Browsing by Subject "tree health"

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  • Junttila, Samuli; Sugano, Junko; Vastaranta, Mikko; Linnakoski, Riikka; Kaartinen, Harri; Kukko, Antero; Holopainen, Markus; Hyyppa, Hannu; Hyyppa, Juha (2018)
    Changing climate is increasing the amount and intensity of forest stress agents, such as drought, pest insects, and pathogens. Leaf water content, measured here in terms of equivalent water thickness (EWT), is an early indicator of tree stress that provides timely information about the health status of forests. Multispectral terrestrial laser scanning (MS-TLS) measures target geometry and reflectance simultaneously, providing spatially explicit reflectance information at several wavelengths. EWT and leaf internal structure affect leaf reflectance in the shortwave infrared region that can be used to predict EWT with MS-TLS. A second wavelength that is sensitive to leaf internal structure but not affected by EWT can be used to normalize leaf internal effects on the shortwave infrared region and improve the prediction of EWT. Here we investigated the relationship between EWT and laser intensity features using multisensor MS-TLS at 690, 905, and 1,550 nm wavelengths with both drought-treated and Endoconidiophora polonica inoculated Norway spruce seedlings to better understand how MS-TLS measurements can explain variation in EWT. In our study, a normalized ratio of two wavelengths at 905 and 1,550 nm and length of seedling explained 91% of the variation (R-2) in EWT as the respective prediction accuracy for EWT was 0.003 g/cm(2) in greenhouse conditions. The relation between EWT and the normalized ratio of 905 and 1,550 nm wavelengths did not seem sensitive to a decreased point density of the MS-TLS data. Based on our results, different EWTs in Norway spruce seedlings show different spectral responses when measured using MS-TLS. These results can be further used when developing EWT monitoring for improving forest health assessments.
  • Junttila, Samuli; Sugano, Junko; Vastaranta, Mikko; Linnakoski, Riikka; Kaartinen, Harri; Kukko, Antero; Holopainen, Markus; Hyyppä, Hannu; Hyyppä, Juha (Frontiers Reseach Foundation, 2018)
    Frontiers in Plant Science
    Changing climate is increasing the amount and intensity of forest stress agents, such as drought, pest insects, and pathogens. Leaf water content, measured here in terms of equivalent water thickness (EWT), is an early indicator of tree stress that provides timely information about the health status of forests. Multispectral terrestrial laser scanning (MS-TLS) measures target geometry and reflectance simultaneously, providing spatially explicit reflectance information at several wavelengths. EWT and leaf internal structure affect leaf reflectance in the shortwave infrared region that can be used to predict EWT with MS-TLS. A second wavelength that is sensitive to leaf internal structure but not affected by EWT can be used to normalize leaf internal effects on the shortwave infrared region and improve the prediction of EWT. Here we investigated the relationship between EWT and laser intensity features using multisensor MS-TLS at 690, 905, and 1,550 nm wavelengths with both drought-treated and Endoconidiophora polonica inoculated Norway spruce seedlings to better understand how MS-TLS measurements can explain variation in EWT. In our study, a normalized ratio of two wavelengths at 905 and 1,550 nm and length of seedling explained 91% of the variation (R2) in EWT as the respective prediction accuracy for EWT was 0.003 g/cm2 in greenhouse conditions. The relation between EWT and the normalized ratio of 905 and 1,550 nm wavelengths did not seem sensitive to a decreased point density of the MS-TLS data. Based on our results, different EWTs in Norway spruce seedlings show different spectral responses when measured using MS-TLS. These results can be further used when developing EWT monitoring for improving forest health assessments.
