城市绿地现状自动提取

运用遥感信息技术,以吉林市为例,在TM影像中对城市绿地进行信息提取。通过4种绿地信息提取方案(彩色合成法、穗帽变换、归一化植被指数NDVI计算、实验波段合成)的比较发现,归一化植被指教NDVI计算方法目视解译的效果最好,彩色合成法的分类效果最佳,研究得出吉林市的绿地面积约占整体的40%,可以得到比较理想的分类结果。     

基于NDVI的黄河三角洲植被覆盖度动态变化研究

植被覆盖度是研究地表植被状况的重要指标,以黄河三角洲为研究区域,利用该地区1995年、2004年和2010年的三期Landsat-5TM影像数据,提取NDVI,采用像元二分模型,反演植被覆盖度及动态变化趋势,并对植被覆盖度变化的原因进行了浅析.研究结果表明,黄河三角洲地区自1995年以来随着耕地面积的增加,以及由于当地政府对生态环境保护的重视,落实环境保护政策,植被覆盖度整体上有了明显提高,Ⅰ级植被面积从1995年的203.8212 km2增加到2010年的1055.6694 km2,生态环境有了较大的改善.另一方面,随着的城市的快速发展,建筑用地及工业用地的增加,出现了局部地区植被退化,植被覆盖度降低.     

遥感技术在森林绿地信息提取中的应用

随着城市的发展,城市森林绿地对于提高人类生活环境的质量,发挥着越来越重要的作用。以太原市为研究区,选择2007年与2009年的LandsmTM数据,对影像数据进行预处理及增强处理,利用NDVI、非监督分类与监督分类相结合的方法获得研究区森林绿地分布信息,利用研究区1：10万地形图等资料和GooSeearth上高分辨率图像对解译结果进行修正,根据研究区森林绿地分布特征确定60个验证点,进行实地验证。结果表明：NDVI、非监督分类与监督分类相结合的方法能快速获得研究区森林绿地分布状况,利用地形图与GooSeearth上的高分辨率数据对解译结果进行修正,通过野外验证60个点,解译精度为90％。     

Urban cover mapping using digital, high-spatial resolution aerial imagery

High-spatial resolution digital color-infrared aerial imagery of Syracuse, NY was analyzed to test methods for developing land cover classifications for an urban area. Five cover types were mapped: tree/shrub, grass/herbaceous, bare soil, water and impervious surface. Challenges in high-spatial resolution imagery such as shadow effect and similarity in spectral response between classes were found. Classification confusion among objects with similar spectral responses occurred between water and dark impervious surfaces, concrete and bare-soil, and grass/herbaceous and trees/shrub. Methods of incorporating texture, band ratios, masking of water objects, sieve functions, and majority filters were evaluated for their potential to improve the classification accuracy. After combining these various techniques, overall cover accuracy for the study area was 81.75%. Highest accuracies occurred for water (100%), tree/shrub (86.2%) and impervious surfaces (82.6%); lowest accuracy were for grass/herbaceous (69.3%) and bare soil (40.0%). Methods of improving cover map accuracy are discussed.     

Bayesian scale space analysis of temporal changes in satellite images

We consider the detection of land cover changes using pairs of Landsat ETM+ satellite images. The images consist of eight spectral bands and to simplify the multidimensional change detection task, the image pair is first transformed to a one-dimensional image. When the transformation is non-linear, the true change in the images may be masked by complex noise. For example, when changes in the Normalized Difference Vegetation Index is considered, the variance of noise may not be constant over the image and methods based on image thresholding can be ineffective. To facilitate detection of change in such situations, we propose an approach that uses Bayesian statistical modeling and simulation-based inference. In order to detect both large and small scale changes, our method uses a scale space approach that employs multi-level smoothing. We demonstrate the technique using artificial test images and two pairs of real Landsat ETM+satellite images.