Spatio-Temporal monitoring of Qeshm mangrove forests through machine learning classification of SAR and optical images on Google Earth Engine

Abstract

Mangrove forests are considered one of the most complex and dynamic ecosystems facing various challenges due to anthropogenic disturbance and climate change. The excessive harvesting and land-use change in areas covered by mangrove ecosystems is critical threats to these forests. Therefore, the continuous and regular monitoring of these forests is essential. Fortunately, remote sensing data has made it possible to regularly and frequently monitor this forest type. This study has two goals. Firstly, it combines optical data of Landsat- 8 and Sentinel-2 with Sentinel-1 radar data to improve land cover mapping accuracy. Secondly, it aims to evaluate the SVM machine learning algorithms and random forest to detection and differentiate forest cover from other land types in the Google Earth Engine system. The results show that the support vector machine (SVM) algorithm in the S2 + S1 dataset with a kappa coefficient of 0.94 performs significantly better than when used in the L8 + S1 combination dataset with a kappa coefficient of 0.88. Conversely, the kappa coefficients of 0.89 and 0.85 were estimated for the random forest algorithm in S2 + S1 and L8 + S1 datasets. This again indicates the superiority of Sentinel-2 and Sentinel-1 datasets over Landsat- 8 and Sentinel-1 datasets. In general, the support vector machine (SVM) algorithm yielded better results than the RF random forest algorithm in optical and radar datasets. The results showed that using the Google Earth engine system and machine learning algorithms accelerates the process of mapping mangrove forests and even change detection.

Keywords

• Mangrove
• Machine learning Algorithm
• Google Earth Engine
• Remote Sensing
• Qeshm Island

References

1. Giri, C., Long, J., Abbas, S., Murali, R. M., Qamer, F. M., Pengra, B., & Thau, D. (2015). Distribution and dynamics of mangrove forests of South Asia. Journal of environmental management, 148, 101-111.
2. Zhen, J., Liao, J., & Shen, G. (2018). Mapping mangrove forests of Dongzhaigang nature reserve in China using Landsat 8 and Radarsat-2 polarimetric SAR data. Sensors, 18(11), 4012.
3. Collins, D. S., Avdis, A., Allison, P. A., Johnson, H. D., Hill, J., Piggott, M. D., ... & Damit, A. R. (2017). Tidal dynamics and mangrove carbon sequestration during the Oligo–Miocene in the South China Sea. Nature communications, 8(1), 15698.
4. Jia, M., Wang, Z., Wang, C., Mao, D., & Zhang, Y. (2019). A new vegetation index to detect periodically submerged mangrove forest using single-tide Sentinel-2 imagery. Remote Sensing, 11(17), 2043.
5. Gupta, K., Mukhopadhyay, A., Giri, S., Chanda, A., Majumdar, S. D., Samanta, S., ... & Hazra, S. (2018). An index for discrimination of mangroves from non-mangroves using LANDSAT 8 OLI imagery. MethodsX, 5, 1129-1139.
6. Vaiphasa, C. (2006). Remote sensing techniques for mangrove mapping. Doctoral Dissertation, Wageningen University and Research.
7. Danehkar, A., Jalali, S.G., 2005. Avicennia marina forest structure using line plot method. Pajouhesh and Sazandegi 67, 18–24
8. Cárdenas, N. Y., Joyce, K. E., & Maier, S. W. (2017). Monitoring mangrove forests: Are we taking full advantage of technology?. International Journal of Applied Earth Observation and Geoinformation, 63, 1-14.

