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

Response of CO₂ and H₂O fluxes in a mountainous tropical rainforest in equatorial Indonesia to El Niño events

The possible impact of El Niño–Southern Oscillation (ENSO) events on the main components of CO₂ and H₂O fluxes between the tropical rainforest and the atmosphere is investigated. The fluxes were continuously measured in an old-growth mountainous tropical rainforest in Central Sulawesi in Indonesia using the eddy covariance method for the period from January 2004 to June 2008. During this period, two episodes of El Niño and one episode of La Niña were observed. All these ENSO episodes had moderate intensity and were of the central Pacific type. The temporal variability analysis of the main meteorological parameters and components of CO₂ and H₂O exchange showed a high sensitivity of evapotranspiration (ET) and gross primary production (GPP) of the tropical rainforest to meteorological variations caused by both El Niño and La Niña episodes. Incoming solar radiation is the main governing factor that is responsible for ET and GPP variability. Ecosystem respiration (RE) dynamics depend mainly on the air temperature changes and are almost insensitive to ENSO. Changes in precipitation due to moderate ENSO events did not have any notable effect on ET and GPP, mainly because of sufficient soil moisture conditions even in periods of an anomalous reduction in precipitation in the region

Синтез излучающей системы, формирующей секторную диаграмму направленности с минимизацией эффекта Гиббса

For the synthesis of radiating systems, which form the flat-topped radiation patterns there are some of the most convenient methods, including Fourier method, method of partial diagram and method of changing projections. These methods are handy for calculation, because they allow us to obtain the flat-topped radiation patterns with predetermined electrical characteristics. These are the following characteristics: side-lobe level, width of the main lobe of the radiation pattern, and amplitude of top ripple of the main lobe of the radiation pattern. Meeting the challenge of synthesizing flat-topped radiation pattern is complicated by the break points available in a predetermined radiation pattern, which prevent the convergence of the Fourier series. The points of discontinuity, in turn, lead to the emergence of extremes that are associated with the Gibb’s phenomenon. To eliminate them, are used different methods for approximating a given radiation pattern (a series of Kotelnikov polynomial and that of Chebyshev). Such approximation, in turn, imposes restrictions on the basic quality indicators the flat-topped radiation patterns, such as the steepness of fronts and top ripple of the main lobe of the radiation pattern. The proposed method provides the lowest side-lobe level with small amplitude of top ripple of the main lobe of the flat-topped radiation pattern. The paper offers a linear approximation option of the given radiation pattern, which allows a synthesized radiation pattern with a diversity of quality parameters. This is achieved by linear approximation coefficients that can be determined using optimization algorithms. Depending on the target function for the optimization algorithm it is possible to determine the best trade-off between making the steepness parameter of the fronts and the top ripple amplitude of the main lobe of the flat-topped radiation pattern. The important feature of the method is that it minimizes the Gibbs phenomenon and is easy to calculate gratings with the small number of radiators.Для синтеза излучающих систем, формирующих секторные диаграммы направленности, существуют несколько наиболее удобных методов, среди которых: метод Фурье, метод парциальных диаграмм и метод изменяющихся проекций. Данные методы удобны для расчета, потому что позволяют получить секторные диаграммы направленности с заданными электрическими характеристиками. Этими характеристиками являются: уровень боковых лепестков, ширина главного лепестка диаграммы направленности и амплитуда переколебаний вершины главного лепестка секторной диаграммы направленности.   Решение задачи синтеза секторной диаграммы направленности осложняется наличием точек разрыва в заданной диаграмме направленности, которые препятствуют сходимости ряда Фурье. Точки разрыва, в свою очередь, приводят к возникновению экстремумов, которые связаны с эффектом Гиббса. Для их устранения применяют различные методы аппроксимации заданной диаграммы направленности (полиномами рядов Котельникова, Чебышева). Подобные аппроксимации накладывают ограничения на основные качественные показатели секторных диаграмм направленности, такие как: крутизну фронтов и переколебания вершины главного лепестка диаграммы направленности. Предложенный метод позволяет получить наименьший уровень боковых лепестков при незначительном переколебании плоской вершины главного лепестка секторной диаграммы направленности. Предложен вариант линейной аппроксимации заданной диаграммы направленности, который позволяет получать синтезированные диаграммы направленности с разного рода параметрами качества. Это достигается с помощью коэффициентов линейной аппроксимации, которые могут быть определены с помощью алгоритмов оптимизации. В зависимости от выбранной целевой функции для алгоритма оптимизации появляется возможность определения наилучших решений компромисса между параметром крутизны фронтов и амплитудой переколебаний вершины главного лепестка секторной диаграммы направленности. Важными особенностями данного метода является то, что он позволяет минимизировать эффект Гиббса и удобен для расчета решеток с малым числом излучателей

Improving land cover classification using input variables derived from a geographically weighted principal components analysis

This study demonstrates the use of a geographically weighted principal components analysis (GWPCA) of remote sensing imagery to improve land cover classification accuracy. A principal components analysis (PCA) is commonly applied in remote sensing but generates global, spatially-invariant results. GWPCA is a local adaptation of PCA that locally transforms the image data, and in doing so, can describe spatial change in the structure of the multi-band imagery, thus directly reflecting that many landscape processes are spatially heterogenic. In this research the GWPCA localised loadings of MODIS data are used as textural inputs, along with GWPCA localised ranked scores and the image bands themselves to three supervised classification algorithms. Using a reference data set for land cover to the west of Jakarta, Indonesia the classification procedure was assessed via training and validation data splits of 80/20, repeated 100 times. For each classification algorithm, the inclusion of the GWPCA loadings data was found to significantly improve classification accuracy. Further, but more moderate improvements in accuracy were found by additionally including GWPCA ranked scores as textural inputs, data that provide information on spatial anomalies in the imagery. The critical importance of considering both spatial structure and spatial anomalies of the imagery in the classification is discussed, together with the transferability of the new method to other studies. Research topics for method refinement are also suggested

