Estimating Plant Dry Matter Productivity for AL-Sweeda Badia Rangeland (Syria) at Deferent Processing Levels of BKA, KVA Satellite Images
الموضوعات :
Ghadir Hmeidan
1
(moslem)
Ahed Alboody
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.
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