Rice leaf chlorophyll content estimation with different crop coverages based on Sentinel-2

Ecological Informatics, Год журнала: 2024, Номер 81, С. 102622 - 102622

Опубликована: Май 1, 2024

Chlorophyll content is an important index for evaluating the health and productivity of crops, environmental stress on them. The real-time, rapid, accurate acquisition chlorophyll plays a key role in crop growth monitoring. Remote sensing can quickly obtain regional global scale, but how to eliminate interference soil background estimation major challenge. statistical analysis method based empirical/semi-empirical model simpler, faster easier implement than that radiative transfer mechanism model. influence by looking Vegetation (VI) sensitive not certain extent. However, accuracy this low fields with different degrees coverage. Additionally, special characteristics rice make doubtful tool estimate To improve leaf (LCC) model, we here propose new We analyzed remote images Sentinel-2 over rice-planting areas Qian Gorlos County Jilin Province China. divided study area into three regions high-, medium-, low-rice canopy Rice LCC each region was estimated identifying vegetation indices are coverages. Compared results without considering coverage, our achieves higher accuracy. In addition, applied Northeast China 2023 verify its strong generalisability robustness. Our provides reference rapidly non-destructively obtaining images. applicability other crops will be verified future.

Язык: Английский

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Effects of precipitation changes on fractional vegetation cover in the Jinghe River basin from 1998 to 2019

Ecological Informatics, Год журнала: 2024, Номер 80, С. 102505 - 102505

Опубликована: Янв. 30, 2024

Studying the spatiotemporal evolutionary characteristics of vegetation and effect precipitation changes is necessary for understanding regional ecological environment. We used trend analysis, partial correlation significance tests, residual analysis to analyze evolution driving factors fractional cover (FVC) in Jinghe River Basin (JRB) from 1998 2019. The results showed that coverage JRB significantly improved FVC an increasing 90.64% areas JRB, overall annual change was extremely significant (p ≤ 0.01). However, insignificant trend; distribution developed a uniform direction centroid tended move backward. area with between concentration index accounted largest proportion (18.47%). Precipitation generally favored recovery; however, limited non-precipitation dominated FVC. Our study contributes more comprehensive effects patterns on facilitate protection.

Язык: Английский

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Explainable machine learning-based fractional vegetation cover inversion and performance optimization – A case study of an alpine grassland on the Qinghai-Tibet Plateau

Ecological Informatics, Год журнала: 2024, Номер 82, С. 102768 - 102768

Опубликована: Авг. 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.

Язык: Английский

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Spatiotemporal evolution of farmland ecosystem stability in the Fenhe River Basin China based on perturbation-resistance-response framework

Ecological Informatics, Год журнала: 2025, Номер unknown, С. 102977 - 102977

Опубликована: Янв. 1, 2025

Язык: Английский

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The World Cup reshaped the urban green space pattern of Qatar

Ecological Informatics, Год журнала: 2024, Номер 81, С. 102551 - 102551

Опубликована: Март 7, 2024

The World Cup stands as the most momentous global sporting event, and significantly impacts urban green space (UGS) of host cities. However, impacts, processes, pattern characteristic on UGS have not yet been fully understood. To fill this gap, we employ time-series satellite imagery compute normalized difference vegetation index (NDVI) across detailed maps in Qatar from 2000 to 2022. In our quantitative assessment, investigate coverage, landscape patterns, exposure both before after Cup. Additionally, compile seven instances greening Qatar, compare them with processes three cities located neighboring countries. This contextual analysis aims unravel nuanced impact Qatar. Our results demonstrate: (1) emerges a significant contributor growth, expansion accounting for 94.3% overall increase built-up area during tournament. surge growth is equivalent an additional 38 Manhattan Central Parks. (2) induces transformation landscapes, rendering more complex fragmented. degree change within 35 times greater than those changes observed pre-World period. (3) brings about enhancement minimum level citizens, marking 8.7-fold increase. event has proven be instrumental propelling towards multifaceted greening, establishing country leading model regional processes. study thus confirms Cup's role promoting reshaping offering fresh insights into its contribution sustainable development.

