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The Influence of Underlying Land Cover on the Accuracy of MODIS C6.1 Aerosol Products-A Case Study over the Yangtze River Delta Region of China

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REMOTE SENSING 2022年第4期14卷 938-938页

作者： Sun, Kun Gao, Yang Qi, Bing Yu, Zhifeng Chinese Acad Sci Inst Geog Sci & Nat Resources Res Key Lab Ecosystem Network Observat & Modeling Beijing 100101 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China Hangzhou Meteorol Bur Hangzhou 310051 Peoples R China Hangzhou Normal Univ Inst Remote Sensing & Earth Sci Hangzhou 311121 Peoples R China Ohio State Univ Sch Earth Sci Div Geodet Sci Columbus OH 43210 USA Zhejiang Prov Key Lab Urban Wetlands & Reg Change Hangzhou 311121 Peoples R China

Due to the significant spatial variation of the performance of Moderate Resolution Imaging Spectrometer (MODIS) aerosol optical depth (AOD) retrievals, validation is very important for applications of MODIS AOD products at regional scales. This study presents a comparative analysis of Collection 6.1 MODIS AOD retrievals and ground measurements from five local sites and one Aerosol Robotic network (AERONET) site in the Yangtze River Delta (YRD) region, which significantly complements the previous validation that utilized limited AERONET measurements. Generally, MODIS AOD retrievals showed a reasonable agreement with collocated ground measurements (R-2 > 0.7), with 66% of Dark Target (DT) 10 km retrievals, 56% of Deep Blue (DB) 10 km retrievals, and 69% of DT 3 km retrievals falling within the expected error (EE = +/-(0.05 + 0.2 x AOD)). Nevertheless, it was found that the DT AOD retrievals tended to be overestimated over urbanized and lakeside sites, while the DB AOD retrievals tended to be underestimated over all ground sites except for lakeside sites. Such patterns appeared to be linked with the systematic biases of the single-scattering albedo estimation in the AOD retrieval algorithms. Another significant finding of this study is that the uncertainties of the MODIS AOD retrievals were highly correlated with the land cover proportions of urbanized features and water (LCP_UW) in the surrounding region, especially for the DT products. An empirical correction method based on these correlations could substantially reduce the uncertainties of DT AOD products over high LCP_UW areas. The results not only highlight the significant impacts of both urban and water areas on the MODIS AOD retrieval algorithms but also create new possibilities to correct such impacts once the universal correlations between LCP_UW and the uncertainty measures are established.

关键词： Yangtze River Delta aerosol optical depth validation MODIS sun photometer land cover

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Enhancements of Light Use Efficiency by Diffuse Photosynthetically Active Radiation Were More Sensitive to Air Temperature and Vapor Pressure Deficit at Four Forests

SSRN

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SSRN 2022年

作者： Han, Jiayin Jin, Xin Zhang, Leiming Li, Shenggong Tang, Yanhong Institute of Ecology Key Laboratory for Earth Surface Processes The Ministry of Education College of Urban and Environmental Sciences Peking University Beijing100871 China Institute of Population Research Peking University Beijing100871 China Peking University Library Peking University Beijing100871 China Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences 11A Datun Road Chaoyang District Beijing100190 China

It is well known that increase of diffuse fraction (kd, defined as the ratio of diffuse photosynthetically active radiation (PAR) to global PAR) in solar radiation improves canopy light use efficiency (LUE, defined as the ratio of estimated gross primary productivity (GPP) to global PAR). However, it is still unclear how other environmental variables affect the impacts of kd on LUE. We examined the enhancements of increasing kd on LUE under different air temperature (Ta), vapor pressure deficit (VPD), and volumetric soil water content (SWC) using eddy covariance flux observations from four typical forests including a temperate deciduous broad-leaved and coniferous mixed forest, two subtropical evergreen coniferous plantations, and a subtropical evergreen broad-leaved and coniferous mixed forest. Results showed that, during the growing season (June–August) from 2009 to 2011, GPP was always higher under complete diffuse conditions (CDC, kd > 0.95) than partial diffuse conditions (PDC, kd ≤ 0.95), with the LUE enhancement (i.e., averaged LUE under CDC / averaged LUE under PDC – 1) from 32.1% to 47.2% depending on forests. The LUE enhancements were more sensitive to Ta under CDC than PDC in all four forests, with the LUE enhancement responded to Ta from 25.7% to 39.1% under CDC and from 12.8% to 26.0% under PDC, respectively. Compared with PDC, the enhancements of changes in LUE which responded to VPD under CDC were more sensitive from 18.2% to 160% depending on forests. Nevertheless, LUE did not change distinctly with increasing SWC under both CDC and PDC at four sites. These findings suggest that the acceleration of kd in global PAR on LUE would decrease under high Ta or high VPD, which provides critical information for future assessments of forest ecosystems carbon budget in a warming world. © 2022, The Authors. All rights reserved.

