Forest Disturbance Mapping with Sentinel-2 Time Series in Austria

2021 ◽  
Author(s):  
Markus Löw ◽  
Tatjana Koukal
Keyword(s):  
Time Series ◽  
Forest Management ◽  
Time Series Analysis ◽  
Bark Beetle ◽  
Management Practices ◽  
Forest Ecosystems ◽  
Forest Disturbance ◽  
Spatial Scales ◽  
Forest Disturbances ◽  
Sentinel 2

<p>Worldwide, forests provide natural resources and ecosystem services. However, forest ecosystems are threatened by increasing forest disturbance dynamics, caused by direct human activities or by altering environmental conditions. It is decisive to reconstruct and trace the intra- to transannual dynamics of forest ecosystems. Therefore, the monitoring of large and small scale vegetation changes such as those caused by natural events (e.g., pest infestation, higher mortality due to altering site conditions) or forest management practices (e.g., thinning or selective timber extraction) becomes more and more crucial. National to local forest authorities and other stakeholders request detailed area-wide maps that delineate forest disturbance dynamics at various spatial scales.</p><p>We developed a time series analysis (TSA) framework that comprises data download, data management, image preprocessing and an advanced but flexible TSA. We use dense Sentinel-2 time series and a dynamic Savitzky–Golay-filtering approach to model robust but sensitive phenology courses. Deviations from the phenology models are used to derive detailed spatiotemporal information on forest disturbances. In a first case study, we apply the TSA to map forest disturbances directly or indirectly linked to recurring bark beetle infestation in Northern Austria.</p><p>In addition to spatiotemporal disturbance maps, we produce zonal statistics on different spatial scales that provide aggregated information on the extent of forest disturbances between 2018 and 2019. The outcomes are (a) area-wide consistent data of individual phenology models and deduced phenology metrics for Austrian forests and (b) operational forest disturbance maps, useful to investigate and monitor forest disturbances, for example to facilitate sustainable forest management.</p><p>At a forest stand level, we reconstruct the origin date of forest disturbances (FDD – Forest Disturbance Date). Theses FDD outputs show the spatiotemporal patterns and the development of damages and indicate that most dynamics are caused by recurring and spreading bark beetle infestation. The validation results based on field data confirm a high detection rate and show that the derived temporal information is reliable. In total, 23400 hectares, i.e., on average 2.8% of the forest area in the study area, are found to be affected by forest disturbance. The zonal statistic maps point out hotspots of significant forest disturbances, where adequate forest management measures are highly needed. Furthermore, this study highlights the TSA’s potential to also depict and monitor minor human impacts on forests, such as thinning, selective timber extraction or other moderate forest management practices.</p><p><strong>Keywords:  </strong><em>forest disturbance; forest monitoring; bark beetle infestation; forest management; time series analysis; phenology modelling; remote sensing; satellite imagery; Sentinel-2</em></p>

Phenology Modelling and Forest Disturbance Mapping with Sentinel-2 time series in Austria

2020 ◽  
Author(s):  
Markus Löw ◽  
Koukal Tatjana
Keyword(s):  
Time Series ◽  
Bark Beetle ◽  
Forest Ecosystems ◽  
Forest Disturbance ◽  
Spatial Scales ◽  
Data Access ◽  
Forest Monitoring ◽  
Forest Disturbances ◽  
Disturbance Dynamics ◽  
Sentinel 2

