Satellite based estimation of forest biomass for structural resource planning using gaussian processes and sentinel-2 imagery DOI

Bandula Aahna

i-manager’s Journal on Structural Engineering, Journal Year: 2024, Volume and Issue: 13(3), P. 34 - 34

Published: Jan. 1, 2024

This study presents a replicable, cost-efficient method for estimating forest biomass critical sustainable structural material sourcing using Sentinel-2 satellite imagery and Gaussian Process Regression. A simplified inventory method, coupled with spectral data in the visible to mid-infrared bands, enables accurate quantification across diverse structures Mediterranean climates. Compared traditional LiDAR-based techniques, this approach offers faster, lower-cost deployment without significant trade-off accuracy, making it suitable applications construction timber forecasting, infrastructure planning, environmental assessments. The has been validated several types is packaged freely accessible programming tool direct integration into engineering planning workflows.

Language: Английский

Optimal Integration of Optical and SAR Data for Improving Alfalfa Yield and Quality Traits Prediction: New Insights into Satellite-Based Forage Crop Monitoring DOI Creative Commons
Jiang Chen, Tong Yu, J. H. Cherney

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(5), P. 734 - 734

Published: Feb. 20, 2024

Global food security and nutrition is suffering from unprecedented challenges. To reach a world without hunger malnutrition by implementing precision agriculture, satellite remote sensing plays an increasingly important role in field crop monitoring management. Alfalfa, global widely distributed forage crop, requires more attention to predict its yield quality traits data since it supports the livestock industry. Meanwhile, there are some key issues that remain unknown regarding alfalfa optical synthetic aperture radar (SAR) data. Using Sentinel-1 Sentinel-2 data, this study developed, compared, further integrated new optical- SAR-based models for improving prediction, i.e., crude protein (CP), acid detergent fiber (ADF), neutral (NDF), digestibility (NDFD). better understand physical mechanism of sensing, unified hybrid leaf area index (LAI) retrieval scheme was developed coupling PROSAIL radiative transfer model, spectral response function desired satellite, random forest (RF) denoted as scalable satellite-based LAI framework. Compared vegetation indices (VIs) only capture canopy information, results indicate had highest correlation (r = 0.701) with due capacity delivering structure characteristics. For traits, chlorophyll VIs presented higher correlations than LAI. On other hand, did not provide significant additional contribution predicting parameters RF prediction model using inputs. In addition, optical-based outperformed yield, CP, NDFD, while showed performance ADF NDF. The integration SAR contributed accuracy either or separately. traditional embedded approach, combination multisource heterogeneous satellites optimized multiple linear regression (yield: R2 0.846 RMSE 0.0354 kg/m2; CP: 0.636 1.57%; ADF: 0.559 1.926%; NDF: 0.58 2.097%; NDFD: 0.679 2.426%). Overall, provides insights into large-scale fields satellites.

Language: Английский

Citations

8
A critical review on multi-sensor and multi-platform remote sensing data fusion approaches: current status and prospects DOI Creative Commons
Farhad Samadzadegan, Ahmad Toosi, Farzaneh Dadrass Javan

et al.

International Journal of Remote Sensing, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 76

Published: Dec. 11, 2024

Numerous remote sensing (RS) systems currently collect data about Earth and its environments. However, each system provides limited in terms of spatial resolution, spectral information, other parameters. Given technological constraints, combining from diverse sources can effectively enhance RS solutions through enrichment. Many studies have investigated the fusion acquired different sensors platforms. This paper a comprehensive review research on multi-platform -sensor fusion, encompassing visible-light images, multi/hyper-spectral RADAR LiDAR point clouds, thermal spectrometry samples, geophysical data. An analysis over 950 papers revealed that feature-level multi-sensor was most commonly employed technique, surpassing pixel- decision-level approaches. Moreover, satellite more prevalent than manned unmanned aerial vehicles. The integration initially gained traction applications such as precision agriculture before expanding to land use cover mapping. addresses previously overlooked issues presents framework facilitate seamless Guidelines for this include ensuring same acquisition time, co-registration, true orthorectification, consistent resolution or information content, radiometric consistency, wavelength band coverage.

