Knowledge-based classification of remote sensing data for the estimation of below- and above-ground organic carbon stocks in riparian forests
Knowledge-based classification of remote sensing data for the estimation of below- and above-ground organic carbon stocks in riparian forests
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DOI：
10.1007/s11273-012-9252-8
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发表时间：
2012-03
影响因子：
1.8
通讯作者：
L. Suchenwirth;M. Förster;A. Cierjacks;Friederike Lang;B. Kleinschmit
中科院分区：
文献类型：
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作者：
L. Suchenwirth;M. Förster;A. Cierjacks;Friederike Lang;B. Kleinschmit
洪泛区森林在有机碳（Corg）的储存中发挥着至关重要的作用。然而，在这些动态生态系统的碳储量建模仍然是固有的困难。在这里，我们提出了在河岸木本植被和土壤（深度为1米）在中欧洪泛区使用非常高的空间分辨率遥感数据和辅助地理数据的Corgstocks的空间估计。研究区域是奥地利的多瑙河洪泛平原国家公园，这是中欧仅存的近自然植被湿地之一。洪泛区内不同的植被类型表现出不同的能力，存储Corg。我们用遥感来区分以下植被类型：草甸，芦苇床和硬木，软木，和棉白杨树林。光谱和知识为基础的分类进行了基于对象的图像分析。其他知识规则包括到河流的距离、对象区域和坡度信息。五种不同的分类方案的基础上的光谱值和额外的知识规则进行了比较和验证。分类准确性的验证数据来自森林清查和地形图。光谱和知识为基础的分类相结合的植被类型的总体准确性高于光谱值单独。虽然水、芦苇床和草地可以清楚地探测到，但区分不同的森林类型仍然具有挑战性。利用蒙特卡罗模拟对所有分类植被类型的土壤和植被的总碳储量进行量化，并绘制空间分布图。研究地点的平均储量为428.9 Mg C ha−1。尽管在植被分类的某些困难，这种方法允许在中欧洪泛区的Corgstocks的间接估计。
Floodplain forests play a crucial role in the storage of organic carbon (Corg). However, modeling of carbon stocks in these dynamic ecosystems remains inherently difficult. Here, we present the spatial estimation of Corgstocks in riparian woody vegetation and soils (to a depth of 1 m) in a Central European floodplain using very high spatial resolution remote sensing data and auxiliary geodata. The research area is the Danube Floodplain National Park in Austria, one of the last remaining wetlands with near-natural vegetation in Central Europe. Different vegetation types within the floodplain show distinct capacities to store Corg. We used remote sensing to distinguish the following vegetation types: meadow, reed bed and hardwood, softwood, and cottonwood forests. Spectral and knowledge-based classification was performed with object-based image analysis. Additional knowledge rules included distances to the river, object area, and slope information. Five different classification schemes based on spectral values and additional knowledge rules were compared and validated. Validation data for the classification accuracy were derived from forest inventories and topographical maps. Overall accuracy for vegetation types was higher for a combination of spectral- and knowledge-based classification than for spectral values alone. While water, reed beds and meadows were clearly detectable, it remained challenging to distinguish the different forest types. The total carbon storage of soils and vegetation was quantified using a Monte Carlo simulation for all classified vegetation types, and the spatial distribution was mapped. The average storage of the study site is 428.9 Mg C ha−1. Despite certain difficulties in vegetation classification this method allows an indirect estimation of Corgstocks in Central European floodplains.