Academic literature on the topic 'Spectral diversity hypothesi'
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Journal articles on the topic "Spectral diversity hypothesi"
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Sun, Hao, Jiaqi Hu, Jiaxiang Wang, Jingheng Zhou, Ling Lv, and Jingyan Nie. "RSPD: A Novel Remote Sensing Index of Plant Biodiversity Combining Spectral Variation Hypothesis and Productivity Hypothesis." Remote Sensing 13, no. 15 (July 30, 2021): 3007. http://dx.doi.org/10.3390/rs13153007.
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Plant diversity (PD) plays an important role in maintaining the healthy function of an ecosystem through affecting the productivity, stability, and nutrient utilization of a terrestrial ecosystem. Remote sensing is a vital way to monitor the status and changes of PD. Most of the existing methods rely on a field botany survey to construct a statistical relationship between PD and remote sensing observations. However, a field botany survey is too costly to be applied widely. In this study, we constructed a new remote sensing index of PD (RSPD), combining the spectral variation hypothesis and productivity hypothesis. Concretely, the RSPD integrated the multi-band spectral reflectance and several spectral greenness, moisture, and red-edge vegetation indices with the principles of Shannon information entropy and Euclidean distance. The RSPD was evaluated by comparing the classical coefficient of variation (CV) method and the Shannon and Simpson diversity indices based on vegetation classification results. Two cases were selected, where Case I was in Beijing and Case II was located in part of Huai’an, China. Sentinel-2 data in three years of 2016, 2018, and 2020 and higher-resolution Pléiades-1 data in 2018 were also utilized. The results demonstrate that: (1) the RSPD is basically consistent with the CV in spatiotemporal variation; (2) the RSPD outperforms the CV as compared with Shannon and Simpson diversity indices that are based on vegetation classification results with Sentinel-2 and Pléiades-1 data; (3) the RSPD outperforms the CV as compared with visual interpretations with Google Earth image. The suggested index can reflect the richness and evenness of plant species, which is inherent in its calculation formula. Moreover, it has a great potential for large-scale regional and long-term series monitoring.
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Xu, Cong, Yuan Zeng, Zhaoju Zheng, Dan Zhao, Wenjun Liu, Zonghan Ma, and Bingfang Wu. "Assessing the Impact of Soil on Species Diversity Estimation Based on UAV Imaging Spectroscopy in a Natural Alpine Steppe." Remote Sensing 14, no. 3 (January 30, 2022): 671. http://dx.doi.org/10.3390/rs14030671.
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Grassland species diversity monitoring is essential to grassland resource protection and utilization. “Spectral variation hypothesis” (SVH) provides a remote sensing method for monitoring grassland species diversity at pixel scale by calculating spectral heterogeneity. However, the pixel spectrum is easily affected by soil and other background factors in natural grassland. Unmanned aerial vehicle (UAV)-based imaging spectroscopy provides the possibility of soil information removal by virtue of its high spatial and spectral resolution. In this study, UAV-imaging spectroscopy data with a spatial resolution of 0.2 m obtained in two sites of typical alpine steppe within the Sanjiangyuan National Nature Reserve were used to analyze the relationships between four spectral diversity metrics (coefficient of variation based on NDVI (CVNDVI), coefficient of variation based on multiple bands (CVMulti), minimum convex hull volume (CHV) and minimum convex hull area (CHA)) and two species diversity indices (species richness and the Shannon–Wiener index). Meanwhile, two soil removal methods (based on NDVI threshold and the linear spectral unmixing model) were used to investigate the impact of soil on species diversity estimation. The results showed that the Shannon–Wiener index had a better response to spectral diversity than species richness, and CVMulti showed the best correlation with the Shannon–Wiener index between the four spectral diversity metrics after removing soil information using the linear spectral unmixing model. It indicated that the estimation ability of spectral diversity to species diversity was significantly improved after removing the soil information. Our findings demonstrated the applicability of the spectral variation hypothesis in natural grassland, and illustrated the impact of soil on species diversity estimation.
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Marzialetti, Flavio, Silvia Cascone, Ludovico Frate, Mirko Di Febbraro, Alicia Teresa Rosario Acosta, and Maria Laura Carranza. "Measuring Alpha and Beta Diversity by Field and Remote-Sensing Data: A Challenge for Coastal Dunes Biodiversity Monitoring." Remote Sensing 13, no. 10 (May 15, 2021): 1928. http://dx.doi.org/10.3390/rs13101928.
