Spectral Heterogeneity Research Articles

Satellite remote sensing reveals the footprint of biodiversity on multiple ecosystem functions across the NEON eddy covariance network

Abstract Biodiversity relates to ecosystem functioning by modulating biogeochemical cycles of carbon, water, energy, and nutrients within and between multiple biotic and abiotic components of the ecosystems. However, large-scale, systematic measurements of plant biodiversity are still lacking, and the effects of biodiversity on measured biogeochemical processes are understudied. Here, we combined alpha (α) and beta (β) taxonomic measurements, spectral diversity from satellite observations, structural properties of the vegetation, and climatic drivers to assess the effect of biodiversity on ecosystem functional properties. Ecosystem functional properties were computed from eddy-covariance fluxes at 44 sites of the National Ecological Observatory Network. Based on the spectral variation hypothesis, we used the near-infrared reflectance of vegetation (NIRv) derived from Sentinel-2 satellite imagery to compute Rao’s quadratic entropy (Rao Q), a distance metric related to spatial heterogeneity. Using an automatic model averaging technique, we found that biodiversity proxies hold substantial explanatory power when predicting several ecosystem functions related to carbon and water exchange. In particular, NIRv-based Rao Q (RaoQNIRv) reflected positive biodiversity effects on productivity, as expected from the literature. In contrast, traditional taxonomic α-diversity indices were generally not selected as relevant predictors of the ecosystem functional properties. Yet, β-diversity strongly contributed to the prediction of carbon use efficiency, surface conductance, and water use efficiency. We also found that the RaoQNIRv is less affected by issues of saturation and bare soil contribution compared to RaoQNDVI. We show that spectral heterogeneity based on remotely sensed NIRv holds the potential for globally characterizing the biodiversity-ecosystem functioning relationship (BEF). While systematic measurements of taxonomic diversity co-located at biogeochemical measurement stations could reduce the uncertainty surrounding the BEF relationship at whole-ecosystem scale, remotely- sensed metrics characterizing important functional and structural diversity aspects of the landscape will be crucial for continuous spatiotemporal monitoring of biodiversity with relevant implications for ecosystem services to humankind.

Fat Suppression in Distal Extremity 3-T MRI Using Spectral Heterogeneity Adaptive Radiofrequency Pulses.

Background Conventional chemical shift selective (CHESS) fat suppression may fail in distal extremity MRI due to sensitivity to field inhomogeneities. Purpose To develop a patient-specific fat-suppression method for distal extremity 3-T MRI by exploiting the spectral heterogeneity adaptive radiofrequency pulse (SHARP) technique and to compare it to fat suppression with CHESS. Materials and Methods SHARP uses the routinely acquired frequency spectrum at MRI calibration to adapt the frequency range and time-bandwidth product of the fat-suppression pulse. In this prospective study, fat suppression by SHARP was assessed by numerical simulations, phantom experiments, and imaging in 15 asymptomatic participants who underwent ankle, foot, and hand (in superman and hand-by-the-side positions) MRI using SHARP, CHESS, and reference standard (short-tau inversion recovery or Dixon) techniques. Three readers ranked the MRI scans from 1 (best) to 3 (worst) regarding fat-suppression homogeneity. The added value of SHARP was defined as the difference between the proportions of images where SHARP outranked CHESS and where CHESS outranked SHARP. Friedman, Wilcoxon signed rank, and χ2 tests were used to compare in vivo data. Results At numerical simulations, SHARP showed 0% water and 62%-70% fat suppression, whereas CHESS showed 2% water and 57% fat suppression. Phantom data demonstrated lower fat-suppression inhomogeneity indexes with Dixon (1.0%) and SHARP (2.4%) compared with CHESS (10.7%). In 15 participants (mean age, 38.5 years ± 12.8 [SD]; six female participants), mean ranking by readers of fat homogeneity in the reference technique (ankle, foot, hand in superman position, and hand-by-the-side position: 1.02, 1.02, 1.03, and 1.06, respectively) was higher than those with SHARP (1.39, 1.46, 1.50, and 1.66, respectively), which were higher than those with CHESS (1.64, 1.80, 1.61, and 1.80, respectively) (all P < .001). The added value of SHARP was highest for images in the foot (389 of 1158; 33.6%; P < .001 vs other joints), followed by the ankle (247 of 971 [25%]; P < .001 vs both hand positions), and lowest for hand-by-the-side and hand in superman positions (158 of 1223; [13%] and 133 of 1193 [11%], respectively; P = .18). Conclusion SHARP provided more homogeneous fat suppression than CHESS. © RSNA, 2024 Supplemental material is available for this article.

