Inferring alpha, beta, and gamma plant diversity across biomes with GEDI spaceborne lidar DOI Creative Commons
Christopher R. Hakkenberg, Jeff W. Atkins, Jedediah F. Brodie

et al.

Environmental Research Ecology, Journal Year: 2023, Volume and Issue: 2(3), P. 035005 - 035005

Published: Sept. 1, 2023

Abstract Biodiversity-structure relationships (BSRs), which describe the correlation between biodiversity and three-dimensional forest structure, have been used to map spatial patterns in based on structural attributes derived from lidar. However, with advent of spaceborne lidar like Global Ecosystem Dynamics Investigation (GEDI), investigators are confronted how predict discrete GEDI footprints, sampled discontinuously across Earth surface often spatially offset where diversity was measured field. In this study, we National Ecological Observation Network data a hierarchical modeling framework assess spatially-coincident BSRs (where field-observed taxonomic measurements airborne coincide at single plot) compare statistical aggregates proximate, but spatially-dispersed samples structure. Despite substantial ecoregional variation, results confirm cross-biome consistency relationship plant/tree alpha data, including outside field plot measured. Moreover, found that generalized profiles footprint were consistently related tree diversity, as well beta gamma diversity. These findings suggest characteristic generated aggregated footprints effective for BSR prediction without incorporation more standard predictors climate, topography, or optical reflectance. Cross-scale comparisons airborne- GEDI-derived provide guidance balancing scale-dependent trade-offs proximity sample size BSR-based gridded products. This study fills critical gap our understanding can be infer specific patterns, those not directly observable remote sensing instruments. it bolsters empirical basis global-scale

Language: Английский

Reviewing the Spectral Variation Hypothesis: Twenty years in the tumultuous sea of biodiversity estimation by remote sensing DOI Creative Commons
Michele Torresani, Christian Rossi, Michela Perrone

et al.

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

Citations

23
A multi-source approach combining GEDI LiDAR, satellite data, and machine learning algorithms for estimating forest aboveground biomass on Google Earth engine platform DOI Creative Commons
Hamdi A. Zurqani

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103052 - 103052

Published: Jan. 1, 2025

Language: Английский

Citations

3
Integrating GEDI, Sentinel-2, and Sentinel-1 imagery for tree crops mapping DOI Creative Commons
Esmaeel Adrah, Jesse P. Wong, He Yin

et al.

Remote Sensing of Environment, Journal Year: 2025, Volume and Issue: 319, P. 114644 - 114644

Published: Feb. 11, 2025

Language: Английский

Citations

2
Monitoring Earth’s climate variables with satellite laser altimetry DOI
Lori A. Magruder, S. L. Farrell, Amy Neuenschwander

et al.

Nature Reviews Earth & Environment, Journal Year: 2024, Volume and Issue: 5(2), P. 120 - 136

Published: Jan. 30, 2024

Language: Английский

Citations

13
Monitoring vegetation- and geodiversity with remote sensing and traits DOI Creative Commons
Angela Lausch, Peter Selsam, Marion Pause

et al.

Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, Journal Year: 2024, Volume and Issue: 382(2269)

Published: Feb. 12, 2024

Geodiversity has shaped and structured the Earth's surface at all spatio-temporal scales, not only through long-term processes but also medium- short-term processes. is, therefore, a key control regulating variable in overall development of landscapes biodiversity. However, climate change land use intensity are leading to major changes disturbances bio- geodiversity. For sustainable ecosystem management, temporal, economically viable standardized monitoring is needed monitor model effects vegetation- RS approaches have been used for this purpose decades. understand detail how capture geodiversity, aim paper describe five features geodiversity captured using technologies, namely: (i) trait diversity, (ii) phylogenetic/genese (iii) structural (iv) taxonomic diversity (v) functional diversity. Trait essential establishing other four. Traits provide crucial interface between situ , close-range, aerial space-based approaches. The approach allows complex data different types formats be linked latest semantic integration techniques, which will enable integrity modelling future. This article part Theo Murphy meeting issue ‘Geodiversity science society’.

Language: Английский

Citations

11
Mapping tree species diversity in a typical natural secondary forest by combining multispectral and LiDAR data DOI Creative Commons

Lang Ming,

Jianyang Liu,

Ying Quan

et al.

Ecological Indicators, Journal Year: 2024, Volume and Issue: 159, P. 111711 - 111711

Published: Feb. 1, 2024

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

Language: Английский

Citations

10
Monitoring wetland plant diversity from space: Progress and perspective DOI Creative Commons
Weiwei Sun, Daosheng Chen, Zhouyuan Li

