基于净初级生产人类占用的1992-2020年全球土地利用数据立方体

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Sarah Matej, Florian Weidinger, Lisa Kaufmann, Nicolas Roux, Simone Gingrich, Helmut Haberl, Fridolin Krausmann, Karl-Heinz Erb
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引用次数: 0

摘要

土地利用与地球系统的关键组成部分密切相关,包括气候系统、生物多样性和生物地球化学循环。深入了解土地利用的模式和动态对于评估对这些系统组成部分的影响和制定确保可持续性的战略至关重要。然而,目前缺乏能够分析土地利用时空动态(包括土地利用强度)的专题详细数据。本研究提出了一个综合土地利用数据立方体(LUIcube),以30角秒的空间分辨率追踪1992 - 2020年间全球土地利用面积和强度的发展。它识别出32种土地使用类别,可以汇总为农田、牧场、林业、建筑用地和荒野。土地利用强度通过净初级生产的人类占用框架来表示,该框架允许量化NPP的变化,分别是由土地转换和土地管理引起的生物量流量。LUIcube为分析土地利用变化的自然和社会经济驱动因素的作用及其生态影响提供了必要的数据库,为可持续土地管理战略提供信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production.

Land use is intimately linked to key components of the Earth system, including the climate system, biodiversity and biogeochemical cycles. Advanced understanding of patterns and dynamics of land use is vital for assessing impacts on these system components and for developing strategies to ensure sustainability. However, thematically detailed data that enable the analyses of spatiotemporal dynamics of land use, including land-use intensity, are currently lacking. This study presents a comprehensive land-use data cube (LUIcube) that traces global land-use area and intensity developments between 1992 and 2020 annually at 30 arcsecond spatial resolution. It discerns 32 land-use classes that can be aggregated to cropland, grazing land, forestry, built-up land and wilderness. Land-use intensity is represented through the framework of Human Appropriation of Net Primary Production, which allows to quantify changes in NPP, respectively biomass flows, induced by land conversion and land-management. The LUIcube provides the necessary database for analyzing the role of natural and socioeconomic drivers of land-use change and its ecological impacts to inform strategies for sustainable land management.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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