Search

Forests are invaluable terrestrial ecosystems with considerable economic, ecological, and environmental benefits. Bark beetles have been recognized as one of the major causes of forest disturbance, and climate change can exacerbate their impact, leading to more tree mortality. Early detection of bark beetle attacks is vital to reduce forest loss and devastating consequences. This study examines the potential for early detection of European spruce bark beetle (Ips typographus L.) attacks in southeastern Sweden using comprehensive harvester data and time series of Sentinel-2 images, 2015–2021. Specifically, it aims at 1) determining the most pronounced wavelength bands and vegetation indices (VIs) of Sentinel-2 for early detection, 2) determining the number of attacked trees in a Sentinel-2 pixel required to enable detection, 3) testing three change detection approaches, Detecting Breakpoints and Estimating Segments in Trend (DBEST), Mean-Level-Shift (MLS), and Cumulative Sum (CUSUM) to investigate the potential for early detection of bark beetle attacks. The greatest separation in reflectance between healthy and attacked pixels, from first swarming peak (May 2018) till harvesting (April 2019), was observed in the SWIR1 (0.018) and SWIR2 (0.011) bands followed by red-edge (0.008), red (0.007), NIR (0.005), and the green band (0.004). The blue band showed the least separation (0.003). All VIs showed a change in their base level after the swarming and this was more prominent for NDRS with an increase of 0.14, followed by NDWI (-0.13), CCI (-0.11) and NDVI (-0.09), all with decreasing values. The observed responses of VIs in relation to the number of attacked trees in a Sentinel-2 pixel increased gradually for pixels having one to ten infested trees, with the strongest response observed for pixels with 9 to 14 attacked trees. Pixels including more than 14 attacked trees did not show any further substantial change in VIs. DBEST, on average, indicated that the infestation impact on VIs is detectable one month after the swarming peak with a 15–31 days precision. MLS and CUSUM with up to two months' accuracies were ranked next. NDRS and CCI showed superior detection performance compared to NDVI and NDWI. The detection was based on smoothed time series of Sentinel-2 data to reduce the influence of noise and missing data and cannot be directly applied to a near real-time detection method. [ABSTRACT FROM AUTHOR]

Monitoring crop growth and estimating crop yield are essential for managing agricultural production, ensuring food security, and maintaining sustainable agricultural development. Combining the mechanistic framework of a crop growth model with remote sensing observations can provide a means of generating realistic and spatially detailed crop growth information that can facilitate accurate crop yield estimates at different scales. The main objective of this study was to develop a robust estimation methodology of within-field winter wheat yield at a high spatial resolution (20 m × 20 m) by combining a light use efficiency-based model and Sentinel-2 data. For this purpose, Sentinel-2 derived leaf area index (LAI) time series were assimilated into the Simple Algorithm for Yield Estimation (SAFY) model using an ensemble Kalman filter (EnKF). The study was conducted on rainfed winter wheat fields in southern Sweden. LAI was estimated using vegetation indices (VIs) derived from Sentinel-2 data with semi-empirical models. The enhanced two-band vegetation index (EVI2) was found to be a useful VI for LAI estimation, with a coefficient of determination (R2) and a root mean square error (RMSE) of 0.80 and 0.65 m2/m2, respectively. Our findings demonstrate that the assimilation of LAI derived from Sentinel-2 into the SAFY model using EnKF enhances the estimation of within-field spatial variability of winter wheat yield by 70% compared to the baseline simulation without the assimilation of remotely sensed data. Additionally, the assimilation of LAI improves the accuracy of winter wheat yield estimation by decreasing the RMSE by 53%. This study demonstrates an approach towards practical applications of freely accessible Sentinel-2 data and a crop growth model through data assimilation for fine-scale mapping of crop yield. Such information is critical for quantifying the yield gap at the field scale, and to aid the optimization of management practices to increase crop production. [ABSTRACT FROM AUTHOR]

