Remote sensing of boreal land cover : estimation of forest attributes and extent

Remote sensing provides methods to infer land cover information over large geographical areas at a variety of spatial and temporal resolutions. Land cover is input data for a range of environmental models and information on land cover dynamics is required for monitoring the implications of global change. Such data are also essential in support of environmental management and policymaking. Boreal forests are a key component of the global climate and a major sink of carbon. The northern latitudes are expected to experience a disproportionate and rapid warming, which can have a major impact on vegetation at forest limits. This thesis examines the use of optical remote sensing for estimating aboveground biomass, leaf area index (LAI), tree cover and tree height in the boreal forests and tundra taiga transition zone in Finland. The continuous fields of forest attributes are required, for example, to improve the mapping of forest extent. The thesis focus on studying the feasibility of satellite data at multiple spatial resolutions, assessing the potential of multispectral, -angular and -temporal information, and provides regional evaluation for global land cover data. Preprocessed ASTER, MISR and MODIS products are the principal satellite data. The reference data consist of field measurements, forest inventory data and fine resolution land cover maps. Fine resolution studies demonstrate how statistical relationships between biomass and satellite data are relatively strong in single species and low biomass mountain birch forests in comparison to higher biomass coniferous stands. The combination of forest stand data and fine resolution ASTER images provides a method for biomass estimation using medium resolution MODIS data. The multiangular data improve the accuracy of land cover mapping in the sparsely forested tundra taiga transition zone, particularly in mires. Similarly, multitemporal data improve the accuracy of coarse resolution tree cover estimates in comparison to single date data. Furthermore, the peak of the growing season is not necessarily the optimal time for land cover mapping in the northern boreal regions. The evaluated coarse resolution land cover data sets have considerable shortcomings in northernmost Finland and should be used with caution in similar regions. The quantitative reference data and upscaling methods for integrating multiresolution data are required for calibration of statistical models and evaluation of land cover data sets. The preprocessed image products have potential for wider use as they can considerably reduce the time and effort used for data processing.Kaukokartoituksella voidaan tuottaa tietoa maanpeitteen ominaisuuksista ja muutoksista laajoilla alueilla. Tietoa maanpeitteestä tarvitaan esimerkiksi ympäristömalleihin, ilmastonmuutoksen vaikutusten seurantaan ja päätöksenteon tueksi. Boreaalisilla metsillä on tärkeä merkitys maapallon ilmastolle ja ne ovat tärkeä hiilinielu. Pohjoisten alueiden ilmaston on ennustettu lämpenevän voimakkaasti ilmastonmuutoksen seurauksena, millä voi olla merkittävä vaikutus metsänrajavyöhykkeen kasvillisuuteen. Väitöskirjassa tarkastellaan optisen alueen satelliittikaukokartoituksen käyttöä metsän ominaisuuksien, kuten biomassan ja puuston peittävyyden arviointiin ja kartoitukseen. Tutkimusalueet sijaitsevat eteläisessä Suomessa ja Pohjois-Suomen metsänrajavyöhykkeessä. Keskeisimpinä tavoitteina oli tutkia satelliittikuva-aineistojen käyttökelpoisuutta ja monikulmaisen ja -aikaisen informaation mahdollisuuksia sekä arvioida globaalien maanpeitetuotteiden luotettavuutta. Satelliittikuva-aineistona käytettiin ASTER, MISR ja MODIS -kuvatuotteita ja vertailuaineistona maastomittauksia, inventointiaineistoja ja maanpeitekarttoja. Tutkimustuloksia voidaan hyödyntää maanpeitteen kartoituksessa ja muutostulkinnassa boreaalisilla alueilla. Korkearesoluutioiset aineistot havainnollistavat kuinka heijastuksen ja biomassan välinen riippuvuus on voimakkaampi harvapuustoisissa tunturikoivikoissa kuin havupuuvaltaisissa metsissä, joiden biomassa on suurempi. Käyttämällä yhdessä kuvioittaista maastoaineistoa ja eri resoluutioisia satelliittikuvia voidaan tuottaa biomassa-arvioita laajoille alueille. Metsänrajavyöhykkeessä monikulmaiset aineistot parantavat metsämuuttujien arvioita vähentäen yliarviointia ongelmallisilla avosoilla ja pensastoisilla alueilla. Myös moniaikainen aineisto parantaa kartoitustarkkuutta. Keskikesän kuvat eivät ole välttämättä ihanteellisimpia kasvipeitteen tulkintaan. Globaalit maanpeitetuotteet osoittautuivat Ylä-Lapissa puutteellisiksi ja niitä tulee käyttää varauksella vastaavilla alueilla, esimerkiksi arvioitaessa metsän laajuutta. Tutkimuksessa korostuivat myös kvantitatiivisen maastoaineiston merkitys maanpeiteaineistojen arvioinnissa sekä maasto- ja satelliittikuva-aineiston yhdistämiseen liittyvät kysymykset. Työssä käytetyt esikäsitellyt kuva-aineistot voivat jatkossa vähentää merkittävästi kuvankäsittelyyn käytettävää aikaa