Disentanglement-inspired single-source domain-generalization network for cross-scene hyperspectral image classification

Cross-scene classification stands as a pivotal frontier in hyperspectral image (HSI) processing, aiming to enhance the generalization capabilities of classification models. However, the diversity of sensor type, shooting environments, and shooting times leads to the spectral heterogeneity problem in HSI. As a result, the same land cover may exhibit varying spectral traits in different domains, posing challenges for cross-scene HSI classification. Drawing inspiration from image disentanglement, we have identified that extracting the latent domain-invariant representation (DIR) of HSI could potentially mitigate the spectral heterogeneity issue. Therefore, we propose a Disentanglement-Inspired Single-Source Domain Generalization Network (DSDGnet) for cross-scene HSI classification in this paper. Firstly, a style transfer module based on a Transformer encoder-transfer-decoder is designed to expand the single source domain to an extended domain. Then, a progressive disentanglement module is proposed to decompose the domain-invariant features and domain-specific features of HSI. Furthermore, a domain combination module is designed to guarantee the accuracy of the progressive disentanglement module and ensure the effectiveness of the domain-invariant feature of HSI. Finally, the domain-invariant features are applied to the classification task, and the domain-specific features are separated to reduce their impact on the generalization ability of classification models. Extensive experiments on three HSI datasets have demonstrated the advanced classification performance of DSDGnet compared to existing domain-generalization methods.

The importance of spatial scale and vegetation complexity in woody species diversity and its relationship with remotely sensed variables

Plant species diversity is key to ecosystem functioning, but in recent decades anthropogenic activities have prompted an alarming decline in this community trait. Thus, developing strategies to understand diversity dynamics based on affordable and efficient remote sensing monitoring is essential, as well as examining the relevance of spatial scale and vegetation structural complexity to these dynamics. Here, we used two mathematical approaches to assess the relationship between tropical woody species diversity and spectral diversity in a human-modified landscape in two vegetation types differing in their degree of complexity. Vegetation complexity was measured through the fraction of species that concentrate different proportions of the cumulative importance value index. Species diversity was assessed using Hill numbers at three spatial scales, and metrics of spectral heterogeneity, vegetation indices, as well as raw data from Landsat 9 and Sentinel-2 sensors were calculated and analysed through general linear models (GLM) and Random Forest. Vegetation complexity emerged as an important variable in modelling species from remote sensing metrics, indicating the need to model species diversity by vegetation type rather than region. Hill numbers showed different relationships with remotely sensed metrics, in consistency with the scale-dependency of ecological processes on species diversity. Contrary to multiple previous reports, in our study, GLMs produced the best fits between Hill numbers of all orders and remotely sensed metrics. If we are to meet the need of conducting efficient and speedy woody species diversity monitoring globally, we propose modelling this diversity from remotely-sensed variables as an attractive strategy, so long as the intrinsic properties of each vegetation type are acknowledged to avoid under- or overestimation biases.

