scholarly journals Stemflow and its controlling factors in the subshrub Artemisia ordosica during two contrasting growth stages in the Mu Us sandy land of northern China

2015 ◽  
Vol 47 (2) ◽  
pp. 409-418 ◽  
Author(s):  
Liu Li ◽  
Xiao-Yan Li ◽  
Si-Yi Zhang ◽  
Zhi-Yun Jiang ◽  
Xiao-Ran Zheng ◽  
...  
Keyword(s):  
Growth Stage ◽  
Canopy Structure ◽  
Northern China ◽  
Growth Stages ◽  
Sandy Land ◽  
Artemisia Ordosica ◽  
Growth Season ◽  
Area Index ◽  
Mu Us Sandy Land ◽  
Canopy Area

The yield of stemflow from vegetation is mostly affected by rainfall and canopy structure, but few past studies have paid attention to the dynamics of canopy structure during the growth season. Artemisia ordosica is a typical subshrub, very different from trees and shrubs. Assessing the influence of canopy structure and rainfall on stemflow yield in A. ordosica during the growth season will fill a knowledge gap in our understanding of stemflow yield from subshrub species. This study therefore examined the effects of those two factors on stemflow at two growth stages of A. ordosica, using 20 experimental individuals in the Mu Us sandy land of northern China. It demonstrated that the mean stemflow percentage of gross rainfall (SF%) for this subshrub was 8.56%, and the average funneling ratio was 75.80. The critical control factors of stemflow volumes were rainfall amount and canopy area, which varied greatly during the growth season. The SF% was significantly lower during the reproductive growth stage than during the vegetative growth stage, because of the rapid increase in leaf area index at the former stage. This evaluation of the effects of vegetation growth dynamics on stemflow yield will improve the accuracy of future hydrological models.

Research on the Succession of Artemisia ordosica Community in Mu Us Sandy Land

2014 ◽  
Vol 989-994 ◽  
pp. 975-977
Author(s):  
Jun Hong Zhang
Keyword(s):  
Soil Water ◽  
Dominant Species ◽  
Sand Dunes ◽  
Biological Soil Crust ◽  
Northern China ◽  
Soil Crust ◽  
Sandy Land ◽  
Artemisia Ordosica ◽  
Mu Us Sandy Land ◽  
Long Time

Mu Us sandy land is an important ecological barrier in Northern China. Artemisia ordosica is the dominant species in Mu Us sandy land, the development of biological soil crust in Artemisia ordosica community seriously impediment precipitations, which result in the deterioration of soil water within 40cm, where most of Artemisia ordosica roots distribution. That led to the recession of Artemisia ordosica in the fixed sand dunes in a long time.

Surface roughness response of biocrust-covered soil to mimicked sheep trampling in the Mu Us sandy Land, northern China

Geoderma ◽  
2020 ◽  
Vol 363 ◽  
pp. 114146
Author(s):  
Yong-Sheng Wu ◽  
Xin-Rong Li ◽  
Hasi-Eerdun ◽  
Rui-Ping Yin ◽  
Tie-Jun Liu
Keyword(s):  
Surface Roughness ◽  
Northern China ◽  
Sandy Land ◽  
Mu Us Sandy Land

Effects of biological crusts on dew deposition and evaporation in the Southern Edge of the Mu Us Sandy Land, Northern China

2013 ◽  
Vol 33 (19) ◽  
pp. 6173-6180 ◽  
Author(s):  
尹瑞平 YIN Ruiping ◽  
吴永胜 WU Yongsheng ◽  
张欣 ZHANG Xin ◽  
哈斯 HA Si ◽  
田秀民 TIAN Xiumin ◽  
...  
Keyword(s):  
Northern China ◽  
Sandy Land ◽  
Mu Us Sandy Land ◽  
Dew Deposition

scholarly journals UAV and ground-based imagery analysis detects canopy structure changes after canopy management applications

OENO One ◽  
2020 ◽  
Vol 54 (4) ◽  
pp. 1093-1103
Author(s):  
Jingyun Ouyang ◽  
Roberta De Bei ◽  
Sigfredo Fuentes ◽  
Cassandra Collins
Keyword(s):  
Vegetation Index ◽  
Management Practices ◽  
Canopy Structure ◽  
Area Index ◽  
Structure Changes ◽  
Grape Quality ◽  
Canopy Volume ◽  
Canopy Area ◽  
Plant Area Index ◽  
Canopy Management