Målarens musik - Harmoniska metoder för modal musik

Title: Strokes of music - Harmonic methods for modal music. The aim of this paper is to develop harmonic methods for music based on different modes or scales. It begins with an exploration of the music of Claude Debussy and then goes on with other methods and scales and also includes an analytic chapter and results at the end. With "strokes of music" I mean a musical technique, which has some parallels to how a painter can choose colors and palettes to express what he is looking for ? the freedom to choose between colors and palettes, or in music tones and scales ? to move between them by sudden or leading modulations, from the diatonic scales with alternative formations, to the symmetric spheres, to the melodic pentatonic scales and other alternative new formations, using plateauic solutions or chords in diatonic, functional and non-functional progressions. A scale can be used as tonality or subordinated and the harmonic rhythm can be both with no movement and fast paced. Syftet med detta dokument är att utveckla harmoniska metoder för musik som bygger på olika modus eller skalor. Det börjar med en explorativ del i musiken av Claude Debussy och går sedan vidare med andra metoder och skalor och innehåller även ett analytiskt kapitel och slutsatser. Med "Målarens musik" menar jag en musikalisk teknik, som har vissa paralleller till hur en målare kan välja färger och paletter för att uttrycka det han söker ? friheten att välja mellan färger och paletter eller inom musik toner och tonförråd ? växla mellan dem genom plötsliga eller ledande moduleringar från de diatoniska skalorna med alternativa formationer, till symmetriska sfärer, till de melodiska pentatoniska skalorna och andra alternativa nya formationer och använda dem platåiskt eller mer ackordiskt med diatoniska, funktionella och icke-funktionella medel. En skala kan användas som tonalitet eller bara förbipasserande och den harmoniska rytmen kan vara både stillastående och fartfylld

