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Leguminous green mulching alters the microbial community structure and increases microbial diversity by improving nitrogen availab.lity in subtropical orchard systems in China

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Science of the Total Environment 2024年 955卷 176891页

作者： Wang, Na Li, Le Gou, Mengmeng Hu, Jianwen Chen, Huiling Xiao, Wenfa Liu, Changfu Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangdong Academy of Sciences Guangzhou510070 China Ecology and Nature Conservation Institute Chinese Academy of Forestry Key Laboratory of Forest Ecology and Environment of National Forestry and Grassland Administration Beijing100091 China Research Institute of Tropical Forestry Chinese Academy of Forestry Guangzhou510000 China Co-Innovation Center for Sustainable Forestry in Southern China Nanjing Forestry University Nanjing210037 China

Microorganisms, the major decomposers of plant residues, are crucial for soil nutrient cycling. Living grass mulching effectively alters microbial community structure and promotes nutrient cycling. However, its consistency with mulching ages and growth periods remains unclear. Therefore, this study aims to clarify the dynamic characteristics of microbial communities and enzyme activities across different mulching ages. In this study, high-throughput sequencing technology was used to investigate bacterial and fungal community evolution in three mulching treatments with Vicia villosa for 8 years (VV_8), 4 years (VV_4), and clean tillage in a citrus orchard. This study covered three growth periods (citrus–grass: spring sprouting to budding period [SSBP], fruit swelling to withering period [FSWP], and fruit maturity to seeding period [FMSP]). The results showed that VV_4 and VV_8 treatments increased bacterial and fungal alpha diversity as well as the activities of nitrogen (N), carbon (C), and phosphorus cycling enzymes. C-cycling enzyme activity was the primary key factor driving changes in microbial diversity across growth periods. Under leguminous green mulching, bacteria alpha diversity increased the most during FSWP, while fungi increased the most during FMSP. Additionally, the relative abundance of Ascomycota and Basidiomycota significantly increased during the FSWP and FMSP, reaching 63.65–73.80 % and 79.73–84.51 %, respectively. With increasing mulching ages, the structural stability and synergistic effects of microorganisms were correspondingly enhanced. Furthermore, availab.e nutrients determined microbial community evolution, with N availab.lity being a key factor influencing microbial diversity, especially fungal diversity. In conclusion, as mulching ages increase, improved nutrient availab.lity gradually enhances microbial diversity, synergistic interactions, and nutrient cycling functions, with copiotrophic taxa occupying a key position in the microbial n

关键词： Citrus orchard Growth period Microbial community Mulching age Nutrient cycling

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Spatiotemporal analysis of COVID-19 risk in Guangdong Province based on population migration

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Journal of geographical Sciences 2020年第12期30卷 1985-2001页

作者： YE Yuyao WANG Changjian ZHANG Hong'ou YANG Ji LIU Zhengqian WU Kangmin DENG Yingbin Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and ApplicationGuangzhou Institute of GeographyGuangdong Academy of SciencesGuangzhou 510070China Southern Marine Science and Engineering Guangdong Laboratory Guangzhou 511458China School of Architecture and Urban Planning Guangdong University of TechnologyGuangzhou 510090China

Population migration,especially population inflow from epidemic areas,is a key source of the risk related to the coronavirus disease 2019(COVID-19)*** paper selects Guangdong Province,China,for a case *** utilizes big data on population migration and the geospatial analysis technique to develop a model to achieve spatiotemporal analysis of COVID-19 *** model takes into consideration the risk differential between the source cities of population migration as well as the heterogeneity in the socioeconomic characteristics of the destination cities of population *** further incorporates a time-lag process based on the time distribution of the onset of the imported *** theory,the model will be able to predict the evolutional trend and spatial distribution of the COVID-19 risk for a certain time period in the future and provide support for advanced planning and targeted prevention *** research findings indicate the following:(1)The COVID-19 epidemic in Guangdong Province reached a turning point on January 29,2020,after which it showed a gradual decreasing trend.(2)Based on the time-lag analysis of the onset of the imported cases,it is common fora time interval to exist between case importation and illness onset,and the proportion of the cases with an interval of 1-14 days is relatively high.(3)There is evident spatial heterogeneity in the epidemic risk;the risk varies significantly between different areas based on their imported risk,susceptibility risk,and ability to prevent the spread.(4)The degree of connectedness and the scale of population migration between Guangdong’s prefecture-level cities and their counterparts in the source regions of the epidemic,as well as the transportation and location factors of the cities in Guangdong,have a significant impact on the risk classification of the cities in *** first-tier cities-Shenzhen and Guangzhou-are high-risk *** cities in the Pearl River Delta that are adjacent

