Semi-empirical modeling of the scene reflectance of snow-covered boreal forest : Validation with airborne spectrometer and LIDAR observations

This work aims at the development and validation of a zeroth order radiative transfer (RT) approach to describe the visible band (555 nm) reflectance of conifer-dominated boreal forest for the needs of remote sensing of snow. This is accomplished by applying airborne and mast-borne spectrometer data sets together with high-resolution information on forest canopy characteristics. In case of aerial spectrometer observations, tree characteristics determined from airborne LIDAR observations are applied to quantify the effect of forest canopy on scene reflectance. The results indicate that a simple RT model is feasible to describe extinction and reflectance properties of both homogeneous and heterogeneous forest scenes (corresponding to varying scales of satellite data footprints and varying structures of forest canopies). The obtained results also justify the application of apparent forest canopy transmissivity to describe the influence of forest to reflectance, as is done e.g. in the SCAmod method for the continental scale monitoring of fractional snow cover (FSC) from optical satellite data. Additionally, the feasibility of the zeroth order RT approach is compared with the use of linear mixing model of scene reflectance. Results suggest that the nonlinear RT approach describes the scene reflectance of a snow-covered boreal forest more realistically than the linear mixing model (in case when shadows on tree crowns and surface are not modeled separately, which is a relevant suggestion when considering the use of models for large scale snow mapping applications). (C) 2014 The Authors. Published by Elsevier Inc.Peer reviewe

The effect of boreal forest canopy to reflectance of snow covered terrain based on airborne imaging spectrometer observations

Volume: 27Peer reviewe

New computational methods for efficient utilisation of public data

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Exploiting the ANN Potential in Estimating Snow Depth and Snow Water Equivalent From the Airborne SnowSAR Data at X- and Ku-Bands

Within the framework of European Space Agency (ESA) activities, several campaigns were carried out in the last decade with the purpose of exploiting the capabilities of multifrequency synthetic aperture radar (SAR) data to retrieve snow information. This article presents the results obtained from the ESA SnowSAR airborne campaigns, carried out between 2011 and 2013 on boreal forest, tundra and alpine environments, selected as representative of different snow regimes. The aim of this study was to assess the capability of X- and Ku-bands SAR in retrieving the snow parameters, namely snow depth (SD) and snow water equivalent (SWE). The retrieval was based on machine learning (ML) techniques and, in particular, of artificial neural networks (ANNs). ANNs have been selected among other ML approaches since they are capable to offer a good compromise between retrieval accuracy and computational cost. Two approaches were evaluated, the first based on the experimental data (data driven) and the second based on data simulated by the dense medium radiative transfer (DMRT). The data driven algorithm was trained on half of the SnowSAR dataset and validated on the remaining half. The validation resulted in a correlation coefficient R ≃ 0.77 between estimated and target SD, a root-mean-square error (RMSE) ≃ 13 cm, and bias = 0.03 cm. ANN algorithms specific for each test site were also implemented, obtaining more accurate results, and the robustness of the data driven approach was evaluated over time and space. The algorithm trained with DMRT simulations and tested on the experimental dataset was able to estimate the target parameter (SWE in this case) with R = 0.74, RMSE = 34.8 mm, and bias = 1.8 mm. The model driven approach had the twofold advantage of reducing the amount of in situ data required for training the algorithm and of extending the algorithm exportability to other test sites

Where do the treeless tundra areas of northern highlands fit in the global biome system: toward an ecologically natural subdivision of the tundra biome

According to some treatises, arctic and alpine sub-biomes are ecologically similar, whereas others find them highly dissimilar. Most peculiarly, large areas of northern tundra highlands fall outside of the two recent subdivisions of the tundra biome. We seek an ecologically natural resolution to this long-standing and far-reaching problem. We studied broad-scale patterns in climate and vegetation along the gradient from Siberian tundra via northernmost Fennoscandia to the alpine habitats of European middle-latitude mountains, as well as explored those patterns within Fennoscandian tundra based on climate–vegetation patterns obtained from a fine-scale vegetation map. Our analyses reveal that ecologically meaningful January–February snow and thermal conditions differ between different types of tundra. High precipitation and mild winter temperatures prevail on middle-latitude mountains, low precipitation and usually cold winters prevail on high-latitude tundra, and Scandinavian mountains show intermediate conditions. Similarly, heath-like plant communities differ clearly between middle latitude mountains (alpine) and high-latitude tundra vegetation, including its altitudinal extension on Scandinavian mountains. Conversely, high abundance of snowbeds and large differences in the composition of dwarf shrub heaths distinguish the Scandinavian mountain tundra from its counterparts in Russia and the north Fennoscandian inland. The European tundra areas fall into three ecologically rather homogeneous categories: the arctic tundra, the oroarctic tundra of northern heights and mountains, and the genuinely alpine tundra of middlelatitude mountains. Attempts to divide the tundra into two sub-biomes have resulted in major discrepancies and confusions, as the oroarctic areas are included in the arctic tundra in some biogeographic maps and in the alpine tundra in others. Our analyses based on climate and vegetation criteria thus seem to resolve the long-standing biome delimitation problem, help in consistent characterization of research sites, and create a basis for further biogeographic and ecological research in global tundra environments.</div

