Authors:A. Sheikhi-Garjan, A. Hosseini-Gharalari, M. Mahjob, M. Rashid, Q. Sabahi, M. Safari, F. Jalilyan, and R. Arbabtafti
Inc. ( 2002 ): SAS/STAT user’s guide. Version 9. 1. SAS Institute Inc., Cary, North Carolina.
Sheikhigarjan , A. , Keyhanyan , A. , Mahjoub , M. and Abdollahi , Gh. ( 2006 ): Study on efficacy and calibration accuracy of conventional
Authors:L.T. Waser, S. Stofer, M. Schwartz, M. Küchler, E. Ivits, and C. Scheidegger
The objective of the present study was to develop a model to predict lichen species richness for six test sites in the Swiss Pre-Alps following a gradient of land use intensity combining airborne remote sensing data and regression models. This study ties in with the European Union Project „BioAssess”, which aimed at quantifying patterns in biodiversity and developing „Biodiversity Assessment Tools” that can be used to rapidly assess biodiversity. For this study, lichen surveys were performed on a circular area of 1 ha in 96 sampling plots in the six test sites. Lichen relevés were made on three different substrates: trees, rocks and soil. In the first step, ecologically meaningful variables derived from airborne remote sensing data were calculated using two levels of detail. 1'st level variables were processed using both spatial and spectral information of the CIR orthoimages. 2'nd level variables - based on 1'st level variables - were implemented using additional lichen expert knowledge. In the second step, all variables were calculated for each sampling plot and correlated with the different lichen relevés. Multiple linear regression models were built, containing all extracted variables, and a stepwise variable selection was applied to optimize the final models. The predictive power of the models (correlation between predicted and measured diversity) in a reference data set can be regarded as good. The obtained R ranging from 0.48 for lichens on soil to 0.79 for lichens on trees can be regarded as satisfactory to good, respectively. The accuracy of models could be further improved by adapting the model and by using additional calibration data and sampling plots. Species richness for each pixel within the six test sites was then calculated. This ecological modeling approach also reveals two main restrictions: 1) this method only indicates the potential presence or absence of species, and 2) the models may only be useful for calculating species richness in neighboring regions with similar landscape structures.
This paper aims to show the relevance of past ecological records, at centennial to millennial timescales, for community ecological principles and theory, mainly in relation to temporal dynamics and the origin of present-day community patterns. The underlying assumption is that ecological time is a continuum and the ecological understanding of the present biosphere needs inputs from multilevel timescales. In particular, the so-called Q-time, embracing the Quaternary (the last 2.6 million years), is proposed as a key time period to understand present-day patterns and the underlying causal processes, as for example the latitudinal diversity gradient, the relationship between species richness and stability, the equilibrium/non-equilibrium conditions between communities and the environment, the main trends and clues on the origin of present-day species and the communities they form, the community succession under changing environmental conditions, or the nature (individual vs collective) of such biotic responses, among others. In this temporal context, neoecological studies and modeling, based on short-term evidence and calibration/validation data sets, are viewed as an important source for hypotheses to be tested with long-term ecological (i.e., palaeoecological) and molecular phylogenetic studies. The considerations around these topics provide valuable insights to address the potential future state of modern communities under the predicted global change, which would be useful to propose suitable conservation strategies. It is hoped that this paper will promote constructive discussions leading to a more close collaboration between neoecologists and palaeoecologists, in the way towards the natural convergence of both into one single, time-independent, discipline as is (or should be) ecology. As this paper has been conceived for both neo- and palaeoecologists, the message is twofold: to neoecologists, care about time; to palaeoecologists, care about ecology.
Authors:M. Küchler, K. Ecker, E. Feldmeyer-Christe, U. Graf, H. Küchler, and L.T. Waser
Bajwa, S. G. and L. Tian. 2002. Multispectral CIR image calibration for cloud shadow and soil background influence using intensity normalization. Applied Engineering in Agriculture 18: 627-635.
Multispectral CIR image
Condit, R., R.B. Foster, S.P. Hubbell, R. Sukumar, E.G. Leigh, N. Manokaran, Suzanne Loo de Lao, J.V.LaFrankie and P.S. Ashton. 1998. Assessing forest diversity on small plots: Calibration using species-individual curves from 50-ha plots. In: F. Dallmeier
Authors:M.E. Schaepman, B. Koetz, G. Schaepman-Strub, N.E. Zimmermann, and K.I. Itten
. Vicarious calibration of airborne hyperspectral sensors in operational environments. Remote Sensing of Environment 76:81-92.
Vicarious calibration of airborne hyperspectral sensors in operational environments
Authors:M. W. Palmer, D. B. Clark, and D. A. Clark
plots: calibration using species-individual curves from 50-ha plots. In: F. Dallmeier and J. A. Comiskey (eds.), Forest Biodiversity Research, Monitoring and Modeling. Conceptual Background and Old World Case Studies. Volume 20. UNESCO, Paris, pp. 247