Vegetation indices play an important role in monitoring variations in vegetation. The Enhanced Vegetation Index (EVI) proposed by the MODIS Land Discipline Group and the Normalized Difference Vegetation Index (NDVI) are both global-based vegetation indices aimed at providing consistent spatial and temporal information regarding global vegetation. However, many environmental factors such as atmospheric conditions and soil background may produce errors in these indices. The topographic effect is another very important factor, especially when the indices are used in areas of rough terrain. In this paper, we theoretically analyzed differences in the topographic effect on the EVI and the NDVI based on a non-Lambertian model and two airborne-based images acquired from a mountainous area covered by high-density Japanese cypress plantation were used as a case study. The results indicate that the soil adjustment factor “L” in the EVI makes it more sensitive to topographic conditions than is the NDVI. Based on these results, we strongly recommend that the topographic effect should be removed in the reflectance data before the EVI was calculated—as well as from other vegetation indices that similarly include a term without a band ratio format (e.g., the PVI and SAVI)—when these indices are used in the area of rough terrain, where the topographic effect on the vegetation indices having only a band ratio format (e.g., the NDVI) can usually be ignored.

As climate change could have considerable influence on hydrology and corresponding water management, appropriate climate change inputs should be used for assessing future impacts.Although the performance of regional climate models (RCMs) has improved over time, systematic model biases still constrain the direct use of RCM output for hydrotogical impact studies. To address this, a distribution-based scaling (DBS) approach was developed that adjusts precipitation and temperature from RCMs to better reflect observations. Statistical properties, such as daily mean, standard deviation, distribution and frequency of precipitation days, were much improved for control periods compared to direct RCM output. DBS adjusted precipitation and temperature from two IPCC Special Report on Emissions Scenarios (SRESAIB) transient climate projections were used as inputs to the HBV hydrological model for several river basins in Sweden for the period 1961-2100. Hydrological results using DBS were compared to results with the widely-used delta change (DC) approach for impact studies. The general signal of a warmer and wetter climate was obtained using both approaches, but use of DBS identified differences between the two projections that were not seen with DC. The DBS approach is thought to better preserve the future variability produced by the RCM, improving usability for climate change impact studies.

Two new remote sensing vegetation parameters derived from spaceborne spectrometers and simulated with a three dimensional radiative transfer model have been evaluated in terms of their prospects and drawbacks for the monitoring of dense vegetation canopies: (i) sun-induced chlorophyll fluorescence (SIF), a unique signal emitted by photosynthetically active vegetation and (ii) the canopy scattering coefficient (CSC), a vegetation parameter derived along with the directional area scattering factor (DASF) and expected to be particularly sensitive to leaf optical properties. Here, we present the first global data set of DASF/CSC and examine the potential of CSC and SIF for providing complementary information on the controversially discussed vegetation seasonality in Amazon forests. A comparison between near-infrared SIF derived from the Global Ozone Monitoring Experiment-2 (GOME-2) instrument and the Orbiting Carbon Observatory-2 (OCO-2) (overpass time in the morning and noon, respectively) reveals the response of SIF to instantaneous photosynthetically active radiation (PAR). Largescale seasonal swings of GOME-2 SIF amount up to 21% (regarding the annual maximum) and peak in October and around February, while OCO-2 SIF peaks in February. However, both time series agree very well if SIF is normalized by overpass time and wavelength. We further examine anistropic reflectance characteristics with the finding that the hot spot effect significantly impacts observed GOME-2 SIF values. On the contrary, our sensitivity analysis suggests that CSC is highly independent of sun-sensor geometry as well as atmospheric effects. The slight annual variability of CSC (3%) shows no clear seaonal cycle, while a relatively high spatial standard deviation points to a high degree of spatial heterogeneity in our study domain within the central Amazon Basin.

No abstract

No abstract