Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments

Remotely sensed data from both in situ and satellite platforms in visible, near-infrared, and shortwave infrared (VNIR–SWIR, 400–2500 nm) regions have been widely used to characterize and model soil properties in a direct, cost-effective, and rapid manner at different scales. In this study, we assess the performance of machine-learning algorithms including random forest (RF), extreme gradient boosting machines (XGBoost), and support vector machines (SVM) to model salt marsh soil bulk density using multispectral remote-sensing data from the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) platform. To our knowledge, use of remote-sensing data for estimating salt marsh soil bulk density at the vegetation rooting zone has not been investigated before. Our study reveals that blue (band 1; 450–520 nm) and NIR (band 4; 770–900 nm) bands of Landsat-7 ETM+ ranked as the most important spectral features for bulk density prediction by XGBoost and RF, respectively. According to XGBoost, band 1 and band 4 had relative importance of around 41% and 39%, respectively. We tested two soil bulk density classes in order to differentiate salt marshes in terms of their capability to support vegetation that grows in either low (0.032 to 0.752 g/cm3) or high (0.752 g/cm3 to 1.893 g/cm3) bulk density areas. XGBoost produced a higher classification accuracy (88%) compared to RF (87%) and SVM (86%), although discrepancies in accuracy between these models were small (<2%). XGBoost correctly classified 178 out of 186 soil samples labeled as low bulk density and 37 out of 62 soil samples labeled as high bulk density. We conclude that remote-sensing-based machine-learning models can be a valuable tool for ecologists and engineers to map the soil bulk density in wetlands to select suitable sites for effective restoration and successful re-establishment practices.

Tannins from senescent Rhizophora mangle mangrove leaves have a distinctive effect on prokaryotic and eukaryotic communities in a Distichlis spicata salt marsh soil

ABSTRACT Due to climate warming, tannin-rich Rhizophora mangle migrates into tannin-poor salt marshes, where the tannins interfere with the biogeochemistry in the soil. Changes in biogeochemistry are likely associated with changes in microbial communities. This was studied in microcosms filled with salt marsh soil and amended with leaf powder, crude condensed tannins, purified condensed tannins (PCT), all from senescent R. mangle leaves, or with tannic acid. Size and composition of the microbial communities were determined by denaturing gradient gel electrophoresis, high-throughput sequencing and real-time PCR based on the 16S and 18S rRNA genes. Compared with the control, the 16S rRNA gene abundance was lowered by PCT, while the 18S rRNA gene abundance was enhanced by all treatments. The treatments also affected the composition of the 16S rRNA and 18S rRNA gene assemblies, but the effects on the 18S rRNA gene were greater. The composition of the 18S rRNA gene, but not of the 16S rRNA gene, was significantly correlated with the mineralization of carbon, nitrogen and phosphorus. Distinctive microbial groups emerged during the different treatments. This study revealed that migration of mangroves may affect both the prokaryotic and the eukaryotic communities in salt marsh soils, but that the effects on the eukaryotes will likely be greater.

Fluxes of methane and distribution of sulfate as influenced by coastal salt-marsh soil ecosystem of Northern Germany

The study was conducted in Schleswig-Holstein at the Wadden sea coast of Northern Germany to evaluate the possible factors controlling methane (CH4) and sulfate (SO4) dynamics along a toposequence of daily to seasonally ?ooded coastal salt marsh soils. The soil at the top end of the salt marsh (with a height of 1.8 m above sea level: a.s.l. and a dense vegetation cover) was salic silty to clayic Typic Sulfaquent, while the soil at the bottom end (with some salt bushes and a 1.4 m a.s.l.) was sandy to silty Haplic Sulfaquent. The mean (depth: 0-100 cm) values of pH were around 7, and of redox potentials in the Typic Sulfaquent were ranged from -162 to +104 mV during all the seasons. The annual average emissions of CH4 were almost 10 fold higher (0.3 g m-2 a-1) in Haplic Sulfaquent than that (0.03 g m-2 a-1) of the Typic Sulfaquent. In all the pro?les, the concentrations of CH4 were very low and varied signi?cantly (p?0.05) with the seasons and soil depths. The concentrations of CH4 showed no dependence to temperature. The SO4 contents were observed maximum in the Typic Sulfaquent followed by Haplic Sulfaquent during all the seasons. There is no noticeable correlation was obtained between the SO4 and CH4 concentrations. Moreover, even CH4 was determined at depths where the SO4 concentration in the soil solution was around 1200 mg SO4 L-1.Bangladesh J. Sci. Ind. Res. 52(3), 177-186, 2017