Browsing by Subject "TIME-SERIES"

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  • Reichenau, Tim G.; Korres, Wolfgang; Schmidt, Marius; Graf, Alexander; Welp, Gerhard; Meyer, Nele; Stadler, Anja; Brogi, Cosimo; Schneider, Karl (2020)
    The development and validation of hydroecological land-surface models to simulate agricultural areas require extensive data on weather, soil properties, agricultural management, and vegetation states and fluxes. However, these comprehensive data are rarely available since measurement, quality control, documentation, and compilation of the different data types are costly in terms of time and money. Here, we present a comprehensive dataset, which was collected at four agricultural sites within the Rur catchment in western Germany in the framework of the Transregional Collaborative Research Centre 32 (TR32) "Patterns in Soil-Vegetation-Atmosphere Systems: Monitoring, Modeling and Data Assimilation". Vegetation-related data comprise fresh and dry biomass (green and brown, predominantly per organ), plant height, green and brown leaf area index, phenological development state, nitrogen and carbon content (overall > 17 000 entries), and masses of harvest residues and regrowth of vegetation after harvest or before planting of the main crop (> 250 entries). Vegetation data including LAI were collected in frequencies of 1 to 3 weeks in the years 2015 until 2017, mostly during overflights of the Sentinel 1 and Radarsat 2 satellites. In addition, fluxes of carbon, energy, and water (> 180 000 half-hourly records) measured using the eddy covariance technique are included. Three flux time series have simultaneous data from two different heights. Data on agricultural management include sowing and harvest dates as well as information on cultivation, fertilization, and agrochemicals (27 management periods). The dataset also includes gap-filled weather data (> 200 000 hourly records) and soil parameters (particle size distributions, carbon and nitrogen content; > 800 records). These data can also be useful for development and validation of remote-sensing products. The dataset is hosted at the TR32 database (, last access: 29 September 2020) and has the DOI (Reichenau et al., 2020).
  • Firozjaei, Mohammad Karimi; Sedighi, Amir; Firozjaei, Hamzeh Karimi; Kiavarz, Majid; Homaee, Mehdi; Arsanjani, Jamal Jokar; Makki, Mohsen; Naimi, Babak; Alavipanah, Seyed Kazem (2021)
    Mining activities and associated actions cause land-use/land-cover (LULC) changes across the world. The objective of this study were to evaluate the historical impacts of mining activities on surface biophysical characteristics, and for the first time, to predict the future changes in pattern of vegetation cover and land surface temperature (LST). In terms of the utilized data, satellite images of Landsat, and meteorological data of Sungun mine in Iran, Athabasca oil sands in Canada, Singrauli coalfield in India and Hambach mine in Germany, were used over the period of 1989-2019. In the first step, the spectral bands of Landsat images were employed to extract historical LULC changes in the study areas based on the homogeneity distance classification algorithm (HDCA). Thereafter, a CA-Markov model was used to predict the future of LULC changes based on the historical changes. In addition, LST and vegetation cover maps were calculated using the single channel algorithm, and the normalized difference vegetation index (NDVI), respectively. In the second step, the trends of LST and NDVI variations in different LULC change types and over different time periods were investigated. Finally, a CA-Markov model was used to predict the LST and NDVI maps and the trend of their variations in future. The results indicated that the forest and green space cover was reduced from 9.95 in 1989 to 5.9 Km(2) in 2019 for Sungun mine, from 42.14 in 1999 to 33.09 Km(2) in 2019 for Athabasca oil sands, from 231.46 in 1996 to 263.95 Km(2) in 2016 for Singrauli coalfield, and from 180.38 in 1989 to 133.99 Km(2) in 2017 for Hambach mine, as a result of expansion and development of of mineral activities. Our findings about Sungun revealed that the areal coverage of forest and green space will decrease to 15% of the total study area by 2039, resulting in reduction of the mean NDVI by almost 0.06 and increase of mean standardized LST from 0.52 in 2019 to 0.61 in 2039. our results further indicate that for Athabasca oil sands (Singrauli coalfield, Hambach mine), the mean values of standardized LST and NDVI will change from 0.5 (0.44 and 0.4) and 0.38 (0.38, 0.35) in 2019 (2016, 2017) to 0.57 (0.5, 0.47) and 0.33 (0.32, 0.28), in 2039 (2036, 2035), respectively. This can be mainly attributed to the increasing mining activities in the past as well as future years. The discussion and conclusions presented in this study can be of interest to local planners, policy makers, and environmentalists in order to observe the damages brought to the environment and the society in a larger picture.