Biogenic volatile organic compound (BVOC) emissions from trees subjected to biotic stress are higher compared to healthy trees, and they may also have a different compound composition. This in turn affects atmospheric chemistry and can lead to either positive or negative feedback to the climate. Climate change favors the abundance of the European spruce bark beetle (Ips typographus) which attacks the bark of Norway spruce (Picea abies) trees, causing induced BVOC emissions from the trees as a response to the insect stress. Here, results are reported from a study analyzing the difference in emission rates between healthy and bark-beetle-infested Norway spruce trees, changes in emission rates over time since the infestation started, and differences in emission rates from bark-beetle-drilled entry and exit holes. Bark chamber measurements on both healthy and infested trees were performed during the summer of 2019 at Hyltemossa and Norunda research stations in Sweden. The measurements showed that induced BVOC emissions following the bark beetle infestation were dominated by entry hole emissions in the early growing season and exit hole emissions in the later season. The results showed a significant difference in emission rates between healthy and infested trees during both seasons. The seasonal average standardized BVOC emission rate of healthy trees was 32 ± 52 µ g m -2 h -1 (mean ± standard deviation), while the average standardized BVOC emission rates of infested trees were 6700 ± 6900 and 2000 ± 1300 µ g m -2 h -1 during the early and late season respectively. BVOC emission rates were highest at the start of the infestation and decreased exponentially with time, showing induced emission rates for up to 1 year after which the emission rates were similar to those from healthy bark. Constitutive needle emission rates from healthy trees were found to be 11 times higher than bark emissions from healthy trees. However, when Norway spruce trees were infested, the bark emission rates were instead 6 to 20 times higher than the needle emissions, causing substantial increases in the total tree BVOC emission rate. This could lead to high impacts on atmospheric processes, specifically the formation of secondary organic aerosols, which have a higher yield from some monoterpene compounds, which increased from infested trees. [ABSTRACT FROM AUTHOR]

Emissions of biogenic volatile organic compound (BVOC) from the bark of Norway spruce (Picea abies) trees can be affected by stress, such as infestation of spruce bark beetles (Ips typographus). We studied the difference in emission rates from healthy spruce bark and infested spruce bark, the influence of time since spruce bark beetle infestation started and the difference in emission rates from bark beetle drilled entry holes and exit holes. Bark chamber measurements on both healthy trees and infested trees were performed during the summer of 2019 at two sites in Sweden. To consider the seasonal pattern of the spruce bark beetle, we divided the emission rates from infested trees into two seasons, an early season dominated by entry holes and a late season with mainly exit holes. Our findings show a significant difference in emission rates from healthy and infested trees, independent of season. The seasonal average standardized emission rate from healthy trees was 31.89 ± 51.67 µg m-2 h-1 (mean ± standard deviation), while the average standardized emission rates from infested trees were 6385 µg m-2 h-1 and 2102 µg m-2 h-1 during early and late season respectively. We also found an exponentially decreasing relationship with BVOC emission rates and time since infestation started where the emission rates reached the same level as the constitutive BVOC emission rates from bark after around one year. When comparing bark monoterpene BVOC emission rates with emission rates from needles, we found that the constitutive needle emission rates were 11 times higher than the constitutive bark emissions. However, the emission rates from infested Norway spruce tree bark were instead 6 to 20 times higher than the constitutive needle emissions, causing substantial increases in the total tree BVOC emission rate (550 % to 1900 % increase). This study adds evidence that spruce bark beetle induced bark BVOC emissions are higher than previously thought and highlights the need for further research with more samples more frequently throughout the season to fully understand the impact, which is required to quantify spruce bark beetle infestations impacts on the atmospheric chemistry and climate change. [ABSTRACT FROM AUTHOR]