Abstract Background Worldwide, forests provide natural resources and ecosystem services. However, forest ecosystems are threatened by increasing forest disturbance dynamics, caused by direct human activities or an altering natural environment. It is decisive to trace the intra- to trans-annual dynamics of these forest ecosystems. National to local forest communities request detailed area-wide maps that delineate forest disturbance dynamics at various spatial scales. Methods We developed a remote sensing based time series analysis (TSA) framework that comprises data access, data management, image pre-processing, and an advanced but flexible TSA. The data basis is a dense time series of multispectral Sentinel-2 images with a spatial resolution of 10 metres. We use a dynamic Savitzky-Golay-filtering approach to reconstruct robust but sensitive phenology courses. Deviations from the latter are further used to derive spatiotemporal information on forest disturbances. In a first case study, we apply the TSA to map forest disturbances directly or indirectly linked to recurring bark beetle infestation in Northern Austria. Finally, we use zonal statistics on different spatial scales to provide aggregated information on the extent of forest disturbances between 2018 and 2019.Results and Conclusion The outcomes are a) individual phenology models and deduced phenology metrics for each 10 metres by 10 metres forest pixel in Austria and b) forest disturbance maps useful to investigate the occurrence, development and extent of bark beetle infestation. The phenology modelling results provide area-wide consistent data, also useful for downstream analyses (e.g. forest type classification). Results of the forest disturbance detection demonstrate that the TSA is capable to systematically delineate disturbed forest areas. Information derived from such a forest monitoring tool is highly relevant for various stakeholders in the forestry sector, either for forest management purposes or for decision-making processes on different levels.

scholarly journals Phenology Modelling and Forest Disturbance Mapping with Sentinel-2 Time Series in Austria

2020 ◽  
Vol 12 (24) ◽  
pp. 4191
Author(s):  
Markus Löw ◽  
Tatjana Koukal
Keyword(s):  
Time Series ◽  
Forest Ecosystems ◽  
Sustainable Forest Management ◽  
Forest Disturbance ◽  
Spatial Scales ◽  
Forest Disturbances ◽  
Disturbance Dynamics ◽  
Spatiotemporal Information ◽  
First Case ◽  
Sentinel 2

Worldwide, forests provide natural resources and ecosystem services. However, forest ecosystems are threatened by increasing forest disturbance dynamics, caused by direct human activities or by altering environmental conditions. It is decisive to reconstruct and trace the intra- to transannual dynamics of forest ecosystems. National to local forest authorities and other stakeholders request detailed area-wide maps that delineate forest disturbance dynamics at various spatial scales. We developed a time series analysis (TSA) framework that comprises data download, data management, image preprocessing and an advanced but flexible TSA. We use dense Sentinel-2 time series and a dynamic Savitzky–Golay-filtering approach to model robust but sensitive phenology courses. Deviations from the phenology models are used to derive detailed spatiotemporal information on forest disturbances. In a first case study, we apply the TSA to map forest disturbances directly or indirectly linked to recurring bark beetle infestation in Northern Austria. In addition to spatially detailed maps, zonal statistics on different spatial scales provide aggregated information on the extent of forest disturbances between 2018 and 2019. The outcomes are (a) area-wide consistent data of individual phenology models and deduced phenology metrics for Austrian forests and (b) operational forest disturbance maps, useful to investigate and monitor forest disturbances to facilitate sustainable forest management.

scholarly journals Forest disturbances and the attribution derived from yearly Landsat time series over 1990–2020 in the Hengduan Mountains Region of Southwest China

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yating Li ◽  
Zhenzi Wu ◽  
Xiao Xu ◽  
Hui Fan ◽  
Xiaojia Tong ◽  
...  
Keyword(s):  
Time Series ◽  
Forest Management ◽  
Biodiversity Conservation ◽  
Forest Disturbance ◽  
Road Construction ◽  
Google Earth ◽  
Forest Area ◽  
Hengduan Mountains ◽  
Natural Forests ◽  
Forest Disturbances

Abstract Background Natural forests in the Hengduan Mountains Region (HDMR) have pivotal ecological functions and provide diverse ecosystem services. Capturing long-term forest disturbance and drivers at a regional scale is crucial for sustainable forest management and biodiversity conservation. Methods We used 30-m resolution Landsat time series images and the LandTrendr algorithm on the Google Earth Engine cloud platform to map forest disturbances at an annual time scale between 1990 and 2020 and attributed causal agents of forest disturbance, including fire, logging, road construction and insects, using disturbance properties and spectral and topographic variables in the random forest model. Results The conventional and area-adjusted overall accuracies (OAs) of the forest disturbance map were 92.3% and 97.70% ± 0.06%, respectively, and the OA of mapping disturbance agents was 85.80%. The estimated disturbed forest area totalled 3313.13 km2 (approximately 2.31% of the total forest area in 1990) from 1990 to 2020, with considerable interannual fluctuations and significant regional differences. The predominant disturbance agent was fire, which comprised approximately 83.33% of the forest area disturbance, followed by logging (12.2%), insects (2.4%) and road construction (2.0%). Massive forest disturbances occurred mainly before 2000, and the post-2000 annual disturbance area significantly dropped by 55% compared with the pre-2000 value. Conclusions This study provided spatially explicit and retrospective information on annual forest disturbance and associated agents in the HDMR. The findings suggest that China’s logging bans in natural forests combined with other forest sustainability programmes have effectively curbed forest disturbances in the HDMR, which has implications for enhancing future forest management and biodiversity conservation.

