Microwave models of snow characteristics for remote sensing

One of the key problems of microwave remote sensing is the development of theoretical microwave models for terrain such as soil, vegetation, snow, forest, etc., due to the complexity of modeling of microwave interaction with the terrain. In this thesis this problem is approached from the new point of view of both empirical models and rigorous theoretical models. New information concerning radar remote sensing of snow-covered terrain and permittivity of snow has been produced. A C-band semi-empirical backscattering model is presented for the forest-snow-ground system. The effective permittivity of random media such as snow, vegetation canopy, soil, etc., describes microwave propagation and attenuation in the media and is a very important parameter in modeling of microwave interaction with the terrain. Good permittivity models are needed in microwave emission and scattering models of terrain. In this thesis, the strong fluctuation theory is applied to calculate the effective permittivity of wet snow. Numerical results for the effective permittivity of wet snow are illustrated. The results are compared with the semi-empirical and the theoretical models. A comparison with experimental data at 6, 18 and 37 GHz is also presented. The results indicate that the model presented in this work gives reasonably good accuracy for calculating the effective permittivity of wet snow. Microwave emission and scattering theoretical models of wet snow are developed based on the radiative transfer and strong fluctuation theory. It is shown that the models agree with the experimental data.reviewe

Ecosystem Services Related to Carbon Cycling - Modeling Present and Future Impacts in Boreal Forests

Forests regulate climate, as carbon, water and nutrient fluxes are modified by physiological processes of vegetation and soil. Forests also provide renewable raw material, food, and recreational possibilities. Rapid climate warming projected for the boreal zone may change the provision of these ecosystem services. We demonstrate model based estimates of present and future ecosystem services related to carbon cycling of boreal forests. The services were derived from biophysical variables calculated by two dynamic models. Future changes in the biophysical variables were driven by climate change scenarios obtained as results of a sample of global climate models downscaled for Finland, assuming three future pathways of radiative forcing. We introduce continuous monitoring on phenology to be used in model parametrization through a webcam network with automated image processing features. In our analysis, climate change impacts on key boreal forest ecosystem services are both beneficial and detrimental. Our results indicate an increase in annual forest growth of about 60% and an increase in annual carbon sink of roughly 40% from the reference period (1981-2010) to the end of the century. The vegetation active period was projected to start about 3 weeks earlier and end ten days later by the end of the century compared to currently. We found a risk for increasing drought, and a decrease in the number of soil frost days. Our results show a considerable uncertainty in future provision of boreal forest ecosystem services.peerReviewe

Networked web-cameras monitor congruent seasonal development of birches with phenological field observations

Ecosystems' potential to provide services, e.g. to sequester carbon, is largely driven by the phonological cycle of vegetation. Timing of phenological events is required for understanding and predicting the influence of climate change on ecosystems and to support analyses of ecosystem functioning. Analyses of conventional camera time series mounted near vegetation has been suggested as a means of monitoring phenological events and supporting wider monitoring of phenological cycle of biomes that is frequently done with satellite earth observation (EO). Especially in the boreal biome, sparsely scattered deciduous trees amongst conifer-dominant forests pose a problem for EO techniques as species phenological signal mix, and render EO data difficult to interpret. Therefore, deriving phonological information from on the ground measurements would provide valuable reference data for earth observed phonology products in a larger scale. Keeping this in mind, we established a network of digital cameras for automated monitoring of phenological activity of vegetation in the boreal ecosystems of Finland. Cameras were mounted at 14 sites, each site having 1-3 cameras. In this study, we used data from 12 sites to investigate how well networked cameras can detect the phenological development of birches (Betula spp.) along a latitudinal gradient. Birches typically appear in small quantities within the dominant species. We tested whether the small, scattered birch image elements allow a reliable extraction of colour indices and the temporal changes therein. We compared automatically derived phenological dates from these birch image elements both to visually determined dates from the same image time series and to independent observations recorded in the phenological monitoring network covering the same region, Automatically extracted season start dates, which were based on the change of green colour fraction in spring, corresponded well with the visually interpreted start of the season, and also to the budburst dates observed in the field. Red colour fraction turned out to be superior to the green colour-based indices in predicting leaf yellowing and fall. The latitudinal gradients derived using automated phenological date extraction corresponded well with the gradients estimated from the phenological field observations. We conclude that small and scattered birch image elements allow reliable extraction of key phonological dates for the season start and end of deciduous species studied here, thus providing important species-specific data for model validation and for explaining the temporal variation in EO phenology products.Peer reviewe

Monitoring changes in forestry and seasonal snow using surface albedo during 1982-2016 as an indicator

