ALBERT

Description: Fractional vegetation cover (FVC) is an important biophysical parameter of terrestrial ecosystems. Variation of FVC is a major problem in research fields related to remote sensing applications. In this study, the global FVC from 1982 to 2011 was estimated by GIMMS NDVI data, USGS global land cover characteristics data and HWSD soil type data with a modified dimidiate pixel model, which considered vegetation and soil types and mixed pixels decomposition. The evaluation of the robustness and accuracy of the GIMMS FVC with MODIS FVC and Validation of Land European Remote sensing Instruments (VALERI) FVC show high reliability. Trends of the annual FVCmax and FVCmean datasets in the last 30 years were reported by the Mann–Kendall method and Sen’s slope estimator. The results indicated that global FVC change was 0.20 and 0.60 in a year with obvious seasonal variability. All of the continents in the world experience a change in the annual FVCmax and FVCmean, which represents biomass production, except for Oceania, which exhibited a significant increase based on a significance level of p = 0.001 with the Student’s t-test. Global annual maximum and mean FVC growth rates are 0.14%/y and 0.12%/y, respectively. The trends of the annual FVCmax and FVCmean based on pixels also illustrated that the global vegetation had turned green in the last 30 years. A significant trend on the p = 0.05 level was found for 15.36% of the GIMMS FVCmax pixels on a global scale (excluding permanent snow and ice), in which 1.8% exhibited negative trends and 13.56% exhibited positive trends. The GIMMS FVCmean similarly produced a total of 16.64% significant pixels with 2.28% with a negative trend and 14.36% with a positive trend. The North Frigid Zone represented the highest annual FVCmax significant increase (p = 0.05) of 25.17%, which may be caused mainly by global warming, Arctic sea-ice loss and an advance in growing seasons. Better FVC predictions at large regional scales, with high temporal resolution (month) and long time series, would advance our ability to understand the characteristics of the global FVC changes in the last 30 years and predict the response of vegetation to global climate change.

Description: Understanding the changes in water consumption structure in order to take measures for demand control is very important for sustainable water resources management. In this study, using the Southern China area of Nanjing as an example, we employed the information entropy method to analyze the water consumption structure, as well as the grey incidence analysis to analyze synthetic incidence degree of the factors associated with agricultural, industrial, domestic, and ecological water consumption. The results show that the degree of balance among water consumption sectors has increased from 0.755 to 0.825 between 1993 and 2014. Gradual decrease of the relative proportion of a single water user structure in a water consumption system has made the utilization of water resources in Nanjing rational and diversified. The study identifies three stages of transformation of water structure in Nanjing, namely, a growth period from 1993 to 2002, an adjustment period from 2003 to 2010, and another growth period from 2011 to 2014. The synthetic incidence degree analysis indicates that adjustments of the agricultural and industrial water consumption as well as water saving measures are the main factors that affected water consumption structure in Nanjing. It is expected that the results obtained from this study will provide references to optimize the utilization of urban water resources.

Description: In recent decades, the area and proportion of planted forests have increased; thus, understanding the responses of planted and natural forests to drought are crucial because it forms the basis for forest risk assessments and management strategies. In this study, we combined the moderate-resolution imaging spectroradiometer (MODIS) enhanced vegetation index (EVI), meteorological aridity indices, and standardized precipitation evapotranspiration indices (SPEI) to identify the drought responses of planted and natural forests. In particular, we used the EVI standard anomaly (ESA) as a physiological drought indicator and analyzed the applicability of SPEIs at time scales of 1–30 months, thereby determining the optimal time scale for the SPEI (SPEIopt), i.e., the SPEI that best represents the drought responses of forests in Yunnan. Next, we employed the optimal SPEI and the ESA as indices to statistically analyze the response characteristics of planted and natural forests under different drought intensities. The results indicated the following: (1) The SPEI in June and a time scale of five months (i.e., SPEIJun,5) comprise the optimal meteorological aridity indicator for forests in Yunnan Province, which had the strongest correlation with the EVI standard anomaly (ESAJun). (2) All forest types were affected by drought in Yunnan, but their responses varied according to the forest type, elevation, and drought intensity. In general, natural forests are more vulnerable and sensitive to drought than planted forests, especially natural coniferous forests at low (0–2000 m) and moderate (2000–4000 m) altitudes, and natural mixed forest at low altitudes (0–2000 m). (3) The remote sensing-based ESA (ESAJun) is sensitive to the intensity of water stress, which makes it a good indicator for drought monitoring. In addition, the forests’ inventory survey revealed that 8.05% of forests were affected by drought; thus, we used this as a guide to estimate an approximate threshold to map forest responses to drought across the region. Below this approximate threshold (i.e., ESAJun 〈 −3.85), severe drought-induced effects on forests may occur. Given that natural forests are more vulnerable and sensitive to drought than the planted forests, natural forests need more careful management, especially in the context of projected increases in extreme drought events in the future.

