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

Fernerkundungsgestützte Untersuchung der Vegetationsdynamik in den Trockengebieten Kasachstans

Die Arbeit untersucht die Zusammenhänge zwischen Vegetationsdynamik und Dynamik von Klimaelementen (Temperatur und Niederschlag) in einem Trockengebiet des Zentral-Kasachstans. Die Datengrundlagen der Arbeit umfassten den Normalized Difference Vegetation Index (NDVI) von dem Advanced Very High Resolution Radiometer (AVHRR) sowie Messwerten der Klimastationen für Niederschlag und Temperatur. Das ganze Spektrum des Zusammenhanges zwischen NDVI auf einer Seite und den Klimaelementen auf anderer Seite, - räumlich, zeitlich, innerhalb der Pflanzenwachstumsperiode, sowie die Langzeitdynamik während eines 20-jährigen Zeitraums, - wurde analysiert und interpretiert. Die statistischen Zusammenhänge wurden auf verschiedenen Skalen räumlicher Generalisierung betrachtet: von dem gesamten Gebiet bis zu einzelnem Pixel. Ein fernerkundungsgestütztes Monitoringssystem für die Bewertung der Vegetationsbedeckung und Trennung ihrer Änderungsursachen wurde entwickelt und erfolgreich angewendet. Dieses System identifiziert und quantifiziert das klimatische Signal in den langjährigen NDVI-Zeitreihen. Nach einer Entfernung des Klimasignals aus den NDVI-Zeitrehen werden die restlichen Störungen für Indikatoren des anthropogenen Einflusses verstanden und den Grad dieses Einflusses bewertet. Die Ergebnisse der Arbeit dienen einer Verbesserung unseres Verständnisses der Natur und den Triebfaktoren der Ökosystemdynamik in den inneren Bereichen des Kontinents Eurasien und schaffen eine Basis für eine verbesserte nachhaltige Nutzung des Raums

Satellite-based monitoring system for assessment of vegetation vulnerability to locust hazard in the River Ili delta (Lake Balkhash, Kazakhstan)

The study investigates the overall vulnerability of vegetation in the River Ili delta (Lake Balkhash, Southern Kazakhstan) to locust hazards using correlation between the normalized difference vegetation index data from advanced very high resolution radiometer and the TOPEX/POSEIDON/JASON-1 satellite altimetry derived water level of Lake Balkhash. The study develops a new monitoring and evaluation system to detect the annual vegetated areas infested by locusts and to quantify the overall recurrence of locust damage in these areas throughout the period 1992 to 2006. The monitoring system-based annual estimations of locust-affected areas were validated against ground survey data. The validation reveals a good correspondence of the estimations with the information from ground truth. The results of the overall vulnerability assessment showed that, for the River Ili delta vegetative land, ∼17% of the total area was characterized by moderate and high vulnerability of vegetation to locust hazards, whereas ∼3% showed low vulnerability. Most parts of the Ili delta area are not sensitive to locust hazard

Retrieval of Coarse-Resolution Leaf Area Index over the Republic of Kazakhstan Using NOAA AVHRR Satellite Data and Ground Measurements

A new multi-decade national-wide coarse-resolution data set of leaf area index (LAI) over the Republic of Kazakhstan has been developed based on data from the Advanced Very High Resolution Radiometer (AVHRR) and in situ measurements of vegetation structure. The Kazakhstan-wide LAI product has been retrieved using an algorithm based on a physical radiative transfer model establishing a relationship between LAI and given patterns of surface reflectance, view-illumination conditions and optical properties of vegetation at the per-pixel scale. The results revealed high consistencies between the produced AVHRR LAI data set and ground truth information and the 30-m resolution Landsat ETM+ LAI estimated using the similar algorithm. Differences in LAI between the AVHRR-based product and the Landsat ETM+-based product are lower than 0.4 LAI units in terms of RMSE. The produced Kazakhstan-wide LAI was also compared with the global 8-km AVHRR LAI (LAI_PAL_BU_V3) and 1-km MODIS LAI (MOD15A2 LAI) products. Results show remarkable consistency of the spatial distribution and temporal dynamics between the new LAI product and both examined global LAI products. However, the results also revealed several discrepancies in LAI estimates when comparing the global and the Kazakhstan-wide products. The discrepancies in LAI estimates were outlined and discussed

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

www.mdpi.com/journal/remotesensing Article Retrieval of Coarse-Resolution Leaf Area Index over the Republic of Kazakhstan Using NOAA AVHRR Satellite Data

Abstract: A new multi-decade national-wide coarse-resolution data set of leaf area index (LAI) over the Republic of Kazakhstan has been developed based on data from the Advanced Very High Resolution Radiometer (AVHRR) and in situ measurements of vegetation structure. The Kazakhstan-wide LAI product has been retrieved using an algorithm based on a physical radiative transfer model establishing a relationship between LAI and given patterns of surface reflectance, view-illumination conditions and optical properties of vegetation at the per-pixel scale. The results revealed high consistencies between the produced AVHRR LAI data set and ground truth information and the 30-m resolution Landsat ETM+ LAI estimated using the similar algorithm. Differences in LAI between the AVHRR-based product and the Landsat ETM+-based product are lower than 0.4 LAI units in terms of RMSE. The produced Kazakhstan-wide LAI was also compared with the global 8-km AVHRR LAI (LAI_PAL_BU_V3) and 1-km MODIS LAI (MOD15A2 LAI) products. Results show remarkable consistency of the spatial distribution and temporal dynamics between the new LAI product and both examined global LAI products. However, the results also revealed several discrepancies in LAI estimates when comparing the global and the Kazakhstan-wide products. The discrepancies in LAI estimates were outlined and discussed. Remote Sens. 2012, 4 22