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Landscape and Ecological Engineering

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26-02-2024 | Technical report

Assessing the characteristics and seasonal changes of mangrove forest in Dong Rui commune, Quang Ninh Province, Vietnam based on multispectral UAV data

Author: Trung Dung Ngo

Published in: Landscape and Ecological Engineering | Issue 2/2024

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Abstract

Mangroves are estuarine and coastal ecosystems strongly influenced by the local tidal regime and climate, so there is a strong variation between seasons of the year. Assessing seasonal characteristics and fluctuations allows for an understanding of changes in mangrove ecosystems during each period of the year. In this study, we used images collected from the Multispectral Unmanned Aerial Vehicle (UAV) to determine the current status and fluctuations between summer and winter of the mangrove area of Dong Rui commune according to five vegetation indicators, including the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Enhanced Vegetation Index (EVI), Visible Atmospherically Resistant Index (VARI), and Green Chlorophyll Index (GCI). The results show that the current state of mangroves in the UAV flight area is relatively good, with the value of vegetation indicators at a high level. In the summer, the value of vegetation indicators is stable and higher than in the winter. In low-tide areas, vegetation index values fluctuate higher than in high-tide areas. Based on the results of the analysis of vegetation indicators, the degree of correlation between NDVI and the remaining four values was determined. The results showed that the correlation coefficient was at a high level, with the highest being between NDVI and SAVI with a correlation coefficient of approximately equal to 1, followed by NDVI and EVI with a correlation coefficient above 0.9. We propose that UAV is an effective tool for monitoring and assessing fluctuations, serving the management and conservation of mangrove ecosystems.

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Abdurahmanov I (2016) Assessment of NDVI and SAVI vegetation indices potential to monitor grazing impact in a rangeland ecosystem. Geoinfomatics Int 12:9–15

Asy’Ari R, Rahmawati A, Sa’diyya N, Gustawan A, Setiawan Y, Zamani N, Pramulya R (2022) Mapping mangrove forest distribution on Banten, Jakarta, and West Java Ecotone Zone from Sentinel-2-derived indices using cloud computing based Random Forest. Jurnal Pengelolaan Sumberdaya Alam Dan Lingkungan J Nat Resour Environ Manag 12:97–111. https://doi.org/10.29244/jpsl.12.1.97-111CrossRef

Bandini F, Butts M, Jacobsen T, Bauer-Gottwein P (2017) Water level observations from Unmanned Aerial Vehicles for improving estimates of surface water-groundwater interaction. Hydrol Process. https://doi.org/10.1002/hyp.11366CrossRef

Berlanga-Robles C, Ruiz-Luna A, Nepita M (2018) Seasonal trend analysis (STA) of MODIS vegetation index time series for the mangrove canopy of theTeacapan-Agua Brava lagoon system, Mexico. Gisci Remote Sens 56:338–361CrossRef

Billah M, Kamal AH, Idris MH, Ismail J (2016) Seasonal variation in the occurrence and abundance of Mangrove Macroalgae in a Malaysian Estuary. Crypt Algol. https://doi.org/10.7872/crya/v37.iss2.2016.109CrossRef

Boerner R (1990) Ecology and our endangered life-support systems. J Environ Qual 19:350. https://doi.org/10.2134/jeq1990.00472425001900020028xCrossRef

Cao J, Leng W, Liu K, Liu L, He Z, Zhu Y (2018) Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models. Remote Sens 10:89. https://doi.org/10.3390/rs10010089CrossRef

Costanza R, Arge R, Groot R, Farberk S, Grasso M, Hannon B et al (1997) The value of the world’s ecosystem services and natural capital. Nature 387:253–260. https://doi.org/10.1016/S0921-8009(98)00020-2CrossRef

Dang Hoi N, Trung Dung N (2021) Seasonal dynamics of tropical forest vegetation in Ngoc Linh Nature Reserve, Vietnam based on UAV data. For Soc 5(2):376–389. https://doi.org/10.24259/fs.v5i2.13027CrossRef