  • Savilahti, Emma M.; Lintula, Sakari; Häkkinen, Laura; Marttunen, Mauri; Granö, Niklas (2021)
    Background The COVID-19-pandemic and especially the physical distancing measures drastically changed the conditions for providing outpatient care in adolescent psychiatry. Methods We investigated the outpatient services of adolescent psychiatry in the Helsinki University Hospital (HUH) from 1/1/2015 until 12/31/2020. We retrieved data from the in-house data software on the number of visits in total and categorized as in-person or remote visits, and analysed the data on a weekly basis. We further analysed these variables grouped according to the psychiatric diagnoses coded for visits. Data on the number of patients and on referrals from other health care providers were available on a monthly basis. We investigated the data descriptively and with a time-series analysis comparing the pre-pandemic period to the period of the COVID-19 pandemic. Results The total number of visits decreased slightly at the early stage of the COVID-19 pandemic in Spring 2020. Remote visits sharply increased starting in 3/2020 and remained at a high level compared with previous years. In-person visits decreased in Spring 2020, but gradually increased afterwards. The number of patients transiently fell in Spring 2020. Conclusions Rapid switch to remote visits in outpatient care of adolescent psychiatry made it possible to avoid a drastic drop in the number of visits despite the physical distancing measures during the COVID-19 pandemic.
  • Wang, Qian; Lintunen, Anna; Zhao, Ping; Shen, Weijun; Salmon, Yann; Chen, Xia; Ouyang, Lei; Zhu, Liwei; Ni, Guangyan; Sun, Dan; Rao, Xinquan; Holtta, Teemu (2020)
    Prerequisite for selection of appropriate tree species in afforestation programs is to understand their water use strategy. Acacia mangium Willd., Schima wallichii Choisy, and Cunninghamia lanceolata (Lamb.) Hook are the three main vegetation restoration pioneer species in southern China, but no comparative research on the water use strategy of these three tree species have been reported. Our objective was to gain a detailed understanding of how photosynthetically active radiation (PAR), vapor pressure deficit (VPD), and soil water content (SWC) at different soil depths control the sap flux density (J(s)) in the dry and wet seasons. We measured the J(s) of these three tree species by using the thermal dissipation method in low subtropical China. We found that both S. wallichii and C. lanceolata differed clearly in their stomatal behavior from one season to another, while A. mangium did not. The canopy conductance per sapwood area of S. wallichii and C. lanceolata was very sensitive to VPD in the dry season, but not in the wet season. The J(s) of A. mangium was negatively correlated to SWC in all soil layers and during both seasons, while the other two species were not sensitive to SWC in the deeper layers and only positively correlated to SWC in dry season. Our results demonstrate that the three species have distinct water use strategies and may therefore respond differently to changing climate.