9. Long, B. G., & Skewes, T. D. (1996). A technique for mapping mangroves with Landsat TM satellite data and geographic information system. Estuarine, Coastal and Shelf Science, 43(3), 373-381.
10. Long, J. B., & Giri, C. (2011). Mapping the Philippines’ mangrove forests using Landsat imagery. Sensors, 11(3), 2972-2981.
11. Pasqualini, V., Iltis, J., Dessay, N., Lointier, M., Guelorget, O., & Polidori, L. (1999). Mangrove mapping in North-Western Madagascar using SPOT-XS and SIR-C radar data. Hydrobiologia, 413, 127-133.
12. Hu, L., Xu, N., Liang, J., Li, Z., Chen, L., & Zhao, F. (2020). Advancing the mapping of mangrove forests at national-scale using Sentinel-1 and Sentinel-2 time-series data with Google Earth Engine: A case study in China. Remote Sensing, 12(19), 3120.
13. Zhu, X., Meng, L., Zhang, Y., Weng, Q., & Morris, J. (2019). Tidal and meteorological influences on the growth of invasive Spartina alterniflora: evidence from UAV remote sensing. Remote Sensing, 11(10), 1208.
14. Tarantino, C., Casella, F., Adamo, M., Lucas, R., Beierkuhnlein, C., & Blonda, P. (2019). Ailanthus altissima mapping from multi-temporal very high resolution satellite images. ISPRS Journal of Photogrammetry and Remote Sensing, 147, 90-103.
15. Campbell, A. D., & Wang, Y. (2020). Salt marsh monitoring along the mid-Atlantic coast by Google Earth Engine enabled time series. PloS one, 15(2), e0229605.
16. Fonteh, M. L., Theophile, F., Cornelius, M. L., Main, R., Ramoelo, A., & Cho, M. A. (2016). Assessing the utility of sentinel-1 c band synthetic aperture radar imagery for land use land cover classification in a tropical coastal systems when compared with landsat 8. Journal of Geographic Information System, 8(4), 495-505.
17. Zhu, Y., Liu, K., Liu, L., Wang, S., & Liu, H. (2015). Retrieval of mangrove aboveground biomass at the individual species level with worldview-2 images. Remote Sensing, 7(9), 12192-12214.
18. Zhen, J., Liao, J., & Shen, G. (2018). Mapping mangrove forests of Dongzhaigang nature reserve in China using Landsat 8 and Radarsat-2 polarimetric SAR data. Sensors, 18(11), 4012.
19. Carrasco, L., O’Neil, A. W., Morton, R. D., & Rowland, C. S. (2019). Evaluating combinations of temporally aggregated Sentinel-1, Sentinel-2 and Landsat 8 for land cover mapping with Google Earth Engine. Remote Sensing, 11(3), 288.
20. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote sensing of Environment, 202, 18-27.
21. Shrestha, S., Miranda, I., Kumar, A., Pardo, M. L. E., Dahal, S., Rashid, T., ... & Mishra, D. R. (2019). Identifying and forecasting potential biophysical risk areas within a tropical mangrove ecosystem using multi-sensor data. International Journal of Applied Earth Observation and Geoinformation, 74, 281-294.
22. Diniz, C., Cortinhas, L., Nerino, G., Rodrigues, J., Sadeck, L., Adami, M., & Souza-Filho, P. W. M. (2019). Brazilian mangrove status: Three decades of satellite data analysis. Remote Sensing, 11(7), 808.
23. Mondal, P., Liu, X., Fatoyinbo, T. E., & Lagomasino, D. (2019). Evaluating combinations of sentinel-2 data and machine-learning algorithms for mangrove mapping in West Africa. Remote Sensing, 11(24), 2928.
24. Dong, D., Wang, C., Yan, J., He, Q., Zeng, J., & Wei, Z. (2020). Combing Sentinel-1 and Sentinel-2 image time series for invasive Spartina alterniflora mapping on Google Earth Engine: A case study in Zhangjiang Estuary. Journal of Applied Remote Sensing, 14(4), 044504.
25. Pham, T. D., & Yoshino, K. (2015, March). Mangrove mapping and change detection using multi-temporal Landsat imagery in Hai Phong city, Vietnam. In International symposium on cartography in internet and ubiquitous environments (pp. 17-19).
26. Shi, T., Liu, J., Hu, Z., Liu, H., Wang, J., & Wu, G. (2016). New spectral metrics for mangrove forest identification. Remote Sensing Letters, 7(9), 885-894.
27. Pimple, U., Simonetti, D., Sitthi, A., Pungkul, S., Leadprathom, K., Skupek, H., ... & Towprayoon, S. (2018). Google earth engine based three decadal landsat imagery analysis for mapping of mangrove forests and its surroundings in the trat province of Thailand. Journal of Computer and Communications, 6, 247-264
28. Gessesse, A. A., & Melesse, A. M. (2019). Temporal relationships between time series CHIRPS-rainfall estimation and eMODIS-NDVI satellite images in Amhara Region, Ethiopia. In Extreme hydrology and climate variability (pp. 81-92). Elsevier.
29. Ghorbanian, A., Zaghian, S., Asiyabi, R. M., Amani, M., Mohammadzadeh, A., & Jamali, S. (2021). Mangrove ecosystem mapping using Sentinel-1 and Sentinel-2 satellite images and random forest algorithm in Google Earth Engine. Remote Sensing, 13(13), 2565.
30. Huang, K., Yang, G., Yuan, Y., Sun, W., Meng, X., & Ge, Y. (2022). Optical and SAR images Combined Mangrove Index based on multi-feature fusion. Science of Remote Sensing, 5, 100040.
31. Shen, Z., Miao, J., Wang, J., Tang, A., & Zhen, J. (2023). Combining Optical and Sar Data for Mapping Mangrove Forests Using Feature Selection and Machine Learning Methods. SSRN
32. Bihamta Toosi, N., Soffianian, A. R., Fakheran, S., Pourmanafi, S., Ginzler, C., & T. Waser, L. (2020). Land cover classification in mangrove ecosystems based on VHR satellite data and machine learning—an upscaling approach. Remote Sensing, 12(17), 2684.
