Spectral Reflectance-Based Mangrove Species Mapping from WorldView-2 Imagery of Karimunjawa and Kemujan Island, Central Java Province, Indonesia

Abstract

Mangrove mapping at the species level enables the creation of a detailed inventory of mangrove forest biodiversity and supports coastal ecosystem management. The Karimunjawa National Park in Central Java Province is one of Indonesia’s mangrove habitats with high biodiversity, namely, 44 species representing 25 true mangroves and 19 mangrove associates. This study aims to (1) classify and group mangrove species by their spectral reflectance characteristics, (2) map mangrove species by applying their spectral reflectance to WorldView-2 satellite imagery with the spectral angle mapper (SAM), spectral information divergence (SID), and spectral feature fitting (SFF) algorithms, and (3) assess the accuracy of the produced mangrove species mapping of the Karimunjawa and Kemujan Islands. The collected field data included (1) mangrove species identification, (2) coordinate locations of targeted mangrove species, and (3) the spectral reflectance of mangrove species measured with a field spectrometer. Dendrogram analysis was conducted with the Ward linkage method to classify mangrove species based on the distance between the closest clusters of spectral reflectance patterns. The dendrogram showed that the 24 mangrove species found in the field could be grouped into four levels. They consisted of two, four, and five species groups for Levels 1 to 3, respectively, and individual species for Level 4. The mapping results indicated that the SID algorithm had the highest overall accuracy (OA) at 49.72%, 22.60%, and 15.20% for Levels 1 to 3, respectively, while SFF produced the most accurate results for individual species mapping (Level 4) with an OA of 5.08%. The results suggest that the greater the number of classes to be mapped, the lower the mapping accuracy. The results can be used to model the spatial distribution of mangrove species or the composition of mangrove forests and update databases related to coastal management.

Mangrove ecosystem species mapping from integrated Sentinel-2 imagery and field spectral data using random forest algorithm

“…The sample locations and number of sample points were determined based on the raw pixel values of mangrove objects on Sentinel-2A, specifically the red band, green band, and NIR band. 45 Additionally, the determination considers access to the sampling location as well since some locations are difficult to reach. The pixel values obtained are plotted onto a map to determine the reflectance value of mangrove species in the field.…”

“…High-resolution data are useful for classifying mangrove species; nonetheless, they are not available for all areas. 45 Previous studies have investigated mangrove reflectance using satellite data; however, observations of the spectral properties of mangrove biota using satellite images are insufficient, owing to the challenge of recognizing mangrove species solely from the canopy. 46 This is supported by previous research showing that object reflectance using a field spectroradiometer can be directly measured for mangrove species mapping.…”

“…46 This is supported by previous research showing that object reflectance using a field spectroradiometer can be directly measured for mangrove species mapping. 45 Other findings have indicated that each mangrove species has a unique spectral reflectance and can be easily identified and mapped with adjacent wavelength bands in the near-infrared (NIR) region. 15 Information on mangrove species is essential for mangrove management; however, insufficient research related to species classification has been conducted, especially in Lampung, Indonesia.…”

Perspective: Improving the accuracy of plant phenology observations and land-cover and land-use detection by optical satellite remote-sensing in the Asian tropics

“…Remote-sensing can detect the color of the canopy surface of various tree species, which indicate the characteristics of leaf traits (leaf size, leaf biomass, leaf thickness, amount of pigments in a leaf, and angle of leaves) and structures (tree structure and height) (Sims and Gamon, 2002;Luke et al, 2013;Noda et al, 2014;Asner et al, 2015;Noda et al, 2021;Rahmandhana et al, 2022). The characteristics of leaf longevity, which is explained by leaf-flush and leaf-fall phenology, were correlated with the type of photosynthesis, leaf traits and structures, and climate (Wright et al, 2004;Kikuzawa, 2005;Onoda et al, 2011;Kikuzawa et al, 2013).…”

“…Also for this purpose, researchers have frequently used data observed by optical sensors on board public satellites with low frequency (16day intervals) but a moderately high spatial resolution (30 m) such as the Landsat series of satellites (Segah et al, 2010;Kou et al, 2015;Li P. et al, 2015;Ishihara and Tadano, 2017;Morozumi et al, 2019). In contrast, for the detection of land cover and land use with a fine-scale, they have frequently used data observed by optical sensors on board commercial satellites (e.g., the RapidEye: Imukova et al, 2015;Pfeifer et al, 2016; the WorldView series satellites: 4 Nomura and Mitchard, 2018;Rahmandhana et al, 2022) with a high spatial resolution (e.g., 50 cm) but quite low frequency (e.g., 46-day intervals).…”