1,721,114 research outputs found
Remote sensing for vegetation science: A virtual special issue on its power and challenges
No full textRemote sensing represents a valuable addition to field-based ap-
proaches in vegetation science, since it allows for a synoptic view
of an area at a broad range of temporal, spectral and spatial res-
olutions (Figure 1). New methods and techniques enable assessing
vegetation properties with direct use in biodiversity research. This
includes the mapping of plant traits, plant functional types and plant
communities with their spatio-temporal variability. The possibility to
map vegetation properties as a continuum in space and time pro-
vides new insights into patterns and processes
Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks
No full textLarge-scale remote sensing reveals that tree mortality in Germany appears to be greater than previously expected
No full textGlobal warming poses a major threat to forests and events of increased tree mortality are observed globally. Studying tree mortality often relies on local-level observations of dieback while large-scale analyses are lacking. Satellite remote sensing provides the spatial coverage and sufficiently high temporal and spatial resolution needed to investigate tree mortality at landscape-scale. However, adequate reference data for training satellite-based models are scarce. In this study, we employed the first maps of standing deadwood in Germany for the years 2018–2022 with 10 m spatial resolution that were created by using tree mortality observations spotted in hundreds of drone images as the reference. We use these maps to study spatial and temporal patterns of tree mortality in Germany and analyse their biotic and abiotic environmental drivers using random forest regression. In 2019, the second consecutive hotter drought year in a row, standing deadwood increased steeply to 334 ± 189 kilohectar (kha) which corresponds to 2.5 ± 1.4% of the total forested area in Germany. Picea abies, Pinus sylvestris, and Fagus sylvatica showed highest shares of standing deadwood. During 2018–2021 978 ± 529 kha (7.9 ± 4.4%) of standing dead trees accumulated. The higher mortality estimates that we report compared to other surveys (such as the ground-based forest condition survey) can be partially attributed to the fact that remote sensing captures mortality from a bird’s eye perspective and that the high spatial detail (10 m) in this study also captures scattered occurrences of tree mortality. Atmospheric drought (i.e. climatic water balance and vapor pressure deficit) and temperature extremes (i.e. number of hot days and frosts after vegetation onset) were the most important predictors of tree mortality. We found increased tree mortality for smaller and younger stands and on less productive sites. Monospecific stands were generally not more affected by mortality than average, but only when interactions with damaging insects (e.g. bark beetles) occurred. Because excess tree mortality rates threaten many forests across the globe, similar analyses of tree mortality are warranted and technically feasible at the global scale. We encourage the international scientific community to share and compile local data on deadwood occurrences (see example: www.deadtrees.earth) as such a collaborative effort is required to help understand mortality events on a global scale
About the link between biodiversity and spectral variation
No full textAim The spectral variability hypothesis (SVH) suggests a link between spectral variation and plant biodiversity. The underlying assumptions are that higher spectral variation in canopy reflectance (depending on scale) is caused by either (1) variation in habitats or linked vegetation types or plant communities with their specific optical community traits or (2) variation in the species themselves and their specific optical traits. Methods The SVH was examined in several empirical remote-sensing case studies, which often report some correlation between spectral variation and biodiversity-related variables (mostly plant species counts); however, the strength of the observed correlations varies between studies. In contrast, studies focussing on understanding the causal relationship between (plant) species counts and spectral variation remain scarce. Here, we