From tree to plot: investigating stem CO efflux and its drivers along a logging gradient in Sabah, Malaysian Borneo.

Stem respiration constitutes a substantial proportion of autotrophic respiration in forested ecosystems, but its drivers across different spatial scales and land-use gradients remain poorly understood. This study quantifies and examines the impact of logging disturbance on stem CO efflux (EA) in Malaysian Borneo. EA was quantified at tree- and stand-level in nine 1-ha plots over a logging gradient from heavily logged to old-growth using the static chamber method.

Climate change, fire return intervals and the growing risk of permanent forest loss in boreal Eurasia.

Climate change has driven an increase in the frequency and severity of fires in Eurasian boreal forests. A growing number of field studies have linked the change in fire regime to post-fire recruitment failure and permanent forest loss. In this study we used four burned area and two forest loss datasets to calculate the landscape-scale fire return interval (FRI) and associated risk of permanent forest loss.

Detection of oil pollution impacts on vegetation using multifrequency SAR, multispectral images with fuzzy forest and random forest methods.

Oil pollution harms terrestrial ecosystems. There is an urgent requirement to improve on existing methods for detecting, mapping and establishing the precise extent of oil-impacted and oil-free vegetation. This is needed to quantify existing spill extents, formulate effective remediation strategies and to enable effective pipeline monitoring strategies to identify leakages at an early stage.

Impact of fertiliser, water table, and warming on celery yield and CO and CH emissions from fenland agricultural peat.

Peatlands are globally important areas for carbon preservation; although covering only 3% of global land area, they store 30% of total soil carbon. Lowland peat soils can also be very productive for agriculture, but their cultivation requires drainage as most crops are intolerant of root-zone anoxia. This leads to the creation of oxic conditions in which organic matter becomes vulnerable to mineralisation.

Mapping terrestrial oil spill impact using machine learning random forest and Landsat 8 OLI imagery: a case site within the Niger Delta region of Nigeria.

Terrestrial oil pollution is one of the major causes of ecological damage within the Niger Delta region of Nigeria and has caused a considerable loss of mangroves and arable croplands since the discovery of crude oil in 1956. The exact extent of landcover loss due to oil pollution remains uncertain due to the variability in factors such as volume and size of the oil spills, the age of oil, and its effects on the different vegetation types. Here, the feasibility of identifying oil-impacted land in the Niger Delta region of Nigeria with a machine learning random forest classifier using Landsat 8 (OLI spectral bands) and Vegetation Health Indices is explored.

Next generation of elevated [CO2] experiments with crops: a critical investment for feeding the future world.

A rising global population and demand for protein-rich diets are increasing pressure to maximize agricultural productivity. Rising atmospheric [CO(2)] is altering global temperature and precipitation patterns, which challenges agricultural productivity. While rising [CO(2)] provides a unique opportunity to increase the productivity of C(3) crops, average yield stimulation observed to date is well below potential gains.