Predicting forest structural parameters using the image texture derived from WorldView-2 multispectral imagery in a dryland forest, Israel

Özdemir, İbrahim; Karnieli, Arnon

doi:10.1016/j.jag.2011.05.006

Cited by 160 publications

(147 citation statements)

References 44 publications

Supporting

Mentioning

135

Contrasting

Unclassified

Order By: Relevance

“…It is a way of extracting second-order statistical texture features, while the spectral derivatives can be considered first-order features, as they do not consider pixel neighbour relationships. GLCM was developed by Haralick, Shanmugam, and Dinstein [31], and has commonly been applied in remote sensing studies [15,17,19,22,32,33]. Ten common textural attributes (contrast, dissimilarity, homogeneity, second moment, energy, max probability, entropy, average, variance, and correlation) were computed using the Sentinel Application Platform (SNAP; http://step.esa.int/main/toolboxes/snap) commissioned by the European Space Agency (ESA).…”

Section: Textural Metricsmentioning

confidence: 99%

Estimation of Boreal Forest Attributes from Very High Resolution Pléiades Data

Persson

2016

Remote Sensing

View full text Add to dashboard Cite

Abstract:In this study, the potential of using very high resolution Pléiades imagery to estimate a number of common forest attributes for 10-m plots in boreal forest was examined, when a high-resolution terrain model was available. The explanatory variables were derived from three processing alternatives. Height metrics were extracted from image matching of the images acquired from different incidence angles. Spectral derivatives were derived by performing principal component analysis of the spectral bands and lastly, second order textural metrics were extracted from a gray-level co-occurrence matrix, computed with an 11 × 11 pixels moving window. The analysis took place at two Swedish test sites, Krycklan and Remningstorp, containing boreal and hemi-boreal forest. The lowest RMSE was estimated with 1.4 m (7.7%) for Lorey's mean height, 1.7 m (10%) for airborne laser scanning height percentile 90, 5.1 m 2 ·ha −1 (22%) for basal area, 66 m 3 ·ha −1 (27%) for stem volume, and 26 tons·ha −1 (26%) for above-ground biomass, respectively. It was found that the image-matched height metrics were most important in all models, and that the spectral and textural metrics contained similar information. Nevertheless, the best estimations were obtained when all three explanatory sources were used. To conclude, image-matched height metrics should be prioritised over spectral metrics when estimation of forest attributes is concerned.

show abstract

Section: Textural Metricsmentioning

confidence: 99%

Estimation of Boreal Forest Attributes from Very High Resolution Pléiades Data

Persson

2016

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Stem volume and basal area have been estimated through a synergy of ALS with airborne colour infrared (CIR) and AVNIR-2 data (Hou et al 2011). Ozdemir and Karnieli (2011) have used WorldView-2 data and texture analysis to approximate a number of additional parameters, including Standard Deviation of Diameters at Breast Heights (SDDBH), Gini Coefficient (GC), and Diameter Differentiation Index (DDI). ETM+ data have outperformed the higher spatial resolution but lower spectral information IKONOS and SPOT-4 High-Resolution Visible and Infrared sensor (HRVIR) in LAI estimation (Soudani et al 2006), with ALS data being reported as alternatives (Zhao and Popescu 2009).…”

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

View full text Add to dashboard Cite

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.

show abstract

“…The structural complexity and increased shadowing in the older (and usually higher-volume/AGB) stands leads to an overall reduction of reflectance from the canopy [32,35]. The reduction is observed even in the NIR range [37], despite the high NIR reflectance at the single-needle level. The observed maximal correlation of the volume and AGB with the NIR band is in contradiction with the results of Muukkonen and Heiskanen [4].…”

Section: Correlationsmentioning

confidence: 99%

Combining SPOT 5 imagery with plotwise and standwise forest data to estimate volume and biomass in mountainous coniferous site

Dimitrov

Roumenina

2013

Open Geosciences

View full text Add to dashboard Cite

Abstract:In this study, regression-based prediction of volume and aboveground biomass (AGB) of coniferous forests in a mountain test site was conducted. Two datasets -one with applied topographic correction and one without applied topographic correction -consisting of four spectral bands and six vegetation indices were generated from SPOT 5 multispectral image. The relationships between these data and ground data from field plots and national forest inventory polygons were examined. Strongest correlations of volume and AGB were observed with the near infrared band, regardless of the topographic correction. The maximal correlation coefficients when using plotwise data were -0.83 and -0.84 for the volume and AGB, respectively. The maximal correlation with standwise data was -0.63 for both parameters. The SCS+C topographic correction did not significantly affect the correlations between spectral data and forest parameters, but visually removed much of the topographically induced shading.Simple linear regression models resulted in relative RMSE of 32-33% using the plotwise data, and 43-45% using the standwise data. The importance of the source and the methodology used to obtain ground data for the successful modelling was pointed out. Keywords

show abstract

Predicting forest structural parameters using the image texture derived from WorldView-2 multispectral imagery in a dryland forest, Israel

Cited by 160 publications

References 44 publications

Estimation of Boreal Forest Attributes from Very High Resolution Pléiades Data

Estimation of Boreal Forest Attributes from Very High Resolution Pléiades Data

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

Combining SPOT 5 imagery with plotwise and standwise forest data to estimate volume and biomass in mountainous coniferous site

Contact Info

Product

Resources

About