Ammattikorkeakouluopiskelijoiden kiinnostus riistametsänhoitoa kohtaan

Riistametsänhoidolla tarkoitetaan metsänhoidon toteuttamista riistan kannalta suotuisin menetelmin. Viime aikoina aihetta on käsitelty esimerkiksi mediassa aiempaa enemmän, mutta lisääntyneestä huomiosta huolimatta sitä ei metsätalouden koulutuksessa ainakaan vielä ole huomioitu erityisen laajasti. Tämän opinnäytetyön tarkoituksena on kyselytutkimuksella saatujen tulosten pohjalta selvittää ammattikorkeakouluopiskelijoiden suhtautumista riistametsänhoitoon ja sen koulutukseen. Lisäksi lukijalle annetaan tietoa riistametsänhoidosta ja siihen liittyvistä menetelmistä. Opinnäytetyön toimeksiantajana toimi Suomen riistakeskus. Tutkimus toteutettiin internet-pohjaisen Surveypal-sovelluksen avulla kevään 2016 aikana. Kyselylomakkeessa oli 36 kysymystä, jotka liittyivät esimerkiksi opiskelijoiden aiempiin kokemuksiin ja koulutuksessa koettuihin kehitystarpeisiin. Lomake lähetettiin niihin Suomen kuuteen ammattikorkeakouluun, joissa tutkimuksen toteutushetkellä toimi metsätalouden koulutusohjelma. Kyselystä ilmeni, että opiskelijat ovat pääosin hyvin kiinnostuneita riistametsänhoidosta ja toivoisivat aihetta käsiteltävän metsätalouden koulutuksessa nykyistä enemmän. Erityisesti maastokohteisiin tutustumista ja muuta käytännön opiskelua toivottiin lisättävän. Kyselyn perusteella on syytä olettaa riistametsänhoidon suosion kasvavan ja alan toimijoiden järjestämille koulutuksille olevan jatkossa kysyntää.Game-based forest management means taking different game species and their needs into account when carrying out different forest management operations. This topic has recently been widely discussed in the media, but despite its increasing popularity, the game-based forest management still has a relatively small role in forestry education. The purpose of this thesis is to determine if forestry students find game-based forest management interesting, and to sum up their opinions on the education of the topic. It also includes a brief introduction to game-based forest management and its methods. The thesis was commissioned by the Finnish Wildlife Agency. The enquiry was carried out using an application software during the spring of 2016. The question form contained 36 questions, concerning students’ own experiences and opinions on different matters. The enquiry was addressed to the third and fourth year forestry students in six Finnish universities of applied sciences giving education in forestry. The results state that most forestry students are highly interested in game-based forest management, and wish the topic to become more visible in education. One of the most common suggestions was to increase the amount of practical introduction to the matter, such as visiting outdoor locations. It is safe to estimate that game-based forest management will gain more popularity in the future, and that different courses and trainings will gather audience

Seasonal changes in canopy leaf area index and MODIS vegetation products for a boreal forest site in central Finland

Peer reviewe

Recruiting Conventional Tree Architecture Models into State-of-the-Art LiDAR Mapping for Investigating Tree Growth Habits in Structure

Mensuration of tree growth habits is of considerable importance for understanding forest ecosystem processes and forest biophysical responses to climate changes. However, the complexity of tree crown morphology that is typically formed after many years of growth tends to render it a non-trivial task, even for the state-of-the-art 3D forest mapping technology-light detection and ranging (LiDAR). Fortunately, botanists have deduced the large structural diversity of tree forms into only a limited number of tree architecture models, which can present a-priori knowledge about tree structure, growth, and other attributes for different species. This study attempted to recruit Halle architecture models (HAMs) into LiDAR mapping to investigate tree growth habits in structure. First, following the HAM-characterized tree structure organization rules, we run the kernel procedure of tree species classification based on the LiDAR-collected point clouds using a support vector machine classifier in the leave-one-out-for-cross-validation mode. Then, the HAM corresponding to each of the classified tree species was identified based on expert knowledge, assisted by the comparison of the LiDAR-derived feature parameters. Next, the tree growth habits in structure for each of the tree species were derived from the determined HAM. In the case of four tree species growing in the boreal environment, the tests indicated that the classification accuracy reached 85.0%, and their growth habits could be derived by qualitative and quantitative means. Overall, the strategy of recruiting conventional HAMs into LiDAR mapping for investigating tree growth habits in structure was validated, thereby paving a new way for efficiently reflecting tree growth habits and projecting forest structure dynamics.Peer reviewe

Effects of livestock and wildlife grazing intensity on soil carbon dioxide flux in the savanna grassland of Kenya

