Biomass estimation as a function of vertical forest structure and forest height. Potential and limiations for remote sensing (radar and LiDAR)

Forest biomass stock, spatial distribution and dynamics are unknown parameters for many regions of the world. Today’s information is largely based on ground measurements on a plot basis without coverage in many remote regions that are fundamental for the global carbon cycle. Thus, a method capable of quantifying biomass by means of Remote Sensing (RS) could help to reduce these uncertainties and contribute to a better understanding of it. In this study the capacity to improve the estimation of above-ground biomass (AGB) with a new approach based on forest vertical structure and its potential to improve RS estimations is analyzed. Height to biomass allometry allows biomass estimations from remote sensing systems capable to resolve forest height (LiDAR and polarimetric SAR interferometry (Pol-InSAR)). However, this approach meets its limitations for forest ecosystems under changing conditions in density and structure. To improve biomass estimation accuracy, additional parameters need to be measured. Pol-InSAR and LiDAR allow getting besides forest height vertical backscattering profiles which are connected to forest vertical structure. Thus, due to the relation between structural parameters and AGB expressed by the Structure to Biomass allometry, AGB can be potentially inverted from these systems. The best characterization of forest vertical structure is obtained using the Legendre polynomials. Biomass profiles can be then characterized by the decomposition into a set of Legendre-Fourier basis functions. This method is able to accurately reconstruct vertical biomass profiles with low frequency features. Vertical backscattering profiles are strongly dependent on the sensor used as the resulting profiles are very sensitive to the wavelength and system geometry. E.g. LiDAR profiles are more sensitive to leaves and crowns while Pol-InSAR tends to reconstruct more the woody compartments (stems and branches). In this study, vertical backscattering profiles from short footprint airborne LiDAR and Pol-InSAR data are evaluated for their potential to reconstruct vertical forest structure. With the Legendre decomposition it is possible to parameterize the vertical backscattering profiles and relate them to forest biomass; even though for each remote sensing system different calibration methodologies must be derived. A first step is achieved using the calibration of backscattering signal with known biomass levels showing optimum results. In order to reduce the need of known parameters a new calibration methodology that exploits height to biomass allometric relations has been derived. Inversions using this methodology are tested for LiDAR and SAR profiles showing good correlations for an optimum subset of samples. As each system (frequency) is sensitive to certain biomass components an underestimation is generally expected. Research in this area is ongoing and will be presented with special focus on each system capacity to reconstruct forest vertical biomass distribution for broader sets of samples

Quantifying Temporal Decorrelation over Boreal Forest at L- and P-band

Temporal decorrelation is probably the most critical factor towards a successful implementation of Pol-InSAR parameter inversion techniques in terms of repeat-pass InSAR scenarios. In this paper the effect and impact of temporal decorrelation at L- and P-band is quantified. For this, data acquired by DLR’s E-SAR system in the frame of the BioSAR campaign (initiated and sponsored by the European Space Agency (ESA)) over boreal forest with variable temporal baseline in 2007 in Sweden are analyzed. For validation lidar data and ground measurements data are used

Fostering energy awareness in residential homes using mobile devices

There is considerable global effort being made towards identifying ways of reducing energy consumption to cope with growing demands. Although there is potential for energy saving in many sectors, our focus is on reducing energy consumption in residential homes. We have developed a system which combines home automation and energy usage monitoring technologies. The system offers a range of tools designed for mobile devices to assist users with monitoring their energy usage and provides mechanisms for setting up and controlling home appliances to conserve energy. In this paper we describe our system and a user study we have conducted to evaluate its effectiveness. The findings of the study show the potential benefits of this type of mobile technology

USEM: A ubiquitous smart energy management system for residential homes

With the ever-increasing worldwide demand for energy, and the limited available energy resources, there is a growing need to reduce our energy consumption whenever possible. Therefore, over the past few decades a range of technologies have been proposed to assist consumers with reducing their energy use. Most of these have focused on decreasing energy consumption in the industry, transport, and services sectors. In more recent years, however, growing attention has been given to energy use in the residential sector, which accounts for nearly 30% of total energy consumption in the developed countries. Here we present one such system, which aims to assist residential users with monitoring their energy usage and provides mechanisms for setting up and controlling their home appliances to conserve energy. We also describe a user study we have conducted to evaluate the effectiveness of this system in supporting its users with a range of tools and visualizations developed for ubiquitous devices such as mobile phones and tablets. The findings of this study have shown the potential benefits of our system, and have identified areas of improvement that need to be addressed in the future

Assisting Inhabitants of Residential Homes with Management of Their Energy Consumption

Although there are already a range of energy monitoring and automation systems available in the market that target residential homes, mostly with the aim of reducing their total energy consumption, very few of these systems are directly concerned with how those energy savings are actually made. As such, these systems do not provide tools that would allow users to make intelligent decisions about their energy usage strategies, and encourage them to change their energy use behaviour. In this paper we describe a system designed to facilitate planning and control of energy usage activities in residential homes. We also report on a user study of this system which demonstrates its potential for making energy savings possible

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Dual Pol-InSAR Forest Height Estimation By Means Of TANDEM-X Data

The TanDEM-X mission [1] provides for the first time single pass (single- and dual-) polarimetric interferometric data from space. This allows the acquisition and analysis of Pol-InSAR data without the disturbing effect of temporal decorrelation on a global scale. Polarimetric interferometric X-band data are now available for different forest ecosystems (from boreal to tropics) in different seasons. The penetration capability of X-band in vegetation is limited and depends strongly on the corresponding forest conditions. However, first data analysis showed sufficient penetration to apply Pol-InSAR height estimation at least for a boreal forest scenario [2]. The limitations of X-band for forest parameter estimation can be identified by analyzing data sets from different forest types. Additionally seasonal effects like leaf fall or freezing conditions may change the backscattering behavior or penetration capability of X-band for forests. It was already shown that the ability to penetration depends on the seasonal stage of a forest. Figure 1 on the left shows the penetration depth of X-band by means of boreal forest (Krycklan forest northern Sweden) for a summer (Figure 1 left side) and a winter (Figure 1 middle) acquisition. Penetration depth of the winter acquisition is with a mean of 11.8m significantly larger than for the summer acquisition with a mean of 9.45m. This is probably due the lower dielectricity of the tree compartments in frozen conditions

Potential of forest height estimation using X band by means of two different inversion scenarios

Polarimetric SAR Interferometry (Pol-InSAR) is a powerful remote sensing method for forest height estimation by using the random volume over ground model (RVoG). At higher frequencies implementation of forest height estimation in X band is limited to less dense and low forest types where X band is able to penetrate through the volume to ground. However, the penetration depth at X band is sufficient to cover all forest types. In the paper height inversion at X band using two different approaches is demonstrated with focus on the impact of extinction on forest height estimation