  • Kinnunen, Aleksi (Helsingin yliopisto, 2021)
    Trees face an increasing variety of health threats. The overall effects of climate change on trees and forests are difficult to predict. As a result of the warming climate, the growing season is lengthening, improving the growth of the trees, but at the same time drought and insect damages may become more common and the risk of storm damage increases. There are many benefits to monitoring tree mortality. It can be used to assess the health status of forests, productivity, carbon sequestration and the ecological impacts of dead trees on forest ecosystems. Causes leading to tree death can include biological, climatic or human related factors. Monitoring can increase understanding of the causes of death and potentially help to protect forests better. Tree-related mortality is a spatially and temporally irregular process that is difficult to monitor using traditional inventory methods. Remote sensing makes it possible to map and monitor tree mortality more effectively. The purpose of this thesis was to find out how remote sensing data can be utilized in monitoring tree mortality. The aim was to find out how tree mortality has varied regionally and quantitatively in the Central Park of Helsinki and how accurately dead trees can be identified from aerial imagery. The study period was 2005–2019, during which orthophotos of seven different years were examined. Reference data of 14 212 trees were collected from the aerial time series covering a 15-year period by visual image interpretation. The data included healthy, weakened and dead trees. Heatmap time series were created from the locations of weakened and dead trees to examine quantitative and regional variability in mortality. The average temperatures over the years as well as the rainfall were compared with the dead tree numbers and the correlations between the observations were examined. The collected reference data was also utilized in health status classifications, which were implemented using semi-automatic machine learning methods. The object of the classifications was to identify healthy, weakened and dead trees as well as possible from each other. The canopies of individual trees were delimited by canopy segments obtained from laser scanning data. From the pixels contained in the delimited canopies, image features describing individual trees were calculated. Considerable changes in tree mortality were observed. The number of dead trees at the beginning of the study period increased significantly from year 2005 to year 2009. An exceptionally dry summer in 2006 was identified as a possible reason. In the following years, the situation remained moderate, but in quantitative and regional terms, mortality was at its highest in 2017. Overall, there was an upward trend in mortality during the study period, and average annual temperatures were found correlating strongly with the number of dead trees (r=0.73). The classification accuracies of tree health status varied annually between 89–96%. The seven-year average accuracy was 93.6% with a kappa value of 0.88. The most important features in the classification were the features calculated from the blue channel, such as the maximum value of the channel (B_max), the difference between the maximum and minimum of the channel (B_range) and the skewness of the distribution (B_skew). The results of the thesis showed that tree mortality can be monitored using remote sensing data. Clear changes in the number of dead trees were observed using the time series and possible causes were identified. By identifying the causes behind rising mortality, the effects of climate change can also be better understood. Tree health status classification accuracies were at a good level and dead trees can be mapped from aerial imagery by semi-automatic methods. Based on the thesis, it can be rightly stated that changes in tree mortality can be observed with aerial imagery time series. In addition, the semi-automatic identification of dead trees from aerial imagery can be said to be accurate enough for large-scale use.
  • Junttila, Samuli; Hölttä, Teemu; Puttonen, Eetu; Katoh, Masato; Vastaranta, Mikko; Kaartinen, Harri; Holopainen, Markus; Hyyppä, Hannu (Elsevier, 2021)
    Remote Sensing of Environment
    During the past decades, extreme events have become more prevalent and last longer, and as a result drought-induced plant mortality has increased globally. Timely in-formation on plant water dynamics is essential for under-standing and anticipating drought-induced plant mortality. Leaf water potential (ΨL), which is usually measured de-structively, is the most common metric that has been used for decades for measuring water stress. Remote sensing methods have been developed to obtain information on water dynamics from trees and forested landscapes. However, the spatial and temporal resolutions of the existing methods have limited our understanding of the water dynamics and diurnal variation of ΨL within single trees. Thus, we investi-gated the capability of terrestrial laser scanning (TLS) in-tensity in observing diurnal variation in ΨL during a 50-h monitoring period. We aimed to improve the understanding on how large a part of the diurnal variation in ΨL can be captured using TLS intensity observations. We found that TLS intensity at the 905 nm wavelength measured from a static position was able to explain 77% of the variation in ΨL for three trees of two tree species with a root mean square error of 0.141 MPa. Based on our experiment with three trees, a time series of TLS intensity measurements can be used in detecting changes in ΨL, and thus it is worthwhile to expand the investigations to cover a wider range of tree species and forests and further increase our understanding of plant water dynamics at wider spatial and temporal scales.