The aim of this study was to determine the content, distribution and behaviour of Al in soils under beech forest with different parent rock, and to assess the role of herbaceous vegetation on soil Al behaviour. We hypothesize that the contents of elements in the soil sorption complex (Al etc.) are strongly influenced by vegetation cover. Also, low molecular mass organic acids (LMMOA) can be considered as an indicator of soil organic matter (SOM) decomposition and vegetation litter turnover. Speciation of LMMOA, nutrition content (PO, Ca, K) and element composition in aqueous extracts were determined by means of ion chromatography and inductively coupled plasma - optical emission spectrometry (ICP-OES) respectively. Active and exchangeable pH, sorption characteristics and exchangeable Al (Al) were determined in BaCl extracts by ICP-OES. Elemental composition of parent rocks was assessed by means of X-ray fluorescence spectroscopy. Herb-poor localities showed lower pH, less nutrients (PO, Ca, K), less LMMOA, a larger stock of SOM and greater cation exchange capacity. There was also lower mobilisation of Al in organic horizons, which explains the larger pools of Al. Generally, we can conclude that LMMOA, and thus soil vegetation cover, play an important role in the Al soil cycle.
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http://dx.doi.org/10.1016/j.jinorgbio.2017.09.017 | DOI Listing |
Front Plant Sci
January 2025
Yellow River Institute of Hydraulic Research, Henan Key Laboratory of Yellow Basin Ecological Protection and Restoration, Zhengzhou, China.
Vegetation productivity and ecosystem carbon sink capacity are significantly influenced by seasonal weather patterns. The time lags between changes in these patterns and ecosystem (including vegetation) responses is a critical aspect in vegetation-climate and ecosystem-climate interactions. These lags can vary considerably due to the spatial heterogeneity of vegetation and ecosystems.
View Article and Find Full Text PDFPlant Cell Environ
January 2025
Remote Sensing Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India.
The generation of spectral libraries using hyperspectral data allows for the capture of detailed spectral signatures, uncovering subtle variations in plant physiology, biochemistry, and growth stages, marking a significant advancement over traditional land cover classification methods. These spectral libraries enable improved forest classification accuracy and more precise differentiation of plant species and plant functional types (PFTs), thereby establishing hyperspectral sensing as a critical tool for PFT classification. This study aims to advance the classification and monitoring of PFTs in Shoolpaneshwar wildlife sanctuary, Gujarat, India using Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and machine learning techniques.
View Article and Find Full Text PDFSci Data
January 2025
Institute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, 100091, China.
The vegetation index is a key satellite-based variable used to monitor global vegetation distribution and growth. However, existing vegetation index datasets face limitations in achieving both high spatial and temporal resolution, restricting their application potential. This study revised a machine learning spatiotemporal fusion model (InENVI) to produce a high-resolution NDVI dataset with 8-day temporal and 30 m spatial resolution, covering China from 2001 to 2020.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Wildlife Fisheries and Aquaculture, College of Forest Resources, Mississippi State University, Mississippi State, MS, 39762-9690, USA.
This study addresses the significant issue of rapid land use and land cover (LULC) changes in Lahore District, which is critical for supporting ecological management and sustainable land-use planning. Understanding these changes is crucial for mitigating adverse environmental impacts and promoting sustainable development. The main goal is to evaluate historical LULC changes from 1994 to 2024 and forecast future trends for 2034 and 2044 utilizing the CA-Markov hybrid model combined with GIS methodologies.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Geography, School of Environment, Education and Development, The University of Manchester, Arthur Lewis Building, Oxford Road, Manchester, M13 9PL, UK.
Urban woodland composition and configuration have strong associations with land surface temperatures (LST), but the evidence is contradictory due to different spatial scales, regional climate zones, woodland types and urban contexts. In this study, we analyse associations between urban woodland and LST within and between five cities in different Köppen climate zones. Our consistent methodology is framed around local climate zones and conducted at a fine spatial scale.
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