Intra-Annual Variabilities of Rubus caesius L. Discrimination on Hyperspectral and LiDAR Data

The study was focused on a plant native to Poland, the European dewberry Rubus caesius L., which is a species with the ability to become excessively abundant within its original range, potentially causing significant changes in ecosystems, including biodiversity loss. Monitoring plant distributions over large areas requires mapping that is fast, reliable, and repeatable. For Rubus, different types of data were successfully used for classification, but most of the studies used data with a very high spectral resolution. The aim of this study was to indicate, using hyperspectral and Light Detection and Ranging (LiDAR) data, the main functional trait crucial for R. caesius differentiation from non-Rubus. This analysis was carried out with consideration of the seasonal variability and different percentages of R. caesius in the vegetation patches. The analysis was based on hyperspectral HySpex images and Airborne Laser Scanning (ALS) products. Data were acquired during three campaigns: early summer, summer, and autumn. Differentiation based on Linear Discriminate Analysis (LDA) and Non-Parametric Multivariate Analysis of Variance (NPMANOVA) analysis was successful for each of the analysed campaigns using optical data, but the ALS data were less useful for identification. The analysis indicated that selected spectral ranges (VIS, red-edge, and parts of the NIR and possibly SWIR ranges) can be useful for differentiating R. caesius from non-Rubus. The most useful indices were ARI1, CRI1, ARVI, GDVI, CAI, NDNI, and MRESR. The obtained results indicate that it is possible to classify R. caesius using images with lower spectral resolution than hyperspectral data

Laboratory for Essential Biodiversity Variables (EBV) Concepts – The “Data Pool Initiative for the Bohemian Forest Ecosystem”

Forest ecosystems respond very sensitively to climate and atmospheric changes. Feedback mechanisms can be measured via changes in albedo, energy balance and carbon storage. The Bavarian Forest National Park is a unique forest ecosystem with large non-intervention zones, which promote a large scale re-wilding process with low human interference. It provides important ecosystem services of clear water, carbon sequestration and recreation, and has fragile habitats with endangered forest species. The national park is therefore a very suitable field of research to study natural and near natural ecosystem processes. Under the leadership of the national park authority, experts from various European research institutions have joined forces to systematically establish a remote sensing data pool on the Bavarian Forest as a resource for their research. This collaborative effort provides an opportunity to combine various methodological approaches and data and to optimize products by sharing knowledge and expertise. The first objective of the data pool is to develop methods for the establishment of Essential Biodiversity Variables (EBV) based on a very sound and comprehensive data base. The recent advances in tighter collaboration of remote sensing and biodiversity science, especially with regard to the newly established EBV and RS-EBV concepts will help to improve the interdisciplinary research. However, such concepts and especially the underlying remote sensing data need to be developed, adapted and validated against biodiversity patterns. Such process needs an extensive set of in-situ and remotely sensed data in order to allow a thorough analysis. The Bavarian data pool fits these requirements through the commitment of all members and hence provides a variety of remote sensing data sets such as hyperspectral, Lidar as well as CIR and multispectral data, as well as a wealth of in-situ data of zoological and botanical transects. This combination allows setting sensor-specific, as well as species-specific analysis on different aspects, i.e. different processes between managed and natural forest, impact of climate change or species distribution mapping. The second objective is to develop concepts for EBV using Sentinel mission data combined with data from future contributing hyperspectral missions such as EnMAP. Spaceborne hyperspectral data has been identified by the remote sensing related biodiversity community as an important data source. However, the acquisition of airborne data is very expensive for regular coverage of forest stands and the entire forest ecosystem. This drawback will be overcome by the launch of the space-borne imaging spectroscopy mission EnMAP. It is a contributing mission to the Copernicus program and will be launched in 2018. EnMAP is expected to provide high quality imaging spectroscopy data on an operational basis and will be suitable for the retrieval of high resolution plant traits at local scales. First studies within the data pool have been focused on e.g. derivation of plant traits like chlorophyll, LAI and nitrogen and tree species classification with a special focus on rare species within the national park, just to name a few. Objective, purpose and content of the data pool will be shown as well as first selective developments

Application of hyperspectral data and artificial neural networks for tree species classification of Karkonoski National Park

