Modelling aboveground biomass of a multistage managed forest through synergistic use of Landsat-OLI, ALOS-2 L-band SAR and GEDI metrics

Ecological Informatics, Journal Year: 2023, Volume and Issue: 77, P. 102234 - 102234

Published: July 26, 2023

Language: Английский

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High-Resolution Canopy Height Mapping: Integrating NASA’s Global Ecosystem Dynamics Investigation (GEDI) with Multi-Source Remote Sensing Data

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(7), P. 1281 - 1281

Published: April 5, 2024

Accurate structural information about forests, including canopy heights and diameters, is crucial for quantifying tree volume, biomass, carbon stocks, enabling effective forest ecosystem management, particularly in response to changing environmental conditions. Since late 2018, NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission has monitored global structure using a satellite Light Detection Ranging (LiDAR) instrument. While GEDI collected billions of LiDAR shots across near-global range (between 51.6°N >51.6°S), their spatial distribution remains dispersed, posing challenges achieving complete coverage. This study proposes evaluates an approach that generates high-resolution height maps by integrating data with Sentinel-1, Sentinel-2, topographical ancillary through three machine learning (ML) algorithms: random forests (RF), gradient boost (GB), classification regression trees (CART). To achieve this, the secondary aims included following: (1) assess performance ML algorithms, RF, GB, CART, predicting heights, (2) evaluate our reference from models (CHMs), (3) compare other two existing maps. RF GB were top-performing best 13.32% 16% root mean squared error broadleaf coniferous respectively. Validation proposed revealed 100th 98th percentile, followed average 75th, 90th, 95th, percentiles (AVG), most accurate metrics real heights. Comparisons between predicted CHMs demonstrated predictions stands (R-squared = 0.45, RMSE 29.16%).

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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UAV-RGB-image-based aboveground biomass equation for planted forest in semi-arid Inner Mongolia, China

Ecological Informatics, Journal Year: 2024, Volume and Issue: 81, P. 102574 - 102574

Published: March 24, 2024

The acquisition of high-resolution above-ground-biomass (AGB) data cost-effectively and expeditiously represents a formidable challenge within the domain current ecosystem surveillance. Plot-based inventory, conventional approach for estimating validating remote sensing data, is nonetheless costly constrained in terms spatial coverage. expeditious advancements unmanned-aerial-vehicle (UAV) technology furnish potential to devise AGB equations that transcend traditional diameter-height-based alongside techniques quantifying forest structural parameters through standard RGB aerial imagery. Since canopy diameter (CD) tree height (H) can be directly ascertained from UAV-derived datasets, biomass parameterized by CD H may more valuable. In present investigation, we established predicated on procured UAV outfitted with camera, specifically planted sparsely Pinus sylvestris central Inner Mongolia, China. Utilizing imagery, generated digital terrain model (DTM), surface (DSM) orthophoto image (DOM). Then, (CHM) was obtained subtracting DSM DTM extract individual trees. This methodology's (R2 = 0.85, RMSE 0.203 m) 0.77 0.671 closely mirrored in-situ measurements. Six prospective were constructed forest, taking extracted survey datasets as dependent variables. accuracy estimation appraised employing extant allometric growth equations, which using ground-measured at breast (DBH) H. most efficacious equation, surveys, delineated W=2.3442CD∗H0.9057(R2 0.731, 2.46 kg), thus presenting convenient tool sparse forests semi-arid locales.

Language: Английский

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Monitoring Water Diversity and Water Quality with Remote Sensing and Traits

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(13), P. 2425 - 2425

Published: July 1, 2024

Changes and disturbances to water diversity quality are complex multi-scale in space time. Although situ methods provide detailed point information on the condition of bodies, they limited use for making area-based monitoring over time, as aquatic ecosystems extremely dynamic. Remote sensing (RS) provides data cost-effective, comprehensive, continuous standardised characteristics changes from local regional scales scale entire continents. In order apply better understand RS techniques their derived spectral indicators quality, this study defines five that can be monitored using RS. These traits, genesis, structural water, taxonomic functional water. It is essential record traits derive other four Furthermore, only most important interface between approaches. The these technologies presented detail discussed numerous examples. Finally, current future developments advance trait approach modelling, prediction assessment a basis successful management strategies.

