Browsing by Author "Rautiainen, Miina, Prof., Aalto University, Department of Built Environment, Finland"

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  • Clumping in Forest Radiation Regime Models
    (2022) Schraik, Daniel
     School of Engineering | Doctoral dissertation (article-based)
    Forest structure is realized in a variety of ways in forest radiation regime models. Depending on model complexity, canopy structure can be quantified by simple parameters such as leaf area index, describing the density of canopies, and an associated clumping index, quantifying the degree of deviation of the spatial distribution of leaves from a uniform distribution, or by realistic three-dimensional models of tree crowns in ray tracing models, for example. Complex models, while being more realistic, are generally difficult to apply to large scale and high temporal frequency observations, as is common in passive optical remote sensing. To this end, simpler forest radiation regime models are useful, and complex models provide valuable insights for calibrating, improving, or training simpler models. The aim of this dissertation is to use a high degree of spatial complexity to analyze how simpler parameters of forest structure behave in different types of forests. In simpler models, forest structure is described through the leaf area index and clumping index. These two variables are capable of quantifying forest structure, however, the clumping index is typically difficult to measure. In the so-called spectral invariants theory, forest structure is summarized by a single parameter, the photon recollision probability, i.e. the probability that a photon, upon being scattered by a canopy element, will again interact with the canopy. Closely associated to this parameter is the silhouette to total area ratio (STAR), i.e. the ratio between the projected area of a body and its total surface area. STAR has commonly been used in shoot clumping correction and been studied in simulated tree crowns. In this dissertation I developed a method to measure STAR using terrestrial laser scanning (TLS) point clouds, and expanded its application to forest stands. I used this method to assess the capability of TLS to analyze forest structure in general, and clumping in particular. To this end, I validated the STAR measurement method using individual tree crowns and destructive reference measurements, and measured STAR at the stand level in 38 forest stands to explore the range of both STAR and clumping index that can be found in different boreal, hemiboreal, and temperate forests. Since STAR is a driver of the forest radiation regime, I explored the relationship between stand STAR and forest reflectance. Forest reflectance was closely related to STAR in spectral regions that are known to be sensitive to forest structure, i.e., especially the red portion of the visible spectrum. In addition, I inverted the forest reflectance model PARAS and showed that realistic prior distributions are required to obtain unbiased LAI estimates. I have shown that TLS may accurately measure stand level STAR, thus allowing to quantify forest structure and clumping in forest radiation regime models. This new method can help to improve forest structure quantification in forest radiation regime models as well as in the measurement of leaf area index in general, and thus contribute to remote sensing applications of forests.
  • Drone-based spectral and 3D remote sensing applications for forestry and agriculture
    (2021) Näsi, Roope
     School of Engineering | Doctoral dissertation (article-based)
    Practising sustainable agriculture and forestry requires information on the state of forests and crops to support management. In precision agriculture, crops are observed in order to treat them precisely in the right place and at the right time, saving both production costs and the environment. Similarly, in forests, information on the composition and state of forest health are crucial to enable their sustainable management. In particular, climate-change-driven insect pests have increased, but economic and ecological losses can be reduced by the right actions if up-to-date and precise information on the health of forests is available. In recent years, drones with cameras have evolved into a flexible way to collect remote sensing data locally. Spectral cameras provide accurate information about the reflection properties of objects, and photogrammetric methods also provide a cost-effective way to collect three-dimensional (3D) data from an object. The objective of this work was to develop and assess drone-based 3D and spectral remote sensing techniques to classify the health status of individual trees and to estimate crop biomass, various biochemical parameters such as nitrogen content, and grass-feeding quality. The work developed a processing chain in which spectral and 3D features were extracted from remote sensing data. Then, combining the features with observations and reference measurements collected from plants, machine learning models were developed for tree health classification and estimation of crop-related parameters.  The effects of different factors related to data collection and processing on classification and estimation accuracies were studied in order to generate knowledge on optimal sensors and methods. In general, radiometric corrections, spectral resolution, and the combined use of spectral and 3D features improved classification and estimation accuracies. However, the optimal sensors as well as the data collection and processing methods depend on the different applications and their accuracy requirements. This work was the first to demonstrate the ability of drone hyperspectral data to map the health status of a forest by classifying individual trees infested by bark beetles. The results of the work also showed that drone-based mapping offers a great tool to estimate agricultural crop parameters which can be applied to the optimization of various precision agriculture tasks.
  • The effect of forest canopy on remote sensing observations in the boreal region
    (2020) Cohen, Juval
     School of Engineering | Doctoral dissertation (article-based)
    Remote 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.
