Remote Sens.2014, 6(4), 3409-3426; doi:10.3390/rs6043409 (doi registration under processing) - published online 17 April 2014 Show/Hide Abstract
Abstract: Frame hyperspectral sensors, in contrast to push-broom or line-scanning ones, produce hyperspectral datasets with, in general, better geometry but with unregistered spectral bands. Being acquired at different instances and due to platform motion and movements (UAVs, aircrafts, etc.), every spectral band is displaced and acquired with a different geometry. The automatic and accurate registration of hyperspectral datasets from frame sensors remains a challenge. Powerful local feature descriptors when computed over the spectrum fail to extract enough correspondences and successfully complete the registration procedure. To this end, we propose a generic and automated framework which decomposes the problem and enables the efficient computation of a sufficient amount of accurate correspondences over the given spectrum, without using any ancillary data (e.g., from GPS/IMU). First, the spectral bands are divided in spectral groups according to their wavelength. The spectral borders of each group are not strict and their formulation allows certain overlaps. The spectral variance and proximity determine the applicability of every spectral band to act as a reference during the registration procedure. The proposed decomposition allows the descriptor and the robust estimation process to deliver numerous inliers. The search space of possible solutions has been effectively narrowed by sorting and selecting the optimal spectral bands which under an unsupervised manner can quickly recover hypercube’s geometry. The developed approach has been qualitatively and quantitatively evaluated with six different datasets obtained by frame sensors onboard aerial platforms and UAVs. Experimental results appear promising.
Remote Sens.2014, 6(4), 3387-3408; doi:10.3390/rs6043387 (doi registration under processing) - published online 17 April 2014 Show/Hide Abstract
Abstract: Accurate mapping of land cover on a regional scale is useful for climate and environmental modeling. In this study, we present a novel land cover classification product based on spectral and phenological information for the Xinjiang Uygur Autonomous Region (XUAR) in China. The product is derived at a 500 m spatial resolution using an innovative approach employing moderate resolution imaging spectroradiometer (MODIS) surface reflectance and the enhanced vegetation index (EVI) time series. The classification results capture regional scale land cover patterns and small-scale phenomena. By applying a regionally specified classification scheme, an extensive collection of training data, and regionally tuned data processing, the quality and consistency of the phenological maps are significantly improved. With the ability to provide an updated land cover product considering the heterogenic environmental and climatic conditions, the novel land cover map is valuable for research related to environmental change in this region.
Remote Sens.2014, 6(4), 3369-3386; doi:10.3390/rs6043369 - published online 16 April 2014 Show/Hide Abstract
Abstract: Describing and quantifying the spatial heterogeneity of land cover in urban systems is crucial for developing an ecological understanding of cities. This paper presents a new approach to quantifying the fine-scale heterogeneity in urban landscapes that capitalizes on the strengths of two commonly used approaches—visual interpretation and object-based image analysis. This new approach integrates the ability of humans to detect pattern with an object-based image analysis that accurately and efficiently quantifies the components that give rise to that pattern. Patches that contain a mix of built and natural land cover features were first delineated through visual interpretation. These patches served as pre-defined boundaries for finer-scale segmentation and classification of within-patch land cover features which were classified using object-based image analysis. Patches were then classified based on the within-patch proportion cover of features. We applied this approach to the Gwynns Falls watershed in Baltimore, Maryland, USA. The object-based classification approach proved to be effective for classifying within-patch land cover features. The overall accuracy of the classification maps of 1999 and 2004 were 92.3% and 93.7%, respectively. This exercise demonstrates that by integrating visual interpretation with object-based classification, the fine-scale spatial heterogeneity in urban landscapes and land cover change can be described and quantified in a more efficient and ecologically meaningful way than either purely automated or visual methods alone. This new approach provides a tool that allows us to quantify the structure of the urban landscape including both built and non-built components that will better accommodate ecological research linking system structure to ecological processes.
Remote Sens.2014, 6(4), 3349-3368; doi:10.3390/rs6043349 - published online 15 April 2014 Show/Hide Abstract
Abstract: Point-like targets are useful in providing surface deformation with the time series of synthetic aperture radar (SAR) images using the multi-temporal interferometric synthetic aperture radar (MTInSAR) methodology. However, the spatial density of point-like targets is low, especially in non-urban areas. In this paper, a hierarchical MTInSAR method is proposed to increase the spatial density of deformation measurements by tracking both the point-like targets and the distributed targets with the temporal steadiness of radar backscattering. To efficiently reduce error propagation, the deformation rates on point-like targets with lower amplitude dispersion index values are first estimated using a least squared estimator and a region growing method. Afterwards, the distributed targets are identified using the amplitude dispersion index and a Pearson correlation coefficient through a multi-level processing strategy. Meanwhile, the deformation rates on distributed targets are estimated during the multi-level processing. The proposed MTInSAR method has been tested for subsidence detection over a suburban area located in Tianjin, China using 40 high-resolution TerraSAR-X images acquired between 2009 and 2010, and validated using the ground-based leveling measurements. The experiment results indicate that the spatial density of deformation measurements can be increased by about 250% and that subsidence accuracy can reach to the millimeter level by using the hierarchical MTInSAR method.
Remote Sens.2014, 6(4), 3321-3348; doi:10.3390/rs6043321 - published online 14 April 2014 Show/Hide Abstract
Abstract: How well parameterization will improve gross primary production (GPP) estimation using the MODerate-resolution Imaging Spectroradiometer (MODIS) algorithm has been rarely investigated. We adjusted the parameters in the algorithm for 21 selected eddy-covariance flux towers which represented nine typical plant functional types (PFTs). We then compared these estimates of the MOD17A2 product, by the MODIS algorithm with default parameters in the Biome Property Look-Up Table, and by a two-leaf Farquhar model. The results indicate that optimizing the maximum light use efficiency (εmax) in the algorithm would improve GPP estimation, especially for deciduous vegetation, though it could not compensate the underestimation during summer caused by the one-leaf upscaling strategy. Adding the soil water factor to the algorithm would not significantly affect performance, but it could make the adjusted εmax more robust for sites with the same PFT and among different PFTs. Even with adjusted parameters, both one-leaf and two-leaf models would not capture seasonally photosynthetic dynamics, thereby we suggest that further improvement in GPP estimaiton is required by taking into consideration seasonal variations of the key parameters and variables.
Remote Sens.2014, 6(4), 3302-3320; doi:10.3390/rs6043302 - published online 11 April 2014 Show/Hide Abstract
Abstract: Airborne LiDAR has been traditionally used for power line cruising. Nevertheless, data acquisition with airborne LiDAR is constrained by the complex environments in urban areas as well as the multiple parallel line structures on the same power line tower, which means it is not directly applicable to the extraction of urban power lines. Vehicle-borne LiDAR system has its advantages upon airborne LiDAR and this paper tries to utilize vehicle-borne LiDAR data for the extraction of urban power lines. First, power line points are extracted using a voxel-based hierarchical method in which geometric features of each voxel are calculated. Then, a bottom-up method for filtering the power lines belonging to each power line is proposed. The initial clustering and clustering recovery procedures are conducted iteratively to identify each power line. The final experiment demonstrates the high precision of this technique.