Leaf Area Index (LAI) estimation of boreal forest using wide optics airborne winter photos

Peer reviewe

Modellering av "Effective Leaf Area Index" med fjärranalysdata

Mapping of e�ective leaf area index (LAIe) over the Swedish boreal forest test site Krycklan (64°N19°E) was performed using ground-based �eld estimates of LAIe and remote sensing data sources. The LAIe data were collected 2017 and 2018 using the LAI-2200 Plant Canopy Analyzer and its later version LAI-2200C Plant Canopy Analyzer. The remote sensing data used were airborne laser scanning (ALS) data, Interferometric Synthetic Aperture Radar (InSAR) data from TanDEM-X, and stereo matched drone images. The stereo matched drone images only covered a small subset of the Krycklan catchment, the ICOS grid area. Point cloud metrics were calculated from the ALS data and the drone data such as height percentiles, intensity percentiles, point cloud density and cover metrics. Three metrics from the TanDEM-X data were evaluated as predictors; interferometric phase height, coherence and backscatter. Estimations were done by �tting regression models of LAIe and the predicting remote sensing data sources. The best ALS regression model for predicting LAIe used the canopy density gap metric, giving an R 2 adj=0.93 for catchment level estimations and R 2 adj=0.97 for the ICOS grid area. The TanDEM-X metric interferometric phase height was the single best predictor of the three InSAR metrics, predicting LAIe with a R 2 adj=0.85 at catchment level and R 2 adj=0.93 at the ICOS grid area. The drone data model included the variables canopy cover gap and the 99th height percentile, which resulted in a R 2 adj value of 0.95. The models were used to generate wall-to-wall rasters and evaluated with the leave-one-out cross validation method. It was concluded that the ALS model was best suited to predict LAIe as it was able to handle varying forestation, which both the other methods struggled with. When applied over mature and homogeneous boreal forest all models performed with similar accuracy

Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties. The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0

Kahden varpukasvin spektrien kaksisuuntaiset heijastussuhdetekijämittaukset

Recent studies have shown the benefits of multiangular remote sensing techniques for characterizing vegetation reflection properties. The study of spectral anisotropy of understory vegetation enables methods for improved plant species identification, and provides valuable input data for radiation scattering models of forests. This thesis presents the applied methods and results of a research effort carried out over the growing season of 2017 for the temporal spectral characterization of two of the economically most important wild berry species in Finland: lingonberry (Vaccinium vitis-idaea) and blueberry (Vaccinium myrtillus). The spectral bidirectional reflectance factor (BRF) data on lingonberry and blueberry shrub samples were collected in a multidirectional measurement geometry using the Finnish Geodetic Institute Goniospectrometer (FIGIFIGO) in laboratory conditions. Leaf reflectance and transmittance spectra on both species were collected with SpectroClip-TR spectral probe. The anisotropic characteristics were analysed in the spectral range from 400 to 2200 nm for view angle dependence (-40° to +40°), illumination angle dependence (+40°, +55°), seasonal dynamics over the growing season (2017), and for berry and flower detection. Both lingonberry and blueberry shrubs have strong backward and notable forward scattering