Remote Sens.2015, 7(4), 3760-3782; doi:10.3390/rs70403760 (registering DOI) - published 27 March 2015 Show/Hide Abstract
Abstract: Long-term global datasets of the Leaf Area Index (LAI) are important for monitoring global vegetation dynamics and are an important input for Earth system models (ESM). The comparison of long-term datasets is based on two recently available datasets both derived from AVHRR (Advanced Very High Resolution Radiometer) time series. The LAI3g dataset is developed from the new improved third generation Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) from AVHRR sensors and best-quality MODIS LAI data. The second long-term LAI dataset is based on the 8-km spatial resolution GIMMS-AVHRR data (Goettingen GIS & Remote Sensing, GGRS dataset). The GGRS-LAI product uses a satellite-based LAI. This algorithm uses a three-dimensional physical radiative transfer model, which establishes the relationship between LAI, vegetation fractional cover and given patterns of surface reflectance, view-illumination conditions and optical properties of vegetation. The model incorporates a number of site-/region-specific parameters, including the vegetation architecture variables, such as leaf angle distribution, clumping index and light extinction coefficient. For the application of the model to Kazakhstan, the vegetation architecture variables were computed at the local (pixel) level based on extensive field surveys of the biophysical properties of vegetation in representative grassland areas of Kazakhstan. As a main result of our study, we could summarize that the differences between both products are most pronounced at the start and the end of the growing season. During the spring and autumn months, the LAI difference maps showed a considerable difference of LAI GGRS and LAI3g. LAI3g is characterized by a considerably earlier start and a later finish to the growing season than LAI GGRS. Moreover, LAI3g showed LAI > 0 during the winter months when any green vegetation is absent in all land covers of Kazakhstan. A direct cause for this could be a too high base level of the LAI3g during the leafless phase.
Remote Sens.2015, 7(4), 3735-3759; doi:10.3390/rs70403735 (registering DOI) - published 27 March 2015 Show/Hide Abstract
Abstract: Active layer thickness (ALT) is a critical parameter for monitoring the status of permafrost that is typically measured at specific locations using probing, in situ temperature sensors, or other ground-based observations. Here we evaluated the Remotely Sensed Active Layer Thickness (ReSALT) product that uses the Interferometric Synthetic Aperture Radar technique to measure seasonal surface subsidence and infer ALT around Barrow, Alaska. We compared ReSALT with ground-based ALT obtained using probing and calibrated, 500 MHz Ground Penetrating Radar at multiple sites around Barrow. ReSALT accurately reproduced observed ALT within uncertainty of the GPR and probing data in ~76% of the study area. However, ReSALT was less than observed ALT in ~22% of the study area with well-drained soils and in ~1% of the area where soils contained gravel. ReSALT was greater than observed ALT in some drained thermokarst lake basins representing ~1% of the area. These results indicate remote sensing techniques based on InSAR could be an effective way to measure and monitor ALT over large areas on the Arctic coastal plain.
Remote Sens.2015, 7(4), 3710-3734; doi:10.3390/rs70403710 (registering DOI) - published 27 March 2015 Show/Hide Abstract
Abstract: On intertidal mudflats, reef-building shellfish, like the Pacific oyster and the blue mussel, provide a myriad of ecosystem services. Monitoring intertidal shellfish with high spatiotemporal resolution is important for fisheries, coastal management and ecosystem studies. Here, we explore the potential of X- (TerraSAR-X) and C-band (Radarsat-2) dual-polarized SAR data to map shellfish densities, species and coverage. We investigated two backscatter models (the integral equation model (IEM) and Oh’s model) for inversion possibilities. Surface roughness (vertical roughness RMSz and correlation length L) was measured of bare sediments and shellfish beds, which was then linked to shellfish density, presence and species. Oysters, mussels and bare sediments differed in RMSz, but because the backscatter saturates at relatively low RMSz values, it was not possible to retrieve shellfish density or species composition from X- and C-band SAR. Using a classification based on univariate and multivariate logistic regression of the field and SAR image data, we constructed maps of shellfish presence (Kappa statistics for calibration 0.56–0.74 for dual-polarized SAR), which were compared with independent field surveys of the contours of the beds (Kappa statistics of agreement 0.29–0.53 when using dual-polarized SAR). We conclude that spaceborne SAR allows one to monitor the contours of shellfish-beds (thus, distinguishing shellfish substrates from bare sediment and dispersed single shellfish), but not densities and species. Although spaceborne SAR cannot replace ground surveys entirely, it could very well offer a significant improvement in efficiency.
