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>

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>

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.

scholarly journals Using Landsat time series imagery to detect forest disturbance in selectively logged tropical forests in Myanmar

2017 ◽  
Vol 47 (3) ◽  
pp. 289-296 ◽  
Author(s):  
Katsuto Shimizu ◽  
Raul Ponce-Hernandez ◽  
Oumer S. Ahmed ◽  
Tetsuji Ota ◽  
Zar Chi Win ◽  
...  
Keyword(s):  
Time Series ◽  
Forest Disturbance ◽  
Landsat Imagery ◽  
Forest Degradation ◽  
Selective Logging ◽  
Mitigation Strategies ◽  
Forest Disturbances ◽  
The Tropics ◽  
Degradation Indicator ◽  
Disturbance Detection

Detecting forest disturbances is an important task in formulating mitigation strategies for deforestation and forest degradation in the tropics. Our study investigated the use of Landsat time series imagery combined with a trajectory-based analysis for detecting forest disturbances resulting exclusively from selective logging in Myanmar. Selective logging was the only forest disturbance and degradation indicator used in this study as a causative force, and the results showed that the overall accuracy for forest disturbance detection based on selective logging was 83.0% in the study area. The areas affected by selective logging and other factors accounted for 4.7% and 5.4%, respectively, of the study area from 2000 to 2014. The detected disturbance areas were underestimated according to error assessments; however, a significant correlation between areas of disturbance and numbers of harvested trees during the logging year was observed, indicating the utility of a trajectory-based, annual Landsat imagery time series analysis for selective logging detection in the tropics. A major constraint of this study was the lack of available data for disturbances other than selective logging. Further studies should focus on identifying other types of disturbances and their impacts on future forest conditions.

scholarly journals The impact of forest regeneration on streamflow in 12 mesoscale humid tropical catchments

2013 ◽  
Vol 17 (7) ◽  
pp. 2613-2635 ◽  
Author(s):  
H. E. Beck ◽  
L. A. Bruijnzeel ◽  
A. I. J. M. van Dijk ◽  
T. R. McVicar ◽  
F. N. Scatena ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Forest Regeneration ◽  
Forest Cover ◽  
Impact Analysis ◽  
Forest Area ◽  
Macro Scale ◽  
Annual Time Series ◽  
The Impact ◽  
Annual Time

Abstract. Although regenerating forests make up an increasingly large portion of humid tropical landscapes, little is known of their water use and effects on streamflow (Q). Since the 1950s the island of Puerto Rico has experienced widespread abandonment of pastures and agricultural lands, followed by forest regeneration. This paper examines the possible impacts of these secondary forests on several Q characteristics for 12 mesoscale catchments (23–346 km2; mean precipitation 1720–3422 mm yr−1) with long (33–51 yr) and simultaneous records for Q, precipitation (P), potential evaporation (PET), and land cover. A simple spatially-lumped, conceptual rainfall–runoff model that uses daily P and PET time series as inputs (HBV-light) was used to simulate Q for each catchment. Annual time series of observed and simulated values of four Q characteristics were calculated. A least-squares trend was fitted through annual time series of the residual difference between observed and simulated time series of each Q characteristic. From this the total cumulative change (Â) was calculated, representing the change in each Q characteristic after controlling for climate variability and water storage carry-over effects between years. Negative values of  were found for most catchments and Q characteristics, suggesting enhanced actual evaporation overall following forest regeneration. However, correlations between changes in urban or forest area and values of  were insignificant (p ≥ 0.389) for all Q characteristics. This suggests there is no convincing evidence that changes in the chosen Q characteristics in these Puerto Rican catchments can be ascribed to changes in urban or forest area. The present results are in line with previous studies of meso- and macro-scale (sub-)tropical catchments, which generally found no significant change in Q that can be attributed to changes in forest cover. Possible explanations for the lack of a clear signal may include errors in the land cover, climate, Q, and/or catchment boundary data; changes in forest area occurring mainly in the less rainy lowlands; and heterogeneity in catchment response. Different results were obtained for different catchments, and using a smaller subset of catchments could have led to very different conclusions. This highlights the importance of including multiple catchments in land-cover impact analysis at the mesoscale.

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 A New Method for Automated Clearcut Disturbance Detection in Mediterranean Coppice Forests Using Landsat Time Series

2020 ◽  
Vol 12 (22) ◽  
pp. 3720 ◽  
Author(s):  
Francesca Giannetti ◽  
Raffaello Pegna ◽  
Saverio Francini ◽  
Ronald E. McRoberts ◽  
Davide Travaglini ◽  
...  
Keyword(s):  
Time Series ◽  
Sustainable Forest Management ◽  
Time Window ◽  
Detection Algorithm ◽  
Forest Disturbances ◽  
Forest Change ◽  
Short Time Window ◽  
Short Time ◽  
Automated Procedures ◽  
Disturbance Detection

