1,720,997 research outputs found
A Sentinel-2 machine learning dataset for tree species classification in Germany
No full textAbstract. We present a machine learning dataset for tree species classification in Sentinel-2 satellite image time series of bottom-of-atmosphere reflectance. It is geared towards training classifiers but is less suitable for validating the resulting maps. The dataset is based on the German National Forest Inventory of 2012 as well as analysis-ready satellite imagery computed using the Framework for Operational Radiometric Correction for Environmental monitoring (FORCE) processing pipeline. From the National Forest Inventory data, we extracted the tree positions, filtered 387 775 trees in the upper canopy layer, and automatically extracted the corresponding bottom-of-atmosphere reflectance time series from Sentinel-2 L2A images. These time series are labeled with the corresponding tree species, which allows pixel-wise classification tasks. Furthermore, we provide auxiliary information such as the approximate tree position, the year of possible disturbance events, or the diameter at breast height. Temporally, the dataset spans the years from July 2015 to the end of October 2022, with approx. 75.3 million data points for trees of 48 species and 3 species groups as well as 13.8 million observations for non-tree backgrounds. Spatially, it covers the whole of Germany. The dataset is available at the following DOI (Freudenberg et al., 2024): https://doi.org/10.3220/DATA20240402122351-0
Monitoring trees outside forests: a review
No full textTrees outside forests (TOFs) are an important natural resource that contributes substantially to national biomass and carbon stocks and to the livelihood of people in many regions. Over the last decades, decision makers have become increasingly aware of the importance of TOF, and as a consequence, this tree resource is nowadays often considered in forest monitoring systems. Our review shows that in many cases, TOF are included in national forest inventories, applying traditional methodologies with relatively sparse networks of field sample plots. Only in some countries, such as India, the design of the inventories has considered the special features of how TOFs occur in the landscape. Several research studies utilising remote sensing for monitoring TOF have been conducted lately, but very few studies include comparative studies to optimise sampling strategies for TOF. Our review indicates that methods combining remote sensing and field surveys appear to be very promising, especially when remote sensing techniques that assess both the horizontal and vertical structures of tree resources are applied. For example, two-phase sampling strategies with laser scanning in the first phase and a field survey in the second phase appear to be effective for assessing TOF resources. However, TOFs often exhibit different characteristics than forest trees. Thus, to improve TOF monitoring, there is often a need to develop models, e.g. for biomass assessment, that are specifically adapted to this tree resource. Alternatively, field-based remote sensing methods that provide structural information about individual trees, notably terrestrial laser scanning, could be further developed for TOF monitoring applications. This also would have a potential to reduce the problem of accessing TOF during field surveys, which is a problem, for example, in countries where TOF are present on intensively utilised private grounds like gardens and agricultural fields
The contribution of trees outside forests to national tree biomass and carbon stocks—a comparative study across three continents
No full textStand Density Management Diagrams for Three Exotic Tree Species in Smallholder Plantations in Vietnam
No full textWhen smallholder farmers establish tree plantations to sell wood to the wood industry, they may run into problems when the plantations are mature and to be marketed because these farmers usually (1) do not know how to estimate the growing stock and (2) do not have sufficient knowledge of the wood markets. In this study, we tackle problem (1) and present stand density management diagrams (SDMDs) as a simple tool that allows rapid estimation of standing volume from data that stem from very basic inventory. Our data come from smallholder plantations in Vietnam, from four communes in the provinces of Binh Dinh and Phu Tho. Immense afforestation activities have been taken place in the country during the past two decades and it is special to Vietnam that a large share of these afforestations are under smallholder management with the goal to generate an additional source of income for these rural poor. A certain type of SDMDs is elaborated for three important exotic tree species commonly used for establishing industrial tree plantations (Acacia hybrid, Acacia mangium and Eucalyptus urophylla). They can be used for volume estimation and are also a tool to guide stand management and silvicultural treatments in general. Both implementation of the inventory and usage of the SDMDs are straightforward and simple so that this tool may be well suited to support smallholders in a better informed marketing of their wood, as well as, a better informed silvicultural management of their plantations
Estimating forest edge length from forest inventory sample dataThis article is one of a selection of papers from Extending Forest Inventory and Monitoring over Space and Time.
