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

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.

Sentinel-1 Time-Series Analysis for Fires Monitoring using Google Earth Engine Tools

2021 ◽  
Author(s):  
Massimiliano Gargiulo ◽  
Antonio Iodice ◽  
Daniele Riccio ◽  
Giuseppe Ruello
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Google Earth ◽  
Series Analysis ◽  
Google Earth Engine

scholarly journals Socio-environmental land cover time series analysis of mining landscapes using Google Earth Engine and web-based mapping

2020 ◽  
pp. 100458
Author(s):  
Michelle Li Ern Ang ◽  
Dirk Arts ◽  
Danielle Crawford ◽  
Bonifacio V. Labatos ◽  
Khanh Duc Ngo ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Analysis ◽  
Google Earth ◽  
Cover Time ◽  
Web Based ◽  
Series Analysis ◽  
Google Earth Engine

scholarly journals Incorporating the Plant Phenological Trajectory into Mangrove Species Mapping with Dense Time Series Sentinel-2 Imagery and the Google Earth Engine Platform

2019 ◽  
Vol 11 (21) ◽  
pp. 2479 ◽  
Author(s):  
Huiying Li ◽  
Mingming Jia ◽  
Rong Zhang ◽  
Yongxing Ren ◽  
Xin Wen
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Google Earth ◽  
Late Winter ◽  
Mangrove Forests ◽  
Mangrove Species ◽  
Ndvi Time Series ◽  
Google Earth Engine ◽  
Species Mapping ◽  
Sentinel 2

Information on mangrove species composition and distribution is key to studying functions of mangrove ecosystems and securing sustainable mangrove conservation. Even though remote sensing technology is developing rapidly currently, mapping mangrove forests at the species level based on freely accessible images is still a great challenge. This study built a Sentinel-2 normalized difference vegetation index (NDVI) time series (from 2017-01-01 to 2018-12-31) to represent phenological trajectories of mangrove species and then demonstrated the feasibility of phenology-based mangrove species classification using the random forest algorithm in the Google Earth Engine platform. It was found that (i) in Zhangjiang estuary, the phenological trajectories (NDVI time series) of different mangrove species have great differences; (ii) the overall accuracy and Kappa confidence of the classification map is 84% and 0.84, respectively; and (iii) Months in late winter and early spring play critical roles in mangrove species mapping. This is the first study to use phonological signatures in discriminating mangrove species. The methodology presented can be used as a practical guideline for the mapping of mangrove or other vegetation species in other regions. However, future work should pay attention to various phenological trajectories of mangrove species in different locations.

scholarly journals Monitoring temperate forest degradation on Google Earth Engine using Landsat time series analysis

2021 ◽  
Vol 265 ◽  
pp. 112648
Author(s):  
Shijuan Chen ◽  
Curtis E. Woodcock ◽  
Eric L. Bullock ◽  
Paulo Arévalo ◽  
Paata Torchinava ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Temperate Forest ◽  
Forest Degradation ◽  
Google Earth ◽  
Series Analysis ◽  
Google Earth Engine

scholarly journals Mapping an Invasive Plant Spartina alterniflora by Combining an Ensemble One-Class Classification Algorithm with a Phenological NDVI Time-Series Analysis Approach in Middle Coast of Jiangsu, China

2020 ◽  
Vol 12 (24) ◽  
pp. 4010
Author(s):  
Xiang Liu ◽  
Huiyu Liu ◽  
Pawanjeet Datta ◽  
Julian Frey ◽  
Barbara Koch
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Spartina Alterniflora ◽  
Vegetation Index ◽  
Invasive Plant ◽  
Training Data ◽  
Support Vector ◽  
Detection Scheme ◽  
Series Analysis ◽  
Ndvi Time Series

Spartina alterniflora (S. alterniflora) is one of the worst plant invaders in the coastal wetlands of China. Accurate and repeatable mapping of S. alterniflora invasion is essential to develop cost-effective management strategies for conserving native biodiversity. Traditional remote-sensing-based mapping methods require a lot of fieldwork for sample collection. Moreover, our ability to detect this invasive species is still limited because of poor spectral separability between S. alterniflora and its co-dominant native plants. Therefore, we proposed a novel scheme that uses an ensemble one-class classifier (EOCC) in combination with phenological Normalized Difference Vegetation Index (NDVI) time-series analysis (TSA) to detect S. alterniflora. We evaluated the performance of the EOCC algorithm in two scenarios, i.e., single-scene analysis (SSA) and NDVI-TSA in the core zones of Yancheng National Natural Reserve (YNNR). Meanwhile, a fully supervised classifier support vector machine (SVM) was tested in the two scenarios for comparison. With these scenarios, the crucial phenological stages and the advantage of phenological NDVI-TSA in S. alterniflora recognition were also investigated. Results indicated the EOCC using only positive training data performed similarly well with the SVM trained on complete training data in the YNNR. Moreover, the EOCC algorithm presented a more robust transferability with notably higher classification accuracy than the SVM when being transferred to a second site, without a second training. Furthermore, when combined with the phenological NDVI-TSA, the EOCC algorithm presented more balanced sensitivity–specificity result, showing slightly better transferability than it performed in the best phenological stage (i.e., senescence stage of November). The achieved results (overall accuracy (OA), Kappa, and true skill statistic (TSS) were 92.92%, 0.843, and 0.834 for the YNNR, and OA, Kappa, and TSS were 90.94%, 0.815, and 0.825 for transferability to the non-training site) suggest that our detection scheme has a high potential for the mapping of S. alterniflora across different areas, and the EOCC algorithm can be a viable alternative to traditional supervised classification method for invasive plant detection.

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