Detection and Mapping of Forest Disturbance in Eurasian Continent

2021 ◽  
Author(s):  
Yusha Zhang ◽  
Yanchen Bo ◽  
Mei Sun ◽  
Tongtong Sun
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Forest Cover ◽  
Forest Disturbance ◽  
Threshold Model ◽  
Adaptive Threshold ◽  
Forest Area ◽  
Forest Disturbances ◽  
Eurasian Continent ◽  
Disturbance Detection

<p>The global distribution and disturbance information of forest have strong impact on the change of Earth’s ecosystems. In the 1990s, the Eurasian continent forest cover an area of 182 million ha, accounting about 33.2% of the Eurasian continent land area. However, we lack a complete mapping of high-resolution forest disturbances in Eurasia. Remote sensing can regularly obtain forest cover data across expansive range. Therefore, a complete set of Landsat time-series-based forest disturbance detection method is constructed in this paper to map a 30-meter forest disturbance detection distribution map of Eurasian continent.</p><p>In the construction of Landsat time series(LTS) data, the Landsat TM, ETM +, and OLI images of forest growth season were selected and synthesized into inter-annual time series over 35 years from 1986 to 2020. And the appropriate indices, NBR and NDVI, were selected as the input data for time series analysis. In time series analysis, the adaptive threshold of model learning is effectively applied in the process of extracting potential disturbance points, and the rich temporal information of LTS is fully mined to optimize and filter the disturbances.</p><p>The LTS images and forest disturbance based on adaptive threshold model are used to map three decades of forest disturbances, including the characteristics of the disturbance, spatiotemporal distribution and disturbance frequency across Eurasian continent. The derived disturbance year maps revealed that the disturbed forest area is 237 million ha and 12.8% of Eurasia’s forest area. In order to validate the accuracy of the map, 10066 interpreted Landsat pixels, including 3932 disturbed samples and 6134 undisturbed samples, are selected as reference data. The overall accuracy of the disturbance map is 86.6%, with a commission error of 13.4% and an omission error of 9.4%. The results indicated that the LTS and adaptive threshold model can effectively support the mapping of forest disturbance in Eurasian continent.</p>

scholarly journals Exploring Archetypes of Tropical Fire-Related Forest Disturbances Based on Dense Optical and Radar Satellite Data and Active Fire Alerts

Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 456
Author(s):  
Johannes Balling ◽  
Jan Verbesselt ◽  
Veronique De Sy ◽  
Martin Herold ◽  
Johannes Reiche
Keyword(s):  
Time Series ◽  
Management Practices ◽  
Forest Disturbance ◽  
Forest Monitoring ◽  
Landsat 8 ◽  
Forest Disturbances ◽  
Salvage Logging ◽  
Active Fire ◽  
The Tropics ◽  
Tree Foliage