Dezhi W, Wan B, Qiu P, Su Y, Guo Q, Wang R et al (2018) Evaluating the performance of Sentinel-2, Landsat 8 and Pléiades-1 in mapping mangrove extent and species. Remote Sens 10:1468. https://doi.org/10.3390/rs10091468CrossRef

Dinh DB, Ngo DT, Nguyen HD, Viet Nguyen HH, Dang NT (2023) Free satellite image data application for monitoring land use cover changes in the Kon Ha Nung Plateau, Vietnam. Heliyon 9(1):e12864. https://doi.org/10.1016/j.heliyon.2023.e12864CrossRefPubMedPubMedCentral

Doughty CL, Ambrose RF, Okin GS, Cavanaugh KC (2021) Characterizing spatial variability in coastal wetland biomass across multiple scales using UAV and satellite imagery. Remote Sens Ecol Conserv 7(3):411–429. https://doi.org/10.1002/rse2.198CrossRef

Duke N, Meynecke J-O, Dittmann S, Ellison A, Anger K, Berger U et al (2007) A world without mangroves? Science (new York, N.y.) 317:41–42. https://doi.org/10.1126/science.317.5834.41bCrossRefPubMed

Eswaran Y, Dharanirajan K, Tamil S, Karpoorasundarapandian (2014) Evaluation of vegetation density of the Mangrove forest in South Andaman Island using Remote Sensing and GIS techniques. Int Res J Environ Sci 3:19–25

Francioni M, Salvini R, Stead D, Coggan J (2018) Improvements in the integration of remote sensing and rock slope modelling. Nat Hazards. https://doi.org/10.1007/s11069-017-3116-8CrossRef

Guo X, Wang M, Jia M, Wang W (2021) Estimating mangrove leaf area index based on red-edge vegetation indices: a comparison among UAV, WorldView-2 and Sentinel-2 imagery. Int J Appl Earth Obs Geoinf 103:102493. https://doi.org/10.1016/j.jag.2021.102493CrossRef

Hese S, Thiel C, Henkel A (2019) UAV based multi seasonal deciduous tree species analysis in the hainich national park using multi temporal and point cloud curvature features. Int Arch Photogramm Remote Sens Spatial Inf Sci XLII-2/W13:363–370CrossRef

Hoa L, Tran T, Gyeltshen S, Nguyen C, Tran D, Luu T, Mẫn D (2020) Characterizing spatiotemporal patterns of mangrove forests in can gio biosphere reserve using Sentinel-2 imagery. Appl Sci 10:4058. https://doi.org/10.3390/app10124058CrossRef

Hong H, Zhang B, Lu H (2021) Seasonal variation and ecological risk assessment of heavy metal in an Estuarine Mangrove Wetland. Water 13:2064. https://doi.org/10.3390/w13152064CrossRef

Huete A, Didan K, Miura T, Rodriguez E, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83:195–213. https://doi.org/10.1016/S0034-4257(02)00096-2CrossRef

Hunt ERJ, Doraiswamy C, McMurtrey J, Daughtry C, Perry E, Akhmedov B (2013) A visible band index for remote sensing leaf chlorophyll content at the canopy scale. Int J Appl Earth Obs Geoinf 21:103–112. https://doi.org/10.1016/j.jag.2012.07.020CrossRef

Huong N, Quynh L, Vinh H, Tu V (2022) Biodegradable and seasonal variation of organic carbon affected by anthropogenic activity: a case in Xuan Thuy Mangrove Forest, North Vietnam. Water. https://doi.org/10.3390/w14050773CrossRef

Iizuka K, Itoh M, Shiodera S, Matsubara T, Dohar M, Watanabe K (2018) Advantages of unmanned aerial vehicle (UAV) photogrammetry for landscape analysis compared with satellite data: a case study of postmining sites in Indonesia. Cogent Geosci. https://doi.org/10.1080/23312041.2018.1498180CrossRef

