land use intensity
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2022 ◽  
Vol 328 ◽  
pp. 107845
Author(s):  
Sandra Müller ◽  
Martin M. Gossner ◽  
Caterina Penone ◽  
Kirsten Jung ◽  
Swen C. Renner ◽  
...  

2022 ◽  
Vol 9 ◽  
Author(s):  
Xue Du ◽  
Dan Song ◽  
Kun Ming ◽  
Jingshuang Yang ◽  
Xing Jin ◽  
...  

Watershed land-use changes have been identified as major threats to lake fauna, subsequently affecting ecosystem functioning. In this study, the functional-based approach was used to examine the effects of land use and environmental changes on phytoplankton communities in four selected lakes in Northeast China. We also identified the sensitive functional traits as indicators of environmental stressors. The integration of RLQ analysis with the fourth-corner approach significantly identified five of 18 functional trait categories, including flagella, filamentous, unicellular, mixotrophic, and chlorophyll c, as potential indicators to changes in watershed land-use intensity and environmental gradients. Significant relationships between traits and land use and water quality highlighted the consequential indirect impact of extensive agricultural and urban development on phytoplankton via allochthonous nutrient inputs and various contaminants. In addition, the functional richness of phytoplankton assemblages generally increased along with surface area and forests, but decreased along with intensive agricultural and urban land use, implying that functional homogenization may cause a reduction in ecosystem productivity and reliability to land-use intensity. Given the superior performance of the functional-based approach, our findings also highlighted the importance of the application of both the biological traits and functional diversity index in monitoring programs for lake ecosystems.


2022 ◽  
Vol 17 (1) ◽  
Author(s):  
Sana Romdhane ◽  
Aymé Spor ◽  
Samiran Banerjee ◽  
Marie-Christine Breuil ◽  
David Bru ◽  
...  

Abstract Background Soil microbial communities are major drivers of cycling of soil nutrients that sustain plant growth and productivity. Yet, a holistic understanding of the impact of land-use intensification on the soil microbiome is still poorly understood. Here, we used a field experiment to investigate the long-term consequences of changes in land-use intensity based on cropping frequency (continuous cropping, alternating cropping with a temporary grassland, perennial grassland) on bacterial, protist and fungal communities as well as on their co-occurrence networks. Results We showed that land use has a major impact on the structure and composition of bacterial, protist and fungal communities. Grassland and arable cropping differed markedly with many taxa differentiating between both land use types. The smallest differences in the microbiome were observed between temporary grassland and continuous cropping, which suggests lasting effects of the cropping system preceding the temporary grasslands. Land-use intensity also affected the bacterial co-occurrence networks with increased complexity in the perennial grassland comparing to the other land-use systems. Similarly, co-occurrence networks within microbial groups showed a higher connectivity in the perennial grasslands. Protists, particularly Rhizaria, dominated in soil microbial associations, as they showed a higher number of connections than bacteria and fungi in all land uses. Conclusions Our findings provide evidence of legacy effects of prior land use on the composition of the soil microbiome. Whatever the land use, network analyses highlighted the importance of protists as a key element of the soil microbiome that should be considered in future work. Altogether, this work provides a holistic perspective of the differential responses of various microbial groups and of their associations to agricultural intensification.


2021 ◽  
Vol 9 ◽  
Author(s):  
Maogang Tang ◽  
Fengxia Hu

The process of land urbanization may result in a great change in land use structure, land use intensity, and efficiency, which could further lead to an increase in carbon dioxide (CO2) emissions. Despite rich literature on the link between urbanization and CO2 emissions, the mechanism through which land urbanization promotes CO2 emissions reductions has not been fully investigated. To address this gap, this study theoretically and empirically explores the mechanism of land urbanization’s influence on CO2 emissions by integrating land use optimization and high-quality industrial development into a unified framework. Firstly, the theoretical mechanism analysis indicates that low-level industrial development and land use management promote the increase of CO2 emissions per unit of land at the extensive land use stage; however, high-quality industrial development and land use optimization lower CO2 emissions per unit of land at the intensive land use stage. Subsequently, a STIRPAT model and a spatial adaptive semi-parametric model are employed to verify the relationship between the land urbanization rate and total CO2 emissions. The results indicate that the land urbanization rate and total CO2 emissions present an inverted U-shaped relationship. In addition, the mediating mechanism of the advanced industrial structure, CO2 emissions per unit of GDP, and CO2 emissions per unit of land, are studied using the mediating effect model. Results indicate that CO2 emissions reduction can be achieved by promoting the advanced industrial structure, reducing CO2 emissions per unit of GDP or reducing CO2 emissions per unit of land. Ultimately, this study showed that the Chinese government may reduce CO2 emissions by promoting land use structure optimization, land use intensity regulation, land use efficiency improvement, and adjusting energy consumption structure, upgrading industrial structure, and promoting emission efficiency technologies.