Tropical forest disturbances linked to fire usage cause large amounts of greenhouse gas (GHG) emissions and environmental damages. Supporting precise GHG estimations and counteracting illegal fire usages in the tropics require timely and thematically detailed large-scale information on fire-related forest disturbances. Multi-sensor optical and radar detection and ranging (radar) remote sensing data combined with active fire alerts shows the potential for a more in-depth characterization of fire-related forest disturbances. We utilized dense optical (Landsat-7, Landsat-8 and Sentinel-2) and radar (Sentinel-1) time series to individually map forest disturbances in the province of Riau (Indonesia) for 2018–2019. We combined the sensor-specific optical and radar forest disturbance maps with daily active fire alerts and classified their temporal relationship (predating, coinciding, postdating) into seven so-called archetypes of fire-related forest disturbances. The archetypes reflect sensor-specific sensitives of optical (e.g., changes in tree foliage) and radar (e.g., changes in tree structure) data to detect varying types of forest disturbances, ranging from either a loss of tree foliage and/or structure predating, coinciding or postdating fires. These can be related to different magnitudes of fire-related forest disturbances and burn severities and can be associated with specific land management practices, such as slash-and-burn agriculture and salvage logging. This can support policy development, local and regional forest management and law enforcement to reduce illegal fire usage in the tropics. Results suggest that a delayed or opposing forest disturbance detection in the optical and radar signal is not only caused by environmental influences or different observation densities but, in some cases, such as fire-related forest disturbances, can be related to their different sensitives to detect changes in tree foliage and structure. Multi-sensor-based forest monitoring approaches should, therefore, not simply combine optical and radar time series on a data level, as it bears the risk of introducing artefacts.

Effect of forest management on the formation and stability of phytolith and PhytOC in forest ecosystem

2021 ◽  
Author(s):  
Lin Xu ◽  
Yongjun Shi ◽  
Wanjie Lv ◽  
Zhengwen Niu ◽  
Ning Yuan ◽  
...  
Keyword(s):  
Carbon Sequestration ◽  
Forest Management ◽  
Forest Ecosystem ◽  
Management Practices ◽  
Forest Ecosystems ◽  
Moso Bamboo ◽  
Soil System ◽  
Silicon Fertilization ◽  
Global Carbon

<p>Forest ecosystem has a high carbon sequestration capacity and plays a crucial role in maintaining global carbon balance and climate change. Phytolith-occluded carbon (PhytOC), a promising long-term biogeochemical carbon sequestration mechanism, has attracted more attentions in the global carbon cycle and the regulation of atmospheric CO<sub>2</sub>. Therefore, it is of practical significance to investigate the PhytOC accumulation in forest ecosystems. Previous studies have mostly focused on the estimation of the content and storage of PhytOC, while there were still few studies on how the management practices affect the PhytOC content. Here, this study focused on the effects of four management practices (compound fertilization, silicon fertilization, cut and control) on the increase of phytolith and PhytOC in Moso bamboo forests. We found that silicon fertilization had a greater potential to significantly promote the capacity of carbon sequestration in Moso bamboo forests. this finding positively corresponds recent studies that the application of silicon fertilizers (e.g., biochar) increase the Si uptake<strong><sup>1</sup></strong> to promote phytolith accumulation and its PhytOC sequestration in the plant-soil system<strong><sup>2</sup></strong>. Of course, the above-mentioned document<strong><sup>2</sup></strong> also had their own shortcomings, i.e., the experimental research time was not long, lacking long-term follow-up trial and the bamboo forest parts were also limited, so that the test results lack certain reliability. We have set up a long-term experiment plot to study the effects of silicon fertilizer on the formation and stability of phytolith and PhytOC in Moso bamboo forests. But anyway, different forest management practices, especially the application of high-efficiency silicon-rich fertilizers<strong><sup>1</sup></strong>, may be an effective way to increase the phytolith and PhytOC storage in forest ecosystems, and thereby improve the long-term CO<sub>2 </sub>sequestration capacity of forest ecosystems. Research in this study provides a good "forest plan" to achieve their national voluntary emission reduction commitments and achieves carbon neutrality goals for all over the world.</p><p>Refences:</p><p><sup>1</sup>Li et al., 2019. Plant and soil, 438(1-2), pp.187-203.</p><p><sup>2</sup>Huang et al., 2020, Science of The Total Environment, 715, p.136846.</p>

scholarly journals Mosaicking Landsat and Sentinel-2 Data to Enhance LandTrendr Time Series Analysis in Northern High Latitude Permafrost Regions

2020 ◽  
Vol 12 (15) ◽  
pp. 2471
Author(s):  
Alexandra Runge ◽  
Guido Grosse
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
High Latitude ◽  
High Latitudes ◽  
Series Analysis ◽  
Data Gaps ◽  
Northern High Latitude ◽  
Study Sites ◽  
Sentinel 2 ◽  
Permafrost Regions