关键词： ecosystems

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Recovered Tibetan antelope at risk again

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SCIENCE 2019年第6462期366卷 194-194页

作者： Pei, Jie Wang, Li Xu, Wenjing Kurz, David J. Geng, Jing Fang, Huajun Guo, Xinlei Niu, Zheng Chinese Acad Sci Inst Remote Sensing & Digital Earth State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Calif Berkeley Dept Environm Sci Policy & Management Berkeley CA 94720 USA Chinese Acad Sci Inst Geog Sci & Nat Resources Res Key Lab Ecosystem Network Observat & Modeling Beijing 100101 Peoples R China

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Changes in Mariculture and Offshore Seawater Quality in China During the Past 20 Years

SSRN

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SSRN 2023年

作者： Liu, Yueming Wang, Zhihua Yang, Xiaomei Wang, Shaoqiang Liu, Xiaoliang Liu, Bin Zhang, Junyao Meng, Dan Ding, Kaimeng Gao, Ku Zeng, Xiaowei Ding, Yaxin State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China Jinling Institute of Technology Nanjing211169 China School of Geography and Information Engineering China University of Geosciences Wuhan430074 China School of Surveying and Land Information Engineering Henan Polytechnic University Jiaozuo454000 China

Mariculture can provide a large amount of food without occupying land resources, which provides important assistance for humans to achieve sustainable development. In this paper, China, where the output of mariculture exceeds half of the global output, is taken as the research area. Based on the spatial distribution data of three periods of mariculture along the coast of the Chinese Mainland in 2000, 2010, and 2020 obtained from remote sensing images and from the perspective of the mariculture center, the spatial development of mariculture in three periods in China is explored. By superposing water quality data from the same period, the relationship between the development of mariculture and water quality changes in China is explored. We found that (1) China’s mariculture exhibited a spatial distribution pattern of dense in the north and sparse in the south. (2) The development speed of mariculture was significantly higher in northern China than in southern China during the past 20 years. (3) The development mode of mariculture in China was mainly the formation of a new center. (4) Good seawater quality is a prerequisite for the formation of mariculture centers. (5) The deterioration of seawater quality is not conducive to the sustainable development of mariculture areas. (6) The formation of large-scale high-density mariculture centers does not necessarily lead to deterioration of the seawater quality. Furthermore, we found that the water depth is an important factor leading to the spatial distribution pattern of mariculture in China, i.e., dense in the north and sparse in the south. The flow velocity is an important factor determining the water quality changes in mariculture centers. © 2023, The Authors. All rights reserved.

关键词： Sustainable development

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Spatial Patterns of Light Response Parameters and Their Regulation on Gross Primary Productivity in China

SSRN

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SSRN 2023年

作者： Lin, Yong Chen, Zhi Yu, Guirui Zhu, Xianjin Yang, Meng Hao, Tianxiang Zhang, Weikang Han, Lang Liu, Zhaogang Ma, Lexin Dou, Xiaojun Luo, Wenxing Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing100049 China Beijing Yanshan Earth Critical Zone National Research Station University of Chinese Academy of Sciences Beijing101408 China College of Agronomy Shenyang Agricultural University Shenyang100161 China Institute of Surface-Earth System Science School of Earth System Science Tianjin University Tianjin300072 China Tianjin Bohai Rim Coastal Earth Critical Zone National Observation and Research Station Tianjin University Tianjin300072 China

Light response parameters are used to model the photosynthetic process of ecosystems, but they are usually considered as fixed or empirical values for specific vegetation type and their geographical variation and influence mechanisms at ecosystem scale remain unclear. In this study, we analyzed the light response parameters (photosynthetic quantum yields, α;maximum gross photosynthetic rate, Amax) at 64 typical ecosystems of ChinaFLUX over 20 years’ measurement, to explore their spatial patterns, controlling factors, and regulation on GPP. The results showed that the light response parameters significantly varied with ecosystem types: in grassland and desert ecosystems, both α and Amax were significantly lower than other ecosystems, and in cropland ecosystems, the Amax was significantly higher than that of other ecosystems. The light response parameters significantly decreased with increasing altitude. Spatial variation of GPP was significantly regulated by light response parameters, hydrothermal conditions regulated vegetation indexes and further modulate light response parameters and GPP. Vegetation index and photosynthetically active radiation were the dominant factor for the spatial variation of α, while the spatial variation of Amax was mainly controlled by moisture conditions. α was more stable than Amax and varied less with hydrothermal conditions. The dominant factors of light response parameters differed among ecosystem types. This study suggested that the quantitative analysis and simulation prediction of photosynthesis should consider the spatial heterogeneity of light response parameters and their different environmental responses at ecosystem scale in future studies. © 2023, The Authors. All rights reserved.

