Yoshiki Yamagata

Land Cover Classification and Change Analysis in the Horqin Sandy Land From 1975 to 2007

Observations over the last three decades show that desertification poses a serious threat to the livelihood and productivity of inhabitants of the Horqin Sandy Land region of China. We evaluated the dynamics and trends of changes of land cover in the Horqin Sandy Land by using Landsat archive images from 1975, 1987, 1999, and 2007. We applied two supervised classification methods, the self-organizing map neural network method and the subspace method. Our analyses revealed significant changes to land cover over the period 1975-2007. The area of cropland doubled over the last three decades. This expansion was accompanied by large increases in water consumption and considerable loss of areas of grassland and woodland. Many lakes and rivers shrank rapidly or disappeared in this region between 1975 and 2007. The sandy area expanded rapidly from 1975 to 1987 but gradually slowed thereafter.

Classification of wetland vegetation by texture analysis methods using ERS-1 and JERS-1 images

Images obtained by the C-band (5.3 GHz, 5.7 cm) VV-polarized ERS-1 SAR and the L-band (1.275 GHz, 23.5 cm) HH-polarized JERS-1 SAR were analyzed for the classification of wetland vegetation types. Both scenes were obtained when the wetland vegetation was at the maximum biomass stage. The authors applied texture analysis method using the cooccurrence matrix to classify the vegetation of the Kushiro mire. The result of the classification was evaluated comparing with the result using the SPOT data. As a result, it is shown that several texture features of SAR images are useful for the wetland vegetation classification. The authors especially demonstrate that the bog and the fen vegetation in the wetland can be segregated using JERS-1 image, and also the swamp forest can be delineated from the fen vegetation using ERS-1 image.<<ETX>>

Combination of AVNIR-2, PALSAR, and Polarimetric Parameters for Land Cover Classification

We evaluate the potential of combined Advanced Land Observing Satellite Advanced Visible and Near-Infrared (AVNIR-2) and fully polarimetric Phased-Array-type L-band Synthetic Aperture Radar (PALSAR) data for land cover classification. Optical AVNIR-2 and fully polarimetric PALSAR can provide both surface spectral information and scattering information of the ground surface. The fully polarimetric PALSAR is particularly important for land cover classification because quad-polarization PALSAR data and its polarimetric parameters contain additional surface information. As a consequence, by combining optical AVNIR-2, PALSAR, and polarimetric parameters into a single data set, land cover classification accuracy may be further improved. For efficient and convenient handling of the combined multisource data, we used a subspace method for the classification and estimated its classification capability for various combinations of optical, PALSAR, and polarimetric parameter data sets in the Lake Kasumigaura region of Japan. We also compared the results obtained using the subspace method with those obtained by the support vector machine (SVM) and maximum-likelihood classification (MLC) methods. The classification results confirm that, when the combined optical AVNIR-2, PALSAR, and polarimetric coherency matrix data were used, the classification accuracy of the subspace method was better than that when other data combinations were used. The subspace method also performed better than the SVM or MLC method in high-dimensional data set classification. Moreover, the experimental results demonstrated that the proposed subspace method is robust for data classification when there is data redundancy and thus allows optimal feature selection procedures to be avoided.

Community-based resilient electricity sharing: Optimal spatial clustering

This paper extends our proposing (Yamagata and Seya 2012) concept of a community-based disaster resilient electricity sharing system (DRESS) as a complement or an alternative to the feed-in-tariff (FiT) to achieve CO2 neutral in cities. In this system, electricity generated from widely introduced solar photovoltaic panels (PVs) is stored to the “cars not in use” in a city. In the central part of the Tokyo metropolitan area, almost half of the cars is used only on weekends and are kept parking during the weekdays. Hence, there exists a huge new potential if those cars are replaced by electric vehicles (EVs) in the future, namely they may be used as new battery storages using vehicle to grid (V2G) at a community level. This study extends our previous paper. Firstly, by using actual ground areas of buildings, we estimate PVs supply potential more accurately. The result shows that the hourly electricity surplus (PV supply minus demand) can be fully stored without waste if 27% of the parking EVs are used as battery storage at the whole city level, although there exist significant spatial differences at local district level. Secondly, based on the geographical demand-supply estimates, we check the possibility of local electricity sharing by combing high and low storage potential districts to form electricity self-sufficient resilient communities. Finally, we analyze the optimal community clustering using Morans I index. We show that the 40%, instead of 27%, is an optimal EV electricity sharing rate, if we consider the resilience against black-out risk.

