Combining GEDI and sentinel data to estimate forest canopy mean height and aboveground biomass

Ecological Informatics, Journal Year: 2023, Volume and Issue: 78, P. 102348 - 102348

Published: Oct. 24, 2023

Language: Английский

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

Forests, Journal Year: 2024, Volume and Issue: 15(6), P. 1055 - 1055

Published: June 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.

Language: Английский

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Correcting forest aboveground biomass biases by incorporating independent canopy height retrieval with conventional machine learning models using GEDI and ICESat-2 data

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103045 - 103045

Published: Jan. 1, 2025

Language: Английский

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Optimising carbon fixation through agroforestry: Estimation of aboveground biomass using multi-sensor data synergy and machine learning

Ecological Informatics, Journal Year: 2023, Volume and Issue: 79, P. 102408 - 102408

Published: Dec. 3, 2023

As agricultural land expansion is the primary driver of deforestation, agroforestry could be an optimal use strategy for climate change mitigation and reducing pressure on forests. Agroforestry a promising method carbon sequestration. With recent advancements in geospatial data science technology, ability to predict aboveground biomass (AGB) assess ecosystem services rapidly expanding. This study was conducted Belpada Block Balangir, Odisha, forest-dominated region eastern India. We recorded species occurrence measured plant parameters, including Circumference at Breast Height (CBH), height, geolocation, 196 plots (0.09 ha) intervention sites noted tree species. used Sentinel-1 Sentinel-2 multi sensor achieve synergy AGB estimation. Three machine learning models were used: Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN). The RF model exhibited highest level prediction accuracy (R2 = 0.69 RMSE 17.07 Mg/ha), followed by ANN 0.63 19.35 SVM 0.54, 21.97 Mg/ha. spectral vegetation indices that are (Normalized Difference Vegetation Index (NDVI), Soil-Adjusted (SAVI), Enhanced (EVI), Modified Simple Ratio (MSR), (MSAVI), (DVI), SAR backscatter values, found important variables prediction. findings revealed interventions plantations resulted average stock increase 15 Mg/ha over five years area. Plant Value (PVI), which indicates importance local economy storage, showed Tectona grandis dominant with PVI value (88.35), Eucalyptus globulus (56.87), Mangifera indica (53.75), Azadirachta (15.45). approach enables monitoring efforts systems, thereby promoting effective management strategies.

Language: Английский

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The utility of Planetscope spectral data in quantifying above-ground carbon stock in an urban reforested landscape

Ecological Informatics, Journal Year: 2024, Volume and Issue: 80, P. 102472 - 102472

Published: Jan. 20, 2024

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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Machine learning feature importance selection for predicting aboveground biomass in African savannah with landsat 8 and ALOS PALSAR data

Machine Learning with Applications, Journal Year: 2024, Volume and Issue: 16, P. 100561 - 100561

Published: May 16, 2024

In remote sensing, multiple input bands are derived from various sensors covering different regions of the electromagnetic spectrum. Each spectral band plays a unique role in land use/land cover characterization. For example, while integrating for predicting aboveground biomass (AGB) is important achieving high accuracy, reducing dataset size by eliminating redundant and irrelevant features essential enhancing performance machine learning algorithms. This accelerates process, thereby developing simpler more efficient models. Our results indicate that compared individual sensor datasets, random forest (RF) classification approach using recursive feature elimination (RFE) increased accuracy based on F score 82.86% 26.19 respectively. The mutual information regression (MIR) method shows slight increase when considering but its decreases all taken into account Overall, combination Landsat 8, ALOS PALSAR backscatter, elevation data selected RFE provided best AGB estimation RF XGBoost contrast to k-nearest neighbors (KNN) support vector machines (SVM), no significant improvement was detected even MIR were used. effect parameter optimization found be than other methods. maps show patterns estimates consistent with those reference dataset. study how prediction errors can minimized selection ML classifiers.

Language: Английский

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Above Ground Biomass Mapping of Tropical Forest of Tripura Using EOS-04 and ALOS-2 PALSAR-2 SAR Data

Journal of the Indian Society of Remote Sensing, Journal Year: 2024, Volume and Issue: 52(4), P. 801 - 811

Published: March 5, 2024

Language: Английский

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Enhancing carbon stock estimation in forests: Integrating multi-data predictors with random forest method

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 102997 - 102997

Published: Jan. 1, 2025

Language: Английский

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Aboveground Biomass and Tree Mortality Revealed Through Multi-Scale LiDAR Analysis

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(5), P. 796 - 796

Published: Feb. 25, 2025

Accurately monitoring aboveground biomass (AGB) and tree mortality is crucial for understanding forest health carbon dynamics. LiDAR (Light Detection Ranging) has emerged as a powerful tool capturing structure across different spatial scales. However, the effectiveness of predicting AGB depends on type instrument, platform, resolution point cloud data. We evaluated three distinct LiDAR-based approaches in 25.6 ha North American temperate forest. Specifically, we following: GEDI-simulated waveforms from airborne laser scanning (ALS), grid-based structural metrics derived unmanned aerial vehicle (UAV)-borne lidar data, individual detection (ITD) ALS Our results demonstrate varying levels performance approaches, with ITD emerging most accurate modeling median R2 value 0.52, followed by UAV (0.38) GEDI (0.11). findings underscore strengths approach fine-scale analysis, while used to analyze showed promise broader-scale monitoring, if more uncertainty acceptable. Moreover, complementary scales each method may offer valuable insights management conservation efforts, particularly dynamics informing strategic interventions aimed at preserving mitigating climate change impacts.

Language: Английский

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