Learning by counterexamples in remote sensing image classification: a case study for road extraction

ABSTRACT The classification of multi- and hyper-spectral imagery is essential for several remote sensing applications. However, several challenges still hinder the accuracy and efficiency of learning algorithms. In diverse remote sensing applications, the high spectral heterogeneity between and within classes is often an issue that adds uncertainties to the classification. Most classifiers do not consider the influence of non-target classes and variations in the spectral similarity level between classes during classification. This paper presents a novel learning strategy for remote sensing image classification, which exploits non-target classes to enhance the generation of classification masks and the performance of supervised methods for specific classes of interest. It produces multiple classification instances, based on the incorporation of counterexample information into the training data set, in order to refine the separation of classes of interest in the available feature space. The experimental results demonstrate the potential of the proposed counterexample assisted learning for road extraction, using high spatial-resolution imagery data.

PaRaVis: An automatic Python graphical package for ensemble analysis of plant beta diversity using remote sensing proxies

The ongoing impacts of climatic changes, coupled with intensified human activities, are leading to a significant loss of plant diversity, prompting urgent calls for comprehensive monitoring of forest ecosystems. This is particularly concerning in regions with the encroachment of human activities into forest zones, and inadequate regulations that primarily view forests as a source of timber without fully accounting for their critical roles in biodiversity. These issues highlight the need for effective geospatial approaches to support conservation strategies and sustained monitoring of plant diversity. The majority of remote sensing studies of plant diversity have focused on alpha-diversity, while beta-diversity plays an important role in providing a comprehensive picture of biodiversity patterns across scales. Among various remote sensing proxies, Rao's Q index stands out as a reliable metric to quantify beta-diversity using multispectral remote sensing indices. However, a holistic approach that can incorporate multiple remote sensing indices and enable comparative analysis using graphical means with computational efficiency is lacking. In response, we developed PaRaVis, an open-source Python-based graphical package for deriving spectral diversity from multispectral remote sensing datasets as a proxy for functional diversity using Rao's Q index. This tool encompasses all the necessary steps for parallelized computation, visualization, and analysis of Rao's index from either single or multiple multispectral indices. It is capable of calculating and visualizing 75 vegetation indices (VIs), from a raster scene, followed by calculating Rao's Q both unidimensionally and multidimensionally in parallel. PaRaVis also offers features for visualizing, analyzing, and comparing Rao's Q outputs using statistical performance diagnostics. It provides a unique means to infer diversity patterns in space and time and is therefore invaluable for researchers or organizations involved with plant diversity monitoring, especially those with limited data analysis or computer programming experience. To demonstrate the tool's effectiveness, we analyzed plant diversity patterns using satellite-derived spectral heterogeneity measures in forest sites within the Hyrcanian Forests of Iran and sites located in Germany. The first case study focused on UNESCO Natural Heritage sites. Our analysis revealed that employing EVI, SR3, and TCI indices as inputs for the multidimensional Rao's Q yielded higher performance in monitoring more heterogeneous forests compared to the unidimensional mode. In the second case study, we utilized field Species Richness and Shannon-Wiener diversity indices to evaluate PaRaVis and our method. We found that using a multi-temporal, multi-index approach enhances the results compared to multi-seasonal and classical Rao based on single time.

Investigation on Fluorescence Origin and Spectral Heterogeneity in Carbon Dots: A Dynamic Perspective

AbstractHeterogeneity in the fluorescence of carbon dots (CDs) has been hard to figure out so far. This could potentially be related to structural factors, size or intermediate fluorophores, especially when employing the bottom‐up synthesis. Herein, we unveil the origin of fluorescence and spectral heterogeneity of CDs using a simple dynamic method. This work reports the room light‐excited green fluorescence and dual‐emissive N‐doped CDs synthesized using a hydrothermal method. Studies were carried out considering the factors of fluorescence phenomena, such as excitation‐independent emission, fluorescent impurities, aggregation and solvation dynamics. The new steady‐state, Excitation‐resolved area‐normalized emission spectroscopy (ERANES) and ensemble measurements, including time‐resolved studies reveal that a heterogeneous environment exists in the ground state. Eventually, the results show the existence of core, edge and surface states in the CDs. Additionally, the fluorescence characteristics depend on the structural functionalization that occurs in both the intrinsic and extrinsic states of the CDs and it is proven that this does not violate the Kasha‐Vavilov rule. Although a highly purified material could still exhibit heterogeneity due to the ensemble emissive states and structural variations. Our results provide insights into the enduring debates about fluorescence and a deeper understanding of the structure‐ property relationship of CDs.

