We investigated the spatiotemporal dynamics of urban expansion in Japan from 1990 to 2006 by using gridded land-use data, population census data, and satellite images of nighttime lights. First, we mapped Defense Meteorological Satellite Program (DMSP) nighttime lights and land-use data onto the 1 km² grid cell system of Japan to determine the proportional areas of DMSP and urban land use within each grid cell. Then, we investigated the relationships among population density, DMSP, and urban area. The urban/built-up area was strongly positively correlated with population density, and rapid expansion of the urban/built-up area around megacities was associated with population increases. In contrast, population density dropped steeply in rural areas and in small towns. Statistical analysis showed that correlation coefficients between population density and DMSP increased as the DMSP nighttime lights brightness value increased. We next estimated population density in the Hokkaido region using an ordinary least squares (OLS) regression model. Numerical evaluation of the results showed that the combination of land-use data and DMSP could be used to predict the population density. Finally, we compared OLS and geographically weighted regression (GWR) model for Sapporo city, Hokkaido. Compared with the OLS, the GWR can improve predictions of population density.

Global urban expansion has created incentives to convert green spaces to urban/built-up area. Therefore, understanding the distribution and dynamics of the land-cover changes in cities is essential for better understanding of the cities' fundamental characteristics and processes, and of the impact of changing land-cover on potential carbon storage. We present a grid square approach using multi-temporal Landsat data from around 1985-2010 to monitor the spatiotemporal land-cover dynamics of 50 global cities. The maximum-likelihood classification method is applied to Landsat data to define the cities' urbanized areas at different points in time. Subsequently, 1 km 2 grid squares with unique cell IDs are designed to link among land-cover maps for spatio-temporal land-cover change analysis. Then, we calculate land-cover category proportions for each map in 1 km 2 grid cells. Statistical comparison of the land-cover changes in grid square cells shows that urban area expansion in 50 global cities was strongly negatively correlated with forest, cropland and grassland changes. The generated land-cover proportions in 1 km 2 grid cells and the spatial relationships between the changes of land-cover classes are critical for understanding past patterns and the consequences of urban development so as to inform future urban planning, risk management and conservation strategies.

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.

A high-dimensional dataset was built with time-series data of vegetation indexes derived from a Terra-Moderate Resolution Imaging Spectroradiometer (MODIS) sensor used for land use/cover classification. The self-organizing map (SOM) neural network technique can reduce the dimensionality of high-dimensional data, yet keep the same topological characters in the low-dimension space after dimension reduction. In this paper, we first employed the SOM neural network technique to classify land cover types using a 17-dimensional dataset that was generated from 16-day interval MODIS Enhanced Vegetation Index (EVI) data with a spatial resolution of 500 m in eastern China during the growing period of plants. Then, we defined an unlabelled class of neuron. Pixels matched to this type of neuron were regarded as unclassified land cover types, so that we could remove the poorly classified areas. Finally, the classification results were compared with those of the maximum likelihood classification (MLC) method. Comparison showed that the accuracy of the former exceeded that of the latter in classifying a high-dimensional dataset.