Modeling projected changes of mangrove biomass in different climatic scenarios in the Sunda Banda Seascapes

Mangroves are critical in the ecological, economic and social development of coastal rural and urban communities. However, they are under threat by climate change and anthropogenic activities. The Sunda Banda Seascape (SBS), Indonesia, is among the world’s richest regions of mangrove biomass and biodiversity. To inform current and future management strategies, it is critical to provide estimates of how mangroves will respond to climate change in this region. Therefore, this paper utilized spatial analysis with model-based climatic indicators (temperature and precipitation) and mangrove distribution maps to estimate a benchmark for the mangrove biomass of the SBS in six scenarios, namely the Last Inter-glacial Period, the current scenario (1950–2000) and all four projected Representative Concentration Pathways in 2070 due to climate change. Despite mangroves gaining more biomass with climate change (the increase in CO2 concentration), this paper highlighted the great proportion of below-ground biomass in mangrove forests. It also showed that the changes in spatial distribution of mangrove biomass became more variable in the context of climate change. As mangroves have been proposed as an essential component of climate change strategies, this study can serve as a baseline for future studies and resource management strategies

Analysing mangrove forest structure and biomass in the Niger Delta

Mangrove forests are important in providing a range of ecosystem services, including food provision to local communities and carbon storage, while being globally restricted to tropical coastlines. The conservation and sustainability of mangrove forests is thus a globally important topic. Mangrove forests in the Niger Delta are known to be under high pressure from urbanisation, development, logging and oil pollution, and invasive species such as nipa palm (Nypa fruticans). These mangrove forests are poorly understood as a result of difficulty of access, social unrest and security restrictions. For example, there is no data on the relationship between disturbance and mangrove structure in the Delta, current area extent and biomass stocks of mangrove forest, its rate of loss, or the rate of nipa palm colonisation in the Niger Delta. The overall objective of this thesis is to utilise a combination of field data and earth observation to resolve these knowledge gaps. This work will estimate area and biomass of mangrove forests in the Niger Delta, and their changes over recent years through disturbance and invasive species. I used an extensive field data collection in 2016-17 to establish 25 geo-referenced 0.25-ha plots across the Niger Delta and collected 567 ground control points. I estimated aboveground biomass (AGB) from a general allometric equation based on stem surveys. Leaf area index (LAI) was recorded using hemispherical photos. I performed and evaluated a land cover classification using a combination of Advanced Land Observatory Satellite Phased Array L-band SAR (ALOS PALSAR), Landsat ETM+ and the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) data. I also compared two supervised classification methods: Maximum Likelihood (ML) and Support Vector machine (SVM) classifiers. I established a relationship between field estimates of AGB and Advanced Land Observatory Satellite (ALOS) L-band radar backscatter. I also estimated the area of nipa palm and mangrove forests in the Niger Delta and generated the first mangrove biomass map for the region, for 2007 and 2017 to obtain change information. Plot estimated mean AGB was 83.7 Mg ha-1 and I found significantly higher plot biomass in close proximity to protected sites and tidal influence, and the lowest in the sites where urbanisation was actively taking place. The mean LAI was 1.45 and there was a significant positive correlation between AGB and LAI (R2= 0.28). Satellite observations of NDVI for the growing season correlated positively with in-situ LAI (R2= 0.63) and AGB (R2= 0.8). Lower stem sizes (5-15cm) accounted for 70% contribution to the total biomass in disturbed plots, while undisturbed plots had a more even contribution of different size classes to AGB. Nipa palm invasion was significantly correlated to plots with larger variations in LAI (i.e. more patchy cover) and proportion of basal area removed within plots. The classification results showed SVM (overall accuracy 99.9 %) performed better than ML (98.7%) across the Niger Delta. I estimated a 2017 mangrove area of 794 561 ha and nipa extent of 11,419 ha. I discovered a 12% decrease in mangrove area and 694 % increase in nipa palm between 2007 and 2017. The highest radar-AGB relationship was from the combination of HH: HV and HV bands (R2= 0.62, p-value < 0.001). Using this relationship, I estimated a mean and total AGB of 90.5 Mg ha-1 and 82 X 106 Mg in 2007; 83.4 Mg ha-1 and 65 X 106 Mg in 2017. Local wood exploitation is removing larger stems (> 15 cm DBH) preferentially from these mangroves and creates an avenue for nipa palm colonisation. I identified opportunities to use remote sensing to estimate biomass, based on the LAI-AGB-NDVI relationship I found, and can serve as a calibration dataset for radar data to provide effective monitoring of mangrove forest degradation. It is clear from these results that remote sensing can be used to map the extent and changes in these land cover types, and thus such mapping efforts should continue for policy targeting and monitoring. I was able to show that mangroves of the Niger Delta are at risk, from rapid clearance as well as from the invasive species nipa palm. I also provide evidence of mangrove cover loss of 11 000 ha yr-1 over a decade, resulting in biomass loss rate of 100 Mg ha-1 yr-1 while mangrove degradation rate of 56 Mg ha-1 yr-1 in the Niger Delta. Assessing carbon stock of mangrove forests in the Niger Delta can create a baseline for regional conservation and regeneration plans. These plans can create opportunities for generating carbon credits under reducing emissions from deforestation and forest degradation (REDD+)