Multi-Temporal Land-Cover Classification of Agricultural Areas in Two European Regions with High Resolution Spotlight TerraSAR-X Data
Multi-Temporal Land-Cover Classification of Agricultural Areas in Two European Regions with High Resolution Spotlight TerraSAR-X Data
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DOI：
10.3390/rs3050859
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发表时间：
2011-05-01
期刊：
影响因子：
5
通讯作者：
Herrmann, Sylvia
中科院分区：
文献类型：
--
作者：
Bargiel, Damian;Herrmann, Sylvia
生态系统为人类的福祉提供多种服务（如：例如，在一个实施例中，食物、淡水、纤维）。农业提供了其中几种服务，但也可能造成负面影响。因此，获取有关农业土地利用及其变化的最新信息至关重要。本文介绍了农业土地利用多时相分类的高分辨率聚束TerraSAR-X图像的基础上。在植被季节采取的l4双极化雷达图像的堆栈已被用于两个不同的研究领域（北部的德国和东南部波兰）。这些地区的人口密度、农业管理以及地质和地貌条件极为不同。从而检验了该分类方法在不同地区的可移植性。最大似然分类基于大量的地面真实样本。两个区域的分类准确度不同。德国地区所有类别的总体准确率为61.78%，波兰地区为39.25%。当单一的植被类被合并成组类时，这两个地区的准确性显着提高（约90%）。这种适用于欧洲不同农业地点的常规土地使用分类可作为农业土地使用及其相关生态系统监测系统的基础。
Functioning ecosystems offer multiple services for human well-being (e. g., food, freshwater, fiber). Agriculture provides several of these services but also can cause negative impacts. Thus, it is essential to derive up-to-date information about agricultural land use and its change. This paper describes the multi-temporal classification of agricultural land use based on high resolution spotlight TerraSAR-X images. A stack of l4 dual-polarized radar images taken during the vegetation season have been used for two different study areas (North of Germany and Southeast Poland). They represent extremely diverse regions with regard to their population density, agricultural management, as well as geological and geomorphological conditions. Thereby, the transferability of the classification method for different regions is tested. The Maximum Likelihood classification is based on a high amount of ground truth samples. Classification accuracies differ in both regions. Overall accuracy for all classes for the German area is 61.78% and 39.25% for the Polish region. Accuracies improved notably for both regions (about 90%) when single vegetation classes were merged into groups of classes. Such regular land use classifications, applicable for different European agricultural sites, can serve as basis for monitoring systems for agricultural land use and its related ecosystems.