关键词： Quantum yield

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Promoting the Nexus Approach of Climate, ecosystems and Livelihoods in Africa through China-Africa Cooperation

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Journal of Resources and Ecology 2018年第3期9卷 232-236页

作者： WANG Guoqin FU Chao LIU Jian ZHANG Linxiu Ayub M.O. Oduor Dagne Mojo Mulubrhan Balehegn International Ecosystem Management Partnership United Nations Environment Programme Beijing 100101 China Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China United Nations Environment Programme P.O. Box 30552 Nairobi 00100 Kenya Center for Chinese Agricultural Policy Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China University of Chinese Academy of Sciences Beijing 100049 China Department of Applied and Technical Biology Technical University of Kenya P.O. Box 52428 Nairobi 00200 Kenya

Africa is facing both challenges and opportunities in pursuing sustainability. The nexus approach of Climate, ecosystems and Livelihoods（CEL） is a promising way to seize the ＂power of integration＂ for achieving sustainability of the African continent. Based on taking stock of the work of various organizations especially the United Nations Environment Programme International ecosystem Management Partnership（UNEP-IEMP） in the Africa, this article explores the demands and opportunities for delivering the CEL approach in Africa and provides perspectives on how to promote it in the framework of China-Africa cooperation in future. It concludes that Africa is one of the focal regions in the delivery of the CEL nexus approach; UNEP-IEMP has launched several major initiatives that lay the foundation for delivering the nexus approach of CEL in Africa; however, more ambitious cooperation should be taken through a broad China-Africa partnership, based on existing institutions, networks and ongoing programmes in both Africa and China, to support future China-Africa cooperation on the nexus approach of CEL.

关键词： Climate ecosystems and Livelihoods （CEL） nexus approach sustainable development China-Africa cooperation

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Model bias in calculating factor importance of climate on vegetation growth

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Global and Planetary Change 2023年第1期228卷

作者： Liang, Boyi Liu, Hongyan Wang, Shaopeng Cressey, Elizabeth L. Dahlsjö, Cecilia A.L. Xu, Chongyang Wang, Jia Wang, Zong Liu, Feng Feng, Siwen Shi, Liang Dai, Jingyu Cao, Jing Li, Fufu Quine, Timothy A. College of Forestry Precision Forestry Key Laboratory of Beijing Beijing Forestry University Beijing100083 China College of Urban and Environmental Sciences and MOE Laboratory for Earth Surface Processes Peking University Beijing100871 China Geography College of Life and Environmental Sciences University of Exeter ExeterEX4 4RJ United Kingdom School of Geography and the Environment University of Oxford OxfordOX1 3QY United Kingdom Faculty of Agriculture Food and Environment The Hebrew University of Jerusalem Rehovot7610001 Israel Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China National Ecosystem Science Data Center Beijing100101 China State Key Laboratory of Environmental Criteria and Risk Assessment State Environmental Protection Key Laboratory of Regional Eco-process and Function Assessment Chinese Research Academy of Environmental Sciences Beijing100012 China

Machine learning is increasingly used to study vegetation growth, however, more often than not, predicting and simulating functions are prioritized over quantitative estimates of the drivers of vegetation growth such as climate. In this paper, we, for the first time, systematically investigate the model bias in calculating factor importance of climate on vegetation growth, especially when various kinds of machine learning models are considered. We undertake two case studies to simulate research in remote sensing and ground-based scenarios from which the difference in quantitative relationships between climate and vegetation is evaluated across multiple models. We found that model complexity increased the determination coefficient (R2) but reduced the absolute importance of the preselected independent variables. As the fitting accuracy increases, the absolute factor importance of dominant factor and all the other influencing factors decreases simultaneously, and factor importance calculated by different models tended to be more normally distributed across the study region. The reduction in factor importance was accompanied by the increased effect of model selection;e.g. the model that was used to estimate vegetation growth played a larger role in producing the factor importance (shown by variance analysis, remote sensing scenario, F-statistic = 555.2;ground based scenario, F-statistic = 30.8) than the climate variables (variance analysis, remote sensing scenario, F = 460.8;ground based scenario, F = 28.8). Critically, for those machine learning models with highest fitting accuracy, the resultant factor importance of climate factors had smaller difference with that of random factor. In contrast, the relative factor influence among the selected climate factor is more robust and reliable (in variation analysis, model was detected no significant impact on the resultant factor importance). For 5 of 8 models, the dominant factor (temperature) has relative influence over 0.