discuss these causal relationships and support our perspectives through simulations and experimental data. Results We reveal that in many situations the spectral variation caused by species or functional traits is subtle in comparison to other factors such as seasonality and physiological status. Moreover, the degree of contrast in reflectance has little to do with the number but rather with the identity of the species or communities involved. Hence, spectral variability should not be expressed based on contrast but rather based on metrics expressing manifoldness. While we describe cases where a certain link between spectral variation and plant species diversity can be expected, we believe that as a scientific hypothesis (which suggests a general validity of this assumed relationship) the SVH is flawed and requires refinement. Conclusions To this end we call for more research examining the drivers of spectral variation in vegetation canopies and their link to plant species diversity and biodiversity in general. Such research will allow critically assessing under which conditions spectral variation is a useful indicator for biodiversity monitoring and how it could be integrated into monitoring networks
Discriminating woody species assemblages from National Forest Inventory data based on phylogeny in Georgia
No full textAbstract Classifications of forest vegetation types and characterization of related species assemblages are important analytical tools for mapping and diversity monitoring of forest communities. The discrimination of forest communities is often based on β‐diversity, which can be quantified via numerous indices to derive compositional dissimilarity between samples. This study aims to evaluate the applicability of unsupervised classification for National Forest Inventory data from Georgia by comparing two cluster hierarchies. We calculated the mean basal area per hectare for each woody species across 1059 plot observations and quantified interspecies distances for all 87 species. Following an unspuervised cluster analysis, we compared the results derived from the species‐neutral dissimilarity (Bray‐Curtis) with those based on the Discriminating Avalanche dissimilarity, which incorporates interspecies phylogenetic variation. Incorporating genetic variation in the dissimilarity quantification resulted in a more nuanced discrimination of woody species assemblages and increased cluster coherence. Favorable statistics include the total number of clusters (23 vs. 20), mean distance within clusters (0.773 vs. 0.343), and within sum of squares (344.13 vs. 112.92). Clusters derived from dissimilarities that account for genetic variation showed a more robust alignment with biogeographical units, such as elevation and known habitats. We demonstrate that the applicability of unsupervised classification of species assemblages to large‐scale forest inventory data strongly depends on the underlying quantification of dissimilarity. Our results indicate that by incorporating phylogenetic variation, a more precise classification aligned with biogeographic units is attained. This supports the concept that the genetic signal of species assemblages reflects biogeographical patterns and facilitates more precise analyses for mapping, monitoring, and management of forest diversity.აბსტრაქტული ტყის მცენარეულობის ტიპების კლასიფიკაცია და მონათესავე სახეობათა შეკრების დახასიათება მნიშვნელოვანი ანალიტიკური ინსტრუმენტებია ტყის ტიპების აღწერისა და მრავალფეროვნების მონიტორინგისთვის. ტყის ტიპების განსხვავება ხშირად ემყარება β‐მრავალფეროვნებას, რომლის რაოდენობრივი დადგენა შესაძლებელია მრავალი ინდექსის მეშვეობით ნიმუშებს შორის კომპოზიციური განსხვავებულობის გამოსათვლელად. ეს კვლევა მიზნად ისახავს შეაფასოს საქართველოს ეროვნული ტყის ინვენტარიზაციის ზედამხედველობის გარეშე კლასიფიკაციის გამოყენებადობა ორი კლასტერული იერარქიის შედარების გზით. ჩვენ გამოვთვალეთ საშუალო ბაზალური ფართობი ჰექტარზე თითოეული მერქნიანი სახეობისთვის 1059 ნაკვეთზე დაკვირვებით და რაოდენობრივად დავადგინეთ სახეობათაშორისი მანძილი 87‐ვე სახეობისთვის. ჩვენ შევადარეთ სახეობების ნეიტრალური განსხვავებულობიდან მიღებული შედეგები (ბრეი‐კურტისი) ზვავის დისკრიმინაციული განსხვავებულობის საფუძველზე, რომელიც აერთიანებს სახეობათაშორის ფილოგენეტიკურ ვარიაციებს. გენეტიკური ცვალებადობის ჩართვამ განსხვავებულობის რაოდენობრივ განსაზღვრებაში გამოიწვია მერქნიანი სახეობების შეკრების უფრო ნიუანსური განსხვავება და გაზრდილი კლასტერული თანმიმდევრულობა. ხელსაყრელი სტატისტიკა მოიცავს მტევანთა საერთო რაოდენობას (23 v. 20), საშუალო მანძილს მტევნის შიგნით (0.773 vs. 0.343) და კვადრატების ჯამის ფარგლებში (344.13 vs. 112.92). განსხვავებებიდან მიღებული კლასტერებმა, რომლებიც ითვალისწინებენ გენეტიკურ ვარიაციებს, აჩვენეს უფრო მძლავრი გასწორება ბიოგეოგრაფიულ ერთეულებთან, როგორიცაა სიმაღლე და ცნობილი ჰაბიტატები. ჩვენ ვაჩვენებთ, რომ სახეობების შეკრების უკონტროლო კლასიფიკაციის გამოყენებადობა ფართომასშტაბიანი ტყის ინვენტარიზაციის მონაცემებზე მტკიცედ არის დამოკიდებული განსხვავებულობის ფუძემდებლური რაოდენობრივი განსაზღვრაზე. ჩვენი შედეგები მიუთითებს, რომ ფილოგენეტიკური ვარიაციით, უფრო ზუსტი კლასიფიკაციაა შესაძლებელი, რომელიც შეესაბამება ბიოგეოგრაფიულ ერთეულებს. ეს ამტიცებს კონცეფციას, რომ სახეობათა შეკრების გენეტიკური სიგნალი ასახავს ბიოგეოგრაფიულ ნიმუშებს და ხელს უწყობს ტყის მრავალფეროვნების აღწერას მონიტორინგისა და მართვის უფრო ზუსტ ანალიზს.Abstract Classifications of forest vegetation types and characterization of related species assemblages are important analytical tools for mapping and diversity monitoring of forest communities. The discrimination of forest communities is often based on β‐diversity, which can be quantified via numerous indices to derive compositional dissimilarity between samples. This study aims to evaluate the applicability of unsupervised classification for National Forest Inventory data from Georgia by comparing two cluster hierarchies. We calculated the mean basal area per hectare for each woody species across 1059 plot observations and quantified interspecies distances for all 87 species. Following an unspuervised cluster analysis, we compared the results derived from the species‐neutral dissimilarity (Bray‐Curtis) with those based on the Discriminating Avalanche dissimilarity, which incorporates interspecies phylogenetic variation. Incorporating genetic variation in the dissimilarity quantification resulted in a more nuanced discrimination of woody species assemblages and increased cluster coherence. Favorable statistics include the total number of clusters (23 vs. 20), mean distance within clusters (0.773 vs. 0.343), and within sum of squares (344.13 vs. 112.92). Clusters derived from dissimilarities that account for genetic variation showed a more robust alignment with biogeographical units, such as elevation and known habitats. We demonstrate that the applicability of unsupervised classification of species assemblages to large‐scale forest inventory data strongly depends on the underlying quantification of dissimilarity. Our results indicate that by incorporating phylogenetic variation, a more precise classification aligned with biogeographic units is attained. This supports the concept that the genetic signal of species assemblages reflects biogeographical patterns and facilitates more precise analyses for mapping, monitoring, and management of forest diversity.აბსტრაქტული ტყის მცენარეულობის ტიპების კლასიფიკაცია და მონათესავე სახეობათა შეკრების დახასიათება მნიშვნელოვანი ანალიტიკური ინსტრუმენტებია ტყის ტიპების აღწერისა და მრავალფეროვნების მონიტორინგისთვის. ტყის ტიპების განსხვავება ხშირად ემყარება β‐მრავალფეროვნებას, რომლის რაოდენობრივი დადგენა შესაძლებელია მრავალი ინდექსის მეშვეობით ნიმუშებს შორის კომპოზიციური განსხვავებულობის გამოსათვლელად. ეს კვლევა მიზნად ისახავს შეაფასოს საქართველოს ეროვნული ტყის ინვენტარიზაციის ზედამხედველობის გარეშე კლასიფიკაციის გამოყენებადობა ორი კლასტერული იერარქიის შედარების გზით. ჩვენ გამოვთვალეთ საშუალო ბაზალური ფართობი ჰექტარზე თითოეული მერქნიანი სახეობისთვის 1059 ნაკვეთზე დაკვირვებით და რაოდენობრივად დავადგინეთ სახეობათაშორისი მანძილი 87‐ვე სახეობისთვის. ჩვენ შევადარეთ სახეობების ნეიტრალური განსხვავებულობიდან მიღებული შედეგები (ბრეი‐კურტისი) ზვავის დისკრიმინაციული განსხვავებულობის საფუძველზე, რომელიც აერთიანებს სახეობათაშორის ფილოგენეტიკურ ვარიაციებს. გენეტიკური ცვალებადობის ჩართვამ განსხვავებულობის რაოდენობრივ განსაზღვრებაში გამოიწვია მერქნიანი სახეობების შეკრების უფრო ნიუანსური განსხვავება და გაზრდილი კლასტერული თანმიმდევრულობა. ხელსაყრელი სტატისტიკა მოიცავს მტევანთა საერთო რაოდენობას (23 v. 20), საშუალო მანძილს მტევნის შიგნით (0.773 vs. 0.343) და კვადრატების ჯამის ფარგლებში (344.13 vs. 112.92). განსხვავებებიდან მიღებული