The Arctic is exposed to even faster temperature changes than most other areas on Earth. Constantly increasing temperature will lead to thawing permafrost and changes in the methane (CH 4) emissions from wetlands. One of the places exposed to those changes is the Abisko–Stordalen Mire in northern Sweden, where climate and vegetation studies have been conducted since the 1970s. In our study, we analyzed field-scale methane emissions measured by the eddy covariance method at Abisko–Stordalen Mire for 3 years (2014–2016). The site is a subarctic mire mosaic of palsas, thawing palsas, fully thawed fens, and open water bodies. A bimodal wind pattern prevalent at the site provides an ideal opportunity to measure mire patches with different permafrost status with one flux measurement system. The flux footprint for westerly winds was dominated by elevated palsa plateaus, while the footprint was almost equally distributed between palsas and thawing bog-like areas for easterly winds. As these patches are exposed to the same climatic and weather conditions, we analyzed the differences in the responses of their methane emission for environmental parameters. The methane fluxes followed a similar annual cycle over the 3 study years, with a gentle rise during spring and a decrease during autumn, without emission bursts at either end of the ice-free season. The peak emission during the ice-free season differed significantly for the two mire areas with different permafrost status: the palsa mire emitted 19 mg-C m -2 d -1 and the thawing wet sector 40 mg-C m -2 d -1. Factors controlling the methane emission were analyzed using generalized linear models. The main driver for methane fluxes was peat temperature for both wind sectors. Soil water content above the water table emerged as an explanatory variable for the 3 years for western sectors and the year 2016 in the eastern sector. The water table level showed a significant correlation with methane emission for the year 2016 as well. Gross primary production, however, did not show a significant correlation with methane emissions. Annual methane emissions were estimated based on four different gap-filing methods. The different methods generally resulted in very similar annual emissions. The mean annual emission based on all models was 3.1 ± 0.3 g-C m -2 a -1 for the western sector and 5.5 ± 0.5 g-C m -2 a -1 for the eastern sector. The average annual emissions, derived from these data and a footprint climatology, were 2.7 ± 0.5 and 8.2 ± 1.5 g-C m -2 a -1 for the palsa and thawing surfaces, respectively. Winter fluxes were relatively high, contributing 27 %–45 % to the annual emissions. [ABSTRACT FROM AUTHOR]

The Artic is exposed to faster temperature changes than most other areas on Earth. Constantly increasing temperature will lead to thawing permafrost and changes in the CH4 emissions from wetlands. One of the places exposed to those changes is the Abisko-Stordalen Mire in northern Sweden, where climate and vegetation studies have been conducted from the 1970s.In our study, we analyzed field-scale methane emissions measured by the eddy covariance method at Abisko-Stordalen Mire for three years (2014-2016). The site is a subarctic mire mosaic of palsas, thawing palsas, fully thawed fens, and open water bodies. A bimodal wind pattern prevalent at the site provides an ideal opportunity to measure mire patches with different permafrost statuses with one flux measurement system. The flux footprint for westerly winds is dominated by elevated palsa plateaus, while the footprint is almost equally distributed between palsas and thawing bog-like areas for easterly winds. As these patches are exposed to the same climatic conditions, we analyzed the differences in the responses of their methane emission for environmental parameters. The methane fluxes followed a similar annual cycle over the three study years, with a gentle rise during spring and a decrease during autumn and with no emission burst at either end of the ice-free season. The peak emission during the ice-free season differed significantly for the mire with two permafrost statuses: the palsa mire emitted 24 mg-CH4 m-2 d-1 and the thawing wet sector 56 mg-CH4 m-2 d-1. Factors controlling the methane emission were analyzed using generalized linear models. The main driver for methane fluxes was peat temperature for both wind sectors. Soil water content above the water table emerged as an explanatory variable for the three years for western sectors and the year 2016 in the eastern sector. Water table level showed a significant correlation with methane emission for the year 2016 as well. Gross primary production, however, did not show a significant correlation with methane emissions. Annual methane emissions were estimated based on four different gap-filing methods. The different methods generally resulted in very similar annual emissions. The mean annual emission based on all models was 4.2 ± 0.4 g- CH4 m-2 a-1 for western sector and 7.3 ± 0.7 g- CH4 m-2 a-1 for the eastern sector. The average annual emissions, derived from this data and a footprint climatology, were 3.6 ± 0.7 g-CH4 m-2 a-1 and 11 ± 2 g- CH4 m-2 a-1 for the palsa and thawing surfaces, respectively. Winter fluxes were relatively high, contributing 27-45 % to the annual emissions. [ABSTRACT FROM AUTHOR]