Aim: To analyse unmanned aerial vehicle (UAV)-based imagery to assess canopy structural changes after the application of different canopy management practices in the vineyard.Methods and results: Four different canopy management practices: i–ii) leaf removal within the bunch zone (eastern side/both eastern and western sides), iii) bunch thinning and iv) shoot trimming were applied to grapevines at veraison, in a commercial Cabernet-Sauvignon vineyard in McLaren Vale, South Australia. UAV-based imagery captures were taken: i) before the canopy treatments, ii) after the treatments and iii) at harvest to assess the treatment outcomes. Canopy volume, projected canopy area and normalized difference vegetation index (NDVI) were derived from the analysis of RGB and multispectral imagery collected using the UAV. Plant area index (PAI) was calculated using the smartphone app VitiCanopy as a ground-based measurement for comparison with UAV-derived measurements. Results showed that all three types of UAV-based measurements detected changes in the canopy structure after the application of canopy management practices, except for the bunch thinning treatment. As expected, ground-based PAI was the only technique to effectively detect internal canopy structure changes caused by bunch thinning. Canopy volume and PAI were found to better detect variations in canopy structure compared to NDVI and projected canopy area. The latter were negatively affected by the interference of the trimmed shoots left on the ground.Conclusions: UAV-based tools can provide accurate assessments to some canopy management outcomes at the vineyard scale. Among different UAV-based measurements, canopy volume was more sensitive to changes in canopy structure, compared to NDVI and projected canopy area, and demonstrated a greater potential to assess the outcomes of a range of canopy management practices.  Significance and impact of the study: Canopy management practices are widely applied to regulate canopy growth, improve grape quality and reduce disease pressure in the bunch zone. Being able to detect major changes in canopy structure, with some limitations when the practice affects the internal structure (i.e., bunch thinning), UAV-based imagery analysis can be used to measure the outcome of common canopy management practices and it can improve the efficiency of vineyard management.  

scholarly journals Spatiotemporal dynamic evolution and driving factors of desertification in the Mu Us Sandy Land in 30 years

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Xueying Han ◽  
Guangpu Jia ◽  
Guang Yang ◽  
Ning Wang ◽  
Feng Liu ◽  
...  
Keyword(s):  
Central Area ◽  
Northern China ◽  
Northern Region ◽  
Sandy Land ◽  
Aeolian Desertification ◽  
Mu Us Sandy Land ◽  
Annual Change Rate ◽  
Three Stages ◽  
Inducing Factors ◽  
Environmental Protection Policies

AbstractThe Mu Us Sandy Land is located in the middle of the farming pastoral ecotone of northern China. The direction of the development of desertification has a direct impact on the economy and development of the northern region. Six remote sensing images acquired during 1990–2017 served as data sources. Using an ENVI 5.3 and ArcGIS 10.3 platform an analysis was conducted of the dynamic changes nearly 30 years in desertified land using a center of gravity moving model, annual change rate, a transfer matrix, and an aeolian desertification index; the factors driving desertification were discussed. The research shows that the time period can be divided into three stages of desertification: development (1990–2000), rapid reversal (2000–2010), and stable reversal (2010–2017). A total of 1680 km2 of desertification were managed over the three stages. Spatially, the distribution of the center of desertification from west to east includes mild, moderate, severe, and extreme desertification, which is consistent with the spatial distribution trends of desertified land in the Mu Us Sandy Land. By the end of 2017, the degree of desertification of the Mu Us Sandy Land was in the central area > northwest > southwest > east > south. Nearly 30 years, the wind speed has decreased year by year at the rate of 0.1 m s−1, which directly reduce the ability to winds to transport soil in the Mu Us Sandy Land and promoted the reversal of desertification. From 1990 to 2010, the climate tended to become warmer and drier. Environmental protection policies along with human intervention and control of desertification have played important roles in reversing desertification. From 2010 to 2020, under the general background of a warm-wet climatic tendency, rational use of sand resources and strengthening scientific control of desertification inducing factors are the keys to reversing desertification.

