Mangrove Species Identification: Comparing WorldView-2 with Aerial Photographs

Abstract

Abstract:Remote sensing plays a critical role in mapping and monitoring mangroves. Aerial photographs and visual image interpretation techniques have historically been known to be the most common approach for mapping mangroves and species discrimination. However, with the availability of increased spectral resolution satellite imagery, and advances in digital image classification algorithms, there is now a potential to digitally classify mangroves to the species level. This study compares the accuracy of mangrove species maps derived from two different layer combinations of WorldView-2 images with those generated using high resolution aerial photographs captured by an UltraCamD camera over Rapid Creek coastal mangrove forest, Darwin, Australia. Mangrove and non-mangrove areas were discriminated using object-based image classification. Mangrove areas were then further classified into species using a support vector machine algorithm with best-fit parameters. Overall classification accuracy for the WorldView-2 data within the visible range was 89%. Kappa statistics provided a strong correlation between the classification and validation data. In contrast to this accuracy, the error matrix for the automated classification of aerial photographs indicated less promising results. In summary, it can be concluded that mangrove species mapping using a support vector machine algorithm is more successful with WorldView-2 data than with aerial photographs.

Object-Based Approach for Multi-Scale Mangrove Composition Mapping Using Multi-Resolution Image Datasets

“…The results showed that the developed rule set was able to map targeted mangrove composition objects from images with different spatial resolutions. Only limited studies have implemented the explicit hierarchical model of objects in mangrove mapping [9,12,16,69]. This study demonstrated the effectiveness of having a conceptual hierarchical model for mangrove mapping.…”

Estimating Mangrove Biophysical Variables Using WorldView-2 Satellite Data: Rapid Creek, Northern Territory, Australia

“…This area is dominated by five different mangrove species: Avicennia marina, Ceriops tagal, Bruguiera exaristata, Lumnitzera racemosa and Rhizophora stylosa [21]. Other species do not represent significant coverage to be considered separately.…”

“…The spatial resolution of multispectral bands was 2.0 m, and the panchromatic band was 0.5 m. Table 1 shows the detailed spectral band description of WV2 images. WV2 images were radiometrically corrected according to the method described by Heenkenda et al [21]. First, digital numbers were converted to at-sensor-radiance values, and then, they were converted to top-of-atmosphere reflectance values.…”

“…Class-specific rules were developed based on the contextual information from the WV2 images, geometry and neighbourhood characteristics of objects at different hierarchical levels to separate mangrove coverage only (see Heenkenda et al [21] for a detailed description).…”

Identification and Mapping of Marine Submerged Aquatic Vegetation in Shallow Coastal Waters with WorldView-2 Satellite Data

“…There are numerous studies that have used highresolution satellite images for identifying and mapping coastal habitats such as coral reefs (Benfield et al, 2007), sea grass meadows (Guimarães et al, 2011), mangroves (Heenkenda et al, 2014;Ibrahim et al, 2015), macroalgae (Garcia et al, 2015;Tin, O'Leary, and Fotedar, 2015), and freshwater/ salt marsh (Carle, Wang, and Sasser, 2014). However, these studies mostly utilized sensors with fewer than four spectral bands in the visible domain, which limited the detailed classification of vegetation (Feilhauer et al, 2013).…”