National-scale 10 m annual maize maps for China and the contiguous United States using a robust index from Sentinel-2 time series

Accurate and continuous maps of maize distribution are essential for food security and sustainable agricultural development. However, there are no continuous national-scale and fine-resolution maize maps and explicit updated information on the spatiotemporal dynamics of maize for most countries. Maize mapping at the national scale is challenging due to the spectral heterogeneity caused by crop growth conditions, cropping patterns, and inter-annual variations. To this end, this study developed a novel crop index-based algorithm for national-scale maize mapping. Compared to other crops, maize is characterized by large-leaf-dominated canopies and high photosynthetic efficiency. Maize shows significant changes in chlorophyll and anthocyanin content. Therefore, a robust maize index was established by exploring the temporal Variation of the Vegetation-Pigment index (VVP) during the growing period. A simple decision rule was coded on the Google Earth Engine (GEE) platform, which was used for maize mapping based on the Sentinel-2 time series in China and the contiguous United States (US) from 2018 to 2022. The national-scale 10 m annual maize maps for China and the contiguous US were developed and in good agreement with the corresponding agricultural statistics data for many years (R2 > 0.94) and 9,412 reference points (overall accuracy of 90.09 %). Compared with simply applying the vegetation index, the VVP index took account of spectral heterogeneity caused by variations in crop growth conditions, cropping patterns, and inter-annual, and the omission error of maize was reduced by over 20 %. Moreover, the VVP index can significantly improve the spatial transferability of the Random Forest (RF) classifier. The first 10 m annual maize maps for China revealed that the planted area trend decreased and then increased from 2018 to 2022. The year 2020 was the turning point. The maize planted area consisted of 68 % single maize and 32 % double cropping with maize in 2020, with the northern boundary for double cropping with maize in the Yanshan Mountains. The maize planted area in China consistently decreased by 39,352 km2 (about 9 %) from 2018 to 2020. This is mainly due to the adjustment of the maize-planted structure in the “Sickle Bend” region of China (the “Sickle Bend” policy). However, the maize planted area gradually recovered from 2020 to 2022, primarily concentrated in regions with ever-planted. This study will provide essential information for cropping structure adjustment and related agricultural policy formulation and contribute to sustainable agricultural development by mapping maize from a national to a global scale.

The relationship between remotely-sensed spectral heterogeneity and bird diversity is modulated by landscape type

To identify areas of high biodiversity and prioritize conservation efforts, it is crucial to understand the drivers of species richness patterns and their scale dependence. While classified land cover products are commonly used to explain bird species richness, recent studies suggest that unclassified remote-sensed images can provide equally good or better results. In our study, we aimed to investigate whether unclassified multispectral data from Landsat 8 can replace image classification for bird diversity modeling. Moreover, we also tested the Spectral Variability Hypothesis.Using the Atlas of Breeding Birds in the Czech Republic 2014-2017, we modeled species richness at two spatial resolutions of approx. 131 km2 (large squares) and 8 km2 (small squares). As predictors of the richness, we assessed 1) classified land cover data (Corine Land Cover 2018 database), 2) spectral heterogeneity (computed in three ways) and landscape composition derived from unclassified remote-sensed reflectance and vegetation indices. Furthermore, we integrated information about the landscape types (expressed by the most prevalent land cover class) into models based on unclassified remote-sensed data to investigate whether the landscape type plays a role in explaining bird species richness.We found that unclassified remote-sensed data, particularly spectral heterogeneity metrics, were better predictors of bird species richness than classified land cover data. The best results were achieved by models that included interactions between the unclassified data and landscape types, indicating that relationships between bird diversity and spectral heterogeneity vary across landscape types.Our findings demonstrate that spectral heterogeneity derived from unclassified multispectral data is effective for assessing bird diversity across the Czech Republic. When explaining bird species richness, it is important to account for the type of landscape and carefully consider the significance of the chosen spatial scale.

