Continued Monitoring and Modeling of Xingfeng Solid Waste Landfill Settlement, China, Based on Multiplatform SAR Images

Continued settlement monitoring and modeling of landfills are critical for land redevelopment and safety assurance. This paper adopts a MTInSAR technique for time-series monitoring of the Xingfeng landfill (XFL) settlement. A major challenge is that the frequent and significant settlement in the initial stage after the closure of landfills can affect the coherence of interferograms, thus hindering the monitoring of settlement by MTInSAR. We analyzed the factors that can directly affect the temporal decorrelation of landfills and adopted a 3D phase unwrapping approach to correct the phase unwrapping errors caused by such deformation gradient. SAR images from four platforms, including 50 Sentinel-1A, 12 Radarsat-2, 4 ALOS-2, and 2 TerraSAR-X/TanDEM-X images, are collected to measure the settlement and thickness of the landfill. The settlement accuracy is evaluated by a cross-evaluation between Radarsat-2 and Sentinel-1A that have similar temporal coverages. We analyzed the spatial characteristics of settlement and the relationship between the settlement and thickness. Further, we modeled the future settlement of the XFL with a hyperbolic function model. The results showed that the coherence in the initial stage after closure of the XFL is primarily affected by temporal decorrelation caused by considerable deformation gradient compared with spatial decorrelation. Settlement occurs primarily in the forward slope of the XFL, and the maximum line-of-sight (LOS) settlement rate reached 0.808 m/year from August 2018 to May 2020. The correlation between the settlement and thickness is 0.62, indicating an obvious relationship between the two. In addition, the settlement of younger areas is usually greater than that of older areas.

A Novel Four-Stage Method for Vegetation Height Estimation with Repeat-Pass PolInSAR Data via Temporal Decorrelation Adaptive Estimation and Distance Transformation

Vegetation height estimation plays a pivotal role in forest mapping, which significantly promotes the study of environment and climate. This paper develops a general forest structure model for vegetation height estimation using polarimetric interferometric synthetic aperture radar (PolInSAR) data. In simple terms, the temporal decorrelation factor of the random volume over ground model with volumetric temporal decorrelation (RVoG-vtd) is first modeled by random motions of forest scatterers to solve the problem of ambiguity. Then, a novel four-stage algorithm is proposed to improve accuracy in forest height estimation. In particular, to compensate for the temporal decorrelation mainly caused by changes between multiple observations, one procedure of temporal decorrelation adaptive estimation via Expectation-Maximum (EM) algorithm is added into the novel method. On the other hand, to extract the features of amplitude and phase more effectively, in the proposed method, we also convert Euclidean distance to a generalized distance for the first time. Assessments of different algorithms are given based on the repeat-pass PolInSAR data of Gabon Lope Park acquired in AfriSAR campaign of German Aerospace Center (DLR). The experimental results show that the proposed method presents a significant improvement of vegetation height estimation accuracy with a root mean square error (RMSE) of 6.23 m and a bias of 1.28 m against LiDAR heights, compared to the results of the three-stage method (RMSE: 8.69 m, bias: 4.81 m) and the previous four-stage method (RMSE: 7.72 m, bias: −2.87 m).

Monthly Deforestation Monitoring with Sentinel-1 Multi-temporal Signatures and InSAR Coherences

<p class="p1">Sentinel-1 interferometric time-series allow for the accurate retrieval of the target’s temporal decorrelation and, therefore, the inversion of land cover information and its temporal monitoring. This paper describes the development of an observation scenario for monitoring monthly deforestation over the Amazon rainforest, which relies on the use of radar for overcoming the physical limitations of optical sensors caused by the presence of cloud coverage. Specifically, we implement a classification scheme that exploits multi-temporal SAR features, such as backscatter, spatial textures, and interferometric parameters, to map forested areas. Distinct forest maps are generated for consecutive months and further processed to detect deforestation phenomena and map clear-cuts evolution. The obtained results are validated by selecting cloud-free Sentinel-2 multispectral data on the selected area and acquired during the same observation time.</p>

Vegetated Target Decorrelation in SAR and Interferometry: Models, Simulation, and Performance Evaluation

The paper addresses the temporal stability of distributed targets, particularly referring to vegetation, to evaluate the degradation affecting synthetic aperture radar (SAR) imaging and repeat-pass interferometry, and provide efficient SAR simulation schemes for generating big dataset from wide areas. The models that are mostly adopted in literature are critically reviewed, and aim to study decorrelation in a range of time (from hours to days), of interest for long-term SAR, such as ground-based or geosynchronous, or repeat-pass SAR interferometry. It is shown that none of them explicitly account for a decorrelation occurring in the short-term. An explanation is provided, and a novel temporal decorrelation model is proposed to account for that fast decorrelation. A formal method is developed to evaluate the performance of SAR focusing, and interferometry on a homogenous, stationary scene, in terms of Signal-to-Clutter Ratio (SCR), and interferometric coherence. Finally, an efficient implementation of an SAR simulator capable of handling the realistic case of heterogeneous decorrelation over a wide area is discussed. Examples are given by assuming two geostationary SAR missions in C and X band.