Open Access Agricultural Journals published by Institute of Agricultural Economics and Information

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We outline an approach combining airborne laser scanning (ALS) and imaging spectroscopy (IS) to quantify and assess patterns of tree density (TD) and forest productivity (FP) in a protected heterogeneous alpine forest in the Swiss National Park (SNP). We use ALS data and a local maxima (LM) approach to predict TD, as well as IS data (Airborne Prism Experiment—APEX) and an empirical model to estimate FP. We investigate the dependency of TD and FP on site related factors, in particular on surface exposition and elevation. Based on reference data (i.e., 1598 trees measured in 35 field plots), we observed an underestimation of ALS-based TD estimates of 40%. Our results suggest a limited sensitivity of the ALS approach to small trees as well as a dependency of TD estimates on canopy heterogeneity, structure, and species composition. We found a weak to moderate relationship between surface elevation and TD (R² = 0.18–0.69) and a less pronounced trend with FP (R² = 0.0–0.56), suggesting that both variables depend on gradients of resource availability. Further to the limitations faced in the sensitivity of the applied approaches, we conclude that the combined application of ALS and IS data was convenient for estimating tree density and mapping FP in north-facing forested areas, however, the accuracy was lower in south-facing forested areas covered with multi-stemmed trees.

Detection and prediction of land-cover changes are powerful tools in natural resources management and ecosystem assessment. This study was carried out to compare multi-criteria decision techniques (AHP and fuzzy) in deforestation risk zoning. The TM images of Landsat 5 were used to produce deforestation map during 1989 to 2011. In the next step, the most important criteria affecting deforestation were determined. The final weights of criteria were computed using expert's judgments, pairwise comparisons by AHP and also linguistic terms by fuzzy technique. Weighted linear combination method was used to combining the criteria, and each of the generated maps with its special weight was integrated into the GIS environment. The final deforestation risk zoning map, in both methods of AHP and fuzzy, were classified into five classes including of very high, high, moderate, low and very low risk.Evaluation of the results showed that 81.07 and 80.65 percentages of deforestation are located in the very high and high risk zones in the maps derived from AHP and fuzzy approaches, respectively. Based on the results, AHP and fuzzy methods have suitable performance in deforestation risk zoning. Thus, despite the different nature of the AHP and fuzzy methods, it was observed that these two methods do not have much difference in deforestation risk zoning of the study area, in practice.

Local economy, based on animal husbandry in Northern Zagros forest leads to increase employing leaves and branches (pollarding) compared to the other parts of Zagros. Pollarding is a convenient method in forest utilization to supply fodder and it has been always trying to obtain its stable production by proper management skills. One of the most important forest management tools in a given forest is to provide up-to-date spatial maps of pollarded regions. The objective of this study was to investigate the capability of multi-temporal Landsat 8 OLI sensor for mapping pollarding areas of Northern Zagros forests. So that, we employed Landsat 8-OLI single and multi-date images acquired on 2014 and 2015. To assess the accuracy of output maps, a complete ground-truth of the study area was used to calculate the accuracy heuristics for the output maps. Different classification approaches were applied including minimum distance and maximum likelihood classifiers, artificial neural networks and fuzzy method. The classification accuracy was calculated on the basis of overall accuracy and kappa coefficient. The results indicated that artificial neural network and fuzzy classifier present the highest accuracy than the other classifiers. It was also found that utilizing the multi-temporal OLI imageries improves the accuracy over employing a single date. The results indicate that the multi-temporal imagery is moderately capable of mapping pollarded stands and classifying pollarding types, using ANN and Fuzzy classifiers.