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首页> 外文会议>Dragon 3 Final Results Dragon 4 Kick-Off Symposium >FOREST TYPE AND ABOVE GROUND BIOMASS ESTIMATION BASED ON SENTINEL-2A ANDWORLDVIEW-2 DATA EVALUATION OF PREDICTOR AND DATA SUITABILITY
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FOREST TYPE AND ABOVE GROUND BIOMASS ESTIMATION BASED ON SENTINEL-2A ANDWORLDVIEW-2 DATA EVALUATION OF PREDICTOR AND DATA SUITABILITY

机译：基于Sentinel-2A和波世界的地面生物量估计的森林类型及地上生物量估计预测器和数据适用性的数据评估

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The present study analyses the two earth observation sensors regarding their capability of modelling forest above ground biomass and forest density. Our research is carried out at two different demonstration sites. The first is located in south-western Germany (region Karlsruhe) and the second is located in southern China in Jiangle County (Province Fujian). A set of spectral and spatial predictors are computed from both, Sentinel-2A and WorldView-2 data. Window sizes in the range of 3*3 pixels to 21*21 pixels are computed in order to cover the full range of the canopy sizes of mature forest stands. Textural predictors of first and second order (grey-level-co-occurrence matrix) are calculated and are further used within a feature selection procedure. Additionally common spectral predictors from WorldView-2 and Sentinel-2A data such as all relevant spectral bands and NDVI are integrated in the analyses. To examine the most important predictors, a predictor selection algorithm is applied to the data, whereas the entire predictor set of more than 1000 predictors is used to find most important ones. Out of the original set only the most important predictors are then further analysed. Predictor selection is done with the Boruta package in R (Kursa and Rudnicki (2010)), whereas regression is computed with random forest. Prior the classification and regression a tuning of parameters is done by a repetitive model selection (100 runs), based on the .632 bootstrapping. Both are implemented in the caret R package (Kuhn et al. (2016)). To account for the variability in the data set 100 independent runs are performed. Within each run 80 percent of the data is used for training and the 20 percent are used for an independent validation. With the subset of original predictors mapping of above ground biomass is performed.
机译：本研究分析了两种地球观测传感器，了解地面生物质和林密度的建模森林能力。我们的研究是在两个不同的演示网站进行的。第一个位于德国西南（地区Karlsruhe），第二个位于中国南方州南县（福建省）。从Sentinel-2a和worldview-2数据计算一组频谱和空间预测器。计算在3 * 3像素范围内的窗口尺寸为21 * 21像素，以覆盖成熟林板的全系列冠层尺寸。计算第一和二阶（灰度级 - 共发生矩阵）的纹理预测器，并进一步在特征选择过程中使用。另外，来自WorldView-2和Sentinel-2a数据的常见频谱预测器，例如所有相关的光谱频带和NDVI都集成在分析中。为了检查最重要的预测因子，将预测器选择算法应用于数据，而整个预测器集超过1000个预测器集用于找到最重要的。从原始设置中，然后进一步分析最重要的预测因子。预测器选择是用R（Kursa和Rudnicki（2010））中的Boruta包完成的，而回归是用随机森林计算的。在分类和回归之前，参数的调整是通过重复模型选择（100运行）完成的，基于.632引导。两者都在地毯R包装中实施（Kuhn等人。（2016））。为了考虑数据集中的可变性，执行独立运行。在每个运行中，80％的数据用于培训，20％用于独立验证。利用原始预测器的子集进行上述生物量的映射。

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