Reflectance in the red edge band of the spectrum (680 to 730 nm) is strongly correlated with leaf chlorophyll concentration and is thus often used in vegetation indices. However, it has not previously been used to classify vegetation types. The RapidEye satellite, which was launched in August 2008, is equipped with multispectral imagery sensor and provides the red edge band. Here, we used two RapidEye images of Mitake, Gifu Prefecture, Japan taken on July 6, 2011 and November 30, 2011 to see if they could be used to classify vegetation types in a forest.The overall accuracy and Kappa coefficient of unsupervised and supervised classification of vegetation in the RapidEye images with red edge band were better than those obtained with RapidEye images with no red edge band. In addition, the accuracy of vegetation classified by the normalized difference red edge index (NDRE) was better than that classified by the normalized difference vegetation index (NDVI), which does not use the red edge band.We developed a new classification index, the DRe (difference of red edge), defined as Re_e – R710, where R710 is the reflectance at Band 4 (710 nm) and Re_e is the reflectance at 710 nm estimated from a linear equation expressing reflectance as a function of wavelength based on the reflectance at Band 3 (657.5 nm) and Band 5 (805 nm). The overall accuracy and Kappa coincident obtained with the DRe were 0.6602 and 0.5438, respectively, indicating that the DRe is sufficient for forest classification. Together, these results show that the red edge band has useful information for forest classification. We are presently testing the DRe's ability to classify vegetation in RapidEye images obtained in other seasons.
A questionnaire survey was conducted using 1,069 farmers about wild boar damage on agricultural land in the former Maebaru City in Fukuoka Prefecture. Abandoned cultivated land in the city was 11,202 ha. The questionnaire contained information about farming, damages attributable to wild boar, and the countermeasures against wild boar. The collection rate of valid questionnaires was 79.6%. The crop damaged the most was rice (53%), followed by vegetables (16%), oranges (13%), potatoes (5%), and others (13%). Statistical analysis of the relationship between land use and farmland damage showed a significant positive correlation between the presence of abandoned cultivated land and agricultural damage by wild boar. A geographic information system analysis revealed that 92.3% of the farmland damage existed within 100m from the forest, indicating that the distance from the forest influenced the damage. Moreover, 54.9% of the farmland damage existed within 100 m from the abandoned cultivated land, which suggested that the proximity of abandoned cultivated land had the possibility to affect agricultural damage. In addition, the effect of abandoned cultivated land on agricultural damage by wild boar was analyzed using regression analysis, and the result suggested that abandoned cultivated land be an important indicator for crop damage by wild boar.
We examined the degree to which we can distinguish parcels planted with rice by visual interpretation of high resolution satellite images in an area in Inashiki-City, Ibaraki Prefecture. In this area, rice planting became difficult over a wide area in 2011 because many irrigation facilities were damaged and sand liquefaction occurred in many paddy fields due to the 2011 off the Pacific coast of Tohoku Earthquake in March 11, 2011. Pansharpened images at a resolution of 0.5 m from the WorldView-2 satellite data acquired on June 29, 2011 were used. Three persons with no prior experience of visual interpretations determined whether rice was planted in paddy parcels by visual interpretation of printed satellite images. We then verified the accuracy of the interpretations by comparing with data from a field survey carried out the next day. The distinction accuracy was 96.0 – 97.6%. Most parcels incorrectly distinguished were fields planted with a small percentage of vegetative cover of oats and the entire soil surface wet, or fallow fields (after plowing or puddling) without vegetative cover and with the entire soil surface wet. The accuracy of distinction was highly dependent on rainfall before the satellite observations.