On the applicability of Landsat TM images to Mediterranean forest inventories

Salvador, Raymond; Pons, Xavier

doi:10.1016/s0378-1127(97)00264-8

Cited by 33 publications

(21 citation statements)

References 22 publications

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“…Other settings guiding the segmentation routine include color-shape 0.8-0.2 and smoothness-compactness 0.5-0.5. The homogeneity criteria included the visible and NIR bands with similar weight, and an aspect layer derived from the DEM to incorporate topographic information as one of the possible structural driving factors [59] was weighted 0.1.…”

Section: Image Segmentationmentioning

confidence: 99%

Modeling Forest Structural Parameters in the Mediterranean Pines of Central Spain using QuickBird-2 Imagery and Classification and Regression Tree Analysis (CART)

Gómez

Wulder

Montes³

et al. 2012

Remote Sensing

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Abstract:Forest structural parameters such as quadratic mean diameter, basal area, and number of trees per unit area are important for the assessment of wood volume and biomass and represent key forest inventory attributes. Forest inventory information is required to support sustainable management, carbon accounting, and policy development activities. Digital image processing of remotely sensed imagery is increasingly utilized to assist traditional, more manual, methods in the estimation of forest structural attributes over extensive areas, also enabling evaluation of change over time. Empirical attribute estimation with remotely sensed data is frequently employed, yet with known limitations, especially over complex environments such as Mediterranean forests. In this study, the capacity of high spatial resolution (HSR) imagery and related techniques to model structural parameters at the stand level (n = 490) in Mediterranean pines in Central Spain is tested using data from the commercial satellite QuickBird-2. Spectral and spatial information derived from multispectral and panchromatic imagery (2.4 m and 0.68 m sided pixels, respectively) served to model structural parameters. Classification and Regression Tree Analysis (CART) was selected for the modeling of attributes. Accurate models were produced of quadratic mean diameter (QMD) (R 2 = 0.8; RMSE = 0.13 m) with an average error of 17% while basal area (BA) models produced an average error of 22% (RMSE = 5.79 m 2 /ha). OPEN ACCESS Remote Sens. 2012, 4 136When the measured number of trees per unit area (N) was categorized, as per frequent forest management practices, CART models correctly classified 70% of the stands, with all other stands classified in an adjacent class. The accuracy of the attributes estimated here is expected to be better when canopy cover is more open and attribute values are at the lower end of the range present, as related in the pattern of the residuals found in this study. Our findings indicate that attributes derived from HSR imagery captured from space-borne platforms have capacity to inform on local structural parameters of Mediterranean pines. The nascent program for annual national coverages of HSR imagery over Spain offers unique opportunities for forest structural attribute estimation; whereby, depletions can be readily captured and successive annual collections of data can support or enable refinement of attributes. Further, HSR imagery and associated attribute estimation techniques can be used in conjunction, not necessarily in competition to, more traditional forest inventory with synergies available through provision of data within an inventory cycle and the capture of forest disturbance or depletions.

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Section: Image Segmentationmentioning

confidence: 99%

Modeling Forest Structural Parameters in the Mediterranean Pines of Central Spain using QuickBird-2 Imagery and Classification and Regression Tree Analysis (CART)

Gómez

Wulder

Montes³

et al. 2012

Remote Sensing

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“…Fazakas et al, 1999;Foody et al, 2001Foody et al, , 2003Gasparri et al, 2010;Hall et al, 2006;Labrecque et al, 2003Labrecque et al, , 2006Lu, 2005;Lu & Batistiella, 2005;Lu et al, 2004;Mäkelä & Pekkarinen, 2004;Mallinis et al, 2004;Meng et al, 2009;Powell et al, 2010;Roy & Ravan, 1996;Salvador & Pons, 1998a, 1998bSteininger, 2000;Tangki & Chappell, 2008;Wulder et al, 2008;Zheng et al, 2004Zheng et al, , 2007. In addition, taking into account the research objectives, there were two other important reasons for using Landsat images.…”

