| Land use and land cover change (LUCC) has been considered to be an important contentof global environment change and sustainable development research. At present, we find landuse and land cover information difficult to achieve present situation, so the real-timeautomatic change detection is an extremely urgent problem. In recent years, at the backgroundof implementation in a series of vegetation construction projects in Shaan-Gan-Ning Region,vegetation coverage has been improved significantly and the ecological effects becomeobvious. These projects can be regarded as LUCC; therefore, the detection of LUCC inShaan-Gan-Ning Region means great important theoretical and practical significance toregional soil and water conservation and ecological environmental construction. In this paper,we compared the temporal and spatial change of vegetation in Shaan-Gan-Ning Region inrecent14years, covering the evolution and future tendency based on the data of SPOT VGTNDVI1km/10d resolution in1999~2012, with difference method including linear regressionmethod and Hurst index R/S analysis method, etc. We calculated the relationship betweenNDVI, precipitation and temperature within the same period by using partial correlationanalysis. Furthermore, we combined land use data and afforestation area data to analyze therelationship between NDVI change together with climatic factors and anthropogenic factors.On the basis of the two parts, we selected the artificial factors dominated area as the studyarea to detect change of vegetation. We discussed the applicability of LUCC detection inShaan-Gan-Ning Region based on coarse resolution NDVI data by the direct comparisonmethod. The results showed that:(1) The yearly average NDVI showed a very significantly increased trend (P<0.001) inShaan-Gan-Ning Region in recent14years. The growing season and seasonal NDVI was alsovery significantly increased (P<0.01). The fast growth speed was summer, followed bygrowth season, autumn, spring, annual and winter. NDVI changed slightly and moderately inmost area, which accounting for more than98%. In general, the improvement in southeastpart is larger than northwest part of the study area, among which the northeast part of Yan’ancity, Pengyang-Guyuan area as well as the river area (Tuwei River, Wuding River and Beiluo River) had a most significantly increased trend with a0.001significance. Only0.05%areahad a degenerate tendency because of the urbanization.(2) The annual average NDVI change from0.149to0.456within one year, which inFebruary was the smallest and the biggest in August in Shaan-Gan-Ning Region. The NDVIvariation curve within one year approximately existing as an down open side parabola, asslowly increase (February to April), rapid increase (April to May), slow increase (May toAugust), rapid decline (August to November), slowly decrease (November to next January),smooth transition（next January to February）six stages. The "green vegetation zone" graduallywidened with the time from spring to summer and from southeast to northwest direction inspace in the study area.(3) In recent14years, the climate became "cold and wet" non-significantly, thetemperature decreased and precipitation increased not obviously, it had an inter-annualfluctuation in Shaan-Gan-Ning Region. The combination of climate factors and human factorscaused the NDVI change which was predominated by the human factors in study area. Themain driving factors were the recent years of conversion of cropland to forest and grasslandproject, the vegetation construction and raising the level of agricultural production, as a result,the ecological environment effect is appearing. Precipitation had a positive effect on NDVIchanges, NDVI respond strongly to rainfall in the less vegetation cover areas. But the positiveeffect was masked by the impact of intense human activities. Temperature and NDVI had noobvious correlation, which mainly performed in the control of the NDVI seasonal rhythm.(4) The main land use types in Shaan-Gan-Ning Region were cultivated land andgrassland, accounting for more than35%and47%respectively. The third largest area in typewas forest which accounted for more than10%, the proportion of other types were less than8%. Cultivated land was greatly reduced that mainly converted to forest and grassland from2000to2005. Forest and grassland area increased a lot, and there is the phenomenon ofreclamation. The overall coverage of grassland increased very significantly whichtransformed from low to high coverage gradually. Conversion of cropland to forest andgrassland and natural recovery of grassland provided a―green basis‖for the NDVI increase inthe study area.(5) The accuracy of change detection results was low in the border region betweenYan’an city and Yulin city, the Kappa coefficient was only0.214. Therefore, it needs to becautious for us by using the change detection method in Shaan-Gan-Ning Region.Nevertheless, there were high precision in the part of vegetation dramatically improved andweak correlation between NDVI and precipitation regions. In this region, the overall accuracywas73.24%and the Kappa coefficient was0.455. Therefore, precipitation has great influence on the accuracy of change detection results. The key to improve the accuracy of changedetection is eliminated the influence of precipitation effectively.(6) The main reasons of errors in change detection result in three aspects: a) the mixedpixel problem. There are3types and7patches in each1km×1km pixel averagely; the landuse types must contain hilly dry land and grassland. Therefore a―changed pixel‖may containno changed part of land use types which lead to commission errors and omission errors. b)The existence of same matter with different spectrums and different matters with samespectrum made it easy to misjudge―grassland-grassland‖and―farmland-farmland‖aschanged state, and misjudge―grassland-farmland‖and―farmland-grassland‖as no changedstate. c) The change detection indexes and determining threshold method in this paper werenot suitable for LUCC detection which needs more reasonable design in the future so as torestrain various kinds of―noise‖effectively and conduct change detection accurately. |
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