Язык: Английский

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Enhanced River Connectivity Assessment Across Larger Areas Through Deep Learning With Dam Detection

Hydrological Processes, Год журнала: 2025, Номер 39(1)

Опубликована: Янв. 1, 2025

ABSTRACT Monitoring river connectivity across large regions is essential for understanding hydrological processes and environmental management. However, comprehensive assessments of are often hindered by inaccurate dam databases, which biased towards larger dams while overlooking smaller or low‐head dams. To enhance the accuracy assessments, we developed three advanced convolutional neural networks (CNNs; YOLOv5, Advance‐You Only Look Once [YOLO], Faster R‐CNN) to accurately classify evaluate using high‐resolution (1 m) remote sensing imagery. The evaluation results showed that Advance‐YOLO performs best with an average mean precision (mAP) 86.6%, R‐CNN mediocrely mAP 77.9%. Applying well‐trained model in Tarim River Basin (China), one largest inland basins around globe, found there currently 135 total on its sources. Conversely, existing public database underestimates 85.9% Notably, a 14.3% decline over past decade, current density four source rivers 1.12 per 10 000 km 2 . overestimated 83.9%. here enhances assessment areas long period, thereby fostering more research effective water resource

Язык: Английский

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Multi-scale feature fusion optical remote sensing target detection method

Optoelectronics Letters, Год журнала: 2025, Номер 21(4), С. 226 - 233

Опубликована: Фев. 14, 2025

Язык: Английский

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Spatiotemporal Heterogeneity of Vegetation Cover Dynamics and Its Drivers in Coastal Regions: Evidence from a Typical Coastal Province in China

Remote Sensing, Год журнала: 2025, Номер 17(5), С. 921 - 921

Опубликована: Март 5, 2025

Studying the spatiotemporal trends and influencing factors of vegetation coverage is essential for assessing ecological quality monitoring regional ecosystem dynamics. The existing research on variations their driving predominantly focused inland ecologically vulnerable regions, while coastal areas received relatively little attention. However, with unique geographical, ecological, anthropogenic activity characteristics, may exhibit distinct distribution patterns mechanisms. To address this gap, we selected Shandong Province (SDP), a representative province in China significant natural socioeconomic heterogeneity, as our study area. investigate coastal–inland differentiation dynamics its underlying mechanisms, SDP was stratified into four geographic sub-regions: coastal, eastern, central, western. Fractional cover (FVC) derived from MOD13A3 v061 NDVI data served key indicator, integrated multi-source datasets (2000–2023) encompassing climatic, topographic, variables. We analyzed characteristics dominant across these sub-regions. results indicated that (1) FVC displayed complex notable gradient where decreased towards coast. (2) influence various significantly varied sub-regions, dominating an east–west polarity, i.e., explanatory power intensified westward resurging zones. (3) intricate interaction multiple influenced spatial FVC, particularly dual-factor synergies interactions between other were crucial determining coverage. Notably, zone exhibited high sensitivity to drivers, highlighting exceptional ecosystems human activities. This provides insights different geographical zones well factors. These findings can help understand challenges faced protecting vegetation, facilitating deeper insight responses enabling formulation effective tailored strategies promote sustainable development areas.

Язык: Английский

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Identification of Salt Marsh Vegetation in the Yellow River Delta Using UAV Multispectral Imagery and Deep Learning

Drones, Год журнала: 2025, Номер 9(4), С. 235 - 235

Опубликована: Март 23, 2025

Salt marsh ecosystems play a critical role in coastal protection, carbon sequestration, and biodiversity preservation. However, they are increasingly threatened by climate change anthropogenic activities, necessitating precise vegetation mapping for effective conservation. This study investigated the effectiveness of spectral features machine learning models separating typical salt types Yellow River Delta using uncrewed aerial vehicle (UAV)-derived multispectral imagery. The results revealed that Normalized Difference Vegetation Index (NDVI), Green (GNDVI), Optimized Soil Adjusted (OSAVI) were pivotal differentiating types, compared with reflectance at individual bands. Among evaluated models, U-Net achieved highest overall accuracy (94.05%), followed SegNet (93.26%). model produced overly distinct abrupt boundaries between lacking natural transitions found real distributions. In contrast, excelled boundary handling, better capturing types. Both deep outperformed Random Forest (83.74%) Extreme Gradient Boosting (83.34%). highlights advantages their potential ecological monitoring conservation efforts.

Язык: Английский

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Exploring the nexus between coastal tourism growth and eutrophication: Challenges for environmental management

Marine Pollution Bulletin, Год журнала: 2025, Номер 216, С. 117922 - 117922

Опубликована: Апрель 16, 2025

Язык: Английский

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