Behavioral aspects for agent-based models of resilient urban systems

This paper discusses behavioral aspects for agent-based models of resilient urban systems. Human behavior in emergency situations is not driven by rational deliberation, but usually based on intuitive decision making and on heuristics. With high levels of risk and uncertainty, mass-psychological propensities tend to occur as well: People no longer orientate themselves to facts, but to other people. Mal-adaptive decision making heuristics might entail information cascades and irrational decisions on the collective level. Based on previously developed agent-based models, we draft a concept for integrating such behavioral aspects into models of urban emergencies.

Bayesian feature selection for classifying multi-temporal SAR and TM data

Remotely sensed imagery data from various satellite sensors are now available for environmental monitoring. However, due to the difficulty in surveying, it is not easy to obtain a sufficient number of training data for classifying these high dimensional imagery data. In order to make use of these imagery data, it is necessary to develop a classification method which can attain a high classification accuracy only using a limited number of training data. In this study, the authors have tested the Bayesian approaches which integrate feature selection and model averaging in the classification process. The experiments are conducted using bayesian neural networks, Gaussian process, and maximum likelihood for classifying wetland vegetation types using multi-temporal LANDAT/TM, JERS1/SAR, and ERS/SAR data. The results shows that the Bayesian approaches work well for classifying these imagery data, and especially the Gaussian process has a very high accuracy which outperforms other methods for classifying the sensor fusion data using JERS1/SAR and LANDSAT/TM.

Assessing nighttime lights for mapping the urban areas of 50 cities across the globe

Nighttime data from the Defense Meteorological Satellite Program Operational Linescan System have been widely used to map urban/built-up areas (hereafter referred to as “built-up area”), but to date there has not been a geographically comprehensive evaluation of the effectiveness of using nighttime lights data to map urban areas. We created accurate, convenient, and scalable grid cells based on Defense Meteorological Satellite Program/Operational Linescan System nighttime light pixels. We then calculated the density of Landsat-derived built-up areas within each grid cell. We explored the relationship between Defense Meteorological Satellite Program/Operational Linescan System nighttime lights data and the density of built-up areas to assess the utility of nighttime lights for mapping urban areas in 50 cities across the globe. We found that the brightness of nighttime lights was only in moderate agreement with the density of built-up areas; moreover, correlations between nighttime lights and Landsat-derived built-up areas were weak. Even in relatively sparsely populated urban regions (where the density of the built-up area is less than 20%), the highest correlation coefficient (R2) was only 0.4. Furthermore, nighttime lights showed lighted areas that extended beyond the area of large cities, and nighttime lights reduced the area of small cities. The results suggest that it is difficult to use the regression model to calibrate the Defense Meteorological Satellite Program/Operational Linescan System nighttime lights to fit urban built up areas.

Revealing Intra-Urban Features using Optical and SAR Images

We developed a method for revealing the intra-urban features and estimating population density using remotely sensed imagery. Remotely sensed imagery has been widely used in urban studies. However, the moderate-resolution optical data (e.g. Landsat) is often too coarse for delineating urban features, since urban areas are far more heterogeneous than most other land cover types. Therefore, we employed both optical and synthetic aperture radar images for estimating population density inside urban areas. This research indicated that the integrated features of the optical and SAR images can increase the accuracy for estimating population density.

Vegetation mapping and change analysis in South-East Asia from NOAA AVHRR LAC imageries

An outline of the vegetation change monitoring program at UNEP/GRID/TSUKUBA is introduced. A satellite image mosaic map and a vegetation index map in South-East Asia were produced from NOAA AVHRR LAC imageries with 1 km spatial resolution to assess vegetation distribution in the region. Also land-cover change detection with multi-temporal AVHRR images was examined for monitoring changes in vegetation on a continental scale.<<ETX>>