区域碳收支能力估算的面向对象遥感分类方法  

Remote Sensing Estimation of the Carbon Balance Ability Based on the Object-Oriented Method for Guangxi Youjiang District

作　　者：童新华[1] 张郭秋晨 韦燕飞[1] 

机构地区：[1]广西师范学院地理科学与规划学院,南宁530001

出　　处：《地球信息科学学报》2016年第12期1675-1683,共9页Journal of Geo-information Science

摘　　要：全球气候变暖问题是人类面临的最艰巨的挑战之一,通过先进的面向对象分类方法可以提高碳排放与碳汇能力的研究水平,对于控制区域气候变化具有推动作用。本文利用面向对象分类方法,以广西百色市右江区为研究区域,选取Landsat 8OLI和Google Earth影像数据提取区域地物信息,并针对研究区地势复杂的特点,采用设置多种尺度参数的方法,选取最优尺度进行影像分割。同时,引入隶属度函数法、最邻近分类法和CART决策树分类器3种方法,基于影像光谱差异、几何形状、对象纹理等特征,逐层逐级地实施面向对象分类,随后加以针对性的精度评价分析并检验分类结果。通过总结分析前人的地物碳系数转换关系并结合高精度面向对象分类结果,构建了基于土地覆被类型的碳收支能力估算模型,并根据已有的基于CASA模型的碳收支能力估算方法加以精度校验,最终估算出右江区碳收支能力为-399.64万t。此外,本文结合右江区行政区划、人口分布、DEM等相关数据对区域碳收支能力进行了专题性剖析。结果表明,面向对象分类方法是研究小区域碳收支能力的有效途径,在区域碳循环评估中具有更好的准确性和预见性,有效促进碳收支平衡研究领域的发展。Global warming is one of the most daunting challenges that our humanity is facing. It has urged the action to study carbon emissions and carbon sequestration ability to control the regional climate change. Some studies show that human activities, especially the combustion of fossil fuel, cause the carbon emissions and the global climate warming. Therefore, it is important to work on energy saving and emission reduction. At the same time, the carbon sink capacity of forest, grassland, crops and other vegetations become one of the most powerful approaches to ease the global climate change. Thus, we conduct a research in this area, in order to improve the carbon source utilization efficiency, reduce the carbon intensity, prefect the energy-saving and emission-reduction works, and well manage the carbon budget capacity and some other problems. Taking Youjiang district of Baise city in Guangxi Province as the experimental area, this article uses the object-oriented classification technology and extracts the area geographic information from the Landsat 8 OLI and Google Earth images. Due to the complex terrain of the study area, different parameter settings of the multi-scale segmentation are used and the optimal scaling for the image segmentation is selected. We also use the membership function method, the closest classification method and the CART decision tree classifier method to complete the object- oriented classification layer by layer and evaluate the accuracy of the classification results, based on the spectral difference,geometric shapes, objects, texture and other characteristics. Through summarizing the conversion relationship between the land and carbon coefficient, combining with the high-precision object-oriented classification results, the estimation model of carbon budget capacity is built based on land-cover types. Finally, according to the CASA model of carbon budget capacity, we check the accuracy of the estimation method. The carbon budget capacity of Youjiang district is estimated to be-3996.4 kt,

关 键 词：e COGNITION 多尺度分割 面向对象分类 碳转换系数 碳收支能力 

分 类 号：X87[环境科学与工程—环境工程] X16[天文地球—大气科学及气象学] P467