კლასტერებმა, რომლებიც ითვალისწინებენ გენეტიკურ ვარიაციებს, აჩვენეს უფრო მძლავრი გასწორება ბიოგეოგრაფიულ ერთეულებთან, როგორიცაა სიმაღლე და ცნობილი ჰაბიტატები. ჩვენ ვაჩვენებთ, რომ სახეობების შეკრების უკონტროლო კლასიფიკაციის გამოყენებადობა ფართომასშტაბიანი ტყის ინვენტარიზაციის მონაცემებზე მტკიცედ არის დამოკიდებული განსხვავებულობის ფუძემდებლური რაოდენობრივი განსაზღვრაზე. ჩვენი შედეგები მიუთითებს, რომ ფილოგენეტიკური ვარიაციით, უფრო ზუსტი კლასიფიკაციაა შესაძლებელი, რომელიც შეესაბამება ბიოგეოგრაფიულ ერთეულებს. ეს ამტიცებს კონცეფციას, რომ სახეობათა შეკრების გენეტიკური სიგნალი ასახავს ბიოგეოგრაფიულ ნიმუშებს და ხელს უწყობს ტყის მრავალფეროვნების აღწერას მონიტორინგისა და მართვის უფრო ზუსტ ანალიზს.Bundesministerium für Bildung und Forschung https://doi.org/10.13039/50110000234
Forage supply of West African rangelands : Towards a better understanding of ecosystem services by application of hyperspectral remote sensing
Full text linkGrazing is the predominant type of land use in savanna regions all over the world. Although large savanna areas in Africa are still grazed by wild herbivores, the West African Sudanian savanna region mainly comprises rangeland ecosystems, providing the important ecosystem service of forage supply for domestic livestock. However, these dryland rangelands are threatened by global change, including a predicted in-crease in climatic aridity and variability as well as land degradation caused by overgrazing. In this context, the international research project WASCAL (West African Science Service Centre on Climate Change and Adapted Land Use) was initiated to investigate the effects of climatic change in this region and to develop effective adaptation and mitigation measures. This cumulative dissertation aims at providing a methodology for a regular knowledge-driven monitoring of forage resources in West Africa. Due to the vast and remote nature of Sudanian savannas, remote sensing technologies are required to achieve this goal. Hence, as a first step, it was necessary to test whether hyperspectral near-surface remote sensing offers the means to model and estimate the two most important aspects of forage supply, i.e. forage quantity (green biomass) and quality (metabolisable energy) (Chapter 2.1). Evidence was provided that partial least squares regression was able to generate robust and transferable forage models. In a second step, direct and indirect drivers of forage supply on the plot and site level were identified by using path modelling within the well-defined concept of social-ecological systems (Chapter 2.2). Results indicate that the provisioning ecosystem service of forage supply is mainly driven by land use, while climatic aridity exerts foremost indirect control by determining the way people use their environment. Building on these findings, upscaling of models was tested to generate maps of forage quality and quantity from satellite images (Chapter 2.3). Here, two different available data sources, i.e. multi- and hyperspectral satellites, were compared to serve the overall objective to install a regular forage monitoring system. In conclusion, preliminary forage maps could be created from both systems. An independent validation would be a research desiderate for future studies. Moreover, both systems feature certain shortcomings that might only be overcome by future satellite missions
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Multiseasonal Remote Sensing of Vegetation with One-Class Classification – Possibilities and Limitations in Detecting Habitats of Nature Conservation Value
Full text linkMapping of habitats relevant for nature conservation often involves the identification of patches of target habitats in a complex mosaic of vegetation types extraneous for conservation planning. In field surveys, this is often a time-consuming and work-intensive task. Limiting the necessary ground reference to a small sample of target habitats and combining it with area-wide remote sensing data could greatly reduce and therefore support the field mapping effort. Conventional supervised classification methods need to be trained with a representative set of samples covering an exhaustive set of all classes. Acquiring such data is work intensive and hence inefficient in cases where only one or few classes are of interest. The usage of one-class