关键词： population migration COVID-19 epidemic risk time-lag process spatiotemporal analysis

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MoCoLSK: Modality Conditioned High-Resolution Downscaling for Land Surface Temperature

arXiv

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arXiv 2024年

作者： Dai, Qun Yuan, Chunyang Dai, Yimian Li, Yuxuan Li, Xiang Ni, Kang Xu, Jianhui Shu, Xiangbo Yang, Jian School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing China PCA Lab VCIP College of Computer Science Nankai University China NKIARI Futian Shenzhen China Guangdong Provincial Key Laboratory of Utilization Remote Sensing and Geographical Information System Guangzhou Institute of Geography Guangdong Academy of Sciences Guangzhou510070 China

Land Surface Temperature (LST) is a critical parameter for environmental studies, but directly obtaining high spatial resolution LST data remains challenging due to the spatio-temporal trade-off in satellite remote sensing. Guided LST downscaling has emerged as an alternative solution to overcome these limitations, but current methods often neglect spatial non-stationarity, and there is a lack of an open-source ecosystem for deep learning methods. In this paper, we propose the Modality-Conditional Large Selective Kernel (MoCoLSK) Network, a novel architecture that dynamically fuses multi-modal data through modality-conditioned projections. MoCoLSK achieves a confluence of dynamic receptive field adjustment and multi-modal feature fusion, leading to enhanced LST prediction accuracy. Furthermore, we establish the GrokLST project, a comprehensive open-source ecosystem featuring the GrokLST dataset, a high-resolution benchmark, and the GrokLST toolkit, an open-source PyTorch-based toolkit encapsulating MoCoLSK alongside 40+ state-of-the-art approaches. Extensive experimental results validate MoCoLSK’s effectiveness in capturing complex dependencies and subtle variations within multispectral data, outperforming existing methods in LST downscaling. Our code, dataset, and toolkit are availab.e at https://***/GrokCV/GrokLST. Copyright © 2024, The Authors. All rights reserved.

关键词： Surface temperature

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The first high spatial resolution multi-scale daily SPI and SPEI raster dataset for drought monitoring and evaluating over China from 1979 to 2018

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Big Earth Data 2023年第3期7卷 860-885页

作者： Rongrong Zhang Virgílio A.Bento Junyu Qi Feng Xu Jianjun Wu Jianxiu Qiu Jianwei Li Wei Shui Qianfeng Wang Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion and Disaster Protection/College of Environmental&Safety Engineering Fuzhou UniversityFuzhouChina Instituto Dom Luiz Faculdade de CiênciasUniversidade de LisboaLisboaPortugal Earth System Science Interdisciplinary Center University of MarylandCollege ParkMDUSA State Key Laboratory of Earth Surface Processes and Resource Ecology/Faculty of Geographical Science Beijing Normal UniversityBeijingChina Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation School of Geography and PlanningSun Yat-sen UniversityGuangzhouChina College of Physics and Information Engineering Fuzhou UniversityFuzhouChina Key Lab of Spatial Data Mining&Information Sharing Ministry of Education of ChinaFuzhouChina