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Combining field collected and remotely sensed (RS) data represents one of the most promising approaches for an extensive and up-to-date ecosystem assessment. We investigated the potential of the so called spectral variability hypothesis (SVH) in linking field-collected and remote-sensed data in Mediterranean coastal dunes and explored if spectral diversity provides reliable information to monitor floristic diversity, as well as the consistency of such information in altered ecosystems due to plant invasions. We analyzed alpha diversity and beta diversity, integrating floristic field and Remote-Sensing PlanetScope data in the Tyrrhenian coast (Central Italy). We explored the relationship among alpha field diversity (species richness, Shannon index, inverse Simpson index) and spectral variability (distance from the spectral centroid index) through linear regressions. For beta diversity, we implemented a distance decay model (DDM) relating field pairwise (Jaccard similarities index, Bray–Curtis similarities index) and spectral pairwise (Euclidean distance) measures. We observed a positive relationship between alpha diversity and spectral heterogeneity with richness reporting the higher R score. As for DDM, we found a significant relationship between Bray–Curtis floristic similarity and Euclidean spectral distance. We provided a first assessment of the relationship between floristic and spectral RS diversity in Mediterranean coastal dune habitats (i.e., natural or invaded). SVH provided evidence about the potential of RS for estimating diversity in complex and dynamic landscapes.
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Pacheco-Labrador, J., U. Weber, X. Ma, M. D. Mahecha, N. Carvalhais, C. Wirth, A. Huth, et al. "EVALUATING THE POTENTIAL OF DESIS TO INFER PLANT TAXONOMICAL AND FUNCTIONAL DIVERSITIES IN EUROPEAN FORESTS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-1/W1-2021 (February 11, 2022): 49–55. http://dx.doi.org/10.5194/isprs-archives-xlvi-1-w1-2021-49-2022.
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Abstract. Tackling the accelerated human-induced biodiversity loss requires tools able to map biodiversity and its changes globally. Remote sensing (RS) offers unique capabilities of characterizing Earth surfaces; therefore, it could map plant biodiversity continuously and globally. This approach is supported by the Spectral Variation Hypothesis (SVH), which states that spectra and species (taxonomic and trait) diversities are linked through environmental heterogeneity. In this work, we evaluate the capability of the DESIS hyperspectral imager to capture plant diversity patterns as measured in dedicated plots of the network FunDivEUROPE. We computed functional and taxonomical diversity metrics from field taxonomic, structural, and foliar measurements in vegetation plots sampled in Spain and Romania. In addition, we also computed functional diversity metrics both from the DESIS reflectance factors and from vegetation parameters estimated via inversion of a radiative transfer model. Results showed that only metrics computed from spectral reflectance were able to capture taxonomic variability in the area. However, the lack of sensitivity was related to the insufficient plot size and the lack of spatial match between remote sensing and field data, but also the differences between the information contained in the field traits and remote sensing data, and the potential uncertainties in the remote estimates of vegetation parameters. Thus, while DESIS showed some sensitivity to plant diversity, further efforts are needed to deploy suitable biodiversity evaluation and validation plots and networks that support the development of biodiversity remote sensing products.
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Liccari, Francesco, Maurizia Sigura, and Giovanni Bacaro. "Use of Remote Sensing Techniques to Estimate Plant Diversity within Ecological Networks: A Worked Example." Remote Sensing 14, no. 19 (October 2, 2022): 4933. http://dx.doi.org/10.3390/rs14194933.
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As there is an urgent need to protect rapidly declining global diversity, it is important to identify methods to quickly estimate the diversity and heterogeneity of a region and effectively implement monitoring and conservation plans. The combination of remotely sensed and field-collected data, under the paradigm of the Spectral Variation Hypothesis (SVH), represents one of the most promising approaches to boost large-scale and reliable biodiversity monitoring practices. Here, the potential of SVH to capture information on plant diversity at a fine scale in an ecological network (EN) embedded in a complex landscape has been tested using two new and promising methodological approaches: the first estimates α and β spectral diversity and the latter ecosystem spectral heterogeneity expressed as Rao’s Quadratic heterogeneity measure (Rao’s Q). Both approaches are available thanks to two brand-new R packages: “biodivMapR” and “rasterdiv”. Our aims were to investigate if spectral diversity and heterogeneity provide reliable information to assess and monitor over time floristic diversity maintained in an EN selected as an example and located in northeast Italy. We analyzed and compared spectral and taxonomic α and β diversities and spectral and landscape heterogeneity, based on field-based plant data collection and remotely sensed data from Sentinel-2A, using different statistical approaches. We observed a positive relationship between taxonomic and spectral diversity and also between spectral heterogeneity, landscape heterogeneity, and the amount of alien species in relation to the native ones, reaching a value of R2 = 0.36 and R2 = 0.43, respectively. Our results confirmed the effectiveness of estimating and mapping α and β spectral diversity and ecosystem spectral heterogeneity using remotely sensed images. Moreover, we highlighted that spectral diversity values become more effective to identify biodiversity-rich areas, representing the most important diversity hotspots to be preserved. Finally, the spectral heterogeneity index in anthropogenic landscapes could be a powerful method to identify those areas most at risk of biological invasion.
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Heumann, Benjamin W., Rachel A. Hackett, and Anna K. Monfils. "Testing the spectral diversity hypothesis using spectroscopy data in a simulated wetland community." Ecological Informatics 25 (January 2015): 29–34. http://dx.doi.org/10.1016/j.ecoinf.2014.10.005.
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Madonsela, Sabelo, Moses A. Cho, Abel Ramoelo, and Onisimo Mutanga. "Investigating the Relationship between Tree Species Diversity and Landsat-8 Spectral Heterogeneity across Multiple Phenological Stages." Remote Sensing 13, no. 13 (June 24, 2021): 2467. http://dx.doi.org/10.3390/rs13132467.
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The emergence of the spectral variation hypothesis (SVH) has gained widespread attention in the remote sensing community as a method for deriving biodiversity information from remotely sensed data. SVH states that spectral heterogeneity on remotely sensed imagery reflects environmental heterogeneity, which in turn is associated with high species diversity and, therefore, could be useful for characterizing landscape biodiversity. However, the effect of phenology has received relatively less attention despite being an important variable influencing plant species spectral responses. The study investigated (i) the effect of phenology on the relationship between spectral heterogeneity and plant species diversity and (ii) explored spectral angle mapper (SAM), the coefficient of variation (CV) and their interaction effect in estimating species diversity. Stratified random sampling was adopted to survey all tree species with a diameter at breast height of > 10 cm in 90 × 90 m plots distributed throughout the study site. Tree species diversity was quantified by the Shannon diversity index (H′), Simpson index of diversity (D2) and species richness (S). SAM and CV were employed on Landsat-8 data to compute spectral heterogeneity. The study applied linear regression models to investigate the relationship between spectral heterogeneity metrics and species diversity indices across four phenological stages. The results showed that the end of the growing season was the most ideal phenological stage for estimating species diversity, following the SVH concept. During this period, SAM and species diversity indices (S, H′, D2) had an r2 of 0.14, 0.24, and 0.20, respectively, while CV had an r2 of 0.22, 0.22, and 0.25, respectively. The interaction of SAM and CV improved the relationship between the spectral data and H′ and D2 (from r2 of 0.24 and 0.25 to r2 of 0.32 and 0.28, respectively) at the end of the growing season. The two spectral heterogeneity metrics showed differential sensitivity to components of plant diversity. SAM had a high relationship with H′ followed by D2 and then a lower relationship with S throughout the different phenological stages. Meanwhile, CV had a higher relationship with D2 than other plant diversity indices and its relationship with S and H′ remained similar. Although the coefficient of determination was comparatively low, the relationship between spectral heterogeneity metrics and species diversity indices was statistically significant (p < 0.05) and this supports the assertion that SVH could be implemented to characterize plant species diversity. Importantly, the application of SVH should consider (i) the choice of spectral heterogeneity metric in line with the purpose of the SVH application since these metrics relate to components of species diversity differently and (ii) vegetation phenology, which affects the relationship that spectral heterogeneity has with plant species diversity.
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Da Re, Daniele, Eva De Clercq, Enrico Tordoni, Maxime Madder, Raphaël Rousseau, and Sophie Vanwambeke. "Looking for Ticks from Space: Using Remotely Sensed Spectral Diversity to Assess Amblyomma and Hyalomma Tick Abundance." Remote Sensing 11, no. 7 (March 30, 2019): 770. http://dx.doi.org/10.3390/rs11070770.
