Reviewing the Spectral Variation Hypothesis: Twenty years in the tumultuous sea of biodiversity estimation by remote sensing

Ecological Informatics, Journal Year: 2024, Volume and Issue: 82, P. 102702 - 102702

Published: July 3, 2024

Twenty years ago, the Spectral Variation Hypothesis (SVH) was formulated as a means to link between different aspects of biodiversity and spatial patterns spectral data (e.g. reflectance) measured from optical remote sensing. This hypothesis initially assumed positive correlation variations computed raster in environment, which would turn correlate with species richness: following SVH, areas characterized by high heterogeneity (SH) should be related higher number available ecological niches, more likely host when combined. The past decade has witnessed major evolution progress both terms remotely sensed available, techniques analyze them, questions addressed. SVH been tested many contexts variety sensing data, this recent corpus highlighted potentials pitfalls. aim paper is review discuss methodological developments based on leading knowledge well conceptual uncertainties limitations for application estimate dimensions biodiversity. In particular, we systematically than 130 publications provide an overview ecosystems, characteristics (i.e., spatial, temporal resolution), metrics, tools, applications strength association SH metrics reported each study. conclusion, serves guideline researchers navigating complexities applying offering insights into current state future research possibilities field estimation data.

Language: Английский

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Using ZY1-02D satellite hyperspectral remote sensing to monitor landscape diversity and its spatial scaling change in the Yellow River Estuary

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 128, P. 103716 - 103716

Published: Feb. 19, 2024

Monitoring and assessing wetland diversity is crucial for its accurate preservation. Hyperspectral satellites have been proven effective detailed investigations of plant in many places. However, it's unclear whether spectral invert landscape diversity, the inversion accuracy varies with spatial scale. In this study, ZY1-02D hyperspectral remote sensing images Yellow River Estuary were supervised classified by support vector machine. Then, indices (i.e., community richness, Shannon-Wiener index, Simpson Pielou index) coefficient variation, convex hull volume, eight vegetation indices) calculated. A random forest model was used to predict using diversity. The scale relationship between explored lastly. Our results showed that overall classification 91.53 %, a Kappa 0.90. Spectral had best on index (14 ∼ 57 average = 38 %), while intermediate (3 56 30 %) richness (2 48 but lowest 43 16 %). increased first then stabilized increase scales, reaching stability at sampling size 2880 m × m. indicated data can be monitor changes systems. affected type scaling effects. findings provide new perspective conservation management large-scale

Language: Английский

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GEE_xtract: High-quality remote sensing data preparation and extraction for multiple spatio-temporal ecological scaling

Ecological Informatics, Journal Year: 2024, Volume and Issue: 80, P. 102502 - 102502

Published: Jan. 28, 2024

Environmental sensing via Earth Observation Satellites (EOS) is critically important for understanding Earth’ biosphere. The last decade witnessed a “Klondike Gold Rush” era ecological research given growing multidisciplinary interest in EOS. Presently, the combination of repositories remotely sensed big data, with cloud infrastructures granting exceptional analytical power, may now mark emergence new paradigm spatio-temporal dynamics systems, by allowing appropriate scaling environmental data to phenomena at an unprecedented level. However, while some efforts have been made combine (near) ground observations, virtually no study has focused on multiple spatial and temporal scales over long time series, integrating different EOS sensors. Furthermore, there still lack applications offering flexible approaches deal limits sensors, ensuring high-quality extraction high resolution. We present GEE_xtract, original EOS-based (Sentinel-2, Landsat, MODIS) code operational within Google Engine (GEE) allow straightforward preparation remote matching which processes occur. GEE_xtract consists three main customisable operations: (1) series imageries filtering calibration; (2) calculation comparable metrics across sensors; (3) from ground-based data. illustrate value complex case concerning seasonal distribution threatened elusive bird, highlight its broad application myriad phenomena. Being user-friendly designed implemented widely used platform (GEE), we believe our approach provides major contribution effectively extracting that can be quickly computed converted any scale, extracted information. Additionally, framework was prepared facilitate comparative initiatives data-fusion research.

Language: Английский

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Is spectral pixel-to-pixel variation a reliable indicator of grassland biodiversity? A systematic assessment of the spectral variation hypothesis using spatial simulation experiments

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 302, P. 113988 - 113988

Published: Jan. 9, 2024

Language: Английский

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Grassland vertical height heterogeneity predicts flower and bee diversity: an UAV photogrammetric approach

