scholarly journals Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data

2017 ◽  
Vol 9 (5) ◽  
pp. 479 ◽  
Author(s):  
Shanshan Liu ◽  
Xinliang Wei ◽  
Dengqiu Li ◽  
Dengsheng Lu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Forest Disturbance ◽  
Subtropical Forest ◽  
Zhejiang Province ◽  
Series Data ◽  
Forest Region ◽  
Disturbance And Recovery

scholarly journals An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data

2014 ◽  
Vol 6 (4) ◽  
pp. 2782-2808 ◽  
Author(s):  
Christopher Neigh ◽  
Douglas Bolton ◽  
Mouhamad Diabate ◽  
Jennifer Williams ◽  
Nuno Carvalhais
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Forest Disturbance ◽  
Series Data ◽  
Insect Mortality ◽  
History Of

scholarly journals Implementation of BFASTmonitor Algorithm on Google Earth Engine to Support Large-Area and Sub-Annual Change Monitoring Using Earth Observation Data

2020 ◽  
Vol 12 (18) ◽  
pp. 2953 ◽  
Author(s):  
Eliakim Hamunyela ◽  
Sabina Rosca ◽  
Andrei Mirt ◽  
Eric Engle ◽  
Martin Herold ◽  
...  
Keyword(s):  
Time Series ◽  
Tropical Forest ◽  
Satellite Data ◽  
Time Series Data ◽  
Forest Disturbance ◽  
Google Earth ◽  
Series Data ◽  
Observation Data ◽  
Large Area ◽  
Change Monitoring

Monitoring of abnormal changes on the earth’s surface (e.g., forest disturbance) has improved greatly in recent years because of satellite remote sensing. However, high computational costs inherently associated with processing and analysis of satellite data often inhibit large-area and sub-annual monitoring. Normal seasonal variations also complicate the detection of abnormal changes at sub-annual scale in the time series of satellite data. Recently, however, computationally powerful platforms, such as the Google Earth Engine (GEE), have been launched to support large-area analysis of satellite data. Change detection methods with the capability to detect abnormal changes in time series data while accounting for normal seasonal variations have also been developed but are computationally intensive. Here, we report an implementation of BFASTmonitor (Breaks For Additive Season and Trend monitor) on GEE to support large-area and sub-annual change monitoring using satellite data available in GEE. BFASTmonitor is a data-driven unsupervised change monitoring approach that detects abnormal changes in time series data, with near real-time monitoring capabilities. Although BFASTmonitor has been widely used in forest cover loss monitoring, it is a generic change monitoring approach that can be used to monitor changes in a various time series data. Using Landsat time series for normalised difference moisture index (NDMI), we evaluated the performance of our GEE BFASTmonitor implementation (GEE BFASTmonitor) by detecting forest disturbance at three forest areas (humid tropical forest, dry tropical forest, and miombo woodland) while comparing it to the original R-based BFASTmonitor implementation (original BFASTmonitor). A map-to-map comparison showed that the spatial and temporal agreements on forest disturbance between the original and our GEE BFASTmonitor implementations were high. At each site, the spatial agreement was more than 97%, whereas the temporal agreement was over 94%. The high spatial and temporal agreement show that we have properly translated and implemented the BFASTmonitor algorithm on GEE. Naturally, due to different numerical solvers being used for regression model fitting in R and GEE, small differences could be observed in the outputs. These differences were most noticeable at the dry tropical forest and miombo woodland sites, where the forest exhibits strong seasonality. To make GEE BFASTmonitor accessible to non-technical users, we developed a web application with simplified user interface. We also created a JavaScript-based GEE BFASTmonitor package that can be imported as a module. Overall, our GEE BFASTmonitor implementation fills an important gap in large-area environmental change monitoring using earth observation data.

scholarly journals MAPPING DISTURBANCE DYNAMICS IN WET SCLEROPHYLL FORESTS USING TIME SERIES LANDSAT

2016 ◽  
Vol XLI-B8 ◽  
pp. 633-641 ◽  
Author(s):  
A. Haywood ◽  
J. Verbesselt ◽  
P. J. Baker
Keyword(s):  
Time Series ◽  
Prescribed Burning ◽  
Time Series Data ◽  
Timber Harvesting ◽  
Series Data ◽  
Response Slope ◽  
Random Forest Models ◽  
Severity Class ◽  
Disturbance And Recovery ◽  
The Individual

