利用卫星ETM与样方统计数据研究西鄂尔多斯珍稀濒危植物种群分布规律

The Application of ETM and Sample Siets Statistic Data to Study the Distribution Pattern of Endangered Rare Plants in West Ordos Plateau

使用 1999年 8月 30m分辨率的ETM遥感数据、DEM数据和 2 5个典型的 4× 4m2 的地面实测样方统计数据、数字化了 1994年的珍稀濒危植物分布图 ,用遥感技术对西鄂尔多斯珍稀濒危植物群落进行了水平和垂直带分布规律的研究。图像处理采用遥感弱信息提取技术 ,识别了建群植物及其种群组合特征 ;并结合三维景观影像对珍稀植物种群生存生境的地貌、土壤等条件的相关性进行了分析 ,揭示了垂直、水平地带分异规律 ,建立了相关分析模型。

ETM data (Aug., 1999), statistics data of 25 sampling sites with size of 4m by 4m and 1:250,000 scale DEM data. and digitized 1 to 100,000 scale distribution map of endangered rare plants are used to study the vertical and horizontal distribution patterns. Endangered rare plants group information was extracted by using Masking Principal component transformation and Hue adjust (MPH) technique. And then the feature imagery was merged with DEM data to produce a virtual reality 3-D imagery to see the differences of rare plant in relation to topographical and soil enviroment. The soil type is affected mainly by topography in this desert area. An analysis model was established to organize different data set. The endangered rare plants in West Ordos Plateau, mainly Tetraena mongolica, Helianthemum soongolicum, Reaumuria trigyna, Reaumuria soongorica, Potaninia mongolica, Ammopiptanthus mongolicus and they are evolved from Tertiary, are for a long time regarded as environment change diagnostic indicators. They also have very precious value to study environmental evolution, plant diversity and geography in global scale. These plants have also played a very important role in maintaining ecosystem in terms of preventing desertification. The vegetation spectral feature ranging from 0.63 to 1.1 μm is called reflectance “shoulder”, which is the spectral character for NDVI, BIOMASS and NPP calculations. In the study we used TM and ETM blue bands( 0.63—0.69μm) and near infrared band4 (0.775—0.90μm). To differentiate spectral features of rare plant groups the MPH technique was used. The technique was implemented in two steps. Masking, and Principal component transformation. Firstly, water bodies and some alluvium were masked out from ETM 1,2,3,4,5,7 bands, which could reduce the variance features for next step processing. Principal component transformation algorithm had the function that can compress the common features into first component. In order to get rid of terrain illumination (mainly BRDF/ALBEDO)from all imported bands, we used the PCA compress function to reduce terrain illumination. After Principal Component Analysis the first component contained most of common BRDF/ALBEDO information. Three MPH resultants, MPH 2, MPH 3and MPH 4 were selected and combined to make a color composite. The method employed in the study can be widely used as a monitorying tool for the future decision making in protection of the endangered rare plants.