Estimating Plant Dry Matter Productivity for AL-Sweeda Badia Rangeland (Syria) at Deferent Processing Levels of BKA, KVA Satellite Images
الموضوعات :Ghadir Hmeidan 1 , Ahed Alboody 2
1 - moslem
2 - moslem
الکلمات المفتاحية: Landsat8, NDVI, Plant dry matter productivity, Top of atmosphere, Ground surface,
ملخص المقالة :
Estimation of plant dry matter to management of rangelands fast as well as high accuracy is important for managers. Research aims to compare Plant Dry Matter Productivity (PDMP) values estimated by Normalized Difference Vegetation Index (NDVI) derived from satellite images BKA, KVA according to different levels of satellite image processing, for AL-Sweeda Badia (Syria), during the April, July of 2015 and October 2014. NDVI calculated according to digital number values (DN) then Top of Atmosphere values (TOA), finally Ground Surface (GS) values after Atmospheric Correction (AC) from L8 satellite image simultaneously with field measurements. Relationship between each two time-dependent satellite images was created. Then relations derived were adopted by L8 PDMP (PDMP) relationship estimation. Matter productivity average values according to TOA and GS was (15-42), (969-214), (3254 -22) and (576-563) kg / h for the previous dates respectively. There was a weak non-significant correlation between DN values and Matter productivity (≤0.063). And for TOA level relationship was relatively weak but significant (≤0.5). After atmospheric correction, it was strong (≥0.7) and significant at (1% and 5%) levels, and field verification measurements were consistent with 2014. Relationship between NDVI and PDMP for each of previous values was determined according to NDVI values of modern images. Previous relationships were applied to estimate PDMP then of objective maps was produced. DN satellite images contain geometrical distortions resulting from terrain, climate, change in velocity and height of sensor and radiation refraction in atmosphere, as well as for TOA values but at lower rate. But using GS after AC was good in rangelands state predicting and estimating PDMP.
Abuzar, M.; K, Sheffield; D, Whitfield; M, O’Connell and A, McAllister. 2014. Comparing inter-sensor NDVI for the analysis of horticulture crops in south-eastern Australia. American Journal of Remote Sensing; 2(1): 1-9.
Al-Khalif, H; N. Daoud; H. Shehab. 2009. Evaluation of the effectiveness of cultivation methods of pastoral plants in palmyra badia. Master Thesis, Department of Environmental Forestry, Faculty of Agriculture, Damascus University, Syria.
Amiri, F., B. Abdul Rashid and M. Shariff. 2010. Using remote sensing data for vegetation cover assessment in semi-arid rangeland of center province of Iran. In World Applied Sciences Journal, 11 (12): 1537-1546, ISSN 1818-4952.
Chander, G & B. Markham. 2003. Revised landsat5 TM radiometric calibration procedures and post calibration dynamic ranges geosciences and remote sensing. IEEE Transactions on, 41: 2674-2677.
Chander, G., B. L. Markham and D. L. Helder. 2009. Summary of current radiometric calibration coefficients for Landsat MSS، TM, ETM+, and EO-1 ALI sensors. Remote Sens. Environ. 2009: 113, 893–903.
Deep. R; Idris, Y., 2006. Using ASTER satellite images in rangeland studying. Diploma Thesis. Department of Geography, University of Damascus, Syria.
Diop, C., 1998. Measurements required in rangeland monitoring. Lecture presented in: Training course on management, improvement and monitoring of rangeland "Directorate of Badia and Sheep, Ministry of Agriculture, Damascus, Syria.
Eremeev.V; V. Ermakov; A. Kuznetcov; O. Nikonov; V. Pobaruev and V. Poshekhonov. 2014. Ground pressing technologies of “CANOPUS-V” and “BKA” information. Proc. SPIE 9241, Sensors, Systems, and Next-Generation Satellites XVIII, 92411V; doi:10.1117/12.2066629
Felde, G. W., G. P. Anderson, S. M. Adler-Golden, M. W. Matthew, and A. Berk, 2003. Analysis of hyperion data with the FLAASH atmospheric correction algorithm. Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX” SPIE Aerosense Conference, Orlando. 21-25 April: 90-92.
Fwal, A., S. Eid, M. Rokaya. 2009. Using remote sensing techniques and geographical information system in estimating rangeland capacity for selected areas of Rakka Badia. PhD Thesis Department of Geography, University of Damascus, Syria.
