Estimation And Uncertainty Assessment Of Spatial Distribution Of Soil Nitrogen Density In Densely Populated Rural

Basing on high-resolution satellite remote sensing image, GPS and GIS technologies, influence factors of soil nitrogen density of a densely populated village-scale district were analyzed and soil nitrogen density was estimated in Sichuan Hilly Region, using escotope method, land cover method, soil type method, ordinary kriging method, multiply regression method and regression kriging method, and besides, monte carlo simulation was used to assess the uncertainty. Finally, the best means was been found, and the content of soil nitrogen density was estimated in the region. The main results were as follows:Soil nitrogen density was influenced by many factors. The variation of soil nitrogen density reached significant at 0.01 probability level between different soil types. To discuss how topographical factors can impact the spatial distribution of soil nitrogen density, the results showed that natual logistic value of soil nitrogen density was notabilitily negatively correlated to elevation and slope at 0.01 probability level. The correlation of soil nitrogen density and soil silt content were notability positive at 0.01 probability level. There was remarkably positive correlation at 0.01 probability level between soil nitrogen density and soil carbon density. So, the factors above could be used to estimate soil nitrogen density in the region.Based on the descriptive statistics, spatial distribution character and area of grade for soil nitrogen density, the results indicated that the methods made the difference in expressing spatial variability of soil nitrogen density, and no one approaches were fit fully except for some aspects. Besides, spatial distribution estimation didnot reflect the smilarlity of estimation and measured value. So, uncertainty assessment needed to be used for the estimation results of soil nitrogen density.The trend of spatial distribution of simulation value of soil nitrogen density residual was the same. High probability region lowing the threshold of 0.060 kg/m~2 was in the valley between two hills in the middle of study aera. Low probability region was at the top of hills. Based on grading statistics of simulation value lowing the threshold of 0.060 kg/m~2, the results indicted that uncertainty of regression kriging method was the smallest in the process of uncertainty assessment.