Latvuston vaikutus kaukokartoitushavaintoihin havumetsävyöhykkellä

The public defense on 12th June 2020 at 12:15 will be available via remote technology. Link: https://aalto.zoom.us/j/66926655027 Zoom Quick Guide: https://www.aalto.fi/en/services/zoom-quick-guide Electronic online display version of the doctoral thesis is available by email by request from [email protected] sensing techniques are often used for monitoring various processes in the boreal environment. Typical satellite sensor types for this purpose are Synthetic Aperture Radar (SAR), optical, and passive microwave sensors. Many of the observed targets on the ground are covered by forest canopy. Vegetation considerably influences the signal behavior, especially for the most commonly used microwave and optical wavelengths. It is therefore necessary to consider the effect of forest canopy on the observed signal in order to provide reliable estimations of geophysical phenomena on the ground. Various models describing the interaction of electromagnetic radiation with forest canopy have been developed, but many of these are overly complex with high ancillary data requirements. For retrieval purposes, simple models are preferred. This thesis aims at increasing the understanding of how vegetation, and particularly forest canopy, influence remote sensing observations in boreal environments. The focus is mainly on SAR instruments, but also passive microwave and optical sensors are investigated. The capability of a simple zeroth-order model in simulating the effect of vegetation on the remote sensing signal is first quantified by a spatial analysis of optical, SAR, and passive microwave remote sensing data. Then, the influence of vegetation in SAR remote sensing is further examined through two practical applications; mapping floods under various forest conditions, and detecting soil freezing/thawing in boreal forests. The results demonstrate that despite using a relatively simple model, the extinction of electromagnetic signals in forest canopy was well estimated. Due to both sufficient estimation accuracy and simplicity, the presented model can be considered applicable in near real-time monitoring applications. Floods were well detected in open areas due to specular reflection of the water surface and in dense forests due to double bouncing between flood surface and tree trunks. Yet, in low tree and sparse forest areas, the detection of floods was less successful. The forest backscattering model was capable of separating between the backscatter contributions originating from the ground surface and from the forest canopy, thus enabling the identification of frozen and thawed terrain in forests.Kaukokartoitusmenetelmiä käytetään usein pohjoisten alueiden prosessien seurannassa. Tyypillisiä satelliittiantureita tähän tarkoitukseen ovat synteettisen apertuurin tutkat (SAR), optiset ja passiiviset mikroaaltoanturit. Monet seurannan alla olevista kohteista ovat kuitenkin metsän peitossa. Kasvillisuus, ja erityisesti metsän latvusto vaikuttavat merkittävästi säteilyn etenemiseen, varsinkin yleisimmin käytetyillä mikroaalto- ja optisilla aallonpituuksilla. Näin ollen, jotta luotettavia arvioita maanpäällisistä geofysikaalisista ilmiöistä voitaisiin saada, on syytä huomioida kasvillisuuden vaikutuksia havaittuun säteilyyn. Sähkömagneettisen säteilyn vuorovaikutusta puuston kanssa selittäviä malleja on jo kehitetty, mutta monet niistä ovat liian monimutkaisia ja vaativat runsaasti oheistietoa. Käytännön sovelluksiin suositaan siksi yksinkertaisia malleja. Tämän opinnäytetyön tavoitteena on lisätä ymmärrystä siitä, miten kasvillisuus ja etenkin metsän latvusto vaikuttavat kaukokartoitushavaintoihin pohjoisella havumetsävyöhykkeellä. Painopiste on pääasiassa SAR-tutkissa, mutta myös passiivisia mikroaalto- ja optisia antureita tutkitaan. Yksinkertaisen nollakertomallin kykyä simuloida kasvillisuuden vaikutusta kaukokartoitussignaaliin arvioidaan ensin analysoimalla optisen-, SAR- ja passiivisen mikroaaltoinstrumentin kaukokartoitushavaintoja. Sitten kasvillisuuden vaikutusta SAR-kaukokartoitukseen tutkitaan yksityiskohtaisemmin kahden käytännön sovelluksen avulla; tulvien kartoittaminen eri metsäolosuhteissa ja maaperän jäätymisen/sulamisen havaitseminen havumetsävyöhykkeen metsissä. Tulokset osoittavat, että suhteellisen yksinkertaisesta mallista huolimatta sähkömagneettisten signaalien käyttäytymistä metsien latvustossa onnistuttiin arvioimaan hyvin. Sekä hyvän arviointitarkkuuden että yksinkertaisuuden vuoksi esitettyä mallia voidaan pitää soveltuvana lähes reaaliaikaisissa seurantasovelluksissa. Tulvat havaittiin hyvin avoimilla alueilla veden pinnalla tapahtuvan peiliheijastuksen, sekä tiheissä metsissä tulvan pinnan ja puiden runkojen välisen kaksinkertaisen sironnan ansiosta. Matala- ja harvapuustoisilla metsäalueilla tulvien havaitseminen oli kuitenkin vaikeampaa. Metsän takaisinsirontamalli pystyi erottamaan maan pinnalta ja puustosta peräisin olevat takaisinsirontaosuudet, mikä mahdollisti routaantuneen ja sulan maaston tunnistamisen metsissä

