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首页> 外文期刊>Journal of Geophysical Research. Biogeosciences >PHYSICALLY BASED CLASSIFICATION AND SATELLITE MAPPING OF BIOPHYSICAL CHARACTERISTICS IN THE SOUTHERN BOREAL FOREST
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PHYSICALLY BASED CLASSIFICATION AND SATELLITE MAPPING OF BIOPHYSICAL CHARACTERISTICS IN THE SOUTHERN BOREAL FOREST

机译：南方针叶林基于物理的分类和卫星映射的生物物理特征

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摘要

Fundamental problems inherent to the existing land cover and biophysical characteristic algorithms are fourfold: (1) their failure to deal physically with global and interannual variations in surface reflectance arising from Sun and view angle variations, (2) decoupling of the land cover classification algorithm from the biophysical characteristic inference algorithm with no ability to control biophysical parameter estimation error arising from misclassification, (3) invalid statistical assumptions within classification algorithms used to model reflectance distribution functions, and (4) sole reliance on vegetation indices that can limit performance for several major land cover classes. To address these problems, we develop an integrated, physically based classification and biophysical characteristics estimation algorithm that utilizes canopy reflectance models to account directly for signature variations from Sun angle, topographic, and other variations. Our approach fuses into a single algorithm both land cover classification and biophysical characteristics estimation, permitting one set of physically based canopy reflectance models to be used for both. The use of canopy reflectance models eliminates the need for unrealistic assumptions, such as multivariate-normal distributions, underlying many classification algorithms. Using the algorithm, we have classified a 10,000 km(2) area of the BOREAS southern study area. Our classification shows that low-productivity wetland conifer is the dominant land cover and that nearly 7% of the area is occupied by boreal fens, a major source of methane. In addition, nearly 23% of the area has been disturbed by either fire or logging in the last 20 years, suggesting an important role for disturbance to the regional carbon budget. A thorough evaluation of the physically based classifier within the southern study area shows accuracies superior to those obtained with conventional statistically based algorithms, implying even better performance when extended over multiple Landsat frames since the physically based approach can account directly for regional variations in reflectance resulting from varying illumination and viewing conditions (topography, Sun angle). The conifer biomass density estimation algorithm is based on our discovery of a convenient natural relationship between crown height and volumetric density that renders the biomass density for black spruce stands independent of tree height, and a function only of sunlit canopy fraction. This permits us to calculate directly the relationship between reflectance and biomass density. An evaluation of the algorithm using ground sites shows our algorithm can estimate black spruce biomass density with a root-mean-square error of 2.73 kg/m(2) for correctly classified sites. Our evaluation also demonstrates the importance of correct classification. Root-mean-square errors for misclassified sites were 3.96 kg/m(2). Using this approach we have estimated the biomass density in the BOREAS southern study area for the dominant land. cover type in the circumpolar boreal ecosystem, wetland black spruce. These results show a bimodality to the biomass density regional distribution, controlled perhaps by underlying topographic and edaphic factors. [References: 33]
机译：现有土地覆盖和生物物理特征算法固有的基本问题有四个方面：（1）无法物理处理由太阳和视角变化引起的表面反射率的全局和年际变化;（2）土地覆盖分类算法与无法控制因分类错误而引起的生物物理参数估计误差的生物物理特征推断算法;（3）用于对反射率分布函数进行建模的分类算法中的无效统计假设;（4）仅依赖植被指数，而该植被指数可能会限制几个主要指标的性能土地覆盖类。为了解决这些问题，我们开发了一种基于物理的集成分类和生物物理特征估计算法，该算法利用树冠反射模型直接解决了太阳角度，地形和其他变化的特征变化。我们的方法融合了土地覆盖分类和生物物理特征估计的单一算法，允许将一套基于物理的树冠反射模型用于这两种算法。冠层反射率模型的使用消除了不现实的假设，例如多元正态分布，这些假设是许多分类算法的基础。使用该算法，我们对BOREAS南部研究区域的10,000 km（2）区域进行了分类。我们的分类显示，低产湿地针叶树是主要的土地覆盖，并且将近7％的面积被北方沼气（甲烷的主要来源）占据。此外，在过去20年中，近23％的地区受到火灾或伐木的干扰，这表明干扰对区域碳预算具有重要作用。对南部研究区域内基于物理的分类器进行的全面评估显示，其精度优于传统基于统计的算法，这意味着当扩展到多个Landsat框架时，其性能甚至更高，因为基于物理的方法可以直接说明由变化的照明和观看条件（地形，太阳角度）。针叶树生物量密度估算算法是基于我们发现树冠高度与体积密度之间存在便利的自然关系而得出的，该关系使得黑云杉林分的生物量密度与树木的高度无关，并且仅与阳光冠层分数有关。这使我们可以直接计算反射率与生物量密度之间的关系。对使用地面站点的算法进行的评估表明，对于正确分类的站点，我们的算法可以估计黑云杉生物量密度，且均方根误差为2.73 kg / m（2）。我们的评估还证明了正确分类的重要性。错误分类的站点的均方根误差为3.96 kg / m（2）。通过这种方法，我们估算了BOREAS南部研究区主要土地的生物量密度。在极地北方生态系统中为覆盖类型，湿地为黑云杉。这些结果显示出生物量密度区域分布的双峰性，可能是由潜在的地形和水生因素控制的。 [参考：33]

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