Mapping beech (Fagus sylvatica L.) forest structure with airborne hyperspectral imagery

Cho, Moses Azong; Skidmore, Andrew K.; Sobhan, Istiak

doi:10.1016/j.jag.2009.01.006

Cited by 37 publications

(31 citation statements)

References 48 publications

(73 reference statements)

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“…In general, as rule of thumb, 25-30 m SAR spatial resolution or 25 m LiDAR footprint diameter are required to capture vegetation structure for biodiversity application (Bergen et al 2009). Other promising alternatives to active sensor data have included multispectral or hyperspectral data, mainly using neighbourhood statistics, spectral indices, or texture features (Wolter et al 2009;Kayitakire et al 2006;Cho et al 2009;White et al 2010;Petrou et al 2012).…”

Section: Forestry Monitoringmentioning

confidence: 99%

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

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Recognizing the imperative need for biodiversity protection, the Convention on Biological Diversity (CBD) has recently established new targets towards 2020, the so-called Aichi targets, and updated proposed sets of indicators to quantitatively monitor the progress towards these targets. Remote sensing has been increasingly contributing to timely, accurate, and cost-effective assessment of biodiversity-related characteristics and functions during the last years. However, most relevant studies constitute individual research efforts, rarely related with the extraction of widely adopted CBD biodiversity indicators. Furthermore, systematic operational use of remote sensing data by managing authorities has still been limited. In this study, the Aichi targets and the related CBD indicators whose monitoring can be facilitated by remote sensing are identified. For each headline indicator a number of recent remote sensing approaches able for the extraction of related properties are reviewed. Methods cover a wide range of fields, including: habitat extent and condition monitoring; species distribution; pressures from unsustainable management, pollution and climate change; ecosystem service monitoring; and conservation status assessment of protected areas. The advantages and limitations of different remote sensing data and algorithms are discussed. Sorting of the methods based on their reported accuracies is attempted, when possible. The extensive literature survey aims at reviewing highly performing methods that can be used for large-area, effective, and timely biodiversity assessment, to encourage the more systematic use of remote sensing solutions in monitoring progress towards the Aichi targets, and to decrease the gaps between the remote sensing and management communities.

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Section: Forestry Monitoringmentioning

confidence: 99%

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

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“…Height was measured for five randomly selected trees within each plot. A Haga hypsometer (Haga GmbH and Co. KG, Nuremberg, Germany) [39] was used to measure the height of the largest tree in terms of DBH and the trees located at the four corners of each plot. It should be noted that we had no a-priori information in term of DBH, height, and species type about the distribution of trees within each randomly selected plot, meaning that each tree has an equal probability of being selected from the respective population.…”

Section: Field Datamentioning

confidence: 99%

“…Previous studies have shown that the narrow band indices can provide significant improvement as compared to broadband indices for estimating vegetation structural variables [39,61,67]. Consequently, we only focused on narrow band indices.…”

Section: Narrow-band Vegetation Indicesmentioning

confidence: 99%

“…As an example, Schlerf et al [61] used HyMap data of highly managed and relatively homogenous Norway spruce (Picea abies) stands and reported significant linear relationships between forest leaf area index (LAI) and crown volume with a narrow-band perpendicular vegetation index (PVI). Cho et al [39] tested the relationship between reflectance factors derived from HyMap data and forest structural attributes including mean DBH, mean tree height and tree density in a homogenous (i.e., homogeneous in DBH, height, tree density, and species) and closed canopy beech (Fagus sylvatica) stand. They conclude that their new vegetation indices, generated from contrasting spectral regions around the red-edge shoulder (756-820 nm) and the water absorption feature centered at 1200 nm (1172-1320 nm), showed higher correlation with structural variables compared with the standard vegetation indices derived from near-infrared and visible reflectance.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Alpine Forest Structural Variables from Imaging Spectrometer Data

et al. 2015

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Spatial information of forest structural variables is crucial for sustainable forest management planning, forest monitoring, and the assessment of forest ecosystem productivity. We investigate a complex alpine forest ecosystem located in the Swiss National Park (SNP) and apply empirical models to retrieve the structural variables canopy closure, basal area, and timber volume at plot scale. We used imaging spectrometer (IS) data from the Airborne Prism EXperiment (APEX) in combination with in-situ measurements of forest structural variables to develop empirical models. These models are based on simple and stepwise multiple regressions, while all potential two narrow-band combinations of the Simple Ratio (SR), the Normalized Difference Vegetation Index (NDVI), the perpendicular vegetation index (PVI), the second soil-adjusted vegetation index (SAVI2), and band depth indices were tested. The accuracy of the estimated structural attributes was evaluated using a leave-one-out cross-validation technique. Using stepwise multiple regression models, we obtained a moderate to good accuracy when estimating canopy closure (R 2 = 0.81, rRMSE = 10%), basal area (R 2 = 0.68, rRMSE = 20%), and timber volume (R 2 = 0.73, rRMSE = 22%). We discuss the reliability of empirical approaches for estimates of canopy structural parameters considering the causality of light interaction and surface information.

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“…From high spatial resolution hyperspectral images, Cho et al (2009) mapped three forest structural parameters including mean diameter-at-breast height (DBH), mean tree height and tree density of a closed canopy beech forest with accuracy 72.4%, 67.4% and 53.6%, respectively. Dehaan et al (2007) quantified the distribution of blackberry in open canopies with an overall accuracy 92%.…”

Section: Introductionmentioning

confidence: 99%

High Spatial Resolution Hyperspectral Mapping for Forest Ecosystem at Tree Species Level

Shen

Sakai

Hoshino

2010

Agricultural Information Research

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Integrated management of forest ecosystems requires an accurate and all-sided mastery of the forest information, of which forest ecosystem cover especially at tree species level is the most basic and important component. The study investigated and demonstrated the mapping potential of the forest ecosystem at tree species level from high spatial resolution hyperspectral images. The mapping performances of eight conventional classification methods including Maximum Likelihood (ML), Mahalanobis Distance (MaD), Minimum Distance (MD), Parallelepiped (P), Binary Encoding (BE), Spectral Angle Mapper (SAM), Spectral Information Divergence (SID) and Support Vector Machine (SVM), were verified based on two noise treatments (noise fraction and noise removal) and three leaf growth periods (tender leaf period, young leaf period and adult leaf period). It could be confirmed that noise removal obviously contributed to improving the classification agreement and young leaf period was most suitable for mapping forest ecosystem at tree species level from high spatial resolution hyperspectral images. ML, P, BE and SID were not considered appropriate according to good results with overall accuracy and kappa coefficient exceeding 85% and 0.80 respectively. Though MD also produced a very high classification agreement, it could not cover up its poor potential to identify tree species by spectral features. Even if SVML, SVMP, SVMR and SVMS performed the stablest and could generate good results across three periods, the best result was obtained by SAM. Except that the difference was significant between MD and SVMS at the 5% significance level in tender leaf period, the comparative tests did not provide more proof to show the significant difference between the methods considered appropriate.

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Mapping beech (Fagus sylvatica L.) forest structure with airborne hyperspectral imagery

Cited by 37 publications

References 48 publications

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

Estimation of Alpine Forest Structural Variables from Imaging Spectrometer Data

High Spatial Resolution Hyperspectral Mapping for Forest Ecosystem at Tree Species Level

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