How unique are spectral signatures?
Abstract
Recent improvements in instrumentation have made possible the acquisition of high spectral resolution image data corresponding to spectral reflectance signatures. It would seem that sufficiently high spectral resolution, of order 0.01 μm, would be adequate to permit unique discrimination of virtually any soils, vegetation types, and rocks, but we show by example that this is not necessarily the case.
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Multi-source remote sensing data and image fusion technology reveal significant spatiotemporal heterogeneity of inundation dynamics in a typical large floodplain lake system
2023, Journal of Hydrology: Regional StudiesThe Poyang Lake, which is located on the south bank of the middle-lower Yangtze River basin. The lake is the largest freshwater lake in China, and also a typical floodplain lake in the world.
The spatiotemporal heterogeneity of inundation dynamics of large floodplain lake system has not been paid enough attention. Based on the reconstructed high spatial and temporal resolution inundation dataset using the image fusion model and multi-source remote sensing data, this study systematically analyzed the spatiotemporal heterogeneity of inundation dynamics in the Poyang Lake- floodplain system.
It is found that within the same floodplain lake, the inundated area and inundation frequency in different regions of the lake (the main lake region and the adjacent floodplain region) can have asynchronous intra-annual fluctuation and opposite inter-annual change trend. This is highly related to the hydrological complexity of the lake: the relative impacts of catchment inflow and the Yangtze River varies in different regions across the lake. The stage-area relationship at the central station along the flow direction of the lake has the highest linear correlation, which might provide more accurate estimates of lake surface/volume. In addition, this study highlights the importance of reconstructed high spatial-temporal resolution of remote sensing data for the accurate assessment of inundation dynamics in floodplain lakes. All the results enrich the understanding of complex hydrological regime of large floodplain lakes and are valuable for the practice of water resources management and ecological conservation in such lakes.
High-resolution multispectral imagery and LiDAR point cloud fusion for the discrimination and biophysical characterisation of vegetable crops at different levels of nitrogen
2022, Biosystems EngineeringHigh-resolution remote sensing data has expanded the scope, precision, and scale of remote sensing applications in agriculture. Availability of spatial information at actionable field units is vital for using remote sensing data in agriculture. Crop discrimination and biophysical characterisation sensitive to nutrient levels have not been addressed at the patch level. This work investigates the synergetic application of high-resolution satellite imagery and terrestrial LiDAR point cloud for object-level discrimination and biophysical characterisation of a few crops at different nitrogen (N) levels. To this end, cabbage, eggplant, and tomato at three levels of N were grown on the experimental fields of the University of Agricultural Sciences, Bengaluru, India, in 2017. Fusing the multispectral imagery (WorldView-III) and LiDAR point cloud (terrestrial laser scanner) at the feature level, object-level supervised classification and estimation of two critical biophysical parameters (crown area and biomass) were performed using the support vector machine (SVM) and Random Forests (RF) algorithms with reference to different N levels. Results suggest discrimination of vegetable crops with high accuracy (92%), about 20% higher than the individual sensors, from the fused imagery sensitive to N levels. The quality of retrievals indicates a contrasting pattern wherein the accuracy of the crown area is high with the LiDAR point cloud at various N levels. For the biomass, there is no perceptible differentiation of N levels within a crop. The accuracy of crop classification with reference to N levels is similar from both RF and SVM algorithms. However, RF algorithm offered substantially higher classification results when the N status is ignored. In contrast, the quality of biophysical modelling is very high and is similar from both the algorithms. Weather conditions and sub-field level environment-induced variations in the crop growth likely are the factors responsible for the reduced sensitivity of remote sensing data to crop N levels at the patch level.
Seasonal patterns of spectral diversity at leaf and canopy scales in the Cedar Creek prairie biodiversity experiment
2022, Remote Sensing of EnvironmentThe relationship between biodiversity and spectral diversity is highly scale-dependent, and temporal variation in leaf morphological, biochemical traits and canopy structure can alter this relationship. However, the temporal dependence of the spectral diversity – biodiversity relationship is poorly understood, in part due to the difficulties of obtaining consistent measurements across space and time. Using leaf pigments and leaf and canopy reflectance throughout a growing season in the Cedar Creek prairie biodiversity experiment, we explored phenological effects on the scale dependence of the spectral biodiversity – biodiversity relationship. Leaf reflectance spectra displayed larger among-species variation than leaf pigments, indicating that leaf reflectance contained more information for distinguishing species than some leaf trait measurements. At the canopy scale, spectral variation derived using reflectance was mainly driven by among-species variation. The canopy scale spectral diversity was also influenced by changing vegetation percent cover, key phenological events (e.g., flowering), and disturbance (drought). Our results revealed that contrasting phenological patterns of spectral diversity metrics emerged at leaf and canopy scales. Because a misunderstanding of these contrasting temporal effects across spatial scales can lead to possible misinterpretations of the spectral diversity – biodiversity relationship or of their underlying causes, more research effort is needed to understand these cross-scale temporal effects.