Land ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1310
Author(s):  
Xiaomin Guo ◽  
Chuanglin Fang

Carbon emission (CE) threatens global climate change severely, leading to the continuous strengthening of the greenhouse effect. Land use changes can greatly affect the ecosystem carbon budget and anthropogenic CE. Based on the land use grids, net ecosystem productivity (NEP), energy consumption-related CE, this study employed various methods to investigate the impact of land use change on carbon balance. The results showed 10.03% of total land use area has land use type changed between 2000 and 2015. Built-up land occupied cropland was the main land use transfer type. The period with the most intense land use changes was 2005–2010, which was constant with the process of China’s urbanization. NEP presented an overall increasing trend excluding built-up land and water areas. Temporally, CE showed an increasing trend in 2000–2015, especially in the industry sector. Spatially, areas with the high energy-related CE were mainly distributed in the south, which has a relatively high economic level. The land use intensity values of cities in Jiangsu all presented an overall increasing trend, which is related to the economic development and local endowment. Cities with higher land use intensity were usually accompanied with high CE, suppressing NEP growth. From 2000 to 2015, soil carbon storage reduced by 0.15 × 108 t, vegetation carbon storage reduced by 0.04 × 108 t, and CE reached 17.42 × 108 t. Total CE caused by land use change reached 15.46 × 108 t. The findings can make references for the low-carbon development from ecological land protection, strengthen land management, and optimize urban planning.


Land ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1254
Author(s):  
Longgao Chen ◽  
Xiaoyan Yang ◽  
Long Li ◽  
Longqian Chen ◽  
Yu Zhang

Intensive land use can support sustainable socioeconomic development, especially in the context of limited land resources and high population. It is measured by land-use intensity that reflects the degree of land-use efficiency. In order to support decision-making for efficient land use, we investigated the mechanism whereby natural and socioeconomic factors influence land-use intensity from the perspectives of overall, region-, and city-based analysis, respectively. This investigation was conducted in Chinese cities using the multiple linear stepwise regression method and geographic information system techniques. The results indicate that: (1) socioeconomic factors have more positive impact on land-use intensity than natural factors as nine of the top 10 indicators with the highest SRC values are in the socioeconomic category according to the overall assessment; (2) education input variously contributes to land-use intensity because of the mobility of a well-educated workforce between different cities; (3) the increase in transportation land may not promote intensive land use in remarkably expanding cities due to the defective appraisal system for governmental achievements; and that (4) in developed cities, economic structure contributes more to land-use intensity than the total economic volume, whereas the opposite is the case in less-developed cities. This study can serve as a guide for the government to prepare strategies for efficient land use, hence promoting sustainable socioeconomic development.


Land ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1237
Author(s):  
Yue Wang ◽  
Ge Song ◽  
Wenying Li

Analyzing the interaction between land use patterns (LUPs) and socioeconomic factors (SEFs) could provide a basis for regional land spatial planning and management decisions in the future. In this study, population, gross domestic product (GDP) and land use intensity were selected to explain the relationship between SEFs and LUPs. The study designed a new method of sample line acquisition for wavelet analysis, and identified the interaction grid scales of LUP changes with SEFs in 1991, 2005 and 2019 by using cross wavelet transform analysis (XWT). Wavelet transform coherent analysis (WTC) was used to reveal the interaction direction and impact strength between LUPs and SEFs. The results showed that: (1) There were two ranges of 2978–5008 m and 24,400–29,738 m in which the grid scales showing interaction between LUPs and SEFs (population, GDP and land use intensity) from 1991 to 2019 were overlapping. (2) The interaction direction between LUPs and SEFs from 1991 to 2019 was almost negative on all sample lines, while the interaction directions of the middle sample line of population and GDP from 1991 to 2019, the end sample line of GDP in 2019, and the start sample line of land use intensity in 1991 were positive. (3) Dry land, grassland and construction land were most affected by SEFs, followed by paddy fields, forest land and other land, and the least affected were water areas during 1991 to 2019. The impact of population and GDP on LUPs was reduced, while the impact of land use intensity on LUPs was increased from 1991 to 2019. Overall, population, GDP and land use intensity were the important SEFs in the changes of LUPs, and were important factors for social progress and economic development.


Author(s):  
Weijie Yu ◽  
Wei Wang ◽  
Xuedong Hua ◽  
Xueyan Wei

With the rapid advance of urbanization, land-use intensity is increasing, and various land-use forms gather to form comprehensive land-use patterns. Traffic demand shows variability and complexity under comprehensive land-use patterns. Accurate analysis of traffic demand in urban transportation is the key to active traffic control and road guidance. Researchers have widely studied the relationship between traffic demand and land-use patterns, while land-use intensity is ignored when classifying land-use patterns, and the traffic demand distribution in each land-use pattern is not studied specifically. Taxi is a flexible public mode in urban areas, and taxi demand is an important component in analyzing traffic demand and identifying traffic hotspots in cities. This paper explores taxi demand distribution of comprehensive land-use patterns using online car-hailing data and points of interest (POI) in Chengdu, China. The demand-driven traffic analysis zones are developed by clustering origin–destination points of online car-hailing services. Using POI data, comprehensive land-use patterns are classified with land-use forms and land-use intensity. The K-shape algorithm is adopted to extract the typical taxi demand distribution in each comprehensive land-use pattern. Finally, two indicators, total taxi demand (TTD) and taxi demand difference (TDD), are computed and further analyzed. Results show that taxi demand distribution is still differential even under the same land-use pattern. Three land-use patterns whose average hourly taxi demand reaches about 300 vehicles per square kilometer have the largest TTD and most uneven TDD. The findings can support traffic management, land-use combination, and land-use adjustment to avoid concentrated taxi demand and mismatched TDD.


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