Your search found 4 records
1 Yan, Y.; Hayter, E. J. 1994. Numerical modeling of fluid mud transport in estuaries. In Cotroneo, G. V.; Rumer, R. R. (Eds.), Hydraulic engineering '94. Vol.2: Proceedings of the 1994 Conference, Buffalo, New York, August 1-5, 1994. New York, NY, USA: ASCE. pp.1070-1074.
(Location: IWMI-HQ Call no: 627 G000 COT Record No: H019301)
2 Yan, Y.; Wightman, W. (Eds.) 1998. China and CGIAR: Proceedings of the China-CGIAR Forum. Beijing, China: Chinese Academy of Agricultural Sciences. 228p.
(Location: IWMI-HQ Call no: 630.72 G592 YAN Record No: H023290)
China-CGIAR Forum co-sponsored by Ministry of Agriculture of China, Chinese Academy of Agricultural Sciences and CGIAR was held from 10-12 November 1997 in Beijing.
3 Yan, Y.; Wu, C.; Wen, Y. 2021. Determining the impacts of climate change and urban expansion on net primary productivity using the spatio-temporal fusion of remote sensing data. Ecological Indicators, 127:107737. (Online first) [doi: https://doi.org/10.1016/j.ecolind.2021.107737]
Climate change ; Urbanization ; Remote sensing ; Net primary productivity ; Moderate resolution imaging spectroradiometer ; Normalized difference vegetation index ; Landsat ; Precipitation ; Fertilization ; Land use ; Land cover ; Ecosystems ; Grasslands ; Farmland ; Forests / China / Beijing
(Location: IWMI HQ Call no: e-copy only Record No: H050393)
https://www.sciencedirect.com/science/article/pii/S1470160X21004027/pdfft?md5=96d56d824ca51ab536802d836e7e164b&pid=1-s2.0-S1470160X21004027-main.pdf
https://vlibrary.iwmi.org/pdf/H050393.pdf
https://vlibrary.iwmi.org/pdf/H050393.pdf
(9.65 MB) (9.65 MB)
Climate change (CLC) and urban expansion (URE) have profoundly altered the terrestrial net primary productivity (NPP). Many studies have determined the effects of CLC and URE on the NPP. However, these studies were conducted at low resolutions (250–1000 m), making it difficult to detect many smaller new urban lands, and thus potentially underestimating the contribution of URE. To accurately determine the contributions of CLC and URE to the NPP, this study takes Beijing as an example and uses an Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to fuse the spatial resolution of the Landsat Normalized Difference Vegetation Index (NDVI) and the temporal resolution of the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI to generate a new NDVI with a high spatio-temporal resolution. Compared with the Landsat NDVI, the NDVI fused by the ESTARFM is found to be reliable. The fused NDVI was then inputted into the Carnegie–Ames–Stanford Approach (CASA) model to generate the NPP with a high spatio-temporal resolution, namely, the 30-m NPP. Compared with the 250-m NPP generated by directly inputting the MODIS NDVI into the CASA model, the 30-m NPP as a new ecological indicator is more accurate than the 250-m NPP. Due to the high resolution of the 30-m NPP and its increased ability to detect more new urban lands, the total loss of the 30-m NPP caused by URE is much higher than that of the 250-m NPP. For the same reason, especially in rapidly urbanized areas, the contribution ratio of URE to the 30-m NPP is much higher than that to the 250-m NPP. Moreover, in natural vegetation cover areas, CLC, which is measured by the interannual changes in temperature, precipitation, and solar radiation, is the leading factor of the change in the NPP. However, within the urban areas, residual factors other than CLC and URE, such as the introduction of exotic high-productivity vegetation, irrigation, fertilization, and pest control, dominate the change in the NPP. The results of this study are expected to contribute to a deeper understanding of the influences of CLC and URE on terrestrial ecosystem carbon cycles and provide an important theoretical reference for urban planning.
4 Yan, Y.; Zhuang, Q.; Zan, C.; Ren, J.; Yang, L.; Wen, Y.; Zeng, S.; Zhang, Q.; Kong, L. 2021. Using the Google Earth Engine to rapidly monitor impacts of geohazards on ecological quality in highly susceptible areas. Ecological Indicators, 132:108258. [doi: https://doi.org/10.1016/j.ecolind.2021.108258]
Geological hazards ; Monitoring ; Remote sensing ; Landsat ; Satellite imagery ; Spatial distribution ; Ecological factors ; Landslides ; Vegetation ; Land use / China / Sichuan / Danba
(Location: IWMI HQ Call no: e-copy only Record No: H050775)
https://www.sciencedirect.com/science/article/pii/S1470160X21009237/pdfft?md5=fc8cae18da106987a406a9feff5a7d79&pid=1-s2.0-S1470160X21009237-main.pdf
https://vlibrary.iwmi.org/pdf/H050775.pdf
https://vlibrary.iwmi.org/pdf/H050775.pdf
(9.62 MB) (9.62 MB)
Frequent geohazards have knock-on effects on ecological quality. Timely and dynamically monitoring the damage of geohazards to ecological quality is important to the geological hazards prevention, ecological restoration, and policy formulation. Existing studies mainly focused on the impacts of climate change, urbanization, and extreme weather on the ecological quality, largely ignoring the role of frequent geohazards in the highly susceptible area. At present, the impact mechanism of the high susceptibility of geohazards on ecological quality remains unknown. To fill this knowledge gap, we use the Remote Sensing Ecological Index (RSEI, a widely accepted ecological quality index) calculated on the Google Earth Engine (GEE) platform, geohazard density data, and the Landsat series of surface reflectance datasets to explore the mechanism that drives spatial–temporal variations of ecological quality. Taking the Danba County as the study area, our results indicate that the total number of geohazards is 944 during 1995–2019, and the number of geohazards fluctuates and rises every year (10 in 1995 and 82 in 2019). A conceptual framework was proposed to quantify the impact of the high susceptibility of geohazards on ecological quality by separately exploring its impact on the 4 ecological components of RSEI (i.e., greenness, wetness, dryness, and heat). We found that the density of geohazards is significantly negatively correlated with greenness (R = 0.48, Pearson Correlation Coefficient (PCC) = -0.528, p < 0.01), and humidity (R = 0.45, PCC = -0.364, p < 0.01), whereas it is significantly positively correlated with dryness (R = 0.63, PCC = -0.335, p < 0.01) and heat (R = 0.47, PCC = -0.368, p < 0.01). Therefore, geohazards make a negative contribution to ecological quality by reducing greenness and humidity and increasing dryness and heat. This study provides insights on the mechanism of geohazards on ecological quality, benefiting stakeholders in designing better management plans for sustainable ecosystem cycling, application of GEE, and geological remote sensing.
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