Denis Gratton

Evaluation Of The Grey-level Co-occurrence Matrix Method For Land-cover Classification Using Spot Imagery

Absfruct-Nine cover types have been classified using a textural/ spectral approach. The texture analysis is based on the grey-level cooccurrence matrix method. Texture features are created from a SPOT near-infrared image using four texture indices, seven window sizes, and two quantization levels. A supervised classification based on the maximum-likelihood algorithm is applied to the three SPOT multispectral bands combined with each texture image individually and to the three bands combined with all four texture images. Classification accuracy is measured by Kappa coefficients calculated from confusion matrices. A factor analysis, based on principal components, is performed to evaluate the contribution to the classification accuracy of each variable involved in the creation of the texture features. The addition of texture features provides a significant improvement in the classification accuracy of each cover type when compared with the results obtained from the multispectral analysis alone. The window size accounts for 90% of the classification variability, 7% is explained by the statistics used as texture measures, and only 3% by the quantization level. There is a window size that optimizes the discrimination of each cover type.

Remote sensing and the measurement of geographical entities in a forested environment. 2. The optimal spatial resolution

Abstract The prime objective of this study was to propose and test a method to identify the optimal spatial resolutions for detection and discrimination of coniferous classes in a temperate forested environment. The approach is based on the paradigm that there is an intricate relationship between the definition and the measurement of geographical entities and implies the following steps: 1) a priori define the geographical entities under investigation, 2)determine an optimization criterion for the choice of a sampling system, 3) progressively aggregate data acquired from a fine spatial sampling grid, 4) apply the optimization criterion on the series of spatially aggregated data, and 5) verify the validity of the results obtained in relation to the goal of the study. Airborne MEIS-II data, acquired at 0.5 m in eight spectral bands of the visible spectrum, were used for the study. Fourteen forest classes, at the stand level, were defined on the basis of four attributes: species, density, height, and organization of the trees. Representative sites for each forest class were selected. From the center of each site, the spatial resolution of the original data was degraded to 29.5 m, with an increment of 1 m, using an averaging window algorithm. The intraclass variance was calculated for each forest class, at every spatial resolution and for the eight spectral bands. The minimal variance was used as the indicator of the optimal spatial resolution. To evaluate the importance of the optimal resolution for class discrimination, a bivariate test of variance was performed for each pair of forest class considered at their optimal spatial resolution. Profiles of spectral separability were also established in relation to the whole series of spatial resolutions. The results show that, for all coniferous classes and for the eight spectral bands considered in the study, there is a minimal value in intraclass variance that indicates the optimal spatial resolution for each class, varying between 2.5 m and 21.5 m. The optimal spatial resolution is primarily affected by the spatial and structural parameters of the forest stands. The analysis of variance between each pair of forest classes considered at their respective optimal spatial resolution reveals that all classes are significantly different in at least two spectral bands, except for 10 pairs. The spectral separability of the forest classes is at a maximum at, or very close to, their optimal spatial resolution. The study confirms the validity of the concept of optimal spatial resolution and proposes an original solution to the problem of the adequate scale of measurement for geographical entities.

Remote sensing and the measurement of geographical entities in a forested environment. 1. The scale and spatial aggregation problem

Abstract The hypothesis tested in this study was that remote sensing constitutes a particular case of an arbitrary uniform spatial sampling grid used to obtain measurements about geographical entities that induces the scale and aggregation effect responsible for haphazard analysis results. The main objective was to evaluate the impact of measurement scale and spatial aggregation on the information content and classification accuracies of airborne MEIS-II data acquired over a midlatitude temperate forested environment. The original MEIS-II data were resampled to four spatial resolutions, namely 5 m, 10 m, 20 m, and 30 m. Forest classes were established according to three progressive levels of spatial aggregation. Descriptive statistics (Wald-Wolfowitz runs test, mean and variance) were calculated on transects of pixels representing each forest class delineated on the images at every spatial resolution. A maximum-likelihood classification was also performed for each combination of spatial resolution and aggregation level. The results reveal that, except for the mean, changing the measurement scale and the aggregation level of the classes greatly affects the values of the descriptive statistics. The Z value of the Wald-Wolfowitz runs test decreases with decreasing spatial resolution. The effect is more pronounced when the classes are progressively aggregated. For most classes, the variance decreases with the decrease of spatial resolution. In such cases, the impact of changing the measurement scale is greater than the change of aggregation level. Per-class accuracies are also considerably modified depending on the measurement scale and the aggregation level. Within a particular aggregation level, some classes are better classified at fine spatial resolutions, while others require coarser spatial resolutions. Three major conclusions can be stated from these results: 1) The information content of remote sensing images is dependent on the measurement scale determined by the spatial resolution of the sensor; 2) neglecting the scale and aggregation level when classifying remote sensing images can produce haphazard results having little correspondence with the objects of the scene; and 3) there is no unique spatial resolution appropriate for the detection and discrimination of all geographical entities composing a complex natural scene such as a forested environment. These conclusions provide a theoretical foundation from which original solutions to the problem of appropriate scales of measurement for geographical entities can be experimented. Logically, there exists an optimal spatial resolution for each entity of interest, corresponding to its intrinsic spatial and spectral characteristics.

Comparison of the Characteristics (Frequency and Timing) of Drought and Wetness Indices of Annual Mean Water Levels in the Five North American Great Lakes

In this study, we compared the frequency and timing of drought and wetness indices of annual mean water levels in the North American Great Lakes as they relate to teleconnection indices over the period from 1918 to 2012. In terms of timing, drought occurred in the Great Lakes watershed during the 1920, 1930 and 2000 decades, and was very intense in the East during the 1930’s and in the West during the 2000 decade. The main cause of extreme drought episodes in the 1920’s and 1930’s was a decrease in precipitation, while the 2000 decade drought is thought to be caused by increased water temperature (enhanced evaporation) due to a significant decrease in winter ice cover. The 1970 and 1980 decades were very wet over the whole watershed as a result of increased precipitation in the region. The succession of these dry and wet episodes did not have the same impacts on the stationarity of annual mean water levels in the five Great Lakes. Lake Superior shows an abrupt shift in mean in 1999, but a smoothed shift in variance since 1994, whereas Lake Erie shows four abrupt shifts in mean. Lake Ontario also shows the two first abrupt shift in mean and one abrupt change in variance. Extreme drought indices are negatively correlated with the North Atlantic Oscillation (NAO) for the two shallowest lakes (Ontario and Erie). In contrast, extreme wetness indices are positively correlated with PDO (positive correlation) and SOI (negative correlation) for Lake Superior only.