An Empirical Assessment of Temporal Decorrelation Using the Uninhabited Aerial Vehicle Synthetic Aperture Radar over Forested Landscapes

Abstract

We present an empirical assessment of the impact of temporal decorrelation on interferometric coherence measured over a forested landscape. A series of repeat-pass interferometric radar images with a zero spatial baseline were collected with UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar), a fully polarimetric airborne L-band radar system. The dataset provided temporal separations of 45 minutes, 2, 7 and 9 days. Coincident airborne lidar and weather data were collected. We theoretically demonstrate that UAVSAR measurement accuracy enables accurate quantification of temporal decorrelation. Data analysis revealed precipitation events to be the main driver of temporal decorrelation over the acquisition period. The experiment also shows temporal decorrelation increases with canopy height, and this pattern was found consistent across forest types and polarization

Mapping Canopy Height and Growing Stock Volume Using Airborne Lidar, ALOS PALSAR and Landsat ETM+

“…In addition, the multi-annual PALSAR acquisitions allowed for the computation of the interferometric repeat-pass coherence, which describes the temporal stability of scattering between two images and generally decreases with increasing forest density and height. Despite the long repeat intervals of 44/46 days of the major hitherto L-band satellite missions (JERS/ALOS PALSAR) and the hence increased risk of temporal decorrelation substantially diminishing the forest related information in the coherence [31,36,37], spaceborne L-band repeat-pass coherence has shown some potential for the retrieval of forest biophysical parameters, in particular in combination with intensity measurements, when the imaging conditions were suitable [19,31,36,38]. We also considered the use of Landsat optical data, which is available globally and free of cost, as in several studies it was shown that a retrieval of forest biophysical parameters based on the fusion of SAR and optical data yielded higher retrieval accuracies than that based on either SAR or optical data alone [17,32,[39][40][41][42].…”

“…Compared to the intensity observations, the effect of rain on the coherence was not as evident. Rain events have been reported to cause substantial decorrelation that strongly diminishes forest related information in repeat-pass coherence images because of the pronounced associated changes in the dielectric properties of the soils and canopies [36,37]. At test site 2, however, the coherence for the HH image pair from 17 February & 4 April revealed the overall highest coherence amongst the available coherence images although all three weather stations reported continuous rainfall of 2 to 4 cm in the two days prior to the sensor overpass on 17 February.…”

Using InSAR Coherence to Map Stand Age in a Boreal Forest

“…Askne et al [18] assumed wind-induced decorrelation related to canopy height, whereas Castel et al [21] demonstrated the impact of wind on temporal decorrelation was more important in tall, mature forest stands. More recent work with airborne L-band data shows the relationship between temporal decorrelation and land cover [22] as well as canopy height [23,24]. Given these observations and the fact forest structure changes with age, it is conceivable that interferometric coherence could be used to map forest age.…”

“…Our analyses are restricted to Montmorency Forest, a 66 km 2 managed boreal forest with stands ranging between 2-42 years. Given UAVSAR observation parameters and zero nominal baseline, the impacts of γ G , γ Z , and γ N are negligible and decorrelation is mainly due its temporal component γ T [24]. This experiment has thus allowed us to focus on and exploit the impacts of temporal decorrelation on forest age mapping.…”

New perspectives on an iconic landscape from comparative international long‐term ecological research

“…These studies will also generate regional maps of shortand long-term changes in land use to determine the differentiated vulnerability of mangrove forests in the Americas to land use change and disturbance. The proof of concept in the application of such remote sensing techniques began in the FCE mangrove ecotone region (Simard et al 2006, Zhang et al 2008, and now has allowed v www.esajournals.org further expansion in other mangrove dominated coastal regions around the world (Fatoyinbo et al 2008, Simard et al 2012, Lagomasino et al 2015. Notably, these studies integrate social science analytical frameworks with the remote sensing data to go beyond identifying the types or proximate sources of change (e.g., mangrove conversion to shrimp ponds, agriculture, urban uses) to evaluating the causal drivers of that change (e.g., population growth, local/global markets for fisheries or agricultural commodities, policies, land tenure, etc.…”