Spectral Heterogeneity Research Articles

Using heterogeneity indices to adjust basal area – Leaf area index relationship in managed coniferous stands

The structure of contemporary managed forests is complex and deviates from experimental forests which are usually even-aged monocultures and single-storied. To apply theoretical growth and yield functions on managed forests, adjustments are required, especially for leaf area index (LAI) which is a key biophysical variable in process-based growth models. To asses this, the performance of canopy LAI in modelling the basal area (BA) of managed boreal forests dominated by Norway spruce (Picea abies (L). Karst) and Scots pine (Pinus sylvestris L.) was investigated by heterogeneity analysis. The study was based on the assumption that canopy LAI and BA are strongly related and are vital for estimating stand productivity and growth. Managed forests were represented by field data from the 2016 and 2017 Swedish National Forest Inventory (NFI) campaigns. Species-specific LAI conversion parameters were applied on the general plant area index (PAI) values from hemispheric fish-eye photos taken from the permanent and temporary NFI sample plots. The heterogeneity analysis was studied in two parts by: (a) ground-based stand structural heterogeneity (SSH) described by species composition, coefficient of tree diameter variation, tree social status and height-diameter ratio, and (b) spectral heterogeneity (SPH) by vegetation and textural indices developed from Sentinel-2. Species-specific final (with heterogeneity metrics) and base (without heterogeneity metrics) models were fitted for BA-LAI and BA-PAI relationships by nonlinear least squares and generalised additive regression functions, respectively. The performance of models was assessed by the root-mean-squared error (RMSE, m2 ha−1) and the relative root-mean-squared error (RelRMSE, %) metrics. For both species, BA-LAI final models (FMs) accounting for heterogeneity resulted in larger explained variance than the base models (BMs). Compared with the BMs, FMs with SSH reduced the variance by 55% in Norway spruce (RMSE = 3.33, RelRMSE = 15.39) and 43% in Scots pine (RMSE = 3.70, RelRMSE = 17.38). The fit between BA-LAI with SPH also showed an improvement for Norway spruce (RMSE = 5.56) and Scots pine (RMSE = 5.66) over the BMs, suggesting the potential use of Sentinel-2 in future growth models. The results of the study suggest that in growth models when extrapolating theoretical growth functions to managed forests, there is a need to calibrate the models with the forest structural heterogeneity. This is important for drawing realistic conclusions from growth and yield modelling of managed stands of Norway spruce and Scots pine.

Stark fluorescence spectroscopy on peridinin-chlorophyll-protein complex of dinoflagellate, Amphidinium carterae.

Because of their peculiar but intriguing photophysical properties, peridinin-chlorophyll-protein complexes (PCPs), the peripheral light-harvesting antenna complexes of photosynthetic dinoflagellates have been unique targets of multidimensional theoretical and experimental investigations over the last few decades. The major light-harvesting chlorophyll a (Chl a) pigments of PCP are hypothesized to be spectroscopically heterogeneous. To study the spectral heterogeneity in terms of electrostatic parameters, we, in this study, implemented Stark fluorescence spectroscopy on PCP isolated from the dinoflagellate Amphidinium carterae. The comprehensive theoretical modeling of the Stark fluorescence spectrum with the help of the conventional Liptay formalism revealed the simultaneous presence of three emission bands in the fluorescence spectrum of PCP recorded upon excitation of peridinin. The three emission bands are found to possess different sets of electrostatic parameters with essentially increasing magnitude of charge-transfer character from the blue to redder ones. The different magnitudes of electrostatic parameters give good support to the earlier proposition that the spectral heterogeneity in PCP results from emissive Chl a clusters anchored at a different sites and domains within the protein network.

