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

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.

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Forest Disturbance Mapping with Sentinel-2 Time Series in Austria

10.5194/egusphere-egu21-7795 ◽

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

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.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&#8211;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 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.At a forest stand level, we reconstruct the origin date of forest disturbances (FDD &#8211; 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&#8217;s potential to also depict and monitor minor human impacts on forests, such as thinning, selective timber extraction or other moderate forest management practices.Keywords:&#160; forest disturbance; forest monitoring; bark beetle infestation; forest management; time series analysis; phenology modelling; remote sensing; satellite imagery; Sentinel-2

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Detection and Mapping of Forest Disturbance in Eurasian Continent

10.5194/egusphere-egu21-10921 ◽

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

The global distribution and disturbance information of forest have strong impact on the change of Earth&#8217;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.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.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&#8217;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.

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Tracking Rates of Forest Disturbance and Associated Carbon Loss in Areas of Illegal Amber Mining in Ukraine Using Landsat Time Series

Remote Sensing ◽

10.3390/rs12142235 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2235

Author(s):

Viktor Myroniuk ◽

Andrii Bilous ◽

Yevhenii Khan ◽

Andrii Terentiev ◽

Pavlo Kravets ◽

...

Keyword(s):

Time Series ◽

Forest Management ◽

Sustainable Forest Management ◽

Quality Management System ◽

Forest Disturbance ◽

Google Earth ◽

Forest Stewardship Council ◽

Carbon Loss ◽

Disturbed Area ◽

Disturbance And Recovery

Mapping forest disturbance is crucial for many applications related to decision-making for sustainable forest management. This study identified the effect of illegal amber mining on forest change and accumulated carbon stock across a study area of 8125.5 ha in northern Ukraine. Our method relies on the Google Earth Engine (GEE) implementation of the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) temporal segmentation algorithm of Landsat time-series (LTS) to derive yearly maps of forest disturbance and recovery in areas affected by amber extraction operations. We used virtual reality (VR) 360 interactive panoramic images taken from the sites to attribute four levels of forest disturbance associated with the delta normalized burn ratio (dNBR) and then calculated the carbon loss. We revealed that illegal amber extraction in Ukraine has been occurring since the middle of the 1990s, yielding 3260 ha of total disturbed area up to 2019. This study indicated that the area of forest disturbance increased dramatically during 2013–2014, and illegal amber operations persist. As a result, regrowth processes were mapped on only 375 ha of total disturbed area. The results were integrated into the Forest Stewardship Council® (FSC®) quality management system in the region to categorize Forest Management Units (FMUs) conforming to different disturbance rates and taking actions related to their certification status. Moreover, carbon loss evaluation allows the responsible forest management systems to be streamlined and to endorse ecosystem service assessment.

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

10.21203/rs.3.rs-26379/v1 ◽

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.

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MAPPING OF STRIP FOREST IN ADAMPUR RANGE (HARYANA) A GEO-INFORMATICS APPROACH

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-5-289-2018 ◽

2018 ◽

Vol XLII-5 ◽

pp. 289-293

Author(s):

K. E. Mothi Kumar ◽

R. Kumar ◽

R. Bishnoi ◽

R. S. Hooda ◽

...

Keyword(s):

