Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling

We used synthetic reflectance spectra generated by a radiative transfer model, PROSPECT-5, to develop statistical relationships between leaf optical and chemical properties, which were applied to experimental data without any readjustment. Four distinct synthetic datasets were tested: two unrealistic, uniform distributions and two normal distributions based on statistical properties drawn from a comprehensive experimental database. Two methods used in remote sensing to retrieve vegetation chemical composition, spectral indices and Partial Least Squares (PLS) regression, were trained both on the synthetic and experimental datasets, and validated against observations. Results are compared to a cross-validation process and model inversion applied to the same observations. They show that synthetic datasets based on normal distributions of actual leaf chemical and structural properties can be used to optimize remotely sensed spectral indices or other retrieval methods for analysis of leaf chemical constituents. This study concludes with the definition of several polynomial relationships to retrieve leaf chlorophyll content, carotenoid content, equivalent water thickness and leaf mass per area using spectral indices, derived from synthetic data and validated on a large variety of leaf types. The straightforward method described here brings the possibility to apply or adapt statistical relationships to any type of leaf.     

Estimating live fuel moisture content from remotely sensed reflectance

Fuel moisture content (FMC) is used in forest fire danger models to characterise the moisture status of the foliage. FMC expresses the amount of water in a leaf relative to the amount of dry matter and differs from measures of leaf water content which express the amount of water in a leaf relative to its area. FMC is related to both leaf water content and leaf dry matter content, and the relationships between FMC and remotely sensed reflectance will therefore be affected by variation in both leaf biophysical properties. This paper uses spectral reflectance data from the Leaf Optical Properties EXperiment (LOPEX) and modelled data from the Prospect leaf reflectance model to examine the relationships between FMC, leaf equivalent water thickness (EWT) and a range of spectral vegetation indices (VI) designed to estimate leaf and canopy water content. Significant correlations were found between FMC and all of the selected vegetation indices for both modelled and measured data, but statistically stronger relationships were found with leaf EWT; overall, the water index (WI) was found to be most strongly correlated with FMC. The accuracy of FMC estimation was very low when the global range of FMC was examined, but for a restricted range of 0-100%, FMC was estimated with a root-mean-square error (RMSE) of 15% in the model simulations and 51% with the measured data. The paper shows that the estimation of live FMC from remotely sensed vegetation indices is likely to be problematic when there is variability in both leaf water content and leaf dry matter content in the target leaves. Estimating FMC from remotely sensed data at the canopy level is likely to be further complicated by spatial and temporal variations in leaf area index (LAI). Further research is required to assess the potential of canopy reflectance model inversion to estimate live fuel moisture content where a priori information on vegetation properties may be used to constrain the inversion process.     

Quantification of bio-physico-chemical properties of intertidal sediment at different spatial scales using remote sensing

Coastal-erosion models require accurate and detailed information on bio-physico-chemical properties of intertidal sediments. In this article, the potential of remotely sensed data at different spatial scales for the quantification of sediment properties was assessed. The focus on specific absorption features of water at 1450 nm, clay at 2206 nm and chlorophyll-a at 673 nm provided a physically based approach and interpretable results. The moisture content in unsaturated sediment could be accurately related to absorption (coefficient of determination r ²?=?0.89 and root-mean-square error of prediction (RMSEP)?=?3% relative gravimetric moisture content) in laboratory and field measurements. The empirical model relating chlorophyll-a content with its field absorption (r ²?=?0.6) was successful on airborne sensor measurements. Though suitable in laboratory conditions, the absorption of shortwave infrared light by clay was not observed in field or airborne spectra. The empirical models showed clear dependency on the study area and sensor, suggesting more research on intercalibration of spectrometers and on factors influencing the interaction of light with sediment properties.     

Learning the “Whys”: Discovering design rationale using text mining — An algorithm perspective

Collecting design rationale (DR) and making it available in a well-organized manner will better support product design, innovation and decision-making. Many DR systems have been developed to capture DR since the 1970s. However, the DR capture process is heavily human involved. In addition, with the increasing amount of DR available in archived design documents, it has become an acute problem to research a new computational approach that is able to capture DR from free textual contents effectively. In our previous study, we have proposed an ISAL (issue, solution and artifact layer) model for DR representation. In this paper, we focus on algorithm design to discover DR from design documents according to the ISAL modeling. For the issue layer of the ISAL model, we define a semantic sentence graph to model sentence relationships through language patterns. Based on this graph, we improve the manifold-ranking algorithm to extract issue-bearing sentences. To discover solution–reason bearing sentences for the solution layer, we propose building up two sentence graphs based on candidate solution-bearing sentences and reason-bearing sentences respectively, and propagating information between them. For artifact information extraction, we propose two term relations, i.e. positional term relation and mutual term relation. Using these relations, we extend our document profile model to score the candidate terms. The performance and scalability of the algorithms proposed are tested using patents as research data joined with an example of prior art search to illustrate its application prospects.     

