Abstract
Mangrove species classification is of great significance to the study of mangrove community structure and biodiversity. Most researches use foreign high-resolution remote sensing images or UAV images for mangrove species classification. In order to improve classification accuracy, LiDAR and hyper-spectral data are often used to assist classification. In this paper, based on the Gaofen-2 image and the CHM data obtained by the UAV Lidar, the mangroves in Qi'ao Island, Zhuhai are classified among species by using the random forest classification method. The classified species include 5 types of true mangroves, 3 types of semi mangroves, Phragmites australis and non-vegetation. The results show that the use of Gaofen-2 image can only effectively distinguish the Sonneratia apetala, Acrostichum aureum and non-vegetation, the accuracy of distinguishing other mangrove species is not ideal; After Gaofen-2 image fusion of CHM data, the classification accuracy of each mangrove species has been significantly improved, with the overall classification accuracy reaching 91.44%, which verifies the effectiveness of Gaofen-2 image fusion of external data in mangrove species classification research.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Giri C, Ochieng E et al (2011) Status and distribution of mangrove forests of the world using earth observation satellite data. Global Ecol Biogeogr
Katherine E, Robert T, Jin O (1998) Different kinds of mangrove forests provide different goods and services. Glob Ecol Biogeogr Lett 7(1):83–94
Deng G (2002) Application of remote sensing technology in mangrove resources investigation. Central South Forest Invent Plan 01:27–28
Liu K, Li X, Wang S, Zhong K, Qian J (2005) Monitoring of the changes of mangrove wetland around the Zhujiang Estuary in the past two decades by remote sensing. Trop Geogr 2005(02):111–116
Wang Y (2018) Estimation of mangrove biomass in Shenzhen Bay based on multi-source remote sensing data. Southwest University
Xu F (2020) mangrove extraction and carbon storage estimation by using Sentinel-2 images. Lanzhou Jiaotong University
Li S (2012) Mangroves spatial distribution extraction and species discrimination based on remote sensing data in Beibu Gulf. Nanjing University
Wan L, Lin Y, Zhang H, Wang F, Liu M, Lin H (2020) GF-5 Hyperspectral data for species mapping of mangrove in Mai Po. Hong Kong Remote Sens 12:656
Zhang H, Wang T, Liu M, Jia M, Lin H, Chu LM, Devlin AT (2018) Potential of combining optical and dual polarimetric SAR data for improving mangrove species discrimination using rotation forest. Remote Sens 10(3):467
Ferrentino E, Nunziata F, Zhang H et al (2020) On the ability of PolSAR measurements to discriminate among mangrove species [J]. IEEE J Sel Top Appl Earth Observ Remote Sens 13:2729–2737
Liu K, Gong H, Cao J, Zhu Y (2019) Comparison of mangrove remote sensing classification based on multi-type UAV data. Tropical Geography 39(4):492–501
Liu X, Bo Y (2015) Object-based crop species classification based on the combination of airborne hyperspectral images and LiDAR data. Remote Sensing 7(1):922–950
Cao J, Liu K, Zhuo L, Liu L, Zhu Y, Peng L (20121) Combining UAV-based hyperspectral and LiDAR data for mangrove species classification using the rotation forest algorithm. Int J Appl Earth Observ Geoinform 102(1):1569–8432
Li X, Liu K, Zhu Y et al (2018) Study on mangrove species classification based on ZY-3 image. Remote Sens Technol Appl 33(2):360–369
Qiu N, Xu S, Qiu P et al (2019) Community distribution and landscape pattern of the mangrove on the Qi’ao Island, Zhuhai. Scientia Silvae Sinicae 55(1):1–10
Zhen W, Weijun C, Wei G et al (2017) Study on Zhuhai Qi’ao island main mangrove community characteristics. J Central South Univ Forest Technol 37(4):86–91
Hu Y, Zhu N, Liao B et al (2019) Carbon density and carbon fixation rate of mangroves of different restoration types in Qi’ao island. J Central South Univ Forest Technol 39(12):101–107
Cao W, Wang W, Wang X et al (2021) Research on crop classification based on GF-2 satellite. Geomatics Spat Inform Technol 44(04):158–161
Wu D, Yu W, Xie T (2020) Application of GF-2 satellite data for monitoring organic pollution delivered to water bodies in the Guangdong-Hong Kong-Macao Greater Bay Area. Trop Geogr 40(4):675–683
Dezhi W, Bo W, Penghua Q, Yanjun S, Qinghua G, Xincai W (2018) Artificial mangrove species mapping using Pléiades-1: an evaluation of pixel-based and object-based classifications with selected machine learning algorithms. Remote Sens 10:294
Breiman L (2001) Random forests. Mach Learn 45(1):5–32
Zhao LJ, Tang P (2016) Scalability analysis of typical remote sensing data classification methods: a case of remote sensing image scene. J Remote Sens 20(2):157–171
Wang W, Dong Z, Fu D et al (2020) Classification of mangrove in Leizhou Bay based on ZY-3. Hydrogr Survey Chart 40(01):35–39
Huang X, Zhang L, Wang L (2009) Evaluation of morphological texture features for mangrove forest mapping and species discrimination using multispectral IKONOS imagery. IEEE Geosci Remote Sens Lett 6:393–397
Rao CN, Sastry SS, Mallika K, Tiong HS, Mahalakshmi KB (2013) Co-occurrence matrix and its statistical features as an approach for identification of phase transitions of mesogens. Int J Innov Res Sci Eng Technol 2:4531–4538
Peng L, Liu K, Cao J, Zhu Y, Li F, Liu L (2020) Combining GF-2 and RapidEye satellite data for mapping mangrove species using ensemble machine-learning methods. Int J Remote Sens 41(3):813–838
Acknowledgements
This research was supported by Independently setting up projects of Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources (MESTA-2021-C005; MESTA-2022-C002); Key Program of Marine Economy Development Special Foundation of Department of Natural Resources of Guangdong Province (GDNRC [2022]19).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sun, Y. et al. (2024). Mangrove Species Classification in Qi’ao Island Based on Gaofen-2 Image and UAV LiDAR. In: Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2023. Lecture Notes in Electrical Engineering, vol 1032. Springer, Singapore. https://doi.org/10.1007/978-981-99-7505-1_9
Download citation
DOI: https://doi.org/10.1007/978-981-99-7505-1_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-7539-6
Online ISBN: 978-981-99-7505-1
eBook Packages: EngineeringEngineering (R0)