Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest

Chen, Xuexia; Vierling, L. A.; Rowell, Eric; DeFelice, Thomas P.

doi:10.1016/j.rse.2003.11.003

Cited by 122 publications

(61 citation statements)

References 29 publications

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“…We speculate that this may be an artifact of the fine resolution of IKONOS imagery, where a larger number of pixels in vegetation rich areas may be picking up numerous small patches of shade cast by vegetation canopies (see [7]), leading to lower perceived values of Greenness. Similar findings have been observed in a study conducted in a pine forest, where the IKONOS derived Enhanced Vegetation Index was found to have a negative relationship with the Leaf Area Index [40].…”

Section: Discussionsupporting

confidence: 90%

Assessing Plant Diversity in a Dry Tropical Forest: Comparing the Utility of Landsat and Ikonos Satellite Images

Nagendra

Rocchini

Ghate³

et al. 2010

Remote Sensing

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While high expectations have been raised about the utility of high resolution satellite imagery for biodiversity assessment, there has been almost no empirical assessment of its use, particularly in the biodiverse tropics which represent a very challenging environment for such assessment challenge. This research evaluates the use of high spatial resolution (IKONOS) and medium spatial resolution (Landsat ETM+) satellite imagery for assessing vegetation diversity in a dry tropical forest in central India. Contrary to expectations, across multiple measures of plant distribution and diversity, the resolution of IKONOS data is too fine for the purpose of plant diversity assessment and Landsat imagery performs better.

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Section: Discussionsupporting

confidence: 90%

Assessing Plant Diversity in a Dry Tropical Forest: Comparing the Utility of Landsat and Ikonos Satellite Images

Nagendra

Rocchini

Ghate³

et al. 2010

Remote Sensing

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“…Similarly, in the literature, other canopy metrics, such as canopy density, canopy area and canopy cover [19], have also been shown to be good secondary predictors (i.e., used in conjunction with a canopy height metric), as they are related to the horizontal and vertical structure of vegetation on the landscape [20,21]. LiDAR data, both spaceborne and airborne, have been utilized in conjunction with optical imagery to quantify the spatial extent of biomass and estimate forest attributes, including forest cover [22][23][24][25]. Therefore, although ICESat-2 heights may not reflect the true top of canopy, they remain poised to be an important measurement for estimating biomass globally, particularly in sparse or degraded ecosystems.…”

Section: Discussionmentioning

confidence: 99%

The Potential Impact of Vertical Sampling Uncertainty on ICESat-2/ATLAS Terrain and Canopy Height Retrievals for Multiple Ecosystems

Neuenschwander

Magruder

2016

Remote Sensing

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With a planned launch no later than September 2018, the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) will provide a global distribution of geodetic elevation measurements for both the terrain surface and relative canopy heights. The Advanced Topographic Laser Altimeter System (ATLAS) instrument on-board ICESat-2 is a LiDAR system sensitive to the photon level. The photon-counting technology has many advantages for space-based altimetry, but also has challenges, particularly with delineating the signal from background noise. As such, a current unknown facing the ecosystem community is the performance of ICESat-2 for terrain and canopy height retrievals. This paper aims to provide the science user community of ICESat-2 land/vegetation data products with a realistic understanding of the performance characteristics and potential uncertainties related to the vertical sampling error, which includes the error in the perceived height value and the measurement precision. Terrain and canopy heights from simulated ICESat-2 data are evaluated against the airborne LiDAR ground truth values to provide a baseline performance uncertainty for multiple ecosystems. Simulation results for wooded savanna and boreal forest result in a mean bias error and error uncertainty (precision) for terrain height retrievals at 0.06 m (0.24 m RMSE) and −0.13 m (0.77 m RMSE). In contrast, results over ecosystems with dense vegetation show terrain errors of 1.93 m (1.66 m RMSE) and 2.52 m (3.18 m RMSE), indicating problems extracting terrain height due to diminished ground returns. Simulated top of canopy heights from ICESat-2 underestimated true top of canopy returns for all types analyzed with errors ranging from 0.28 m (1.39 m RMSE) to 1.25 m (2.63 m RMSE). These results comprise a first step in a comprehensive evaluation of ICESat-2 anticipated performance. Future steps will include solar noise impact analysis and investigation into performance discrepancy between visible and near-infrared wavelengths.

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“…Leaf Area Index (LAI), a critical structural property of vegetation canopies used to predict mass and energy fluxes, was reliably predicted from LiDAR in coniferous forest; however, the inclusion of SPOT image-derived spectral vegetation indices in an integrated model provided only negligible improvement [110]. A method was formulated for using LiDAR and effective LAI to validate vegetation cover estimated from multispectral IKONOS and Landsat ETM+ imagery [111] in ponderosa pine forest. In the Black Hills of South Dakota, USA, vegetation height layers identified from LiDAR were combined with IKONOS multispectral satellite imagery to assess avian species occurrence, density, and diversity [112].…”

Section: Sensor Integrationmentioning

confidence: 99%

LiDAR Utility for Natural Resource Managers

2009

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Applications of LiDAR remote sensing are exploding, while moving from the research to the operational realm. Increasingly, natural resource managers are recognizing the tremendous utility of LiDAR-derived information to make improved decisions. This review provides a cross-section of studies, many recent, that demonstrate the relevance of LiDAR across a suite of terrestrial natural resource disciplines including forestry, fire and fuels, ecology, wildlife, geology, geomorphology, and surface hydrology. We anticipate that interest in and reliance upon LiDAR for natural resource management, both alone and in concert with other remote sensing data, will continue to rapidly expand for the foreseeable future.

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Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest

Cited by 122 publications

References 29 publications

Assessing Plant Diversity in a Dry Tropical Forest: Comparing the Utility of Landsat and Ikonos Satellite Images

Assessing Plant Diversity in a Dry Tropical Forest: Comparing the Utility of Landsat and Ikonos Satellite Images

The Potential Impact of Vertical Sampling Uncertainty on ICESat-2/ATLAS Terrain and Canopy Height Retrievals for Multiple Ecosystems

LiDAR Utility for Natural Resource Managers

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