Computational Assessment of Spectral Heterogeneity within Fresh Glioblastoma Tissue Using Raman Spectroscopy and Machine Learning Algorithms.

Understanding and classifying inherent tumor heterogeneity is a multimodal approach, which can be undertaken at the genetic, biochemical, or morphological level, among others. Optical spectral methods such as Raman spectroscopy aim at rapid and non-destructive tissue analysis, where each spectrum generated reflects the individual molecular composition of an examined spot within a (heterogenous) tissue sample. Using a combination of supervised and unsupervised machine learning methods as well as a solid database of Raman spectra of native glioblastoma samples, we succeed not only in distinguishing explicit tumor areas-vital tumor tissue and necrotic tumor tissue can correctly be predicted with an accuracy of 76%-but also in determining and classifying different spectral entities within the histomorphologically distinct class of vital tumor tissue. Measurements of non-pathological, autoptic brain tissue hereby serve as a healthy control since their respective spectroscopic properties form an individual and reproducible cluster within the spectral heterogeneity of a vital tumor sample. The demonstrated decipherment of a spectral glioblastoma heterogeneity will be valuable, especially in the field of spectroscopically guided surgery to delineate tumor margins and to assist resection control.

Tree species diversity mapping from spaceborne optical images: The effects of spectral and spatial resolution

AbstractIncreasingly available spaceborne sensors provide unprecedented opportunities for large‐scale, timely and continuous tree species diversity (TSD) monitoring. However, given differences in spectral and spatial resolutions, the choice of sensor is not always straightforward. In this work, we investigated the effects of spatial and spectral resolutions for four spaceborne sensors (RapidEye, Landsat‐8, Sentinel‐2 and PlanetScope) on TSD mapping in an area of approximately 4000 km2 within the Black Forest, Germany. We employed a random forest (RF) regression model to predict Shannon–Wiener diversity based on seven types of spectral heterogeneity metrics (texture, coefficient of variation, Rao's Q, convex hull volume, spectral angle mapper, convex hull area and spectral species diversity) and a full survey dataset from 135 one‐ha sample plots. We compared the RF model's performance across sensors and spatial resolutions. Our results demonstrated that the Sentinel‐2‐based TSD model achieved the highest accuracy (mean R2: 0.477, mean root‐mean‐square error (RMSE): 0.274). The RapidEye‐based TSD model produced lower accuracy (mean R2: 0.346, mean RMSE: 0.303), but it was better than the PlanetScope‐ and Landsat‐based TSD models. The 10 m (for Sentinel‐2 and RapidEye) and 15 m (for PlanetScope) were the best spatial resolutions for predicting TSD. The NIR band was the most favourable spectral band for predicting TSD. Texture metrics and Rao's Q outperformed the other spectral heterogeneity metrics. Our results highlighted that spaceborne optical imagery (especially Sentinel‐2) can be successfully used for large‐scale TSD mapping but that the choice of sensors can significantly affect the resulting mapping accuracy in temperate montane forests.

Mapping tree species diversity in a typical natural secondary forest by combining multispectral and LiDAR data

