Based on remote sensing and GIS techniques, land use maps in 2000, 2005 and 2010 in China′s coastal zone were produced, and structural raster data of land use were further generated to calculate land use intensity comprehensive index (LUICI) for analyzing land use spatial-temporal characteristics at 1 km scale. Results show that: 1) from the perspective of spatial patterns of landforms at a macro scale, there is a significant difference in land use intensity between the north and the south of China′s coastal zone. Hotspots of changes mainly concentrated in metropolitan areas, estuaries and coastal wetlands; 2) elevation is an important factor that controlling land use spatial patterns at local scale. Land use intensity is much higher within areas below the elevation of 400 m and it decreased significantly as the elevation increasing; 3) there is a significant land-ocean gradient for land use intensity, which is low in island and near-shore areas, but high in the regions that 4-30 km far away the coastline because of much intensive human activities; however, in recent decades land use intensity had been promoted significantly in low near-shore area due to extensive sea reclamations; 4) significant differences of land use intensity were also found among provincial administrative units. A rising trend of land use intensity was found in provincial-level administrative units from 2000 to 2010. To sum up, elevation, land-ocean gradient, socio-economic status and policy are all influencing factors to the spatial patterns and temporal variations of land use intensity in China′s coastal zone.
Markov chain is one of the most widely used methods for land use change forecasting, however, it's a nonspatial model and few papers have discussed the effects of timeduration on its performance. In this paper, we first present the primary methodologies of the Spatial-Markov model, which endows the ordinary Markov chain with spatial dimension using spatial analysis techniques, and then explore the effects of forecasting time-duration on the model's performance. By taking Shandong province, China as a case study area, land use maps in 1990, 1995, 2000, 2005 and 2010 were created using on Landsat images and then the Spatial-Markov model was developed at 1 km spatial scale. In detail, we repeatedly run the model by choosing different initial time points and the same time step (five year interval) to simulate the spatial-temporal dynamics of land use change from 1990 to 2010. The forecasting results of a single run included a stack of ratio scale images and a derived nominal scale image, χ 2 test and Kappa coefficient were adopted to evaluate their accuracy respectively. It turned out that the Spatial-Markov model could achieve very good performance for short period forecasting. For the case study, it was quite qualified for the prediction of three time steps (up to 15 years) or more within which the results had much high reliability, however, time-duration of forecasting had much significant impact on the model's performance, the longer the forecasting duration, the lower the model's accuracy.
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