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The aim of this study is to adapt and evaluate the effectiveness of a multi-temporal downscaled images technique for classifying the typical vegetation types of a reclaimed area. The areas reclaimed from estuarine tidal flats show high spatial heterogeneity in soil salinity conditions. There are three typical vegetation types for which the distribution is restricted by the soil conditions. A halophyte-dominated vegetation is located in a high saline area, grass vegetation is found in a mid-or low saline area, and reed/small-reed vegetation is situated in a low saline area. Multi-temporal satellite images were used to classify the vegetation types. Landsat images were downscaled to take into account spatial heterogeneity using cokriging. A random forest classifier was used for the classification, with downscaled Landsat and RapidEye images. Classification with RapidEye images alone demonstrated a lower level of accuracy than when combined with multi-temporal downscaled images. The results demonstrate the usefulness of a downscaling technique for mapping. This approach can provide a framework which is able to maintain low costs whilst producing richer images for the monitoring of a large and heterogeneous ecosystem.

Invasion by non-native species due to human activities is a major threat to biodiversity. The niche hypothesis for invasive species that rapidly disperse and disturb ecosystems is easily discarded owing to eradication activities or unsaturated dispersal. Here, we used spatial and non-spatial models to model the distribution of two invasive plant species (Ambrosia artemisiifolia and Ambrosia trifida), which are widely distributed, but are also being actively eradicated. Regression kriging (RK) and Maxent were used to predict the spatial distribution of the two plant species having eradication targets for decades in South Korea. In total, 1,478 presence/absence data points in the Seoul metropolitan area (∼11,000 km2 in northeastern South Korea) were used. For regression kriging, the presence/absence data were first fitted with environmental covariates using a generalized linear model (GLM), and then the residuals of the GLM were modeled using ordinary kriging. The residuals of GLM showed significant spatial autocorrelation. The spatial autocorrelation was modeled using kriging. Regression kriging, which considers the spatial structure of data, yielded area under the receiver operating curve values of 0.785 and 0.775 for A. artemisiifolia and A. trifida, respectively; however, the values of Maxent, a non-spatial model, were 0.619 and 0.622, respectively. Thus, regression kriging was advantageous as it considers the spatial autocorrelation of the data. However, species distribution modeling encounters difficulties when the current species distribution does not reflect optimal habitat conditions (the niche habitat preferences) or when colonization is disturbed by artificial interference (e.g., removal activity). This greatly reduces the predictive power of the model if the model is based solely on the niche hypotheses that do not reflect reality. Managers can take advantage of regression modeling when modeling species distributions under conditions unfavorable to the niche hypothesis.

Entropy is widely used for measuring the degree of urban sprawl. However, despite the intense use of the entropy concept in urban sprawl, entropy’s spatial context has been largely ignored. In this study, we analyzed urban sprawl in Changwon and Gimhae cities, as they shared a common boundary but differed in their population growth and urban expansion. The land cover type, “urban and dry area,” was used to identify urban areas in the two cities, and a land cover map showed the areas of expansion in the 1980s, 1990s, 2000s, and 2010s. Different zoning schemes, namely concentric rings and regular partitioning, were applied. Shannon’s and Batty’s spatial entropy indices were used to measure urban sprawl. The results showed that concentric ring zoning was not suitable for measuring urban sprawl in a decentralized and polycentric city. Batty’s spatial entropy was less affected by the zoning scheme used and reflected the pattern of urban expansion more accurately. Urban sprawl, a phenomenon occurring within a spatial context, can be better understood by measuring spatial entropy with appropriate zoning schemes.