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Land use dynamic in the catchment was assessed through the analysis of four land use maps corresponding to the land use status in 1990, 2000, 2007, and 2013.
The vector maps and data from two National Land Investigations in China, socio-economic information from government departments are used, and land use dynamic models and landscape metrics with mathematical statistical method are applied.
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Here, we develop and describe the use of four land-use dynamic indicators to capture historical land-use changes of catchments and to measure the rate of deforestation (annual deforestation rate), forest regeneration level (secondary forest mean proportion), time since disturbance (mean time since deforestation) and deforestation profile (deforestation profile curvature).
The equation to calculate a single land use type dynamic index (Liu et al. 2002) is given as: {L}_c=frac{U_b-{U}_a}{U_a}times frac{1}{T}times 100% (1).
The specific examples included are: the management of fisheries; tropical and temperate forestry; urban land use and the dynamic process of suburbanization.
We suggest that the diversity of livelihood strategies and land use types is dynamic and continuously in transition; this creates a complex and changing landscape.
The objective of this study was to evaluate the impacts of static and dynamic land use input conditions on the performance of non-point source (NPS) model and find out whether dynamic land use input can improve the model accuracy.
The integrated approach of, urban dynamic assessment using land use modeling is useful for spatiotemporal representation of, distinct urban development forms.
Urban vegetation is influenced by prior land use due to the dynamic nature of urban landscape.
Dynamic land use and transport interaction models provide a good platform to study this mutual interaction.
Our simulations demonstrated that dynamic land use supports urban biodiversity in terms of species richness and rarity.
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