Znajomość składu gatunkowego lasu jest ważnym zagadnieniem w zarządzaniu zasobamiśrodowiska leśnego. Główny nacisk powinien być położony na monitoring składu gatunkowegoposzczególnych zbiorowisk i ich rozmieszczenia przestrzennego. Praca skupiła się na opracowaniumetod identyfikacji gatunków drzew wykorzystując lotnicze dane hiperspektralne.Wysokorozdzielczy skaner hiperspektralny APEX (288 kanałów spektralnych w zakresie 413-2440nm o wielkości piksela 3,35 m) został użyty jako źródło danych do opracowania maprozmieszczenia wybranych gatunków drzew na obszarze Karkonoskiego Parku Narodowego. Wbadaniach wykonano mapę lokalizacji przestrzennej następujących gatunków: buk (Fagus sylvaticaL.), brzoza (Betula pendula Roth), olcha (Alnus Mill.), modrzew (Larix decidua Mill), sosna (Pinussylvestris L.) i świerk (Picea abies L. Karst). W celu zredukowania czasu przetwarzania danych,przeprowadzono procedurę wyboru najlepszych kanałów spektralnych. Zaszumione kanałyzobrazowania oraz te o niskiej jakości zostały usunięte (66 kanałów) przed analizą składowychgłównych (Principal Component Analysis – PCA). Po transformacji, zawartość informacji wkażdym kanale została obliczona wykorzystując współczynnik użyteczności kanału (band loading).Analiza PCA pozwoliła wybrać 40 kanałów spektralnych o największej zawartości informacji, którezostały użyte do klasyfikacji drzewostanów. Jako klasyfikator wykorzystano perceptronwielowarstwowy z jedną warstwą ukrytą. Symulowanie działania sztucznej sieci neuronowejprzeprowadzono przy użyciu programu R oraz paczki nnet. Przeprowadzono proceduręoptymalizacji parametrów uczenia oraz struktury (liczba neuronów w warstwie ukrytej) w celuotrzymania jak najlepszych wyników. Uzyskane wyniki zostały zweryfikowane na podstawiemarszruty terenowej. Rezultatem badań jest mapa rozmieszczenia gatunków drzewiastych.Uzyskane dane statystyczne (mediana dokładności całkowitej wyniosła 87% oraz współczynnikkappa 0,81) potwierdziły przydatność opracowanej metody oraz obrazów hiperspektralnych APEX,gdyż wszystkie sklasyfikowane gatunki uzyskały medianę dokładności producenta wyższą niż 68%.Najlepiej sklasyfikowały się świerki, buki i brzozy (mediana dokładności producenta wyniosłaodpowiednio 93, 88 i 83%. Sosna sklasyfikowała się uzyskując medianę dokładności producenta napoziomie 68% oraz mediana dokładności użytkownika 75%. Opracowana metoda potwierdziłapotencjał teledetekcji hiperspektralnej oraz sztucznych sieci neuronowych jako narzędzi dokartowania gatunków drzew.Knowledge of tree species composition in forest is an important topic in forest management.Accurate tree species maps allow acquiring more details of forest biophysical variables. Thisresearch focused on developing methods of tree species identification using aerial hyperspectraldata. Research area was the Karkonoski National Park located in south-western Poland. Highresolution (3,35m) APEX hyperspectral data (288 spectral bands in range from 413 to 2440 nm)were used as a basis for tree species classification. Beech (Fagus sylvatica L.), birch (Betulapendula Roth), alder (Alnus Mill.), larch (Larix decidua Mill), pine (Pinus sylvestris L.) and spruce(Picea abies L. Karst) were classified. Noisy bands (including water vapor absorption range) weretaken out of whole dataset before band selection procedure. Remaining bands went thought PCA(Principal Component Analysis) analysis to find out bands with highest information load. Eachband had its information load assessed and was ranked based on amount of information it held.Finally 40 most informative bands were selected for final classifications. Feed forward multilayered-perceptron with single hidden layer was applied. To simulate such network we used Rstatistical program and package nnet. Methods of the best artificial neural network architecturedetermination (number of neurons in hidden layer) and network training parameters were used. Theoutput maps were verified using field collected data. Final tree species maps cover whole area ofKPN; achieved median overall accuracy of 87%, with median producer accuracies for all classesexceeding 68%. Best classified classes were spruce, beech and birch with median produceraccuracies of 93%, 88% and 83% respectively. Class pine achieved lowest median producer anduser accuracies of 68% and 75%. Results show great potential in hyperspectral data as tool foridentifying tree species location in diverse mountainous forest

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

Asbestos—Cement Roofing Identification Using Remote Sensing and Convolutional Neural Networks (CNNs)