In this study, we characterised the temporal-spectral patterns associated with identifying acute-severity disturbances and low-severity disturbances between 1985 and 2011 with the objective to test whether different disturbance agents within these categories can be identified with annual Landsat time series data. We analysed a representative State forest within the Central Highlands which has been exposed to a range of disturbances over the last 30 years, including timber harvesting (clearfell, selective and thinning) and fire (wildfire and prescribed burning). We fitted spectral time series models to annual normal burn ratio (NBR) and Tasseled Cap Indices (TCI), from which we extracted a range of disturbance and recovery metrics. With these metrics, three hierarchical random forest models were trained to 1) distinguish acute-severity disturbances from low-severity disturbances; 2a) attribute the disturbance agents most likely within the acute-severity class; 2b) and attribute the disturbance agents most likely within the low-severity class. Disturbance types (acute severity and low-severity) were successfully mapped with an overall accuracy of 72.9 %, and the individual disturbance types were successfully attributed with overall accuracies ranging from 53.2 % to 64.3 %. Low-severity disturbance agents were successfully mapped with an overall accuracy of 80.2 %, and individual agents were successfully attributed with overall accuracies ranging from 25.5 % to 95.1. Acute-severity disturbance agents were successfully mapped with an overall accuracy of 95.4 %, and individual agents were successfully attributed with overall accuracies ranging from 94.2 % to 95.2 %. Spectral metrics describing the disturbance magnitude were more important for distinguishing the disturbance agents than the post-disturbance response slope. Spectral changes associated with planned burning disturbances had generally lower magnitudes than selective harvesting. This study demonstrates the potential of landsat time series mapping for fire and timber harvesting disturbances at the agent level and highlights the need for distinguishing between agents to fully capture their impacts on ecosystem processes.

scholarly journals Investigating Forest Disturbance Using Landsat Data in the Nagagamisis Central Plateau, Ontario, Canada

2021 ◽  
Author(s):  
K. Wayne Forsythe ◽  
Grant McCartney
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Forest Disturbance ◽  
Timber Harvest ◽  
Series Data ◽  
Cultural Significance ◽  
Forest Disturbances ◽  
Moisture Index ◽  
Central Plateau ◽  
Provincial Park

The Nagagamisis Central Plateau (located in Northern Ontario, Canada) is an area of distinct natural and cultural significance. The importance of this land was officially recognized in 1957 through the establishment of the Nagagamisis Provincial Park Reserve. The park has experienced significant expansion since its inception and is currently under development as one of Ontario Parks ‘Signature Sites’. Since the 1980s, timber harvest activity has led to widespread forest disturbance just outside of the park boundaries. This research is focused on the detection of stand level forest disturbances associated with timber harvest occurring near Nagagamisis Provincial Park. The image time-series data selected for this project were Landsat TM and ETM+; spanning a twenty-five year period from 1984 to 2009. The Tasselled Cap Transformation and Normalized Difference Moisture Index were derived for use in unsupervised image classification to determine the land cover for each image in the time-series. Image band differencing and raster arithmetic were performed to create maps illustrating the size and spatial distribution of stand level forest disturbances between image dates. A total area of 1649 km2 or 26.1% of the study area experienced stand level disturbance during the analysis period.

scholarly journals Investigating Forest Disturbance Using Landsat Data in the Nagagamisis Central Plateau, Ontario, Canada

2021 ◽  
Author(s):  
K. Wayne Forsythe ◽  
Grant McCartney
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Forest Disturbance ◽  
Timber Harvest ◽  
Series Data ◽  
Cultural Significance ◽  
Forest Disturbances ◽  
Moisture Index ◽  
Central Plateau ◽  
Provincial Park

The Nagagamisis Central Plateau (located in Northern Ontario, Canada) is an area of distinct natural and cultural significance. The importance of this land was officially recognized in 1957 through the establishment of the Nagagamisis Provincial Park Reserve. The park has experienced significant expansion since its inception and is currently under development as one of Ontario Parks ‘Signature Sites’. Since the 1980s, timber harvest activity has led to widespread forest disturbance just outside of the park boundaries. This research is focused on the detection of stand level forest disturbances associated with timber harvest occurring near Nagagamisis Provincial Park. The image time-series data selected for this project were Landsat TM and ETM+; spanning a twenty-five year period from 1984 to 2009. The Tasselled Cap Transformation and Normalized Difference Moisture Index were derived for use in unsupervised image classification to determine the land cover for each image in the time-series. Image band differencing and raster arithmetic were performed to create maps illustrating the size and spatial distribution of stand level forest disturbances between image dates. A total area of 1649 km2 or 26.1% of the study area experienced stand level disturbance during the analysis period.

