Remote Sensing and GIS as an Advance Space Technologies for Rare Vegetation Monitoring in Gobustan State National Park, Azerbaijan

Abstract

This paper describes remote sensing methodologies for monitoring rare vegetation with special emphasis on the Image Statistic Analysis for set of training samples and classification. At first 5 types of Rare Vegetation communities were defined and the Initial classification scheme was designed on that base. After preliminary Statistic Analysis for training samples, a modification algorithm of the classification scheme was defined: one led us to creating a 4 class's scheme (Final classification scheme). The different methods analysis such as signature statistics, signature separability and scatter plots are used. According to the results, the average separability (Transformed Divergence) is 1951.14, minimum is 1732.44 and maximum is 2000 which shows an acceptable level of accuracy. Contingency Matrix computed on the results of the training on Final classification scheme achieves better results, in terms of overall accuracy, than the training on Initial classification scheme.

Classification of rural landscapes from low-density lidar data: is it theoretically possible?

“…Because transformed measures have been shown to be more powerful than other approaches (Gambarova et al 2010), two transformed measures (transformed divergence and JM distance) have been used in this article to estimate class separability.…”

“…Traditionally, these methods have been used mainly with the spectral bands of satellite or aerial imagery. Gambarova et al (2010) used TD and the JM distance to determine the best band combination of four Système Pour l'Observation de la Terre 5 (SPOT5) image data sets for four types of rare vegetation communities. TD results were more encouraging than JM distance results, something that was expected because TD overestimates classification performance.…”

Mapping of peanut crops in Queensland, Australia, using time-series PROBA-V 100-m normalized difference vegetation index imagery

“…If the distance is insignificant, the layers can be eliminated from the classification to ensure the best result. 58 JM distance measures the separability between a pair of two classes based on the average distance between their spectral means. Its output value ranges from 0 to 2, where a good separability is indicated by a larger value.…”

Improving the potential of pixel-based supervised classification in the absence of quality ground truth data

“…If the spectral distance between any two ROIs is not significant for any combination of bands, then the ROIs may not be distinct enough to produce a valuable classification (Gambarova et al, 2010). Both the Jeffries-Matusita (J-M) and the Transformed Divergence distance (separability) measures are available when using the ENVI 4.8 image analysis software.…”