Testing the Contribution of Multi-Source Remote Sensing Features for Random Forest Classification of the Greater Amanzule Tropical Peatland.

Tropical peatlands such as Ghana's Greater Amanzule peatland are highly valuable ecosystems and under great pressure from anthropogenic land use activities. Accurate measurement of their occurrence and extent is required to facilitate sustainable management. A key challenge, however, is the high cloud cover in the tropics that limits optical remote sensing data acquisition. In this work we combine optical imagery with radar and elevation data to optimise land cover classification for the Greater Amanzule tropical peatland. Sentinel-2, Sentinel-1 and Shuttle Radar Topography Mission (SRTM) imagery were acquired and integrated to drive a machine learning land cover classification using a random forest classifier. Recursive feature elimination was used to optimize high-dimensional and correlated feature space and determine the optimal features for the classification. Six datasets were compared, comprising different combinations of optical, radar and elevation features. Results showed that the best overall accuracy (OA) was found for the integrated Sentinel-2, Sentinel-1 and SRTM dataset (S2+S1+DEM), significantly outperforming all the other classifications with an OA of 94%. Assessment of the sensitivity of land cover classes to image features indicated that elevation and the original Sentinel-1 bands contributed the most to separating tropical peatlands from other land cover types. The integration of more features and the removal of redundant features systematically increased classification accuracy. We estimate Ghana's Greater Amanzule peatland covers 60,187 ha. Our proposed methodological framework contributes a robust workflow for accurate and detailed landscape-scale monitoring of tropical peatlands, while our findings provide timely information critical for the sustainable management of the Greater Amanzule peatland.

Author Correction: Greenhouse gas emissions resulting from conversion of peat swamp forest to oil palm plantation.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Greenhouse gas emissions resulting from conversion of peat swamp forest to oil palm plantation.

Conversion of tropical peat swamp forest to drainage-based agriculture alters greenhouse gas (GHG) production, but the magnitude of these changes remains highly uncertain. Current emissions factors for oil palm grown on drained peat do not account for temporal variation over the plantation cycle and only consider CO2 emissions. Here, we present direct measurements of GHGs emitted during the conversion from peat swamp forest to oil palm plantation, accounting for CH4 and N2O as well as CO2. Our results demonstrate that emissions factors for converted peat swamp forest is in the range 70-117 t CO2 eq ha-1 yr-1 (95% confidence interval, CI), with CO2 and N2O responsible for ca. 60 and ca. 40% of this value, respectively. These GHG emissions suggest that conversion of Southeast Asian peat swamp forest is contributing between 16.6 and 27.9% (95% CI) of combined total national GHG emissions from Malaysia and Indonesia or 0.44 and 0.74% (95% CI) of annual global emissions.

Methane emissions from tree stems in neotropical peatlands.

Neotropical peatlands emit large amounts of methane (CH4 ) from the soil surface, but fluxes from tree stems in these ecosystems are unknown. In this study we investigated CH4 emissions from five tree species in two forest types common to neotropical lowland peatlands in Panama. Methane from tree stems accounted for up to 30% of net ecosystem CH4 emissions. Peak CH4 fluxes were greater during the wet season when the water table was high and temperatures were lower. Emissions were greatest from the hardwood tree Campnosperma panamensis, but most species acted as emitters, with emissions declining exponentially with height along the stem for all species. Overall, species identity, stem diameter, water level, soil temperature and soil CH4 fluxes explained 54% of the variance in stem CH4 emissions from individual trees. On the landscape level, On the landscape level, the high emissions from C. panamensis forests resulted in that they emitted at 340 kg CH4  d-1 during flooded periods despite their substantially lower areal cover. We conclude that emission from tree stems is an important emission pathway for CH4 flux from Neotropical peatlands, and that these emissions vary strongly with season and forest type.