characteristics on the principal plane. In the interspecies comparison, lingonberry is brighter into all view direction in the visible and near infrared wavelengths but darker in the short-wave infrared. Increasing the illumination zenith angle by 15° improves the spectral discrimination of the two dwarf shrub species by inducing a 12% ratio of the spectral responses. Vegetation indices that are commonly used in remote sensing of forests (NDVI, NDVI705, MSI, PSRI) show low sensitivity to the changes in the view- and illumination angles. The presence of lingonberries and lingonberry flowers is indicated as a spectral peak around 679 nm in the spectral ratio of samples with berries or flowers to samples without berries or flowers. It was shown that the analysis of spectral data on the reflectance anisotropy improves the spectral discrimination of the dwarf shrub species. The contribution of the berries on the obtained shrub spectra was shown to be notable enough to justify further studies by applying unmanned aerial vehicle (UAV) platforms. Future studies on the aerial spectral data are suggested to evaluate the potential of berry mapping in larger-scale.Viimeaikaiset tutkimukset ovat osoittaneet monisuunta-spektrometrian hyödyt kasvillisuuden heijastusominaisuuksien karakterisoinnissa kaukokartoituksessa. Aluskasvillisuuden spektrien anisotropian tutkiminen edesauttaa kehittämään menetelmiä kasvilajien tunnistamiseksi ja tarjoaa validointiaineistoa metsien sirontamalleihin. Tämä diplomityö esittää menetelmät ja tulokset Suomen kahden taloudellisesti tärkeimmän luonnonmarjoja tuottavan varpukasvin, mustikan (Vaccinium myrtillus) ja puolukan (Vaccinium vitis-idaea), spektrien temporaalisesta karakterisointikampanjasta kasvukauden 2017 yli. Kaksisuuntainen heijastussuhdetekijä spektriaineisto mitattiin mustikan ja puolukan varpunäytteistä monisuuntamittausgeometriassa FIGIFIGO (Finnish Geodetic Institute Goniospectrometer) goniospektrometrillä laboratorio-olosuhteissa. Lehtien heijastus- ja läpäisyspektrit mitattiin molemmista lajeista käyttäen SpectroClip-TR mittalaitetta. Anisotropiset ominaispiirteet analysointiin aallonpituuksien 400 - 2200 nm välillä katselukulmariippuvuudelle (-40° to +40°), valaistuskulmariippuvuudelle (+40°, +55°), vuodenajan aiheuttamille muutoksille (kasvukausi 2017) sekä marja ja kukintojen tunnistamiselle. Sekä puolukka että mustikka osoittavat voimakasta taaksepäin suuntautuvaa ja huomattavaa eteenpäin suuntautuvaa ominaissirontaa päätasossa. Lajien välisessä vertailussa puolukka on kirkkaampi kaikkiin mitattuihin katselukulmiin näkyvän valon ja lähi-infrapunan aallonpituuksilla, mutta tummempi lyhytaaltoisen infrapunan alueella. Valaistuskulman zeniitin kasvattaminen 15° parantaa lajien spektrien erotettavuutta aiheuttamalla 12 %:n eron lajien heijastusvasteisiin. Yleisesti metsän kaukokartoituksessa käytetyt kasvillisuusindeksit (NDVI, NDVI705, MSI, PSRI) osoittavat matalaa herkkyyttä katselu- ja valaistuskulman muutoksille. Näytteessä olevat puolukanmarjat ja -kukat erottuvat spektrissä piikkinä 679 nm:n kohdalla, kun tarkastellaan marjallisten ja kukallisten näytteiden suhdetta marjattomiin ja kukattomiin. Spektriaineiston heijastus-anisotropian analysoinnin näytettiin edesauttavan varpukasvien erotettavuutta. Marjojen vahva kontribuutio varpunäytteistä mitattuihin spektreihin osoitettiin niin selkeästi, että jatkotutkimuksia UAV (unmanned aerial vehicle) -alustalla voidaan pitää perusteltuina. Ilma-aluksilla kerättyä aineistoa ehdotetaan käytettävän marjojen laajemman kartoituksen potentiaalin selvittämiseksi

