Your search found 5 records
1 Hong, L.; Li, Y. H.; Deng, L.; Chen, C. D.; Dawe, D.; Loeve, R.; Barker, R. 2000. Impact of water-saving irrigation techniques in China: analysis of changes in water allocations and crop production in the Zhanghe Irrigation System and district, 1996 to 1998. In International Water Management Institute (IWMI). Annual report 1999-2000. Colombo, Sri Lanka: International Water Management Institute (IWMI) pp.27-35.
Irrigation programs ; Crop production ; Water allocation ; Water conservation ; Reservoirs ; Rice ; Crop yield / China / Zhanghe
(Location: IWMI HQ Call no: IIMI 631.7.3 G000 IIM Record No: H026792)
(0.38 MB)
2 Hong, L.; Li, Y. H.; Deng, L.; Chen, C. D.; Dawe, D.; Barker, R. 2001. Analysis of changes in water allocations and crop production in the Zhanghe Irrigation System and District - 1966-1998. Barker, R.; Loeve, R.; Li, Y. H.; Tuong, T. P. (Eds.). Water-saving irrigation for rice: proceedings of an international workshop held in Wuhan, China, 23-25 March 2001. Colombo, Sri Lanka: International Water Management Institute (IWMI) pp.11-23.
Water allocation ; Crop production ; Rice ; Irrigated farming ; Reservoirs ; Water use ; Water conservation / China / Yangtze River / Zhanghe
(Location: IWMI-HQ Call no: IWMI 631.7.2 G592 BAR Record No: H027861)
3 Dong, B.; Loeve, R.; Li, Y. H.; Chen, C. D.; Deng, L.; Molden, D. 2001. Water productivity in Zhanghe Irrigation System: issues of scale. Barker, R.; Loeve, R.; Li, Y. H.; Tuong, T. P. (Eds.). Water-saving irrigation for rice: proceedings of an international workshop held in Wuhan, China, 23-25 March 2001. Colombo, Sri Lanka: International Water Management Institute (IWMI) pp.97-115.
Irrigation systems ; Water productivity ; Reservoirs ; Water use ; Water stress ; Water conservation ; Rice ; Paddy fields ; Crop yield / China / Hubei Province / Zhanghe
(Location: IWMI-HQ Call no: IWMI 631.7.2 G592 BAR Record No: H027865)
4 Deng, L.; Cai, L.; Sun, F.; Li, G.; Che, Y. 2020. Public attitudes towards microplastics: perceptions, behaviors and policy implications. Resources, Conservation and Recycling, 163:105096. [doi: https://doi.org/10.1016/j.resconrec.2020.105096]
Microplastics ; Public opinion ; Attitudes ; Awareness ; Behaviour ; Marine environment ; Emission reduction ; Pollution control ; Socioeconomic environment ; Public health ; Policies ; Models / China / Shanghai
(Location: IWMI HQ Call no: e-copy only Record No: H050081)
(1.14 MB)
Microplastics are ubiquitous and have been found in marine environments, organisms, salt, and even human bodies. Concern about the impact of microplastics on the ecological environment, as well as the threat of microplastics to food safety and public health is increasing among the society. However, there is currently no effective technical way to tackle and remove microplastics from the environment. Thus, public attitudes are key to reducing microplastic emissions. This study investigated the public's perceptions and attitudes towards microplastics in Shanghai and used an ordered regression model to explore the public's willingness to reduce microplastics and its influencing factors. We used random face-to-face interviews to complete a total of 437 valid questionnaires. The survey results show that only 26% of the respondents had heard of microplastics before the survey, and the majority were relatively unfamiliar with microplastics. Although the public's awareness of microplastics is low compared to that of other substances, when informed with the possibility that microplastics may affect human health, 75% of respondents became worried or even overly worried. In addition, the higher the respondents' knowledge of plastics and microplastics is, the stronger their willingness to behave. Public's concern is also an important impact factor. We found that women's willingness to reduce emissions is higher than men's and environmental protection-related practitioners are also more willing to act. Therefore, this article focuses on the public's understanding of microplastics to propose measures and policy implications to reduce microplastic emissions during the process of microplastic production and recycling.
5 Wu, S.; Deng, L.; Guo, L.; Wu, Y. 2022. Wheat leaf area index prediction using data fusion based on high-resolution unmanned aerial vehicle imagery. Plant Methods, 18:68. [doi: https://doi.org/10.1186/s13007-022-00899-7]
Leaf area index ; Forecasting ; Unmanned aerial vehicles ; Thermal infrared imagery ; Data fusion ; Machine learning ; Estimation ; Wheat ; Vegetation index ; Remote sensing ; Satellites ; Biomass ; Models / China / Henan
(Location: IWMI HQ Call no: e-copy only Record No: H051401)
https://plantmethods.biomedcentral.com/counter/pdf/10.1186/s13007-022-00899-7.pdf
https://vlibrary.iwmi.org/pdf/H051401.pdf
https://vlibrary.iwmi.org/pdf/H051401.pdf
(7.53 MB) (7.53 MB)
Background: Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion.
Methods : To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.
Results: The results show that: (1) the soil background reduced the accuracy of the LAI prediction of wheat, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data could achieve better accuracy (R2 = 0.815 and RMSE = 1.023), compared with using only one data; (3) A simple LAI prediction method could be found, that is, after selecting a few features by machine learning, high prediction accuracy can be obtained only by simple multiple linear regression (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction of wheat.
Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.
Methods : To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.
Results: The results show that: (1) the soil background reduced the accuracy of the LAI prediction of wheat, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data could achieve better accuracy (R2 = 0.815 and RMSE = 1.023), compared with using only one data; (3) A simple LAI prediction method could be found, that is, after selecting a few features by machine learning, high prediction accuracy can be obtained only by simple multiple linear regression (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction of wheat.
Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.
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