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

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.

Download Full-text

Land Cover-Specific Local Incidence Angle Correction: A Method for Time-Series Analysis of Forest Ecosystems

Remote Sensing ◽

10.3390/rs13091743 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1743

Author(s):

Daniel Paluba ◽

Josef Laštovička ◽

Antonios Mouratidis ◽

Přemysl Štych

Keyword(s):

Time Series ◽

Land Cover ◽

Forest Type ◽

Incidence Angle ◽

Correction Method ◽

Google Earth ◽

Forest Change ◽

Corine Land Cover ◽

Angle Correction ◽

Wide Range

This study deals with a local incidence angle correction method, i.e., the land cover-specific local incidence angle correction (LC-SLIAC), based on the linear relationship between the backscatter values and the local incidence angle (LIA) for a given land cover type in the monitored area. Using the combination of CORINE Land Cover and Hansen et al.’s Global Forest Change databases, a wide range of different LIAs for a specific forest type can be generated for each scene. The algorithm was developed and tested in the cloud-based platform Google Earth Engine (GEE) using Sentinel-1 open access data, Shuttle Radar Topography Mission (SRTM) digital elevation model, and CORINE Land Cover and Hansen et al.’s Global Forest Change databases. The developed method was created primarily for time-series analyses of forests in mountainous areas. LC-SLIAC was tested in 16 study areas over several protected areas in Central Europe. The results after correction by LC-SLIAC showed a reduction of variance and range of backscatter values. Statistically significant reduction in variance (of more than 40%) was achieved in areas with LIA range >50° and LIA interquartile range (IQR) >12°, while in areas with low LIA range and LIA IQR, the decrease in variance was very low and statistically not significant. Six case studies with different LIA ranges were further analyzed in pre- and post-correction time series. Time-series after the correction showed a reduced fluctuation of backscatter values caused by different LIAs in each acquisition path. This reduction was statistically significant (with up to 95% reduction of variance) in areas with a difference in LIA greater than or equal to 27°. LC-SLIAC is freely available on GitHub and GEE, making the method accessible to the wide remote sensing community.

Download Full-text

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

Forest Ecosystems ◽

10.1186/s40663-021-00352-6 ◽

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.

Download Full-text

Extraction of Old Towns in Hangzhou (2000–2018) from Landsat Time Series Image Stacks

Remote Sensing ◽

10.3390/rs13132438 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2438

Author(s):

Hao Ni ◽

Peng Gong ◽

Xuecao Li

Keyword(s):

Time Series ◽

Random Forest ◽

City Planning ◽

Large City ◽

Google Earth ◽

Urban Land Use ◽

Specific Method ◽

Rapid Urbanization ◽

Random Forest Classification ◽

Forest Classification

With rapid urbanization in recent decades, more and more urban renewal has taken place in China. Meanwhile, the early developed areas without change have become old towns, which need special attention in future city planning. However, other than field surveys, there is no specific method to identify old towns. To fill this gap, we used time-series image stacks established from Landsat Surface Reflectance Tier 1 data on the Google Earth Engine (GEE) platform, facilitated by Global Urban Boundary (GUB), Essential Urban Land Use Categories (EULUC) and Global Artificial Impervious Area (GAIA) data. The LandTrendr change detection algorithm was applied to extract detailed information from 14 band/index trajectories. These features were then used as inputs to two methods of old town identification: statistical thresholding and random forest classification. We assessed these two methods in a rapidly developing large city, Hangzhou, and subsequently obtained overall accuracies of 81.33% and 90.67%, respectively. Red band, NIR band and related indices show higher importance in random forest classification, and the magnitude feature plays an outstanding role. The final map of Hangzhou during the 2000–2018 period shows that the old towns were concentrated in the downtown region near West Lake within the urban boundaries in 2000, and far fewer than the renewed areas. The results could serve as references in the provincial and national planning of future urban developments.

Download Full-text

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

Remote Sensing ◽

10.3390/rs12182953 ◽

2020 ◽

Vol 12 (18) ◽

pp. 2953 ◽

Cited By ~ 2

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.

Download Full-text

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.

Download Full-text

Satellite Time Series and Google Earth Engine Democratize the Process of Forest-Recovery Monitoring over Large Areas

Remote Sensing ◽

10.3390/rs13234745 ◽

2021 ◽

Vol 13 (23) ◽

pp. 4745

Author(s):

Jennifer N. Hird ◽

Jahan Kariyeva ◽

Gregory J. McDermid

Keyword(s):

Time Series ◽

Forest Health ◽

Forest Degradation ◽

Google Earth ◽

Forest Recovery ◽

Percent Recovery ◽

Health Initiatives ◽

Spectral Recovery ◽

Google Earth Engine ◽

Normalized Burn Ratio

Contemporary forest-health initiatives require technologies and workflows that can monitor forest degradation and recovery simply and efficiently over large areas. Spectral recovery analysis—the examination of spectral trajectories in satellite time series—can help democratize this process, particularly when performed with cloud computing and open-access satellite archives. We used the Landsat archive and Google Earth Engine (GEE) to track spectral recovery across more than 57,000 forest harvest areas in the Canadian province of Alberta. We analyzed changes in the normalized burn ratio (NBR) to document a variety of recovery metrics, including year of harvest, percent recovery after five years, number of years required to achieve 80% of pre-disturbance NBR, and % recovery the end of our monitoring window (2018). We found harvest areas in Alberta to recover an average of 59.9% of their pre-harvest NBR after five years. The mean number of years required to achieve 80% recovery in the province was 8.7 years. We observed significant variability in pre- and post-harvest spectral recovery both regionally and locally, demonstrating the importance of climate, elevation, and complex local factors on rates of spectral recovery. These findings are comparable to those reported in other studies and demonstrate the potential for our workflow to support broad-scale management and research objectives in a manner that is complimentary to existing information sources. Measures of spectral recovery for all 57,979 harvest areas in our analysis are freely available and browseable via a custom GEE visualization tool, further demonstrating the accessibility of this information to stakeholders and interested members of the public.