Improving accuracy of Landsat-8 OLI classification using image composite and multisource data with Google Earth Engine

ABSTRACTLand use and land cover (LULC) mapping is very important for evaluation and management of natural resources. Classification of remote sensing data in mountainous and other heterogeneous environments are particularly problematic because of their high spectral heterogeneity. The aim of this study is to conduct accuracy analyses of LULC classification of Landsat-8 OLI composite images and multisource ancillary data using four machine learning classifiers on the Google Earth Engine (GEE) platform in the Golden Gate Highland National Park (GGHNP) and its near surrounding. Our results indicate that the inclusion of short-wave infrared bands increased accuracy of image composites by more than 10% in landcover types with wider range of spectral characteristics. Best performance was achieved by MaxEnt with overall accuracy and kappa statistics of 0.846 and 0.775, respectively. Support Vector Machine (SVM), however, produces relatively higher accuracy for handling different characteristics of multisource data. In elevated areas, aspect was found to outperform other topographic indices in reducing misclassification errors associated with confusion of grasslands with bare lands and water. The LULC produced from image composites can provide some reliable information on vegetation, land and water characteristics and reduce confusion associated with complex biophysical environments on vegetation spectral signatures in sub-tropical mountainous grasslands.

P1605Increased irregularity and spectral complexity of the intrinsic pacemaker beat-to-beat variability correlates with increased metabolic syndrome components

Abstract Background Metabolic syndrome (MetS) is becoming one of the future potential leading risk factors for heart and cardiovascular disease. MetS relates to a condition associated with at least three metabolic risk factors raising risk for health diseases concomitantly such as diabetes, stroke, hypertension, obesity and dyslipidemia. This can lead to chest pain, heart attack, heart damage and overall higher prevalence of cardiovascular disease, atrial fibrillation and sudden cardiac death. One of the underlying mechanisms of the progressive remodeling in presence of MetS components could be altered automaticity, which would reflect modifications of sinus node activity. These phenomena can be evaluated analyzing the components of heart rate variability (HRV). Purpose Our aim was to examine the modifications of sinus node variability in an isolated heart model of diet-induced obesity and MetS. Methods Male NZW rabbits were randomly assigned to high-fat (HF, n=8), control (HF-C, n=7), high-fat, high-sucrose (HFHS, n=9), and control (HFHS-C, n=9) groups, fed with their respective diets during 18/28 weeks. After euthanasia their hearts were isolated in a Langendorff system. We recorded 10–15 minutes of spontaneous activity. Short RR time series were analyzed, and standard HRV parameters were determined with special interest in the time-course of spectral, time-frequency and non-linear components. One-way ANOVA, Kruskal-Wallis test and bivariate correlations were used for statistical analysis (p<0.05). Results We did find an increase in the complexity and irregularity of intrinsic pacemaker activity as shown by modifications of entropy (ApEn: p=0.011 vs HF, p=0.002 vs HF-C, p=0.019 vs HFHS-C) and the complexity index (CI: p=0.006 vs HF, p=0.047 vs HF-C, p=0.027 vs HFHS-C) in HFHS animals (Figure). Higher dispersion on RR differences distributions was observed in the HFHS group. Time-frequency spectral heterogeneity increased in HFHS group (p=0.002 vs HF, p=0.050 vs HF-X, p=0.027 vs HFHS-C) even though no differences were found in standard time and frequency-domain analyses. High-band and low-band spectral concentration ratios showed decreased organization in HFHS when compared to HF (p=0.002) and controls HFHS-C (p=0.027) and HF-C (p=0.050). Interestingly, animal weight and glucose intolerance were highly correlated with the modifications of intrinsic pacemaker variability. Modifications of intrinsic IHRV. Conclusions Modifications of intrinsic HRV seemed to be reliant on the number of components of MetS present, given that only HFHS group showed significant changes towards an increased complexity and irregularity of intrinsic pacemaker variability. Acknowledgement/Funding This work was partially supported by: GV2015-062, CB16/11/00486.