Due to the pathogenic nature of asbestos, a statutory ban on asbestos-containing products has been in place in Poland since 1997. In order to protect human health and the environment, it is crucial to estimate the quantity of asbestos–cement products in use. It has been evaluated that about 90% of them are roof coverings. Different methods are used to estimate the amount of asbestos–cement products, such as the use of indicators, field inventory, remote sensing data, and multi- and hyperspectral images; the latter are used for relatively small areas. Other methods are sought for the reliable estimation of the quantity of asbestos-containing products, as well as their spatial distribution. The objective of this paper is to present the use of convolutional neural networks for the identification of asbestos–cement roofing on aerial photographs in natural color (RGB) and color infrared (CIR) compositions. The study was conducted for the Chęciny commune. Aerial photographs, each with the spatial resolution of 25 cm in RGB and CIR compositions, were used, and field studies were conducted to verify data and to develop a database for Convolutional Neural Networks (CNNs) training. Network training was carried out using the TensorFlow and R-Keras libraries in the R programming environment. The classification was carried out using a convolutional neural network consisting of two convolutional blocks, a spatial dropout layer, and two blocks of fully connected perceptrons. Asbestos–cement roofing products were classified with the producer’s accuracy of 89% and overall accuracy of 87% and 89%, depending on the image composition used. Attempts have been made at the identification of asbestos–cement roofing. They focus primarily on the use of hyperspectral data and multispectral imagery. The following classification algorithms were usually employed: Spectral Angle Mapper, Support Vector Machine, object classification, Spectral Feature Fitting, and decision trees. Previous studies undertaken by other researchers showed that low spectral resolution only allowed for a rough classification of roofing materials. The use of one coherent method would allow data comparison between regions. Determining the amount of asbestos–cement products in use is important for assessing environmental exposure to asbestos fibres, determining patterns of disease, and ultimately modelling potential solutions to counteract threats

Mapping Invasive Plant Species with Hyperspectral Data Based on Iterative Accuracy Assessment Techniques

Recent developments in computer hardware made it possible to assess the viability of permutation-based approaches in image classification. Such approaches sample a reference dataset multiple times in order to train an arbitrary number of machine learning models while assessing their accuracy. So-called iterative accuracy assessment techniques or Monte-Carlo-based approaches can be a useful tool when it comes to assessment of algorithm/model performance but are lacking when it comes to actual image classification and map creation. Due to the multitude of models trained, one has to somehow reason which one of them, if any, should be used in the creation of a map. This poses an interesting challenge since there is a clear disconnect between algorithm assessment and the act of map creation. Our work shows one of the ways this disconnect can be bridged. We calculate how often a given pixel was classified as given class in all variations of a multitude of post-classification images delivered by models trained during the iterative assessment procedure. As a classification problem, a mapping of Calamagrostis epigejos, Rubus spp., Solidago spp. invasive plant species using three HySpex hyperspectral datasets collected in June, August and September was used. As a classification algorithm, the support vector machine approach was chosen, with training hyperparameters obtained using a grid search approach. The resulting maps obtained F1-scores ranging from 0.87 to 0.89 for Calamagrostis epigejos, 0.89 to 0.97 for Rubus spp. and 0.99 for Solidago spp

Comparison of Random Forest, Support Vector Machines, and Neural Networks for Post-Disaster Forest Species Mapping of the Krkonoše/Karkonosze Transboundary Biosphere Reserve

Mountain forests are exposed to extreme conditions (e.g., strong winds and intense solar radiation) and various types of damage by insects such as bark beetles, which makes them very sensitive to climatic changes. Therefore, continuous monitoring is crucial, and remote-sensing techniques allow the monitoring of transboundary areas where a common policy is needed to protect and monitor the environment. In this study, we used Sentinel-2 and Landsat 8 open data to assess the forest stands classification of the UNESCO Krkonoše/Karkonosze Transboundary Biosphere Reserve, which is undergoing dynamic changes in recovering woodland vegetation due to an ecological disaster that led to damage and death of a large portion of the forests. Currently, in this protected area, dry big trunks and branches coexist with naturally occurring young forests. This heterogeneity generates mixes, which hinders the automation of classification. Thus, we used three machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN)—to classify dominant tree species (birch, beech, larch and spruce). The best results were obtained for the SVM RBF classifier, which offered an average median F1-score that oscillated around 67.2–91.5% depending on the species. The obtained maps, which were based on multispectral satellite images, were also compared with classifications made for the same area on the basis of hyperspectral APEX imagery (288 spectral bands with three-meter resolution), indicating high convergence in the recognition of woody species

Intraspecific differences in spectral reflectance curves as indicators of reduced vitality in high-arctic plants

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