable was used as a surrogate for moisture and nutrient gradients which are a result of hillslope process. Estimates of the distance from each image pixel to the nearest ridgeline were required. The algorithm (path 2 in Figure 11.4) for estimating this variable was a compromise between […]

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