Publisher Copyright: © 2021 The AuthorsAlthough grazing is the primary land use in the savanna lowland of southern Kenya, the effects of grazing on soil carbon dioxide flux (RS) remain unclear. A 12-month study was conducted from January to December 2020 on the effects of six grazing intensities sites (overgrazed (OG), heavily grazed (HG), moderately grazed (MG), moderately to lightly grazed (M-LG), lightly grazed (LG) and no grazing (NG)) on RS on. A camera trap was used to monitor the total number of animals at each site, indicating the grazing intensity. Weekly measurements of RS were taken using static greenhouse gas chambers along with simultaneous measurements of soil temperature (TS) and volumetric soil water content (WS) (depth of 5 cm). Mean RS at HG, MG, M-LG and LG sites was approximately 15–25% higher than at NG and OG sites (p 45%) than those in the dry seasons, and WS accounted for 71% of the temporal variability in RS (p < 0.05). In addition, the enhanced vegetation index (EVI, interpreted as a proxy for vegetation cover) explained 60% of the variance of RS, and WS and EVI together explained 75%. EVI showed a negative relationship (p < 0.05) with animal intensity, indicating that more grazing reduced vegetation cover and, consequently, soil organic carbon and biomass. Soil bulk density was lower at less grazed sites. While RS variability was unaffected by total nitrogen content, pH, and texture, correspondence analysis demonstrated that the main factors influencing RS dynamics across the year under different grazing intensities were WS and vegetation cover. Our results contribute to closing the existing knowledge gap regarding the effects of grazing intensity on RS in East Africa savannas. Therefore, this information is of great importance in understanding carbon cycling in savanna grassland, as well as the identification of the potential consequences of increasing land pressure caused by rising livestock numbers, and will assist in the development of climate-smart livestock management in East Africa.Peer reviewe

Impact of Preprocessing on Tree Canopy Cover Modelling : Does Gap-Filling of Landsat Time Series Improve Modelling Accuracy?

Preprocessing of Landsat images is a double-edged sword, transforming the raw data into a useful format but potentially introducing unwanted values with unnecessary steps. Through recovering missing data of satellite images in time series analysis, gap-filling is an important, highly developed, preprocessing procedure, but its necessity and effects in numerous Landsat applications, such as tree canopy cover (TCC) modelling, are rarely examined. We address this barrier by providing a quantitative comparison of TCC modelling using predictor variables derived from Landsat time series that included gap-filling versus those that did not include gap-filling and evaluating the effects that gap-filling has on modelling TCC. With 1-year Landsat time series from a tropical region located in Taita Hills, Kenya, and a reference TCC map in 0–100 scales derived from airborne laser scanning data, we designed comparable random forest modelling experiments to address the following questions: 1) Does gap-filling improve TCC modelling based on time series predictor variables including the seasonal composites (SC), spectral-temporal metrics (STMs), and harmonic regression (HR) coefficients? 2) What is the difference in TCC modelling between using gap-filled pixels and using valid (actual or cloud-free) pixels? Two gap-filling methods, one temporal-based method (Steffen spline interpolation) and one hybrid method (MOPSTM) have been examined. We show that gap-filled predictors derived from the Landsat time series delivered better performance on average than non-gap-filled predictors with the average of median RMSE values for Steffen-filled and MOPSTM-filled SC’s being 17.09 and 16.57 respectively, while for non-gap-filled predictors, it was 17.21. MOPSTM-filled SC is 3.7% better than non-gap-filled SC on RMSE, and Steffen-filled SC is 0.7% better than non-gap-filled SC on RMSE. The positive effects of gap-filling may be reduced when there are sufficient high-quality valid observations to generate a seasonal composite. The single-date experiment suggests that gap-filled data (e.g. RMSE of 16.99, 17.71, 16.24, and 17.85 with 100% gap-filled pixels as training and test datasets for four seasons) may deliver no worse performance than valid data (e.g. RMSE of 15.46, 17.07, 16.31, and 18.14 with 100% valid pixels as training and test datasets for four seasons). Thus, we conclude that gap-filling has a positive effect on the accuracy of TCC modelling, which justifies its inclusion in image preprocessing workflows.Peer reviewe

A method for predicting large-area missing observations in Landsat time series using spectral-temporal metrics