scholarly journals Groundwater evapotranspiration under psammophilous vegetation covers in the Mu Us Sandy Land, northern China

2016 ◽  
Vol 9 (1) ◽  
pp. 98-108 ◽  
Author(s):  
Donghui Cheng ◽  
Jibo Duan ◽  
Kang Qian ◽  
Lijun Qi ◽  
Hongbin Yang ◽  
...  
Keyword(s):  
Northern China ◽  
Sandy Land ◽  
Mu Us Sandy Land ◽  
Groundwater Evapotranspiration

scholarly journals Seasonal Variation of the Canopy Structure Parameters and Its Correlation with Yield-Related Traits in Sugarcane

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Jun Luo ◽  
Youxiong Que ◽  
Hua Zhang ◽  
Liping Xu
Keyword(s):  
Transmission Coefficient ◽  
Canopy Structure ◽  
Diffuse Radiation ◽  
Growth Stages ◽  
Beam Radiation ◽  
Area Index ◽  
Sugarcane Varieties ◽  
Sugarcane Yield ◽  
Leaf Distribution ◽  
Different Growth Stages

Population structure determines sugarcane yield, of which canopy structure is a key component. To fully understand the relations between sugarcane yield and parameters of the canopy structure, 17 sugarcane varieties were investigated at five growth stages. The results indicated that there were significant differences between characterized parameters among sugarcane populations at different growth stages. During sugarcane growth after planting, leaf area index (LAI) and leaf distribution (LD) increased, while transmission coefficient for diffuse radiation (TD), mean foliage inclination angle (MFIA), transmission coefficient for solar beam radiation penetration (TR), and extinction coefficient (K) decreased. Significant negative correlations were found between sugarcane yield and MFIA, TD, TR, andKat the early elongation stage, while a significant positive correlation between sugarcane yield and LD was found at the same stage. A regression for sugarcane yield, with relative error of yield fitting less than 10%, was successfully established: sugarcane yield = 2380.12 + 46.25 × LD − 491.82 × LAI + 1.36 × MFIA + 614.91 × TD − 1908.05 × TR − 182.53 ×  K+ 1281.75 × LD − 1.35 × MFIA + 831.2 × TR − 407.8 ×  K+ 8.21 × MFIA − 834.50 × TD − 1695.49 ×  K  (R2=0.94**).

scholarly journals Improvement of Wheat Grain Yield Prediction Model Performance Based on Stacking Technique

2021 ◽  
Vol 11 (24) ◽  
pp. 12164
Author(s):  
Changchun Li ◽  
Yilin Wang ◽  
Chunyan Ma ◽  
Weinan Chen ◽  
Yacong Li ◽  
...  
Keyword(s):  
Prediction Model ◽  
Grain Yield ◽  
Growth Stage ◽  
Prediction Models ◽  
Vegetation Indices ◽  
Model Performance ◽  
Grain Filling ◽  
Growth Stages ◽  
Wheat Grain ◽  
Area Index

Crop growth and development is a dynamic and complex process, and the essence of yield formation is the continuous accumulation of photosynthetic products from multiple fertility stages. In this study, a new stacking method for integrating multiple growth stages information was proposed to improve the performance of the winter wheat grain yield (GY) prediction model. For this purpose, crop canopy hyperspectral reflectance and leaf area index (LAI) data were obtained at the jointing, flagging, anthesis and grain filling stages. In this case, 15 vegetation indices and LAI were used as input features of the elastic network to construct GY prediction models for single growth stage. Based on Stacking technique, the GY prediction results of four single growth stages were integrated to construct the ensemble learning framework. The results showed that vegetation indices coupled LAI could effectively overcome the spectral saturation phenomenon, the validated R2 of each growth stage was improved by 10%, 22.5%, 3.6% and 10%, respectively. The stacking method provided more stable information with higher prediction accuracy than the individual fertility results (R2 = 0.74), and the R2 of the model validation phase improved by 236%, 51%, 27.6%, and 12.1%, respectively. The study can provide a reference for GY prediction of other crops.

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