Monitoring high latitude wetlands is required to understand feedbacks between terrestrial carbon pools and climate change. Hydrological variability is a key factor driving biogeochemical processes in these ecosystems and effective assessment tools are critical for accurate characterization of surface hydrology, soil moisture, and water table fluctuations. Operational satellite platforms provide opportunities to systematically monitor hydrological variability in high latitude wetlands. The objective of this research application was to integrate high temporal frequency Synthetic Aperture Radar (SAR) and high spatial resolution Light Detection and Ranging (LiDAR) observations to assess hydroperiod at a mire in northern Sweden. Geostatistical and polarimetric (PLR) techniques were applied to determine spatial structure of the wetland and imagery at respective scales (0.5 m to 25 m). Variogram, spatial regression, and decomposition approaches characterized the sensitivity of the two platforms (SAR and LiDAR) to wetland hydrogeomorphology, scattering mechanisms, and data interrelationships. A Classification and Regression Tree (CART), based on random forest, fused multi-mode (fine-beam single, dual, quad pol) Phased Array L-band Synthetic Aperture Radar (PALSAR) and LiDAR-derived elevation to effectively map hydroperiod attributes at the Swedish mire across an aggregated warm season (May-September, 2006-2010). Image derived estimates of water and peat moisture were sensitive (R-2 = 0.86) to field measurements of water table depth (cm). Peat areas that are underlain by permafrost were observed as areas with fluctuating soil moisture and water table changes.

We propose an algorithm to estimate surface roughness and moisture level of active layer of permafrost over permafrost area. This algorithm is based on the Oh's semi-empirical model, and PALSAR data observed both in winter and summer seasons with vh polarization. PALSAR vh polarization data observed in winter is used to estimate surface roughness of permafrost. Then, the estimated surface roughness and PALSAR vh polarization data observed in summer is used to estimate the moisture level of the active layer of the permafrost. The moisture levels estimated from PALSAR data moderately matched with those of validation data taken in the field, while the surface roughness value shows some difference. The possible cause of this difference is that the surface roughness derived from the field data collection represents the roughness of the top of the sphagnum moss layer covered on the active layer of the permafrost, while the one estimated from PALSAR represents the roughness of the underlying active layer of the permafrost.

随着青藏铁路的开通,对青藏高原地区进行形变监测显得尤为重要,由于ALOS卫星PALSAR传感器采用的是L波段,相干性得到明显改善,适合于地形较复杂区域和冻土地区监测。本文选择了青藏高原地区部分时间段的PALSAR数据,采用外部DEM,运用GAMMA软件进行二轨法差分干涉测量,获得该地区的形变图。结合实地情况进行定性分析,其形变较好的符合冻土的物理变化规律,证明了PALSAR数据在青藏高原冻土地区运用于形变监测具有良好的前景。

季节性冻胀和融沉导致的地面形变是青藏高原冻土区建设施工与维护的主要问题。对冻融造成的形变进行有效监测是青藏铁路建设与维护的前提。差分干涉测量技术是地表形变监测的重要手段之一,PALSAR(L波段的合成孔径雷达)数据在非城市区域具有较高的相关性,适合青藏高原冻土区的地表形变监测。本文选用4景覆盖研究区域的PALSAR数据,研究利用该数据进行冻土形变检测的方法,并对其检测结果进行了分析。结果表明,该方法与水准测量方法有较好的一致性。