  • Colston, Josh M.; Zaitchik, Benjamin F.; Badr, Hamada S.; Burnett, Eleanor; Ali, Syed Asad; Rayamajhi, Ajit; Satter, Syed M.; Eibach, Daniel; Krumkamp, Ralf; May, Jurgen; Chilengi, Roma; Howard, Leigh M.; Sow, Samba O.; Hossain, M. Jahangir; Saha, Debasish; Nisar, M. Imran; Zaidi, Anita K. M.; Kanungo, Suman; Mandomando, Inacio; Faruque, Abu S. G.; Kotloff, Karen L.; Levine, Myron M.; Breiman, Robert F.; Omore, Richard; Page, Nicola; Platts-Mills, James A.; Ashorn, Ulla; Fan, Yue-Mei; Shrestha, Prakash Sunder; Ahmed, Tahmeed; Mduma, Estomih; Yori, Pablo Penatero; Bhutta, Zulfiqar; Bessong, Pascal; Olortegui, Maribel P.; Lima, Aldo A. M.; Kang, Gagandeep; Humphrey, Jean; Prendergast, Andrew J.; Ntozini, Robert; Okada, Kazuhisa; Wongboot, Warawan; Gaensbauer, James; Melgar, Mario T.; Pelkonen, Tuula; Freitas, Cesar Mavacala; Kosek, Margaret N. (2022)
    Diarrheal disease, still a major cause of childhood illness, is caused by numerous, diverse infectious microorganisms, which are differentially sensitive to environmental conditions. Enteropathogen-specific impacts of climate remain underexplored. Results from 15 studies that diagnosed enteropathogens in 64,788 stool samples from 20,760 children in 19 countries were combined. Infection status for 10 common enteropathogens-adenovirus, astrovirus, norovirus, rotavirus, sapovirus, Campylobacter, ETEC, Shigella, Cryptosporidium and Giardia-was matched by date with hydrometeorological variables from a global Earth observation dataset-precipitation and runoff volume, humidity, soil moisture, solar radiation, air pressure, temperature, and wind speed. Models were fitted for each pathogen, accounting for lags, nonlinearity, confounders, and threshold effects. Different variables showed complex, non-linear associations with infection risk varying in magnitude and direction depending on pathogen species. Rotavirus infection decreased markedly following increasing 7-day average temperatures-a relative risk of 0.76 (95% confidence interval: 0.69-0.85) above 28 degrees C-while ETEC risk increased by almost half, 1.43 (1.36-1.50), in the 20-35 degrees C range. Risk for all pathogens was highest following soil moistures in the upper range. Humidity was associated with increases in bacterial infections and decreases in most viral infections. Several virus species' risk increased following lower-than-average rainfall, while rotavirus and ETEC increased with heavier runoff. Temperature, soil moisture, and humidity are particularly influential parameters across all enteropathogens, likely impacting pathogen survival outside the host. Precipitation and runoff have divergent associations with different enteric viruses. These effects may engender shifts in the relative burden of diarrhea-causing agents as the global climate changes. Plain Language Summary Diarrheal disease is a big health problem for children. It can be caused by different bugs, which can be caught more easily in certain weather conditions, though not much is understood about this because the climate varies so much from one place to the next. This study combined data from many different countries where diarrhea-causing bugs were diagnosed in children's stool. Satellites recorded what the weather was like on the day each sample was collected. Rotavirus is easiest to catch in cold weather and when water washes over the ground after rain. Dry weather also makes it and other viruses easy to catch. Bacteria spread best when the air is warm and humid, and the soil moist, though one type of E. coli can also be spread in rainwater. Climate change will make dry places drier, wet places wetter and everywhere warmer. This might lead to more diarrhea caused by bacteria and less by viruses in some places, though places with moist soil might see more of every kind of bug.
  • Koolen, Ninah; Oberdorfer, Lisa; Rona, Zsofia; Giordano, Vito; Werther, Tobias; Klebermass-Schrehof, Katrin; Stevenson, Nathan; Vanhatalo, Sampsa (2017)
    Objective: To develop a method for automated neonatal sleep state classification based on EEG that can be applied over a wide range of age. Methods: We collected 231 EEG recordings from 67 infants between 24 and 45 weeks of postmenstrual age. Ten minute epochs of 8 channel polysomnography (N = 323) from active and quiet sleep were used as a training dataset. We extracted a set of 57 EEG features from the time, frequency, and spatial domains. A greedy algorithm was used to define a reduced feature set to be used in a support vector machine classifier. Results: Performance tests showed that our algorithm was able to classify quiet and active sleep epochs with 85% accuracy, 83% sensitivity, and 87% specificity. The performance was not substantially lowered by reducing the epoch length or EEG channel number. The classifier output was used to construct a novel trend, the sleep state probability index, that improves the visualisation of brain state fluctuations. Conclusions: A robust EEG-based sleep state classifier was developed. It performs consistently well across a large span of postmenstrual ages. Significance: This method enables the visualisation of sleep state in preterm infants which can assist clinical management in the neonatal intensive care unit. (C) 2017 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
