Comparison of vegetation indices for estimating above-ground mangrove carbon stocks using PlanetScope image
Introduction
Global warming is one of the most feared problems, and carbon emissions are considered its strongest influencing factors (Bindu et al., 2018). Global climate change has been attributed to the loss of forest area due to advances in crop and livestock agriculture, forest fires, and resource overuse. Coupled with the release of greenhouse gases into the atmosphere by industrial activities and transportation development, it creates a daunting challenge for humans to survive (Smith et al., 2017, Grant et al., 2017, Pant et al., 2018). To overcome this circumstance, many have suggested enhancing the role of forests as CO2 absorbers by devising natural and planted forest management systems (Brown, 1997). Coastal ecosystems such as mangroves and seagrass play a crucial part in providing benefits and services related to climate change impact reduction and adaptation (Howard et al., 2014). Mangroves are a group of vegetation species that can absorb carbon, an element controlling CO2 concentrations in the atmosphere (Sidik et al., 2019). Mangrove forests in Indonesia have the most extensive area worldwide (Kusmana, 1996), making it tremendous potential for carbon absorption. The global mangrove area is about 16,530,000 hectares, with 42% of it found in Asia, 20% in Africa, and 15% in North and Central America (Giri et al., 2011).
Considering that mangrove forests often grow in hardly accessible terrain, it is necessary that constraints related to time, cost, and energy in mangrove studies to be minimized. Remote sensing satellites are now commonly used for estimating extension and dynamics, like detecting changes in mangrove areas (Sulaiman et al., 2013). In 2016, the latest remote sensing data product was released, namely PlanetScope. This high-resolution satellite imagery offers several advantages in data acquisition, including the ability to sense and record 200 million km2 of the ground surface area daily with 130 complete satellite constellations. According to Planet Labs (2020), each PlanetScope satellite is 3U in size (CubeSat form factor 10 cm × 10 cm × 30 cm). It has a 98° inclination angle, with an orbital altitude of 475 km in a sun-synchronous orbit, allowing object sensing in the morning and during the day and, especially along the equator, at 09:3011:30 am. PlanetScope has a 12-bit radiometric resolution carrying four spectral bands (blue, green, red, and near-infrared/NIR), a 3 m spatial resolution, and a high temporal resolution (one day) at nadir (Planet Labs, 2020).
Mangrove ecosystems are one of the objects detectable in remote sensing data because they are geographically situated at the interface between land and sea, resulting in unique sensing products compared to other vegetation objects (Faizal and Amran, 2005). Because remote sensing acquires information quickly and with a wide area of coverage, it can provide data required in mangrove carbon stock estimation (Hastuti et al., 2017). This method generally uses image transformation like vegetation indices to measure vegetation density levels, which are related to biomass, by applying a specific algorithm to a satellite image. The vegetation index transforms the spectral values of several remote sensing bands to accentuate those of vegetation objects. In practical terms, it is a mathematical transformation that involves several bands at once and produces a new, more representative image of vegetation objects (Danoedoro, 2012).
In this research, three vegetation indices (VIs) were selected: Normalized Difference Vegetation Index (NDVI), Difference Vegetation Index (DVI), and Enhanced Vegetation Index (EVI). These VIs were selected after factoring in numerous literature studies and research reporting their strengths and weaknesses in different circumstances (Wicaksono et al., 2011, Wicaksono, 2017, Kamal et al., 2016, Nguyen et al., 2019, Xia et al., 2020, Zhu et al., 2020, Aljahdali et al., 2021). Thus, it was intended to assess their accuracy in estimating above-ground carbon stocks from PlanetScope images in the Bedul Mangrove Area, Banyuwangi, East Java Province, Indonesia. The research differs from previous studies in remote sensing data used and area observed. Accordingly, it was also designed to investigate whether the three vegetation indices produced accurate estimates with such differences.
Section snippets
Research site
This research was conducted in the Bedul Mangrove Block, a mangrove forest part of the Alas Purwo National Park in Bloksolo Sub-village, Sumberasri Village, Purwoharjo District, Banyuwangi Regency. According to the minutes of the areal measurement on May 27, 1983, the national park covers 43,420 hectares of land. Lowland tropical rainforest ecosystem composes most of the park, while the mangrove forest area, situated at the Grajagan Bay, is currently about 1200 hectares. The Grajagan Bay is
Identified mangrove species characteristics
Table 2 presents the list of mangrove species found at several locations during the fieldwork. Data were collected from only 20 of the planned 40 points due to the mangrove forest’s harsh condition, making it difficult to access all sample sites. These 20 samples data were split up equally into two groups: ten samples were used for modeling carbon stocks, while the other ten were for accuracy testing. To obtain an optimum prediction from limited samples, we selected these sampling sets
Conclusion
There are 14 mangrove species found along the Segara Anak River in the Alas Purwo National Park. Among those species, Rhizophora sp. were found to be dominant in this area and contributed to the high values of AGC. Overall, PlanetScope image has a potential for mangrove AGC estimation and mapping. All the three vegetation indices resulted almost similar pattern of estimated AGC value distribution. However, based on the accuracy testing results, DVI has the lowest standard error of estimate
CRediT authorship contribution statement
Eva Purnamasari: Conceptualization, Methodology, Formal analysis, Writing - original draft. Muhammad Kamal: Validation, Interpreting, Writing - review & editing. Pramaditya Wicaksono: Methodology, Data curation, Writing - review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
This research was funded by the 2020 Penelitian Dasar (Fundamental Research) Grant scheme provided by the Ministry of Research and Technology/ National Research and Innovation Agency of the Republic of Indonesia (contract number 2979/UN1.DITLIT/DIT-LIT/PT/2020). The authors would like to thank (1) the Department of Geographic Information Science at the Faculty of Geography, Universitas Gadjah Mada, for providing research facilities and equipment, (2) the Alas Purwo National Park management for
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