To sustain a stable food supply without losses from various hazards, such as insects, diseases, or abnormal climate conditions induced by global change, application of genetically modified (GM) organisms is one potential tool for agriculture. However, GM crops do not exist in traditional agricultural environments; thus, the risks resulting from the application of GM crops to agriculture must be made clear. Crops, such as rice belonging to the Gramineae family, can be cross-pollinated by wind. This enables non-GM crops to be pollinated by GM crops. Prior to the cultivation of GM crops, the degree of cross-pollination must be estimated. This should take into consideration meteorological conditions, because of their importance in pollen flow. We constructed a system model to calculate the cross-pollination distribution by using data on the geographical distribution of GM donor and non-GM recipient crop fields, meteorological elements, and flowering data. The system consists of a main program to calculate cross-pollination rates, a program to include the isolation distance, and a sub-program to average the maps of the cross-pollination rates in recipient fields. The system can predict the regional cross-pollination rate by incorporating the isolation distance and differences in the flowering period. The system was applied to three areas around Tsukuba City, Ibaraki Prefecture, with different distributions of paddy fields and hypothetical donor (ratio 30%) and recipient (ratio 70%) fields. The general trend was that the cross-pollination rates were lower in areas of clustered donor fields. The calculated cross-pollination rate can mostly be explained by the length of the border between the donor and recipient fields. This is because rice pollen is dispersed only within short distances from donor fields. In order to clarify the influence of meteorological conditions on the variation in the cross-pollination rate, 10 years of simulations were performed. The cross-pollination rate varied by about a factor of three (0.03-0.09% for one of the simulated fields) during the simulated 10 years.
Numerous studies have used the satellite-derived Normalized Difference Vegetation Index (NDVI) to estimate the phenology of vegetation cover. However, little is known about the effect of species difference on the susceptibility of NDVI-based estimation approaches, such as the threshold approach and the abrupt variation approach, for estimating the phenology of forest trees. In this study, to clarify the utility of NDVI in cool temperate deciduous forests, which consist of many tree species, we investigated the effect of the species difference on the estimation accuracy of two traditional approaches at the scale of the individual tree. We observed a canopy NDVI of 6 tree species by using a high resolution spectral camera, and compared the NDVI-based estimate of the phenological stages (green-up, green peak, senescence and leaf fall) and the ground truth data on the basis of foliar chlorophyll content. In the threshold approach, the optimal threshold value of NDVI was higher in the autumn leaf fall than the spring green-up. Species difference did not strongly affect the threshold of the green-up, but the threshold of the leaf fall was higher in tree species which flower in summer. The mean estimation error of the leafy period was +1.3 days in this approach when the simple threshold value was used for all species. In the abrupt variance approach the estimation error was larger and the leafy period was over estimated (mean: +26.1 days). The degree of overestimation in the leaf fall tended to be larger in species that flower and have a late abscission. These results suggest that the threshold approach is a better method than the abrupt variation approach if the optimal threshold value can be calculated by using a ground truth data set. Furthermore, species specific leaf senescence type and the existence of flowering affect the accuracy of NDVI-based estimates, indicating that we should confirm the composition of tree species when evaluating the NDVI-based phenology data of cool temperate deciduous forests.
Remotely sensed vegetation indices such as the normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI) have been used to scale up flux-based gross primary production (GPP) measurements. Recently, the use of visible-band (VIS) indices for estimation of GPP has been proposed, and VIS_indices derived from digital cameras have been used for detecting phenological changes. To confirm the utility of remotely sensed VIS_indices for the evaluation of GPP in a Japanese larch forest, we investigated the relationships between flux-based GPP measurements and indices derived from both moderate resolution imaging spectroradiometer (MODIS) data and tower-mounted digital camera images. We evaluated the suitability of both traditional (NDVI and EVI) and VIS_indices (the greenred vegetation index (GRVI) and green ratio (GR)) at both satellite and near-surface scales for GPP estimation. We also used the MODIS data to evaluate the sensitivity of the indices to the effects of a severe forest disturbance. The results showed that VIS_indices had several advantages over the traditional indices: (1) seasonal variations in VIS_indices were more strongly correlated with GPP variations; (2) the vegetation growing season could be easily discriminated from the winter dormant period, because ground surface conditions affect VIS_indices less than they affect traditional indices; (3) the seasonal dynamics of vegetation could be determined at a satellite scale from MODIS data, and possibly even at a canopy scale from digital camera images; and (4) inter-annual variations of VIS_indices were likely to be more sensitive to vegetation changes after a disturbance. These results demonstrate the utility of VIS_indices for estimating GPP at satellite scales and possibly at the canopy scale. We suggest that multi-scale visible-band remote sensing could help our understanding of the ecosystem by improving the temporal and spatial resolutions of satellite data.
Weeping love grass (Eragrostis curvula) has become a well-established invasive species along the Kinu River, Japan and is now considered a problematic invasive weed species. The aim of this study was to map the probability of the establishment of this invasive grass in the shore of the Kinu River using airborne hyperspectral imagery. Binary logistic regression analysis was used to model the probable presence/absence of weeping love grass. This study tried entering two types of input variables, original reflectance bands and MNF (Minimum Noise Fraction) transformed bands, into the regression model. No available variable of original reflectance data was selected, but two bands of MNF were selected in the regression analysis. The final classification, using the selected MNF bands, has distinguished weeping love grass from pseudo-absence pixels with user's and producer's accuracies of 100% and 66.7% respectively. The kappa coefficient was 0.74. These results indicate that the MNF transformed hyperspectral bands are more suitable than the original reflectance data to estimate the distribution of invasive weeping love grass in the shore of the Kinu River.