Remote Sensing Application in Ecological Restoration Monitoring: A Systematic Review

Remote Sensing, Год журнала: 2024, Номер 16(12), С. 2204 - 2204

Опубликована: Июнь 17, 2024

In the context of continuous degradation global environment, ecological restoration has become a primary task in environmental governance. this process, remote sensing technology, as an advanced monitoring and analysis tool, plays key role restoration. This article reviews application technology monitoring. Based on comprehensive literature field sensing, it systematically summarizes major in-orbit spaceborne airborne sensors their related products. further proposes series evaluation indicators for from four aspects: forests, soil, water, atmosphere, elaborates calculation methods these indicators. addition, paper also evaluating effectiveness restoration, including subjective evaluation, objective methods. Finally, we analyze challenges faced by effectiveness, such issues with precision extraction, limitations spatial resolution, diversity review looks forward to future technologies, potential applications integrated aerospace terrestrial multi-data fusion, machine learning technologies. study reveals monitoring, aiming provide efficient tools innovative strategies assessment

Язык: Английский

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Improving Soil Heavy Metal Lead Inversion Through Combined Band Selection Methods: A Case Study in Gejiu City, China

Sensors, Год журнала: 2025, Номер 25(3), С. 684 - 684

Опубликована: Янв. 23, 2025

Hyperspectral technology has become increasingly important in monitoring soil heavy metal pollution, yet hyperspectral data often contain substantial band redundancy, and selection methods are typically limited to single algorithms or simple combinations. Multi-algorithm combinations for remain underutilized. To address this gap, study, conducted Gejiu, Yunnan Province, China, proposes a multi-algorithm method enable the rapid prediction of lead (Pb) contamination levels soil. construct preliminary Pb content model, initial spectral bands utilized including CARS (Competitive Adaptive Reweighted Sampling), GA (Genetic Algorithm), MI (mutual information), SPA (Successive Projections WOA (Whale Optimization Algorithm). The results indicated that achieved highest modeling accuracy. Building on this, combined WOA-based was developed, such as WOA-CARS, WOA-GA, WOA-MI, WOA-SPA, with multi-level optimization further refined by (e.g., WOA-GA-MI, WOA-CARS-MI, WOA-SPA-MI). showed WOA-GA-MI model exhibited optimal performance, achieving an average R2 0.75, improvements 0.32, 0.11, 0.02 over full-spectrum WOA-selected WOA-GA respectively. Additionally, response analysis identified 22 common essential inversion. proposed not only significantly enhances accuracy but also provides new insights into optimizing selection, serving valuable scientific foundation assessing contamination.

Язык: Английский

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Ensemble and transfer learning of soil inorganic carbon with visible near-infrared spectra

Geoderma, Год журнала: 2025, Номер 456, С. 117257 - 117257

Опубликована: Март 15, 2025

Язык: Английский

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Prediction of Vanadium Contamination Distribution Pattern Through Remote Sensing Image Fusion and Machine Learning

Remote Sensing, Год журнала: 2025, Номер 17(7), С. 1164 - 1164

Опубликована: Март 25, 2025

Soil vanadium contamination poses a significant threat to ecosystems. Hyperspectral remote sensing plays critical role in extracting spectral features of heavy metal contamination, mapping its spatial distribution, and monitoring trends over time. This study targets vanadium-contaminated area Panzhihua City, Sichuan Province. sampling measurements occurred the laboratory. (Gaofen-5, GF-5) multispectral (Gaofen-2, GF-2; Sentinel-2) images were acquired preprocessed, feature bands extracted by combining laboratory data. A dual-branch convolutional neural network (DB-CNN) fused hyperspectral confirmed fusion’s effectiveness. Six prevalent machine learning models adopted, unified framework leveraged Random Forest (RF) as second-layer model enhance predictive performance these base models. Both ensemble evaluated based on accuracy. The fusion process enhanced models, improving R2 values for (V) pentavalent (V5+) from 0.54 0.3 0.58 0.39, respectively, at 4 m resolution. Further optimization using RF refine Extreme Trees (ETs) significantly increased 0.83 0.75 V V5+, this scale. 934 nm 464 wavelengths identified most predicting soil contamination. integrated approach robustly delineates distribution characteristics V5+ soils, facilitating precise ecological risk assessments through comparative analysis accuracy across diverse

Язык: Английский

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Enhancing field-scale soil moisture content monitoring using UAV hyperspectral-derived multi-dimensional spectral response indices of crop comprehensive phenotypic traits

Computers and Electronics in Agriculture, Год журнала: 2025, Номер 235, С. 110399 - 110399

Опубликована: Апрель 15, 2025

Язык: Английский

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Evaluation of machine learning models for accurate prediction of heavy metals in coal mining region soils in Bangladesh

Environmental Geochemistry and Health, Год журнала: 2025, Номер 47(5)

Опубликована: Апрель 23, 2025

Язык: Английский

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Machine learning models with innovative outlier detection techniques for predicting heavy metal contamination in soils

Journal of Hazardous Materials, Год журнала: 2024, Номер 481, С. 136536 - 136536

Опубликована: Ноя. 19, 2024

Язык: Английский

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Hyperspectral Inversion of Soil Cu Content in Agricultural Land Based on Continuous Wavelet Transform and Stacking Ensemble Learning

Опубликована: Янв. 1, 2024

Язык: Английский

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A Comparative Study of Different Dimensionality Reduction Algorithms for Hyperspectral Prediction of Salt Information in Saline–Alkali Soils of Songnen Plain, China

Agriculture, Год журнала: 2024, Номер 14(7), С. 1200 - 1200

Опубликована: Июль 21, 2024

Hyperspectral technology is widely recognized as an effective method for monitoring soil salinity. However, the traditional sieved samples often cannot reflect true condition of surface. In particular, there a lack research on spectral response cracked salt-affected soils despite common occurrence cohesive saline shrinkage and cracking during water evaporation. To address this research, laboratory was designed to simulate desiccation progress 57 soda saline–alkali with different salinity levels in Songnen Plain China. After completion drying process, spectroscopic analysis conducted surface all samples. Moreover, study aimed evaluate predictive ability multiple linear regression models (MLR) four main salt parameters. The hyperspectral reflectance data analyzed using three band screening methods, namely random forest (RF), principal component (PCA), Pearson correlation (R). findings revealed significant between salinity, suggesting that primary factor influencing Plain. results modeling also indicated that, regardless dimensionality reduction employed, exhibited highest prediction accuracy followed by electrical conductivity (EC) sodium (Na+), while pH model weakest performance. addition, usage RF selection has best effect compared PCA which allows information be predicted precisely.

Язык: Английский

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Effects of salt content and particle size on spectral reflectance and model accuracy: Estimating soil salt content in arid, saline-alkali lands

Microchemical Journal, Год журнала: 2024, Номер unknown, С. 111666 - 111666

Опубликована: Сен. 1, 2024

Язык: Английский

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