scholarly journals FOREST DISTURBANCE DETECTION BY LANDSAT-BASED NDVI TIME SERIES FOR AYUQUILA RIVER BASIN, JALISCO, MEXICO

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 31-35
Author(s):  
Y. Gao ◽  
A. Quevedo ◽  
J. Loya
Keyword(s):  
Time Series ◽  
River Basin ◽  
Time Series Data ◽  
Forest Disturbance ◽  
Series Data ◽  
Forest Loss ◽  
Validation Data ◽  
Early Action ◽  
Ndvi Time Series ◽  
Disturbance Detection

Abstract. Time series data have been applied for forest disturbance detection. The validation of detected changes is challenging partially because the validation data are often not readily available. Unlike multi-temporal change analysis, time series analysis not only detects areas of change but also reports time of change. Both spatial and temporal accuracy are therefore important for the accuracy assessment. Ayuquila River Basin (ARB) is one of the early action areas in Mexico for the implementation of REDD+ initiatives under UNFCCC. In ARB, shifting cultivation and cattle grazing often take place, resulting in degraded forestland. Sub-annual forest disturbance detection and estimation contribution to the improved local forest management and REDD+ implementation. Landsat-based NDVI time series data covering 1999–2018 were analysed using linear regression and the breakpoints of change and the magnitude of change were detected. Breakpoints with magnitude of change ranging from (−0.05) to (−0.2) were verified during a field campaign in October 2018. Here the magnitude of change is related with NDVI differences. Areas with magnitude of change higher than (−0.2) were identified as false changes. Verification data were generated by visually interpreting time series Landsat images of 2016–2018. In this way, areas with forest loss were identified. By stratified random sampling, 683 points were applied for the verification including 511 points of forests and 172 points of forest loss. It yields 75.84% for the overall accuracy of the change detection; for the detected forest loss as a category, the user’s accuracy is 88.89% and the producer’s accuracy is 0.46%. A possible reason for the very low producer’s accuracy is that the selected magnitude value (−0.2) is too low and some of the detected changes were filtered out.

scholarly journals MAPPING DISTURBANCE DYNAMICS IN WET SCLEROPHYLL FORESTS USING TIME SERIES LANDSAT

2016 ◽  
Vol XLI-B8 ◽  
pp. 633-641 ◽  
Author(s):  
A. Haywood ◽  
J. Verbesselt ◽  
P. J. Baker
Keyword(s):  
Time Series ◽  
Prescribed Burning ◽  
Time Series Data ◽  
Timber Harvesting ◽  
Series Data ◽  
Response Slope ◽  
Random Forest Models ◽  
Severity Class ◽  
Disturbance And Recovery ◽  
The Individual

In this study, we characterised the temporal-spectral patterns associated with identifying acute-severity disturbances and low-severity disturbances between 1985 and 2011 with the objective to test whether different disturbance agents within these categories can be identified with annual Landsat time series data. We analysed a representative State forest within the Central Highlands which has been exposed to a range of disturbances over the last 30 years, including timber harvesting (clearfell, selective and thinning) and fire (wildfire and prescribed burning). We fitted spectral time series models to annual normal burn ratio (NBR) and Tasseled Cap Indices (TCI), from which we extracted a range of disturbance and recovery metrics. With these metrics, three hierarchical random forest models were trained to 1) distinguish acute-severity disturbances from low-severity disturbances; 2a) attribute the disturbance agents most likely within the acute-severity class; 2b) and attribute the disturbance agents most likely within the low-severity class. Disturbance types (acute severity and low-severity) were successfully mapped with an overall accuracy of 72.9 %, and the individual disturbance types were successfully attributed with overall accuracies ranging from 53.2 % to 64.3 %. Low-severity disturbance agents were successfully mapped with an overall accuracy of 80.2 %, and individual agents were successfully attributed with overall accuracies ranging from 25.5 % to 95.1. Acute-severity disturbance agents were successfully mapped with an overall accuracy of 95.4 %, and individual agents were successfully attributed with overall accuracies ranging from 94.2 % to 95.2 %. Spectral metrics describing the disturbance magnitude were more important for distinguishing the disturbance agents than the post-disturbance response slope. Spectral changes associated with planned burning disturbances had generally lower magnitudes than selective harvesting. This study demonstrates the potential of landsat time series mapping for fire and timber harvesting disturbances at the agent level and highlights the need for distinguishing between agents to fully capture their impacts on ecosystem processes.

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