Soil microbial diversity plays an important role in resisting and restoring degraded ecosystems

Plant and Soil, Год журнала: 2024, Номер 500(1-2), С. 325 - 349

Опубликована: Янв. 30, 2024

Язык: Английский

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Dynamics of land cover changes and driving forces in China’s drylands since the 1970 s

Land Use Policy, Год журнала: 2024, Номер 140, С. 107097 - 107097

Опубликована: Фев. 27, 2024

Язык: Английский

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A comprehensive framework for evaluating ecosystem quality changes and human activity contributions in Inner Mongolia and Xinjiang, China

Land Use Policy, Год журнала: 2025, Номер 151, С. 107494 - 107494

Опубликована: Фев. 5, 2025

Язык: Английский

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Characteristics of spatial and temporal dynamics of vegetation and its response to climate extremes in ecologically fragile and climate change sensitive areas – A case study of Hexi region

CATENA, Год журнала: 2024, Номер 239, С. 107910 - 107910

Опубликована: Фев. 21, 2024

Язык: Английский

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Random forest-based analysis of land cover/land use LCLU dynamics associated with meteorological droughts in the desert ecosystem of Pakistan

Ecological Indicators, Год журнала: 2024, Номер 159, С. 111670 - 111670

Опубликована: Фев. 1, 2024

Dry land ecosystems extend over 40 % of the Earth, supporting an estimated 3 billion human population. Thus, quantifying LCLU changes in such is essential for achieving sustainable development goals. In this context, research aimed to examine past three decades (1990 – 2020) arid ecosystem Pakistan, i.e., Cholisatn desert. Three remote sensing indices, normalized difference vegetation index (NDVI), barren (NDBaI), and top grain soil (TGSI) are taken as representatives their temporal relationship associated with meteorological drought, e.g. standardized precipitation (SPI). Moreover, machine learning-based random forest (RF) classification followed by change detection techniques was implemented. Results from RF classifier revealed applicability accurately predicting LULC validation overall accuracy 0.99. Output interesting finding where desert experienced significant last decades. The highest expansion (4.4 %) took place 2014 2020 at expense reduction (-6.3 %). Mann-Kendall trend (MK) Sen's slope (SS) analysis showed a (P < 0.001) increasing NDVI (SS = 0.004), SPI 0.01 0.04) decreasing NDBaI TGSI -0.001, −0.005). Interestingly, positive Pearson correlation range (r 0.6–0.8) SPI-1 6, negative 0.5–0.7) indices reveals strong linear between drought. provides substantial implications policy makers stakeholders emphasizing need proactive strategies drought resistant improve maintain ecological health combating impacts climatic change.

Язык: Английский

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Detection and Attribution of Vegetation Dynamics in the Yellow River Basin Based on Long-Term Kernel NDVI Data

Remote Sensing, Год журнала: 2024, Номер 16(7), С. 1280 - 1280

Опубликована: Апрель 5, 2024

Detecting and attributing vegetation variations in the Yellow River Basin (YRB) is vital for adjusting ecological restoration strategies to address possible threats posed by changing environments. On basis of kernel normalized difference index (kNDVI) key climate drivers (precipitation (PRE), temperature (TEM), solar radiation (SR), potential evapotranspiration (PET)) basin during period from 1982 2022, we utilized multivariate statistical approach analyze spatiotemporal patterns dynamics, identified variables, discerned respective impacts change (CC) human activities (HA) on these variations. Our analysis revealed a widespread greening trend across 93.1% YRB, with 83.2% exhibiting significant increases kNDVI (p < 0.05). Conversely, 6.9% vegetated areas displayed browning trend, particularly concentrated alpine urban areas. With Hurst exceeding 0.5 97.5% areas, YRB tends be extensively greened future. Climate variability emerges as pivotal determinant shaping diverse spatial temporal patterns, PRE exerting dominance 41.9% followed TEM (35.4%), SR (13%), PET (9.7%). Spatially, increased significantly enhanced growth arid zones, while controlled non-water-limited such irrigation zones. Vegetation dynamics were driven combination CC HA, relative contributions 55.8% 44.2%, respectively, suggesting that long-term dominant force. Specifically, contributed seen region southeastern part basin, human-induced factors benefited Loess Plateau (LP) inhibiting pastoral These findings provide critical insights inform formulation adaptation conservation thereby enhancing resilience environmental conditions.

Язык: Английский

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Analysis and Prediction of Land Use/Land Cover Changes in Korgalzhyn District, Kazakhstan

Agronomy, Год журнала: 2024, Номер 14(2), С. 268 - 268

Опубликована: Янв. 25, 2024

Changes occurring because of human activity in protected natural places require constant monitoring land use (LU) structures. Therefore, Korgalzhyn District, which occupies part the State Natural Reserve territory, is considerable interest. The aim these studies was to analyze changes composition use/land cover (LULC) District from 2010 2021 and predict LU transformation by 2030 2050. Landsat image classification performed using Random Forest on Google Earth Engine. combined CA-ANN model used LULC 2050, were carried out MOLUSCE plugin. results showed that 2021, there a steady increase share ploughable an adequate reduction grassland. It established that, this trend will continue. At same time, be no drastic other classes. obtained can helpful for development management plans policies District.

Язык: Английский

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Monitoring Land Use Changes in the Yellow River Delta Using Multi-Temporal Remote Sensing Data and Machine Learning from 2000 to 2020

Remote Sensing, Год журнала: 2024, Номер 16(11), С. 1946 - 1946

Опубликована: Май 28, 2024

The Yellow River Delta (YRD), known for its vast and diverse wetland ecosystem, is the largest estuarine delta in China. However, human activities climate change have significantly degraded ecosystem recent decades YRD. Therefore, an understanding of land use modifications essential efficient management preservation ecosystems this region. This study utilized time series remote sensing data extreme gradient boosting method to generate maps YRD from 2000 2020. Several methods, including transition matrix, dynamic degree, standard deviation ellipse, were employed explore characteristics transitions. results underscore significant spatial variations over past two decades. most rapid increase was observed built-up area, followed by terrestrial water tidal flats, while unutilized experienced fastest decrease, forest–grassland. distribution patterns agricultural land, water, forest–grassland demonstrated stronger directionality compared other types. wetlands expanded size improved structure. Unutilized has been converted into artificial comprising ponds, reservoirs, salt shrimp crab natural featuring mudflats conservation efforts after 2008 proven very effective, playing a positive role ecological environmental preservation, as well regional sustainable development.

Язык: Английский

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Dryland Social-Ecological Systems in Changing Environments

Опубликована: Янв. 1, 2024

This open access book presents a timely synthesis of up-to-date knowledge in various thematic fields relevant to dryland SESs

Язык: Английский

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Evaluating evapotranspiration models for precise aridity mapping based on UNEP- aridity classification

Earth Science Informatics, Год журнала: 2025, Номер 18(2)

Опубликована: Янв. 20, 2025

Язык: Английский

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