Standardized Precipitation Index(SPI)and Standardized Precipitation Evapotranspiration Index(SPEI),traditionally derived at a monthly scale,are widely used drought *** overcome temporalresolution limitations,we have previously developed and published a well-validated daily SPI/SPEI in situ *** having a high temporal resolution,this in situ dataset presents low spatial resolution due to the scarcity of ***,based on the China Meteorological Forcing Dataset,which is composed of data from more than 1,000 ground-based observation sites and multiple remote sensing grid meteorological dataset,we present the first high spatiotemporal-resolution daily SPI/SPEI raster datasets over *** spans from 1979 to 2018,with a spatial resolution of 0.1°×0.1°,a temporal resolution of 1-day,and the timescales of 30-,90-,and *** show that the spatial distributions of drought event characteristics detected by the daily SPI/SPEI are consistent with the monthly SPI/*** correlation between the daily value of the 12-month scale and the monthly value of SPI/SPEI is the strongest,with linear correlation,Nash-Sutcliffe coefficient,and normalized root mean square error of 0.98,0.97,and 0.04,*** daily SPI/SPEI is shown to be more sensitive to flash drought than the monthly SPI/*** improved SPI/SPEI shows high accuracy and credibility,presenting enhanced results when compared to the monthly SPI/*** total data volume is up to 150 GB,compressed to 91 GB in Network Common Data Form(NetCDF).It can be availab.e from Figshare(https://***/10.6084/***.c.5823533)and ScienceDB(https://***/10.57760/sciencedb.j00076.00103).

关键词： Drought index climate change climatic disaster Generalized Extreme Value

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Spectral Characteristics and Model Inversion of Common Greening Plants in Guangzhou Under Dust Pollution 7th

Spectral Characteristics and Model Inversion of Common Green...

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7th International Conference on Geoinformatics in Sustainable Ecosystem and Society, GSES 2019, and 1st International Conference on Geospatial Artificial Intelligence for Urban Computing, GeoAI 2019

作者： Zhang, Chen Zhou, Xia Li, Yong Yang, Ji Yang, Chuanxun Wen, Kaixiang Chen, Yuchan Yang, Libo Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangzhou510070 China Guangzhou511458 China State Grid Hunan Maintenance Company Hengyang421000 China

ISBN: (纸本)9789811561054

Hyperspectral remote sensing provides a highly efficient, convenient, and non-destructive technical means for the quantitative study of the dust retention content (DRC) of plants. Based on the common greening plants in Guangzhou, this study used hyperspectral technology to explore the spectral characteristics of different plant leaves under dust pollution, and used the Pearson correlation method to construct the DRC estimation models of plants. The results show that the spectral characteristics of the dust-retention effect of plants are mainly concentrated in the visible and near-infrared wavelengths (350–1360 nm), which are important band intervals for the study of spectral characteristics and the inversion band selection. The first derivative of spectral reflectance is more suitable for constructing the DRC estimation model than the spectral reflectance, and CF has a good fitting effect, and the deviation between the Model estimated DRC and the measured DRC is small, and its R2 is 0.55 and the RMSE is 0.300 g/m2. Through this research, the application of hyperspectral remote sensing technology in the dust retention effect research of plants can be effectively promoted, which can help to quickly and non-destructively evaluate the DRC of plants, and provide a scientific basis for rational selection of green plant types and urban air quality monitoring, and lay a foundation for large-scale DRC estimation. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Reflection

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Satellite-Based Water Depth Estimation: A Review 7th

Satellite-Based Water Depth Estimation: A Review

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7th International Conference on Geoinformatics in Sustainable Ecosystem and Society, GSES 2019, and 1st International Conference on Geospatial Artificial Intelligence for Urban Computing, GeoAI 2019

作者： Wen, Kaixiang Li, Yong Wang, Hua Jing, Wenlong Yang, Ji Zhang, Chen Wang, Zhou Guangdong University of Technology Guangzhou510070 China Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangzhou510070 China State Grid Panzhihua Power Supply Company Panzhihua617000 China

ISBN: (纸本)9789811561054

This paper reviews the advances in water depth estimations from satellite images. According to the sensor type, current satellite-based water depth estimations are divided into two categories: optical-based and SAR-based (synthetic aperture radar) methods. By analyzing and discussing on the advantages and disadvantages of various methods. The following points can be summarized: First, the accuracy of optical remote sensing water depth detection method is higher than that of SAR-based models. Besides, optical-based method is simpler and less restricted than the SAR-based methods. Second, hyperspectral-based approach performed better than the multispectral optical remote sensing approaches. Since hyperspectral images provide richer spectral information than multispectral, which significantly matter in the water depth detection. Third, remote sensing water depth detection method is the best way to obtain the underwater topographic features of the inaccessible waters. Finally, on the basis of summarizing the previous water depth estimation methods, the development prospects of shallow seawater deep inversion technology are discussed and analyzed. For example, the establishment of an inversion model by fusing multiple remote sensing data, and the use of artificial intelligence technology for water depth inversion. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Synthetic aperture radar

