Differentiating forest types using TerraSAR–X spotlight images based on inferential statistics and multivariate analysis

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet


This study investigated the potential of applying statistical analysis tests, for example, two sample Z-test and the Factor Analysis (FA) tool, on the TerraSAR-X backscattering coefficient, for distinguishing between different types of forests and detecting changes in distribution and extent of them. Two sample Z-test is an inferential statistical test that determines whether there is a statistically significant difference between the means in the data from two independent groups. FA is a multivariate analysis that can examine the structure or relationship between variables. Twelve pilot plots for forests of 17 ha were surveyed in a water protection catchment near Hanover, Germany. The forest types were deciduous, coniferous, and mixed. In order to sustain groundwater quality, deciduous trees were planted over a period of several years to gradually replace the coniferous trees in the catchment area. Regular forest observations were required to ensure that the percentages of deciduous and mixed forests in this catchment area were increasing relative to coniferous forests. Fourteen dual-co-polarized TerraSAR-X (HH/VV) images were used to monitor the forests in the period from March 2008 to January 2009. The values of the backscattering coefficient (σ0) for the test plots were statistically analyzed using the two sample Z-test and the Factor Analysis tools. The study showed that Factor analysis tools succeeded in differentiating between the coniferous forest and both the deciduous forest and the mixed forest, but failed to discriminate between the deciduous and the mixed forest. Only one factor was extracted for each sample plot of the coniferous forest with approximately equal loadings during the whole acquisition period from March 2008 to January 2009. However, two factors were extracted for each deciduous or mixed forest sample plot, where one factor had high loadings during the leaf-on period from May to October, and the other one had high loadings during the leaf-off period from November to April. Furthermore, the research revealed that the two sample Z-test differentiated the deciduous and mixed forests from the coniferous forest, and discriminated between deciduous forest and mixed forest. Statistically significant differences were observed between the mean backscatter values of the HH-polarized acquisitions for the deciduous forest and the mixed forest during the leaf-off period from November to April, but no statistically significant difference was found during the leaf-on period from May to October. Moreover, plot samples for the deciduous forest had slightly higher mean backscattering coefficients than those for the mixed forest during the leaf-off period. Applying the Factor Analysis and the two sample Z-test on the backscattering coefficient of multi-temporal TerraSAR-X data facilitates distinction of forest types, tracks changes in forest patterns, and estimates the extent of environmental disasters in forest regions. This accomplishes sustainable forest management, which can play an important role not only in preserving groundwater quality but also in achieving climate change adaptation goals.

ZeitschriftRemote Sensing Applications: Society and Environment
PublikationsstatusErschienen - 01.08.2019