Estimating Salt Concentrations Based on Optimized Spectral Indices in Soils with Regional Heterogeneity

Soil salinity is one of the most damaging environmental problems worldwide, especially in arid and semiarid regions. The objectives of this study were to improve the inversion accuracy of soil salt content (SSC) in soils with spectral heterogeneity by using optimized spectral indices. Soil samples at a 0–20 cm depth were taken from Keriya Oasis (98 soil samples), Ugan-Kuqa Oasis (49 soil samples), and Ebinur Lake Basin (57 soil samples). SSC and spectral reflectance (SR) of all the 204 soil samples were determined. To comprehensively analyze the field-collected hyperspectral data, various band combinations were used to calculate a normalized difference spectral index (NDSI) and ratio spectral index (RSI). Then, the relationships between the indices and SSC were examined, and the most robust relationships were demonstrated. The partial least squares regression (PLSR) method was utilized to develop a predictive model of SSC, and the variable importance in the projection (VIP) method was used during modeling. The results revealed that (i) the salinized soils in different regions had apparent differences in both reflectance and spectral curve morphology, but the optimized spectral indices method effectively overcame the regional heterogeneity of salinized soil hyperspectral characteristics, and the correlation with SSC was always kind, with correlation coefficients up to 0.748 at 0.001 level of significance; (ii) the VIP filtering method effectively selected the optimal independent model, and the modeling accuracy was better than the single optimization index (R2Pre = 0.83 and RMSEPre = 2.31 g·kg−1) by using the combination of two optimal indices; (iii) although the global modeling accuracy was significantly lower than the local modeling accuracy due to the inconsistent salt sensitivity bands of salinized soils in different regions, combined with cross-validation analysis, the global model had the ability to predict soil salinization accurately (R2Pre = 0.69 and RMSEPre = 8.45 g·kg−1). The methods developed in this study can be applied in other arid and semiarid areas. Besides, the study also provides examples for aerospace hyperspectral remote sensing of cross-regional soil salinization.

Optimizing multiscale segmentation with local spectral heterogeneity measure for high resolution remote sensing images

Image segmentation is a vital and fundamental step in Geographic Object-Based Image Analysis (GEOBIA). Many multiscale segmentation algorithms have been widely used in high resolution (HR) remote sensing images. These segmentation algorithms need a preset parameter, named scale parameter, to control the average size of each object. However, due to the spatial variation, single scale parameter can hardly describe the boundaries of regions with different land covers. To overcome this limitation, in this study, an adaptive parameter optimization method is proposed for multiscale segmentation. To find the optimal scale of objects, a local spectral heterogeneity measure is applied by calculating the spectral angle between inter and intra objects. Different from selecting a global optimal scale parameter, this study aims to directly search the optimal objects from results of all different scales and combine them into final segmentation results. In experiments, a multi-resolution segmentation is used to generate segmentation results of different scales and the QuickBird-2 images are used as test data. Optimization results over four HR test images reveal that the proposed method provides better segmentation performance than single scale segmentation result.

The potential of satellite greenness to predict plant diversity among wetland types, ecoregions, and disturbance levels

The unprecedented global biodiversity loss has massive implications for the capacity of ecosystems to maintain functions critical to human well-being, urgently calling for rapid, scalable, and reproducible strategies for biodiversity monitoring, particularly in threatened ecosystems with difficult field access such as wetlands. Remote sensing indicators of spectral variability and greenness may predict the diversity of plant communities based on their optical diversity; however, most evidence is based on narrowband spectral data or terrestrial ecosystems. We investigate how spectral greenness and heterogeneity from publicly available broadband multi-spectral Landsat satellite imagery explain variation in vegetation diversity across different wetland types, ecoregions, and disturbance levels using 1,138 sites surveyed by U.S. EPA's National Wetland Condition Assessment. We found positive correlations of plant species richness and diversity with indicators of annual maximum spectral greenness and its spatial heterogeneity, explaining up to 43% variation within the global sample, 48% within wetland types or ecoregions, and up to 61% with abiotic covariates. The combined effect of spectral greenness and heterogeneity was stronger than the best-performing model using climatic, topographic, and edaphic factors alone. When compared among major U.S. watersheds and individual states, the fit of diversity-greenness models increased when more wetland types were included within the corresponding region's boundaries, up to 61% at the watershed and 77% at the state level, respectively, for diversity models and up to 73% and 80%, respectively, for richness models. Model outliers were characterized by a significantly greater diversity of nonnative species (P<0.0001), suggesting that changes in model performance and greenness distributions could be used as indicators of shifts in plant community composition, particularly in tidal wetlands making the majority of outliers with significantly lower than predicted diversity. This study represents a first-time national-scale effort to use publicly available remote sensing, climatic, and topographic data to predict plant diversity in wetlands, which tend to be understudied compared to terrestrial ecosystems despite being among the most stressed ecosystems on Earth. Our study suggests that multi-temporal broadband satellite imagery could provide a low-cost assessment of regional and national wetland biodiversity for prioritization of conservation efforts and early detection of biodiversity loss.