No full text Forest edge length is important for landscape ecological analysis, including the analysis of fragmentation. In this paper, we estimate forest edge length using field sample data from the German National Forest Inventory as an example. The complex plot design of many large-area forest inventories allows for the estimation of forest edge length at different spatial resolutions. As expected, estimates depend on the spatial resolution: longer estimated edge lengths resulted from observations at finer spatial resolutions. From the comparison of estimated edge lengths at different spatial resolutions, conclusions about the irregularity of forest edges can be drawn: more irregular forest boundaries resulted in greater differences between the estimated lengths for different spatial resolutions. One conclusion is of particular relevance: reported forest edge length values are meaningless unless their spatial resolution is also reported. The analysis presented is an add-on to the standard estimations from a forest inventory, producing additional, ecologically meaningful information. It is contended that many more nonstandard analyses of forest inventory data are possible either immediately or with minor modifications to the plot design. </jats:p
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
A unified framework for land cover monitoring based on a discrete global sampling grid (GSG)
No full textEnvironmental monitoring and assessment of the extent and change of land uses and their renewable natural resources over time is a key element in many
international processes and one crucial basis for sustainable management. Remote sensing plays an increasingly important role in these monitoring systems, especially
if the interest is in large areas. Integration of remote sensing requires comprehensive and careful preprocessing and a high level of expertise which is not always at hand in all applications. However, easy-to-implement sampling techniques based on visual interpretation are an alternative approach for utilizing remote sensing imagery, including the evolving archives of georeferenced
and preprocessed data provided by virtual globes like Google Earth, Bing, and others. The goal of this paper is to propose a simple unified framework that may
be used in the context of sampling studies and environmental monitoring from local to global scale. Besides the definition of a sampling design, the observation or plot
design, i.e., defining how observations are to be made and recorded, has a strong influence on the precision of estimates as well as the overall efficiency of a sampling
exercise.As an example, we present a simulation study focusing on the estimation of forest cover in artificial landscapes with different coverage and degree of fragmentation. The sampling units we compare are point clusters with different configuration and spatial extent
Comparison of estimators of variance for forest inventories with systematic sampling-results from artificial populations
Full text linkBackground: Large area forest inventories often use regular grids (with a single random start) of sample locations to ensure a uniform sampling intensity across the space of the surveyed populations. A design-unbiased estimator of variance does not exist for this design. Oftentimes, a quasi-default estimator applicable to simple random sampling (SRS) is used, even if it carries with it the likely risk of overestimating the variance by a practically important margin. To better exploit the precision of systematic sampling we assess the performance of five estimators of variance, including the quasi default. In this study, simulated systematic sampling was applied to artificial populations with contrasting covariance structures and with or without linear trends. We compared the results obtained with the SRS, Matérn’s, successive difference replication, Ripley’s, and D’Orazio’s variance estimators. Results: The variances obtained with the four alternatives to the SRS estimator of variance were strongly correlated, and in all study settings consistently closer to the target design variance than the estimator for SRS. The latter always produced the greatest overestimation. In populations with a near zero spatial autocorrelation, all estimators, performed equally, and delivered estimates close to the actual design variance. Conclusion: Without a linear trend, the SDR and DOR estimators were best with variance estimates more narrowly distributed around the benchmark; yet in terms of the least average absolute deviation, Matérn’s estimator held a narrow lead. With a strong or moderate linear trend, Matérn’s estimator is choice. In large populations, and a low sampling intensity, the performance of the investigated estimators becomes more similar. Keywords: Spatial autocorrelation, Linear trend, Model based, Design biased, Matérn variance, Successive difference replication variance, Geary contiguity coefficient, Random site effect
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