Combined with increasing computing ability, the free and open access to Landsat archive has enabled the changes on the Earth’s surface to be monitored for almost 50 years. However, due to missing observations that result from clouds, cloud shadows, and scan line corrector failure, the Landsat data record is neither a continuous nor consistent time series. We present a new gap-filling method, Missing Observation Prediction based on Spectral-Temporal Metrics (MOPSTM), which uses spectral-temporal metrics computed from Landsat one-year time series and the k-Nearest Neighbor (k-NN) regression. Herein, we demonstrate the performance of MOPSTM by using five, nearly cloud-free, full scene Landsat images from Kenya, Finland, Germany, the USA, and China. Cloud masks from the images with extensive cloud cover were used to simulate large-area gaps, with the highest value we tested being 92% of missing data. The gap-filling accuracy was assessed quantitatively considering all five sites and different land use/land cover types, and the MOPSTM algorithm performed better than the spectral angle-mapper based spatiotemporal similarity (SAMSTS) gap-filling algorithm. The mean RMSE values of MOPSTM were 0.010, 0.012, 0.025, 0.012, and 0.018 for the five sites, while those of SAMSTS were 0.011, 0.017, 0.038, 0.014, and 0.023, respectively. Furthermore, MOPSTM had mean coefficient of determination (R2) values of 0.90, 0.86, 0.78, 0.92, and 0.89, which were higher than those for SAMSTS (0.84, 0.75, 0.55, 0.89, and 0.83). The performance of MOPSTM was not considerably affected by image gap sizes as images ranging from gap sizes of 51% of the image all the way to 92% of the image yielded similar gap-filling accuracy. Also, MOPSTM does not require local parametertuning except for the k values in the k-NN regression, and it can make a gap-free image from any acquisition date. MOPSTM provides a new spectral-temporal approach to generate the gap-free imagery for typical Landsat applications, such as land use, land cover, and forest monitoring.Peer reviewe

Land Surface Temperature Trend and Its Drivers in East Africa

Land surface temperature (LST) is affected by surface-atmosphere interaction. Yet, the degree to which surface and atmospheric factors impact the magnitude of LST trend is not well established. Here, we used surface energy balance, boosted regression tree model, and satellite observation and reanalysis data to unravel the effects of surface factors (albedo, sensible heat, latent heat, and ground heat) as well as incoming radiation (shortwave and longwave) on LST trends in East Africa (EA). Our result showed that 11% of EA was affected by significant (p <0.05) daytime annual LST trends, which exhibited both cooling of -0.19 K year(-1) (mainly in South Sudan and Sudan) and warming of 0.22 K year(-1) (mainly in Somalia and Kenya). The nighttime LST trends affected a large part of EA (31%) and were dominated by significant warming trend (0.06 K year(-1)). Influenced by contrasting daytime and nighttime LST trends, the diurnal LST range reduced in 15% of EA. The modeling result showed that latent heat flux (32%), incoming longwave radiation (30%), and shortwave radiation (23%) were stronger in explaining daytime LST trend. The effects of surface factors were stronger in both cooling and warming trends, whereas atmospheric factors had stronger control only on surface cooling trends. These results indicate the differential control of surface and atmospheric factors on warming and cooling trends, highlighting the importance of considering both factors for accurate evaluation of the LST trends in the future.Peer reviewe

Assessing Leaf Biomass of Agave sisalana Using Sentinel-2 Vegetation Indices

Biomass is a principal variable in crop monitoring and management and in assessing carbon cycling. Remote sensing combined with field measurements can be used to estimate biomass over large areas. This study assessed leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre production in tropical and subtropical regions. Furthermore, the residue from fibre production can be used to produce bioenergy through anaerobic digestion. First, biomass was estimated for 58 field plots using an allometric approach. Then, Sentinel-2 multispectral satellite imagery was used to model biomass in an 8851-ha plantation in semi-arid south-eastern Kenya. Generalised Additive Models were employed to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (explained deviance = 76%, RMSE = 5.15 Mg ha−1) was achieved with ratio and normalised difference VIs based on the green (R560), red-edge (R740 and R783), and near-infrared (R865) spectral bands. Heterogeneity of ground vegetation and resulting background effects seemed to limit model performance. The best performing VI (R740/R783) was used to predict plantation biomass that ranged from 0 to 46.7 Mg ha−1 (mean biomass 10.6 Mg ha−1). The modelling showed that multispectral data are suitable for assessing sisal leaf biomass at the plantation level and in individual blocks. Although these results demonstrate the value of Sentinel-2 red-edge bands at 20-m resolution, the difference from the best model based on green and near-infrared bands at 10-m resolution was rather small