A spatialized classification approach for land cover mapping using hyperspatial imagery

Maps of classified surface features are a key output from remote sensing. Conventional methods of pixel-based classification label each pixel independently by considering only a pixel's spectral properties. While these purely spectral-based techniques may be applicable to many medium and coarse-scale remote sensing analyses, they may become less appropriate when applied to high spatial resolution imagery in which the pixels are smaller than the objects to be classified. At this scale, there is often higher intra-class spectral heterogeneity than inter-class spectral heterogeneity, leading to difficulties in using purely spectral-based classifications. A solution to these issues is to use not only a pixel's spectral characteristics but also its spatial characteristics. In this study, we develop a generalizable "spatialized" classification approach for high spatial resolution image classification. We apply the proposed approach to map vegetation growth forms such as trees, shrubs, and herbs in a forested ecosystem in the Sierra Nevada Mountains. Our results found that the spatialized classification approach outperformed spectral-only approaches for all cover classes examined, with the largest improvements being in discriminating vegetation classes.

Forest beta-diversity analysis by remote sensing: How scale and sensors affect the Rao’s Q index

Space-borne remote sensing missions provide robust, timely and continuous data to assess biodiversity in remote or protected areas, where direct field observations can be prohibited by difficult accessibility. The objective of this study was to extend the concept of remote sensing based assessment of beta-diversity to multi-scale domain by multi-resolution optical satellite data. This study was conducted in a reserved forest of western Himalaya, India; a region affected by the invasive Lantana camara L (lantana). We calculated and compared Rao’s Q and Shannon indices at different spatial resolutions (0.5, 5, and 30 m) and scales (window sizes) by using imageries from Pléiades 1A, RapidEye, and Landsat-8 acquired in April 2013, the pre-monsoon season. Rao’s Q index explained diversity more accurately than Shannon index for the three analyzed stand densities. Diversity was better approximated by Rao’s Q index calculated by Pléiades 1A at a resolution of 0.5 m at low stand density. We observed higher correlations of the average coefficient of variation (CV) with Rao’s Q and Shannon indices for areas associated with mixed spectral reflectance caused by overstory and understory vegetation. Furthermore, CV was lower in open areas dominated by lantana. These results indicated a strong scale and spatial resolution dependence of Rao’s Q index on remote sensing-derived spectral heterogeneity information. When applied in heterogeneous forest environments, Rao’s Q index could represent a better remote sensing proxy to estimate beta-diversity than the conventional Shannon index.

A spatiotemporal cube model for analyzing satellite image time series: Application to land-cover mapping and change detection

Satellite image time series (SITS) data have been widely used in resource investigations and environmental monitoring. However, three characteristics of geographic objects (i.e., spatiotemporal heterogeneity, spatiotemporal correlation and scale characteristics) pose great challenges to SITS analysis; while, existing studies ignore these characteristics, leading to unsatisfactory SITS analysis results including land-cover classification and change detection. Aiming at integratively measuring and expressing these three characteristics, this study proposes a spatiotemporal cube model and a spatiotemporal multi-resolution segmentation (ST-MRS) method for analyzing SITS. First, the novel spatiotemporal model, ST-cube, is introduced to reduce the spectral heterogeneity in spatial and temporal domains. Both spatial and temporal heterogeneities are considered and defined while constructing the spatiotemporal segmentation model. Second, the ST-MRS method is proposed by extending the existing mono-temporal MRS method into spatiotemporal domain to segment SITS into relatively homogeneous ST-cubes. Third, a novel unsupervised method is developed to evaluate the segmentation results of the ST-MRS method. In experiments, we apply this method to Shunyi District in Beijing, China, generate ST-cubes at different spatiotemporal scales, and compare our method with existing ones for SITS classification. Furthermore, the intra-annual seasonal changes of land covers are detected based on ST-cubes classification results. The results indicate the effectiveness and superiority of the ST-cubes in representing geographic objects and analyzing spatiotemporal characteristics over existing pixel- and object-based methods, contributing to a wider range of applications and analysis of SITS.

