To effectively display hyperspectral imagery for visualization purposes, the three RGB channels should be selected or extracted from a hyperspectral image under the criteria of maximum information or maximum between-class separability. Seven band selection (OIF, SI, Cl, divergence, transformed divergence, B-distance, JM-distance) and five feature extraction (principal component analysis, linear discriminant analysis, class-based PCA, segmented PCT (SPCT), independent component analysis) methods and their variations are examined and compared using CASI hyperspectral imagery with the goal of detecting Centaurea solstitialis (yellow starthistle or YST), an invasive weed, in an annual grassland in California. Three indicators, information index (Infodex), separability index (Sepadex) and average correlation coefficient (ACC) are proposed to evaluate the quality of the generated images. The results suggest that both the combination of the three SPCT channels and the combination of the second PCA channel with the positive and negative of the first LDA channels (PCA2, LDA1, -LDA1) can enhance our ability to visualize the distribution of YST in contrast to the surrounding vegetation.

Detection of yellow starthistle through band selection and feature extraction from hyperspectral imagery

Agronomic traits and vegetation indices of two onion hybrids

The study examined the Landuse and Landcover (LULC) dynamics of the central cocoa cultivation area of southwestern Nigeria between 1972 and 2002 using remotely sensed multi-temporal datasets. The datasets are Landsat 1972, 1986, 1991 and 2002 imageries. The datasets were each subjected to supervised classification techniques employing the maximum likelihood classifier using ILWIS software. This implies that field observation for identification and completion of ambiguous features and other details supported by GPS locations was carried out. Seven dominant classes of feature: agro-forest/light forest, built-up area, exposed rock surfaces/bare land, forest reserve, shrub and arable land, ridge forest and water body were identified. A time series analysis of the LULC changes was carried out to provide the necessary understanding of the changes required for policy formulation and decision-making with respect to cocoa production, forest reserve management and landuse planning, control, coordination and budgeting while being mindful of environmental conservation. This indispensable geo-information is yet lacking in the cocoa growing belt of southwestern Nigeria. ArcView software was used to prepare the corresponding time series LULC maps of the study area. The study showed that the forest reserves within the study area reduced at an average rate of 2.78% per year while agro-forest/light forest reduced to 46.39% (i.e., at an average rate of 1.55% per year) and, shrub and arable land increased by 323.06% (i.e., at an average rate of 10.77% per year) for food production farming to feed the rapidly increasing population between 1972 and 2002.

Spatio-Temporal Assessment and Mapping of the Landuse Landcover Dynamics in The Central Forest Belt of Southwestern Nigeria

Airborne archaeological prospection in arable crops relies on detecting features using contrasts in the growth of the overlying crop as a proxy. This is possible because thecomposition of the soil in the features differs from the unmodified subsoil, and this exerts influence on the state of the crop. This influence is expressed as changes in crop canopydensity, structure, and in periods of resource constraint, variations in vegetation stressand vigour. These contrasts are dynamic, and vary temporally with local weather, andspatially with variations in drift geology and land use. This means that the archaeologicalfeatures have no unique spectral signature usable for classification. Rather, contrast isexpressed as relative, local variation in the crop.
The extent to which the features are detectable using a particular technique is dependanton the strength of the contrast and the ability of the sensor to resolve it. Current practicerelies heavily on photography in the visible spectrum, but other sensors and processingtechniques have the potential to improve our ability to resolve subtle contrasts. This isimportant, as it affords the opportunity to extend the detection temporally and in soiltypes not normally considered conducive to detection.
This work uses multi-temporal spectro-radiometry and ground-based survey to studycontrast at two sites in southern England. From these measurements leaf area index, vegetationindices, the red-edge position, chlorophyll fluorescence and continuum removalof foliar absorption features were derived and compared to evaluate contrast. The knowledgegained from the ground-based surveys was used to inform the analysis of the airbornesurveys. This included the application of vegetation indices to RGB cameras, theuse of multi-temporal and full-waveform LiDAR to detect biomass variations, and the useof various techniques with hyper-spectral imaging spectroscopy. These methods providea demonstrable improvement in contrast, particularly in methods sensitve to chlorophyllfluorescence, which afford the opportunity to record transient and short term contraststhat are not resolved by other sensors.

Improving contrast for the detection of archaeological vegetation marks using optical remote sensing techniques.

Land use and land cover mapping of the Simiyu catchment (Tanzania) using remote sensing techniques

There have been extensive evaluations of different remote sensing digital classification techniques. In contrast, there has been very little systematic evaluation of various enhancements, such as different colour composites, for visual interpretations. This study examined three Landsat Thematic Mapper (TM) colour composites for visual classification of agricultural crops in an Imperial Valley, California study area. Accuracy results varied considerablywith the composite from the best three bands as determined by transformed divergence calculations being more useful than an image obtained via principle components or the standard infrared simulated false colour composite.

Colour composite comparisons for agricultural assessments

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Colour composite comparisons for agricultural assessments