Section: Image Pre-processing and Development Of New Spectral Indicesmentioning

confidence: 99%

“…scrublands, farmlands) or because of high variability within the forested area (presence of different tree species, ages). In addition, other problems detected in previous research in Mediterranean forests are inaccuracies in the localization of inventory field plots, small plot sizes, and the small number of plots used in the analysis (Mallinis et al, 2004;Maselli & Chiesi, 2006;Salvador & Pons, 1998a, 1998bShoshany, 2000;Vázquez de la Cueva, 2005). The present study tested three different methods to extract the radiometric data in order to overcome the problems outlined above and achieve accurate FRB regression models: (i) fixed pixel windows or kernels, (ii) visual analysis, and (iii) spectral segmentation.…”

Section: Image Pre-processing and Development Of New Spectral Indicesmentioning

confidence: 99%

“…Images derived from remote sensing register continuous and complete information across a landscape and such images can be obtained at frequent intervals. These characteristics help to overcome some of the problems associated with inventory methods exclusively based on field work, interpolation techniques and GIS (Franklin, 2001;Lu, 2006;Salvador & Pons, 1998a).…”

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confidence: 99%

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Using Remote Sensing to Estimate a Renewable Resource: Forest Residual Biomass

García-Martín¹,

Fernández²,

Pérez‐Cabello³

et al. 2012

Remote Sensing of Biomass - Principles and Applications

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“…Remote sensing has potential to provide, at lower cost, robust forest information with greater coverage and more limited time extent than is attainable using field sampling. However, forest areas in the Mediterranean region are known for the high spatiotemporal heterogeneity of their vegetation patterns with respect to species composition and stand (Salvador and Pons 1998;Shoshany, 2000). While this heterogeneity makes them aesthetically attractive, accurate mapping of these areas has been the weak point of applied remote sensing technology, especially for local scale mapping (Mallinis et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Tree number estimation with the use of VHR natural colour orthophotos over a heterogeneous landscape in northern Greece

Stournara

Tsakiri–Strati

Siachalou

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Spatial explicit knowledge regarding the quantity and the spatial distribution of forest parameters is crucial for sustainable forest management, as well as in fulfilling national reporting needs in the framework of international treaties (i.e. Kyoto Protocol, FAO, EFFIS etc). Especially, tree number which can be used for assessing forest tree density (tree number/ha), is among the most important and laboursome parameters to be measured in the field. The aim of this study is to estimate tree number based on the use of nationwide, freely available, very high spatial resolution orthophotos acquired from Greek National Cadastre and Mapping Agency during the 2007-2009 period. The study area is the University Forest of Taxiarchis, which is located in central Halkidiki, Northern Greece. The dominant species of the forest includes both broadleaves (oak, beech) and coniferous species (Black pine, Calabrian pine), which are found in both pure and mixed stands. Tree crown detection was tested on natural color orthophoto bands in several plots. The principal components and intensity-hue-saturation transformations were also applied in order to enhance tree detection accuracy. Local maxima technique was utilized for tree crown detection. Accuracy results were evaluated based on field plot data available from the official forest management plan of the area completed in 2011. Overall, the detection accuracy exceeded 50% which is deemed satisfactory considering also the heterogeneity of the Mediterranean landscape and the limited spectral resolution of the remote sensing data available.

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On the applicability of Landsat TM images to Mediterranean forest inventories

Cited by 33 publications

References 22 publications

Modeling Forest Structural Parameters in the Mediterranean Pines of Central Spain using QuickBird-2 Imagery and Classification and Regression Tree Analysis (CART)

Modeling Forest Structural Parameters in the Mediterranean Pines of Central Spain using QuickBird-2 Imagery and Classification and Regression Tree Analysis (CART)

Using Remote Sensing to Estimate a Renewable Resource: Forest Residual Biomass

Tree number estimation with the use of VHR natural colour orthophotos over a heterogeneous landscape in northern Greece

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