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Landscape heterogeneity, as measured by the spectral diversity of satellite imagery, has the potential to provide information on the resources available within the movement capacity range of arthropod vectors, and to help predict vector abundance. The Spectral Variation Hypothesis states that higher spectral diversity is positively related to a higher number of ecological niches present in the landscape, allowing more species to coexist regardless of the taxonomic group considered. Investigating the landscape heterogeneity as a proxy of the resources available to vectors may be relevant for complex and continuous agro-forest mosaics of small farmlands and degraded forests, where land cover classification is often imprecise. In this study, we hypothesized that larger spectral diversity would be associated with higher tick abundance due to the potentially higher number of hosts in heterogeneous landscapes. Specifically, we tested whether spectral diversity indices could represent heterogeneous landscapes, and if so, whether they explain Amblyomma and Hyalomma tick abundance in Benin and inform on their habitat preferences. Benin is a West-African country characterized by a mosaic landscape of farmland and degraded forests. Our results showed that both NDVI-derived and spectral predictors are highly collinear, with NDVI-derived predictors related to vegetated land cover classes and spectral predictors correlated to mosaic landscapes. Amblyomma abundance was not related to the predictors considered. Hyalomma abundance showed positive relationships to spectral diversity indices and negative relationships to NDVI-derived-ones. Though taxa dependent, our approach showed moderate performance in terms of goodness of fit (ca. 13–20% R2), which is a promising result considering the sampling and scale limitations. Spectral diversity indices coupled with classical SRS vegetation indices could be a complementary approach for providing further ecological aspects in the field of disease biogeography.
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Thornley, Rachael H., France F. Gerard, Kevin White, and Anne Verhoef. "Prediction of Grassland Biodiversity Using Measures of Spectral Variance: A Meta-Analytical Review." Remote Sensing 15, no. 3 (January 23, 2023): 668. http://dx.doi.org/10.3390/rs15030668.
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Over the last 20 years, there has been a surge of interest in the use of reflectance data collected using satellites and aerial vehicles to monitor vegetation diversity. One methodological option to monitor these systems involves developing empirical relationships between spectral heterogeneity in space (spectral variation) and plant or habitat diversity. This approach is commonly termed the ‘Spectral Variation Hypothesis’. Although increasingly used, it is controversial and can be unreliable in some contexts. Here, we review the literature and apply three-level meta-analytical models to assess the test results of the hypothesis across studies using several moderating variables relating to the botanical and spectral sampling strategies and the types of sites evaluated. We focus on the literature relating to grasslands, which are less well studied compared to forests and are likely to require separate treatments due to their dynamic phenology and the taxonomic complexity of their canopies on a small scale. Across studies, the results suggest an overall positive relationship between spectral variation and species diversity (mean correlation coefficient = 0.36). However, high levels of both within-study and between-study heterogeneity were found. Whether data was collected at the leaf or canopy level had the most impact on the mean effect size, with leaf-level studies displaying a stronger relationship compared to canopy-level studies. We highlight the challenges facing the synthesis of these kinds of experiments, the lack of studies carried out in arid or tropical systems and the need for scalable, multitemporal assessments to resolve the controversy in this field.
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Richards, Lora A., Lee A. Dyer, Matthew L. Forister, Angela M. Smilanich, Craig D. Dodson, Michael D. Leonard, and Christopher S. Jeffrey. "Phytochemical diversity drives plant–insect community diversity." Proceedings of the National Academy of Sciences 112, no. 35 (August 17, 2015): 10973–78. http://dx.doi.org/10.1073/pnas.1504977112.
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What are the ecological causes and consequences of variation in phytochemical diversity within and between plant taxa? Despite decades of natural products discovery by organic chemists and research by chemical ecologists, our understanding of phytochemically mediated ecological processes in natural communities has been restricted to studies of either broad classes of compounds or a small number of well-characterized molecules. Until now, no studies have assessed the ecological causes or consequences of rigorously quantified phytochemical diversity across taxa in natural systems. Consequently, hypotheses that attempt to explain variation in phytochemical diversity among plants remain largely untested. We use spectral data from crude plant extracts to characterize phytochemical diversity in a suite of co-occurring plants in the tropical genus Piper (Piperaceae). In combination with 20 years of data focused on Piper-associated insects, we find that phytochemical diversity has a direct and positive effect on the diversity of herbivores but also reduces overall herbivore damage. Elevated chemical diversity is associated with more specialized assemblages of herbivores, and the cascading positive effect of phytochemistry on herbivore enemies is stronger as herbivore diet breadth narrows. These results are consistent with traditional hypotheses that predict positive associations between plant chemical diversity, insect herbivore diversity, and trophic specialization. It is clear from these results that high phytochemical diversity not only enhances the diversity of plant-associated insects but also contributes to the ecological predominance of specialized insect herbivores.
Book chapters on the topic "Spectral diversity hypothesi"
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"Community Ecology of Stream Fishes: Concepts, Approaches, and Techniques." In Community Ecology of Stream Fishes: Concepts, Approaches, and Techniques, edited by Marlis R. Douglas and Michael E. Douglas. American Fisheries Society, 2010. http://dx.doi.org/10.47886/9781934874141.ch8.