Remote Sens.2015, 7(4), 3690-3709; doi:10.3390/rs70403690 (registering DOI) - published 27 March 2015 Show/Hide Abstract
Abstract: Most land surface models require information on aerodynamic roughness length and its temporal and spatial variability. This research presents a practical approach for determining the aerodynamic roughness length at fine temporal and spatial resolution over the landscape by combining remote sensing and ground measurements. The basic framework of Raupach, with the bulk surface parameters redefined by Jasinski et al., has been applied to optical remote sensing data collected by the HJ-1A/1B satellites. In addition, a method for estimating vegetation height was introduced to derive the aerodynamic roughness length, which is preferred by users over the height-normalized form. Finally, mapping different vegetation classes was validated taking advantage of the data-dense field experiments conducted in the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project. Overall, the roughness model performed well against the measurements collected at most HiWATER flux tower sites. However, deviations still occurred at some sites, which have been further analyzed.
Remote Sens.2015, 7(4), 3670-3689; doi:10.3390/rs70403670 (registering DOI) - published 27 March 2015 Show/Hide Abstract
Abstract: Urban heat islands (UHIs) created through urbanization can have negative impacts on the lives of people living in cities. They may also vary spatially and temporally over a city. There is, thus, a need for greater understanding of these patterns and their causes. While previous UHI studies focused on only a few cities and/or several explanatory variables, this research provides a comprehensive and comparative characterization of the diurnal and seasonal variation in surface UHI intensities (SUHIIs) across 67 major Chinese cities. The factors associated with the SUHII were assessed by considering a variety of related social, economic and natural factors using a regression tree model. Obvious seasonal variation was observed for the daytime SUHII, and the diurnal variation in SUHII varied seasonally across China. Interestingly, the SUHII varied significantly in character between northern and southern China. Southern China experienced more intense daytime SUHIIs, while the opposite was true for nighttime SUHIIs. Vegetation had the greatest effect in the day time in northern China. In southern China, annual electricity consumption and the number of public buses were found to be important. These results have important theoretical significance and may be of use to mitigate UHI effects.
Remote Sens.2015, 7(4), 3651-3669; doi:10.3390/rs70403651 (registering DOI) - published 27 March 2015 Show/Hide Abstract
Abstract: An accurate estimation of carbon fluxes is very important in carbon cycle studies. A remote sensing based gross primary production (GPP) and net ecosystem production (NEP) algorithm, RS-CFLUX, was presented in this work. The algorithm was calibrated with Markov Chain Monte Carlo (MCMC) method at Daman superstation and Zhangye wetland station in the midstream of the Heihe River Basin. Results indicated that both of the stations present high GPP (1442.04 g C/m2/year at Daman superstation and 928.89 g C/m2/year at Zhangye wetland station) and NEP (409.38 g C/m2/year at Daman superstation and 422.60 g C/m2/year at Zhangye wetland station). The RS-CFLUX model can correctly simulate the seasonal dynamics and quantities of carbon fluxes at both stations, using photosynthetically active radiation (PAR), land surface temperature (LST), normalized difference water index (NDWI) and enhanced vegetation index (EVI) as input. RS-CFLUX model were sensitive to maximum light use efficiency, respiration at reference temperature, activation energy parameter of respiration.