Estimating tree species diversity from space in an alpine conifer forest: The Rao's Q diversity index meets the spectral variation hypothesis

Forests cover about 30% of the Earth surface, they are among the most biodiverse terrestrial ecosystems and represent the bulk of many ecological processes and services. The assessment of biodiversity is an important and essential goal to achieve but it can results difficult, time consuming and expensive when based on field data. Remote sensing covers large areas and provides consistent quality and standardized data, which can be used to estimate species diversity. One method to estimate species diversity from remote sensing data is based on the Spectral Variation Hypothesis (SVH), which assumes that the higher the spectral variation of an image, the higher the environmental heterogeneity and the species diversity of the considered area. SVH has been tested using different spectral heterogeneity (SH) indices and measures, recently the Rao's Q index has been proposed as a new spectral variation measure to be applied to remote sensing data. In this paper, we tested the SVH in an alpine coniferous forest to estimate tree species diversity. We evaluated the performance of the Rao's Q index and compared it with another widely used SH index, the Coefficient of Variation (CV), validating them against values of Shannon's H (used as species diversity index) derived from in-situ collected data. A NDVI time-series (for 2016 and 2017) derived from the Sentinel-2A and 2B and Landsat 8 OLI satellites has been used to test the effect of the spatial grain of both the sensors and to understand the seasonality of the SVH. The results showed that the SVH is season and sensor dependent. For both years and satellites, the relation between Rao's Q and field data reached the highest R2 between June and July, decreasing towards winter and spring similarly to the NDVI time-series. This relationship could be given because, when NDVI reaches its highest values, it is able to capture small variation in reflectance of different leaf traits typical of specific trees. The relation between field and spectral diversity reached a value of R2 = 0.70 (2017) and R2 = 0.48 (2016) for Sentinel-2 and of R2 = 0.42 (2017) and R2 = 0.47 (2016) for Landsat 8. CV showed the same NDVI temporal tendency. However, the relation between field-derived Shannon's H and CV was on average lower than that we found for Rao's Q. This research underlined the goodness of the Rao's Q index, the relevance of the NDVI in the study of the SVH and the importance of the multi-temporal approach.

Modifications of short-term intrinsic pacemaker variability in diet-induced metabolic syndrome: a study on isolated rabbit heart.

Metabolic syndrome (MetS) describes a condition associated with multiple diseases concomitantly such as diabetes, hypertension, obesity, and dyslipidemia. It has been linked with higher prevalence of cardiovascular disease, atrial fibrillation, and sudden cardiac death. One of the underlying mechanisms could be altered automaticity, which would reflect modifications of sinus node activity. These phenomena can be evaluated analyzing the components of heart rate variability (HRV). Our aim was to examine the modifications of sinus node variability in an isolated heart model of diet-induced obesity and MetS. Male NZW rabbits were randomly assigned to high-fat (HF, n = 8), control (HF-C, n = 7), high-fat, high-sucrose (HFHS, n = 9), and control (HFHS-C, n = 9) groups, fed with their respective diets during 18/28weeks. After euthanasia, their hearts were isolated in a Langendorff system. We recorded 10-15min of spontaneous activity. Short RR time series were analyzed, and standard HRV parameters were determined. One-way ANOVA, Kruskal-Wallis test, and bivariate correlation were used for statistical analysis (p < 0.05). We did find an increase in the complexity and irregularity of intrinsic pacemaker activity as shown by modifications of approximate entropy, sample entropy, minimum multiscale entropy, and complexity index in HFHS animals. Even though no differences were found in standard time and frequency-domain analyses, spectral heterogeneity increased in HFHS group. Animal weight and glucose intolerance were highly correlated with the modifications of intrinsic pacemaker variability. Finally, modifications of intrinsic HRV seemed to be reliant on the number of components of MetS present, given that only HFHS group showed significant changes towards an increased complexity and irregularity of intrinsic pacemaker variability.

