Impact of understory vegetation on forest canopy reflectance and remotely sensed LAI estimates

Eriksson, Helena; Eklundh, Lars; Kuusk, Andres; Nilson, Tiit

doi:10.1016/j.rse.2006.04.005

Cited by 158 publications

(87 citation statements)

References 41 publications

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“…The lower understory canopy may show the influences of additive reflectance [17] allowing it to be used to predict the NHP 15% layer by the spectral data. As has been pointed out by previous researchers [60][61][62], the understory reflectance can be a major influence on spectral remote sensing of forest biophysical variables. Of the three epochs, the first two were to ensure inclusion of all 14 independent variables at least once in an epoch.…”

Section: Hyperion To Predict Normalized Height Percentilesmentioning

confidence: 84%

Deciduous Forest Structure Estimated with LIDAR-Optimized Spectral Remote Sensing

Chávez

Tullis

2013

Remote Sensing

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Coverage and frequency of remotely sensed forest structural information would benefit from single orbital platforms designed to collect sufficient data. We evaluated forest structural information content using single-date Hyperion hyperspectral imagery collected over full-canopy oak-hickory forests in the Ozark National Forest, Arkansas, USA. Hyperion spectral derivatives were used to develop machine learning regression tree rule sets for predicting forest neighborhood percentile heights generated from near-coincident Leica Geosystems ALS50 small footprint light detection and ranging (LIDAR). The most successful spectral predictors of LIDAR-derived forest structure were also tested with basal area measured in situ. Based on the machine learning regression trees developed, Hyperion spectral derivatives were utilized to predict LIDAR forest neighborhood percentile heights with accuracies between 2.1 and 3.7 m RMSE. Understory predictions consistently resulted in the highest accuracy of 2.1 m RMSE. In contrast, hyperspectral prediction of basal area measured in situ was only found to be 6.5 m 2 /ha RMSE when the average basal area across the study area was ~12 m 2 /ha. The results suggest, at a spatial resolution of 30 × 30 m, that orbital hyperspectral imagery alone can provide useful structural information related to vegetation height. Rapidly calibrated biophysical remote sensing techniques will facilitate timely assessment of regional forest conditions.

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Section: Hyperion To Predict Normalized Height Percentilesmentioning

confidence: 84%

Deciduous Forest Structure Estimated with LIDAR-Optimized Spectral Remote Sensing

Chávez

Tullis

2013

Remote Sensing

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“…LAI is highly related to rates of evapotranspiration and photosynthesis, forest production and site water balance (Larcher 1977). LAI is also an important variable in carbon balance models (Chen & Cihlar 1996, Eriksson et al 2006. Bréda (2003) summarizes the importance of LAI.…”

Section: Introductionmentioning

confidence: 99%

A simplified methodology for the correction of Leaf Area Index (LAI) measurements obtained by ceptometer with reference to Pinus Portuguese forests

Lopes¹,

Nunes²,

Walford³

et al. 2014

iForest

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“…These parameters can best be estimated when 3D information on forest, i.e., a canopy height model, is integrated into the classification process [4]. However, forest is generally hard to map with optical sensors due to the influence from ground vegetation, shadow, cloud coverage and saturation (when the amount of biomass reaches a certain level), which may be especially high over rain forests [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Forest Assessment Using High Resolution SAR Data in X-Band

et al. 2011

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Novel radar satellite missions also include sensors operating in X-band at very high resolution. The presented study reports methodologies, algorithms and results on forest assessment utilizing such X-band satellite images, namely from TerraSAR-X and COSMO-SkyMed sensors. The proposed procedures cover advanced stereo-radargrammetric and interferometric data processing, as well as image segmentation and image classification. A core methodology is the multi-image matching concept for digital surface modeling based on geometrically constrained matching. Validation of generated surface models is made through comparison with LiDAR data, resulting in a standard deviation height error of less than 2 meters over forest. Image classification of forest regions is then based on X-band backscatter information, a canopy height model and interferometric coherence information yielding a classification accuracy above 90%. Such information is then directly used to extract forest border lines. High resolution X-band sensors deliver imagery that can be used for automatic forest assessment on a large scale.

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Impact of understory vegetation on forest canopy reflectance and remotely sensed LAI estimates

Cited by 158 publications

References 41 publications

Deciduous Forest Structure Estimated with LIDAR-Optimized Spectral Remote Sensing

Deciduous Forest Structure Estimated with LIDAR-Optimized Spectral Remote Sensing

A simplified methodology for the correction of Leaf Area Index (LAI) measurements obtained by ceptometer with reference to Pinus Portuguese forests

Forest Assessment Using High Resolution SAR Data in X-Band

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