Geographical Area ◽

Forest Policy ◽

Google Earth ◽

Wood Products ◽

Forest Area ◽

Environmental Stability ◽

Tree Cover ◽

Natural Forests ◽

Forest Department ◽

The North

Abstract. Haryana state is an intensively cultivated state, and deficient in natural forests. One of the mandate of Haryana Forest Department (HFD) is to afforest for maintenance of environmental stability and restoration of ecological balance affected by serious depletion of forests, woodlands and water, and to increase tree cover in the state. National Forest Policy (1988) has set a goal to bring one third of Country’s area under forest and tree cover. Stock and dynamics of Trees Outside Forests (TOF) along with natural forests need to be understood holistically to appreciate the ecosystem services e.g., timber and non-wood products as tangible benefits along with services like carbon, water and weather moderation. The present study has attempted to demonstrate the utility of High Resolution Worldview-II (WV) satellite data (ortho rectified) that offeres immense scope to analyze the strip forests in Hisar district (Haryana, India). The study area Adampur Range (Hisar District) lies between the north latitudes 29&deg;0′52.229″ to 29&deg;25′6.746″ and east longitudes 75&deg;14′0.266″ to 75&deg;45′11.093″ with a total geographical area of about 1092.04sq.km. The adopted methodology involves onscreen digitization of the strip forest areas in the Adampur range (Hisar Distirct). The ToF formation identification and delineation includes the forest land besides roads, river, streams, canals, distributaries and railway lines etc. The shape files were converted into .kml files and overlaid on the Google Earth data for validation. An attempt has been made to compare the area difference between the Haryana Forest Department (HFD) notification details with that of the digitized strip forest lands. It was observed that the surveyed forest area is found to be 1717.37ha. against the notified forest area of 1714.45ha. showing a difference of 2.92ha. approximately in the studied beat boundaries.

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Ash Presence and Abundance Derived from Composite Landsat and Sentinel-2 Time Series and Lidar Surface Models in Minnesota, USA

Remote Sensing ◽

10.3390/rs12081341 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1341 ◽

Cited By ~ 1

Author(s):

Trevor K. Host ◽

Matthew B. Russell ◽

Marcella A. Windmuller-Campione ◽

Robert A. Slesak ◽

Joseph F. Knight

Keyword(s):

Time Series ◽

Forest Management ◽

Tree Mortality ◽

Emerald Ash Borer ◽

Google Earth ◽

Agrilus Planipennis ◽

The State ◽

Average Height ◽

Position Information ◽

Ash Trees

Ash trees (Fraxinus spp.) are a prominent species in Minnesota forests, with an estimated 1.1 billion trees in the state, totaling approximately 8% of all trees. Ash trees are threatened by the invasive emerald ash borer (Agrilus planipennis Fairmaire), which typically results in close to 100% tree mortality within one to five years of infestation. A detailed, wall-to-wall map of ash presence is highly desirable for forest management and monitoring applications. We used Google Earth Engine to compile Landsat time series analysis, which provided unique information on phenologic patterns across the landscape to identify ash species. Topographic position information derived from lidar was added to improve spatial maps of ash abundance. These input data were combined to produce a classification map and identify the abundance of ash forests that exist in the state of Minnesota. Overall, 12,524 km2 of forestland was predicted to have greater than 10% probability of ash species present. The overall accuracy of the composite ash presence/absence map was 64% for all ash species and 72% for black ash, and classification accuracy increased with the length of the time series. Average height derived from lidar was the best model predictor for ash basal area (R2 = 0.40), which, on average, was estimated as 16.1 m2 ha−1. Information produced from this map will be useful for natural resource managers and planners in developing forest management strategies which account for the spatial distribution of ash on the landscape. The approach used in this analysis is easily transferable and broadly scalable to other regions threatened with forest health problems such as invasive insects.

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

Remote Sensing ◽

10.3390/rs12244191 ◽

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.

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

Canadian Journal of Forest Research ◽

10.1139/cjfr-2016-0244 ◽

2017 ◽

Vol 47 (3) ◽

pp. 289-296 ◽

Cited By ~ 16

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.