Language: Английский

Citations

6
Digital mapping of soil salinity with time-windows features optimization and ensemble learning model DOI Creative Commons
Shuaishuai Shi, Nan Wang, Songchao Chen

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: unknown, P. 102982 - 102982

Published: Dec. 1, 2024

Language: Английский

Citations

4
Temporal segmentation method for 30-meter long-term mapping of abandoned and reclaimed croplands in Inner Mongolia, China DOI Creative Commons
Deji Wuyun, Liang Sun, Zhongxin Chen

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2025, Volume and Issue: 136, P. 104399 - 104399

Published: Feb. 1, 2025

Language: Английский

Citations

0
Fusion-Based Approaches and Machine Learning Algorithms for Forest Monitoring: A Systematic Review DOI Open Access

Abdullah Al Saim,

Mohamed H. Aly

Wild, Journal Year: 2025, Volume and Issue: 2(1), P. 7 - 7

Published: March 11, 2025

Multi-source remote sensing fusion and machine learning are effective tools for forest monitoring. This study aimed to analyze various techniques, their application with algorithms, assessment in estimating type aboveground biomass (AGB). A keyword search across Web of Science, Science Direct, Google Scholar yielded 920 articles. After rigorous screening, 72 relevant articles were analyzed. Results showed a growing trend optical radar fusion, notable use hyperspectral images, LiDAR, field measurements fusion-based Machine particularly Random Forest (RF), Support Vector (SVM), K-Nearest Neighbor (KNN), leverage features from fused sources, proper variable selection enhancing accuracy. Standard evaluation metrics include Mean Absolute Error (MAE), Root Squared (RMSE), Overall Accuracy (OA), User’s (UA), Producer’s (PA), confusion matrix, Kappa coefficient. review provides comprehensive overview prevalent data by synthesizing current research highlighting fusion’s potential improve monitoring The underscores the importance spectral, topographic, textural, environmental variables, sensor frequency, key gaps standardized protocols exploration multi-temporal dynamic change

Language: Английский

Citations

0
Improving the estimation accuracy of alfalfa quality based on UAV hyperspectral imagery by using data enhancement and synergistic band selection strategies DOI
Shuai Fu, Jie Liu, Jinlong Gao

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 234, P. 110305 - 110305

Published: March 19, 2025

Language: Английский

Citations

0
Alfalfa yield estimation using the combination of Sentinel-2 and meteorological data DOI Creative Commons
Angie L. Gámez, Joel Segarra, Thomas Vatter

et al.

Field Crops Research, Journal Year: 2025, Volume and Issue: 326, P. 109857 - 109857

Published: March 19, 2025

Language: Английский

Citations

0
Crop Mapping and Monitoring Using Sentinel-2: A Review DOI

Kenzie M. Prado,

Luis Alberto Riancho Martín,

Rheyent L. Boton

et al.

Smart innovation, systems and technologies, Journal Year: 2025, Volume and Issue: unknown, P. 173 - 185

Published: Jan. 1, 2025

Language: Английский

Citations

0
Improving the estimation of alfalfa yield based on multi-source satellite data and the synthetic minority oversampling strategy DOI

Lanxiang Li,

Shuai Fu, Jinlong Gao

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 236, P. 110497 - 110497

Published: May 9, 2025

Language: Английский

Citations

0
Research on the impact and mechanism of digital economy on China’s food production capacity DOI Creative Commons
Jue Wang, Yanyan Dong, Heng Wang

et al.

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

Published: Nov. 8, 2024

Enhancing and strengthening food production capacity has always been a top priority in agricultural research, serving as cornerstone for ensuring national security stable economic development. This study, based on panel data spanning from 2011 to 2021 across 30 provinces China, delves into the mechanism through which digital economy impacts capacity. Employing double fixed effect model, mediation threshold we uncover several key findings: The significantly boosts capacity, with robustness tests affirming reliability of our results. Mechanism analysis reveals that enhances by elevating total factor productivity bolstering resilience. underscores urbanization levels exhibit single-threshold impact, wherein influence intensifies upon crossing this threshold. Heterogeneity central primary grain-producing regions, while its impact is comparatively weaker eastern western well non-primary areas. In summary, research sheds light pivotal role augmenting offering valuable insights regional variations thresholds China.

Language: Английский

Citations

3