Accurate monitoring of tree species diversity is crucial for understanding the dynamic changes in tree species diversity and its relationships with other services and functions in forest ecosystems. Traditional optical remote sensing data have been widely used for monitoring tree species diversity based on the spectral variation hypothesis (SVH). However, this method cannot capture the three-dimensional structural variations in complex species compositions under different stand conditions. In this study, we modeled tree species diversity in terms of spectral variation and stand structural complexity in a typical natural secondary forest in Northeast China by combining Sentinel-2 data and UAV-borne light detection and ranging (LiDAR) point cloud data. First, species diversity indices (including the Shannon index H' and Simpson index D1) were derived from 60 field-measured plots. Second, recursive feature elimination (RFE) was utilized for feature filtering of ten spectral bands and four vegetation indices extracted from Sentinel-2 data and Rao's Q index, as well as eleven features extracted from LiDAR point clouds reflecting the complexity of the stand structure. Subsequently, the random forest method was utilized to fit and predict the relationship between the remote sensing feature set and tree species diversity. The results showed that the use of multisource remote sensing feature set to estimate tree species diversity had the highest accuracy (R2 = 0.44, RMSE = 0.28 for H') compared to the use of only one data source. Moreover, when using a single remote sensing feature set, the estimation accuracy of the optical remote sensing feature set is higher than that of the LiDAR feature set for H' and D1, and the NIRv is the most influential spectral feature. This study clarified the value of spectral variation and productivity heterogeneity embodied in optical remote sensing features for monitoring tree species diversity, as well as evaluating the shortcomings and possibilities of using LiDAR point cloud features independently, and fully confirmed the positive significance of complementary effects between multisource remote sensing feature sets.

Visualizing nanoscale heterogeneity in perylene thin films via tip-enhanced photoluminescence with unsupervised machine learning.

We investigate the properties of ultrathin 3,4,9,10-perylenetetracarboxylic diimide (PTCDI) films using a combination of tip-enhanced photoluminescence and unsupervised machine learning. We expose nanoscale spectral heterogeneities that can be understood on the basis of the interplay between vibronic effects, intermolecular excitons, and intramolecular excitons in PTDCI films.

INDIVIDUAL TREE-BASED FOREST SPECIES DIVERSITY ESTIMATION USING UAV-BORNE HYPERSPECTRAL AND LIDAR DATA

Abstract. Forest biodiversity is essential in maintaining ecosystem functions and services. Recently, unmanned aerial vehicle (UAV) remote sensing technology has emerged as a cost-effective and flexible tool for biodiversity monitoring. In this study, we compared the optimal clustering algorithm, classification method (spectral angle mapper, SAM), spectral diversity metric and structural heterogeneity index for forest species diversity estimation in two complex subtropical forests, Mazongling (MZL) and Gonggashan (GGS) National Nature Forest Reserves in China, using UAV-borne hyperspectral and LiDAR data. The results showed that the SAM classification method performed better with higher values of R2 than the clustering algorithm for predicting both species richness (MZL: 0.62 &gt; 0.46 and GGS: 0.55 &gt; 0.46) and Shannon-Wiener index (MZL: 0.64 &gt; 0.58 and GGS: 0.52 &gt; 0.47), while the optimal clustering algorithm had the highest prediction accuracy for the Simpson index, followed by the SAM classification method, spectral diversity metric and structural heterogeneity index (MZL: 0.83&gt;0.44&gt;0.31&gt;0.12, GGS: 0.62&gt;0.44&gt;0.38&gt;0.00). Our study indicated that the SAM classification method had the advantage of identifying rare species and estimating species richness, while the clustering method could capture forest diversity patterns rapidly without distinguishing the specific tree species and predict the Simpson index more accurately. Overall, both clustering and classification methods exhibited superior performance compared to spectral or structural diversity indices. Our findings highlight the applicability of UAV remote sensing in monitoring forest species diversity in complex subtropical forests.

Spectral Heterogeneity Analysis and Soil Organic Matter Inversion across Differences in Soil Types and Organic Matter Content in Dryland Farmland in China

