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Dynamic Monitoring and Prediction of Aboveground Biomass of Natural Grassland

Author: BaoHaiMing
Tutor: ZuoXuJiang；ZhangDeZuo；LiuAiJun
School: Gansu Agricultural University
Course: Grassland
Keywords: Compensatory Growth Enhanced vegetation index Time Series Aboveground biomass Forage yield forecasts Changes in vegetation dynamics
CLC: S812
Type: Master's thesis
Year: 2010
Downloads: 105
Quote: 0
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Abstract


In recent years, the development of the natural grassland ecosystem increasingly important focus of the healthy development of the grassland ecosystem and livestock balance relations, but also many scholars who have studied the natural grassland of Gansu Xiahe County as the research object, based on the 2000 to 2009 the EVI time sequence data using the 3S technology and dynamic monitoring of grassland vegetation, natural grassland vegetation growth dynamics and aboveground biomass prediction method, and vegetation compensatory growth in natural grassland made simple set. The main findings are as follows: 1) through dynamic monitoring of Xiahe natural grassland, come to the of Xiahe mountain meadow and alpine meadow without grazing disturbance as well as the respective average stocking rate of vegetation growth the dynamic curves law and feed intake variation . The results of the study provide a basis not only for of natural grassland compensatory growth dynamics of and provide data to support exploratory study of the natural grassland aboveground biomass dynamic forecasting methods. 2) natural grassland vegetation growth compensation coefficient curve: wide variety of vegetation due to the vast area of ??natural grassland vegetation compensatory growth conditions, not a quantitative or qualitative, but by the hydrothermal conditions, vegetation many factors affect the type, time and feed intake of a gradual process. 3) of natural grassland whole, starting from the start grazing, the grassland vegetation compensatory growth conditions generally first performance for ultra compensation growth, and gradually change with the change of time and feed strength into undercompensation state time mainly determined by the grazing intensity and environmental factors. 4) Gansu Xiahe mountain meadow and alpine meadow vegetation growth status in the same year, consistent performance, relative to the average state in nearly a decade, 2002,2005,2009 performance for harvest, showed non-leap year in 2004,2006,2007,2008 , 2000,2001,2003 underdamped years. 5) due to the saturation problem of NDVI, EVI is more suitable for monitoring in high-density vegetation area relative to NDVI vegetation index. 6) Xiahe mountain meadow and alpine meadow perennial aboveground biomass time series based forecast late after reviving a period of above-ground biomass production, verification and dynamic monitoring data in the 2009 state of the average stocking rates average prediction accuracy, respectively: 92.09% and 88.82%, respectively; measured and predicted values ??for paired samples T-test results indicate: between the measured and predicted values ??highly significant correlation (mountain meadow r = 0.988 ** P lt; 0.01; the alpine meadow r = 0.956 **, P lt; 0.01), the prediction method is feasible.

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CLC: > Agricultural Sciences > Livestock, animal medicine,hunting,silkworm,bee > General Animal Science > Grassland Science,prairie school
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