A Colourimetric Approach to Ecological Remote Sensing: Case Study for the Rainforests of South-Eastern Australia

Abstract

To facilitate the simplification, visualisation and communicability of satellite imagery classifications, this study applied visual analytics to validate a colourimetric approach via the direct and scalable measurement of hue angle from enhanced false colour band ratio RGB composites. A holistic visual analysis of the landscape was formalised by creating and applying an ontological image interpretation key from an ecological-colourimetric deduction for rainforests within the variegated landscapes of south-eastern Australia. A workflow based on simple one-class, one-index density slicing was developed to implement this deductive approach to mapping using freely available Sentinel-2 imagery and the super computing power from Google Earth Engine for general public use. A comprehensive accuracy assessment based on existing field observations showed that the hue from a new false colour blend combining two band ratio RGBs provided the best overall results, producing a 15 m classification with an overall average accuracy of 79%. Additionally, a new index based on a band ratio subtraction performed better than any existing vegetation index typically used for tropical evergreen forests with comparable results to the false colour blend. The results emphasise the importance of the SWIR1 band in discriminating rainforests from other vegetation types. While traditional vegetation indices focus on productivity, colourimetric measurement offers versatile multivariate indicators that can encapsulate properties such as greenness, wetness and brightness as physiognomic indicators. The results confirmed the potential for the large-scale, high-resolution mapping of broadly defined vegetation types.

Holistic Reduction to Compare and Create New Indices for Global Inter-Seasonal Monitoring: Case Study for High Resolution Surface Water Mapping

“…In the context of remote sensing, holistic reduction and multi-scalar SII facilitate the effective discrimination of landscape features by identifying their appearance globally and across the seasons, and reducing or grouping them into land class 'primitives' [71] which can be classified distinctly with one-class classifications, rather than considering them together with every other class in and across landscapes [24,72,73]. Consequently, classifying sub-classes only from the extents of reduced super-classes (such as water or forest masks) can be more effective because their multispectral overlap with other classes will have been eliminated, reducing the analytical complexity of the data space to be considered.…”

“…Interpretation keys with colourimetric descriptions were created for the water and non-water features that were compared in the decision matrix. Interpretation keys are formalized by a set of examples, which are as mutually exclusive as possible to support reliable recognition and communication of features [24,[41][42][43]. The examples can be based on existing reference data or maps.…”

“…Taking a colourimetric approach [24] to decorrelate RGB composite hues from saturation and brightness, bands from CHI and CAWI were also tested to create a hue-based index from an HSV (Hue, Saturation, Value) colour space transformation to provide a linear gradient where water was at an extreme, to be referred to as CWI (Colourimetric Water Index). This HSV transformation also provided a Saturation that was able to mask out features such as dark agricultural features, urban/built-up, and coal and mining areas, which might be misclassified by the hue angle alone.…”

“…Indices are widely used for surface water mapping (Table 1), especially in large-scale and time series analyses because of their high computational efficiency and easy implementation [10]. The perception of colour does not require knowledge of optical properties [23] and colourimetry provides the potential for quantifiable measurements and easily communicable ontologies [24]. Hue angle has been considered the most perceptibly intuitive way for humans to consistently differentiate and communicate their observations about land cover features [25,26].…”

“…Satellite Imagery Interpretation (SII) has previously been proposed as a more rigorous analogue to Aerial Photographic Interpretation (API) for multispectral imagery analysis, with the potential for direct and repeatable quantitative measurement [24]. SII is formalized by the creation of interpretation keys that summarize visual perception cues for the identification of land cover classes for the development of colourimetric or index-based ontologies for their classification.…”