  • Hyperspectral imaging of tree stems
    (2023) Juola, Jussi
     School of Engineering | Doctoral dissertation (article-based)
    With the recent development of portable hyperspectral imaging spectrometers, there is a unique opportunity to acquire detailed information on the spectral properties of individual forest canopy elements, such as tree stems. Information on the spectral properties of stem bark is important for remote sensing of forests, biodiversity mapping, and forestry applications. In remote sensing, information on the fine scale physical interactions between shortwave solar radiation and stem bark is needed to interpret remotely sensed signals of forests more accurately. For biodiversity mapping and forestry applications, information on the spectral properties of stem bark could be used to support accurate identification of tree species. The aim of this dissertation was to develop close-range imaging spectroscopy as a method to measure the reflectance of stem bark in laboratory and field conditions, explore the spectral characteristics of stem bark, and assess the use of spectral data on bark in the identification of boreal and temperate tree species. The results demonstrated that there is potential in utilizing close-range imaging spectroscopy as a novel data source for studying the spectral characteristics of stem bark in the laboratory and in the field. The reflectance spectra of the measured boreal and temperate tree species had a similar shape in the visible to near-infrared region, but the overall levels of reflectance varied substantially within-and between-species. In general, stem bark reflectance for the measured boreal and temperate tree species was highly variable and anisotropic. Stem bark reflectance was affected by the spatial location of the sample along the stem and by the angular effects associated with the view-illumination geometry. Greatest interspecific differences in stem bark reflectance were in the near-infrared region and varying absorption features were observed at around 670–700 nm. The spectral features of stem bark were robust for identifying tree species but combining them with the texture features extracted from the hyperspectral reflectance images improved results further. The results also underlined the importance of accounting for meteorological and radiation conditions when measuring the spectra of stem bark in field conditions with pushbroom sensor technology. This dissertation gave a unique perspective into the spectral characteristics of stem bark for boreal and temperate tree species. The results contributed towards understanding the spectral diversity of forests more comprehensively and the portable hyperspectral imaging spectrometer showed promising results as a new technology for developing tree species identification methods for the forest industry and biodiversity mapping applications.
  • Remotely sensed monitoring of land surface albedo and ecosystem dynamics  
    (2020) Alibakhshi, Sara
     School of Engineering | Doctoral dissertation (article-based)
    The earth is under unprecedented pressure, which is reflected in rapid ecosystem changes around the globe. Over just the past three decades, the earth has lost over 178 million hectares of its forests. The rapidly growing evidence of the loss of resilience in ecosystems (i.e., recovery from disturbances slows down) due to climate change has become a global concern. Our limited knowledge of ecosystem dynamics and their key parameters, such as albedo, has also hindered our ability to manage ecosystems appropriately. The main aim of this dissertation is to contribute to elucidating ecosystem dynamics by exploiting remotely sensed satellite data. Additionally, this dissertation aims to explore the dynamics of albedo (reflectivity) in response to forest structure (forest density, tree cover, and leaf area index) variations and forest disturbances (fire and drought). To address specific research questions discussed throughout this dissertation, various study sites extending from local to global scales are considered. The results showed that using an appropriate ecosystem state variable that can represent the state of an ecosystem makes it is possible to achieve a reliable and timely evaluation of wetlands or forests state change. To that end, a new remotely sensed index called the modified vegetation water ratio (MVWR) was developed which improved the ability to understand the dynamics of wetlands. A new approach was also developed based on incorporating local spatial autocorrelation. The efficiency of this approach in measuring the state of drought-affected forests was demonstrated. To provide a convenient implementation of the presented approaches, a new R package, "stew", was developed. Investigating the dynamics of forest albedo on disturbances revealed that precipitation and burn severity only weakly explained the temporal dynamics of albedo. In contrast, it was shown that the number of fire events and the leaf area index strongly explained temporal albedo dynamics. According to the results, although fires could lead to abrupt decreases in the temporal dynamics of albedo, droughts caused abrupt increases in the temporal dynamics of albedo. Finally, a global-scale study was conducted to explore the links between forest structure and albedo during the peak growing season. The results demonstrated that forest structure might significantly explain albedo in most of the forests around the world. It was also found that the response of the shortwave albedo (300–5000 nm) to the variations of the leaf area index was always positive. The first map representing the links between forest structure and albedo was provided which highlights the importance of the role of forests in modulating albedo on a global scale. It is expected that the implications of the results of this dissertation contribute to future climate mitigation plans, the monitoring of ecosystem dynamics, and sustainable development in general.