Multicolor super-resolution imaging using spectroscopic single-molecule localization microscopy with optimal spectral dispersion.

We developed transmission diffraction grating-based spectroscopic single-molecule localization microscopy (sSMLM) to collect the spatial and spectral information of single-molecule blinking events concurrently. We characterized the spectral heterogeneities of multiple far-red emitting dyes in a high-throughput manner using sSMLM. We also investigated the influence of spectral dispersion on the single-molecule identification performance of fluorophores with large spectral overlapping. The careful tuning of spectral dispersion in grating-based sSMLM permitted simultaneous three-color super-resolution imaging in fixed cells with a single objective lens at a relatively low photon budget. Our sSMLM has a compact optical design and can be integrated with conventional localization microscopy to provide add-on spectroscopic analysis capability.

Wide-Field Spectral Super-Resolution Mapping of Optically Active Defects in Hexagonal Boron Nitride.

Point defects can have significant impact on the mechanical, electronic, and optical properties of materials. The development of robust, multidimensional, high-throughput, and large-scale characterization techniques of defects is thus crucial for the establishment of integrated nanophotonic technologies and material growth optimization. Here, we demonstrate the potential of wide-field spectral single-molecule localization microscopy (SMLM) for the determination of ensemble spectral properties as well as the characterization of spatial, spectral, and temporal dynamics of single defects in chemical vapor deposition (CVD)-grown and irradiated exfoliated hexagonal boron-nitride materials. We characterize the heterogeneous spectral response of our samples and identify at least two types of defects in CVD-grown materials, while irradiated exfoliated flakes show predominantly only one type of defects. We analyze the blinking kinetics and spectral emission for each type of defects and discuss their implications with respect to the observed spectral heterogeneity of our samples. Our study shows the potential of wide-field spectral SMLM techniques in material science and paves the way toward the quantitative multidimensional mapping of defect properties.

Spectral Heterogeneity Predicts Local-Scale Gamma and Beta Diversity of Mesic Grasslands

Plant species diversity is an important metric of ecosystem functioning, but field assessments of diversity are constrained in number and spatial extent by labor and other expenses. We tested the utility of using spatial heterogeneity in the remotely-sensed reflectance spectrum of grassland canopies to model both spatial turnover in species composition and abundances (β diversity) and species diversity at aggregate spatial scales (γ diversity). Shannon indices of γ and β diversity were calculated from field measurements of the number and relative abundances of plant species at each of two spatial grains (0.45 m2 and 35.2 m2) in mesic grasslands in central Texas, USA. Spectral signatures of reflected radiation at each grain were measured from ground-level or an unmanned aerial vehicle (UAV). Partial least squares regression (PLSR) models explained 59–85% of variance in γ diversity and 68–79% of variance in β diversity using spatial heterogeneity in canopy optical properties. Variation in both γ and β diversity were associated most strongly with heterogeneity in reflectance in blue (350–370 nm), red (660–770 nm), and near infrared (810–1050 nm) wavebands. Modeled diversity was more sensitive by a factor of three to a given level of spectral heterogeneity when derived from data collected at the small than larger spatial grain. As estimated from calibrated PLSR models, β diversity was greater, but γ diversity was smaller for restored grassland on a lowland clay than upland silty clay soil. Both γ and β diversity of grassland can be modeled by using spatial heterogeneity in vegetation optical properties provided that the grain of reflectance measurements is conserved.

Surface Plasmon Coupling in Dimers of Gold Nanoparticles: Experiment and Theory for Ideal (Spherical) and Nonideal (Faceted) Building Blocks

The surface plasmon (SP) coupling in dimers of spherical and faceted gold nanoparticles was investigated experimentally and computationally at the single-particle level. Individual ideal dimers of two spherical gold nanoparticles with a constant gap, filled by a self-assembled monolayer of 1,8-octanedithiol linker molecules, exhibit highly uniform dark-field (DF) scattering spectra. In contrast, single nonideal dimers of two faceted gold nanoparticles with the same constant gap exhibit a high degree of spectral nonuniformity. We attribute this significant spectral heterogeneity to the many possible gap morphologies, that is, the many possible configurations of the crystal facet orientations at the junction of the two faceted particles in nonideal dimers. This configurational complexity is reduced in so-called hybrid dimers, which comprise an ideal spherical particle and a nonideal faceted particle. Hybrid dimers were therefore investigated theoretically and experimentally for revealing the influence of cr...

