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Abstract: Forests are important from many perspectives. Forestry delivers products such as timber, fiber and fuel; forests are also important for recreational activities as well as for the global carbon balance. Consequently, it is important to develop methods that enable efficient monitoring of disturbances, such as insect attacks, over vast forested areas. These methods can be based on remote sensing, since satellites provide images with frequent spatial coverage. Insect attacks can be detected in these satellite data if the resulting defoliation or discolouration is sufficiently severe. Satellite data also facilitates monitoring of migration patterns of invasive insects since some sensors provide time series that enables tracing of insect attacks back in time. In this study, SPOT and MODIS data were utilized to map damage in Norway spruce (Picea abies) caused by Physokermes inopinatus, and the associated black encrustation formed by sooty mold during an attack occurring 2010 in Scania, the southernmost province of Sweden. This attack is the first known presence of P. inopinatus in Sweden. The study shows that damage can be detected with high accuracy in satellite data. In SPOT-data, 78% of the damage was detected with an overestimation of 46%. The larger damaged areas could be detected with MODIS 16-days composite NDVI-product with 250m resolution. In addition, the study indicates that there was damage already in 2009, the year before any damage was detected in field. Prior to 2009 no damage was detected, suggesting that this was the first year of the outbreak. This study documents the outbreak of P. inopinatus in S. Sweden and highlights the potential for remote sensing for monitoring and early detection of damage of this invasive insect. [Copyright &y& Elsevier]

Miniaturized thermal infrared (TIR) cameras that measure surface temperature are increasingly available for use with unmanned aerial vehicles (UAVs). However, deriving accurate temperature data from these cameras is non-trivialsince they are highly sensitive to changes in their internal temperature and low-cost models are often not radiometrically calibrated. We present the results of laboratory and field experiments that tested the extent of the temperature-dependency of a non-radiometric FLIR Vue Pro 640. We found that a simple empirical line calibration using at least three ground calibration points was sufficient to convert camera digital numbers to temperature values for images captured during UAV flight. Although the camera performed well under stable laboratory conditions (accuracy ±0.5 °C), the accuracy declined to ±5 °C under the changing ambient conditions experienced during UAV flight. The poor performance resulted from the non-linear relationship between camera output and sensor temperature, which was affected by wind and temperature-drift during flight. The camera's automated non-uniformity correction (NUC) could not sufficiently correct for these effects. Prominent vignetting was also visible in images captured under both stable and changing ambient conditions. The inconsistencies in camera output over time and across the sensor will affect camera applications based on relative temperature differences as well as user-generated radiometric calibration. Based on our findings, we present a set of best practices for UAV TIR camera sampling to minimize the impacts of the temperature dependency of these systems. [ABSTRACT FROM AUTHOR]

• We used thermal imaging to model and upscale ecosystem respiration (ER). • Thermal-based ER models are as accurate as those using air or soil temperature. • High spatial resolution (<7 cm) ER maps were produced using UAV thermal data. • Accounting for differences in peatland microform ER is key to accurate upscaling. • Peatland ER plateaus at high temperatures during a hot drought. Field-based thermal infrared cameras provide surface temperature information at very high spatial and temporal resolution and could complement existing phenological camera and spectral sensor networks. Since temperature is one of the main drivers of ecosystem respiration (ER), field-based thermal cameras offer a new opportunity to model and upscale ER in unprecedented detail. We present such an approach based on manual chamber CO 2 flux measurements and thermal imagery from a tower-based camera and from Unmanned Aerial Vehicle (UAV) flights. Data were collected over two growing seasons, including the hot drought of 2018, for the two main vegetation microforms (hummock and hollow) of a hemi-boreal peatland in Sweden. Thermal imagery proved suitable for modelling ER in this ecosystem: ER model accuracies were similar when air, soil or surface temperature measurements were used as input. Our findings allowed us to upscale ER using UAV-derived thermal images and we present maps of ER at sub-decimeter resolution (<7 cm). The significantly different ER measured for each microform highlighted the importance of modelling their ER separately. Not accounting for these differences and the microforms' spatial distribution across the peatland led to a bias in upscaled ER of up to 18%. As a result of the severity and timing of the hot drought in 2018, we observed reductions in the ER of both microforms, but more so for hummocks (-48%) than for hollows (-15%), and modelled ER leveled off at high temperatures. These findings indicate that peatland carbon loss during hot droughts may be lower than expected and strongly relates to vegetation composition. The presented upscaling approach offers a new method to analyse how ER varies across a peatland or within a flux-tower footprint, and to interpret biases that occur when using coarse resolution satellite data to upscale chamber or tower-based flux measurements. Image, graphical abstract [ABSTRACT FROM AUTHOR]