Abstract
Mangrove forests in the Niger Delta are very valuable, providing ecosystem services, such as carbon storage, fish nurseries, coastal protection, and aesthetic values. However, they are under threat from urbanization, logging, oil pollution, and the proliferation of the invasive Nipa Palm (Nypa fruticans). However, there are no reliable data on the current extent of mangrove forest in the Niger Delta, its rate of loss, or the rate of colonization by the invasive Nipa Palm. Here, we estimate the area of Nipa Palm and mangrove forests in the Niger Delta in 2007 and 2017, using 567 ground control points, Advanced Land Observatory Satellite Phased Array L-band SAR (ALOS PALSAR), Landsat and the Shuttle Radar Topography Mission Digital Elevation Model 2000 (SRTM DEM). We performed the classification using Maximum Likelihood (ML) and Support Vector Machine (SVM) methods. The classification results showed SVM (overall accuracy 93%) performed better than ML (77%). Producers (PA) and User’s accuracy (UA) for the best SVM classification were above 80% for most classes; however, these were considerably lower for Nipa Palm (PA—32%, UA—30%). We estimated a 2017 mangrove area of 801,774 ± 34,787 ha (±95% Confidence Interval) ha and Nipa Palm extent of 11,447 ± 7343 ha. Our maps show a greater landward extent than other reported products. The results indicate a 12% (7–17%) decrease in mangrove area and 694 (0–1304)% increase in Nipa Palm. Mapping efforts should continue for policy targeting and monitoring. The mangroves of the Niger Delta are clearly in grave danger from both rapid clearance and encroachment by the invasive Nipa Palm. This is of great concern given the dense carbon stocks and the value of these mangroves to local communities for generating fish stocks and protection from extreme events.
Highlights
Mangrove ecosystems are intertidal regions at the land-sea, fresh-salt water interface; they have characteristics of both zones
We discovered that while the highest producer’s accuracy (100%) on the Nipa Palm invasive species was in the Maximum Likelihood (ML) method, it had a very low user’s accuracy (5%) (Table 3), with further investigation showing this was due to overestimation (Figure 4)
The accuracy of Nipa Palm classification was low using all other methods, with even the best Support Vector Machine (SVM) method (RBF) achieving a User’s and Producer’s Accuracy of 32% and 30% respectively. As these were relatively balanced, we hope that the area estimate of Nipa Palm from the classification remains reasonably accurate, and certainly these low accuracies will feed into the confidence interval estimates so useful ranges of area are still available from the analysis (See Table 6)
Summary
Mangrove ecosystems are intertidal regions at the land-sea, fresh-salt water interface; they have characteristics of both zones. Mangrove forests serve as a direct means of coastal protection from storm surges, tidal waves, and, over longer time scales, provide resilience to climate-change driven sea level rise. The superimposition of mangroves with the high population density of coastal communities has resulted in rapid and increasing deforestation [5]. Deforestation can be because of mangrove clearance for fuelwood and fisheries, agriculture, industrial pollution, and urbanization in coastal regions. In south-east Asia, aquaculture, and agriculture are the major drivers of deforestation in mangrove forests [6], while in oil producing countries, oil pollution, which results in fire largely contributes to mangrove deforestation in these regions [7]. Mapping mangrove forest cover across space and time can help provide information on the progress of restoration programs and detect areas of high mangrove deterioration, enabling conservation targeting, and policy implementation [5]
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