Comparing Pixel and Object-Based Approaches to Map an Understorey Invasive Shrub in Tropical Mixed Forests

Abstract

The establishment of invasive alien species in varied habitats across the world is now recognized as a genuine threat to the preservation of biodiversity. Specifically, plant invasions in understory tropical forests are detrimental to the persistence of healthy ecosystems. Monitoring such invasions using Very High Resolution (VHR) satellite remote sensing has been shown to be valuable in designing management interventions for conservation of native habitats. Object-based classification methods are very helpful in identifying invasive plants in various habitats, by their inherent nature of imitating the ability of the human brain in pattern recognition. However, these methods have not been tested adequately in dense tropical mixed forests where invasion occurs in the understorey. This study compares a pixel-based and object-based classification method for mapping the understorey invasive shrub Lantana camara (Lantana) in a tropical mixed forest habitat in the Western Ghats biodiversity hotspot in India. Overall, a hierarchical approach of mapping top canopy at first, and then further processing for the understorey shrub, using measures such as texture and vegetation indices proved effective in separating out Lantana from other cover types. In the first method, we implement a simple parametric supervised classification for mapping cover types, and then process within these types for Lantana delineation. In the second method, we use an object-based segmentation algorithm to map cover types, and then perform further processing for separating Lantana. The improved ability of the object-based approach to delineate structurally distinct objects with characteristic spectral and spatial characteristics of their own, as well as with reference to their surroundings, allows for much flexibility in identifying invasive understorey shrubs among the complex vegetation of the tropical forest than that provided by the parametric classifier. Conservation practices in tropical mixed forests can benefit greatly by adopting methods which use high resolution remotely sensed data and advanced techniques to monitor the patterns and effective functioning of native ecosystems by periodically mapping disturbances such as invasion.

Mapping Wild Leek through the Forest Canopy Using a UAV

“…When using high resolution imagery to detect bush honeysuckle, higher accuracies were obtained (90-95% overall accuracy) with an object-based image processing technique compared to pixel-based methods [29]. Even in more complex environments, an object-based method performed relatively well when incorporating textural attributes and vegetation indices for multiple species [32]. Using a set threshold has the benefit of being easily implemented, but might lack the flexibility and precision to separate other species present on the forest floor.…”

“…Multispectral and hyperspectral data has also proven to be effective in classifying invasive species using objectbased classification methods [31][32][33]. An important disadvantage of using such data is the typically high cost associated with obtaining or producing the data.…”

Regional mapping of spekboom canopy cover using very high resolution aerial imagery

“…Recently, a number of studies have used LiDAR (light detection and ranging) data for describing plant growth forms as a complement to spectral information (Fassnacht et al 2016;Vaz et al 2018). Measures of plant phenological characteristics derived from multi-temporal image sets have also been found to assist in species mapping (Niphadkar et al 2017;Tarantino et al 2019).…”

“…Compared to snapshot imagery, multi-temporal images provide more detailed descriptions of spectral variation relating to seasonal growth patterns. This form of data is seeing increasing use and can be useful for species identification where there are species discriminating phenological differences (Michez et al 2016;Niphadkar et al 2017;Tarantino et al 2019). Some species exhibit phenology that depends on spatially varying environmental factors such as weather, geology and altitude (Fassnacht et al 2016;Niphadkar & Nagendra 2016).…”

“…Challenges with LiDAR data include multiple scattering within the canopy (Korpela et al 2010), which creates uncertainty as to the true source of returns and intra-species variation of plant structure (Hovi et al 2016), which can hamper classifier performance. (Boyden et al 2007;De Castro et al 2012;Clark, Roberts & Clark 2005;Mehner et al 2004;Niphadkar et al 2017;Tarantino et al 2019). Algorithms implemented in the eCognition software package (Trimble 2018) such as the fuzzy and hierarchical approaches are also frequently used for vegetation identification (Johansen et al 2007;Kollár, Vekerdy & Márkus 2013;Niphadkar et al 2017;Ouyang et al 2011).…”