classifiers (OCC) seems to be more suitable for this task – but has up until now neither been tested nor applied for large scale mapping and monitoring in programs such as those requested for the Natura 2000 European Habitat Directive or the High Nature Value (HNV) farmland Indicator. It is important to uncover the possibilities and mark the obstacles of this new approach since the usage of remote sensing for conservation purposes is currently controversially discussed in the ecology community as well as in the remote sensing community. Thus, the focal and innovative point of this thesis is to explore possibilities and limitations in the application of one-class classifiers for detecting habitats of nature conservation value with the help of multi-seasonal remote sensing and limited field data. The first study ascertains the usage of an OCC is suitable for mapping Natura 2000 habitat types. Applying the Maxent algorithm in combination with a low number of ground reference points of four habitat types and easily available multi-seasonal satellite imagery resulted in a combined habitat map with reasonable accuracy. There is potential in one-class classification for detecting rare habitats – however, differentiating habitats with very similar species composition remains challenging. Motivated by these positive results, the topic of the second study of this thesis is whether low and HNV grasslands can be differentiated with remotely-sensed reflectance data, field data and one-class classification. This approach could supplement existing field survey-based monitoring approaches such as for the HNV farmland Indicator. Three one-class classifiers together with multi-seasonal, multispectral remote sensing data in combination with sparse field data were analysed for their usage A) to classify HNV grassland against other areas and B) to differentiate between three quality classes of HNV grassland according to the current German HNV monitoring approach. Results indicated reasonable performances of the OCC to identify HNV grassland areas, but clearly showed that a separation into several HNV quality classes is not possible. In the third study the robustness and weak spots of an OCC were tested considering the effect of landscape composition and sample size on accuracy measurements. For this purpose artificial landscapes were generated to avoid the common problem of case-studies which usually can only make locally valid statements on the suitability of a tested approach. Whereas results concerning target sample size and the amount of similar classes in the background confirm conclusions of earlier studies from the field of species distribution modelling, results for background sample size and prevalence of target class give new insights and a basis for further studies and discussions. In conclusion the utilisation of an OCC together with reflectance and sparse field data for addressing rare vegetation types of conservation interest proves to be useful and has to be recommended for further research
Fernerkundungsgestützte Modellierung kleinräumiger Biodiversität am Beispiel des Turtmanntals in der Schweiz
Full text linkHochgebirge sind Regionen mit extrem hoher Biodiversität. Sie erschließen die dritte Dimension, das heißt sie enthalten eine vertikale Klimazonierung auf sehr kurzer Distanz, was zu einer Komprimierung der Vegetationszonen entlang des Höhengradienten auf engem Raum führt. Steilheit (Neigung) und damit einhergehende Störung führen zusätzlich zu einem kleinräumigen Mosaik an Habitatvielfalt. Diese Habitatdiversität wird ferner durch die kleinräumig wechselnden Standortbedingungen gefördert. Für den Umwelt- und Naturschutz sind aktuelle, flächendeckende Karten vor dem Hintergrund des weltweit zunehmenden Biodiversitätsverlustes von großem Interesse. Da flächendeckende Kartierungen im Gelände gerade in schwer zugänglichen Gebieten wie den alpinen Hochgebirgsräumen sehr zeit- und arbeitsaufwändig, wenn nicht gar unmöglich sind, können Fernerkundungsdaten als kontinuierliche Ressource zur indirekten Vorhersage der Biodiversität über daraus abgeleitete Relief-, Spektral- und Texturparameter genutzt werden. Das Ziel dieser Arbeit war es, aus Fernerkundungsdaten Umweltparameter abzuleiten, um in statistischen Modellen die kleinräumige α-Diversität alpiner