Unifying remote sensing and web GIS infrastructure design and implementation of weighted overlay analysis on vegetation indices

Development momentum of satellite technologies; • enables us tracking seasonal changes in vegetation regionally or globally via remote sensing phenology, • leads us observing the invisible light spectrum and its meanings inside. As living in information age, people have started to understand mathematical chains through the nature and environment. We get values identified by sensors as inputs, write functions for various analysis that helps us to visualize and figure out of enhanced outputs from raw imagery. This thesis study concentrates on the topic of GIS and remote sensing using a multispectral imaging system for vegetative research and its use mainly in agricultural applications. Applications may be used for classification of the forests, change detection on burned area, site selection of specific varieties of agricultural goods, acquiring the health or stress level of vegetation, resource optimization along with sustainable environment and more else with researchers vision. 'Spectr-Agri-ITU' app is a GIS based modelling, geo-design collaborated web app which spectrally transforms the raw imagery into valued information. The input must be an image containing red, green, blue (RGB), near infra-red (NIR), and shortwave infrared (SWIR 1-2) spectral bands. Otherwise some analyzes will not work. Spectral imagery use for vegetation manner is mainly based on red and infrared light because of reflectance and scatter. Selectable analysis and indices which user may specify, applied to uploaded spectral image. Mostly known and used analysis is normalized difference vegetation index (NDVI) which is a key for quickly identifying vegetated areas and their "condition," and it remains the most used index to detect live green plant canopies in multispectral remote sensing data. There are more vegetation indices which are mathematical combination or transformation of spectral bands that emphasize the spectral characteristics of greenness. Enhanced imagery is created as numerous raster data, presents information for next step. Weighted overlay analysis (WOA) provides us to associate, weight and rank various types of information so to evaluate multiple factors at once. Percentage settings of weights form the final output. Finally output raster data has meanings to visualize with smart mapping styles, color ramps and symbology. Customizable styles let user to understand spectral imagery insights of research. This innovative application puts these cut-edge technologies forward in World Wide Web environment with Web-GIS architecture.