Estimating the leaf area index in Indian tropical forests using Landsat-8 OLI data

Middinti, Suresh; Thumaty, Kiran Chand; Rajashekar, G.; Jha, Chandra Shekhar; Thatiparthi, Byragi Reddy

doi:10.1080/01431161.2017.1363436

Cited by 15 publications

(3 citation statements)

References 56 publications

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“…Chen et al [50] found that when using multiple sensors, the ones that had a larger discrepancy between plot size and sensor resolution did not perform as well as the sensors that were closer to the plot size. Middinti et al [51] found that when combining MODIS and OLI data there were a significant number of high LAI values when compared to a solely OLI-derived map. This supports the notion that the larger the sensor resolution difference when combining multiple sensor types, the less apt the combination is at estimating LAI.…”

Section: Vegetation Indices Comparisonmentioning

confidence: 99%

Landsat 8 Based Leaf Area Index Estimation in Loblolly Pine Plantations

Blinn

House

Wynne

et al. 2019

Forests

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Leaf area index (LAI) is an important biophysical parameter used to monitor, model, and manage loblolly pine plantations across the southeastern United States. Landsat provides forest scientists and managers the ability to obtain accurate and timely LAI estimates. The objective of this study was to investigate the relationship between loblolly pine LAI measured in situ (at both leaf area minimum and maximum through two growing seasons at two geographically disparate study areas) and vegetation indices calculated using data from Landsat 7 (ETM+) and Landsat 8 (OLI). Sub-objectives included examination of the impact of georegistration accuracy, comparison of top-of-atmosphere and surface reflectance, development of a new empirical model for the species and region, and comparison of the new empirical model with the current operational standard. Permanent plots for the collection of ground LAI measurements were established at two locations near Appomattox, Virginia and Tuscaloosa, Alabama in 2013 and 2014, respectively. Each plot is thirty by thirty meters in size and is located at least thirty meters from a stand boundary. Plot LAI measurements were collected twice a year using the LI-COR LAI-2200 Plant Canopy Analyzer. Ground measurements were used as dependent variables in ordinary least squares regressions with ETM+ and OLI-derived vegetation indices. We conclude that accurately-located ground LAI estimates at minimum and maximum LAI in loblolly pine stands can be combined and modeled with Landsat-derived vegetation indices using surface reflectance, particularly simple ratio (SR) and normalized difference moisture index (NDMI), across sites and sensors. The best resulting model (LAI = −0.00212 + 0.3329SR) appears not to saturate through an LAI of 5 and is an improvement over the current operational standard for loblolly pine monitoring, modeling, and management in this ecologically and economically important region.

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Section: Vegetation Indices Comparisonmentioning

confidence: 99%

Landsat 8 Based Leaf Area Index Estimation in Loblolly Pine Plantations

Blinn

House

Wynne

et al. 2019

Forests

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“…• LAI-there are many algorithms for calculating the LAI vegetation index [19][20][21]. The LAI used in the present study is the modified simple ratio (MSR) that can be linearly related to LAI [22]:…”

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confidence: 99%

Field Testing Satellite-Derived Vegetation Health Indices for a Koala Habitat Managers Toolkit

2022

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A Central Queensland University (CQU) partnership with the Queensland Government National Park management agency has developed a koala (Phascolarctos cinereus) habitat managers’ toolkit for vegetation health assessment. Private and public landholders use the field-based toolkit to assess habitat suitability or monitor conservation outcomes for the koala—an iconic Australian arboreal herbivorous marsupial. The toolkit was upgraded recently with instructions to process European Space Agency (ESA) Sentinel-2 multispectral satellite-derived selected vegetation maps for areal vegetation health trend monitoring. A field campaign sought to validate the relatively coarse spatial resolution derived indices (photosynthetic health, leaf area index and leaf water content) to verify their suitability for the habitat management decision-support toolkit. Other user requirement-driven criteria for including remote sensing in the toolkit were imagery and associated processing software costs and ease of map production for habitat managers without cost-effective access to spatial science skills. Despite moderate-to-low field and image vegetation proxy correlations, discussing the results with stakeholders indicates that, at a landscape scale, the use of cost-free, suitable temporal resolution, 10-m spatial resolution imagery is satisfactory when aligned with the design outcomes of a habitat health toolkit.

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“…The plant communities in any ecosystem largely determine the energy exchange, biomass accumulation, and gaseous exchange between plant canopies, thus regulating atmospheric concentrations of carbon dioxide (CO 2 ) which in turn is useful for understanding the carbon budgets of the vegetation type (Nelson et al, 1999;Houghton et al, 2015;Anderson-Teixeira et al, 2016;Chazdon et al, 2016;Kumar et al, 2017;Wang et al, 2019;Tripati et al, 2020;Chaturvedi et al, 2021). In the past, a few studies have indicated that the multispectral satellite images are useful and can assist in monitoring the structure, and composition of a forest type, its diversity, and spatial arrangements (Lepine et al, 2016;Shiklomanov et al, 2016;Ali et al, 2017;Middinti et al, 2017), before addressing any functional ecological and biophysical processes of an ecosystem (such as above-ground biomass (AGB), net primary production (NPP), evapotranspiration, energy exchange, and biomass allocation patterns). Tropical forests are considered as productive terrestrial environments with a maximum potential of carbon sink and NPP per unit area (Fearnside, 1996;Gaston et al, 1998;Chave et al, 2001;Clark et al, 2001;Malhi et al, 2004;Malhi et al, 2009;Beer et al, 2010;Bijalwan et al, 2010;Mohommad and Joshi, 2015;Anderson-Teixeira et al, 2016;Poorter et al, 2016;Moore et al, 2018;Wallis et al, 2019;Wang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Table2.DOCX

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Estimating the leaf area index in Indian tropical forests using Landsat-8 OLI data

Cited by 15 publications

References 56 publications

Landsat 8 Based Leaf Area Index Estimation in Loblolly Pine Plantations

Landsat 8 Based Leaf Area Index Estimation in Loblolly Pine Plantations

Field Testing Satellite-Derived Vegetation Health Indices for a Koala Habitat Managers Toolkit

Table2.DOCX

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