Lebensräume am Beispiel des Turtmanntals in der Schweiz zu modellieren. Dazu wurden sowohl Vegetationskartierungen im Gelände auf 2x2m² und 10x10m² durchgeführt und ausgewertet, als auch Fernerkundungsdaten, wie das 1m digitale Geländemodell des höchstauflösenden Sensors (HRSC – High Resolution Stereo Camera), eine QuickBird-Szene und eine SPOT-Szene verwendet. Aus den Fernerkundungsdaten wurden auf unterschiedlichen Skalen Umweltparameter zur Beschreibung des Reliefs (einfache und komplexe Reliefparameter) und der Textur abgeleitet, sowie einzelne Spektralkanäle extrahiert und der Vegetationsindex NDVI berechnet. Der Zusammenhang zwischen den Umweltparametern und den Biodiversitätsmaßen wurde mit statistischen Verfahren bivariat, in multivariaten ökologisch-statistischen Modellen (kanonische Korrespondenzanalyse - CCA) so wie in rein statistisch-basierten Modellen (Partial Least Square Regression – PLS) analysiert. Schließlich wurde die α-Diversität für das gesamte Untersuchungsgebiet auf Grundlage der fernerkundungsgestützten Umweltparameter mit Hilfe der berechneten Modellen vorhergesagt. Aus dieser Arbeit geht hervor, dass eine fernerkundungsgestützte Modellierung kleinräumiger Biodiversität im Turtmanntal in der Schweiz möglich ist. Die besten Modelle sind die statistisch basierten PLS Regressionen, die bis zu 74% der Varianz des Artenreichtums erklären. Die stärksten Zusammenhänge zur α-Diversität weisen Höhe, NDVI und naher Infrarotkanal von QuickBird auf. Unter Verwendung der abgeleiteten Umweltparameter, vor allem aus den optischen Spektraldaten, können flächendeckende, naturnahe Karten des Artenreichtums pro Fläche, das heißt der α-Diversität, auf einer Skalenebene von etwa 1m-25m berechnet werden.Modeling species richness and habitat diversity from HRSC, Quickbird and SPOT data in the Turtmann valley, Switzerland High mountains are regions of high geodiversity and biodiversity. Extreme high geodiversity within a relative small area implies different topoclimatic conditions over short distances and therefore, generates high biodiversity. Disturbances caused by steep slopes create small scale mosaics of vegetation habitats. Different abiotic factors additionally effect the local biological variety. For nature conservation actual maps of species richness and habitat variety within high moun-tains are useful to identify areas with a particular high biodiversity and which need a special protection, especially because of the actual high rates of global biodiversity loss. As area-wide mappings during field campaigns are very time-consuming, cost-intensive and in hard accessable areas nearly impossible, observations with remote sensing could serve as a continuous source for extracting diversity information. The main focus of this study was to predict local α-diversity of alpine regions with environ-mental parameters derived by remote sensing data in the Turtmann valley, Switzerland. In a first step vegetation parameters were mapped during field campaigns using 2x2m² and 10x10m² plots. In a second step, topographic parameters (primary and secondary topographic attributes) were derived from a digital elevation model of a high resolution sensor (HRSC – High Resolution Stereo Camera), spectral data, NDVI as well as texture parameters were cal-culated using QuickBird and SPOT images. The parameters were linked to the diversity pat-tern using bivariate and multivariate regressions at different scales. Multivariate ecological methods like canonical correspondence analysis (CCA) und statistical methods like partial least square regressions (PLS) were used to model and predict the local α-diversity of the study area and consequential create area-wide biodiversity maps. This study shows that it was possible to model local α-diversity of alpine regions in the Turt-mann valley, Switzerland, using environmental parameters derived by remote sensing data. PLS models performed best, explaining up to 74% of the variance of species richness. Strong-est relationships could be found between α-diversity and elevation, NDVI as well as near infrared reflectance of QuickBird. Using environmental parameters derived by remote sensing, especially from spectral data, area-wide and natural maps of species richness (local α-diversity) can be produced within a scale of 1m-25m
- …