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Jan. 8, 2024

Abstract The ecosystem services offered by pollinators are vital for supporting agriculture and functioning, with bees standing out as especially valuable contributors among these insects. Threats such habitat fragmentation, intensive agriculture, climate change contributing to the decline of natural bee populations. Remote sensing could be a useful tool identify sites high diversity before investing into more expensive field survey. In this study, ability Unoccupied Aerial Vehicles (UAV) images estimate biodiversity at local scale has been assessed while testing concept Height Variation Hypothesis (HVH). This hypothesis states that higher vegetation height heterogeneity (HH) measured remote information, vertical complexity associated species diversity. further developed understand if HH can also considered proxy abundance. We tested approach in 30 grasslands South Netherlands, where an data campaign (collection flower abundance) was carried 2021, along UAV true color-RGB-images spatial resolution). Canopy Models (CHM) were derived using photogrammetry technique “Structure from Motion” (SfM) horizontal resolution (spatial) 10 cm, 25 50 cm. accuracy CHM comparing them through linear regression against LiDAR (Light Detection Ranging) Airborne Laser Scanner completed 2020/2021, yielding $$R^2$$ R 2 0.71. Subsequently, on CHMs three resolutions, four different indices (Rao’s Q, Coefficient Variation, Berger–Parker index, Simpson’s D index), correlated ground-based abundance data. Rao’s Q index most effective reaching correlations (0.44 diversity, 0.47 0.34 abundance). Interestingly, not significantly influenced photogrammetry. Our results suggest used large-scale, standardized, cost-effective inference quality bees.

Language: Английский

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A machine learning scheme for estimating fine-resolution grassland aboveground biomass over China with Sentinel-1/2 satellite images

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 311, P. 114317 - 114317

Published: July 16, 2024

Language: Английский

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“Flower power”: How flowering affects spectral diversity metrics and their relationship with plant diversity

Ecological Informatics, Journal Year: 2024, Volume and Issue: 81, P. 102589 - 102589

Published: April 9, 2024

Biodiversity monitoring is constrained by cost- and labour-intensive field sampling methods. Increasing evidence suggests that remotely sensed spectral diversity (SD) linked to plant diversity, holding promise for applications. However, studies testing such a relationship reported conflicting findings, especially in challenging ecosystems as grasslands, due their variety high temporal dynamism. It follows thorough investigation of the key factors influencing these relationships, metrics applied (i.e., continuous, categorical) phenology (e.g., flowering), necessary. The present study aims assess effect flowering on applicability six different SD at local scale investigate how spatial resolution affects results. Taxonomic was calculated based data collected 159 plots 1.5 m × with experimental mesic grassland communities. Spectral information using UAV-borne sensor measuring reflectance across bands visible near-infrared range ~2 cm resolution. Our results showed that, presence flowering, between significant positive only when categorical metrics. Despite observed significance, variance explained models very low, no evident differences resampling coarser pixel sizes. Such findings suggest new insights into possible confounding effects ~ communities are needed use purposes.

Language: Английский

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Mind the gaps: horizontal canopy structure affects the relationship between taxonomic and spectral diversity

International Journal of Remote Sensing, Journal Year: 2024, Volume and Issue: 45(9), P. 2833 - 2864

Published: April 17, 2024

Spectral diversity (SD) in reflectance can be used to estimate plant taxonomic (TD) according the Variation Hypothesis (SVH). However, contrasting relationships between SD and TD have been reported by different studies. Indeed, multiple factors may affect SD, including spatial spectral scales, vegetation characteristics adopted computational method. Here, we tested SVH over 171 plots within a large heterogeneous forest area North-Eastern Italy using Sentinel-2 data, aiming at identifying possible affecting strength direction of SD-TD relationship. was determined 'biodivMapR' (BD) 'rasterdiv' (RD) R packages 38 combinations indices, both α (within community) β (among communities) levels, parameters accounting for scales. Information on structure either retrieved from ground-based or LiDAR data. A Random Forest approach disentangle structure, identify best combination parameters. At α-level, found negative relationship RD which mainly driven presence gaps canopy. As regards BD, that this algorithm reduced background contribution able differentiate major types (broadleaves vs conifers), but derived α-SD indices were marginally correlated with α-TD. β-level, observed statistically significant positive correlation BD (maximum r = 0.24). Finally, stronger correlations R2 when calculated smaller computation windows larger pixels extraction area. Our findings suggest cover play role, respect inter-species differences, determining α-SD, might better capture differences species composition landscape-level rather than richness individual communities.

Language: Английский

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Parcel level temporal variance of remotely sensed spectral reflectance predicts plant diversity

Environmental Research Letters, Journal Year: 2024, Volume and Issue: 19(7), P. 074023 - 074023

Published: June 5, 2024

Abstract Over the last two decades, considerable research has built on remote sensing of spectral diversity to assess plant diversity. The variation hypothesis (SVH) proposes that spatial in reflectance data an area is positively associated with While SVH exhibited validity dense forests, it performs poorly highly fragmented and temporally dynamic agricultural landscapes covered mainly by grasslands. Such underperformance can be attributed mosaic-like structure human-dominated fields varying phenological management stages. Therefore, we argued for re-evaluating SVH’s flawed window-based analysis underutilized temporal component. In particular, captured assessed relationships between components at parcel level as a unit relates patterns. Our investigation spanned three grasslands continents covering wide spectrum usage intensities. To calculate different diversity, used multi-temporal spaceborne Sentinel-2 data. We showed was negatively component across all sites. contrast, related sites larger parcels. findings highlighted landscapes, drives diversity-plant associations. Consequently, our results offer novel perspective globally.

Language: Английский

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Assessing eco-physiological patterns of Ailanthus altissima (Mill.) Swingle and differences with native vegetation using Copernicus satellite data on a Mediterranean Island

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103080 - 103080

Published: Feb. 1, 2025

Language: Английский

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