  • Hurskainen, Pekka; Adhikari, Hari; Siljander, Mika; Pellikka, Petri; Hemp, Andreas (2019)
    Classifying land use/land cover (LULC) with sufficient accuracy in heterogeneous landscapes is challenging using only satellite imagery. To improve classification accuracy inclusion of features from auxiliary geospatial datasets in classification models is applied since 1980s. However, the method is mostly limited to pixel-based classifications, and the coverage, accuracy and resolution of free and open-access auxiliary datasets have been poor until recent years. We evaluated how recent global coverage open-access geospatial datasets improve object-based LULC classification accuracy compared to using only spectral and texture features from satellite images. We applied feature sets topography, population, soil, canopy cover, distance to watercourses and spectral-temporal metrics from Landsat-8 time series on the southern foothills and savanna of Mt. Kilimanjaro, Tanzania, where the landscape is characterized by heterogeneous and fragmented mosaic of disturbed savanna vegetation, croplands, and settlements. The classification was based on image objects (groups of spectrally similar pixels) derived from segmentation of four Formosat-2 scenes with 8m spatial resolution using 1370 ground reference points for training, validation, and for defining 17 LULC classes. We built six Random Forest classification models with different sets of object features in each. The baseline model having only spectral and texture features was compared with five other models supplemented with auxiliary features. Inclusion of auxiliary features significantly improved classification overall accuracy (OA). The baseline model gave a median OA of 60.7%, but auxiliary features in other models increased median OA between 6.1 and 16.5 percentage points. The best OA was achieved with a model including all features of which elevation was the most important auxiliary feature followed by Enhanced Vegetation Index temporal range and slope degree. Applying object-based classification to geospatial information on topography, soil, settlement patterns and vegetation phenology, the discriminatory potential of challenging LULC classes can be significantly improved. We demonstrated this for the first time, and the technique shows good potential for improving LULC mapping across a multitude of fragmented landscapes worldwide.
  • Olsson, Per-Ola; Kantola, Tuula; Lyytikäinen-Saarenmaa, Päivi; Jönsson, Anna Maria; Eklundh, Lars (2016)
    We investigated if coarse-resolution satellite data from the MODIS sensor can be used for regional monitoring of insect disturbances in Fennoscandia. A damage detection method based on z-scores of seasonal maximums of the 2-band Enhanced Vegetation Index (EVI2) was developed. Time-series smoothing was applied and Receiver Operating Characteristics graphs were used for optimisation. The method was developed in fragmented and heavily managed forests in eastern Finland dominated by Scots pine (Pinus sylvestris L.) (pinaceae) and with defoliation of European pine sawfly (Neodiprion sertifer Geoffr.) (Hymenoptera: Diprionidae) and common pine sawfly (Diprion pini L.) (Hymenoptera: Diprionidae). The method was also applied to subalpine mountain birch (Betula pubescens ssp. Czerepanovii N. I. Orlova) forests in northern Sweden, infested by autumnal moth (Epirrita autumnata Borkhausen) and winter moth (Operophtera brumata L.). In Finland, detection accuracies were fairly low with 50% of the damaged stands detected, and a misclassification of healthy stands of 22%. In areas with long outbreak histories the method resulted in extensive misclassification. In northern Sweden accuracies were higher, with 75% of the damage detected and a misclassification of healthy samples of 19%. Our results indicate that MODIS data may fail to detect damage in fragmented forests, particularly when the damage history is long. Therefore, regional studies based on these data may underestimate defoliation. However, the method yielded accurate results in homogeneous forest ecosystems and when long-enough periods without damage could be identified. Furthermore, the method is likely to be useful for insect disturbance detection using future medium-resolution data, e. g. from Sentinel-2.