Hanqiu, X and T. Zhang. 2011. Comparison of landsat-7 ETM+ and ASTER NDVI measurements. Remote Sensing of the Environment: The 17th China Conference on Remote Sensing; Proc. of SPIE vol. 8203 82030K-1. Proc. SPIE 8203, Remote Sensing of the Environment: doi: 10.1117/12.910397.
Hmeidan, G., A. Alboody; S. Moussa; N. Daoud; A. Yaghi; E.Alkhaled. 2016. Analysis of NDVI changes and using NDVI to estimate plant productivity of dry matter for an area of Sweda Badia by reflectance-at-ground level for landSat 7&8 satellite images. The Arab Journal for Arid Environments. (In press)
Liqin ,C; T, LIU and L, Wei. 2014. A comparison of multi-resource remote sensing data for vegetation indices. IOP Conf. Ser.: Earth Environ. Sci. 17, 012067 doi:10.1088/1755-1315/17/1/012067.
Mishra , N; M. O. Haque, L. Leigh , A. David , H. Dennis and B. Markham. 2014. Radiometric cross calibration of landsat 8 operational land imager (OLI) and Landsat 7 enhanced thematic mapper plus (ETM+). Remote Sens., 6: 12619-12638.
Miura, T; H, Yoshioka; K, Fujiwara and H, Yamamoto. 2008. Inter-comparison of ASTER and MODIS surface reflectance and vegetation index products for synergistic applications to natural resource monitoring. Sensors, 8, 2480-2499.
Nekrasova, V and E. Makushevaa. 2012. Satellite CANOPUS-V' images Processing technology development for cartography purposes based on prelaunch simulation. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B4, 2012 XXII ISPRS Congress, Melbourne, Australia.
Nordblom, T. L, A. V.Goddchild, F. Shomo, and G. Gintzburger. 1997. Dynamics of feed resources in mixed farming systems of West/Central Asia-North Africa. In C. Renard (Ed.), Crop residues in sustainable mixed crops/livestock farming systems (pp. 131–147). Wallingfold (UK): CAB International.
Owensby, C.E. 1973. Technical notes: modified step-point system for botanical composition and basal cover estimates. Journal of Range Manage. 26:302-303
Parente,C., 2013. TOA reflectance and NDVI calculation for Landsat 7 ETM+ images of Sicily. Electronic International Interdisciplinary Conference, SECTION11. Ecology, Forestry, Earth Science.
Roland. G., and M. Ilaiwib. 2004. Assessment of rangeland degradation and development of a strategy for rehabilitation. Remote Sensing of Environment, 90: 490 – 504.
Ronald B. S., 2005. Computing radiances, reflectance and albedo from DN’s". Report. http:// www.yale.edu/ceo/Documentation /Computing Reflectance from DN. Downloaded at 10/05/2013.
Rouse, J. W., R. H. Haas, J. A. Schell, and D. W. Deering. 1973. Monitoring vegetation systems in the Great plains with ERTS", Third ERTS Symposium, NASA SP-351 I: 309- 317.
Soudani, K; C, François; G, Maire; V, Dantec and E, Dufrêne. 2006. Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands. Remote Sensing of Environment Vol:102, Issues 1–2, 30, P:161–175.
Tanser, F. C and A. R. Palmer. 1999. The application of a remotely-sensed diversity index to monitor degradation patterns in a semi-arid, heterogeneous, South African landscape. Journal of Arid Environments, 43: 477– 484.
The Arab Center for the Studies of Arid Zones and Dry Lands, Syria (ACSAD). 2004. Report of natural resources survey project in Syrian Badia using remote sensing techniques and geographical information system. Prepared for the Ministry of Agriculture in Syrian Arab Republic.
Théau, J; Temuulen T and W, Keith. 2010. Multi-sensor analyses of vegetation indices in a semi-arid environment. GIScience & Remote Sensing, 47, No. 2, p. 1–XXX. DOI: 10.2747/1548-1603.47.2.1
Thenkabail, P. S., 2004. Inter-sensor relationships between IKONOS and Landsat-7 ETM+ NDVI data in three ecoregions of Africa. International Journal of Remote Sensing Vol:25, P:389-408.
Yin, H; T, Udelhoven; R, Fensholt; D, Pflugmacher and P, Hostert. 2012. How normalized difference vegetation index (NDVI) trends from advanced very high resolution radiometer (AVHRR) and systeme probatoired 'Observation de la Terre VEGETATION (SPOT VGT) time series differ in agricultural areas: An inner Mongolian case study. Remote Sens., 4, 3364-3389; doi:10.3390/rs4113364.
