Search results for "data reconstruction"
showing 3 items of 3 documents
Using NASA'S Long Term Data Record version 3 for the monitoring of land surface vegetation
2011
Numerous datasets have been made available for the observation of our planet from space. The aim of this work is the observation of changes in vegetation, through the use of a recent remote sensing dataset, NASA's Long Term Data Record (LTDR). Several authors have pointed out that vegetation monitoring benefits of the simultaneous use of Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST). Therefore, this work presents the procedure developed to monitor vegetation with the LTDR dataset, using both NDVI and LST parameters. This procedure includes data preprocessing (estimation of NDVI and LST, orbital drift correction, atmospherically contaminated data reconstruc…
SHIFTS OF START AND END OF SEASON IN RESPONSE TO AIR TEMPERATURE VARIATION BASED ON GIMMS DATASET IN HYRCANIAN FORESTS
2018
Abstract. Climate change is one of the most important environmental challenges in the world and forest as a dynamic phenomenon is influenced by environmental changes. The Hyrcanian forests is a unique natural heritage of global importance and we need monitoring this region. The objective of this study was to detect start and end of season trends in Hyrcanian forests of Iran based on biweekly GIMMS (Global Inventory Modeling and Mapping Studies) NDVI3g in the period 1981-2012. In order to find response of vegetation activity to local temperature variations, we used air temperature provided from I.R. Iran Meteorological Organization (IRIMO). At the first step in order to remove the existing g…
A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor Networks
2019
International audience; In a resource-constrained Wireless Sensor Networks (WSNs), the optimization of the sampling and the transmission rates of each individual node is a crucial issue. A high volume of redundant data transmitted through the network will result in collisions, data loss, and energy dissipation. This paper proposes a novel data reduction scheme, that exploits the spatial-temporal correlation among sensor data in order to determine the optimal sampling strategy for the deployed sensor nodes. This strategy reduces the overall sampling/transmission rates while preserving the quality of the data. Moreover, a back-end reconstruction algorithm is deployed on the workstation (Sink)…