Tree density estimation in a tropical woodland ecosystem with multiangular MISR and MODIS data
- 作者：Fern ; o Sedano ; Daniel Gó ; mez ; Peng Gong ; Gregory S. Biging
- 关键词：MODIS ; MISR ; Neural networks ; Band selection ; Tropical woodland
- 刊名：Remote Sensing of Environment
- 出版年：2008
- 期刊代码：79_00344257
- 类别：ear
- 出版时间：15 May 2008
- 卷：112
- 期：5
- 页码：2523-2537
- 文件大小：1394 K
摘要
In this paper we evaluate the potential of spectral, temporal and angular aspect of remotely sensed data for quantitative extraction of forest structure information in tropical woodlands. Moderate resolution imaging spectroradiometer (MODIS) multispectral data at 500-meter spatial resolution from different dates, multiangle imaging spectroradiometer (MISR) bidirectional reflectance factors (BRF) and normalized difference angular index (NDAI) derived from MISR data at 275-meter spatial resolution were used as input data. The number of trees per hectare bigger than 20cm in diameter at breast height was taken as variable of interest. Simple and multiple ordinary least square regressions and artificial neural networks (ANN) were tested to understand the relationships between the various sources of remotely sensed data and the output variable. An experimental design technique, followed by a classification of the input variables and a factor analysis were implemented in order to understand the structure, reduce the dimensionality of the data and avoid the overfitting of the neural network. The results show that there is a significant amount of independent information in the angular dimension, and this information is highly relevant to the estimation of tree densities in the study area. The MISR NDAI indexes improved the performance of the MISR BRF. The non-linear ANN outperformed the linear regressions. The best results were obtained with the ANN after selecting the input variables according to the results of the experimental design, the classification and the factor analysis, with a 0.71 correlation coefficient against the 0.58 of the best linear regression model.