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Mapping Forest Disturbances between 1987–2016 Using All Available Time Series Landsat TM/ETM+ Imagery: Developing a Reliable Methodology for Georgia, United States

Forests ◽

10.3390/f11030335 ◽

2020 ◽

Vol 11 (3) ◽

pp. 335 ◽

Cited By ~ 1

Author(s):

Shingo Obata ◽

Pete Bettinger ◽

Chris J. Cieszewski ◽

Roger C. Lowe III

Keyword(s):

Forest Management ◽

Spatial Data ◽

Forest Disturbance ◽

The State ◽

Thematic Mapper ◽

Detection Methods ◽

Forest Resource ◽

Forest Disturbances ◽

Disturbance History ◽

Resource Sustainability

Forest resources have a high economic value in the State of Georgia (USA) and the landscape is frequently disturbed as a part of forest management activities, such as plantation forest management activities. Thus, tracking the stand-clearing disturbance history in a spatially referenced manner might be pivotal in discussions of forest resource sustainability within the State. The two major objectives of this research are (i) to develop and test a reliable methodology for statewide tracking of forest disturbances in Georgia, (ii) to consider and discuss the use and implications of the information derived from the forest disturbance map. Two primary disturbance detection methods, a threshold algorithm and a statistical boundary method, were combined to develop a robust estimation of recent forest disturbance history. The developed model was used to create a forest disturbance record for the years 1987–2016, through the use of all available Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+) data. The final product was a raster database, where each pixel was assigned a value corresponding to the last disturbance year. The overall accuracy of the forest disturbance map was 87%, and it indicated that 4,503,253 ha, equivalent to 29.2% of the total land area in Georgia, experienced disturbances between 1987 and 2016. The estimated disturbed area in each year was highly variable and ranged between 84,651 ha (±36,354 ha) to 211,780 ha (±49,504 ha). By combining the use of the disturbance map along with the 2016 database from the National Land Cover Database (NLCD), we also analyzed the regional variation in the disturbance history. This analysis indicated that disturbed forests in urban areas were more likely to be converted to other land-uses. The forest disturbance record created in this research provides the necessary spatial data and address forest resource sustainability in Georgia. Additionally, the methodology used has application in the analysis of other resources, such as the estimation of the aboveground forest biomass.

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Detecting Forest Disturbance and Recovery in Primorsky Krai, Russia, Using Annual Landsat Time Series and Multi–Source Land Cover Products

Remote Sensing ◽

10.3390/rs12010129 ◽

2020 ◽

Vol 12 (1) ◽

pp. 129 ◽

Cited By ~ 1

Author(s):

Yang Hu ◽

Yunfeng Hu

Keyword(s):

Time Series ◽

Forest Disturbance ◽

Far East ◽

Google Earth ◽

Forest Recovery ◽

Dynamic Monitoring ◽

Forest Change ◽

Primorsky Krai ◽

Forest Classification ◽

Classification Probability

The spatial distribution and dynamic changes of the forests in Primorsky Krai, Russia, are of great significance for regional ecological security and sustainable economic and societal development. With the support of the Google Earth Engine cloud computing platform, we first synthesized yearly Landsat surface reflectance images of the best quality of the research area and then used the random forest method to calculate the forest classification probability of the study area year by year from 1998 to 2015. Furthermore, we used a time–series segmentation algorithm to perform temporal trajectory segmentation for forest classification probability estimation, and determined the spatial and temporal distribution characteristics and change laws of the forest. We extended the existing algorithms and parameters of forest classification probability trajectory analysis, achieving a high overall accuracy (86.2%) in forest change detection in the study area. The extended method can accurately capture the time node information of the changes. In the present research we observed: (1) that from 1998 to 2015, the forest area of the whole district showed a net loss state, with a loss area of 0.56 × 106 ha, of which the cumulative forest disturbance area reached 1.12 × 106 ha, and the cumulative forest recovery area reached 0.55 × 106 ha; and (2) that more than 90% of the forest change occurred in areas with a slope of less than 18°, at a distance of less than 20 km from settlements, and at a distance of less than 10 km from roads. The forest disturbance monitoring results are consistent with the changes in official statistical results over time, but there was a 20% overestimation. The technical method we extended in this study can be used as a reference for large–scale and high–precision dynamic monitoring of the forests in Russia’s Far East and other regions of the world; it also provides a basis for estimating illegal timber harvesting and determining the appropriate amount of forest harvested.

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