Invasion in the Niger Delta: remote sensing of mangrove conversion to invasive Nypa fruticans from 2015 to 2020

Abstract

Invasive species are a leading threat to biodiversity worldwide. Nypa palm (Nypa fruticans) has emerged as the predominant invasive species in the Niger Delta region of Nigeria. While endemic mangroves have high rates of carbon sequestration, stabilize coastlines and protect biodiversity, Nypa does not provide these services outside its native region of Southeast Asia. Oil exploration and urbanization in this region also exacerbate mangrove loss and Nypa spread. As Nypa is difficult to distinguish from endemic mangrove species in remotely sensed data, estimates of mangrove and ecosystem services losses in Nigeria are highly uncertain. Here, we analyse multisensor satellite data with machine learning to quantify the rapid expansion of Nypa from 2015 to 2020 in Nigeria. Using Landsat imagery and random forest classification, we quantify total potential Nypa extent in Nigeria in 2019. We then produced a Nypa extent map using iterative combinations of Sentinel‐1 SAR, Sentinel‐2 MSI and ALOS PALSAR. Random forest classifications using SAR data from ALOS and Sentinel‐1 were best suited for mapping Nypa extent with similar accuracies (78% and 75%, respectively). Based on data availability and accuracy, we focussed our change analysis on Sentinel‐1 SAR. Our results show ~28 000 ha of mangroves were converted to Nypa in Nigeria by 2020 and covered a larger extent than endemic mangroves, compounding the effect of the existing degradation and deforestation in the region. We also compared forest height and complexity estimates from Global Ecosystem Dynamics Investigation LiDAR to further distinguish between endemic mangroves and Nypa in three dimensions. Nypa structural variability, measured by top‐of‐canopy height, vegetation cover, plant area index, and foliage height diversity, was lower than that of mangroves. At current rates of Nypa expansion, the entire area of study would be invaded by Nypa by 2028, with potentially detrimental consequences to the ecosystem services provided by mangroves.

Nonparametric assessment of mangrove ecosystem in the context of coastal resilience in Ghana

“…As a result of using the same pixels for classifier training and validation, any systematic mistake was eliminated by this process (Geiß et al, 2017; Pimple et al, 2018; Shi et al, 2016). Utilizing 20% of the training sample, stratified random samples were created for an independent accuracy evaluation, which follows the procedure outlined by Barenblitt and Fatoyinbo (2020). High‐resolution satellite imagery that is accessible in ArcGIS was used to verify each point.…”