Spatiotemporal dynamics of wetlands and their future multi-scenario simulation in the Yellow River Delta, China

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 353, P. 120193 - 120193

Published: Feb. 1, 2024

Language: Английский

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Dynamic landscapes and the influence of human activities in the Yellow River Delta wetland region

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 899, P. 166239 - 166239

Published: Aug. 10, 2023

The Yellow River Delta (YRD) wetland is one of the largest and youngest ecosystems in world. It plays an important role regulating climate maintaining ecological balance region. This study analyzes spatiotemporal changes land use, migration, landscape pattern from 2013 to 2022 using Landsat-8 Sentinel-1 data YRD. Then impact human activities are determined by analyzing correlation between socio-economic indicators including nighttime light centroid, total intensity, cultivated area building economic population. results show that increased 1426 km2 during this decade. However, tended be fragmented 2022, with wetlands different types interlacing connectivity decreasing, distribution becoming more concentrated. Different had influences on aspects landscape, expansion mainly compressing core edge, buildings disrupting connectivity, such as intensity centroid causing fragmentation. YRD provide explanation how effect change its which provides available achieve sustainable development goals 6.6 may give access measure human-activity data, could help adject behaviors protect wetlands.

Language: Английский

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Study on spatiotemporal changes of wetlands based on PLS-SEM and PLUS model: The case of the Sanjiang Plain

Ecological Indicators, Journal Year: 2024, Volume and Issue: 169, P. 112812 - 112812

Published: Nov. 9, 2024

Language: Английский

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Environmental sensitivity assessment of land desertification in the Hexi Corridor, China

CATENA, Journal Year: 2022, Volume and Issue: 220, P. 106728 - 106728

Published: Oct. 31, 2022

Language: Английский

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Desertification process and its effects on vegetation carbon sources and sinks vary under different aridity stress in Central Asia during 1990–2020

CATENA, Journal Year: 2022, Volume and Issue: 221, P. 106767 - 106767

Published: Nov. 14, 2022

Language: Английский

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Transferability of Recursive Feature Elimination (RFE)-Derived Feature Sets for Support Vector Machine Land Cover Classification

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(24), P. 6218 - 6218

Published: Dec. 8, 2022

Remote sensing analyses frequently use feature selection methods to remove non-beneficial variables from the input data, which often improve classification accuracy and reduce computational complexity of classification. Many remote report results process provide insights on important variable for future analyses. Are these generalizable other models, or are they specific dataset model were derived from? To investigate this, a series radial basis function (RBF) support vector machines (SVM) supervised machine learning land cover classifications Sentinel-2A Multispectral Instrument (MSI) imagery conducted assess transferability recursive elimination (RFE)-derived sets between different models using training acquired same remotely sensed image, similar imagery. Feature various image images widely varied small (n = 108). Variability in was reduced as set size increased; however, each RFE-derived unique, even when sample increased over 10-fold 1895). The an high performing was, average, slightly more accurate comparison but provided, lower accuracies generalized other, However, effects inconsistent per model. Specific analyses, while useful providing general variables, may not always generalize comparable dataset, datasets. Thus, should be individually within analysis determine optimal

Language: Английский

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Fine-grained wetland classification for national wetland reserves using multi-source remote sensing data and Pixel Information Expert Engine (PIE-Engine)

GIScience & Remote Sensing, Journal Year: 2023, Volume and Issue: 60(1)