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 130, P. 103943 - 103943

Published: May 28, 2024

Wetlands are the one of ecosystems with highest biodiversity, ecological service functions and carbon storage. Affected by synergistic impacts human activities climate change, global wetland area has decreased 35 % since 1970, far-reaching implications on biodiversity loss. Compared manual ground investigations, remote sensing is considered to be most promising method for monitoring change in order formulate effective conservation strategies due its characteristics non-contact detection, low cost timely. Here we used bibliometric analyze study sites, methods, conclusions shortcomings published papers globally over past 60 years monitoring. We show that distribution wetlands was uneven, mostly concentrated United States, China Northern Europe. Current researches mainly focused coastal, marsh estuarine wetlands, while other (e.g., lake riparian artificial peatlands high-altitude high-latitude peatlands) were still lacking. Overall, 20 platforms sensors used, near infrared shortwave length (780 ∼ 1100 nm) reliable sensitive spectral region. Among various estimation accuracy nonlinear, multi-independent variables, hyperspectral models generally higher than those linear, single-factor multispectral models, respectively. The affected both sampling time plant phenology. Most studies taxonomic within-habitat diversity (α-diversity) single-layer communities (grassland), few paid attentions functional phylogenetic inter-habitat (β-diversity) region (γ-diversity) multi-layer (forest shrubland), biodiversity-ecosystem functioning (BEF) relationships. suggest prospective should strengthen globally. multi-dimensional data mined fused provide new high accuracy. focus scale effects (α, β γ), BEF relationships, environmental gradients.

Language: Английский

Citations

8
Accuracy assessment of GEDI terrain elevation, canopy height, and aboveground biomass density estimates in Japanese artificial forests DOI Creative Commons

Hantao Li,

Xiaoxuan Li, Tomomichi Kato

et al.

Science of Remote Sensing, Journal Year: 2024, Volume and Issue: 10, P. 100144 - 100144

Published: June 15, 2024

Global forests face severe challenges owing to climate change, making dynamic and accurate monitoring of forest conditions critically important. Forests in Japan, covering approximately 70% the country's land area, play a vital role yet often overlooked global forestry. Japanese are unique, with 50% comprising artificial forests, predominantly coniferous forests. Despite government's extensive use airborne Light Detecting Ranging (LiDAR) assess conditions, these data need more availability frequency. The Ecosystem Dynamics Investigation (GEDI), first Spaceborne LiDAR explicitly designed for vegetation monitoring, is expected provide significant value high-frequency high-accuracy monitoring. To accuracy GEDI reference were gathered from 53,967,770 trees via Aichi Prefecture, Japan. This was then compared corresponding GEDI-derived terrain elevations, canopy heights (GEDI RH98), aboveground biomass density (AGBD) estimates data. research also explored how different factors influence elevation estimates, including type beam, time acquisition (day or night), beam sensitivity, slope. Additionally, effects various structural parameters, such as height-to-diameter ratio, crown length number on height AGBD, investigated. results showed that demonstrated high across slope rRMSE ranging 2.28% 3.25% RMSE 11.68 m 16.54 m. After geolocation adjustment, comparison derived LiDAR-derived accuracy, exhibiting 22.04%. In contrast, AGBD product moderate 52.79%. findings indicated RH98 influenced by whereas mainly impacted ratio. study provided baseline assessment elevation, RH98, Furthermore, this valuable insights into metrics examining potential factors.

Language: Английский

Citations

8
Mapping alpha diversity of plant species using scale effects of remote sensing DOI Creative Commons
Xingchen Yang, Shaogang Lei,

Jun Xu

et al.

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 102993 - 102993

Published: Jan. 1, 2025

Language: Английский

Citations

1
Integrating remote sensing and field inventories to understand determinants of urban forest diversity and structure DOI Creative Commons
Vinícius Marcilio‐Silva,

Sally Donovan,

Sarah E. Hobbie

et al.

Ecology, Journal Year: 2025, Volume and Issue: 106(2)

Published: Feb. 1, 2025

Abstract Understanding the determinants of urban forest diversity and structure is important for preserving biodiversity sustaining ecosystem services in cities. However, comprehensive field assessments are resource‐intensive, landscape‐level approaches may overlook heterogeneity within regions. To address this challenge, we combined remote sensing with inventories to comprehensively map analyze attributes patches across Minneapolis‐St. Paul Metropolitan Area (MSPMA) a multistep process. First, developed predictive machine learning models by integrating data from (from 40 12.5‐m‐radius plots) Global Ecosystem Dynamics Investigation (GEDI) observations Sentinel‐2‐derived land surface phenology (LSP). These enabled accurate predictions attributes, specifically nine metrics plant (tree species richness, tree abundance, understory abundance), (average canopy height, dbh, density), structural complexity (variability density) relative errors ranging between 11% 21%. Second, applied these predict 804 additional plots GEDI Sentinel‐2. Finally, Bayesian multilevel predicted assess influence multiple factors—patch dimensions, landscape plot position, jurisdictional agency—on plots. The showed all predictors have some degree effect on presenting varying explanatory power R 2 values 0.071 0.405. Overall, characteristics (e.g., distance nearest trail, proximity edge) agency explained large portion variability patches, whereas patch did not. versus management sets marginal Δ was heterogeneous ecological subsections (an classification designation). multiplicity influencing forests emphasizes intricate nature ecosystems highlights nuanced, relationships anthropogenic factors that determine properties. Effectively enhancing requires assessments, management, conservation strategies tailored context‐specific characteristics.

Language: Английский

Citations

1