Analyses of Time Series InSAR Signatures for Land Cover Classification: Case Studies over Dense Forestry Areas with L-Band SAR Images

Abstract

As demonstrated in prior studies, InSAR holds great potential for land cover classification, especially considering its wide coverage and transparency to climatic conditions. In addition to features such as backscattering coefficient and phase coherence, the temporal migration in InSAR signatures provides information that is capable of discriminating types of land cover in target area. The exploitation of InSAR signatures was expected to provide merits to trace land cover change in extensive areas; however, the extraction of suitable features from InSAR signatures was a challenging task. Combining time series amplitudes and phase coherences through linear and nonlinear compressions, we showed that the InSAR signatures could be extracted and transformed into reliable classification features for interpreting land cover types. The prototype was tested in mountainous areas that were covered with a dense vegetation canopy. It was demonstrated that InSAR time series signature analyses reliably identified land cover types and also recognized tracing of temporal land cover change. Based on the robustness of the developed scheme against the temporal noise components and the availability of advanced spatial and temporal resolution SAR data, classification of finer land cover types and identification of stable scatterers for InSAR time series techniques can be expected. The advanced spatial and temporal resolution of future SAR assets combining the scheme in this study can be applicable for various important applications including global land cover changes monitoring.

Differential SAR Interferometry Using Sentinel-1 Imagery-Limitations in Monitoring Fast Moving Landslides: The Case Study of Cyprus

“…A comparison of pre-event and co-event coherence maps would need to be conducted to study the coherence loss and differentiate between coherence loss due to the landslides and due to the temporal baseline of SAR images. Coherent Change Detection has been widely used for monitoring land use/land cover changes [62][63][64]; agricultural studies [65][66][67]; forestry applications [68][69][70]; and lately earthquake impact assessment [71][72][73][74]. In fact, the Coherent Change Detection methodology has been applied successfully for the detection of the same landslides, determining accurately the extents of the areas affected by the landslides [75].…”

Reconstructed Aeolian Surface Erosion in Southern Mongolia by Multi-Temporal InSAR Phase Coherence Analyses

“…Therefore, construction of a phase coherence loss model to simulate surface change is extremely difficult owing to the prerequisites of determination of decay constants in accordance with the target landcover types and imaging conditions. For instance, vegetation usually causes random decreases in the phase coherence in the presence of biomass and the interactions with wind and moisture (Yun et al, 2019). Further, migration on base topography also decreases the phase coherence, however the aforementioned interactions with vegetation produces additional effects on the measured coherence.…”

“…First, the advantageous time series analysis, specifically that with the principle component analysis (PCA) of phase coherences, was exploited as the outputs of PCA analyses were expected to remove temporal outliers such as the responses of forest cover to the temporal wind and moisture variations and the remnants of thermal/spatial coherence components. In Yun et al (2019), PCA analysis was successfully applied to suppress random and climatic factorinduced fluctuations and maintain only contributions by the topographic characteristics on phase coherence. Thus, a similar approach to only preserve the surface change component of phase coherence induced by the eolian erosion was employed in this study.…”

Performance Analysis of Open Source Time Series InSAR Methods for Deformation Monitoring over a Broader Mining Region

“…The land cover classes in the area can be categorized into three main classes according to Corine level-1 land cover classes [40]: artificial surfaces, agricultural areas and forest/semi-natural areas. Interferometric coherence is different for each land cover class and is exploited in a lot of land cover classification studies [41,42]. The different backscattering properties of each land cover can affect the density of the TSInSAR measurements [43] as well as their expected accuracies.…”