Permafrost is warming in the northern high latitudes, inducing highly dynamic thaw-related permafrost disturbances across the terrestrial Arctic. Monitoring and tracking of permafrost disturbances is important as they impact surrounding landscapes, ecosystems and infrastructure. Remote sensing provides the means to detect, map, and quantify these changes homogeneously across large regions and time scales. Existing Landsat-based algorithms assess different types of disturbances with similar spatiotemporal requirements. However, Landsat-based analyses are restricted in northern high latitudes due to the long repeat interval and frequent clouds, in particular at Arctic coastal sites. We therefore propose to combine Landsat and Sentinel-2 data for enhanced data coverage and present a combined annual mosaic workflow, expanding currently available algorithms, such as LandTrendr, to achieve more reliable time series analysis. We exemplary test the workflow for twelve sites across the northern high latitudes in Siberia. We assessed the number of images and cloud-free pixels, the spatial mosaic coverage and the mosaic quality with spectral comparisons. The number of available images increased steadily from 1999 to 2019 but especially from 2016 onward with the addition of Sentinel-2 images. Consequently, we have an increased number of cloud-free pixels even under challenging environmental conditions, which then serve as the input to the mosaicking process. In a comparison of annual mosaics, the Landsat+Sentinel-2 mosaics always fully covered the study areas (99.9–100 %), while Landsat-only mosaics contained data-gaps in the same years, only reaching coverage percentages of 27.2 %, 58.1 %, and 69.7 % for Sobo Sise, East Taymyr, and Kurungnakh in 2017, respectively. The spectral comparison of Landsat image, Sentinel-2 image, and Landsat+Sentinel-2 mosaic showed high correlation between the input images and mosaic bands (e.g., for Kurungnakh 0.91–0.97 between Landsat and Landsat+Sentinel-2 mosaic and 0.92–0.98 between Sentinel-2 and Landsat+Sentinel-2 mosaic) across all twelve study sites, testifying good quality mosaic results. Our results show that especially the results for northern, coastal areas was substantially improved with the Landsat+Sentinel-2 mosaics. By combining Landsat and Sentinel-2 data we accomplished to create reliably high spatial resolution input mosaics for time series analyses. Our approach allows to apply a high temporal continuous time series analysis to northern high latitude permafrost regions for the first time, overcoming substantial data gaps, and assess permafrost disturbance dynamics on an annual scale across large regions with algorithms such as LandTrendr by deriving the location, timing and progression of permafrost thaw disturbances.

scholarly journals Abundance and genetic diversity responses of a lizard (Eulamprus heatwolei) to logging disturbance

2017 ◽  
Vol 65 (6) ◽  
pp. 362
Author(s):  
Francesca Lyndon-Gee ◽  
Joanna Sumner ◽  
Yang Hu ◽  
Claudio Ciofi ◽  
Tim S. Jessop
Keyword(s):  
Genetic Diversity ◽  
Forest Management ◽  
Management Practices ◽  
Forest Disturbance ◽  
Population Level ◽  
Landscape Scale ◽  
Population Abundance ◽  
Animal Populations ◽  
Eastern Australia ◽  
Logging Disturbance

Rotational logging practices are used with the goal of reducing forest disturbance impacts on biodiversity. However, it is poorly understood whether such forest management practices conserve the demographic and genetic composition of animal populations across logged landscapes. Here we investigated whether rotational logging practices alter patterns of landscape-scale population abundance and genetic diversity of a forest-dwelling lizard (Eulamprus heatwolei) in south-eastern Australia. We sampled lizards (n = 407) at up to 48 sites across a chronosequence of logging disturbance intervals (<10 to >60 years after logging) to assess site-specific population changes and genetic diversity parameters. Lizard abundances exhibited a significant curvilinear response to time since logging, with decreased numbers following logging (<10 years), increased abundance as the forest regenerated (10–20 years), before decreasing again in older regenerated forest sites (>30 years). Lizard genetic diversity parameters were not significantly influenced by logging disturbance. These results suggest that logging practices, whilst inducing short-term changes to population abundance, had no measurable effects on the landscape-scale genetic diversity of E. heatwolei. These results are important as they demonstrate the value of monitoring for evaluating forest management efficacy, and the use of different population-level markers to make stronger inference about the potential impacts of logging activities.