Description: The direction of arrival (DOA) tracking problem based on an angle sensor is an important topic in many fields. In this paper, a nonlinear filter named the feedback M-estimation based robust cubature Kalman filter (FMR-CKF) is proposed to deal with measurement outliers from the angle sensor. The filter designs a new equivalent weight function with the Mahalanobis distance to combine the cubature Kalman filter (CKF) with the M-estimation method. Moreover, by embedding a feedback strategy which consists of a splitting and merging procedure, the proper sub-filter (the standard CKF or the robust CKF) can be chosen in each time index. Hence, the probability of the outliers’ misjudgment can be reduced. Numerical experiments show that the FMR-CKF performs better than the CKF and conventional robust filters in terms of accuracy and robustness with good computational efficiency. Additionally, the filter can be extended to the nonlinear applications using other types of sensors.

Description: Sample data plays an important role in land cover (LC) map validation. Traditionally, they are collected through field survey or image interpretation, either of which is costly, labor-intensive and time-consuming. In recent years, massive geo-tagged texts are emerging on the web and they contain valuable information for LC map validation. However, this kind of special textual data has seldom been analyzed and used for supporting LC map validation. This paper examines the potential of geo-tagged web texts as a new cost-free sample data source to assist LC map validation and proposes an active data collection approach. The proposed approach uses a customized deep web crawler to search for geo-tagged web texts based on land cover-related keywords and string-based rules matching. A data transformation based on buffer analysis is then performed to convert the collected web texts into LC sample data. Using three provinces and three municipalities directly under the Central Government in China as study areas, geo-tagged web texts were collected to validate artificial surface class of China’s 30-meter global land cover datasets (GlobeLand30-2010). A total of 6283 geo-tagged web texts were collected at a speed of 0.58 texts per second. The collected texts about built-up areas were transformed into sample data. User’s accuracy of 82.2% was achieved, which is close to that derived from formal expert validation. The preliminary results show that geo-tagged web texts are valuable ancillary data for LC map validation and the proposed approach can improve the efficiency of sample data collection.

Description: Automatic discovery of isolated land cover web map services (LCWMSs) can potentially help in sharing land cover data. Currently, various search engine-based and crawler-based approaches have been developed for finding services dispersed throughout the surface web. In fact, with the prevalence of geospatial web applications, a considerable number of LCWMSs are hidden in JavaScript code, which belongs to the deep web. However, discovering LCWMSs from JavaScript code remains an open challenge. This paper aims to solve this challenge by proposing a focused deep web crawler for finding more LCWMSs from deep web JavaScript code and the surface web. First, the names of a group of JavaScript links are abstracted as initial judgements. Through name matching, these judgements are utilized to judge whether or not the fetched webpages contain predefined JavaScript links that may prompt JavaScript code to invoke WMSs. Secondly, some JavaScript invocation functions and URL formats for WMS are summarized as JavaScript invocation rules from prior knowledge of how WMSs are employed and coded in JavaScript. These invocation rules are used to identify the JavaScript code for extracting candidate WMSs through rule matching. The above two operations are incorporated into a traditional focused crawling strategy situated between the tasks of fetching webpages and parsing webpages. Thirdly, LCWMSs are selected by matching services with a set of land cover keywords. Moreover, a search engine for LCWMSs is implemented that uses the focused deep web crawler to retrieve and integrate the LCWMSs it discovers. In the first experiment, eight online geospatial web applications serve as seed URLs (Uniform Resource Locators) and crawling scopes; the proposed crawler addresses only the JavaScript code in these eight applications. All 32 available WMSs hidden in JavaScript code were found using the proposed crawler, while not one WMS was discovered through the focused crawler-based approach. This result shows that the proposed crawler has the ability to discover WMSs hidden in JavaScript code. The second experiment uses 4842 seed URLs updated daily. The crawler found a total of 17,874 available WMSs, of which 11,901 were LCWMSs. Our approach discovered a greater number of services than those found using previous approaches. It indicates that the proposed crawler has a large advantage in discovering LCWMSs from the surface web and from JavaScript code. Furthermore, a simple case study demonstrates that the designed LCWMS search engine represents an important step towards realizing land cover information integration for global mapping and monitoring purposes.