The use of NDVI and its Derivatives for Monitoring Lake Victoria’s Water Level and Drought Conditions

Normalized Difference Vegetation Index (NDVI), which is a measure of vegetation vigour, and lake water levels respond variably to precipitation and its deficiency. For a given lake catchment, NDVI may have the ability to depict localized natural variability in water levels in response to weather patterns. This information may be used to decipher natural from unnatural variations of a given lake’s surface. This study evaluates the potential of using NDVI and its associated derivatives (VCI (vegetation condition index), SVI (standardised vegetation index), AINDVI (annually integrated NDVI), green vegetation function (F g ), and NDVIA (NDVI anomaly)) to depict Lake Victoria’s water levels. Thirty years of monthly mean water levels and a portion of the Global Inventory Modelling and Mapping Studies (GIMMS) AVHRR (Advanced Very High Resolution Radiometer) NDVI datasets were used. Their aggregate data structures and temporal co-variabilities were analysed using GIS/spatial analysis tools. Locally, NDVI was found to be more sensitive to drought (i.e., responded more strongly to reduced precipitation) than to water levels. It showed a good ability to depict water levels one-month in advance, especially in moderate to low precipitation years. SVI and SWL (standardized water levels) used in association with AINDVI and AMWLA (annual mean water levels anomaly) readily identified high precipitation years, which are also when NDVI has a low ability to depict water levels. NDVI also appears to be able to highlight unnatural variations in water levels. We propose an iterative approach for the better use of NDVI, which may be useful in developing an early warning mechanisms for the management of lake Victoria and other Lakes with similar characteristics

Remote sensing of annual terrestrial gross primary productivity from MODIS: an assessment using the FLUXNET La Thuile data set

Gross primary productivity (GPP) is the largest and most variable component of the global terrestrial carbon cycle. Repeatable and accurate monitoring of terrestrial GPP is therefore critical for quantifying dynamics in regional-to-global carbon budgets. Remote sensing provides high frequency observations of terrestrial ecosystems and is widely used to monitor and model spatiotemporal variability in ecosystem properties and processes that affect terrestrial GPP. We used data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and FLUXNET to assess how well four metrics derived from remotely sensed vegetation indices (hereafter referred to as proxies) and six remote sensing-based models capture spatial and temporal variations in annual GPP. Specifically, we used the FLUXNET La Thuile data set, which includes several times more sites (144) and site years (422) than previous studies have used. Our results show that remotely sensed proxies and modeled GPP are able to capture significant spatial variation in mean annual GPP in every biome except croplands, but that the percentage of explained variance differed substantially across biomes (10–80%). The ability of remotely sensed proxies and models to explain interannual variability in GPP was even more limited. Remotely sensed proxies explained 40–60% of interannual variance in annual GPP in moisture-limited biomes, including grasslands and shrublands. However, none of the models or remotely sensed proxies explained statistically significant amounts of interannual variation in GPP in croplands, evergreen needleleaf forests, or deciduous broadleaf forests. Robust and repeatable characterization of spatiotemporal variability in carbon budgets is critically important and the carbon cycle science community is increasingly relying on remotely sensing data. Our analyses highlight the power of remote sensing-based models, but also provide bounds on the uncertainties associated with these models. Uncertainty in flux tower GPP, and difference between the footprints of MODIS pixels and flux tower measurements are acknowledged as unresolved challenges.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by Copernicus Publications on behalf of the European Geosciences Union. The published article can be found at: http://www.biogeosciences.net/

Spatiotemporal climate and vegetation greenness changes and their nexus for Dhidhessa River Basin, Ethiopia

Background Understanding spatiotemporal climate and vegetation changes and their nexus is key for designing climate change adaptation strategies at a local scale. However, such a study is lacking in many basins of Ethiopia. The objectives of this study were (i) to analyze temperature, rainfall and vegetation greenness trends and (ii) determine the spatial relationship of climate variables and vegetation greenness, characterized using Normalized Difference in Vegetation Index (NDVI), for the Dhidhessa River Basin (DRB). Quality checked high spatial resolution satellite datasets were used for the study. Mann–Kendall test and Sen’s slope method were used for the trend analysis. The spatial relationship between climate change and NDVI was analyzed using geographically weighted regression (GWR) technique. Results According to the study, past and future climate trend analysis generally showed wetting and warming for the DRB where the degree of trends varies for the different time and spatial scales. A seasonal shift in rainfall was also observed for the basin. These findings informed that there will be a negative impact on rain-fed agriculture and water availability in the basin. Besides, NDVI trends analysis generally showed greening for most climatic zones for the annual and main rainy season timescales. However, no NDVI trends were observed in all timescales for cool sub-humid, tepid humid and warm humid climatic zones. The increasing NDVI trends could be attributed to agroforestry practices but do not necessarily indicate improved forest coverage for the basin. The change in NDVI was positively correlated to rainfall (r2 = 0.62) and negatively correlated to the minimum (r2 = 0.58) and maximum (r2 = 0.45) temperature. The study revealed a strong interaction between the climate variables and vegetation greenness for the basin that further influences the biophysical processes of the land surface like the hydrologic responses of a basin. Conclusion The study concluded that the trend in climate and vegetation greenness varies spatiotemporally for the DRB. Besides, the climate change and its strong relationship with vegetation greenness observed in this study will further affect the biophysical and environmental processes in the study area; mostly negatively on agricultural and water resource sectors. Thus, this study provides helpful information to device climate change adaptation strategies at a local scale