A Landsat time series has been recognized as a viable source of information for monitoring and assessing forest disturbances and for continuous reporting on forest dynamics. This study focused on developing automated procedures for detecting disturbances in Mediterranean coppice forests which are characterized by rapid regrowth after a cut. Specifically, new methods specific to Mediterranean coppice forests are needed for mapping clearcut disturbances over time and for estimating related indicators in the context of Sustainable Forest Management and Biodiversity International monitoring frameworks. The aim of this work was to develop a new change detection algorithm for mapping clearcut disturbances in Mediterranean coppice forests with Landsat time series (LTS) using a short time window. Accuracy for the new algorithm, characterized as the Two Thresholds Method (TTM), was evaluated using an independent clearcut reference dataset over a temporal period of the 13 years between 2001 and 2013. TTM was also evaluated against two benchmark approaches: (i) LandTrendr, and (ii) the forest loss category of the Global Forest Change Map. Overall Accuracy for LandTrendr and TTM were greater than 0.94. Meanwhile, smaller accuracies were always obtained for the GFC. In particular, Producer’s Accuracy ranged between 0.45 and 0.84 for TTM and between 0.49 and 0.83 for LT, while for the GFC, PA ranged between 0 and 0.38. User’s Accuracy ranged between 0.86 and 0.96 for TTM and between 0.73 and 0.91 for LT, while for the GFC UA ranged between 0.19 and 1.00. Moreover, to illustrate the utility of TTM for mapping clearcut disturbances in Mediterranean coppice forests, we applied TTM to a Landsat scene that covered almost the entirety of the Tuscany region in Italy.

scholarly journals Dendroecological reconstructions of forest disturbance history using time-series analysis with intervention detection

2005 ◽  
Vol 35 (4) ◽  
pp. 868-876 ◽  
Author(s):  
Daniel L Druckenbrod
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Tree Ring ◽  
Forest Disturbance ◽  
Ring Width ◽  
Fagus Grandifolia ◽  
Disturbance History ◽  
Series Analysis ◽  
Growth Increments ◽  
Width Measurements

The detection of release events in the annual growth increments of trees has become a central and widely applied method for reconstructing the disturbance history of forests. While numerous approaches have been developed for identifying release events, the preponderance of these methods relies on running means that compare the percent change in growth rates. These methods do not explicitly account for the autocorrelation present within tree-ring width measurements and may introduce spurious events. This paper utilizes autoregressive integrated moving-average (ARIMA) processes to model tree-ring time series and incorporates intervention detection to identify pulse and step outliers as well as changes in trends indicative of a deterministic exogenous influence on past growth. This approach is evaluated by applying it to three chronologies from the Forest Responses to Anthropogenic Stress (FORAST) project that were impacted by prior disturbance events. The examples include a hemlock (Tsuga canadensis (L.) Carrière) chronology from New Hampshire, a white pine (Pinus strobus L.) chronology from Pennsylvania, and an American beech (Fagus grandifolia Ehrh.) chronology from Virginia. All three chronologies exhibit a clustering of step, pulse, and trend interventions subsequent to a known or likely disturbance event. Time-series analysis offers an alternative approach for identifying prior forest disturbances via tree rings based on statistical methods applicable across species and disturbance regimes.

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 Detecting Forest Changes Using Dense Landsat 8 and Sentinel-1 Time Series Data in Tropical Seasonal Forests

2019 ◽  
Vol 11 (16) ◽  
pp. 1899 ◽  
Author(s):  
Katsuto Shimizu ◽  
Tetsuji Ota ◽  
Nobuya Mizoue
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Data ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Landsat 8 ◽  
Forest Disturbances ◽  
Disturbance Detection ◽  
Seasonal Forests

The accurate and timely detection of forest disturbances can provide valuable information for effective forest management. Combining dense time series observations from optical and synthetic aperture radar satellites has the potential to improve large-area forest monitoring. For various disturbances, machine learning algorithms might accurately characterize forest changes. However, there is limited knowledge especially on the use of machine learning algorithms to detect forest disturbances through hybrid approaches that combine different data sources. This study investigated the use of dense Landsat 8 and Sentinel-1 time series data for detecting disturbances in tropical seasonal forests based on a machine learning algorithm. The random forest algorithm was used to predict the disturbance probability of each Landsat 8 and Sentinel-1 observation using variables derived from a harmonic regression model, which characterized seasonality and disturbance-related changes. The time series disturbance probabilities of both sensors were then combined to detect forest disturbances in each pixel. The results showed that the combination of Landsat 8 and Sentinel-1 achieved an overall accuracy of 83.6% for disturbance detection, which was higher than the disturbance detection using only Landsat 8 (78.3%) or Sentinel-1 (75.5%). Additionally, more timely disturbance detection was achieved by combining Landsat 8 and Sentinel-1. Small-scale disturbances caused by logging led to large omissions of disturbances; however, other disturbances were detected with relatively high accuracy. Although disturbance detection using only Sentinel-1 data had low accuracy in this study, the combination with Landsat 8 data improved the accuracy of detection, indicating the value of dense Landsat 8 and Sentinel-1 time series data for timely and accurate disturbance detection.

Sign in / Sign up

Export Citation Format

Share Document