Short summary The surface albedo time series CLARA-A2 SAL was used to study trends in the timing of the melting season of snow and preceding albedo value in Finland during 1982–2016 to assess climate change. The results were in line with operational snow depth data, JSBACH land ecosystem model, SYKE fractional snow cover and greening-up data. In the north a clear trend to earlier snowmelt onset, increasing melting season length, and decrease in pre-melt albedo (related to increased stem volume) was observed.The surface albedo time series, CLARA-A2 SAL, was used to study trends in the snowmelt start and end dates, the melting season length and the albedo value preceding the melt onset in Finland during 1982–2016. In addition, the melt onset from the JSBACH land surface model was compared with the timing of green-up estimated from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Moreover, the melt onset was compared with the timing of the greening up based on MODIS data. Similarly, the end of snowmelt timing predicted by JSBACH was compared with the melt-off dates based on the Finnish Meteorological Institute (FMI) operational in situ measurements and the Fractional Snow Cover (FSC) time-series product provided by the EU FP7 CryoLand project. It was found that the snowmelt date estimated using the 20 % threshold of the albedo range during the melting period corresponded well to the melt estimate of the permanent snow layer. The longest period, during which the ground is continuously half or more covered by snow, defines the permanent snow layer (Solantie et al., 1996). The greening up followed within 5–13 days the date when the albedo reached the 1 % threshold of the albedo dynamic range during the melting period. The time difference between greening up and complete snowmelt was smaller in mountainous areas than in coastal areas. In two northern vegetation map areas (Northern Karelia–Kainuu and Southwestern Lapland), a clear trend towards earlier snowmelt onset (5–6 days per decade) and increasing melting season length (6–7 days per decade) was observed. In the forested part of northern Finland, a clear decreasing trend in albedo (2 %–3 % per decade in absolute albedo percentage) before the start of the melt onset was observed. The decreasing albedo trend was found to be due to the increased stem volume

Preface to the special issue on Monitoring and Modelling of Carbon-Balance-, Water- and Snow-Related Phenomena at Northern Latitudes

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Refining the role of phenology in regulating gross ecosystem productivity across European peatlands

Abstract The role of plant phenology as regulator for gross ecosystem productivity (GEP) in peatlands is empirically not well constrained. This is because proxies to track vegetation development with daily coverage at the ecosystem scale have only recently become available and the lack of such data has hampered the disentangling of biotic and abiotic effects. This study aimed at unraveling the mechanisms that regulate the seasonal variation in GEP across a network of eight European peatlands. Therefore, we described phenology with canopy greenness derived from digital repeat photography and disentangled the effects of radiation, temperature and phenology on GEP with commonality analysis and structural equation modeling. The resulting relational network could not only delineate direct effects but also accounted for possible effect combinations such as interdependencies (mediation) and interactions (moderation). We found that peatland GEP was controlled by the same mechanisms across all sites: phenology constituted a key predictor for the seasonal variation in GEP and further acted as distinct mediator for temperature and radiation effects on GEP. In particular, the effect of air temperature on GEP was fully mediated through phenology, implying that direct temperature effects representing the thermoregulation of photosynthesis were negligible. The tight coupling between temperature, phenology and GEP applied especially to high latitude and high altitude peatlands and during phenological transition phases. Our study highlights the importance of phenological effects when evaluating the future response of peatland GEP to climate change. Climate change will affect peatland GEP especially through changing temperature patterns during plant-phenologically sensitive phases in high latitude and high altitude regions.Peer reviewe

Diffraction by thin dielectric strip and its applicaton to modeling of microwave scattering and absorption by plant elements

TEZ2005Tez (Yüksek Lisans) -- Çukurova Üniversitesi, Adana, 1995.Kaynakça (s. 36-37) var.iv, 37 s. ; 30 cm.

Special issue on remote sensing of snow and its applications

Snow cover is an essential climate variable directly affecting the Earth's energy balance. Snow cover has a number of important physical properties that exert an influence on global and regional energy, water, and carbon cycles. Remote sensing provides a good understanding of snow cover and enable snow cover information to be assimilated into hydrological, land surface, meteorological, and climate models for predicting snowmelt runoff, snow water resources, and to warn about snow-related natural hazards. The main objectives of this Special Issue, Remote Sensing of Snow and Its Applications in Geosciences are to present a wide range of topics such as (1) remote sensing techniques and methods for snow, (2) modeling, retrieval algorithms, and in-situ measurements of snow parameters, (3) multi-source and multi-sensor remote sensing of snow, (4) remote sensing and model integrated approaches of snow, and (5) applications where remotely sensed snow information is used for weather forecasting, flooding, avalanche, water management, traffic, health and sport, agriculture and forestry, climate scenarios, etc. It is very important to understand (a) differences and similarities, (b) representativeness and applicability, (c) accuracy and sources of error in measuring of snow both in-situ and remote sensing and assimilating snow into hydrological, land surface, meteorological, and climate models. This Special Issue contains nine articles and covers some of the topics we listed above

Online Hybrid Learning Methods for Real-Time Structural Health Monitoring Using Remote Sensing and Small Displacement Data

Structural health monitoring (SHM) by using remote sensing and synthetic aperture radar (SAR) images is a promising approach to assessing the safety and the integrity of civil structures. Apart from this issue, artificial intelligence and machine learning have brought great opportunities to SHM by learning an automated computational model for damage detection. Accordingly, this article proposes online hybrid learning methods to firstly deal with some major challenges in data-driven SHM and secondly detect damage via small displacement data from SAR images in a real-time manner. The proposed methods contain three main parts: (i) data augmentation by Hamiltonian Monte Carlo and slice sampling for addressing the problem of small displacement data, (ii) data normalization by an online deep transfer learning algorithm for removing the effects of environmental and/or operational variability from augmented data, and (iii) feature classification via a scalar novelty score. The major contributions of this research include proposing two online hybrid unsupervised learning methods and providing effective frameworks for online damage detection. A small set of displacement samples extracted from SAR images of TerraSar-X regarding a long-term monitoring scheme of the Tadcaster Bridge in United Kingdom is applied to validate the proposed methods