Vegetation Net Primary Productivity Estimation Based on Multispectral Remote Sensing Images in Qinghai Lake Basin

Author(s):  
Jie Zhan ◽  
Dianjun Zhang ◽  
Lingjuan Cao ◽  
Quan Guo
2021 ◽  
Vol 12 ◽  
Author(s):  
Chunli Li ◽  
Yonghui Li ◽  
Xinwei Li ◽  
Li Ma ◽  
Yuanming Xiao ◽  
...  

Climate, land-use changes, and nitrogen (N) deposition strongly impact plant primary productivity, particularly in alpine grassland ecosystems. In this study, the differential responses of plant community primary productivity to N and phosphorus (P) nutrient application were investigated in the natural (NG) and “Grain for Green” restored (RG) alpine grasslands by a continuous 3-year experiment in the Qinghai Lake Basin. N addition only significantly promoted plant aboveground biomass (AGB) by 42% and had no significant effect on belowground biomass (BGB) and total biomass (TB) in NG. In comparison with NG, N addition elevated AGB and BGB concurrently in RG by 138% and 24%, respectively, which further significantly increased TB by 41% in RG. Meanwhile, N addition significantly decreased BGB and the AGB ratio (R/S) both in NG and RG. Compared with N addition, P addition did not perform an evident effect on plant biomass parameters. Additionally, AGB was merely negatively influenced by growing season temperatures (GST) under the N addition treatment in NG. AGB was negatively associated with GST but positively related to growing season precipitation (GSP) in RG. By contrast, changes in the R/S ratio in RG were positively correlated with GST and negatively related to GSP. In sum, the results revealed that plant community biomass exhibited convergent (AGB and R/S) and divergent (BGB and TB) responses to N addition between NG and RG. In addition, the outcomes suggested that climate warming would enhance plant biomass allocation to belowground under ongoing N deposition, and indicated the significance of precipitation for plant growth and AGB accumulation in this restored alpine grassland ecosystem.


2006 ◽  
Vol 49 (8) ◽  
pp. 851-861 ◽  
Author(s):  
Zhisheng An ◽  
Ping Wang ◽  
Ji Shen ◽  
Yixiang Zhang ◽  
Peizhen Zhang ◽  
...  

2021 ◽  
Vol 2089 (1) ◽  
pp. 012064
Author(s):  
P. Lokeshwara Reddy ◽  
Santosh Pawar ◽  
S.L. Prathapa Reddy

Abstract With the advent of sensor technology, the exertion of multispectral image (MSI) is comely omnipresent. Denoising is an essential quest in multispectral image processing which further improves recital of unmixing, classification and supplementary ensuing praxis. Explication and ocular analysis are essential to extricate data from remote sensing images for broad realm of supplications. This paper describes curvelet transform based denoising of multispectral remote sensing images. The implementation of curvelet transform is done by using both wrapping function and unequally spaced fast Fourier transform (USFFT) and they diverge in selection of spatial grid which is used to construe curvelets at every orientation and scale. The coefficients of curvelets are docket by a scaling factor, angle and spatial location criterion. This paper crisps on denoising of Linear Imaging Self Scanning Sensor (LISS) III images. The proposed denoising approach has also been collated with some existing schemes for assessment. The efficacy of proposed approach is analyzed with calculation of facet matrices such as Peak signal to noise ratio and Structural similarity at distinct variance of noise..


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