  • Satellite optical remote sensing of forest canopy cover in boreal and tropical biomes
    (2018) Hadi
     School of Engineering | Doctoral dissertation (article-based)
    Continuous field of tree cover, or canopy cover (CC) is an important information required in ecology and international forestry. In ecology, CC quantitatively depicts the spatial heterogeneity of tree cover, and is therefore useful for spatially-explicit characterizations of ecosystem state, changes, structure, and functioning. In international forestry, CC threshold is a main criteria for a consistent definition of forest land cover, thus facilitating a globally comparable forest area statistics. In boreal forests, CC information supports the social and ecological aspects of forest management objectives. In tropical rainforests, CC is a biophysical indicator for forest degradation, and hence supports international climate policy mechanisms. The aim of this dissertation was to investigate the capability of the freely-available medium resolution, passive optical satellite data for estimating CC in boreal and tropical forests. The boreal study area included sites that span a large latitudinal gradient in Finland, encompassing a large variation in forest structure, species composition, and site fertility. The tropical study area included sites which have experienced forest degradation and deforestation, in the Borneo mega-island, South East Asia. The results showed that, in boreal forests, large area CC prediction across multiple Landsat scenes was feasible, with accuracy comparable to single-site (single-scene) CC prediction. Beta regression model with individual red spectral band as predictor was found optimal. Physically-based analysis of the sources of variations in canopy reflectance indicated that, in red band, canopy reflectance was most sensitive to CC variations, in both the boreal and tropical biomes. The new Sentinel-2 data provided a slight improvement in CC prediction accuracy, compared to Landsat-8 data. The improvement was associated with the new 705 nm red edge spectral band. In tropical rainforests, current CC variations due to varying intensities of past selective logging, could not be estimated from Landsat data. Discriminating rainforests with different degrees of past selective logging using Landsat data was not possible, due to similarity in the present canopy structural properties that drive forest reflectance. Finally, sub-annual deforestation monitoring in the insular South East Asia was feasible, using a continuous change detection algorithm based on a consecutive anomalies criterion applied to dense Landsat time series. This dissertation concluded that, in boreal forests, CC estimation accuracy can be improved most logically by accounting for the sources behind the scatter in the relationship between canopy reflectance and CC, using a physically-based approach. It was inferred that the most important sources are the reflectance adjacency effect, variability in understory reflectance, and variability in canopy shadows and scattering. In tropical rainforests, complete or partial changes in CC can be most accurately detected if done immediately as it happens, and thus continuous monitoring with integrated Landsat and Sentinel-2 data is essential. 
  • Water quality monitoring and assessment of the Northern Baltic Sea using Earth Observation
    (2019) Attila, Jenni
     School of Engineering | Doctoral dissertation (article-based)
    This thesis concentrates on determining how accurate Earth Observation (EO), i.e. satellite instrument observation methods are for water quality estimation in the coastal waters of Finland, in the Northern Baltic Sea. Water quality refers to the characteristics of water that define its chemical, physical or biological properties. These are used to assess the ecological and chemical status of the waters based on various environmental regulations, such as the European Union (EU) Water Framework Directive (WFD). In water areas, where the quality is not good, such as in eutrophicated sea areas, the water quality parameters have high spatial and temporal variability. EO observations capability to cover this variability is beyond the possibilities of conventional station-wise water sampling. This is valuable in Finland with numerous coastal and lake water bodies. In this thesis, phytoplankton chlorophyll-a (chl-a) – as a direct indicator of eutrophication and one of the WFD's biological quality elements – is the key water quality parameter. Water quality parameters derived from one airborne and two satellite instruments algorithms are analysed against two types of field measurements: automated flow-through measurement systems and monitoring station water samples. The flow-through systems, either installed on smaller coastal boats or on board merchant ships (ferrybox), provide an efficient way for collecting a wide range of ground truth samples for EO algorithm development and validation. The routine water samples are collected at the station sites throughout the coastal waters of Finland and are used as the basis for the status assessment of water bodies. Finnish coastal waters represent an optically extreme and complex water type: absorption dominated humic waters, where EO algorithms often produce incorrect results. A Quality Grade (QG) method confirmed that the analysed EO model to determine chl-a concentrations is reliable for 62% (80 of the examined 129) coastal water bodies. Summarising results over the coastal monitoring sites confirm that the accuracy of the EO method is close to the determination accuracy of station sampling (0.6 μg l-1) near the chl-a target for WFD. The difference in statistical metrics calculated by EO and station sampling is 23% – well within the uncertainty limits of chl-a laboratory analyses. The statistical measures (mode and the geometric mean) are optimal for non-normally distributed EO observations for utilising them in the water bodies for assessment. Only in the optically specific estuaries (2.8 % of the examined area 7 out of 129 water bodies), the optical properties of the water can reach the extremes of their ranges and the chl-a estimation fails. This occurs particularly when the loading from the drainage basin is intensive (such as in the melting season in spring and after heavy rains). Moreover, EO algorithms for determining turbidity, absorption of coloured dissolved organic matter and Secchi disk depth were analysed and found to perform well. The work done with past EO instruments – medium resolution satellite and high resolution airborne - anticipated the efficient use of the EU Copernicus programme Sentinel-series instruments for the monitoring of the water areas of Finland.