Remote Sens.2015, 7(10), 13410-13435; doi:10.3390/rs71013410 (registering DOI) - published 13 October 2015 Show/Hide Abstract
Abstract: The development of near-surface remote sensing requires the accurate extraction of leaf area index (LAI) from networked digital cameras under all illumination conditions. The widely used directional gap fraction model is more suitable for overcast conditions due to the difficulty to discriminate the shaded foliage from the shadowed parts of images acquired on sunny days. In this study, a new LAI extraction method by the sunlit foliage component from downward-looking digital photography under clear-sky conditions is proposed. In this method, the sunlit foliage component was extracted by an automated image classification algorithm named LAB2, the clumping index was estimated by a path length distribution-based method, the LAD and G function were quantified by leveled digital images and, eventually, the LAI was obtained by introducing a geometric-optical (GO) model which can quantify the sunlit foliage proportion. The proposed method was evaluated at the YJP site, Canada, by the 3D realistic structural scene constructed based on the field measurements. Results suggest that the LAB2 algorithm makes it possible for the automated image processing and the accurate sunlit foliage extraction with the minimum overall accuracy of 91.4%. The widely-used finite-length method tends to underestimate the clumping index, while the path length distribution-based method can reduce the relative error (RE) from 7.8% to 6.6%. Using the directional gap fraction model under sunny conditions can lead to an underestimation of LAI by (1.61; 55.9%), which was significantly outside the accuracy requirement (0.5; 20%) by the Global Climate Observation System (GCOS). The proposed LAI extraction method has an RMSE of 0.35 and an RE of 11.4% under sunny conditions, which can meet the accuracy requirement of the GCOS. This method relaxes the required diffuse illumination conditions for the digital photography, and can be applied to extract LAI from downward-looking webcam images, which is expected for the regional to continental scale monitoring of vegetation dynamics and validation of satellite remote sensing products.
Remote Sens.2015, 7(10), 13390-13409; doi:10.3390/rs71013390 (registering DOI) - published 12 October 2015 Show/Hide Abstract
Abstract: A data-driven method for describing the benthic cover type based on full-waveform bathymetric LiDAR data analysis is presented. The waveform of the bathymetric LiDAR return pulse is first modeled as a sum of three functions: a Gaussian pulse representing the surface return, a function modeling the backscatter and another Gaussian pulse modeling the return from the bottom surface. Two sets of variables are formed: one containing features describing the bottom return and the other describing various conditions, such as water quality and the depth of the seabed. Regression analysis is used to eliminate the effect of the condition variables on the features, after which the features are mapped onto a cell lattice using a self-organizing map (SOM). The cells of the SOM are grouped into seven clusters using the neighborhood distance matrix method. The clustering result is evaluated using the seabed substrate map based on sonar measurements, as well as delineation of photic zones in the study area. High correspondence between the clusters and the substrate type/photic zone has been obtained indicating that the proposed clustering method adequately describes the benthic cover in the study area. The bottom return pulse waveforms corresponding to the clusters and a cluster map of the study area are also presented. The method can be used for clustering full waveform bathymetric LiDAR data acquired from large areas to discover the structure of benthic cover types and to focus the field studies accordingly.
Remote Sens.2015, 7(10), 13367-13389; doi:10.3390/rs71013367 (registering DOI) - published 12 October 2015 Show/Hide Abstract
Abstract: Geosynchronous synthetic aperture radar (GEO SAR) will move in a high orbit of ~36,000 km with a long integration time of hundreds of seconds. It is obviously impacted by orbital perturbations and the Earth’s rotation, which can give rise to un-parallel repeated tracks and induce a squint-looking angle in the repeat-track SAR interferometry (InSAR). Thus, the traditional data acquisition method using in the zero-Doppler centroid (ZDC) configuration to generate the GEO InSAR pair will bring about the obvious rotation-induced decorrelation. Moreover, the conventional height retrieval model with the broadside mode imaging geometry and the approximate expression of the interferometric baseline will induce large height and localization errors in the GEO InSAR processing. In this paper, a novel data acquisition method is firstly presented based on a criterion of optimal minimal rotational-induced decorrelation (OMRD). It can significantly improve the coherence of the InSAR pair. Then, considering the localization equations in the squint-looking mode and the accurate expression of the interferometric baseline, a modified GEO InSAR height retrieval model is proposed to mitigate the height and localization errors induced by the conventional model. Finally, computer simulations are carried out for the verification of the proposed methods. In a typical inclined GEO InSAR configuration, the averaged total correlation coefficient increases more than 0.4, and height errors of hundreds of meters and localization errors of more than 10 degrees are removed.