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<em>Abstract</em>.—Our capabilities to address pertinent questions in stream fish ecology, such as population connectivity, biotic homogenization, species invasions, introgression, and effects of habitat alterations on population structure and demography, have been significantly expanded by the development of molecular genetic approaches. A broad spectrum of molecular markers can now be tailored to address specific questions while newer statistical approaches accommodate larger data sets and permit the test of alternative hypotheses. Furthermore, molecular approaches facilitate the evaluation of ecological processes across both spatial and temporal scales, which are often mutually exclusive parameters. Population expansions, declines, and movements can be examined from recent to deep history and scaled from local to continental drainages. The intrinsic properties of stream ecosystems also make them particularly amenable to molecular approaches. The hierarchical order reflected in streams is directly translatable into an expanding spatial scale, from restricted headwaters through entire basins. Additionally, stream habitats are generally linear, and consequently, fish populations are often distributed sequentially, with interactions constrained to neighboring populations. Finally, streams tend to develop vicariant barriers over time, thus isolating populations and promoting local adaptation, a process easily deciphered using molecular markers. The latter have also contributed to the resolution of conservation issues and guided appropriate adaptive management of stream fauna. Molecular approaches in stream fish ecology are far too diverse to be comprehensively reviewed herein. Instead, we illustrate their facility by emphasizing three case studies demonstrating their broad utility: (1) a range-wide analysis of mitochondrial DNA diversity in flannelmouth sucker <em>Catostomus latipinnis</em>, pointing to a population bottleneck likely induced by severe post-Pleistocene drought in the Colorado River basin; (2) single nucleotide polymorphism screening to evaluate hybridization and introgression among native flannelmouth sucker, bluehead sucker <em>C. discobolus </em>(also known as <em>Pantosteus discobolus</em>), and the introduced white sucker <em>C. commersonii </em>in the upper Colorado River basin; and (3) microsatellite DNA analysis to evaluate gene flow and contemporary relationships in the Grand Canyon among populations of an endangered cyprinid fish (the humpback chub <em>Gila cypha</em>). In an appendix, we outline several recent molecular approaches that have expanded our opportunities to study stream fish ecology. We review relevant literature by emphasizing new statistical approaches and potential pitfalls of marker selection and data, rather than by delving into abstruse technical details regarding protocol development.
Conference papers on the topic "Spectral diversity hypothesi"
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Stanciu, Ionutdorin, and Carmen Bal. "UTILIZING CROSSWORD CREATION SOFTWARE TO DEEPEN LEARNING IN UNDERGRADUATE STUDENTS." In eLSE 2015. Carol I National Defence University Publishing House, 2015. http://dx.doi.org/10.12753/2066-026x-15-218.
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Learning support software varies from dedicated learning software that emulates the didactics of learning as close as possible, to non-dedicated software capable of providing support or scaffolds for the cognitive processes involved in learning. Software such as Algebrator (TM) or Cabri 3D (TM) are closer to the first end the spectrum. Regarding the other end of the spectrum, many steps were taken since the advent of the first concept and mind mapping tools, with software like Argunet (TM), Austhink's Rationale (TM), or Hot Potatoes (TM) bringing considerable diversity to the digital cognitive tools available today. While the implementation of complex learning systems is not adequate for certain educational establishments as well as for certain courses, teachers are open to any efficient tool that can adequately contribute to the instructional and learning processes. Our research included a between-subjects post-test only study of the various manners of utilization of Hot Potatoes and explore the research hypothesis that the use of digital tools to increase information processing leads to observable increase in academic performance. Crosswords created in Hot Potatoes were used to enrich the information processing involved in students' solving of homework. 1st year undergraduate students enrolled in the Classroom Management course participated in the study and were distributed in one control group and two experimental groups. The control group received regular homework assignments, while the experimental groups were tasked to solve crosswords created with Hot Potatoes (TM) only, or, respectively, for the second experimental group, to solve regular homework assignments and crosswords given the first experimental group. ANOVA processing indicated statistically significant differences between the group receiving homework and crosswords, as compared to both the control and the experimental group receiving only crosswords assignments. The best academic performances were achieved in the experimental group using both regular assignments and crosswords, followed by the group receiving regular assignments and by the group receiving only crosswords assignments. Also, a measure of perceived difficulty of knowledge test was made, indicating that the use of crosswords assignments contributed to a decrease in perceived difficulty of the knowledge test. While the obtained results support the research hypothesis, further study is needed to clarify issues and the role of other variables, such as the role of the perceived task value in motivating the learning and, consequently, in academic performance.