Published: Nov. 27, 2023

Timely and accurate wetland information is necessary for resource management. Recent advances in machine learning remote sensing have facilitated cost-effective monitoring of wetlands. However, reliable methods fine-grained rapid mapping are still lacking. To address the issue, a sample set with 20 categories China was collected based on sampling strategy that combines automatic generation visual interpretation. Simultaneously, novel multi-stage method classification proposed, which integrates pixel-based object-based strategies using ensemble algorithms multi-source data. First, algorithm implemented to classify five rough six non-wetland categories. Second, an approach designed separate water cover results into eight detailed Third, merged were refined knowledge-based post-processing procedures identify 14 Results Pixel Information Expert Engine (PIE-Engine) cloud platform proved effectiveness proposed method. The overall accuracy, kappa, weighted F1 reached 87.39%, 82.80%, 86.02%, respectively. adopted yielded better performance than classifiers such as CatBoost, random forest, XGBoost. incorporation spectral, texture, shape, topographic, geographic features from data contributed differentiating According relative contribution, spectral indexes (NDVI NDWI), texture (sum average contrast), topographic (slope elevation) identified important leading predictors first-stage classification. Shape (shape index compactness) auxiliary (geographic location) crucial second-stage Compared other products, our 10-m national reserves rich detail fine Overall, constructed developed show promise laying foundation large-scale mapping. derived maps can provide support protection restoration.

Language: Английский

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Evolution of Small and Micro Wetlands and Their Driving Factors in the Yangtze River Delta—A Case Study of Wuxi Area

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(4), P. 1152 - 1152

Published: Feb. 20, 2023

Understanding the long-term dynamics and driving factors behind small micro wetlands is critical for their management future sustainability. This study explored impacts of natural anthropogenic on spatiotemporal evolution these areas in Wuxi area using support vector machine (SVM) classification method geographic detector model based Landsat satellite image data from 1985 to 2020. The results revealed that: (1) Natural were prominent area, with an average proportion 70%, although they exhibited a downward trend over last ten years, scale increased 1.5-fold—from 4349.59 hm2 10,841.59 (2) had obvious seasonal variations, most being 0.1–1 1–3 hm2, respectively. From perspective spatial distribution, primarily distributed Yixing district, which accounts 34% area. (3) distribution was systematically affected by human activities. main that annual temperature GDP, interactions between all nonlinear bi-linear. influences weakened, activities steadily emerging as dominant factor distribution. this can provide supportive scientific basis ecological restoration protection wetlands.

Language: Английский

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Monitoring Wetland Changes and Analyzing the Spartina alterniflora Invasion in the Yellow River Delta over the Past 30 Years Based on Google Earth Engine

IEEE Geoscience and Remote Sensing Letters, Journal Year: 2024, Volume and Issue: 21, P. 1 - 5

Published: Jan. 1, 2024

The spectral similarity of regular and irregular ground objects the problem image noise abnormal pixels in a wide range object extraction have been difficult for accurate wetland classification. This paper proposes two-order hierarchical classification method (HSNIC), three new indices are constructed to extract salt marsh vegetation more accurately. Finally, results Yellow River Delta wetlands from 1990 2023 were obtained, invasion mechanism Spartina alterniflora was further analyzed. show that: (1) accuracy by HSNIC reached 88.48%. Compared with RF object-oriented classification, is improved. (2) Introducing significantly enhanced accuracy. (3) xmlns:xlink="http://www.w3.org/1999/xlink">S.alterniflora influenced survival environment local vegetation. These research findings provide basis decision-making regarding restoration conservation Delta.

Language: Английский

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Estimation of carbon stock in the reed wetland of Weishan county in China based on Sentinel satellite series

Carbon Research, Journal Year: 2025, Volume and Issue: 4(1)

Published: March 11, 2025

Abstract Reed wetlands in Weishan County, Shandong provinces, are typical and representative wetland ecosystems with exceptional carbon sequestration potential. Evaluating the spatial temporal characteristics of land use stock these reed wetlands, exploring their sink value is crucial for climate change mitigation adaptation, provides a potential to as solution neutrality China. Using Sentinel active passive remote sensing data within Google Earth Engine (GEE) platform, we employed random forest classification method identify features County. By combining density obtained from bibliometric sources InVEST model, evaluated dynamics well other types. The results indicated that optical more effective than radar classification, achieving mean overall accuracy 89%. contribute significantly stock, accounting 28% total Other major contributors include forest, water body, agricultural land, artificial unused mudflat land. highest concentration found along shores four lakes northeastern mountainous areas capacity County expected generate 4.95–54 × 10 8 RMB, up 1%–12% county’s GDP. These findings provide scientific foundation subsequent restoration management efforts offer valuable insights developing relevant strategies.

Language: Английский

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