High tolerance of a high-arctic willow and graminoid to simulated ice encasement

Source at http://www.borenv.net/BER/ber231-6.htm.Climate change-induced snow thaw and subsequent accumulation of ice on the ground is a potential, major threat to snow-dominated ecosystems. While impacts of ground-ice on arctic wildlife are well explored, the impacts on tundra vegetation is far from understood. We therefore tested the vulnerability of two high-arctic plants, the prostrate shrub Salix polaris and the graminoid Luzula confusa, to ice encasement for 60 days under full environmental control. Both species were tolerant, showing only minor negative responses to the treatment. Subsequent exposure to simulated late spring frost increased the amount of damaged tissue, particularly in S. polaris, compared to the pre-frost situation. Wilting shoot tips of S. polaris increased nearly tenfold, while the proportion of wilted leaves of L. confusa increased by 15%. During recovery, damaged plants of S. polaris responded by extensive compensatory growth of new leaves that were much smaller than leaves of non-damaged shoots. The results suggest that S. polaris and L. confusa are rather tolerant to arctic winter-spring climate change, and this may be part of the reason for their wide distribution range and abundance in the Arctic

Feasibility of hyperspectral vegetation indices for the detection of chlorophyll concentration in three high Arctic plants: Salix polaris, Bistorta vivipara, and Dryas octopetala

Remote sensing, which is based on a reflected electromagnetic spectrum, offers a wide range of research methods. It allows for the identification of plant properties, e.g., chlorophyll, but a registered signal not only comes from green parts but also from dry shoots, soil, and other objects located next to the plants. It is, thus, important to identify the most applicable remote-acquired indices for chlorophyll detection in polar regions, which play a primary role in global monitoring systems but consist of areas with high and low accessibility. This study focuses on an analysis of in situ-acquired hyperspectral properties, which was verified by simultaneously measuring the chlorophyll concentration in three representative arctic plant species, i.e., the prostrate deciduous shrub Salix polaris, the herb Bistorta vivipara, and the prostrate semievergreen shrub Dryas octopetala. This study was conducted at the high Arctic archipelago of Svalbard, Norway. Of the 23 analyzed candidate vegetation and chlorophyll indices, the following showed the best statistical correlations with the optical measurements of chlorophyll concentration: Vogelmann red edge index 1, 2, 3 (VOG 1, 2, 3), Zarco-Tejada and Miller index (ZMI), modified normalized difference vegetation index 705 (mNDVI 705), modified normalized difference index (mND), red edge normalized difference vegetation index (NDVI 705), and Gitelson and Merzlyak index 2 (GM 2). An assessment of the results from this analysis indicates that S. polaris and B. vivipara were in good health, while the health status of D. octopetala was reduced. This is consistent with other studies from the same area. There were also differences between study sites, probably as a result of local variation in environmental conditions. All these indices may be extracted from future satellite missions like EnMAP (Environmental Mapping and Analysis Program) and FLEX (Fluorescence Explorer), thus, enabling the efficient monitoring of vegetation condition in vast and inaccessible polar areas

Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images

Knowledge of tree species composition in a forest is an important topic in forest management. Accurate tree species maps allow for much more detailed and in-depth analysis of biophysical forest variables. The paper presents a comparison of three classification algorithms: support vector machines (SVM), random forest (RF) and artificial neural networks (ANN) for tree species classification using airborne hyperspectral data from the Airborne Prism EXperiment sensor. The aim of this paper is to evaluate the three nonparametric classification algorithms (SVM, RF and ANN) in an attempt to classify the five most common tree species of the Szklarska Poręba area: spruce (Picea alba L. Karst), larch (Larix decidua Mill.), alder (Alnus Mill), beech (Fagus sylvatica L.) and birch (Betula pendula Roth). To avoid human introduced biases a 0.632 bootstrap procedure was used during evaluation of each compared classifier. Of all compared classification results, ANN achieved the highest median overall classification accuracy (77%) followed by SVM with 68% and RF with 62%. Analysis of the stability of results concluded that RF and SVM had the lowest variance of overall accuracy and kappa coefficient (12 percentage points) while ANN had 15 percentage points variance in results