Non-linear mixture modelling without end-members using an artificial neural network

Many methods of analysing remotely sensed data assume that pixels are pure, and so a failure to accommodate mixed pixels may result in significant errors in data interpretation and analysis. The analysis of data containing a large proportion of mixed pixels may therefore benefit from the decomposition of the pixels into their component parts. Methods for unmixing the composition of pixels have been used in a range of studies and have often increased the accuracy of the analyses. However, many of the methods assume linear mixing and require end-member spectra, but mixing is often non-linear and end-member spectra are difficult to obtain. In this paper, an alternative approach to unmixing the composition of image pixels, which makes no assumptions about the nature of the mixing and does not require end-member spectra, is presented. The method is based on an artificial neural network (ANN) and shown in a case study to provide accurate estimates of sub-pixel land cover composition. The results of this case study showed that accurate estimates of the proportional cover of a class and its areal extent may be made. It was also shown that there was a tendency for the accuracy of the unmixing to increase with the complexity of the network and the intensity of training. The results indicate the potential to derive accurate information from remotely sensed data sets dominated by mixed pixels.     

Data and information as property

Digital technologists concern themselves with data - conventional representations of facts or ideas - and with machines for storing, transforming, and transmitting it. Although computing professionals also concern themselves with digital technology, they focus primarily on people and information-the meaning that people give to data. The use of data to convey information is vitally important to our social systems. This is underlined by recent research showing that dogs are much more able to get meaning from data than chimpanzees, which probably explains why dogs make better pets. The sharing of meaning has been the foundation of social development. The different data technologies have been used both to empower and to constrain members of our society as technology and society have evolved together. Computing professionals should thus always be sensitive to the social uses of data and information. They should also be alert to legal developments related to using data and to digital technology's role in producing data.     

Spatial context mining approach for transport mode recognition from mobile sensed big data

Knowledge about what transport mode people use is important information of any mobility or travel behaviour research. With ubiquitous presence of smartphones, and its sensing possibilities, new opportunities to infer transport mode from movement data are appearing. In this paper we investigate the role of spatial context of human movements in inferring transport mode from mobile sensed data. For this we use data collected from more than 8000 participants over a period of four months, in combination with freely available geographical information. We develop a support vectors machines-based model to infer five transport modes and achieve success rate of 94%. The developed model is applicable across different mobile sensed data, as it is independent on the integration of additional sensors in the device itself. Furthermore, suggested approach is robust, as it strongly relies on pre-processed data, which makes it applicable for big data implementations in (smart) cities and other data-driven mobility platforms.     

Searching in peer-to-peer networks

As peer-to-peer networks are proving capable of handling huge volumes of data, the need for effective search tools is lasting and imperative. During the last years, a number of research studies have been published, which attempt to address the problem of search in large, decentralized networks. In this article, we mainly focus on content and concept-based retrieval. After providing a useful discussion on terminology, we introduce a representative sample of such studies and categorize them according to basic functional and non-functional characteristics. Following our analysis and discussion we conclude that future work should focus on information filtering, re-ranking and merging of results, relevance feedback and content replication as well as on related user-centric aspects of the problem.     

Identifying the controls on coastal cliff landslides using machine-learning approaches

Transformations are underway in our ability to collect and interrogate remotely sensed data. Here we explore the utility of three machine-learning methods for identifying the controls on coastal cliff landsliding using a dataset from Auckland, New Zealand. Models were built using all available data with a resampling approach used to evaluate uncertainties. All methods identify two dominant landslide predictors (unfailed cliff slope angle and fault proximity). This information could support a range of management approaches, from the development of ‘rules-of-thumb’ to detailed models that incorporate all predictor information. In our study all statistical approaches correctly predict a high proportion (>85%) of cases. Similar ‘success’ has been shown in other studies, but important questions should be asked about possible error sources, particularly in regard to absence data. In coastal landslide studies sign decay is a vexing issue, because sites prone to landsliding may also be sites of rapid evidence removal.     

Mitigating the effects of soil and residue water contents on remotely sensed estimates of crop residue cover

Crop residues on the soil surface decrease soil erosion and increase soil organic carbon and the management of crop residues is an integral part of many conservation tillage systems. Current methods of measuring residue cover are inadequate for characterizing the spatial variability of residue cover over large fields. The objectives of this research were to determine the effects of water content on the remotely sensed estimates of crop residue cover and to propose a method to mitigate the effects of water content on remotely sensed estimates of crop residue cover. Reflectance spectra of crop residues and soils were measured in the lab over the 400-2400 nm wavelength region. Reflectance of scenes with various residue cover fractions and water contents was simulated using a linear mixture model. Additional spectra of scenes with mixtures of crop residues and soil were also acquired in corn, soybean, and wheat fields with different tillage treatments and different water content conditions. Crop residue cover was linearly related to the cellulose absorption index (CAI), which was defined as the relative intensity of an absorption feature near 2100 nm. Water in the crop residue significantly attenuated CAI and changed the slope of the residue cover vs. CAI relationship. Without an appropriate correction, crop residue covers were underestimated as scene water content increased. Spectral vegetation water indices were poorly related to changes in the water contents of crop residues and soils. A new reflectance ratio water index that used the two bands located on the shoulders of the cellulose absorption feature to estimate scene water conditions was proposed and tested with data from corn, soybean, and wheat fields. The ratio water index was used to describe the changes in the slope of crop residue cover vs. CAI and improve the predictions of crop residue cover. These results indicate that spatial and temporal adjustments in the spectral estimates of crop residue cover are possible. Current mutispectral imaging systems will not provide reliable estimates of crop residue cover when scene water content varies. Hyperspectral data are not required, because the three narrow bands that are used for both CAI and the scene moisture correction could be incorporated in advanced multispectral sensors. Thus, regional surveys of soil conservation practices that affect soil carbon dynamics may be feasible using either advanced multispectral or hyperspectral imaging systems.