A 10‐Year Record of Aerosol Optical Properties and Radiative Forcing Over Three Environmentally Distinct AERONET Sites in Kenya, East Africa

AbstractIn the framework of Aerosol Robotic Network (AERONET), the aerosol optical, microphysical, and radiative properties were investigated over three sites (CRPSM_Malindi, Nairobi, and ICIPE_Mbita) in Kenya, East Africa, during 2006–2015. The annual mean (±σ) aerosol optical depth at 440 nm (AOD440) was found high at Mbita (0.27 ± 0.09) followed by Malindi (0.26 ± 0.07), and low at Nairobi (0.19 ± 0.04). Whereas the seasonal mean AOD440 noticed high (low) values during the local dry (wet) seasons. The aerosol optical properties: AOD, single scattering albedo, asymmetry parameter, and complex aerosol refractive index exhibited significant temporal and spectral heterogeneities illustrating the complexity of aerosol types with an abundance of fine‐mode aerosols during the local dry (June–July–August) season. Characterization of major aerosol types revealed the dominance of mixed‐type followed by biomass burning aerosols. The aerosol volume size distribution revealed that the coarse‐ over fine‐mode aerosols showed a significant contribution to the total volume particle concentration, especially at high (&gt;0.3) AOD440. Further, the aerosol columnar number size distribution retrieved from the King's inversion of spectral AOD exhibited a power law distribution affirming multiplicity of aerosol sources. The direct aerosol radiative forcing values simulated in the shortwave region using the Santa Barbara DISORT Atmospheric Radiative Transfer model showed good correlation (r = &gt;0.85) with the AERONET‐derived ones at the top‐of‐atmosphere, bottom‐of‐atmosphere, and within the atmosphere. The annual mean (±σ) top‐of‐atmosphere, bottom‐of‐atmosphere, and within the atmosphere forcing values were found in the range from −8.10 ± 3.75 to −13.23 ± 4.87, −34.54 ± 4.86 to −46.11 ± 10.27, and 26.63 ± 6.43 to 36.24 ± 7.26 W/m2, respectively, with an atmospheric heating rate of 0.74 ± 0.12–1.02 ± 0.20 K/day. The Santa Barbara DISORT Atmospheric Radiative Transfer‐derived direct aerosol radiative forcing exhibited significant temporal heterogeneity with high (low) during the local dry (wet) seasons. Results derived from the present study forms a basis for regional climate change studies and could increase the accuracy of climate models over this unexplored region of Africa.

Ground-State Heterogeneity along with Fluorescent Byproducts Causes Excitation-Dependent Fluorescence and Time-Dependent Spectral Migration in Citric Acid-Derived Carbon Dots.

The integrity of fluorescent carbon dot (FCD) emission deserves its highest appreciation when sample purification is performed with extreme care. Several controversial phenomena of FCD fluorescence including excitation-dependent emission, spectral migration with time, and thereby violation of the Kasha-Vavilov rule, which sparked intense debate during recent reports, disappeared when we rigorously purified the as-synthesized FCD sample. Purification was performed by first visual silica column chromatography (observing the emissions under UV illumination) and subsequently prolonged membrane dialysis. Most of the surprising phenomena of FCD fluorescence reported earlier apparently arose from ground-state spectral heterogeneity of FCD sample containing a large amount of fluorescent impurities (mostly polymeric or oligomeric in nature). Observation of our ensemble spectroscopic measurements, albeit nicely matched with recent reports based on single-particle experiments, differed largely from that of other ensemble measurements. Our results reconciled a number of long-standing controversies on FCD emission mostly by emphasizing the urgency of sample purification with more scientific rigor.