  • Holopainen, Jari; Helama, Samuli; Väre, Henry (2018)
    Abstract Long records of phenological observations are commonly used as data in global change and palaeoclimate research and to analyse plants' responses to climatic changes. Here we delve into the historical archives of plant phenological observations (1750–1875) compiled and published previously by Professor Adolf Moberg (Imperial Alexander University of Finland). The digitized dataset represents 44,487 observations of 450 different plant species for their 15 different phenological phases made in 193 sites across Finland, and results in 662 different phenological variables. The five most frequently observed variables are the blooming of rye, the sowing of barley, the blooming of bird cherry, the leaf outbreak of birch, and the sowing of oat. The spring and summer observations demonstrate positive relationships between the onset date and the site latitude, this relationship becoming negative for observations made in the autumn. This latitudinal effect is evident in the raw data as demonstrated by the temporal correlations between the unadjusted mean phenological records and the mean latitude of the sites. After the latitudinal effect is removed from the original data such correlations are much reduced and the new set of phenological records based on the adjusted dates can be computed. The resulting mean phenological records correlate negatively and statistically significantly with the mean temperatures from April through July. Linear trends indicate (i) summer onsets having become delayed by more than one week over the full period and (ii) shortening of the growing seasons since 1846. The dataset is made available in an open repository.
  • Kollanus, Virpi; Tiittanen, Pekka; Niemi, Jarkko V.; Lanki, Timo (2016)
    Introduction: Fine particulate matter (PM2.5) emissions from vegetation fires can be transported over long distances and may cause significant air pollution episodes far from the fires. However, epidemiological evidence on health effects of vegetation-fire originated air pollution is limited, particularly for mortality and cardiovascular outcomes. Objective: We examined association between short-term exposure to long-range transported PM2.5 from vegetation fires and daily mortality due to non-accidental, cardiovascular, and respiratory causes and daily hospital admissions due to cardiovascular and respiratory causes in the Helsinki metropolitan area, Finland. Methods: Days significantly affected by smoke from vegetation fires between 2001 and 2010 were identified using air quality measurements at an urban background and a regional background monitoring station, and modelled data on surface concentrations of vegetation-fire smoke. Associations between daily PM2.5 concentration and health outcomes on i) smoke-affected days and ii) all other days (i.e. non smoke days) were analysed using Poisson time series regression. All statistical models were adjusted for daily temperature and relative humidity, influenza, pollen, and public holidays. Results: On smoke-affected days, 10 mu g/m(3) increase in PM2.5 was associated with a borderline statistically significant increase in cardiovascular mortality among total population at a lag of three days (12.4%, 95% CI -0.2% to 26.5%), and among the elderly (>= 65 years) following same-day exposure (13.8%, 95% CI -0.6% to 30.4%) and at a lag of three days (11.8%, 95% CI -2.2% to 27.7%). Smoke day PM2.5 was not associated with non-accidental mortality or hospital admissions due to cardiovascular causes. However, there was an indication of a positive association with hospital admissions due to respiratory causes among the elderly, and admissions due to chronic obstructive pulmonary disease or asthma among the total population. In contrast, on non-smoke days PM2.5 was generally not associated with the health outcomes, apart from suggestive small positive effects on non-accidental mortality at a lag of one day among the elderly and hospital admissions due to all respiratory causes following same-day exposure among the total population. Conclusions: Our research provides suggestive evidence for an association of exposure to long-range transported PM2.5 from vegetation fires with increased cardiovascular mortality, and to a lesser extent with increased hospital admissions due to respiratory causes. Hence, vegetation-fire originated air pollution may have adverse effects on public health over a distance of hundreds to thousands of kilometres from the fires. (C) 2016 The Authors. Published by Elsevier Inc.
  • Böttcher, Kristin; Markkanen, Tiina; Thum, Tea; Aalto, Tuula; Aurela, Mika; Reick, Christian H.; Kolari, Pasi; Arslan, Ali N.; Pulliainen, Jouni (2016)
    The objective of this study was to assess the performance of the simulated start of the photosynthetically active season by a large-scale biosphere model in boreal forests in Finland with remote sensing observations. The start of season for two forest types, evergreen needle-and deciduous broad-leaf, was obtained for the period 2003-2011 from regional JSBACH (Jena Scheme for Biosphere-Atmosphere Hamburg) runs, driven with climate variables from a regional climate model. The satellite-derived start of season was determined from daily Moderate Resolution Imaging Spectrometer (MODIS) time series of Fractional Snow Cover and the Normalized Difference Water Index by applying methods that were targeted to the two forest types. The accuracy of the satellite-derived start of season in deciduous forest was assessed with bud break observations of birch and a root mean square error of seven days was obtained. The evaluation of JSBACH modelled start of season dates with satellite observations revealed high spatial correspondence. The bias was less than five days for both forest types but showed regional differences that need further consideration. The agreement with satellite observations was slightly better for the evergreen than for the deciduous forest. Nonetheless, comparison with gross primary production (GPP) determined from CO2 flux measurements at two eddy covariance sites in evergreen forest revealed that the JSBACH-simulated GPP was higher in early spring and led to too-early simulated start of season dates. Photosynthetic activity recovers differently in evergreen and deciduous forests. While for the deciduous forest calibration of phenology alone could improve the performance of JSBACH, for the evergreen forest, changes such as seasonality of temperature response, would need to be introduced to the photosynthetic capacity to improve the temporal development of gross primary production.
