Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms

2010 ◽  
Vol 114 (12) ◽  
pp. 2897-2910 ◽  
Author(s):  
Robert E. Kennedy ◽  
Zhiqiang Yang ◽  
Warren B. Cohen
Keyword(s):  
Time Series ◽  
Forest Disturbance ◽  
Temporal Segmentation ◽  
Segmentation Algorithms ◽  
Disturbance And Recovery

Detecting forest disturbance in the Pacific Northwest from MODIS time series using temporal segmentation

2014 ◽  
Vol 151 ◽  
pp. 114-123 ◽  
Author(s):  
Damien Sulla-Menashe ◽  
Robert E. Kennedy ◽  
Zhiqiang Yang ◽  
Justin Braaten ◽  
Olga N. Krankina ◽  
...  
Keyword(s):  
Time Series ◽  
Pacific Northwest ◽  
Forest Disturbance ◽  
Temporal Segmentation ◽  
The Pacific

scholarly journals Monitoring Mangrove Forest Degradation and Regeneration: Landsat Time Series Analysis of Moisture and Vegetation Indices at Rabigh Lagoon, Red Sea

Forests ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 52
Author(s):  
Mohammed Othman Aljahdali ◽  
Sana Munawar ◽  
Waseem Razzaq Khan
Keyword(s):  
Time Series ◽  
Red Sea ◽  
Vegetation Index ◽  
Forest Disturbance ◽  
Road Construction ◽  
Least Square ◽  
Eastern Coast ◽  
Positive Trend ◽  
Study Region ◽  
Disturbance And Recovery

Rabigh Lagoon, located on the eastern coast of the Red Sea, is an ecologically rich zone in Saudi Arabia, providing habitat to Avicennia marina mangrove trees. The environmental quality of the lagoon has been decaying since the 1990s mainly from sedimentation, road construction, and camel grazing. However, because of remedial measures, the mangrove communities have shown some degree of restoration. This study aims to monitor mangrove health of Rabigh Lagoon during the time it was under stress from road construction and after the road was demolished. For this purpose, time series of EVI (Enhanced Vegetation Index), MSAVI (Modified, Soil-Adjusted Vegetation Index), NDVI (Normalized Difference Vegetation Index), and NDMI (Normalized Difference Moisture Index) have been used as a proxy to plant biomass and indicator of forest disturbance and recovery. Long-term trend patterns, through linear, least square regression, were estimated using 30 m annual Landsat surface-reflectance-derived indices from 1986 to 2019. The outcome of this study showed (1) a positive trend over most of the study region during the evaluation period; (2) most trend slopes were gradual and weakly positive, implying subtle changes as opposed to abrupt changes; (3) all four indices divided the times series into three phases: degraded mangroves, slow recovery, and regenerated mangroves; (4) MSAVI performed best in capturing various trend patterns related to the greenness of vegetation; and (5) NDMI better identified forest disturbance and recovery in terms of water stress. Validating observed patterns using only the regression slope proved to be a challenge. Therefore, water quality parameters such as salinity, pH/dissolved oxygen should also be investigated to explain the calculated trends.

scholarly journals A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series

2017 ◽  
Vol 194 ◽  
pp. 303-321 ◽  
Author(s):  
Joanne C. White ◽  
Michael A. Wulder ◽  
Txomin Hermosilla ◽  
Nicholas C. Coops ◽  
Geordie W. Hobart
Keyword(s):  
Time Series ◽  
Forest Disturbance ◽  
Disturbance And Recovery

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

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.

Boreal Shield forest disturbance and recovery trends using Landsat time series

2015 ◽  
Vol 170 ◽  
pp. 317-327 ◽  
Author(s):  
Ryan J. Frazier ◽  
Nicholas C. Coops ◽  
Michael A. Wulder
Keyword(s):  
Time Series ◽  
Forest Disturbance ◽  
Boreal Shield ◽  
Disturbance And Recovery

scholarly journals Landsat Time-Series for Estimating Forest Aboveground Biomass and Its Dynamics across Space and Time: A Review

2019 ◽  
Vol 12 (1) ◽  
pp. 98 ◽  
Author(s):  
Trung H. Nguyen ◽  
Simon Jones ◽  
Mariela Soto-Berelov ◽  
Andrew Haywood ◽  
Samuel Hislop
Keyword(s):  
Time Series ◽  
Aboveground Biomass ◽  
Forest Disturbance ◽  
Forest Monitoring ◽  
Space And Time ◽  
Long Time ◽  
Data Policy ◽  
Disturbance And Recovery ◽  
Forest Aboveground Biomass

The free open access data policy instituted for the Landsat archive since 2008 has revolutionised the use of Landsat data for forest monitoring, especially for estimating forest aboveground biomass (AGB). This paper provides a comprehensive review of recent approaches utilising Landsat time-series (LTS) for estimating AGB and its dynamics across space and time. In particular, we focus on reviewing: (1) how LTS has been utilised to improve the estimation of AGB (for both single-date and over time) and (2) recent LTS-based approaches used for estimating AGB and its dynamics across space and time. In contrast to using single-date images, the use of LTS can benefit forest AGB estimation in two broad areas. First, using LTS allows for the filling of spatial and temporal data gaps in AGB predictions, improving the quality of AGB products and enabling the estimation of AGB across large areas and long time-periods. Second, studies have demonstrated that spectral information extracted from LTS analysis, including forest disturbance and recovery metrics, can significantly improve the accuracy of AGB models. Throughout the last decade, many innovative LTS-based approaches for estimating forest AGB dynamics across space and time have been demonstrated. A general trend is that methods have evolved as demonstrated through recent studies, becoming more advanced and robust. However, most of these methods have been developed and tested in areas that are either supported by established forest inventory programs and/or can rely on Lidar data across large forest areas. Further investigations should focus on tropical forest areas where inventory data are often not systematically available and/or out-of-date.

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