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]

The aim of this paper is to present a proposal for a national building standard in Sweden. We define requirements for the proposed standard, e.g., it should support development of 3D city models, connect to building information models (BIM) and national registers and be based on a national classification system for the urban environment. Based on these requirements we develop an Application Domain Extension (ADE) of the building model in the proposed CityGML 3.0 standard denoted CityGML Sve-Test. CityGML 3.0 includes several new features of interest, e.g., the space concept, enhanced possibilities to convert data, and to link to other standards. In our study we create test data according to CityGML Sve-Test and evaluate it against the requirements. It is shown that BIM models (in Industry Foundation Classes, IFC, format) can be converted to CityGML Sve-Test and that a classification system facilitates this conversion. The CityGML Sve-Test dataset can be used to increase the automation level in building permissions checking and a related study shows that CityGML 3.0 has capabilities to link to legal information and be a base for 3D cadastral index maps. Based on our experience, we suggest that the national building standard should conform to international standards and, if possible, include a classification system. The exchange format (GML, JSON etc.) might change, but to be based on a standardized data model ensures harmonized structures and concepts. [ABSTRACT FROM AUTHOR]

The current three-dimensionally (3D) delimited property units are in most countries registered using two-dimensional (2D) documentation and textual descriptions. This approach has limitations if used for representing the actual extent of complicated 3D property units, in particular in city centers. 3D digital models such as building information model (BIM) and 3D geographic information system (GIS) could be utilized for accurate identification of property units, better representation of cadastral boundaries, and detailed visualization of complex buildings. To facilitate this, several requirements need to be identified considering organizational, legal, and technical aspects. In this study, we formulate these requirements and then develop a framework for integration of 3D cadastre and 3D digital models. The aim of this paper is that cadastral information stored based on the land administration domain model (LADM) are integrated with BIM on building level for accurate representation of legal boundaries and with GIS on city level for visualization of 3D cadastre in urban environments. The framework is implemented and evaluated against the requirements in a practical case study in Sweden. The conclusion is that the integration of the cadastral information and BIM/GIS is possible on both conceptual level and data level which will facilitate that organizations dealing with cadastral information (cadastral units), BIM models (architecture, engineering, and construction companies), and GIS (surveying units on e.g., municipality level) can exchange information; this facilitates better representation and visualization of 3D cadastral boundaries. [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]

Temperature is a key constraint on ecosystem respiration but is typically measured at only one or a few points across a study site. Given the exponential relationship between temperature and respiration, small differences in temperature across a heterogeneous ecosystem may result in large differences in respiration fluxes. These differences are not captured in current respiration models which use single measurements of air or soil temperature to represent a whole ecosystem. As a result, uncertainties are introduced when upscaling modelled respiration estimates or partitioning eddy covariance fluxes. Thermal cameras provide a new method for observing the spatial variability of surface temperature across an ecosystem at a high temporal resolution. We used thermal images, air and soil temperature data to model chamber respiration measurements collected over the growing season at a peatland in central Sweden. The measurements covered the two main vegetation communities (bryophyte-dominated hollows and graminoid-dominated hummocks) present at the site. We tested whether surface temperature data from the tower-based thermal camera predicted ecosystem respiration more accurately than air or soil temperature. We also assessed the impact of vegetation heterogeneity on the modelled fluxes by comparing model accuracy when including either one or both vegetation communities in a single model. Finally, we upscaled our modelled predictions to the whole site using a vegetation classification and thermal data collected with an Umanned Aerial Vehicle (UAV). We could thus quantify the uncertainties in modelled fluxes when using single versus spatially-distributed temperature estimates at the ecosystem scale. [ABSTRACT FROM AUTHOR]