STRATEGIES FOR ALTERNATE APPROACHES FOR VEGETATION INDICES COMPOSITING USING PARALLEL TEMPORAL MAP ALGEBRA

Transcription

1 STRATEGIES FOR ALTERNATE APPROACHES FOR VEGETATION INDICES COMPOSITING USING PARALLEL TEMPORAL MAP ALGEBRA Bijay Shrestha, Dr. Charles O Hara Dr. Nicholas H. Younan Mississippi State University

2 OUTLINE Introduction Map Algebra Temporal Map Algebra Vegetation Index Compositing Parallel Temporal Map Algebra Quality Metrics Results Conclusions

3 INTRODUCTION Satellite images with wide area coverage and high temporal resolution are highly useful in performing land use analysis, vegetation vigor, and/or analysis of change. Eg. AVHRR, MODIS Areas of cloud or water cover preclude analysis and interpretation of terrestrial land cover. Multi-temporal image compositing techniques, using Temporal Map Algebra (TMA), can be employed to create a synthetic cloud free image that contains representative values derived from a set of possibly cloudy satellite images collected during a given time period of interest.

4 Map Algebra An approach to raster data handling which treats spatial data layers as variables which may be combined using mathematical operators. Source: Geographic Information Systems and Cartographic Modeling, Tomlin

5 Map Algebra (Contd.) Local Operation = Arithmetic, Relational, Bitwise, Boolean, Logical, Accumulative and Assignment Operators can be used for Map Algebra. 3 x 3 Focal Neighborhood

6 Temporal Map Algebra (TMA) TMA is the temporal extension to conventional map algebra. Treats time series of imagery as three dimensional data set. XY plane represent Earth s surface. Z dimension represents time. Y Y X Image n Z Time Image 1 X

7 Temporal Map Algebra (Contd.) Temporal Map Algebra Local Function Temporal Map Algebra 3x3x3 Focal Neighborhood Row Time Column Source: Analyzing time series satellite imagery using temporal map algebra, J.Mennis and R. Viger

8 Vegetation Index Compositing Vegetation Indices are dimensionless, radiometric measures that function as indicators green vegetation. Normalized Difference Vegetation Index (NDVI) NDVI = NIR R NIR + R Healthy, chlorophyll-based vegetation strongly reflects nearinfrared wavelengths and reflects relatively weakly in the visible red. Range [-1, +1] Maximum Value Compositing Maximum Value Compositing with Angular Constraint

9 Parallel Processing Global coverage requires large volume of satellite data Need for intensive computing to integrate and process large datasets. Parallel processing is the decomposition of a large problem into smaller problems that can be solved simultaneously to provide faster execution time. Many spatial programs are inherently parallel. Parallel processing can provide leap in performance.

10 Performance Metrics Serial Execution Time (T S ) Parallel Execution Time (T P ) Speedup S= T S / T P Efficiency E = S/ p; Cost = pt P Total Parallel Overhead T O = pt P -T S * p represents the number of processors used in parallel processing

11 Tools Used Matlab MatlabMPI LibTIFF

12 Pre-processing Surface reflectance day 1 NDVI day 1 Surface reflectance day 2 NDVI day 2 NDVI cube Surface reflectance day N NDVI day N Surf.Refl. Quality day 1 Surf. Refl. Quality day 2 QMask day 1 QMask day 2 Quality Mask cube Input to Compositing Algorithm Surf. Refl. Quality day N QMask day N Geolocation Angles day 1 view zenith angle day 1 Geolocation Angles day 1 Geolocation Angles day N view zenith angle day 2 view zenith angle day N View zenith angle cube

13 Storage Requirement Raw MODIS data MYD09GQK - 250m surface reflectance data MYD09GST - 1 Km Geo-location angles data MYDMGGAD - 1 Km surface reflectance quality data Total storage space required for 90 days is around 10 TB. To facilitate the orderly and hierarchical analysis, the world is divided into 6 o 6 o grids such that each tile represents a unique spatial location on earth. For a 6 o 6 o Tile Image Size pixels Number of Cubes 3 Bits per Pixel 32/16/8 Total Data per 90 Days 4 TB Total storage requirement for image cubes and the preprocessed data is 14 TB

14 TMA Compositing NDVI Cube View Angle Cube Surface reflectance Quality Cube Model based constraints to create masked NDVI Masking Masked NDVI Cube TMA Operations NDVI Composite

15 Parallel Processing (Contd.) Temporal Cube Block Distribution & Processing 1 2 n Temporal Composite

16 Experimental Results

17 Experimental Results (Contd.) Compared the results of various compositing algorithms using temporal map algebra. Compositing using Focal Maximum criteria produces cloud free composites with good results. Performance evaluation of the compositing algorithm using Focal maximum criteria were compared. Considered the surface reflectance quality and geolocation angles metadata derived from the MODIS data products.

18 Experimental Results (Contd.) The evaluation of parallel Temporal Map Algebra consists of three parts A measurement on the performance of serial processes The performance profiling of the parallel processes to compute composites The performance profiling of the parallel processes to compute composites using distributed data sources

19 Experimental Results (Contd.) For the evaluation compositing was performed on Bi-weekly MODIS data cubes using focal maximum criteria of Temporal Map Algebra. The experiments were conducted on the EMPIRE cluster at ERC at Mississippi State University. Initializing Matlab processes on different machines in cluster using MatlabMPI. Each Process Read the cubes stored in Network File Server Perform computations Store results in common storage Readings taken Metrics computed

20 Experimental Results (Contd.) Number of Processors Time Speedup Efficiency Overhead

21 Experimental Results (Contd.) Parallel Run-times With Centralized Data Parallel Run-times With Distributed Data Run-times for 6x6 grids Run-time using Distributed Data for 6x6 Time (Seconds) Number of Processors Avg Time Max Time Min Time Time (Seconds) Number of Processors Max Min Distributed Average

22 Experimental Results (Contd.) Speedup and Efficiency Speedup Efficiency Speedup Number of Processors Efficiency Number of Processors

23 Experimental Results (Contd.) Overhead Centralized VS Distributed comparison Overhead Centralized Vs Distributed Time(Seconds) Number of processors Time (Seconds) Number of Processors Distributed Average Central

24 Conclusions TMA is embarrassingly parallelizable. The performance improvements using the parallel implementation can be impacted by various factors such as network bandwidth, network traffic, processor speed, and network/cluster architecture. The use of Matlab and MatlabMPI as the programming environment added overhead to the total serial and parallel times for the computation. These overheads can be reduced by programming in C/C++ that have lower overhead. Temporal Map Algebra offers novel analytical capabilities and computational implementation methods may be employed to efficiently address complex spatio-temporal analysis problems on large multi-dimensional datasets.

25 Acknowledgements This work was funded by the Mississippi State University GeoResource Institute as a part of the National Aeronautical and Space Administration contract number NCC Special thanks to Preeti Mali and Veeraraghavan Vijayaraj for their help in data prepartion. Dr. Kepner for MatlabMPI

26 THANK YOU