Leaf Area Index Variations in Ecoregions of Ardabil Province, Iran

Abstract

The leaf area index (LAI) is an important vegetation biophysical index that provides broad information on the dynamic behavior of an ecosystem’s productivity and related climate, topography, and edaphic impacts. The spatiotemporal 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. Moreover, 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 the Enhanced Vegetation Index calculated in GEE (EVIG) and ENVI5.3 software (EVIE), as well as the Normalized Difference Vegetation Index estimated in ENVI5.3 software (NDVIE). Moreover, 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 in 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 a 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), respectively, 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, the 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 underestimate compared to LP 100-LAI (r > 0.42). The findings of the present study could be promising for effective monitoring and proper management of vegetation and land use in the Ardabil Province and other similar areas.

Vegetation Indices for Spatio-Temporal Analysis of the Quality of Can Gio Mangrove for Biodiversity and Conservation

“…Most of the chosen images of the study area had land cloud cover less than 20 percent, and they were described in the Table 1 below [2,7,18]. NDVI is commonly used for vegetation segregation, plant growth, and vegetation cover estimation [9]. This index ranges from −1.0 to 1.0.…”

“…However, NDVI cannot identify some effects on vegetation (such as dust, atmospheric aerosols and soil background) to obtain precise calculation [20,21]. Therefore, EVI was developed to overcome the NDVI problems with temporal and spatial vegetation variations and clarify topographic differences [9,20]. In the urban area, EVI is as a proxy for analysing vegetation variations due to urbanization.…”

“…The well-known indices used by many researchers are normalized difference vegetation indices (NDVI) and enhanced vegetation index (EVI) [6][7][8]. The leaf area index (LAI) is another important index to analyse vegetation structure and density [9][10][11]. Other indices such as PTC (percentage tree cover), SAVI (soil adjusted vegetation index), MSAVI (modified soil adjusted vegetation index), OSAVI (optimized soil adjusted vegetation index), TSAVI (transformed soil adjusted vegetation index), FAPAR (fraction of absorbed photosynthetically active radiation), etc., can well support analyses of vegetation quality and health [2,7,10,12].…”

Spatio-temporal dynamics of mangrove extent in coastal Bekasi Regency, Indonesia

“…Mangrove mapping has used machine learning and satellite images extensively [8][9][10][11]. The Google Earth Engine (GEE) cloud computing platform allows us to filter and sort satellite imagery, which additionally simplifies the method of obtaining time-series data.…”

Multisensor Assessment of Leaf Area Index across Ecoregions of Ardabil Province, Northwestern Iran

“…After considering the type of plant under study and determining a coefficient in the obtained value, it becomes the effective LAI. Remote sensing observations are sensitive to the effective LAI [14]. The difference between the actual and effective LAI may be determined by the population index [15], which varies approximately between 0.5 (highly clustered canopies) and 1 (leaves with random distribution) [16].…”