关键词： Machine learning

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Application of Multi-Temporal MODIS NDVI Data to Assess Practiced Maize Calendars in Rwanda

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Journal of Resources and Ecology 2018年第3期9卷 273-280页

作者： MUGABOWINDEKWE Maurice MUYIZERE Aline LI Fadong QIAO Yunfeng RWANYIZIRI Gaspard Centre for Geographic Information Systems and Remote Sensing College of Science and Technology University of Rwanda 3900 Kigali Rwanda Department of Environmental Management Bright Green Nature Ltd 2916 Kigali Rwanda Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing 100190 China Department of Geography and Urban Planning College of Science and Technology University of Rwanda 3900 Kigali Rwanda

Crop calendar is an important tool providing relevant information on crops cycles in a specific area for effective agricultural management. Crop calendars vary in different areas given dissimilarities in agro-ecosystems＇ characteristics. This research used multi-temporal MODIS NDVI stratification to assess differences in practiced maize crop calendars in various areas of Rwanda. Four（4） sample NDVI strata dominated by agriculture were purposively chosen, and 433 local farmers were randomly selected from the strata for interviews. The collected information helped to know about their maize planting as well as harvesting dates in order to generate maize calendars per NDVI strata. The generated crop calendars were later classified using k-means unsupervised classification, and produced 4 groupings of practiced maize calendars irrespective of NDVI strata. ANOVA results revealed significant differences between both the generated maize crop calendars by NDVI strata and the practiced crop calendars irrespective of NDVI strata, at p = 0.05. Moreover, chi-square tests and t-tests revealed not only a significant relationship between maize calendars and number of crop growing seasons, but also a significant relationship between maize calendars and NDVI strata, at p = 0.05. Finally, findings of this research contrasted the present conviction that there exist a single general maize calendar all over the country. Instead, the results were in accordance with the fact that Rwanda agro-ecosystems differ from East to West in terms of, mainly, altitude and rainfall patterns variations.

关键词： NDVI data stratification crop calendar maize growing season Rwanda

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Corrigendum to “Water use efficiency threshold for terrestrial ecosystem carbon sequestration in China under afforestation” [Agric. For. Meteorol. 195–196 (2014) 32–37]

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Agricultural and Forest Meteorology 2014年 198-199卷 81-81页

作者： Yang Gao Xianjin Zhu Guirui Yu Nianpeng He Qiufeng Wang Jing Tian Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research CAS Beijing 100101 China USDA-ARS National Soil Erosion Research Laboratory Purdue University IN 47907 United States

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Mega-cities dominate China's urban greening

arXiv

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arXiv 2023年

作者： Zhang, Xiaoxin Brandt, Martin Tong, Xiaoye Tong, Xiaowei Zhang, Wenmin Reiner, Florian Li, Sizhuo Tian, Feng Yue, Yuemin Zhou, Weiqi Chen, Bin Xiao, Xiangming Fensholt, Rasmus Department of Geosciences and Natural Resource Management University of Copenhagen Copenhagen Denmark Key Laboratory for Agro-ecological Processes in Subtropical Region Institute of Subtropical Agriculture Chinese Academy of Sciences Changsha China Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere School of Remote Sensing and Information Engineering Wuhan University Wuhan China Huanjiang Observation and Research Station for Karst Ecosystem Chinese Academy of Sciences Huanjiang China State Key Laboratory of Urban and Regional Ecology Research Center for Eco-Environmental Sciences Chinese Academy of Sciences Beijing100085 China Lab Division of Landscape Architecture Faculty of Architecture The University of Hong Kong Hong Kong Department of Microbiology and Plant Biology Center for Earth Observation and Modeling University of Oklahoma NormanOK United States

Trees play a crucial role in urban environments, offering various ecosystem services that contribute to public health and human well-being. China has initiated a range of urban greening policies over the past decades, however, monitoring their impact on urban tree dynamics at a national scale has proven challenging. In this study, we deployed nano-satellites to quantify urban tree coverage in all major Chinese cities larger than 50 km2 in 2010 and 2019. Our findings indicate that approximately 6000 km2 (11%) of urban areas were covered by trees in 2019, and 76% of these cities experienced an increase in tree cover compared to 2010. Notably, the increase in tree cover in mega-cities such as Beijing, and Shanghai was approximately twice as large as in most other cities (7.69% vs 3.94%). The study employs a data-driven approach towards assessing urban tree cover changes in relation to greening policies, showing clear signs of tree cover increases but also suggesting an uneven implementation primarily benefiting a few mega-cities. © 2023, CC BY.

关键词： ecosystems

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