  • Herwartz, Helmut; Maxand, Simone; Walle, Yabibal (2019)
    Time-varying volatility and linear trends are common features of several macroeconomic time series. Recent articles have proposed panel unit root tests (PURTs) that are pivotal in the presence of volatility shifts, excluding linear trends, however. This article proposes a new PURT that works well for data that is both heteroskedastic and trending. Under the null hypothesis, the test statistic has a limiting Gaussian distribution. We derive the local asymptotic power to underpin the consistency of the test statistic. Simulation results reveal that the test performs well in small samples. As an empirical illustration, we examine the stationarity of energy use per capita in OECD economies. While the series are in general difference stationary, they could also be considered as trend stationary for specific time spans.
  • Abera, Temesgen; Heiskanen, Janne; Maeda, Eduardo; Hailu, Binyam Tesfaw; Pellikka, Petri (2022)
    While cropland expansion and demand for woodfuel exert increasing pressure on woody vegetation in East Africa, climate change is inducing woody cover gain. It is however unclear if these contrasting patterns have led to net fractional woody cover loss or gain. Here we used non-parametric fractional woody cover (WC) predictions and breakpoint detection algorithms driven by satellite observations (Landsat and MODIS) and airborne laser scanning to unveil the net fractional WC change during 2001-2019 over Ethiopia and Kenya. Our results show that total WC loss was 4-times higher than total gain, leading to net loss. The contribution of abrupt WC loss (59%) was higher than gradual losses (41%). We estimated an annual WC loss rate of up to 5% locally, with cropland expansion contributing to 57% of the total loss in the region. Major hotspots of WC loss and degradation corridors were identified inside as well as surrounding protected areas, in agricultural lands located close to agropastoral and pastoral livelihood zones, and near highly populated areas. As the dominant vegetation type in the region, Acacia-Commiphora bushlands and thickets ecosystem was the most threatened, accounting 69% of the total WC loss, followed by montane forest (12%). Although highly outweighed by loss, relatively more gain was observed in woody savanna than in other ecosystems. These results reveal the marked impact of human activities on woody vegetation and highlight the importance of protecting endangered ecosystems from increased human activities for mitigating impacts on climate and supporting sustainable ecosystem service provision in East Africa.
  • Huong Thi Thanh Nguyen; Trung Minh Doan; Tomppo, Erkki; McRoberts, Ronald E. (2020)
    Information on land use and land cover (LULC) including forest cover is important for the development of strategies for land planning and management. Satellite remotely sensed data of varying resolutions have been an unmatched source of such information that can be used to produce estimates with a greater degree of confidence than traditional inventory estimates. However, use of these data has always been a challenge in tropical regions owing to the complexity of the biophysical environment, clouds, and haze, and atmospheric moisture content, all of which impede accurate LULC classification. We tested a parametric classifier (logistic regression) and three non-parametric machine learning classifiers (improved k-nearest neighbors, random forests, and support vector machine) for classification of multi-temporal Sentinel 2 satellite imagery into LULC categories in Dak Nong province, Vietnam. A total of 446 images, 235 from the year 2017 and 211 from the year 2018, were pre-processed to gain high quality images for mapping LULC in the 6516 km(2) study area. The Sentinel 2 images were tested and classified separately for four temporal periods: (i) dry season, (ii) rainy season, (iii) the entirety of the year 2017, and (iv) the combination of dry and rainy seasons. Eleven different LULC classes were discriminated of which five were forest classes. For each combination of temporal image set and classifier, a confusion matrix was constructed using independent reference data and pixel classifications, and the area on the ground of each class was estimated. For overall temporal periods and classifiers, overall accuracy ranged from 63.9% to 80.3%, and the Kappa coefficient ranged from 0.611 to 0.813. Area estimates for individual classes ranged from 70 km(2) (1% of the study area) to 2200 km(2) (34% of the study area) with greater uncertainties for smaller classes.
