Estimating Aboveground Net Primary Production (ANPP) Using Landsat 8-Based Indices: A Case Study From Hir-Neur Rangelands, Iran

Ghorbani, Ardavan; Dadjou, Farid; Moameri, Mehdi; Biswas, Asim

doi:10.1016/j.rama.2020.06.006

Cited by 11 publications

(14 citation statements)

References 49 publications

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“…To mitigate spatial misalignments, the LAI estimates from the aerial images were extracted with pixel size resolutions for each PFT, and subsequently averaged for each ecoregion/sub-ecoregion. To evaluate the performance of the data obtained from satellite images compared to the ground data, five error assessment criteria were used [6,68] (Equations ( 6) to ( 10)):…”

Section: Statistical Analysis and Accuracy Assessmentmentioning

confidence: 99%

“…1. Mean bias error (MBE) indicates the difference between the mean of remote sensing (LAI RS ) and LP 100 (LAI LP ) derived LAIs (Equation (6). Although this criterion is the average bias error of the overall remote sensing method, and not a good error measure-it primarily facilitates seeing how much remote sensing methods overestimate or underestimate the LAI values.…”

Section: Statistical Analysis and Accuracy Assessmentmentioning

confidence: 99%

“…2021, 13 indicator [1][2][3]. Since the ecological values of vegetation in natural ecosystems have a particular priority in regional security supply, the study of leaf production added to the soil every year plays an essential role in soil quality improvement [4][5][6]. Therefore, it is necessary to be aware of the changing pattern of the leaf area index (LAI) in different ecosystems to choose the right land management strategy [7,8].…”

Section: Introductionmentioning

confidence: 99%

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Leaf Area Index Variations in Ecoregions of Ardabil Province, Iran

et al. 2021

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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.

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Section: Statistical Analysis and Accuracy Assessmentmentioning

confidence: 99%

Section: Statistical Analysis and Accuracy Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Leaf Area Index Variations in Ecoregions of Ardabil Province, Iran

et al. 2021

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show abstract

“…To mitigate spatial misalignments, the LAI estimates from the aerial images were extracted with pixel size resolution for each PFT and subsequently averaged for each ecoregion/sub-ecoregion. To evaluate the performance of the data obtained from satellite images in comparison with the ground data, five error assessment criteria were used [6,69] (Eqs. 6 to 10):…”

Section: Statistical Analysis and Accuracy Assessmentmentioning

confidence: 99%

“…Leaf area is the main component to the exchange of matter and energy between tree canopies and the atmosphere and is considered an important ecological and biological indicator [1][2][3]. Since the ecological values of vegetation in natural ecosystems have a particular priority in regional security supply, the study of leaf production that is added to the soil every year plays an essential role in soil quality improvement [4][5][6]. Therefore, it is necessary to be aware of changing pattern of leaf area index (LAI) in different ecosystems to choose the right land management strategy [7,8].…”

Section: Introductionmentioning

confidence: 99%

Leaf Area Index Variations in Ecoregions of Ardabil Province, Iran

Andalibi¹,

Ghorbani²,

Moameri³

et al. 2021

Preprint

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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&gt;0.83 and R2&gt;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&gt;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.

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