Lassalle G, Souza Filho C (2022) Tracking canopy gaps in mangroves remotely using deep learning. Remote Sens Ecol Conserv. https://doi.org/10.1002/rse2.289CrossRef

Lorenz S, Zimmermann R, Gloaguen R (2017) The need for accurate geometric and radiometric corrections of drone-borne hyperspectral data for mineral exploration: MEPHySTo—a toolbox for pre-processing drone-borne hyperspectral data. Remote Sens. https://doi.org/10.3390/rs9010088CrossRef

Mallmann CL, Zaninni AF, Filho WP (2020). Vegetation index based in unmanned aerial vehicle (UAV) to improve the management of invasive plants in protected areas, Southern Brazil. In: 2020 IEEE Latin American GRSS & ISPRS remote sensing conference (LAGIRS), pp 66–69

Mitsch W, Gosselink J (2000) The value of wetlands: importance of scale and landscape setting. Ecol Econ 35:25–33. https://doi.org/10.1016/S0921-8009(00)00165-8CrossRef

Modi A, Das P (2019) Multispectral imaging camera sensing to evaluate vegetation index from UAV. 8:30–42

Mondal B, Das R (2022) Mangrove forest dynamics and vulnerability of Indian Sundarban: a geospatial analysis

Morocho Cuenca R, Gonzalez I, Otero X (2022) Mangrove forests in Ecuador: a two-decade analysis. Forests 13:656. https://doi.org/10.3390/f13050656CrossRef

Morris J, Sundareshwar P, Nietch C, Kjerfve B, Cahoon D (2002) Responses of coastal wetlands to rising sea level. Ecology 83:2869–2877. https://doi.org/10.1890/0012-9658(2002)083[2869:ROCWTR]2.0.CO;2CrossRef

Nagelkerken I, Blaber S, Bouillon S, Green P, Haywood M, Kirton LG et al (2008) The habitat function of mangroves for terrestrial and marine fauna: a review. Aquat Bot. https://doi.org/10.1016/j.aquabot.2007.12.007CrossRef

Ngo DT, Nguyen HD, Nguyen HC (2023) Assessment of fluctuations in wetland ecosystem areas resulting from anthropogenic activities in the Dong Rui commune, Quang Ninh Province, Vietnam. Heliyon 9(6):e16984. https://doi.org/10.1016/j.heliyon.2023.e16984CrossRefPubMedPubMedCentral

Ngo D, Nguyen H, Dang C, Kolesnikov S (2020) UAV application for assessing rainforest structure in Ngoc Linh nature reserve, Vietnam. In: E3S web conf., vol 203. p 03006

Nicholls R (2004) Coastal flooding and wetland loss in the 21st century: changes under the SRES climate and socio-economic scenarios. Glob Environ Chang 14:69–86. https://doi.org/10.1016/j.gloenvcha.2003.10.007CrossRef

Omar H, Misman MA, Musa S (2019) GIS and remote sensing for mangroves mapping and monitoring, pp 1–15)

Pellegrini J, Soares M, Chaves F, Estrada G, Cavalcanti V (2009) A method for the classification of mangrove forests and sensitivity/vulnerability analysis. J Coast Res 56:443–447

Polidoro B, Carpenter K, Collins L, Duke N, Ellison A, Ellison J et al (2010) The loss of species: mangrove extinction risk and geographic areas of global concern. PLoS ONE 5:e10095. https://doi.org/10.1371/journal.pone.0010095CrossRefPubMedPubMedCentral

Purwanto A, Wikantika K, Deliar A, Darmawan S (2022) Decision tree and random forest classification algorithms for mangrove forest mapping in Sembilang National Park, Indonesia. Remote Sens 15:16. https://doi.org/10.3390/rs15010016CrossRef

Qiu F, Wang C, Zou, Yang H, Xie H, Xu L, Zhong F (2019) Finer resolution estimation and mapping of mangrove biomass using UAV LiDAR and worldview-2 data. Forests 10:871. https://doi.org/10.3390/f10100871CrossRef