Description: Plasmonic nanostructures have attracted intensive attention for many applications in recent years because of the field enhancement at the metal/dielectric interface. First, this strong field enhancement makes it possible to break the diffraction limit and enable subwavelength optical waveguiding, which is desired for nanophotonic integrated circuits with ultra-high integration density. Second, the field enhancement in plasmonic nanostructures occurs only for the polarization mode whose electric field is perpendicular to the metal/dielectric interface, and thus the strong birefringence is beneficial for realizing ultra-small polarization-sensitive/selective devices, including polarization beam splitters, and polarizers. Third, plasmonic nanostructures provide an excellent platform of merging electronics and photonics for some applications, e.g., thermal tuning, photo-thermal detection, etc. Finally, the field enhancement at the metal/dielectric interface helps a lot to realize optical sensors with high sensitivity when introducing plasmonic nanostrutures. In this paper, we give a review for recent progresses on the utilization of field enhancement in plasmonic nanostructures for these applications, e.g., waveguiding, polarization handling, heating, as well as optical sensing.

Description: Drought is expected to increase in frequency and severity due to global warming, and its impacts on vegetation are typically extensively evaluated with climatic drought indices, such as multi-scalar Standardized Precipitation Evapotranspiration Index (SPEI). We analyzed the covariation between the SPEIs of various time scales and the anomalies of the normalized difference vegetation index (NDVI), from which the vegetation type-related optimal time scales were retrieved. The results indicated that the optimal time scales of needle-leaved forest, broadleaf forest and shrubland were between 10 and 12 months, which were considerably longer than the grassland, meadow and cultivated vegetation ones (2 to 4 months). When the optimal vegetation type-related time scales were used, the SPEI could better reflect the vegetation’s responses to water conditions, with the correlation coefficients between SPEIs and NDVI anomalies increased by 5.88% to 28.4%. We investigated the spatio-temporal characteristics of drought and quantified the different responses of vegetation growth to drought during the growing season (April–October). The results revealed that the frequency of drought has increased in the 21st century with the drying trend occurring in most of China. These results are useful for ecological assessments and adapting management steps to mitigate the impact of drought on vegetation. They are helpful to employ water resources more efficiently and reduce potential damage to human health caused by water shortages.

Description: We theoretically propose a method based on the combination of a nonlinear optical mass sensor using a hybrid spin-microcantilever and the nanoparticle-enhanced technique, to detect and monitor DNA mutations. The technique theoretically allows the mass of external particles (ssDNA) landing on the surface of a hybrid spin-microcantilever to be detected directly and accurately at 300 K with a mass responsivity 0.137 Hz/ag in situ in liquid. Moreover, combined with the nanoparticle-enhanced technique, even only one base pair mutation in the target DNA sequence can be identified in real time accurately, and the DNA hybridization reactions can be monitored quantitatively. Furthermore, in situ detection in liquid and measurement of the proposed nonlinear optical spin resonance spectra will minimize the experimental errors.

Description: Understanding the spatial patterns of urban land use at both the macro and the micro levels is a central issue in global change studies. Due to the nonlinear features associated with land use spatial patterns, it is currently necessary to provide some distinct analysis methods to analyze them across a range of remote sensing imagery resolutions. The objective of our study is to quantify urban land use patterns from various perspectives using multidimensional fractal methods. Three commonly used fractal dimensions, i.e., the boundary dimension, the radius dimension, and the information entropy dimension, are introduced as the typical indices to examine the complexity, centrality and balance of land use spatial patterns, respectively. Moreover, a new lacunarity dimension for describing the degree of self-organization of urban land use at the macro level is presented. A cloud-free Landsat ETM+ image acquired on 17 September 2010 was used to extract land use information in Wuhan, China. The results show that there are significant linear relationships represented by good statistical fitness related to these four indices. The results indicate that rapid urbanization has substantially affected the urban landscape pattern, and different land use types show different spatial patterns in response. This analysis reveals that multiple fractal/nonfractal indices provides a more comprehensive understanding of the spatial heterogeneity of urban land use spatial patterns than any single fractal dimension index. These findings can help us to gain deeper insight into the complex spatial patterns of urban land use.