Burak Berk Ustundag

Multiscale object-based drought monitoring and comparison in rainfed and irrigated agriculture from Landsat 8 OLI imagery

Abstract Drought is a rapidly rising environmental issue that can cause hardly repaired or unrepaired damages to the nature and socio-economy. This is especially true for a region that features arid/semi-arid climate, including the Turkeys most important agricultural district — Southeast Anatolia. In this area, we examined the uncertainties of applying Landsat 8 Operational Land Imager (OLI) NDVI data to estimate meteorological drought – Standardized Precipitation Index (SPI) — measured from 31 in-situ agro-meteorological monitoring stations during spring and summer of 2013 and 2014. Our analysis was designed to address two important, yet under-examined questions: (i) how does the co-existence of rainfed and irrigated agriculture affect remote sensing drought monitoring in an arid/semi-arid region? (ii) What is the role of spatial scale in drought monitoring using a GEOBIA (geographic object-based image analysis) framework? Results show that spatial scale exerted a higher impact on drought monitoring especially in the drier year 2013, during which small scales were found to outperform large scales in general. In addition, consideration of irrigated and rainfed areas separately ensured a better performance in drought analysis. Compared to the positive correlations between SPI and NDVI over the rainfed areas, negative correlations were determined over the irrigated agricultural areas. Finally, the time lag effect was evident in the study, i.e., strong correlations between spring SPI and summer NDVI in both 2013 and 2014. This reflects the fact that spring watering is crucial for the growth and yield of the major crops (i.e., winter wheat, barley and lentil) cultivated in the region.

An improved ELGossiping data distribution technique with emphasis on reliability and resource constraints in wireless sensor network

Sensor networks are composed of many sensors usually far from the region is available. Routing in wireless sensor networks for the transfer of information from sensor nodes to base station is especially important. Optimum consumption of energy is important requirements in wireless sensor networks because sensor systems typically use battery power. In this paper with a routing algorithm to determine the optimal path of energy consumption viewpoint for information transfer from sensor nodes to base station with the data transmission we have presented multi-skip. In this algorithm when a sensor felt event, the node that was in transmission radius of sensor and it had minimum distance to the main station as next step is selected and send to only one node is selected. Finally, the proposed algorithm compare with Gossiping and LGossiping algorithms as simulation results show the proposed algorithm has better performance.

Spatial estimation of wind speed: a new integrative model using inverse distance weighting and power law

ABSTRACT Spatial interpolation (SI) is currently one of the most common ways to estimate wind speed (Ws). However, classic SI models either ignore the complex geography [e.g. inverse distance weighting (IDW)], or demand high computational resources (e.g. cokriging). This study aimed to develop a simple yet effective SI model for estimating Ws in Eastern Thrace of Turkey. This new method, named MIDW(Ws), is a modified IDW through the integration of IDW with wind profile model, power law (PL), representing the influence of land cover and topography on Ws. Terrain features and elevation data of PL were obtained using normalized difference vegetation index (NDVI) and digital elevation model (DEM), respectively. Results showed superior and comparable performance of MIDW(Ws) to standard IDW and ordinary kriging (OK) across all months of year. Compared to ordinary cokriging (OCK) using DEM as covariate, MIDW(Ws) generated better results in the arid–semiarid seasons (around summer). Local complex atmospheric conditions during rainy seasons (around winter) may have affected the performance of incorporating PL with MIDW(Ws). Generally, the proposed MIDW(Ws) is simpler and easier to implement compared to OCK. For landscape-scale projects, its high computational efficiency and relatively robust performance show potential to deal with large volumes of datasets.

Spatial interpolation of climatic variables using land surface temperature and modified inverse distance weighting

Accurate spatial interpolation (SI) of climate data is vital for the management and supervision of natural resources and agriculture. Owing to the lack of an adequate number of meteorological stations, meteorological-station-data-based SI methods may not always reflect the real climatic conditions of an interpolated point. Land surface temperature (LST) data obtained from satellite sensors enable the characterization of meteorological conditions of areas without meteorological stations. The aim of this article is to present a new modified inverse distance weighting (M-IDW) SI method for air temperature (Ta), total precipitation (Pt), and relative humidity (RH) by integrating Landsat LST data with meteorological station data for the interpolation process. The M-IDW approach is based on the correlation relationship between the climate data and LST at each meteorological station, which is incorporated into the traditional IDW to improve the estimation of the climate data at an interpolation location of interest. The proposed method, M-IDW, is applied for the interpolation of long years’ (i.e. long term) monthly average (LYMA) Ta, Pt, and RH climate data from meteorological stations in the Eastern Thrace region, which is 23,764 km2, located in southeast Europe. The LYMA of the Ta, Pt, and RH has been constructed using data obtained from 27 meteorological stations that had functioned at least 10 years between 2000 and 2012 and from the corresponding satellite data. The outputs of the interpolation are in the form of LYMA, so are the analysed climate data. The spatial resolution of the predicted surface was taken as 30 m, similar to the original data presented by United States Geological Survey. The results were compared with those of the standard IDW, ordinary kriging (OK), and ordinary cokriging (OCK) methods to analyse the performance and accuracy of the proposed method. The results show that the proposed M-IDW method has the potential for SI of climate data, if enough number of images and cloudless pixels are incorporated in the LYMA LST computation. The proposed method, in general, yields better results than standard IDW and OK methods, especially during spring, summer, and partially in autumn for the interpolation of Ta (with 0.72°C, 0.53°C, and 0.66°C root mean square error (RMSE) values, respectively) and Pt (with 11.07 mm, 7.64 mm, and 4.85 mm RMSE values, respectively). OCK and M-IDW results were comparable in spring, summer, and autumn where M-IDW was slightly better for Ta in autumn and spring and was slightly better for Pt in summer. For the RH interpolation, although M-IDW results were found to be close to the results of IDW, OK, and OCK in spring, summer, and autumn, for the overall seasonal interpretation, the RMSE values of M-IDW were worse than the others. In general, M-IDW yields worse results for the winter months, which in turn is related to cloudiness and availability of satellite images.