Soil organic matter (SOM) plays an important role in agricultural production and arable land quality improvement. Hyperspectral technology enables frequent surveys over large areas. In this study, we explored the spectral heterogeneity of differences in soil types and SOM content, and proposed a method for measuring SOM content in large areas using spectroscopy. The results indicate regional variations in factors affecting soil spectral absorption peaks, with noticeable latitudinal disparities. The first-order differential partial-least-squares method provided the best prediction for the SOM inversion. The coefficient of determination (R2) for the SOM inversion model was 0.93, and the root mean square error (RMSE) was 3.42, with an 8.49 g/kg difference in the SOM content. When the difference in SOM content fell between 8 and 15 g/kg, the inversion effect model performed best. The optimal model R2 exceeded 0.95, and the RMSE was less than 5. The comprehensive analysis showed that the organic matter content was an important factor affecting the SOM content estimate and must be considered in the real process. In addition, it is crucial to categorize soil samples on the basis of distinct soil types while maintaining a consistent range of SOM content within the same soil type, ideally between 8 and 15 g/kg. Subsequently, the first-order differential partial least squares method is applicable. These results are expected to contribute to the acquisition of high-quality information on variations in the SOM of complex large-scale areas.

Spectral Heterogeneity of Phenol Blue in Protic Solvents Revealed by Ultrafast Nonradiative Decay Dynamics

AbstractPhenol blue (PhB) is a dye that exhibits positive solvatochromism which is also nonfluorescent due to its ultrashort excited state lifetime. We have conducted femtosecond time‐resolved transient absorption (TA) spectroscopic measurements of a PhB solution using two distinct excitation wavelengths, at shorter and longer wavelength sides of the visible absorption band. The results revealed that a long‐lived heterogeneity exists in protic solvents whereas, in aprotic solvents, the system rapidly returns to the original thermal equilibrium on the ultrafast timescale. Interestingly, the lifetime of the heterogeneity is longer for methanol (MeOH) compared to ethanol (EtOH) solution suggesting stronger interaction between PhB and MeOH. Additionally, since the excited state lifetime of PhB was still in the sub‐picosecond domain even in a polymer matrix, we suggest a nonradiative decay mechanism through high‐frequency vibrational modes without involving large structural reconfiguration such as twisting of the benzoquinone (BzQ) moiety.

Using spectral diversity and heterogeneity measures to map habitat mosaics: An example from the Classical Karst

AbstractQuestionsCan we map complex habitat mosaics from remote‐sensing data? In doing this, are measures of spectral heterogeneity useful to improve image classification performance? Which measures are the most important? How can multitemporal data be integrated in a robust framework?LocationClassical Karst (NE Italy).MethodsFirst, a habitat map was produced from field surveys. Then, a collection of 12 monthly Sentinel‐2 images was retrieved. Vegetation and spectral heterogeneity (SH) indices were computed and aggregated in four combinations: (1) monthly layers of vegetation and SH indices; (2) seasonal layers of vegetation and SH indices; (3) yearly layers of SH indices computed across the months; and (4) yearly layers of SH indices computed across the seasons. For each combination, a Random Forest classification was performed, first with the complete set of input layers and then with a subset obtained by recursive feature elimination. Training and validation points were independently extracted from field data.ResultsThe maximum overall accuracy (0.72) was achieved by using seasonally aggregated vegetation and SH indices, after the number of vegetation types was reduced by aggregation from 26 to 11. The use of SH measures significantly increased the overall accuracy of the classification. The spectral β‐diversity was the most important variable in most cases, while the spectral α‐diversity and Rao's Q had a low relative importance, possibly because some habitat patches were small compared to the window used to compute the indices.ConclusionsThe results are promising and suggest that image classification frameworks could benefit from the inclusion of SH measures, rarely included before. Habitat mapping in complex landscapes can thus be improved in a cost‐ and time‐effective way, suitable for monitoring applications.

