Ecosystem services provided by tropical peat swamp forests, such as carbon (C) storage and water regulation, are under threat due to encroachment and replacement of these natural forests by drainage-based agriculture, commonly oil palm plantation. This study aims to quantify how the chemical and physical properties of peat change during land conversion to oil palm. This will be addressed by comparing four separate stages of conversion; namely, secondary peat swamp forests, recently deeply drained secondary forests, cleared and recently planted oil palm, and mature oil palm plantation in North Selangor, Malaysia. Results indicate accelerated peat decomposition in surface peats of mature oil palm plantations due to the lowered water table and altered litter inputs associated with this land-use change. Surface organic matter content and peat C stocks at secondary forest sites were higher than at mature oil palm sites (e.g. C stocks were 975 ± 151 and 497 ± 157 Mg ha− 1 at secondary forest and mature oil palm sites, respectively). Land conversion altered peat physical properties such as shear strength, bulk density and porosity, with mirrored changes above and below the water table. Our findings suggest close links between the organic matter and C content and peat physical properties through the entire depth of the peat profile. We have demonstrated that conversion from secondary peat swamp forest to mature oil palm plantation may seriously compromise C storage and, through its impact on peat physical properties, the water holding capacity in these peatlands

Detailed land cover information is valuable for mapping complex urban environments. Recent enhancements to satellite sensor technology promise fit-for-purpose data, particularly when processed using contemporary classification approaches. We evaluate this promise by comparing the influence of spatial resolution, spectral band set and classification approach for mapping detailed urban land cover in Nottingham, UK. A WorldView-2 image provides the basis for a set of 12 images with varying spatial and spectral characteristics, and these are classified using three different approaches (maximum likelihood (ML), support vector machine (SVM) and object-based image analysis (OBIA)) to yield 36 output land cover maps. Classification accuracy is evaluated independently and McNemar tests are conducted between all paired outputs (630 pairs in total) to determine which classifications are significantly different. Overall accuracy varied between 35% for ML classification of 30 m spatial resolution, 4-band imagery and 91% for OBIA classification of 2 m spatial resolution, 8-band imagery. The results demonstrate that spatial resolution is clearly the most influential factor when mapping complex urban environments, and modern “very high resolution” or VHR sensors offer great advantage here. However, the advanced spectral capabilities provided by some recent sensors, coupled with contemporary classification approaches (especially SVMs and OBIA), can also lead to significant gains in mapping accuracy. Ongoing development in instrumentation and methodology offer huge potential here and imply that urban mapping opportunities will continue to grow.

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.

A method was developed to transform a soft land cover classification into hard land cover classes at the sub-pixel scale for subsequent per-field classification. First, image pixels were segmented using vector boundaries. Second, the pixel segments (ranked by area) were labelled with a land cover class (ranked by class typicality). Third, a hard per-field classification was generated by examining each polygon (representing a land cover parcel, or field) in its entirety (by grouping the fragments of the polygon contained within different image pixels) and assigning to it the modal land cover class. The accuracy of this technique was considerably higher than that of both a corresponding hard per-pixel classification and a perfield classification based on hard per-pixel classified imagery.