城市乔木树种多样性遥感反演方法研究

PDF HTML阅读 XML下载 导出引用 引用提醒 城市乔木树种多样性遥感反演方法研究 DOI: 10.5846/stxb201808131728 作者: 作者单位: 中国科学院生态环境研究中心城市与区域生态国家重点实验室,中国科学院生态环境研究中心城市与区域生态国家重点实验室,中国科学院生态环境研究中心城市与区域生态国家重点实验室 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金重大基金项目（41590841）；国家自然科学基金面上项目（41771203）；中国科学院前沿科学重点研究项目（QYZDB-SSW-DQC034）；科技部国家重点研发计划项目课题（2016YFC0503004） A new approach to mapping tree diversity based on remote sensing imagery Author: Affiliation: State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences,State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences,State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences Fund Project: The Key Program of the Chinese Academy of Sciences,省、部研究计划基金 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:量化城市乔木树种多样性是定量研究其生态系统服务的前提和基础。目前，城市树种多样性水平的定量研究以地面调查方法为主，存在效率低、成本高的问题。针对上述问题，首先验证了光谱变异性假说和生产力假说在城市中的适用性，进而提出了快速量化城市树种多样性水平的方法。该方法基于神经网络刻画了乔木斑块多样性与光谱异质性之间的关系，能够较准确地反演城市乔木的香浓维纳指数和辛普森指数。为城市乔木树种多样性的调查提供了新的思路，并将为定量研究城市乔木多样性的生态系统服务提供基础数据。 Abstract:Urban tree diversity is responsible for many ecosystem services. Quantifying the level and spatial pattern of tree diversity is crucial for the assessment of ecosystem services. However, previous quantitative studies on urban tree diversity are mainly based on field surveys, which are highly expensive and restricted by poor efficiency. In this study, we firstly tested the applicability of Spectral Variation Hypothesis (SVH) and Productivity Hypothesis in urban areas. Then, we developed a new approach for quickly and efficiently quantifying tree diversity using remote sensing imagery. This new approach described the relationship between urban tree diversity and spectral heterogeneity based on Neural Network Model, which could be used to calculate the Shannon-Wiener index and the Simpson index of urban trees. This study provides a new direction for studying urban tree diversity, and our results are critical basic data for studying ecosystem services of urban tree diversity. 参考文献 相似文献 引证文献

Individual Tree Crown Detection and Delineation From Very-High-Resolution UAV Images Based on Bias Field and Marker-Controlled Watershed Segmentation Algorithms

Individual tree crown detection and delineation (ITCD) mainly depend on high-resolution aerial photos and satellite images or LiDAR, and these data can be costly to obtain. The advent of unmanned aerial vehicle (UAV) remote sensing technology provides an economic and effective method for data acquisition. Therefore, the research of ITCD based on UAV high-resolution images is of significance to improve the efficiency and accuracy of forest resource inventory and remote sensing validation. However, in very high-resolution (defined here with the pixel size smaller than 10 cm) images, the inhomogeneities in the canopy texture can be detrimental to the correct detection of individual trees by computer processing. We applied the bias field estimation, which is used in medical image segmentation, to reduce the within-canopy spectral heterogeneity in the very high-resolution UAV-derived orthophoto. By selecting young Osmanthus and Podocarpus trees that grow in a nursery as the study objects, we tested our method in an orthophoto (with a ground resolution of 2.5 cm) generated from overlapping UAV images. A local intensity clustering was first applied to produce a smoothed bias field image; then, by using morphological operation of opening and closing, the fine texture of the canopy was further smoothed. Finally, individual tree crowns were extracted by applying the marker-controlled watershed segmentation algorithm. The segmentation results were validated by comparing to the manually drawn individual tree crown polygons. The $F$ -scores of the detection rate for Osmanthus and Podocarpus were 98.2% and 93.1%, respectively. The result is considerably better than those achieved with similar processing steps but without the bias field treatment. Our study proves that it is feasible and effective to detect and delineate individual tree crowns based on the bias field and marker-controlled watershed segmentation in very-high-resolution images obtained from UAV.