33. Worthington, T. A., Zu Ermgassen, P. S., Friess, D. A., Krauss, K. W., Lovelock, C. E., Thorley, J., ... & Spalding, M. (2020). A global biophysical typology of mangroves and its relevance for ecosystem structure and deforestation. Scientific reports, 10(1), 1-11.
34. Walker, W. (2014). Introduction to RADAR Remote Sensing for Vegetation Mapping and Monitoring. A Ph. D. presentation: Woods Hole Research Center, 22.
35. Baghdadi, N., El Hajj, M., Zribi, M., & Bousbih, S. (2017). Calibration of the water cloud model at C-band for winter crop fields and grasslands. Remote Sensing, 9(9), 969.
36. Vrieling, A., De Leeuw, J., & Said, M. Y. (2013). Length of growing period over Africa: Variability and trends from 30 years of NDVI time series. Remote sensing, 5(2), 982-1000.
37. Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of Environment, 8(2), 127-150.
38. Zhu, Z., Bi, J., Pan, Y., Ganguly, S., Anav, A., Xu, L., ... & Myneni, R. B. (2013). Global data sets of vegetation leaf area index (LAI) 3g and fraction of photosynthetically active radiation (FPAR) 3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) for the period 1981 to 2011. Remote sensing, 5(2), 927-948.
39. Viana, C. M., Oliveira, S., Oliveira, S. C., & Rocha, J. (2019). Land use/land cover change detection and urban sprawl analysis. In Spatial modeling in GIS and R for earth and environmental sciences (pp. 621-651). Elsevier.
40. Javadnia, E., Mobasheri, M. R., & Kamali, G. A. (2009). MODIS NDVI quality enhancement using ASTER images. Journal of Agricultural Science and Technology, 11, 549-558.
41. McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International journal of remote sensing, 17(7), 1425-1432.
42. Jensen, J. R. (1996). Introductory digital image processing: a remote sensing perspective (No. Ed. 2). Prentice-Hall Inc.
43. Schulz, K., Hänsch, R., & Sörgel, U. (2018). Machine learning methods for remote sensing applications: an overview. Earth resources and environmental remote sensing/GIS applications IX, 10790, 1079002.
44. Liu, K., Li, X., Shi, X., & Wang, S. (2008). Monitoring mangrove forest changes using remote sensing and GIS data with decision-tree learning. Wetlands, 28, 336-346.
45. Heumann, B. W. (2011). An object-based classification of mangroves using a hybrid decision tree—Support vector machine approach. Remote Sensing, 3(11), 2440-2460.
46. Feizizadeh, B., Omarzadeh, D., Kazemi Garajeh, M., Lakes, T., & Blaschke, T. (2023). Machine learning data-driven approaches for land use/cover mapping and trend analysis using Google Earth Engine. Journal of Environmental Planning and Management, 66(3), 665-697.
47. Torres, M., & Qiu, G. (2014). Automatic habitat classification using image analysis and random forest. Ecological informatics, 23, 126-136.
48. Fu, B., Wang, Y., Campbell, A., Li, Y., Zhang, B., Yin, S., ... & Jin, X. (2017). Comparison of object-based and pixel-based Random Forest algorithm for wetland vegetation mapping using high spatial resolution GF-1 and SAR data. Ecological indicators, 73, 105-117.
49. Millard, K., & Richardson, M. (2015). On the importance of training data sample selection in random forest image classification: A case study in peatland ecosystem mapping. Remote sensing, 7(7), 8489-8515.
50. Phan, T. N., Kuch, V., & Lehnert, L. W. (2020). Land cover classification using Google Earth Engine and random forest classifier—The role of image composition. Remote Sensing, 12(15), 2411.
51. Amani, M., Mahdavi, S., Afshar, M., Brisco, B., Huang, W., Mohammad Javad Mirzadeh, S., ... & Hopkinson, C. (2019). Canadian wetland inventory using Google Earth Engine: The first map and preliminary results. Remote Sensing, 11(7), 842.
52. Tappan, G. G., Sall, M., Wood, E. C., & Cushing, M. (2004). Ecoregions and land cover trends in Senegal. Journal of arid environments, 59(3), 427-462.
53. Mountrakis, G., Im, J., & Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS journal of photogrammetry and remote sensing, 66(3), 247-259.
54. Toosi, N. B., Soffianian, A. R., Fakheran, S., Pourmanafi, S., Ginzler, C., & Waser, L. T. (2019). Comparing different classification algorithms for monitoring mangrove cover changes in southern Iran. Global Ecology and Conservation, 19, e00662.
55. Andrew, A. M. (2000). An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods by Nello Christianini and John Shawe-Taylor, Cambridge University Press, Cambridge, 2000, xiii+ 189 pp., ISBN 0-521-78019-5 (Hbk,£ 27.50). Robotica, 18(6), 687-689.
56. Ding, H. Y., & Bian, Z. F. (2008). Theory of support vector machine and its applications in remote sensing image processing. Computer Engineering Design, 5, 62.
57. Foody, G. M. (2020). Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification. Remote Sensing of Environment, 239, 111630.
58. Mohammadi, A., Karimzadeh, S., Valizadeh Kamran, K., & Matsuoka, M. (2020). Extraction of land information, future landscape changes and seismic hazard assessment: A case study of Tabriz, Iran. Sensors, 20(24), 7010.
59. Jahanbakhshi, F., & Ekhtesasi, M. R. (2019). Performance evaluation of three image classification methods (Random Forest, Support Vector Machine and the Maximum Likelihood) in land use mapping. Journal of Water and Soil Science, 22(4), 235-247