The Indonesian government committed to restoring over 2 million ha of degraded peatland by the end of 2020, mainly to reduce peat fires and greenhouse gas emissions. Although it is unlikely the government will meet this target, restoration projects are still underway. One restoration strategy involves blocking peatland drainage canals, but the consequences of this for smallholder farmers whose livelihoods are dependent on agriculture are unclear. This paper investigates perceived impacts of canal blocks on smallholder farmers and identifies factors that affect their willingness to accept canal blocks on their land. We use data from 181 household questionnaires collected in 2018 across three villages in Jambi province, Sumatra. We found that the majority of respondents would accept canal blocks on their farms, perceiving that the blocks would have no impact on yields or farm access, and would decrease fire risk. Respondents who would not accept blocks on their farms were more likely to use canals to access their farms and perceive that canal blocks would decrease yields. The majority of farmers unwilling to accept canal blocks did not change their mind when provided with an option of a block that would allow boat travel. Our results improve understanding of why some smallholders may be unwilling to engage with peatland restoration. Further research is needed to understand the impact of canal blocks on smallholders’ yields. Engaging with stakeholders from the outset to understand farmers’ concerns, and perceptions is key if the government is to succeed in meeting its peatland restoration target and to ensure that the costs and benefits of restoration are evenly shared between local stakeholders and other actors.

The loss of huge areas of peat swamp forest in Southeast Asia and the resulting negative environmental effects, both local and global, have led to an increasing interest in peat restoration in the region. Satellite remote sensing offers the potential to provide up‐to‐date information on peat swamp forest loss across large areas, and support spatial explicit conservation and restoration planning. Fusion of optical and radar remote sensing data may be particularly valuable in this context, as most peat swamp forests are in areas with high cloud cover, which limits the use of optical data. Radar data can ‘see through’ cloud, but experience so far has shown that it doesn't discriminate well between certain types of land cover. Various approaches to fusion exist, but there is little information on how they compare. To assess this untapped potential, we compare three different classification methods with Sentinel‐1 and Sentinel‐2 images to map the remnant distribution of peat swamp forest in the area surrounding Sungai Buluh Protection Forest, Sumatra, Indonesia. Results show that data fusion increases overall accuracy in one of the three methods, compared to the use of optical data only. When data fusion was used with the pixel‐based classification using the original pixel values, overall accuracy increased by a small, but statistically significant amount. Data fusion was not beneficial in the case of object‐based classification or pixel‐based classification using principal components. This indicates optical data are still the main source of information for land cover mapping in the region. Based on our findings, we provide methodological recommendations to help those involved in peatland restoration capitalize on the potential of big data.

The recent availability of high spatial and temporal resolution optical and radar satellite imagery has dramatically increased opportunities for mapping land cover at fine scales. Fusion of optical and radar images has been found useful in tropical areas affected by cloud cover because of their complementarity. However, the multitemporal dimension these data now offer is often neglected because these areas are primarily characterized by relatively low levels of seasonality and because the consideration of multitemporal data requires more processing time. Hence, land cover mapping in these regions is often based on imagery acquired for a single date or on an average of multiple dates. The aim of this work is to assess the added value brought by the temporal dimension of optical and radar time series when mapping land cover in tropical environments. Specifically, we compared the accuracies of classifications based on (a) optical time series, (b) their temporal average, (c) radar time series, (d) their temporal average, (e) a combination of optical and radar time series and (f) a combination of their temporal averages for mapping land cover in Jambi province, Indonesia, using Sentinel‐1 and Sentinel‐2 imagery. Using the full information contained in the time series resulted in significantly higher classification accuracies than using temporal averages (+14.7% for Sentinel‐1, +2.5% for Sentinel‐2 and +2% combining Sentinel‐1 and Sentinel‐2). Overall, combining Sentinel‐2 and Sentinel‐1 time series provided the highest accuracies (Kappa = 88.5%). Our study demonstrates that preserving the temporal information provided by satellite image time series can significantly improve land cover classifications in tropical biodiversity hotspots, improving our capacity to monitor ecosystems of high conservation relevance such as peatlands. The proposed method is reproducible, automated and based on open‐source tools satellite imagery.

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1. Tropical peat swamp forests retain large carbon stocks and support unique biodiversity, but clearance and drainage for agriculture have resulted in fires, carbon emissions and biodiversity losses. Initiatives to re-wet cultivated peatlands may benefit biodiversity if this protects remaining forests from fire and agricultural encroachment, but there are concerns that re-wetting could reduce yields and damage livelihoods, as relationships between drainage, on-farm biodiversity, and crop yields have not been studied.2. We examined oil palm fruit yields and bird diversity on 41 smallholder farms in Jambi (Sumatra, Indonesia), which varied in drainage intensity (12-month mean water table per plot from August 2018 to August 2019: −52 to −3 cm belowground). We also compared farm bird diversity with a neighbouring area of protected peat swamp forest (11,000 ha, 21 plots; mean water table per plot −3 to +15 cm).3. Bird species richness (3-18 species per plot), species composition and oil palm yields (4.5-19.2 t fresh fruit bunch ha −1 year −1 ) varied among farms, but were not detectably affected by water table depth, although groundlevel vegetation was more complex on wetter farms. Bird richness in oil palm (mean = 10.3 species per plot) was <50% of that in forest (26 species per