  • Saarinen, Ninni; White, Joanne C.; Wulder, Michael A.; Kangas, Annika; Tuominen, Sakari; Kankare, Ville; Holopainen, Markus; Hyyppä, Juha; Vastaranta, Mikko (2018)
    There is growing interest in the use of Landsat data to enable forest monitoring over large areas. Free and open data access combined with high performance computing have enabled new approaches to Landsat data analysis that use the best observation for any given pixel to generate an annual, cloud-free, gap-free, surface reflectance image composite. Finland has a long history of incorporating Landsat data into its National Forest Inventory to produce forest information in the form of thematic maps and small area statistics on a variety of forest attributes. Herein we explore the spatial and temporal characteristics of the Landsat archive in the context of forest monitoring in Finland. The United States Geological Survey Landsat archive holds a total of 30 076 images (1972-2017) for 66 scenes (each 185 km by 185 km in size) representing the terrestrial area of Finland, of which 93.6% were acquired since 1984 with a spatial resolution of 30 m. Approximately 16.3% of the archived images have desired compositing characteristics (acquired within August 1 +/- 30 days,
  • Morley, S. K.; Brito, T. V.; Welling, D. T. (2018)
    Quantitative assessment of modeling and forecasting of continuous quantities uses a variety of approaches. We review existing literature describing metrics for forecast accuracy and bias, concentrating on those based on relative errors and percentage errors. Of these accuracy metrics, the mean absolute percentage error (MAPE) is one of the most common across many fields and has been widely applied in recent space science literature and we highlight the benefits and drawbacks of MAPE and proposed alternatives. We then introduce the log accuracy ratio and derive from it two metrics: the median symmetric accuracy and the symmetric signed percentage bias. Robust methods for estimating the spread of a multiplicative linear model using the log accuracy ratio are also presented. The developed metrics are shown to be easy to interpret, robust, and to mitigate the key drawbacks of their more widely used counterparts based on relative errors and percentage errors. Their use is illustrated with radiation belt electron flux modeling examples.
  • Järvinen, Heikki; Seitola, Teija; Silen, Johan; Räisänen, Jouni (2016)
    A performance expectation is that Earth system models simulate well the climate mean state and the climate variability. To test this expectation, we decompose two 20th century reanalysis data sets and 12 CMIP5 model simulations for the years 1901-2005 of the monthly mean near-surface air temperature using randomised multi-channel singular spectrum analysis (RMSSA). Due to the relatively short time span, we concentrate on the representation of multi-annual variability which the RMSSA method effectively captures as separate and mutually orthogonal spatio-temporal components. This decomposition is a unique way to separate statistically significant quasi-periodic oscillations from one another in high-dimensional data sets. The main results are as follows. First, the total spectra for the two reanalysis data sets are remarkably similar in all timescales, except that the spectral power in ERA-20C is systematically slightly higher than in 20CR. Apart from the slow components related to multi-decadal periodicities, ENSO oscillations with approximately 3.5- and 5-year periods are the most prominent forms of variability in both reanalyses. In 20CR, these are relatively slightly more pronounced than in ERA-20C. Since about the 1970s, the amplitudes of the 3.5- and 5-year oscillations have increased, presumably due to some combination of forced climate change, intrinsic low-frequency climate variability, or change in global observing network. Second, none of the 12 coupled climate models closely reproduce all aspects of the reanalysis spectra, although some models represent many aspects well. For instance, the GFDL-ESM2M model has two nicely separated ENSO periods although they are relatively too prominent as compared with the reanalyses. There is an extensive Supplement and YouTube videos to illustrate the multi-annual variability of the data sets.
