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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. 

We study a game where agents interacting over a network engage in two coupled activities and have to strategically decide their production for each of these activities. Agent interactions involve local and global network effects, as well as a coupling between activities. We consider the general case where the network effects are heterogeneous across activities, i.e., the underlying graph structures of the two activities differ and/or the parameters of the network effects are not equal. In particular, we apply this game in the context of palm oil tree cultivation and timber harvesting, where network structures are defined by spatial boundaries of concessions. We first derive a sufficient condition for the existence and uniqueness of a Nash equilibrium. This condition can be derived using the potential game property of our game or by employing variational inequality framework. We show that the equilibrium can be expressed as a linear combination of two Bonacich centrality vectors. 

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.