Rouse J, Haas R, Schell J, Deering D (1974) Monitoring vegetation systems in the great plains with ERTS. NASA special publication, 1

Samsuri S, Zaitunah A, Meliani S, Hasnanda Syahputra O, Budiharta S, Susilowati A et al (2021) Mapping of mangrove forest tree density using SENTINEL 2A satelit image in remained natural mangrove forest of Sumatra eastern coastal. IOP Conf Ser Earth Environ Sci 912:012001. https://doi.org/10.1088/1755-1315/912/1/012001CrossRef

Sari N, Rokhmatuloh, Manessa M (2022) Sentinel-2 image based analysis of spatiotemporal variation of vegetation quality in Majalengka Regency, Indonesia. IOP Conf Ser Earth Environ Sci 1030:012013. https://doi.org/10.1088/1755-1315/1030/1/012013CrossRef

Singgalen Y (2022) Vegetation index and mangrove forest utilization through ecotourism development in Dodola and Guraping of North Maluku Province. Jurnal Manajemen Hutan Tropika J Trop for Manag 28:150–161. https://doi.org/10.7226/jtfm.28.2.150CrossRef

Singgalen Y, Gudiato C, Prasetyo S, Fibriani C (2021) Mangrove monitoring using normalized difference vegetation index (NDVI): case study in North Halmahera, Indonesia. Jurnal Ilmu Dan Teknologi Kelautan Tropis 13:2. https://doi.org/10.2944/jitkt.v13i2.34771CrossRef

Souza B, Cantalice W, Braga C, Silva M, Silva E (2022) Response of the EVI Vegetation Index to precipitation anomalies in the State of Paraíba. J Hyperspectr Remote Sens 12:10–19CrossRef

Sremongkontip S, Yousif, Hussin Y, Groenendijk L (2000) Detecting changes in the mangrove forests of southern Thailand using remotely sensed data and GIS. 33

Taylor J R. (1997). An introduction to error analysis: the study of uncertainties in physical measurements. Sausalito

Thi Thuy Van T, Anh LT, Thuy HLT, Bien LB (2017) Bioclimate and mangrove development in Thai Binh Coastal Zone. VNU J Sci Earth Environ Sci 33(1):90–99

Tian J, Wang L, Li X, Gong H, Shi C, Zhong R, Liu X (2017) Comparison of UAV and WorldView-2 imagery for mapping leaf area index of mangrove forest. Int J Appl Earth Obs Geoinf 61:22–31. https://doi.org/10.1016/j.jag.2017.05.002CrossRef

Tucker C (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ. https://doi.org/10.1016/0034-4257(79)90013-0CrossRef

Webber M, Calumpong H, Ferreira B, Granek E, Green S, Ruwa R, Soares M (2016). Mangroves UN world ocean assessment, p 18

Yaney-Keller A, Tomillo P, Marshall J, Paladino F (2019) Using unmanned aerial systems (UAS) to assay mangrove estuaries on the Pacific coast of Costa Rica. PLoS ONE 14:e0217310. https://doi.org/10.1371/journal.pone.0217310CrossRefPubMedPubMedCentral

Zahra N, Setiawan Y, Prasetyo L (2022) Estimation of mangrove canopy cover using unmanned aerial vehicle (UAV) in Indramayu Regency, West Java. IOP Conf Ser Earth Environ Sci 950:012032. https://doi.org/10.1088/1755-1315/950/1/012032CrossRef

Title: Assessing the characteristics and seasonal changes of mangrove forest in Dong Rui commune, Quang Ninh Province, Vietnam based on multispectral UAV data
Author: Trung Dung Ngo
Publication date: 26-02-2024
Publisher: Springer Japan
Published in: Landscape and Ecological Engineering / Issue 2/2024
Print ISSN: 1860-1871
Electronic ISSN: 1860-188X
DOI: https://doi.org/10.1007/s11355-024-00595-9

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