Mineral abundance quantification of lunar surface materials using remotely acquired spectra can be achieved with spectral unmixing methods, but this has remained a challenging problem. The existing unmixing methods neglect the existence of widespread glass material and the spectral similarity of different minerals. To solve this problem, this study proposes a method to calculate the main mineral abundances in lunar regolith based on reflectance spectra by utilizing Fisher transformation combined with multiple endmember spectral mixture analysis (MESMA). A set of spectra of lunar main constituents, including plagioclase, pyroxene, ilmenite, olivine and glass material, were selected from the RELAB spectral database and used as endmembers. The endmember spectra were utilized to compute the projection vectors, with which the spectral characteristics can be transformed into low-dimensional Fisher space in which intraclass spectral variability can be minimized while the interclass variability of different mineral spectra can be maximized. The endmember spectra and the lunar soil spectra in the Lunar Soil Characterization Consortium (LSCC) dataset were both transformed into Fisher space, in which MESMA was executed to acquire the fraction of each endmember. MESMA allows for comprehensive endmember expressions by representing a type of mineral using multiple spectra rather than a single endmember. Compared with laboratory mineral measurements, this approach estimated the mineral abundances with determination coefficient R2 values equal to 0.88 and 0.65 in the lunar mare and highland regions, respectively. After validation with laboratory data, the method was further applied at the regional scale where Apollo 12 and Apollo 16 landed by using M3 images, and the estimated accuracy of different minerals was similar to that of the laboratory spectra.
Explaining discrepancies between spectral and in-situ plant diversity in multispectral satellite earth observation
2021, Remote Sensing of EnvironmentIn light of the ongoing global biodiversity crisis, the urge to monitor and map terrestrial plant biodiversity at large spatial extents has spurred research on adequate quantitative methods. The use of spectral diversity metrics from different remote sensing platforms has emerged as a promising tool for such biodiversity assessments. Satellite remote sensing presents the next frontier for implementation of these methods to assess plant diversity with spatial and temporal continuity at truly regional or global scales. However, the question of what exactly is monitored by spectral diversity metrics from relatively coarse multi-spectral satellite observations has remained largely unanswered.
In this research, we examined which components contribute to satellite remotely sensed spectral diversity. We assessed the relationships between spectral diversity and in-situ taxonomic and trait diversity, and evaluated the role of confounding factors, vegetation cover, and landscape morphology (slope and elevation), in shaping these relationships. Hereto, we used Sentinel-2 imagery and in-situ field trait and species count data collected in the Montesinho-Nogueira Natura 2000 site (Portugal) together with radiative transfer models to quantify the theoretical link between in-situ trait diversity and simulated spectral diversity.
Through the use of linear mixed-effect models, our results highlight that variation in vegetation cover dominates the Sentinel-2's spectral diversity signal (contributing 53–84% of the R2marginal). The vegetation cover component encompasses spatial variability in canopy architecture traits as well as the fraction of bare soil and plant litter spectra. These elements together strongly impact the overall spectral diversity signal, as shown both in our radiative transfer simulations and empirical comparisons. Next to vegetation cover, we found that taxonomic diversity is a significant predictor and covariate of spectral diversity, while the role of leaf trait diversity appeared insignificant in our multispectral dataset.
Variation in vegetation cover dominated the spectral diversity signal in our study while it is not necessarily correlated with plant diversity. We, therefore, recommend that future applications of multi-spectral diversity metrics consider the impact of vegetation cover, including soil variability and the role of morphological traits, in shaping leaf trait - canopy reflectance relationships to better understand the ambiguous performance of spectral diversity as a proxy of plant diversity. This will result in higher robustness, consistency, and scalability of spectral diversity metrics for predicting in-situ plant diversity across scales, sensors, and ecosystems in regional biodiversity assessments.
Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion
2021, Remote Sensing of EnvironmentRemote sensors, onboard orbital platforms, aircraft, or unmanned aerial vehicles (UAVs) have emerged as a promising technology to enhance our understanding of changes in ecosystem composition, structure, and function of forests, offering multi-scale monitoring of forest restoration. UAV systems can generate high-resolution images that provide accurate information on forest ecosystems to aid decision-making in restoration projects. However, UAV technological advances have outpaced practical application; thus, we explored combining UAV-borne lidar and hyperspectral data to evaluate the diversity and structure of restoration plantings. We developed novel analytical approaches to assess twelve 13-year-old restoration plots experimentally established with 20, 60 or 120 native tree species in the Brazilian Atlantic Forest. We assessed (1) the congruence and complementarity of lidar and hyperspectral-derived variables, (2) their ability to distinguish tree richness levels and (3) their ability to predict aboveground biomass (AGB). We analyzed three structural attributes derived from lidar data—canopy height, leaf area index (LAI), and understory LAI—and eighteen variables derived from hyperspectral data—15 vegetation indices (VIs), two components of the minimum noise fraction (related to spectral composition) and the spectral angle (related to spectral variability). We found that VIs were positively correlated with LAI for low LAI values, but stabilized for LAI greater than 2 m2/m2. LAI and structural VIs increased with increasing species richness, and hyperspectral variability was significantly related to species richness. While lidar-derived canopy height better predicted AGB than hyperspectral-derived VIs, it was the fusion of UAV-borne hyperspectral and lidar data that allowed effective co-monitoring of both forest structural attributes and tree diversity in restoration plantings. Furthermore, considering lidar and hyperspectral data together more broadly supported the expectations of biodiversity theory, showing that diversity enhanced biomass capture and canopy functional attributes in restoration. The use of UAV-borne remote sensors can play an essential role during the UN Decade of Ecosystem Restoration, which requires detailed forest monitoring on an unprecedented scale.
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Agricultural Research Service, Beltsville, Maryland