Remote Sens.2015, 7(10), 13364-13366; doi:10.3390/rs71013364 (registering DOI) - published 12 October 2015 Show/Hide Abstract
Abstract: Studies of phytoplankton ecology in Monterey Bay, CA, USA, using the Hyperspectral Imager for the Coastal Ocean (HICO) and other satellite remote sensing and in-situ observations, were presented in . [...]
Remote Sens.2015, 7(10), 13337-13363; doi:10.3390/rs71013337 (registering DOI) - published 12 October 2015 Show/Hide Abstract
Abstract: Photogrammetric documentation can provide a sound database for the needs of architectural heritage preservation. However, the major part of photogrammetric documentation production is not used for subsequent architectural heritage projects, due to lack of knowledge of photogrammetric documentation accuracy. In addition, there are only a few studies with rigorous analysis of the requirements for photogrammetric documentation of architectural heritage. In particular, requirements focusing on the geometry of the models generated by fully digital photogrammetric processes are missing. Considering these needs, this paper presents a procedure for architectural heritage documentation with photogrammetric techniques based on a previous review of existing standards of architectural heritage documentation. The data product specification proposed was elaborated conforming to ISO 19131 recommendations. We present the procedure with two case studies in the context of Brazilian architectural heritage documentation. Quality analysis of the produced models were performed considering ISO 19157elements, such as positional accuracy, logical consistency and completeness, meeting the requirements. Our results confirm that the proposed requirements for photogrammetric documentation are viable.
Remote Sens.2015, 7(10), 13319-13336; doi:10.3390/rs71013319 (registering DOI) - published 12 October 2015 Show/Hide Abstract
Abstract: Environmental heterogeneity in space and time plays a key role in influencing trait variability in animals, and can be particularly relevant to animal phenology. Until recently, the use of remotely sensed imagery in understanding animal variation was limited to analyses at the population level, largely because of a lack of high-resolution data that would allow inference at the individual level. We evaluated the potential of SPOT 4 (Take 5) satellite imagery data (with observations every fifth day at 20 m resolution and equivalent to acquisition parameters of Sentinel-2) in animal ecology research. We focused on blue tit Cyanistes caeruleus reproduction in a study site containing 227 nestboxes scattered in a Mediterranean forest dominated by deciduous downy oaks Quercus pubescens with a secondary cover of evergreen holm oaks Quercus ilex. We observed high congruence between ground data collected in a 50 m radius around each nestbox and NDVI values averaged across a 5 by 5 pixel grid centered around each nestbox of the study site. The number of deciduous and evergreen oaks around nestboxes explained up to 66% of variance in nestbox-centered, SPOT-derived NDVI values. We also found highly equivalent patterns of spatial autocorrelation for both ground- and satellite-derived indexes of environmental heterogeneity. For deciduous and evergreen oaks, the derived NDVI signal was highly distinctive in winter and early spring. June NDVI values for deciduous and evergreen oaks were higher by 58% and 8% relative to February values, respectively. The number of evergreen oaks was positively associated with later timing of breeding in blue tits. SPOT-derived, Sentinel-2 like imagery thus provided highly reliable, ground-validated information on habitat heterogeneity of direct relevance to a long-term field study of a free-living passerine bird. Given that the logistical demands of gathering ground data often limit our understanding of variation in animal reproductive traits across time and space, there appears to be great promise in applying fine-resolution satellite data in evolutionary ecology research.