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Effect of Water Application Methods on Salinity Leaching Efficiency in Different Textured Soils Based on lab.ratory Measurements and Model Simulations

SSRN

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SSRN 2022年

作者： Yang, Ting Cherchian, Setrag Liu, Xinmin Shahrokhnia, Hossein Mo, Minghao Šimůnek, Jirka Wu, Laosheng Key lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangzhou Institute of Geography Guangdong Academy of Sciences Guangzhou510070 China Department of Environmental Sciences University of California RiversideCA92512 United States Chongqing Key Laboratory of Soil Multi-Scale Interfacial Process College of Resources and Environment Southwest University Chongqing China Jiangxi Academy of Water Science and Engineering Nanchang330029 China

Irrigated agriculture has been in a quandary of sustaining its productivity for centuries while attempting to cope with soil and water salinity issues that continue to devastate crop production. Several of the research gaps associated with current irrigation methods include how to assess leaching requirement and efficiency for different soils, crops, and irrigation regimes. The objective of this study was to test irrigation water application methods on salinity leaching efficiency by lab.ratory column experiments and computer model simulations. Three water application methods, continuous ponding (CP), intermittent ponding (IP), and unsaturated application (UA), were imposed on the packed columns to evaluate salinity leaching efficiency. The HYDRUS-1D model was employed to inversely estimate hydraulic and solute transport parameters from the column experimental data, while the HYDRUS-2D model was used to simulate water and salinity transport under field irrigation regimes (every 7, 5, and 3 days). Among the different textured soils, water application methods have greater impact on leaching efficiency in coarse soils than that in finer soils. The quality of soil in terms of water transmission (measured by S) changes with different water application methods. Continuous ponding is the best choice for coarse soils to maintain soil quality at very good level. For finer soils, intermittent ponding and unsaturated application are better water application methods, and unsaturated application was the best for the loamy soil. When the irrigation scheme changed from flexible irrigation frequency to fixed frequency (every 7, 5, and 3 days), salinity around the root zone under flexible frequency irrigation is lower than that under fixed frequency irrigation (every 7, 5 or 3 days). For fixed frequency irrigation, more frequent application of smaller quantity of water is preferable to the application of large quantity at extended intervals in the loamy soil at almond orchard. The w

关键词： Efficiency

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Characterizing the temporally stable structure of community evolution in intra-urban origin-destination networks

arXiv

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arXiv 2023年

作者： Chen, Xiao-Jian Zhao, Yuhui Kang, Chaogui Xing, Xiaoyue Dong, Quanhua Liu, Yu Institute of Remote Sensing and Geographical Information Systems School of Earth and Space Sciences Peking University Beijing China Beijing Key Lab of Spatial Information Integration & Its Applications Peking University Beijing China Research Centerfor Intelligent Society and Governance Zhejiang Lab Zhejiang China Zhejiang Pilot Laboratory of Philosophy and Social Science Zhejiang China National Engineering Research Center of Geographic Information System China University of Geosciences Wuhan China Peking University Beijing100871 China

Intra-urban origin-destination (OD) network communities evolve throughout the day, indicating changing groups of closely connected regions. Under this variation, groups of regions with high consistency of community affiliation characterize the temporally stable structure of the evolution process, aiding in comprehending urban dynamics. However, how to quantify this consistency and identify these groups are open questions. In this study, we introduce the consensus OD network to quantify the consistency of community affiliation among regions. Furthermore, the temporally stable community decomposition method is proposed to identify groups of regions with high internal and low external consistency (named "stable groups"), where each group consists of temporally stable cores and attaching peripheries. Wuhan taxi data is used to verify our methods. On the hourly time scale, eleven stable groups containing 82.9% of regions are identified. This high percentage suggests that dynamic communities can be well organized via cores. Moreover, stable groups are spatially closed and more likely to distribute within a single district and separated by water bodies. Cores exhibit higher POI entropy and more healthcare and shopping services than peripheries. Our methods and empirical findings contribute to some practical issues, such as urban area division, polycentric evaluation and construction, and infectious disease control. Copyright © 2023, The Authors. All rights reserved.

