Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Leaf Area Index Variations in Ecoregions of Ardabil Province, Iran

Version 1 : Received: 16 June 2021 / Approved: 17 June 2021 / Online: 17 June 2021 (09:26:34 CEST)

A peer-reviewed article of this Preprint also exists.

Andalibi, L.; Ghorbani, A.; Moameri, M.; Hazbavi, Z.; Nothdurft, A.; Jafari, R.; Dadjou, F. Leaf Area Index Variations in Ecoregions of Ardabil Province, Iran. Remote Sens. 2021, 13, 2879. Andalibi, L.; Ghorbani, A.; Moameri, M.; Hazbavi, Z.; Nothdurft, A.; Jafari, R.; Dadjou, F. Leaf Area Index Variations in Ecoregions of Ardabil Province, Iran. Remote Sens. 2021, 13, 2879.

Journal reference: Remote Sens. 2021, 13, 2879
DOI: 10.3390/rs13152879

Abstract

The leaf area index (LAI) is an important vegetation biophysical index that provides broad information on the dynamic behavior of ecosystems productivity and related climate, topography, and edaphic impacts. The spatio-temporal changes of LAI were assessed throughout Ardabil Province, a host of relevant plant communities within the critical ecoregion of a semi-arid climate. In a comparative study, novel data from Google Earth Engine- GEE was tested against traditional ENVI measures to provide LAI estimations. Besides, it is of important practical significance for institutional networks to quantitatively and accurately estimate LAI at large areas in a short time and using appropriate baseline vegetation indices. Therefore, LAI was characterized for ecoregions of Ardabil Province using remote sensing indices extracted from Landsat 8 Operational Land Imager (OLI), including Enhanced Vegetation Index calculated in GEE (EVIG) and ENVI5.3 software (EVIE), as well as Normalized Difference Vegetation Index estimated in ENVI5.3 software (NDVIE). Besides, a new field measurement method, i.e., the LaiPen LP 100 portable device (LP 100), was used to evaluate the accuracy of the derived indices. Accordingly, the LAI was measured on June and July 2020 in 822 ground points distributed in 16 different ecoregions-sub ecoregions having various Plant Functional Types (PFTs) of the shrub, bush, and tree. The analyses revealed heterogeneous spatial and temporal variability in vegetation indices and LAIs within and between ecoregions. The mean (standard deviation) value of EVIG, EVIE, and NDVIE at Province scale yielded 1.1 (0.41), 2.20 (0.78), and 3.00 (1.01), respectively in June, and 0.67 (0.37), 0.80 (0.63), and 1.88 (1.23), in that respect in July. The highest mean values of EVIG-LAI, EVIE-LAI, and NDVIE-LAI in June are found in Meshginshahr (1.40), Meshginshahr (2.80), and Hir (4.33) ecoregions and in July are found in Andabil ecoregion respectively with values of 1.23, 1.5, and 3.64. The lowest mean values of EVIG-LAI, EVIE-LAI, and NDVIE-LAI in June were observed for Kowsar (0.67), Meshginshahr (1.8), and Neur (2.70), ecoregions and in July were for Bilesavar ecoregion respectively with values of 0.31, 0.31, and 0.81. High correlation and determination coefficients (r>0.83 and R2>0.68) between LP 100 and remote sensing derived LAI were observed in all three PFTs (except for NDVIE-LAI in June with r=0.56 and R2=0.31). On average, all three examined LAI measures tended to underestimation compared to LP 100-LAI (r>0.42). The findings of the present study can be promising for effective monitoring and proper management of vegetation and land use in Ardabil Province and other similar areas.

Subject Areas

LaiPen; Management Tools; Remote sensing; Vegetation indices; Spatio-temporal changes

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