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These images depict drainage canals and roads in peatlands in Borneo, Sumatra, and Peninsular Malaysia at 5 meter resolution. These canals were detected from July-September 2017 Planet Basemaps satellite imagery using a convolutional neural network. Please contact Nathan Dadap (ndadap@stanford.edu) with any questions. 

Abstract When organic peat soils are sufficiently dry, they become flammable. In Southeast Asian peatlands, widespread deforestation and associated drainage create dry conditions that, when coupled with El Niño-driven drought, result in catastrophic fire events that release large amounts of carbon and deadly smoke to the atmosphere. While the effects of anthropogenic degradation on peat moisture and fire risk have been extensively demonstrated, climate change impacts to peat flammability are poorly understood. These impacts are likely to be mediated primarily through changes in soil moisture. Here, we used neural networks (trained on data from the NASA Soil Moisture Active Passive satellite) to model soil moisture as a function of climate, degradation, and location. The neural networks were forced with regional climate model projections for 1985–2005 and 2040–2060 climate under RCP8.5 forcing to predict changes in soil moisture. We find that reduced precipitation and increased evaporative demand will lead to median soil moisture decreases about half as strong as those observed during recent El Niño droughts in 2015 and 2019. Based on previous studies, such reductions may be expected to accelerate peat carbon emissions. Our results also suggest that soil moisture in degraded areas with less tree cover may be more sensitive to climate change than in other land use types, motivating urgent peatland restoration. Climate change may play an important role in future soil moisture regimes and by extension, future peat fire in Southeast Asian peatlands. 

This repository contains soil moisture and vegetation optical depth (VOD) retrievals from the Multi-Temporal Dual Channel Algorithm applied to SMAP observations. The data are subset for Insular Southeast Asia (ISEA), which encompasses Sumatra, Borneo, and Peninsular Malaysia, as well as the two year period from April 1, 2015 to March 31, 2017. 

Abstract Fires that emit massive amounts of CO₂and particulate matter now burn with regularity in Southeast Asian tropical peatlands. Natural peatlands in Southeast Asia are waterlogged for most of the year and experience little or no fire, but networks of canals constructed for agriculture have drained vast areas of these peatlands, making the soil vulnerable to fire during periods of low rainfall. While soil moisture is the most direct measure of peat flammability, it has not been incorporated into fire studies due to an absence of regional observations. Here, we create the first remotely sensed soil moisture dataset for tropical peatlands in Sumatra, Borneo and Peninsular Malaysia by applying a new retrieval algorithm to satellite data from the Soil Moisture Active Passive (SMAP) mission with data spanning the 2015 El Niño burning event. Drier soil up to 30 days prior to fire correlates with larger burned area. The predictive information provided by soil moisture complements that of precipitation. Our remote sensing-derived results mirror those from a laboratory-based peat ignition study, suggesting that the dependence of fire on soil moisture exhibits scale independence within peatlands. Soil moisture measured from SMAP, a dataset spanning 2015-present, is a valuable resource for peat fire studies and warning systems. 

Abstract Drainage canals associated with logging and agriculture dry out organic soils in tropical peatlands, thereby threatening the viability of long‐term carbon stores due to increased emissions from decomposition, fire, and fluvial transport. In Southeast Asian peatlands, which have experienced decades of land use change, the exact extent and spatial distribution of drainage canals are unknown. This has prevented regional‐scale investigation of the relationships between drainage, land use, and carbon emissions. Here, we create the first regional map of drainage canals using high resolution satellite imagery and a convolutional neural network. We find that drainage is widespread—occurring in at least 65% of peatlands and across all land use types. Although previous estimates of peatland carbon emissions have relied on land use as a proxy for drainage, our maps show substantial variation in drainage density within land use types. Subsidence rates are 3.2 times larger in intensively drained areas than in non‐drained areas, highlighting the central role of drainage in mediating peat subsidence. Accounting for drainage canals was found to improve a subsidence prediction model by 30%, suggesting that canals contain information about subsidence not captured by land use alone. Thus, our data set can be used to improve subsidence and associated carbon emissions predictions in peatlands, and to target areas for hydrologic restoration.