关键词： Disease control

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Ecological Environment Evaluation of Liuxi River Basin During Rapid Urbanization 7th

Ecological Environment Evaluation of Liuxi River Basin Durin...

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7th International Conference on Geoinformatics in Sustainable Ecosystem and Society, GSES 2019, and 1st International Conference on Geospatial Artificial Intelligence for Urban Computing, GeoAI 2019

作者： Xie, Yunwen Liu, Xulong Wu, Zhifeng Yang, Ji Zhang, Jingjun Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangzhou510070 China School of Geographical Sciences Guangzhou University Guangzhou510006 China Guangzhou511458 China Xinhua College of Sun Yat-Sen University Guangzhou510520 China

ISBN: (纸本)9789811561054

This article takes Liuxi River Basin in Guangzhou as the research object, selects Landsat remote sensing data of 1993, 2000, 2011 and 2018 as data source, to access the five indices, including vegetation coverage, DEM, land use, soil index and slope. The comprehensive index method is used to obtain the ecological environment comprehensive index of this study area. The environmental quality is divided into 4 grades. The ecological environment of the basin and its dynamic changes have been comprehensively evaluated in the past 25 years. The results show that: In the Liuxi River Basin in 2018, the percentage of excellent natural ecological environment quality was 4.80%, the percentage of the areas with good quality was 44.53%;the percentage of the areas with general quality was 45.97% and the percentage of the areas with poor quality was 4.68%. Overall, the good and the general ecological environment quality is the majority. From the perspective of time from 1993 to 2018, the percentage of the area with excellent environmental quality increased by 1.69%;the percentage of the area with good environmental quality gradually decreased, with a total of 32.88%, but still occupied the largest proportion;The percentage of area with general environmental quality went up and down, with a total of 29.64%;the percentage of the area with poor environmental quality rose by 1.54%, the overall change was not big. The downstream ecological environment is showing a trend of deterioration. In upstream, the excellent ecological environment is the majority. The overall ecological environment of the river basin is developing for the better. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Landsat

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Fusing Visual and Mobility Data for City sensing: A Case Study of Urban Village Recognition

SSRN

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SSRN 2023年

作者： Huang, Yingjing Zhang, Fan Gao, Yong Tu, Wei Duarte, Fabio Ratti, Carlo Guo, Diansheng Liu, Yu Hong Kong University of Science and Technology Hong Kong Institute of Remote Sensing and Geographical Information System School of Earth and Space Sciences Peking University Beijing China Senseable City Lab Massachusetts Institute of Technology United States Guangdong Key Laboratory of Urban Informatics Research Institute for Smart Cities Shenzhen Key Laboratory of Spatial Smart Sensing and Services Department of Urban Informatics School of Architecture and Urban Planning Shenzhen University Shenzhen518060 China Tencent Corporation Beijing China

Using multimodal data fusion for city sensing can lead to more accurate, comprehensive and reliable results. Existing studies have used the strategy of fusion of remote sensing and social sensing to make the results least resource-intensive and most accurate. However, it is difficult to distinguish some similar urban functions using only remote sensing to represent visual information in spatial units. To fill the gap, this paper designs sensing Blender, an end-to-end deep learning model that integrates remote sensing, street view imagery and social sensing to comprehensively characterize urban space. Specifically, this model combines physical environment features from satellite imagery and street view imagery with the dynamic mobility features from taxi trajectory data. In particular, a novel module in sensing Blender is proposed to extract features from varying numbers of street view images. To validate the performance of the proposed model, a series of experiments of urban village recognition were conducted in Shenzhen, China, with a grid resolution of 500 meters. The results indicate that sensing Blender achieved good performance with an overall accuracy (OA) of 92.0% and the Kappa of 0.720. Compared with unimodal models, our multimodal model improved the OA by 9.2% and the Kappa by 0.179. The proposed model provides an effective and efficient method for monitoring the distribution of urban villages, potentially supporting urban management, decision-making, and research on urban expansion and urban renewal. © 2023, The Authors. All rights reserved.

关键词： remote sensing

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