## 2 Non Destructive Sensing
### Remote Sensing
A major challenge of invasion biology lies in the development of pre and post predictive models and understanding of the invasion processes. Introduced species vary in their invasive behavior in different regions [101]. Predicting the ecological behavior of a species in a new environment may be effectively impossible [102]. Estimating animal species numbers, population size and related features is rather difficult in comparison to plants. However, indicated clear relationships between the characteristics of releases and the species involved [103], and the successful establishment and spread of invaders. A modified change vector analysis (CVA) was developed using normalized multi-date data from Landsat TM and examined _Adelges piceae_ infestation. Although high spectral and spatial resolution provide the ability to classify canopy species, precise classification of a species is still difficult. Several such studies of the spectral properties of invasive species have been derived, mostly from low altitude aerial photography or field spectrographs, but minimum information will reach the remote observer. Other factors like atmospheric noise, humidity, shadow, contribution from soil add to the confusion and the low chance of discrimination of separate species [105]. Furthermore, variation in orientation of leaves, age of a leaf, variation in leaf area index, different slopes of the locations where the individuals are found could make the spectral signature of a species difficult to define. Also, all leaves are not exposed to the same level of incoming radiant energy and often do not reflect back to the sky due to distortions in the leaf surface. It is not however practically feasible to determine the ideal wavelengths for discrimination when large numbers of invasive species are present. Furthermore, if the presence of number of invasive species per pixel increases, the difficulty in identifying the individual components that contribute to the mixed spectrum also increases. These problems will be further aggravated if species variability in spectral signatures is high. For large scale direct remotely sensed monitoring of several invasive species, the possibility of correctly identifying all individuals through direct mapping thus appears doubtful. Absorption of incident light in the visible range and in the NIR range changes when plants are disease-stressed [106, 107]. Damage of foliar internal structure, intracellular water content, decrease of chlorophyll content and pigment changes result from plant-disease interaction. This consequently influences their reflectance obtained from airborne remote sensing. Therefore, comparing the spectrum difference of plants in stress from the healthy ones, theoretically we are able to identify the stress severity of green vegetation. Reflectance tends to increase in individual leaves as the leaf matures but the changes are wavelength dependent. Water stress by reducing the internal water content increases the reflectance from an individual leaf. Information gathered from individual leaves provides a basic set of information about the mechanism of the changes occurring within a plant [108, 109]; however, to be of practical application it must be extended to a canopy or field level [110]. Canopies exhibit the same properties of individual leaves but several variables that now must be considered. Leaf surfaces often act as polarizing filters and reflect back to portions of the sky that are not always detected by viewing the canopy only from the vertical direction. Leaf fluorescence has been observed in all plants and can be related to the efficiency of the photosynthetic process. It is possible that leaf fluorescence could be used to assess the impact of diseases on the physiological status of a plant. This technique has only been used on individual leaves; however, it could be extended to canopies through the use of laser-induced fluorescence [110].