  • Ershov, Dmitry V.; Gavrilyuk, Egor A.; Koroleva, Natalia V.; Belova, Elena I.; Tikhonova, Elena V.; Shopina, Olga V.; Titovets, Anastasia V.; Tikhonov, Gleb N. (2022)
    Remote monitoring of natural afforestation processes on abandoned agricultural lands is crucial for assessments and predictions of forest cover dynamics, biodiversity, ecosystem functions and services. In this work, we built on the general approach of combining satellite and field data for forest mapping and developed a simple and robust method for afforestation dynamics assessment. This method is based on Landsat imagery and index-based thresholding and specifically targets suitability for limited field data. We demonstrated method's details and performance by conducting a case study for two bordering districts of Rudnya (Smolensk region, Russia) and Liozno (Vitebsk region, Belarus). This study area was selected because of the striking differences in the development of the agrarian sectors of these countries during the post-Soviet period (1991-present day). We used Landsat data to generate a consistent time series of five-year cloud-free multispectral composite images for the 1985-2020 period via the Google Earth Engine. Three spectral indices, each specifically designed for either forest, water or bare soil identification, were used for forest cover and arable land mapping. Threshold values for indices classification were both determined and verified based on field data and additional samples obtained by visual interpretation of very high-resolution satellite imagery. The developed approach was applied over the full Landsat time series to quantify 35-year afforestation dynamics over the study area. About 32% of initial arable lands and grasslands in the Russian district were afforested by the end of considered period, while the agricultural lands in Belarus' district decreased only by around 5%. Obtained results are in the good agreement with the previous studies dedicated to the agricultural lands abandonment in the Eastern Europe region. The proposed method could be further developed into a general universally applicable technique for forest cover mapping in different growing conditions at local and regional spatial levels.
  • Hossein Motlagh, Naser; Kapoor, Shubham; Alhalaseh, Rola; Tarkoma, Sasu; H ätönen, Kimmo (2022)
    5G networks and beyond introduce a larger number of Network Elements (NEs) and functions than former cellular generations. The increase in NEs will, thus, result in significantly increasing the Management-Plane (M-Plane) data collected from the NEs. Therefore, the conventional centralized Network Management Systems (NMSs) will face fundamental challenges in processing the M-Plane data. In this paper, we present the concept of Quality of Monitoring (QoM) as a solution, which is able to reduce the M-Plane data already at the NEs. First, QoM aggregates the raw M-Plane data into Key Performance Indicators (KPIs). To these KPIs, the QoM applies a data-driven algorithm to define information loss limits for QoM classes specific for each KPI time series. Then, the QoM applies the classes for compressing the KPI data utilizing a lossy-compression method, which is a derivative of the Piece-Wise Constant Approximation (PWCA) algorithm. To evaluate the performance of the QoM solution, we use M-Plane raw data from a live LTE network and calculate four KPIs, while each KPI has different statistical characteristics. We also define three QoM classes named Exact, Optimized, and Sharp. For all KPIs, the class Optimized has a higher compression rate than the class Exact, while the class Sharp has the highest compression rate. Assuming that, for example, NEs of a network produce 280 MB of raw data containing information that needs to be transferred to the network operations center; we use KPIs to represent the information contents of the data, and QoM solution to transfer the data over the network. As a result, the QoM solution achieves an estimated 95% compression gain from the raw data in transfer.
  • Seitola, Teija; Silen, Johan; Järvinen, Heikki (2015)
    In this article, we introduce a new algorithm called randomised multichannel singular spectrum analysis (RMSSA), which is a generalisation of the traditional multichannel singular spectrum analysis (MSSA) into problems of arbitrarily large dimension. RMSSA consists of (1) a dimension reduction of the original data via random projections, (2) the standard MSSA step and (3) a recovery of the MSSA eigenmodes from the reduced space back to the original space. The RMSSA algorithm is presented in detail and additionally we show how to integrate it with a significance test based on a red noise null-hypothesis by Monte-Carlo simulation. Finally, RMSSA is applied to decompose the 20th century global monthly mean near-surface temperature variability into its low-frequency components. The decomposition of a reanalysis data set and two climate model simulations reveals, for instance, that the 2-6 yr variability centred in the Pacific Ocean is captured by all the data sets with some differences in statistical significance and spatial patterns.