| Desertification is one of most important reason which was cause the ecological deterioration and productivity decline of grassland. And cause serious threat to grassland sustainable utilization. Ruoergai grassland is main water source for Huang He River and Yangtze River, but recent year the degree of desertification was incersed which was caused by natural and artificial factors. Remote sensing monitoring of the degree of grassland desertification in Ruoergai and analysis of characteristics of vegetation and soil nutrient was help to scientifically understand the effect ion of desertification. And provide scientific basis for control alpine grassland desertification.The paper takes the desertification grassland of the Ruoergai County in south-west of Sichuan province in south-east of Tibet plateau. To supervised classification the desertification grassland of the study area using the Thematic Mapper images. To calculate area of different desertification level grassland under the national standard of classification the desertification grassland. Extraction NDVI index from TM images and created a regression model with coverage. Then inversion study area desertification level of coverage distribution, and accuracy evaluation. At the same time, by using method which was combining remote sensing information with ground investigation of different levels of desertification grassland (non-desertification, slight-desertification, middle-desertification, and serious-desertification), to analysis the Vegetation Community Characteristics(height, coverage, above-ground biomass, Species richness, Shannon-Wiener index, Pielou index), nutriment content of surface soil(pH, Soil organic matter, total N, available N, available P, available K) and correlation between Vegetation and soil in the Ruoergai County. This study wanted to demonstrate the vegetation characteristics and soil properties of grasslands at different desertification successions; a remote sensing image for grassland desertification classification in this area and model for vegetation coverage monitoring were obtained.The main results are as follows:1. Processing the TM image (August,2010; Path/Row:132/039) Under the ENVI4.4software system, for the supervision classification (Maximum Likelihood) and Accuracy evaluation. The results showed that the Overall Accuracy was86.1644%and the Kappa coefficient was0.8777. Also the area of non-desertification grassland was3346.00km2, slight-desertification grassland2201.80km2, middle-desertification grassland349.17km2, and serious-desertification grassland was67.52km2. Desertification grassland, occupying24.52%of the total study area, was2600.49km2.2. Extraction NDVI from TM image of study area under ENVI4.4system. Build a linear regression model with ground vegetation coverage and NDVI. The complex correlation coefficient (R2) of model is0.8675, regression model was: y=146.3x-3.0064. Using the model, to inversion coverage which was base on NDVI. And output study area grassland desertification coverage grading distribution. The accuracy of this model is86%and the average error is13.98%. An estimation was conducted which model in the vegetation coverage monitoring a feasible in practice. While the coverage distribution can be clearly resolved on the inversion image.3. With the desertification increasing, the vegetation cover, height and above-ground biomass showed significantly downtrend. Comparing to non-desertification grassland, height decreased26.09%,57.89%and89.22%, respectively and living above-ground biomass decreased19.80%,54.15%and88.11%. The difference of coverage and above-ground biomass between all levels desertification grassland was showed the greatly significant (p<0.0.01). Species richness showed downtrend with the desertification increasing, respectively, the decrease range between2.0-3.9species. The important value of Elymus sibiricus, Kobresia. Setchwanensis and Elymus nutans downtrend with the desertification increasing. But important value of Car ex. Praeclara and Leymus secalinus was increasing. The results showed that the effect of desertification was obviously working on characters of plant communities. 4. The pH of the surface soil among different desertification levels has showed the weak alkaline. There was significant downtrend among different desertification of soil0-20cm organic matter and total nitrogen content (P<0.05). Comparing to non-desertification grassland, slight-desertification grassland decreased64.13%(35.37g/Kg) and23.83%(0.25mg/Kg), the difference was extremely significant (p<0.01). Available nitrogen and available potassium varied inapparently, while available phosphorus showed a downward trend.5. Correlation analysis showed that the coverage, up-ground-biomass, Shannon-Wiener index, height and surface soil nutrient relevance was not significant. Available potassium correlated with the vegetation characteristic, and reached significant level P<0.05,2-tailed),0.9284,0.9457. Soil pH was negatively correlated with the vegetation characteristic, but soil organic matter and total nitrogen was correlated with the vegetation cover and height, correlation coefficient higher than0.7.To sum up, a feasible method which was combined with remote sensing information and ground survey for monitor condition in Ruoergai has been proved. It can be clearly grasp the grassland desertification area distribution, degree of desertification, vegetation condition and soil nutrient. This study results has the reference to prevention and control of desertification at Ruoergai. |
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