Mapping plant communities in a local Arctic landscape applying a scanned infrared aerial photograph in a geographical information system

Nilsen, Lennart

doi:10.1080/014311699213541

Cited by 35 publications

(18 citation statements)

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“…Different studies have concluded that the characterization of individual phyto-sociological units cannot be obtained by Landsat-TM data due to its spatial resolution limitations (Spjelkavik 1995, Nilsen et al 1999. In this study, high-resolution infrared images with pixel array dimensions of 101261524 and spatial resolution of 1 m61 m pixels were acquired with a Kodak DCS 420 digital, colour-infrared camera.…”

Section: Digital Camera Imagery Acquisition and Processingmentioning

confidence: 99%

The use of high resolution digital camera imagery to characterize the distribution of Pteronia incana invader species in Ngqushwa (formerly Peddie) District, Eastern Cape, South Africa

Kakembo

Palmer²,

Rowntree

2006

International Journal of Remote Sensing

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High resolution infrared digital camera imagery was used to determine the extent of the invasion of hill slopes by Pteronia incana, an unpalatable dwarf shrub. On the basis of a priori field surveys, the imagery was classified into different degrees of the invasion and other surface cover types using Idrisi32 GIS. The imagery was noted to have limited constraints, as evidenced by the high level of classification accuracy. A range of vegetation indices was examined to identify one that best characterized the spatial distribution of the shrub and its degree of invasion. Distinct spectral separability of the invader from other surface cover types was achieved by means of the perpendicular vegetation index (PVI), as opposed to the ratio based vegetation indices (NDVI, SAVI, and MSAVI). Depending on the local geographical characteristics, the PVI could be particularly suited for the identification of perennial shrubs with characteristics similar to P. incana, which are usually interspersed with considerable bare zones.

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Section: Digital Camera Imagery Acquisition and Processingmentioning

confidence: 99%

The use of high resolution digital camera imagery to characterize the distribution of Pteronia incana invader species in Ngqushwa (formerly Peddie) District, Eastern Cape, South Africa

Kakembo

Palmer²,

Rowntree

2006

International Journal of Remote Sensing

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“…To represent such a composite feature on the map requires a special technical solution. A method was used which rests on the binomial law and empirical probability modelling as presented in Nilsen et al (1999). Table 2 provides the key data useful to explain how the model works.…”

Section: Satellite Datamentioning

confidence: 99%

Mapping and analysing arctic vegetation: Evaluating a method coupling numerical classification of vegetation data with SPOT satellite data in a probability model

Nilsen¹,

Elvebakk²,

Brossard³

et al. 1999

International Journal of Remote Sensing

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Vegetation and environmental data were collected at 266 sampling points distributed in a regular manner along transects covering the Brù ggerhalvù ya peninsula, on the north-western coast of Spitsbergen. Transects with sampling points were drawn in advance on aerial photographs. The analysis of releve Â s and collection of ground data along transects represent an e cient, representative and precise way of sampling. The vegetation data were classi® ed and 19 plant communities distinguished. The plant communities were subjected to detrended correspondence analysis (DCA). Among the recorded variables, moisture is the one with the highest correlation along axes one and two, and re¯ects a coincidental moisture and vegetation cover gradient. The vegetation component responsible for this positive correlation is the bryophytes. Likewise, the TWINSPAN classi® cation con® rms this gradient in a dendrogram re¯ecting the hierarchical structure of the plant communities.Plant communities constitute the base of a statistical model that links the communities and the SPOT satellite data. The model then classi® es and maps plant communities by means of satellite data, covering the entire Brù ggerhalvù ya peninsula. Satellite data and environmental data were analysed regarding their ability to distinguish the plant communities in a discriminant function analysis (DFA). The results of the DFA indicate that it may be reasonable to include all the information from the di erent satellite channels when using satellite data for vegetation classi® cation purposes. Among the satellite data the panchromatic channel is the one adding the most unique information to the power of the model in separating plant communities.The classi® cation of satellite data using the probability model indicates that plant communities with less than 30% vegetation cover could be classi® ed with the same degree of con® dence or better, as compared with plant communities with more than 30% vegetation cover. The overall percentage of correctly classi-® ed releve Â s increased by 13% when using probability level two instead of level one (57.8 to 71.1%). The probability classi® cation model makes it possible

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“…Examples of the successful use of DEM for predictive vegetation mapping are mostly from mountainous regions (Nilsen et al 1999;Dirnböck et al 2003;Wright and Gallant 2007), where elevation and its derivates serve as proxies for multiple environment variables including soil properties and water status (Lassueur et al 2006). In our study area, the range of absolute elevation is only 35-207 m. The soil properties in the database of the 1:10 000 soil map of Estonia also indirectly describe land forms-this way the soil map serves as a proxy for DEM.…”

Section: Indicator Value Of Data Layers and Featuresmentioning

confidence: 98%

Similarity-based large-scale distribution mapping of orchids

Remm

2008

Biodivers Conserv

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The expected presence/absence of a target species outside the area of actual observations is commonly estimated using statistical models or decision criteria. This investigation demonstrates a similarity-based solution for predictive mapping as an alternative to generalizing models. The maps of the expected distribution of 12 orchids were created using Wnd sites from Weld observations and absence sites generated onto the observation track. The expected presence/absence of a species in a location was calculated according to the similarity between the location and selected examples of presence and absence sites. A machine learning system selected the best predictive sets for each species out of 161 cartographic and remote sensing features. The usefulness of the predictive distribution maps was expressed as the ratio of the density of Wnd sites per track in the predicted presence area relative to the density per track in the predicted absence area. The predictive mapping was more eYcient for Dactylorhiza incarnata, D. russowii, Gymnadenia conopsea, and Goodyera repens. Soil properties and the proportion of Wnd sites for the other species in the vicinity were the most indicative site characteristics. The rarer species were found to be better indicators of the occurrence of the other species than were the more common orchids. The proposed approach-to direct subsequent Weld observations to sites where the occurrence of the target species was predicted but has not yet been recorded-helped discover new populations of orchids and enhance the representativeness of absence sites.Keywords Orchidaceae · Similarity-based reasoning · Predictive distribution mapping · Indicator value of site characteristics Abbreviations EELIS Estonian Nature Infosystem, a government database of protected areas and veriWed permanent Wnd sites of protected species

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Mapping plant communities in a local Arctic landscape applying a scanned infrared aerial photograph in a geographical information system

Cited by 35 publications

References 0 publications

The use of high resolution digital camera imagery to characterize the distribution of Pteronia incana invader species in Ngqushwa (formerly Peddie) District, Eastern Cape, South Africa

The use of high resolution digital camera imagery to characterize the distribution of Pteronia incana invader species in Ngqushwa (formerly Peddie) District, Eastern Cape, South Africa

Mapping and analysing arctic vegetation: Evaluating a method coupling numerical classification of vegetation data with SPOT satellite data in a probability model

Similarity-based large-scale distribution mapping of orchids

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