Towards Single-Molecule Analyte Sensing with Single-Walled Carbon Nanotubes

Single-walled carbon nanotubes (SWCNTs) have been at the forefront of research for well over a decade due to their unique optical properties. Specifically, the inherent and photostable fluorescence of SWCNTs has been used to create sensitive near-infrared (NIR) sensors for a variety of target analytes, such as dopamine and cholesterol. However, most of the SWCNT sensors are employed in the solution-phase, severely limiting the efficacy due to ensemble measurements which can significantly confound any optical readout. One potential solution involves imaging individual SWCNTs deposited on a surface, unfortunately it is well known that SWCNT florescence is adversely affected by the inherently rough surfaces that are conventionally used for their observation, which emphasizes the need for a novel platform. We therefore devised a spin-coated hydrogel platform that contained SWCNTs in a single focal plane and enabled fluorescent imaging without significantly perturbing their optical properties when compared to the solution-phase. As SWCNT fluorescent microcopy is limited by diffraction, several strategies were employed to improve the sensitivity of analyte detection. When single SWCNTs longer than the diffraction limit were examined, spectral heterogeneity was observed along the length of each SWCNT, suggesting an application in single-molecule analyte detection. A common surfactant sodium deoxycholate (SDC) was investigated as an analyte as it exchanges with DNA oligonucleotides on the surface of DNA-SWCNTs, by which the kinetic response can be used to estimate DNA binding strength. We employed DNA-SWCNTs in the hydrogel platform and observed the kinetic response of individual SWCNTs in response to the addition of SDC. Importantly, the diffusion was intentionally slowed by modifying the top hydrogel layer to examine the kinetic response on a longer time scale, in contrast to the nearly instantaneous response in solution. We observed a decoupling of the spectral properties of each SWCNT, where the fluorescence intensity responded later than the emission wavelength. We then utilized a 1-dimensional diffusion model to estimate the local concentration of each SWCNT. We hypothesize that differences in the SDC functionalization morphology on SWCNTs have a significant impact on this observed effect.

Forest Tree Species Diversity Mapping Using ICESat-2/ATLAS with GF-1/PMS Imagery

Forest ecosystems depend on species of tree variety. Remote sensing for obtaining large-scale spatial distribution information of tree species diversity is a geoscience research hotspot to overcome the limitations of conventional tree species diversity survey approaches. Airborne LiDAR or synergy with airborne optical imagery has been used to model and estimate tree species diversity for specific forest communities, with many revealing results. However, the data collection for such research is costly, the breadth of monitoring findings is limited, and obtaining information on the geographical pattern is challenging. To this end, we propose a method for mapping forest tree species diversity by synergy satellite optical remote sensing and satellite-based LiDAR based on the spectral heterogeneity hypothesis and structural variation hypothesis to improve the accuracy of the remote sensing monitoring of forest tree species diversity while considering data cost. The method integrates horizontal spectral variation from GF-1/PMS image data with vertical structural variation from ICESat-2 spot data to estimate the species diversity of trees. The findings reveal that synergistic horizontal spectral variation and vertical structural variation overall increase tree species diversity prediction accuracy compared to a single remote sensing variation model. The synergistic approach improved Shannon and Simpson indices prediction accuracy by 0.06 and 0.04, respectively, compared to the single horizontal spectral variation model. The synergistic model, single vertical structural variation model, and single horizontal spectral variation model were the best prediction models for Shannon, Simpson, and richness indices, with R2 of 0.58, 0.62, and 0.64, respectively. This research indicates the potential of synergistic satellite-based LiDAR and optical remote sensing in large-scale forest tree species diversity mapping.

Tuning the Colloidal Softness of CsPbBr3 Nanocrystals for Homogeneous Superlattices.

Perovskite nanocrystal superlattices (NC SLs), made from millions of ordered crystals, support collective optoelectronic phenomena. Coupled NC emitters are highly sensitive to the structural and spectral inhomogeneities of the NC ensemble. Free electrons in scanning electron microscopy (SEM) are used to probe the cathodoluminescence (CL) properties of CsPbBr3 SLs with a ∼20 nm spatial resolution. Correlated CL-SEM measurements allow for simultaneous characterization of structural and spectral heterogeneities of the SLs. Hyperspectral CL mapping shows multipole emissive domains within a single SL. Consistently, the edges of the SLs are blue-shifted relative to the central domain by up to 65 meV. We discover a relation between NC building block colloidal softness and the extent of the CL shift. Residual uniaxial compressive strains accompanying SL formation are contributors to these emission shifts. Therefore, precise control over the colloidal softness of the NC building blocks is critical for SL engineering.