Abandoned Farmland Extraction and Feature Analysis Based on Multi-Sensor Fused Normalized Difference Vegetation Index Time Series—A Case Study in Western Mianchi County DOI Creative Commons
Jiqiu Deng, Yiwei Guo, Xiaohong Chen

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(5), P. 2102 - 2102

Published: March 2, 2024

Farmland abandonment monitoring is one of the key aspects land use and cover research, as well being an important prerequisite for ecological environmental protection food security. A Normalized Difference Vegetation Index (NDVI) time series analysis a common method used farmland data extraction; however, extracting this information using high-resolution still difficult due to limitations caused by cloud influence low temporal resolution. To address problem, study STARFM GF-6 Landsat 8 fusion enhance continuity cloudless images. dataset was constructed combining phenological cycle crops in area then abandoned based on NDVI analysis. The overall accuracy results STARFM-fused 93.42%, which 15.5% higher than obtained only 28.52% those data. Improvements were also achieved when SVM fused dataset, indicating that can effectively improve results. Then, we analyzed spatial distribution pattern concluded rate increased with increase road network density decreased distance residential areas. This provide decision-making guidance scientific technological support facilitate mechanisms area, conducive sustainable development farmland.

Language: Английский

Comparing the quantum use efficiency of red and far-red sun-induced fluorescence at leaf and canopy under heat-drought stress DOI Creative Commons
Sebastian Wieneke, Javier Pacheco‐Labrador, Miguel D. Mahecha

et al.

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 311, P. 114294 - 114294

Published: June 27, 2024

Sun-Induced chlorophyll Fluorescence (SIF) is the most promising remote sensing signal to monitor photosynthesis in space and time. However, under stress conditions its interpretation often complicated by factors such as light absorption plant morphological physiological adaptations. To ultimately derive quantum yield of fluorescence (ΦF) at photosystem from canopy measurements, so-called escape probability (fesc) needs be accounted for. In this study, we aim compare ΦF measured leaf- canopy-scale evaluate influence responses on two signals based a potato mesocosm heat-drought experiment. First, compared performance recently proposed reflectance-based approaches estimate leaf red fesc using data-supported simulations radiative transfer model SCOPE. While showed strong correlation (r2 ≥ 0.76), exhibited no relationship with SCOPE retrieved our We therefore propose modifications address limitation. then used modified models fesc, along an existing for far-red analyse dynamics increasing drought heat conditions. By incorporating obtained closer agreement between measurements. Specifically, r2 variables increased 0.3 0.50, 0.36 0.48. When comparing (ΦF,687 ΦF,760) stress, observed statistically significant decrease both ΦF,687 well ΦF,760, intensified. Canopy contrary, did not exhibit same trend, since measurements low wider spread lower median than high Finally, analysed sensitivity ΦF,760 changing solar incidence angle, variability without rotation. Our results suggest that variation strongly angle. These findings highlight need further research understand causes discrepancies scale ΦF,760. On underutilised understudied great potential assessing stress.

Language: Английский

Citations

7
The relationship between structure and ecosystem services of forest and grassland based on pattern analysis method: A case study of the Mongolian Plateau DOI
Jikai Zhao, Qiang Yu,

Buyanbaatar Avirmed

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 948, P. 174700 - 174700

Published: Oct. 1, 2024

Language: Английский

Citations

7
Explainable machine learning-based fractional vegetation cover inversion and performance optimization – A case study of an alpine grassland on the Qinghai-Tibet Plateau DOI Creative Commons
Xinhong Li, Jianjun Chen, Zizhen Chen

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: 82, P. 102768 - 102768

Published: Aug. 10, 2024

Fractional Vegetation Cover (FVC) serves as a crucial indicator in ecological sustainability and climate change monitoring. While machine learning is the primary method for FVC inversion, there are still certain shortcomings feature selection, hyperparameter tuning, underlying surface heterogeneity, explainability. Addressing these challenges, this study leveraged extensive field data from Qinghai-Tibet Plateau. Initially, selection algorithm combining genetic algorithms XGBoost was proposed. This integrated with Optuna tuning method, forming GA-OP combination to optimize learning. Furthermore, comparative analyses of various models inversion alpine grassland were conducted, followed by an investigation into impact heterogeneity on performance using NDVI Coefficient Variation (NDVI-CV). Lastly, SHAP (Shapley Additive exPlanations) employed both global local interpretations optimal model. The results indicated that: (1) exhibited favorable terms computational cost accuracy, demonstrating significant potential tuning. (2) Stacking model achieved among seven (R2 = 0.867, RMSE 0.12, RPD 2.552, BIAS −0.0005, VAR 0.014), ranking follows: > CatBoost LightGBM RFR KNN SVR. (3) NDVI-CV enhanced result reliability excluding highly heterogeneous regions that tended be either overestimated or underestimated. (4) revealed decision-making processes perspectives. allowed deeper exploration causality between features targets. developed high-precision scheme, successfully achieving accurate proposed approach provides valuable references other parameter inversions.

Language: Английский

Citations

7
Analysis of vegetation dynamics from 2001 to 2020 in China's Ganzhou rare earth mining area using time series remote sensing and SHAP-enhanced machine learning DOI Creative Commons
Ming Lei, Yuandong Wang, Guangxu Liu

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: 84, P. 102887 - 102887

Published: Nov. 9, 2024

Language: Английский

Citations

7
Abandoned Farmland Extraction and Feature Analysis Based on Multi-Sensor Fused Normalized Difference Vegetation Index Time Series—A Case Study in Western Mianchi County DOI Creative Commons
Jiqiu Deng, Yiwei Guo, Xiaohong Chen

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(5), P. 2102 - 2102

Published: March 2, 2024

Farmland abandonment monitoring is one of the key aspects land use and cover research, as well being an important prerequisite for ecological environmental protection food security. A Normalized Difference Vegetation Index (NDVI) time series analysis a common method used farmland data extraction; however, extracting this information using high-resolution still difficult due to limitations caused by cloud influence low temporal resolution. To address problem, study STARFM GF-6 Landsat 8 fusion enhance continuity cloudless images. dataset was constructed combining phenological cycle crops in area then abandoned based on NDVI analysis. The overall accuracy results STARFM-fused 93.42%, which 15.5% higher than obtained only 28.52% those data. Improvements were also achieved when SVM fused dataset, indicating that can effectively improve results. Then, we analyzed spatial distribution pattern concluded rate increased with increase road network density decreased distance residential areas. This provide decision-making guidance scientific technological support facilitate mechanisms area, conducive sustainable development farmland.

Language: Английский

Citations

6