scholarly journals Time-Series of Vegetation Indices (VNIR/SWIR) Derived from Sentinel-2 (A/B) to Assess Turgor Pressure in Kiwifruit

2020 ◽  
Vol 9 (11) ◽  
pp. 641
Author(s):  
Alberto Jopia ◽  
Francisco Zambrano ◽  
Waldo Pérez-Martínez ◽  
Paulina Vidal-Páez ◽  
Julio Molina ◽  
...  
Keyword(s):  
Time Series ◽  
Crop Production ◽  
Management Practices ◽  
Water Status ◽  
Vegetation Indices ◽  
Turgor Pressure ◽  
Security Risk ◽  
Vegetative Development ◽  
Water Sensor ◽  
Sentinel 2

For more than ten years, Central Chile has faced drought conditions, which impact crop production and quality, increasing food security risk. Under this scenario, implementing management practices that allow increasing water use efficiency is urgent. The study was carried out on kiwifruit trees, located in the O’Higgins region, Chile for season 2018–2019 and 2019–2020. We evaluate the time-series of nine vegetation indices in the VNIR and SWIR regions derived from Sentinel-2 (A/B) satellites to establish how much variability in the canopy water status there was. Over the study’s site, eleven sensors were installed in five trees, which continuously measured the leaf’s turgor pressure (Yara Water-Sensor). A strong Spearman’s (ρ) correlation between turgor pressure and vegetation indices was obtained, having −0.88 with EVI and −0.81 with GVMI for season 2018–2019, and lower correlation for season 2019–2020, reaching −0.65 with Rededge1 and −0.66 with EVI. However, the NIR range’s indices were influenced by the vegetative development of the crop rather than its water status. The red-edge showed better performance as the vegetative growth did not affect it. It is necessary to expand the study to consider higher variability in kiwifruit’s water conditions and incorporate the sensitivity of different wavelengths.

scholarly journals Modelling Non-timber Forest Products for Forest Management Planning in Europe

2020 ◽  
Vol 6 (4) ◽  
pp. 309-322
Author(s):  
Jari Miina ◽  
Mikko Kurttila ◽  
Rafael Calama ◽  
Sergio de-Miguel ◽  
Timo Pukkala
Keyword(s):  
Forest Management ◽  
Management Practices ◽  
Spatial Scales ◽  
Forest Products ◽  
Joint Production ◽  
Forest Owners ◽  
Management Planning ◽  
Forest Management Planning ◽  
Forest Management Practices ◽  
Non Timber Forest Products

Abstract Purpose of Review The increased popularity and commercial use of non-timber forest products (NTFPs) calls for the development of models for NTFPs to include their predicted yields in forest management planning and to evaluate the potential of multi-functional forest management. This study assesses and discusses the current state of the art and trends in NTFP yield modelling in Europe and the integration of the models in multi-functional forest management planning at different spatial scales. Recent Findings Climate-sensitive empirical yield models already exist not only for a variety of NTFPs that are economically important to forest owners (e.g. cork and pine nuts) but also for wild-gathered berries and mushrooms, the harvesting of which cannot be controlled by the forest landowner in all European countries. Several studies on multi-functional forest management planning consider the economic profitability of the joint production of timber and NTFP. Harvesting NTFPs can create significant additional incomes for forest owners, compared with timber production only. However, maximizing the economic returns from the joint production of timber and NTFPs often calls for changes in forest management practices. Summary Continued efforts in modelling and predicting the yields of NTFPs have enabled forest managers to further expand the analyses of multi-functional forest planning and management in Europe. Climate-sensitive models also allow analyses on the potential effects of climate change on NTFP yields. New models and forest management practices are still needed for tree fruits, birch sap, a wider variety of wild edible mushrooms, specialty mushrooms cultivated on live trees as well as medicinal and edible forest herbs harvested for commercial value in Europe.

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