基于光谱角度匹配方法提取黑土边界

Extracting Black Soil Border in Heilongjiang Province Based on Spectral Angle Match Method

在RS与GIS环境下,用土地利用、土壤图等获取遥感监督分类的感兴趣区,基于MODIS反射率产品、运用光谱角度匹配等方法进行黑土边界提取研究。结果表明:基于MODIS反射率数据的土壤遥感分类方法,可以提取黑龙江省黑土边界,光谱角度匹配方法分类结果最好,黑龙江省黑土带北部分类精度高于南部;由于植被覆盖、光谱特征相似等原因,其他土壤分类结果相对较差;由于东北地区裸土时间相对较长,MODIS的高时间分辨率特性有利于提高土壤遥感分类精度;在GIS的支持下,充分利用辅助信息选择遥感分类的感兴趣区,可以提高土壤遥感分类精度;引入地形、气候等信息,分类精度得到显著提高、提取的黑土边界信息更准确。

As soils are generally covered by vegetation most time of a year, the spectral reflectance collected by remote sensing technique is from the mixture of soil and vegetation, so the classifieation precision based on remote sensing （RS） technique is unsatisfied. Under RS and geographic information systems （GIS） environment and with the help of buffer and overlay analysis methods, land use and soil maps were used to derive regions of interest （ROI） for RS supervised classification, which plus MODIS reflectance products were chosen to extract black soil border, with methods including spectral single match. The results showed that the black soil border in Heilong, iiang province can be extracted with soil remote sensing method based on MODIS reflectance products, especially in the north part of black soil zone; the classification precision of spectral angel mapping method is the highest, but the classifying accuracy of other soils can not meet the need, because of vegetation covering and similar spectral characteristics; even for the same soil, black soil, the classifying aceuracy has obvious spatial heterogeneity, in the north part of black soil zone in Heilongjiang province it is higher than in the south, which is because of spectral differences; as soil uncovering period in Northeastern China is relatively longer, high temporal resolution make MODIS images get the advantage over soil remote sensing classification; with the help of GIS, extracting ROIs by making the best of auxiliary data can improve the precision of soil classification; with the help of auxiliary information, such as topography and climate, the classification aecuracy was enhanced significantly. As there are five main factors determining soil classes, much data of different types, such as DEM, terrain factors, climate （temperature, precipitation, etc. ）, parent material, vegetation map, and remote sensing images, were introduced to classify soils, so how to choose some of the data and quantify the weights of different data layers needs further study.