This paper describes a methodology of early-stage water stress detection for forest vegetation using a newly proposed index, CDNT (Compensated Day Night Temperature difference), which uses only remotely sensed surface temperature and has a close relationship with latent heat flux compared to simple differences of day-night surface temperatures. The comparison between CDNT calculated from the MODIS surface temperature and the ground-measured latent heat flux data at 8 Ameriflux sites in a period of 11 years, from year 2000 to 2010, showed a good relationship. The authors also found that a reduction in precipitation significant enough to cause a drought introduces an anomalously high positive value of CDNT. Anomalous CDNT values, based on the 11-year average daily CDNT value, can be used as indicators of water stress for forest vegetation. Since this method uses only remotely sensed surface temperature data, it is suitable for use in large- or global-scale water stress monitoring.
Here we propose an accurate and robust method for large-area land-use and land-cover (LULC) mapping using multi-temporal optical data. The conventional method for LULC classification usually uses time-series data at regular intervals to consider the seasonality of LULC. However, high-resolution optical data have considerable seasonal biases, making it difficult to use time-series data. Our basic idea for the accurate classification of LULC using high-resolution optical satellite data is to implement a classification for each scene considering seasonality first, and to then integrate multi-temporal classification results. In the per-scene classification, we accurately estimated the class-conditional spectral-seasonal densities of observation values from training data by conducting a kernel density estimation (KDE), and we used the densities in a Bayesian inference to obtain the class posterior probability. After the multi-temporal per-scene classification, we calculated the classification score by integrating class posterior probabilities in multi-temporal scenes. We conducted an 8-class classification for the entirety of Japan with 10-m spatial resolution using 1,876 scenes from the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) low-cloud-cover data, and we evaluated the accuracy of the classification by conducting a cross validation test and comparing the results to that obtained with existing methods: maximum likelihood classifier (MLC) and support vector machines (SVMs). The evaluation results showed that the overall accuracy of the proposed method is the best of all of the methods examined.
Low-altitude remote sensing, using a digital camera suspended from a vessel-towed balloon, was adopted to monitor jellyfish patchiness in coastal waters. The balloon, towed by a rope from a research vessel at about 200m altitude, takes photographs of the sea surface while the vessel moves through jellyfish patchiness. This balloon photography satisfies two essential technical requirements for surveying relatively small objects drifting in coastal waters. First, the photographs must be converted into images to which our sight line is perpendicular, because they are generally taken at oblique angles to the horizontal plane; otherwise positions and areas of jellyfish patchiness cannot be computed accurately. Hence, a projective transformation was applied for this geo-referencing, using polystyrene form boards with a GPS receiver for providing reference positions. In addition, to enhance the reference points sufficiently, GPS data recorded on the vessel were augmented in a single photograph by superimposing multiple photographs taken within a short time interval. Second, jellyfish patchiness must be extracted from the photographs efficiently and accurately. The color difference between the jellyfish and the background color of the ocean permitted extraction of the patchiness from photographs on a color space based on hue, saturation, and intensity components. This color space was useful in removing the strong levels of reflected sunlight at the sea surface by lowering the intensity of colors artificially, under the assumption that any white color was due to reflected sunlight.
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