Combining field and image spectral reflectance for mangrove species identification and mapping using WorldView-2 image

Kamal, Muhammad; Ningam, Muhammad Ulul Lizamun; Alqorina, Finni; Wicaksono, Pramaditya; Murti, Sigit H.

doi:10.1117/12.2325629

Cited by 7 publications

(5 citation statements)

References 15 publications

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“…Spectral Angle Mapper successfully mapping distribution of species of mangrove in Mangkang Kulon. This research mirrored research from [28] and [29] that SAM has potential for extracted mangrove species distribution using remote sensing. This application supports an effort for mangrove conservation in long-term monitoring.…”

Section: Discussionsupporting

confidence: 55%

Analysis of Mangrove Species Distribution Mapping and the Environmental Problem in Mangkang Kulon, Semarang City

Sanjoto¹,

Husna²,

Sidiq³

2021

Proceedings of the 6th International Conference on Education &Amp; Social Sciences (ICESS 2021)

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The pressure on mangrove forest have been increasing due to abrasion and industrial development. Mangkang Kulon Sub-district as one coastal area in Semarang City experience loss of mangrove and coast itself due to abrasion from pond activities. We use Spectral Angle Mapper to classify species of mangrove in Mangkang Kulon. Spectral library was obtained from fieldwork using USB 4000. As the result, Spectral Angle Mapper successfully mapping distribution of species of mangrove in Mangkang Kulon. The area of mangrove in Mangkang Kulon is about 8.6 hectares or only 2% of Mangkang Kulon. Most of the mangrove in Mangkang Kulon is Bruguiera gymnorrhiza, followed by Rhizophora mucronata and Xylocarpus granatum. For future existence of mangrove ecosystem in Mangkang Kulon, several methods have been utilizing to improve extend and quality of mangrove.

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Section: Discussionsupporting

confidence: 55%

Analysis of Mangrove Species Distribution Mapping and the Environmental Problem in Mangkang Kulon, Semarang City

Sanjoto¹,

Husna²,

Sidiq³

2021

Proceedings of the 6th International Conference on Education &Amp; Social Sciences (ICESS 2021)

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“…According to this study, the spectral reflectance curves recorded at close range to the leaf (i.e., 2 cm) and from the furthest distance (i.e., 5 m) showed the lowest curve variation between readings. Therefore, this study used a distance of 2 cm for leaf spectral measurements to ensure that only the mangrove leaf was read (Figure 3a) and ensure highly consistent spectrometer results across readings [20,21]. The spectrometer's measurement angle was set at 45 • to the nadir and facing the sun to avoid shadows on the target object.…”

Section: Field Data Collectionmentioning

confidence: 99%

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

2022

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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.

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“…The sample points were evenly distributed in mangrove areas to represent each mangrove species. The spectral reflectance of the mangroves was measured using a field spectroradiometer, 45 a commonly used method to analyze the spectrum of light reflected or emitted 49 . Reflectance data were collected at the leaf level by selecting mangrove leaves that represent various species or conditions in the area.…”

Section: Methodsmentioning

confidence: 99%

“…The spectral reflectance of the mangroves was measured using a field spectroradiometer, 45 a commonly used method to analyze the spectrum of light reflected or emitted. 49 Reflectance data were collected at the leaf level by selecting mangrove leaves that represent various species or conditions in the area. The spectroradiometer was placed above the leaf to measure the reflectance of light at various wavelengths, ranging from ultraviolet (315 nm) to SWIR (1100 nm).…”

Section: Field Datamentioning

confidence: 99%

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

Simarmata,

Wikantika,

Darmawan

et al. 2024

J. Appl. Rem. Sens.

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Mangroves maintain coastal balance and have the greatest potential for carbon sequestration. Most mapping studies on mangroves have focused on their extent and distribution and rarely featured mangrove species. Therefore, the objective of our study is to investigate mangrove species mapping from integrated Sentinel-2 imagery and field spectral data using a random forest (RF) algorithm. Study areas are located in East and South Lampung, Indonesia. The field samples used represented 144 points of mangrove species. The classification method used an RF algorithm and four models with varying parameters: model 1 with Sentinel-2; model 2 with both Sentinel-2 and field spectral data; model 3 with Sentinel-2, field spectral data, and spectrally transformed data; and model 4 only with spectrally transformed data. The results showed that Rhizophora mucronata, Sonneratia alba, Avicennia lanata, and Avicennia marina were the most common mangrove species in these areas, with reflectance values in the range of 0.002 to 0.493, 0.006 to 0.833, 0.014 to 0.768, and 0.002 to 0.758. Permutation importance (PI) that affects the classification model is the red band, near-infrared, and green normalized difference vegetation index, where the most PI in model 3 is 0.283. The highest level of agreement for mangrove species is found in model 3. Model 3 is the best parameter for RF classification that showed the best mapping accuracy, with the overall accuracy, user accuracy, producer accuracy, and kappa value being 81.25%, 81.68%, 81.25%, and 0.80, respectively.

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Combining field and image spectral reflectance for mangrove species identification and mapping using WorldView-2 image

Cited by 7 publications

References 15 publications

Analysis of Mangrove Species Distribution Mapping and the Environmental Problem in Mangkang Kulon, Semarang City

Analysis of Mangrove Species Distribution Mapping and the Environmental Problem in Mangkang Kulon, Semarang City

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

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

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