Application of Geographically Weighted Regression to Investigate the Impact of Scale on Prediction Uncertainty by Modelling Relationship between Vegetation and Climate

Scale-dependence of spatial relationship between vegetation and rainfall in Central Sulavesi has been modelled using Normalized Difference Vegetation Index (NDVI) and rainfall data from weather stations. The modelling based on application of two statistical approaches: conventional ordinary least squares (OLS) regression, and geographically weighted regression (GWR). The analysis scales ranged from the entire study region to spatial unities with a size of 750*750 m. The analysis revealed the presence of spatial non-stationarity for the NDVI-precipitation relationship. The results support the assumption that dealing with spatial non-stationarity and scaling down from regional to local modelling significantly improves the model’s accuracy and prediction power. The local approach also provides a better solution to the problem of spatially autocorrelated errors in spatial modelling

Modified Light Use Efficiency Model for Assessment of Carbon Sequestration in Grasslands of Kazakhstan: Combining Ground Biomass Data and Remote-sensing

A modified light use efficiency (LUE) model was tested in the grasslands of central Kazakhstan in terms of its ability to characterize spatial patterns and interannual dynamics of net primary production (NPP) at a regional scale. In this model, the LUE of the grassland biome (n) was simulated from ground-based NPP measurements, absorbed photosynthetically active radiation (APAR) and meteorological observations using a new empirical approach. Using coarse-resolution satellite data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), monthly NPP was calculated from 1998 to 2008 over a large grassland region in Kazakhstan. The modelling results were verified against scaled up plot-level observations of grassland biomass and another available NPP data set derived from a field study in a similar grassland biome. The results indicated the reliability of productivity estimates produced by the model for regional monitoring of grassland NPP. The method for simulation of n suggested in this study can be used in grassland regions where no carbon flux measurements are accessible

Modelling of population dynamics: GIS versus Remote sensing – a case study for Istanbul

Over the last decades, the rapid growth of the world population has led to a large number of emerging megacities. The 1999 Izmit (Turkey) earthquake is a striking example of the impact of natural hazards on megacities. On August 17 1999, a magnitude 7.6 earthquake struck the area of Izmit in Turkey, causing about 20.000 fatalities and US$6.5 billion economic loss. The probability of a magnitude 7 earthquake striking Istanbul within the next 30 years ranges between 30% to 70%. In order to reduce the impact of natural hazards on human lives, emergency management plans are essential. The development of these plans strongly relies on up-to-date population and inventory data. However, existing techniques for population data generation do not meet the requirements of today’s dynamic cities. In this context remote sensing has become an important source of information in the last years. However, a rational discourse on the suitability of remote sensing for urban applications is still missing. In this study a quantitative evaluation of the suitability of IKONOS imagery for population modelling using the district of Zeytinburnu (Istanbul, Turkey) is conducted. The results reveal that IKONOS images can be used for complementing existing inventory data set. The automated extraction of single buildings was identified as the major source of error in the population estimation. Further advantages and limitations such as the associated costs are discussed in this present paper

Mapping leaf area index over semi-desert and steppe biomes in kazakhstan using satellite imagery and ground measurements

Maps of peak seasonal leaf area index (LAI) were produced using the Normalised Difference Vegetation Index (NDVI) from SPOT VEGETATION (VEG) satellite at 1 km resolution over a large region in the semi-arid zone of Kazakhstan. Ground measurements of LAI were acquired using indirect and direct techniques across a 150·150 km2 large region. A Landsat Enhanced Thematic Mapper (ETM+) scene at 30 m spatial resolution was used to locate ground sites and to facilitate spatial scaling to 1 km pixels. A high-resolution LAI map retrieved from the Landsat ETM+ data was aggregated to 1 km resolution and afterwards used as reference data. The methods tested for transfer function between ETM+ LAI and SPOT-VEG were ordinary least squares (OLS) regres-sion, non-linear regression, and reduced major axis (RMA) regression. In this paper, final maps of peak season LAI at a 1 km resolution are presented after an assessment of their accuracy using the aggregated ETM+ LAI scene. The most appropriate results were attained by RMA. Advantages and shortages of the used regression approaches were analysed and discussed. Errors were mostly caused by uncertainties in co-registration of Landsat ETM+ and SPOT-VEG images as it was demonstrated by a pixel degradation experiment. The methodology presented in this paper can serve as a basis for generation of medium- and coarse-resolution LAI satellite products for wide areas of Central Asia and Kazakhstan. The study exposed a general transferability of the de-veloped model for LAI estimations at coarser scales. The 1000 m SPOT-VEG model has proved to be fully suitable for utilising with the SPOT-VEG data with resolution of 2 km