Spectrodirectional investigation of a geometric-optical canopy reflectance model by laboratory simulation

Canopy reflectance models (CRMs) can accurately estimate vegetation canopy biophysical-structural information such as Leaf Area Index (LAI) inexpensively using satellite imagery. The strict physical basis which geometric-optical CRMs employ to mathematically link canopy bidirectional reflectance and structure allows for the tangible replication of a CRM's geometric abstraction of a canopy in the laboratory, enabling robust CRM validation studies. To this end, the ULGS-2 goniometer was used to obtain multiangle, hyperspectral (Spectrodirectional) measurements of a specially-designed tangible physical model forest, developed based upon the Geometric-Optical Mutual Shadowing (GOMS) CRM, at three different canopy cover densities. GOMS forwardmodelled reflectance values had high levels of agreement with ULGS-2 measurements, with obtained reflectance RMSE values ranging from 0.03% to 0.1%. Canopy structure modelled via GOMS Multiple-Forward-Mode (MFM) inversion had varying levels of success. The methods developed in this thesis can potentially be extended to more complex CRMs through the implementation of 3D printing

Puistute takseertunnuste hindamine aerolidari mõõtmisandmete põhjal hemiboreaalsetes metsades

A Thesis for applying for the degree of Doctor of Philosophy in Forestry.Forest management and planning requires up-to-date data, which commonly is acquired using field experts and ground measurements. Nowadays, more and more of data about forest stands is measured using remotely sensing methods. Most common methods include aerial photography and laser scanning from airplanes, also spectral measurements from satellites or even drone images and applications. This doctoral thesis focuses on developing applications and methods for utilising the airborne laser scanning (ALS) data that is freely available for the whole Estonia. The ALS measurements are carried out by the Estonian Land Board on a routine basis twice a year – in spring and summer. The first variable that was studied in this thesis was forest height. Based on the thesis, the most reliable method for forest height assessment was using the ALS point-cloud 80th height percentile (HP80). The small circular plot (radius of 15…30 m) and stand based studies showed high correlations with the field-measured forest heights and with great confidence it can be said, that ALS-based forest height estimations are close or even with higher accuracy, than field inspected. The second studied variable was standing wood volume. The ALS-based methods and models that were developed throughout this thesis used the idea, that standing wood volume is based on forest height and density. For this the HP80 and a threshold-based point count ratio was used (canopy cover - CC). ALS-based CC (CCALS) estimates were studied and compared with digital hemispherical photo based measurements. The results showed similar errors as were shown in other similar studies, with around 10-15% root mean square error (RMSE). The strongest correlation was shown using all echoes above a 1.3 metre threshold. Combining the CCALS and HP80 showed standing wood volume estimates with a similar error as we would receive from field measurements (<20%). The freely available multitemporal ALS data showed promising results for forest height growth monitoring and detecting small-scale disturbances. CCALS was shown to have strong predictive value, when compared with a four year difference in thinned and unthinned stands. The nation-wide ALS data can also be combined with forest height predictions from satellites, providing a faster update compared to the ALS data. Promising results were shown using the interferometric synthetic aperture radar (InSAR). Stand species maps generated using self-learning algorithms and satellite based spectral data can be used for developing species specific models of standing wood volume prediction. By combining these different datasets we can construct a nation-wide forest resource to help make better decisions for forest management and targeting fieldwork.Metsades majandamisotsuste langetamiseks ja metsamajanduslike tööde planeerimiseks on metsaomanikel vaja andmeid. Harjumuspäraselt on andmete kogumiseks tehtud metsas maapealseid mõõtmisi. Viimastel aastakümnetel on metsade inventeerimiseks üha enam aga kasutatud mittekontaktseid mõõtmisi - lennukitelt tehtavad aerofotosid, laserskaneerimist, satelliitidelt tehtavaid kiirgusmõõtmisi või viimastel aastatel ka droonidelt tehtud pilte. Antud doktoritöö on võtnud fookusesse aerolaserskaneerimise (ALS) andmete põhjal Eesti metsadesse sobilike rakenduste väljatöötamise. ALS mõõtmisi teeb Eesti Maa-amet rutiinsete lendude käigus kaks korda aastas, nii kevadel kui ka suvel. Aastast 2008 alustatud mõõtmiste tulemusel on Eesti üks väheseid riike maailmas, kus on vabalt kasutada mitmekordselt kogu riiki kattev ALS andmestik. Doktoritöö tulemusel töötati välja metsa kõrguse hindamiseks sobilikud meetodid, kasutades selleks punktipilvede kõrgusprotsentiile. Tugevamaid seoseid metsas proovitükkidel mõõdetud kõrgustega näitas punktipilve 80-protsentiil (HP80) ja uuringute põhjal võib väita, et metsa kõrguse määramine suvistelt aerolidari andmetelt on ligilähedane täpsustele, mida saadakse metsas kohapeal mõõtes. Teine oluline tunnus, mida metsade majandamise planeerimisel silmas peetakse, on kasvava metsa tagavara. Teadustöö põhjal töötati välja mudelite kujud ja metoodika, mille abil prognoositud tagavara oli sarnase veapiiriga, mis on lubatud metsas hinnanguid tegevatele taksaatoritele (<20%). Väljatöötatud ALS-põhine mudeli kuju järgib loogikat, et metsa tagavara on otseselt seotud mõõdetud kõrguse ja metsa tihedusega. Tihenduse hindamiseks aerolidari andmetelt kasutatakse nivoopõhist punktide suhtearvu ehk nn katvushinnangut (CCALS). Katvushinnangu täpsuse valideerimiseks ja tihedas metsas sobiva prognoosimeetodi väljatöötamiseks tehti välimõõtmisi kasutades poolsfäärikaameraid. Poolsfääripiltide põhjal tehtud valideerimise tulemused andsid sarnaseid veahinnanguid, mida on ka varasemates teadusuuringutes esitletud (RMSE = 10…15%). Kahe sarnasest fenoloogilisest perioodist ALS andmestiku lahutamisel uuriti ka muutuste tuvastamise võimalikkust. Uuringud andsid paljulubavaid tulemusi metsade kõrguskasvu hindamiseks ja CCALS osutus ka oluliseks tunnuseks väiksemate häiringute, nagu näiteks harvendusraie, tuvastamiseks. Kogu riiki katva ALS andmestiku kombineerimisel erinevate satelliitandmetega või näiteks spektraalsete mõõtmiste põhjal tehtud puistu liigiliste koosseisu kaartidega on võimalik antud töös välja pakutud meetodite abil anda igal aastal kogu Eesti metsaressursside ülevaade. Samuti on võimalik koostada vaid kaugseirevahendeid ja proovitükkidel lähendatud mudeleid kasutades eraldiste põhised takseerkirjeldused, mida siis taksaatorid saavad näiteks kasutada oma välitööde kavandamisel.  Publication of this thesis is supported by the Estonian University of Life Sciences

Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations