Estimation of Aboveground Biomass for Different Forest Types Using Data from Sentinel-1, Sentinel-2, ALOS PALSAR-2, and GEDI

Forests, Год журнала: 2024, Номер 15(1), С. 215 - 215

Опубликована: Янв. 21, 2024

Forest aboveground biomass (AGB) is integral to the global carbon cycle and climate change study. Local regional AGB mapping crucial for understanding stock dynamics. NASA’s ecosystem dynamics investigation (GEDI) combination of multi-source optical synthetic aperture radar (SAR) datasets have great potential local estimation mapping. In this study, GEDI L4A data ground sample plots worked as true values explore their difference estimating forest using Sentinel-1 (S1), Sentinel-2 (S2), ALOS PALSAR-2 (PALSAR) data, individually in different combinations. The effects types validation were investigated well. S1 S2 performed best with R2 ranging from 0.79 0.84 RMSE 7.97 29.42 Mg/ha, used truth data. While product working reference, range 0.36 0.47 31.41 37.50 Mg/ha. between plot reference shows obvious dependence on types. summary, dataset its SAR better when average less than 150 predictions underperformed across study sites. However, can work source a certain level accuracy.

Язык: Английский

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Development of forest aboveground biomass estimation, its problems and future solutions: A review

Ecological Indicators, Год журнала: 2024, Номер 159, С. 111653 - 111653

Опубликована: Фев. 1, 2024

Forest aboveground biomass (AGB) is crucial as it serves a fundamental indicator of the productivity, biodiversity, and carbon storage forest ecosystems. This paper presents targeted literature review advancements in AGB estimation methods. We conducted an extensive published using Web Science, ResearchGate, Semantic Scholar, Google Scholar. Our findings highlight importance accurate studies terrestrial cycle, ecosystem management, climate change. Moreover, contributes valuable ecological knowledge supports effective natural resource management. Unfortunately, during data collection process for estimation, we have identified two critical yet often overlooked issues: (1) reliability manual survey accuracy, (2) impact overlap between ground plots remote sensing pixels on estimation. Drawing existing technologies analysis, propose potentially solution to address these challenges. In conclusion, mapping parameters, such AGB, will remain priority forestry research foreseeable future. To ensure practical applicability findings, our future efforts focus understanding accuracy determining optimal pixels.

Язык: Английский

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Aboveground biomass modeling using simulated Global Ecosystem Dynamics Investigation (GEDI) waveform LiDAR and forest inventories in Amazonian rainforests

Forest Ecology and Management, Год журнала: 2025, Номер 578, С. 122491 - 122491

Опубликована: Янв. 5, 2025

Язык: Английский

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

Ecological Informatics, Год журнала: 2023, Номер 77, С. 102234 - 102234

Опубликована: Июль 26, 2023

Язык: Английский

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Estimation of above ground biomass in tropical heterogeneous forests in India using GEDI

Ecological Informatics, Год журнала: 2024, Номер 82, С. 102712 - 102712

Опубликована: Июнь 30, 2024

Quantifying above ground biomass (AGB) and its spatial distribution can significantly contribute to monitor carbon stocks as well the storage dynamics in forests. For effective forest monitoring management case of complex tropical Indian forests, there is a need obtain reliable estimates amount sequestration at regional national levels, but estimation quite challenging. The main objective study validate usefulness gridded density (AGBD) (ton/ha) spaceborne LiDAR Global Ecosystem Dynamics Investigation data (GEDI L4B, Version 2) across two heterogeneous forests India, Betul Mudumalai Methodology includes, for each area, linear regression model which predicts AGB from Sentinel-2 MSI was developed using reference comparing it with GEDI AGBD values. Central India had RMSE 13.9 ton/ha, relative = 8.7% R2 0.88, bias −0.28 comparison between modelled 1 km resolution show relatively strong correlation (0.66) no or little bias. It also found that footprint value underestimated compared according model. southern an 29.1 10.8%, 0.79 −0.022. 0.84, field values lies 42.2 ton/ha 238.8 75.9 353.6 ton/ha. results indicates underestimates AGB, used produce product needs be adjusted provide information on balance changes over time type exists test areas.

Язык: Английский

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Forest aboveground biomass estimation by GEDI and multi-source EO data fusion over Indian forest

International Journal of Remote Sensing, Год журнала: 2024, Номер 45(4), С. 1304 - 1338

Опубликована: Фев. 2, 2024

Monitoring changes in carbon stocks through forest biomass assessment is crucial for cycle studies. However, challenges obtaining timely and reliable ground measurements hinder creation of the spatially continuous maps aboveground density (AGBD). This study proposes an approach generating (AGBD) by combining Global Ecosystem Dynamics Investigation (GEDI) LiDAR-based data with open-access earth observation (EO) data. The key contribution lies systematic evaluation various model configurations to select optimal AGBD generation. considered configurations, including predictor sets, spatial resolution, beam selection, sensitivity thresholds. We used a Random Forest model, trained five-fold cross-validation on 80% total data, estimate Indian region. Model performance was assessed using 20% independent test dataset. Results, Sentinel-1 2 predictors, yielded R2 values 0.55 0.60 RMSE 48.5 56.3 Mg/ha. Incorporating agroclimatic zone attributes improved (R2: 0.59 0.69, RMSE: 42.2 53.3 Mg/ha). selection top 15 which favoured features from Sentinel-2, DEM, attributes, zones, GEDI >0.98, 0.64 46.59 results underscore significance incorporating like agro-climatic zones need considering types shot characteristics. top-performing validated Simdega, Jharkhand 0.74, 39.3 Mg/ha), demonstrating methodological potential this approach. Overall, emphasizes prospects integrating multi-source EO produce (AGB) fusion.

Язык: Английский

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

Science of Remote Sensing, Год журнала: 2024, Номер 10, С. 100144 - 100144

Опубликована: Июнь 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.

Язык: Английский

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Forest Aboveground Biomass Estimation and Inventory: Evaluating Remote Sensing-Based Approaches

Forests, Год журнала: 2024, Номер 15(6), С. 1055 - 1055

Опубликована: Июнь 18, 2024

Remote sensing datasets offer robust approaches for gaining reliable insights into forest ecosystems. Despite numerous studies reviewing aboveground biomass estimation using remote approaches, a comprehensive synthesis of synergetic integration methods to map and estimate AGB is still needed. This article reviews the integrated discusses significant advances in estimating from space- airborne sensors. review covers research articles published during 2015–2023 ascertain recent developments. A total 98 peer-reviewed journal were selected under Preferred Reporting Items Systematic Reviews Meta-Analysis (PRISMA) guidelines. Among scrutinized studies, 54 relevant spaceborne, 22 airborne, datasets. empirical models used, random regression model accounted most (32). The highest number utilizing dataset originated China (24), followed by USA (15). datasets, Sentinel-1 2, Landsat, GEDI, Airborne LiDAR widely employed with parameters that encompassed tree height, canopy cover, vegetation indices. results co-citation analysis also determined be objectives this review. focuses on provides accuracy reliability modeling.

Язык: Английский

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Improving aboveground biomass density mapping of arid and semi-arid vegetation by combining GEDI LiDAR, Sentinel-1/2 imagery and field data

Science of Remote Sensing, Год журнала: 2025, Номер 11, С. 100204 - 100204

Опубликована: Фев. 6, 2025

Язык: Английский

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Integrating GEDI, Sentinel-2, and Sentinel-1 imagery for tree crops mapping

Remote Sensing of Environment, Год журнала: 2025, Номер 319, С. 114644 - 114644

Опубликована: Фев. 11, 2025

Язык: Английский

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