Kasvillisuuden vaikutus lumen sulamiseen ja pinta-albedoon

Surface albedo has a great influence on Earth's energy balance, because it determines the relation between reflected and absorbed solar radiation on the ground. Therefore it is important to examine the factors which have a high influence on the surface albedo. One of these factors is snow cover. The albedo difference between snow covered and snow free ground in tundra areas is usually around 0.6. Therefore, by slowing or delaying the snowmelt process, it is possible to decrease the amount of absorbed solar energy on the earth surface. The main goal in this study is to examine the influence of vegetation on snowmelt timing and through this on the surface albedo in Northern tundra areas. This is done by studying first the vegetation on the ground using Corine Land Cover 2006, Globcover 2009 and Landsat products. Then, statistical analyses of different time series remote sensing products such as MODIS, Globsnow and Landsat, considering the vegetation, snowmelt and surface albedo are done. The test site is located in the Northern parts of Norway and Finland, where tundra vegetation is the most common land type. A comparison of NDVI, fractional snow cover and albedo between the Finnish and the Norwegian side of the test area is done, based on a presumption, that vegetation on the Norwegian side is denser than on the Finnish side. The results of this work indicate that indeed vegetation is more abundant, and also snow melts faster on the Norwegian side. This of course has an affect also on surface albedo, which is lower on the Norwegian side during the melting season. Lower albedo causes higher solar energy absorption on the Norwegian side. The magnitude of this difference in the absorbed energy between the Norwegian and the Finnish side is calculated in this study.Pinta-albedo vaikuttaa suuresti maapallon energiatasapainoon, koska se määrittää maan pinnalta heijastuneen ja pinnalla imeytyneen auringon säteilyn välisen suhteen. Tämän takia on tärkeää tutkia niitä tekijöitä, jotka vaikuttavat olennaisesti maan pinnan albedoon. Yksi albedoon vaikuttavista merkittävistä tekijöistä on lumipeite. Ero lumen ja paljaan maan albedon välillä tundra alueilla on noin 0,6, ja siksi hidastamalla ja lykkäämällä lumen sulamista on mahdollista vähentää maan pinnalla imeytyvän auringosta säteilevän energian määrää. Tämän työn tavoite on tutkia kasvillisuuden määrän vaikutusta lumen sulamisen ajankohtaan, ja sitä kautta myös maan pinnan albedoon pohjoisilla tundra-alueilla. Aihetta selvitetään ensin tutkimalla tutkimusalueen kasvillisuuden ominaisuuksia käyttäen Corine Land Cover 2006-, GlobCover 2009-ja Landsat tuotteita. Sen jälkeen alueen kasvillisuutta, lumen sulantaa ja albedoa analysoidaan käyttämällä niihin liittyvien kaukokartoitustuotteiden aikasarjoja. Tutkimusalue sijoittuu Pohjois-Suomen ja -Norjan alueelle, jossa tundrakasvillisuus on vallitsevaa. Työssä vertaillaan kasvillisuusindeksiä (NDVI) lumen sulamista ja albedoa tutkimusalueella Suomen ja Norjan välillä siitä ennakko-oletuksesta käsin, että Norjan puolella kasvillisuus olisi runsaampaa. Tulokset osoittavat, että kasvillisuuden määrä Norjan puolella on todellakin suurempi, ja että lumi siellä sulaa nopeammin kuin Suomen puolella. Tämä tietenkin vaikuttaa myös pinnan albedoon, joka on sulamiskaudella suurempi Suomen puolella. Työssä myös lasketaan ero Suomen ja Norjan puolella imeytyvän auringon energian välillä, jotta voitaisiin paremmin ymmärtää albedon vaikutusta maan energiatasapainoon

NetCDF files containing raster layers of the Airborne SnowSAR observations land cover (LC), elevation (DEM), canopy cover (CC), tree height (TH) and stem volume (VOL) over the Finnish sites Sodankylä and Saariselkä in march 2011 and winter 2011-2012

NetCDF files containing raster layers of the Airborne SnowSAR observations, land cover (LC), elevation (DEM), canopy cover (CC), tree height (TH) and stem volume (VOL) over the Finnish sites; Sodankylä and Saariselkä. The Airborne SnowSAR observations include the mean and the standard deviation of the X- and Ku-band backscatter (sigma nought) in VV- and VH-polarization, as well as the incidence angle in 10 m pixel size. The airborne data was collected in winter 2011-2012, except mission00 from Sodankylä, which was collected in March 2011