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Tropical peatland, which dominantly distributes in Indonesia and Malaysia, has experienced recurring fires in the last few decades. Constraining the enhancement ratios and emission factors of gas and particulate matter emitted by the wildfires is necessary to evaluate their environmental and climatic impacts. We analyzed continuous observation data at Pekanbaru in Indonesia and Muar in Malaysia to investigate the emissions of gas and particulate matter. The enhancement ratios of particulate matter to carbon monoxide (PM10/CO) of wildfires in Riau province in June 2013 and February–March 2014 were analyzed. The PM10/CO ratios of peatland burning plumes ranged from 77 to 97 μg mg−1 for the event in June 2013, whereas the corresponding value was 127 μg mg−1 in February–March 2014. These enhancement ratios were translated to the emission factors of particulate matter using previous data on the emission factors of CO, assuming that secondary formation was ignorable. The estimated emission factors for PM10 were 13 ± 2 g kg−1 (2013) and 19 ± 2 g kg−1 (2014). These values are comparable to those reported by recent field observations in Indonesia and Malaysia (17.3 ± 6.0 to 34.4 ± 18.8 g kg−1). The estimated emission factors from both the present study and recent field work are consistently higher than that used in the current emission inventory, which suggests that it should be updated. A caveat for this analysis is possible influence of secondary formation, which will still be needed to be investigated in future studies.

The high demand for unmanned aerial systems (UASs) reflects the notable impact that these systems have had on the remote sensing field in recent years. Such systems can be used to discover new findings and develop strategic plans in related scientific fields. In this work, a case study is performed to describe a novel approach that uses a UAS with two different sensors and assesses the possibility of monitoring peatland in a small area of a plantation forest in West Kalimantan, Indonesia. First, a multicopter drone with an onboard camera was used to collect aerial images of the study area. The structure from motion (SfM) method was implemented to generate a mosaic image. A digital surface model (DSM) and digital terrain model (DTM) were used to compute a canopy height model (CHM) and explore the vegetation height. Second, a multicopter drone combined with a thermal infrared camera (Zenmuse-XT) was utilized to collect both spatial and temporal thermal data from the study area. The temperature is an important factor that controls the oxidation of tropical peats by microorganisms, root respiration, the soil water content, and so forth. In turn, these processes can alter the greenhouse gas (GHG) flux in the area. Using principal component analysis (PCA), the thermal data were processed to visualize the thermal characteristics of the study site, and the PCA successfully extracted different feature areas. The trends in the thermal information clearly show the differences among land cover types, and the heating and cooling of the peat varies throughout the study area. This study shows the potential for using UAS thermal remote sensing to interpret the characteristics of thermal trends in peatland environments, and the proposed method can be used to guide strategical approaches for monitoring the peatlands in Indonesia.

Understanding the information on land conditions and especially green vegetation cover is important for monitoring ecosystem dynamics. The fraction of vegetation cover (FVC) is a key variable that can be used to observe vegetation cover trends. Conventionally, satellite data are utilized to compute these variables, although computations in regions such as the tropics can limit the amount of available observation information due to frequent cloud coverage. Unmanned aerial systems (UASs) have become increasingly prominent in recent research and can remotely sense using the same methods as satellites but at a lower altitude. UASs are not limited by clouds and have a much higher resolution. This study utilizes a UAS to determine the emerging trends for FVC estimates at an industrial plantation site in Indonesia, which utilizes fast-growing Acacia trees that can rapidly change the land conditions. First, the UAS was utilized to collect high-resolution RGB imagery and multispectral images for the study area. The data were used to develop general land use/land cover (LULC) information for the site. Multispectral data were converted to various vegetation indices, and within the determined resolution grid (5, 10, 30 and 60 m), the fraction of each LULC type was analyzed for its correlation between the different vegetation indices (Vis). Finally, a simple empirical model was developed to estimate the FVC from the UAS data. The results show the correlation between the FVC (acacias) and different Vis ranging from R 2 = 0.66-0.74, 0.76-0.8, 0.84-0.89 and 0.93-0.94 for 5, 10, 30 and 60 m grid resolutions, respectively. This study indicates that UAS-based FVC estimations can be used for observing fast-growing acacia trees at a fine scale resolution, which may assist current restoration programs in Indonesia.