Estimating forest aboveground carbon sink based on landsat time series and its response to climate change

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 2, 2025

Accurately estimating forest carbon sink and exploring their climate-driven mechanisms are critical to achieving neutrality sustainable development. Fewer studies have used machine learning-based dynamic models estimate sink. The in Shangri-La yet be explored. In this study, a genetic algorithm (GA) was optimize the parameters of random (RF) establish intensity (CSI) Pinus densata analyze combined effects multi-climatic factors on CSI. We found that (1) GA can effectively improve estimation accuracy RF, R2 improved by up 34.8%, optimal GA-RF model is 0.83. (2) CSI 0.45–0.72 t C·hm− 2 from 1987 2017. (3) Precipitation has most significant effect weak drive precipitation, temperature, surface solar radiation dominant for These results indicate large-scale long-term highland forest, providing feasible method. Clarifying driving mechanism will provide scientific basis resource management.

Язык: Английский

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A Novel Model for Soil Organic Matter and Total Nitrogen Detection Based on Visible/Shortwave Near-Infrared Spectroscopy

Land, Год журнала: 2025, Номер 14(2), С. 329 - 329

Опубликована: Фев. 6, 2025

Soil organic matter (SOM) and total nitrogen (TN) are critical indicators for assessing soil fertility. Although laboratory chemical analysis methods can accurately measure their contents, these techniques time-consuming labor-intensive. Spectral technology, characterized by its high sensitivity convenience, has been increasingly integrated with machine learning algorithms nutrient monitoring. However, the process of spectral data remains complex requires further optimization simplicity efficiency to improve prediction accuracy. This study proposes a novel model enhance accuracy SOM TN predictions in northeast China’s black soil. Visible/Shortwave Near-Infrared Spectroscopy (Vis/SW-NIRS) within 350–1070 nm range were collected, preprocessed, dimensionality-reduced. The scores first nine principal components after partial least squares (PLS) dimensionality reduction selected as inputs, measured contents used outputs build back-propagation neural network (BPNN) model. results show that processed combination standard normal variate (SNV) multiple scattering correction (MSC) have best modeling performance. To stability this model, three named random search (RS), grid (GS), Bayesian (BO) introduced. demonstrate Vis/SW-NIRS provides reliable PLS-RS-BPNN achieving performance (R2 = 0.980 0.972, RMSE 1.004 0.006 TN, respectively). Compared traditional models such forests (RF), one-dimensional convolutional networks (1D-CNNs), extreme gradient boosting (XGBoost), proposed improves R2 0.164–0.344 predicting 0.257–0.314 respectively. These findings confirm potential technology effective tools prediction, offering valuable insights application sensing information.

Язык: Английский

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Evaluating Airborne Hyperspectral Scanner (AHS) for the mapping of soil organic matter and clay in a Mediterranean forest ecosystem

CATENA, Год журнала: 2025, Номер 252, С. 108889 - 108889

Опубликована: Март 4, 2025

Язык: Английский

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Estimating and mapping tailings properties of the largest iron cluster in China for resource potential and reuse: A new perspective from interpretable CNN model and proposed spectral index based on hyperspectral satellite imagery

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2025, Номер 139, С. 104512 - 104512

Опубликована: Апрель 7, 2025

Язык: Английский

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Analytical Study of the Detection Model for Sulphate Saline Soil Based on Mid-Infrared Spectrometry

Chemosensors, Год журнала: 2025, Номер 13(5), С. 173 - 173

Опубликована: Май 8, 2025

High soil sulfate levels can inhibit crop growth and accelerate concrete infrastructure degradation, highlighting the critical importance of rapid accurate content determination. Nevertheless, conventional analytical techniques are laborious intricate, delays in processing may result alterations to material, owing oxidation. We recognized accuracy, reproducibility, non-invasiveness mid-infrared (MIR) spectroscopy as a straightforward technique for analysis. In this study, samples were collected from two depths (0–20 cm 20–40 cm) across three regions China: arid northwestern region, cold-temperate northeastern zone, subtropical southwestern region. One group was mixed with Na2SO4 (a readily soluble salt) at mass fractions ranging 0.1% 7%, while other FeS2 sulfide) 1% 70%. This study aimed develop spectroscopy-based method analyzing sulfide soil. Three chemometric methods evaluated: partial least squares regression (PLSR), principal component (PCR), multivariate linear (MLR). Results showed that MLR model provided superior predictive performance. For sodium sulfate-mixed exhibited best performance an Rp2 0.9535, RMSEP 0.0030, RPD 4.96, RPIQ 6.26. iron disulfide-mixed demonstrated results Rp2, RMSEP, RPD, values 0.9590, 0.042, 5.97, 10.94, respectively. 0–20 achieved 0.9848, 0.0025, 14.20, 25.48. Despite regional variations properties, successfully predicted contents soils diverse areas using combined appropriate methods. approach provides reliable technical support detection offers significant practical value assessment both agricultural production engineering construction.

Язык: Английский

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LimeSoDa: A dataset collection for benchmarking of machine learning regressors in digital soil mapping

Geoderma, Год журнала: 2025, Номер 459, С. 117337 - 117337

Опубликована: Май 20, 2025

Язык: Английский

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Multifactorial Analysis of Fluorescence Detection for Soil Total Petroleum Hydrocarbons Using Random Forest and Multiple Linear Regression

Chemometrics and Intelligent Laboratory Systems, Год журнала: 2025, Номер unknown, С. 105444 - 105444

Опубликована: Май 1, 2025

Язык: Английский

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Mapping the carbon mitigation potential of photovoltaic development in the Gobi and desert regions of China

Energy, Год журнала: 2024, Номер 308, С. 132936 - 132936

Опубликована: Авг. 24, 2024

Язык: Английский

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Estimating forest aboveground carbon sink based on Landsat time-series and its response to climate change

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Ноя. 22, 2024

Accurately estimating forest carbon sink and exploring their climate-driven mechanisms are essential for achieving neutrality sustainable development. Taking Pinus densata in Shangri-La as the research object, we established three Random Forest (RF) dynamic models based on Landsat time series ground data with 5-year interval variation, 10-year annual average variation. Then, Genetic Algorithm (GA) was applied to optimize parameters of RF establish GA-RF models, selected optimal model estimate intensity (CSI) densata. Finally, were explored by correlation analysis. We found that 1) variation had highest accuracy an R² 0.83. 2) The CSI 7.84–12.35×10⁴ t C·hm^{− 2} from 1987 2017. 3) Precipitation greatest effect CSI. joint weak drive precipitation, temperature surface solar radiation most dominant form These results suggest can be used large-scale long-term estimation above-ground sinks highland forests. In addition, precipitation-led multifactorial synergistic driving mechanism will stabilize capacity long term.

Язык: Английский

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Application of chemometrics based on digital image analysis for simultaneous determination of tartrazine and sunset yellow in food samples

Food Chemistry, Год журнала: 2024, Номер 470, С. 142619 - 142619

Опубликована: Дек. 24, 2024

Язык: Английский

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