Language: Английский

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Accuracy Assessment of Gedi Terrain Elevation, Canopy Height, and Aboveground Biomass Density Estimates in Japanese Artificial Forests

Published: Jan. 1, 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 we gathered reference 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 January 2019 November 2023. research also explored how different factors influence elevation estimates, including type beam, time acquisition (day or night), beam sensitivity, slope. Additionally, investigated effects various structural parameters, such as height-to-diameter ratio, crown length number on height AGBD. Our results showed that demonstrates high across slope rRMSE ranging 2.28% 3.25%. After geolocation adjustment, comparison derived demonstrated accuracy, exhibiting an 22.04%. In contrast, AGBD product lower 52.79%. findings indicated RH98 significantly influenced by whereas mainly impacted ratio. study provided baseline validation elevation, RH98, Furthermore, this provides valuable insights into precision metrics examining potential factors.

Language: Английский

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Current and Near-Term Earth-Observing Environmental Satellites, Their Missions, Characteristics, Instruments, and Applications

Sensors, Journal Year: 2024, Volume and Issue: 24(11), P. 3488 - 3488

Published: May 28, 2024

Among the essential tools to address global environmental information requirements are Earth-Observing (EO) satellites with free and open data access. This paper reviews those EO from international space programs that already, or will in next decade so, provide of importance sciences describe Earth’s status. We summarize factors distinguishing pioneering placed over past half century, their links modern ones, changing priorities for spaceborne instruments platforms. illustrate broad sweep instrument technologies useful observing different aspects physio-biological surface, spanning wavelengths UV-A at 380 nanometers microwave radar out 1 m. a background on technical specifications each mission its primary instrument(s), types collected, examples applications these observations. websites additional details instrument, history context behind measurements, about design, specifications, measurements.

Language: Английский

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Enhancing Wetland Mapping: Integrating Sentinel-1/2, GEDI Data, and Google Earth Engine

Sensors, Journal Year: 2024, Volume and Issue: 24(5), P. 1651 - 1651

Published: March 3, 2024

Wetlands are amongst Earth's most dynamic and complex ecological resources, serving productive biodiverse ecosystems. Enhancing the quality of wetland mapping through Earth observation (EO) data is essential for improving effective management conservation practices. However, achievement reliable accurate faces challenges due to heterogeneous fragmented landscape wetlands, along with spectral similarities among different classes. The present study aims produce advanced 10 m spatial resolution classification maps four pilot sites on Island Newfoundland in Canada. Employing a comprehensive multidisciplinary approach, this research leverages synergistic use optical, synthetic aperture radar (SAR), light detection ranging (LiDAR) data. It focuses hydrological interpretation using multi-source multi-sensor EO evaluate their effectiveness identifying diverse sources include Sentinel-1 -2 satellite imagery, Global Ecosystem Dynamics Investigation (GEDI) LiDAR footprints, Multi-Error-Removed Improved-Terrain (MERIT) Hydro dataset, European ReAnalysis (ERA5) dataset. Elevation topographical derivatives, such as slope aspect, were also included analysis. evaluates added value incorporating these new into mapping. Using Google Engine (GEE) platform Random Forest (RF) model, two main objectives pursued: (1) integrating GEDI footprint heights datasets generate vegetation canopy height (VCH) map (2) seeking enhance by utilizing VCH an input predictor. Results highlight significant role variable derived from samples enhancing accuracy, it provides vertical profile vegetation. Accordingly, reached highest accuracy coefficient determination (R2) 0.69, root-mean-square error (RMSE) 1.51 m, mean absolute (MAE) 1.26 m. Leveraging procedure improved maximum overall 93.45%, kappa 0.92, F1 score 0.88. This underscores importance approaches address various factors results expected benefit future studies.

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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Using airborne LiDAR and enhanced-geolocated GEDI metrics to map structural traits over a Mediterranean forest

Science of Remote Sensing, Journal Year: 2025, Volume and Issue: unknown, P. 100195 - 100195

Published: Jan. 1, 2025

Language: Английский

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