Search

Tropical peatland fires play a significant role in the context of global warming through emissions of substantial amounts of greenhouse gases. However, the state of knowledge on carbon loss from these fires is still poorly developed with few studies reporting the associated mass of peat consumed. Furthermore, spatial and temporal variations in burn depth have not been previously quantified. This study presents the first spatially explicit investigation of fire-driven tropical peat loss and its variability. An extensive airborne Light Detection and Ranging data set was used to develop a prefire peat surface modelling methodology, enabling the spatially differentiated quantification of burned area depth over the entire burned area. We observe a strong interdependence between burned area depth, fire frequency and distance to drainage canals. For the first time, we show that relative burned area depth decreases over the first four fire events and is constant thereafter. Based on our results, we revise existing peat and carbon loss estimates for recurrent fires in drained tropical peatlands. We suggest values for the dry mass of peat fuel consumed that are 206 t ha−1 for initial fires, reducing to 115 t ha−1 for second, 69 t ha−1 for third and 23 t ha−1 for successive fires, which are 58-7% of the current IPCC Tier 1 default value for all fires. In our study area, this results in carbon losses of 114, 64, 38 and 13 t C ha−1 for first to fourth fires, respectively. Furthermore, we show that with increasing proximity to drainage canals both burned area depth and the probability of recurrent fires increase and present equations explaining burned area depth as a function of distance to drainage canal. This improved knowledge enables a more accurate approach to emissions accounting and will support IPCC Tier 2 reporting of fire emissions. [ABSTRACT FROM AUTHOR]

Tropical peatlands cover over 25 Mha in Southeast Asia and are estimated to contain around 70 Gt of carbon. Peat swamp forest ecosystems are an important part of the region's natural resources supporting unique flora and fauna endemic to Southeast Asia. Over recent years, industrial plantation development on peatland, especially for oil palm cultivation, has created intense debate due to its potentially adverse social and environmental effects. The lack of objective up-to-date information on the extent of industrial plantations has complicated quantification of their regional and global environmental consequences, both in terms of loss of forest and biodiversity as well as increases in carbon emissions. Based on visual interpretation of high-resolution (30 m) satellite images, we find that industrial plantations covered over 3.1 Mha (20%) of the peatlands of Peninsular Malaysia, Sumatra and Borneo in 2010, surpassing the area of Belgium and causing an annual carbon emission from peat decomposition of 230-310 Mt CO2e. The majority (62%) of the plantations were located on the island of Sumatra, and over two-thirds (69%) of all industrial plantations were developed for oil palm cultivation, with the remainder mostly being Acacia plantations for paper pulp production. Historical analysis shows strong acceleration of plantation development in recent years: 70% of all industrial plantations have been established since 2000 and only 4% of the current plantation area existed in 1990. 'Business-as-usual' projections of future conversion rates, based on historical rates over the past two decades, indicate that 6-9 Mha of peatland in insular Southeast Asia may be converted to plantations by the year 2020, unless land use planning policies or markets for products change. This would increase the annual carbon emission to somewhere between 380 and 920 Mt CO2e by 2020 depending on water management practices and the extent of plantations. [ABSTRACT FROM AUTHOR]

Water management in lowland areas usually aims to keep water tables within a narrow range to avoid flooding and drought conditions. A common water management target parameter is the depth of the canal water table below the surrounding soil surface. We demonstrated a method that rapidly determines canal water table depth (CWD) from airborne LiDAR data. The water table elevation was measured as the minimum value determined in a grid of 100 m × 100 m applied to a 1 m × 1 m digital terrain model (DTM), and the soil surface was calculated as the median value of values in each grid cell. Results for areas in eastern Sumatra and West Kalimantan, Indonesia, were validated against 145 field measurements at the time of LiDAR data collection. LiDAR-derived CWD was found to be accurate within 0.25 m and 0.5 m for 86% and 99% of field measurements, respectively, with an R2 value of 0.74. We demonstrated the method for CWD conditions in a drained peatland area in Central Kalimantan, where we found CWD in the dry season of 2011 to be generally below −1.5 and often below −2.5 m indicating severely overdrained conditions. We concluded that airborne LiDAR can provide an efficient and rapid mapping tool of CWD at the time of LiDAR data collection, which can be cost-effective especially where LiDAR data or derived DTMs are already available. The method can be applied to any LiDAR-based DTM that represents a flat landscape that has open water bodies. [ABSTRACT FROM AUTHOR]

Coastal lowland areas support much of the world population on only a small part of its terrestrial surface. Yet these areas face rapidly increasing land surface subsidence and flooding, and are most vulnerable to future sea level rise. The accurate and up to date digital terrain models (DTMs) that are required to predict and manage such risks are absent in many of the areas affected, especially in regions where populations are least developed economically and may be least resilient to such changes. Airborne LiDAR is widely seen as the most accurate data type for elevation mapping but can be prohibitively expensive, as are detailed field surveys across a broad geographic scale. We present an economical method that utilizes airborne LiDAR data along parallel flight lines ('strips') covering between 10% and 35% of the land depending on terrain characteristics, and manual interpolation. We present results for lowland areas in Central Kalimantan and East Sumatra (Indonesia), for which no accurate DTM currently exists. The study areas are covered with forest, plantations and agricultural land, on mineral soils and peatlands. The method is shown to yield DTM differences within 0.5 m, relative to full coverage LiDAR data, for 87.7–96.4% of the land surface in a range of conditions in 15 validation areas, and within 1.0 m for 99.3% of the area overall. After testing, the method was then applied to the entire eastern coastal zone of Sumatra, yielding a DTM at 100 m spatial resolution covering 7.1 Mha of lowland area from 1.45 Mha of effective LiDAR coverage. The DTM shows that 36.3%, or 2.6 Mha, of this area is below 2 m +MSL and, therefore, at risk of flooding in the near future as sea level rise continues. This DTM product is available for use in flood risk mapping, peatland mapping and other applications. [ABSTRACT FROM AUTHOR]