Rapid Mangrove Forest Loss and Nipa Palm (Nypa fruticans) Expansion in the Niger Delta, 2007–2017

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.

Land Cover Dynamics and Mangrove Degradation in the Niger Delta Region

“…These figures differ by almost a fifth, and can play a significant role in the setting of conservation targets, management policies, and sustainability goals. Moreover, our mangrove results compare favourably with three studies that mapped mangroves as one class accurately: the study of Nwobi et al [19], who found that mangroves occupied an area of 9115 km 2 in 2007 and 8017 km 2 in 2017; the study of Ayanlade and Drake [23] (9965 km 2 in 1987, 9255 km 2 in 2001, and 8430 km 2 in 2011); and the study by James et al [9] (7037 km 2 in 1987 and 6824 km 2 in 2002).…”

“…Based on our knowledge of the region and on the classification systems of the ESA 20m African land cover data for 2016 and the GlobeLand 30 m data for 2010, we included a grassland class in our mapping efforts, which were found to decrease in the first period (from 9421 to 7089 km 2 ) and increase in the second (8102 km 2 in 2013). Our figures for the agricultural class are significantly lower to those in Ayanlade and Drake [23], Kuenzer et al [27], and Nwobi et al [19]. However, none of these studies included a separate class for grassland but, according to their spatial outputs, appear to have mapped this together with the agricultural class.…”

“…With the exception of the grassland and bareland classes, user's accuracies were also high (from 70% to 91%). Our results compare favourably with other studies in the NDR [19,23,44,45]. Regarding the mapping of degraded mangroves, one of the main objectives of this paper, our study is the first to map this accurately with user's accuracies between 77% and 87% and producer's consistently above 82%.…”

“…They report far smaller areas than both our study and the study by Ayanlade and Drake [23]: 18,325 km 2 in 1987 and 15,408 km 2 in 2013. Finally, the Nwobi et al [19] study also agrees that "tropical forests" were reduced but reported that these occupied 29,000 km 2 in 2007 and 25,500 km 2 in 2017. As all of these studies, including ours, reported high per-class accuracies in the mapping of forests, it is difficult to ascertain which on is closer to the true figure.…”

A Simple Approach for Mapping Forest Cover from Time Series of Satellite Data

“…However, using time series data greatly increases the number of input features, reaching dozens or even hundreds. The classifiers, such as random forest, decision tree and support vector machine [13][14][15][16], are generally required to extract the forest extent from such a large number of features. They usually need a huge amount of training samples to calibrate the classifiers.…”