Download Full-text

DETECTION OF FOREST DISTURBANCE USING TANDEM-X POL-INSAR COHERENCE

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

10.5194/isprs-archives-xlii-5-295-2018 ◽

2018 ◽

Vol XLII-5 ◽

pp. 295-297 ◽

Cited By ~ 1

Author(s):

M. Musthafa ◽

G. Singh ◽

U. Khati

Keyword(s):

Time Series ◽

Forest Structure ◽

Forest Disturbance ◽

High Reliability ◽

Selective Logging ◽

Forest Change ◽

Teak Plantation

Abstract. This current study shows the potential of TanDEM-X pol-InSAR coherence to identify progressive selective logging of Teak plantation in Uttarakhand, India. Pol-InSAR data were acquired over four months with 11 days interval with perpendicular baseline varying from 111 to 689m. Progressive selective logging of mature teak plantation from January to February was analyzed using time-series pol-InSAR coherences. The results shows the baseline selection critical for forest change studies. TanDEM-X derived pol-InSAR coherence would enable us to detect the change in forest structure with high reliability.

Download Full-text

Comparison of Winter Wheat Extraction Methods Based on Different Time Series of Vegetation Indices in the Northeastern Margin of the Qinghai–Tibet Plateau: A Case Study of Minhe, China

Remote Sensing ◽

10.3390/rs14020343 ◽

2022 ◽

Vol 14 (2) ◽

pp. 343

Author(s):

Fujue Huang ◽

Xingsheng Xia ◽

Yongsheng Huang ◽

Shenghui Lv ◽

Qiong Chen ◽

...

Keyword(s):

Time Series ◽

Winter Wheat ◽

Vegetation Index ◽

Optimal Parameter ◽

Arable Land ◽

Vegetation Indices ◽

Google Earth ◽

Dynamic Monitoring ◽

Tibet Plateau ◽

Qinghai Tibet Plateau

The northeastern margin of the Qinghai–Tibet Plateau (QTP) is an agricultural protection area in China’s new development plan, and the primary region of winter wheat growth within QTP. Winter wheat monitoring is critical for understanding grain self-sufficiency, climate change, and sustainable socioeconomic and ecological development in the region. However, due to the complex terrain and high altitude of the region, with discontinuous arable land and the relatively low level of agricultural development, there are no effective localization methodologies for extracting and monitoring the detailed planting distribution information of winter wheat. In this study, Sentinel-2A/B data from 2019 to 2020, obtained through the Google Earth Engine platform, were used to build time series reference curves of vegetation indices in Minhe. Planting distribution information of winter wheat was extracted based on the phenology time-weighted dynamic time warping (PT-DTW) method, and the effects of different vegetation indices’ time series and their corresponding threshold parameters were compared. The results showed that: (1) the three vegetation indices—normalized difference vegetation index (NDVI), normalized differential phenology index (NDPI), and normalized difference greenness index (NDGI)—maintained high mapping potential; (2) under the optimal threshold, >88% accuracy of index identification for winter wheat extraction was achieved; (3) due to improved extraction accuracy and resulting boundary range, NDPI and its corresponding optimal parameter (T = 0.05) performed the best. The process and results of this study have certain reference value for the study of winter wheat planting information change and the formulation of dynamic monitoring schemes in agricultural areas of QTP.

Download Full-text

POST-DISASTER ASSESSMENT OF MANGROVE FOREST RECOVERY IN LAWAAN-BALANGIGA, EASTERN SAMAR USING NDVI TIME SERIES ANALYSIS

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-3-2021-243-2021 ◽

2021 ◽

Vol V-3-2021 ◽

pp. 243-250

Author(s):

K. E. Cabello ◽

M. Q. Germentil ◽

A. C. Blanco ◽

E. G. Macatulad ◽

S. G. Salmo III

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Google Earth ◽

Forest Recovery ◽

Mangrove Forests ◽

Series Analysis ◽

Disaster Assessment ◽

Ndvi Time Series ◽

Extent Of Damage ◽

Gains And Losses

Abstract. In 2013, Typhoon Haiyan (Yolanda) struck the Eastern Philippines. Mangrove forests in the area were destroyed and were estimated to have at least 86% of damage. Some studies done on the typhoon-stricken mangroves had collected data such as measurements of mangrove trunk, height, roots, and seedlings to investigate the extent of damage and recovery. While these studies were proven to effectively identify mangrove gains and losses, these methods are only applicable in sites that are relatively accessible. This paper highlights the relevance of effective remote monitoring of mangrove forests that are vulnerable to typhoons including post-typhoon recovery. In this study, a Time Series Analysis using Google Earth Engine (GEE) was applied in assessing the damages and recovery of mangroves struck by Super Typhoon Haiyan in Lawaan and Balangiga, Samar (Eastern Philippines). The changes in mangrove extent followed the changes in NDVI; however, there were significant site-specific differences. Based on NDVI values, it was estimated that 83% of the mangrove area was damaged. After three years, regeneration from 2014–2017 was about 144%. Mangroves steadily developed but with a minimal change of 2.83% from 2017–2019. Most villages followed the general recovery trends in Lawaan and Balangiga. However, based on the time series analysis, some villages have minimal recovery than others. It suggests that the recovery of mangroves may be a function of the pre-typhoon mangrove extent and possibly vegetation condition. Even if there were new spaces for mangroves to colonize, some of the sites may not be conducive for plant regrowth.

Download Full-text