Tree Species Classification of the UNESCO Man and the Biosphere Karkonoski National Park (Poland) Using Artificial Neural Networks and APEX Hyperspectral Images

Knowledge of tree species composition is obligatory in forest management. Accurate tree species maps allow for detailed analysis of a forest ecosystem and its interactions with the environment. The research presented here focused on developing methods of tree species identification using aerial hyperspectral data. The research area is located in Southwestern Poland and covers the Karkonoski National Park (KNP), which was significantly damaged by acid rain and pest infestation in the 1980s. High-resolution (3.35 m) Airborne Prism Experiment (APEX) hyperspectral images (288 spectral bands in the range of 413 to 2440 nm) were used as a basis for tree species classification. Beech (Fagus sylvatica), birch (Betula pendula), alder (Alnus incana), larch (Larix decidua), pine (Pinus sylvestris), and spruce (Picea abies) were classified. The classification algorithm used was feed-forward multilayered perceptron (MLP) with a single hidden layer. To simulate such a network, we used the R programming environment and the nnet package. To provide more accurate measurement of accuracy, iterative accuracy assessment was performed. The final tree species maps cover the whole area of KNP; a median overall accuracy (OA) of 87% was achieved, with median producer accuracy (PA) for all classes exceeding 68%. The best-classified classes were spruce, beech, and birch, with median producer accuracy of 93%, 88% and 83%, respectively. The pine class achieved the lowest median producer and user accuracies (68% and 75%, respectively). The results show great potential for the use of hyperspectral data as a tool for identifying tree species locations in diverse mountainous forest

Variability in spectral characteristics of trampled high-mountain grasslands

The goal of the paper is a presentation of field remote sensing methods for the analysis of the trampled plants of a highly protected mountain meadow ecosystem (M&B UNESCO Reserve and one of the most important Polish National Parks). The research area covers a core part of the Western Tatras - the Gąsienicowa Valley and Kasprowy Wierch summit, which are among the most visited destinations of the Polish Tatras. The research method is based on field hyperspectral measurements, using the ASD FieldSpec 3 spectrometer, on the dominant plant species of alpine swards. Sampling sites were located on trampled areas (next to trails) and reference plots, with the same species, but located more than 10 m from the trail (where the probability of trampling was very low, but the same composition of analysed plants). In each case, homogenous plots with a domination of one plant species were investigated. Based on the hyperspectral measurements, spectral characteristics as well as vegetation indices were analysed with the ANOVA statistical test. This indicated a varied resistance to trampling of the studied plant species. The analysis of vegetation indices enabled the selection of those groups which are the most useful for research into mountain vegetation condition: the broadband greenness group; the narrowband greenness group, measuring chlorophyll content and cell structure; and the canopy water content group. The results of the analyses show that vegetation of the High Tatras is characterised by optimal ranges of remote sensing indices. Only plants located nearest to the trails were in a worse condition (chlorophyll and water content was lower for the reference targets). These differences are statistically significant, but the measured values indicate a good condition of vegetation along trampled trails, within the range of optimum plant characteristics

The Electronic Spatial Information System – tools for the monitoring of asbestos in Poland

On January 1, 2005 the use of asbestos-containing products was banned in the European Union. According to the Act of 19 June 1997 banning the use of these products, their usage in Poland should be abated by the end of 2032. The whole process is being monitored by the Electronic Spatial Information System for the Monitoring of Asbestos Products Removal. The system design was based on a geodatabase. The research area of the study is the whole territory of Poland at the national, provincial and local level of detail. The monitoring process embraces spatial analysis through the preparation and interpretation of a range of maps. The results obtained from the deployed methods proved that the system has been useful for decision making purposes during the monitoring process. The proposed solutions were appreciated by the EU