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

In 2016, Central Italy was hit by a months-lasting earthquake sequence that started off in August 24th 2016 with a Mw 6.2 earthquake which provoked severe damage to the towns of Accumoli (RI) and Amatrice (RI). The following October 30th 2016 earthquake (Mw 6.5), with epicenter in Norcia (PG) about 20 km NW of the first shock, triggered landslides in the area of Visso (MC), as reported by local newspapers.The purpose of this work is to individuate the areas affected by such landslides using the radiance variation recorded by multispectral images acquired by Sentinel 2. The time series analysis of the images has been carried out in Google Earth Engine environment, that allows access to the entire suite of available images. Due to the steep terrain, the shadowing effect of the hills was taken into account and comparison of images have been made only for those taken in the same seasonal moment of different years, thus guaranteeing the same solar elevation.It was found that the band of red was instrumental in identifying landslides along slopes made up of limestone, which is the typical outcrop of the area. Due to the extended time period between the images (July 2015 and July 2017), anthropogenic changes in land-use were present and had to be distinguished from landslides. A criterion involving the slope angle was developed, maintaining only the changes that had occurred on slopes steeper than 25&#176;, since man-made interventions giving similar spectral response are hardly done in steep areas. The slope analysis and correlation study with the extension and location of landslides was carried out using a Geographic Information System. (ESRI ArcGIS 10.5) The total extent of the area affected by the surveyed landslides is very large, having&#160; been estimated to be more than 200 000 m2.

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Earth Observation-Based Detectability of the Effects of Land Management Programmes to Counter Land Degradation: A Case Study from the Highlands of the Ethiopian Plateau

Remote Sensing ◽

10.3390/rs13071297 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1297

Author(s):

Esther Barvels ◽

Rasmus Fensholt

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Land Management ◽

Land Degradation ◽

Google Earth ◽

Small Scale ◽

Ols Regression ◽

Series Analysis ◽

Vegetation Condition ◽

Ethiopian Plateau

In Ethiopia land degradation through soil erosion is of major concern. Land degradation mainly results from heavy rainfall events and droughts and is associated with a loss of vegetation and a reduction in soil fertility. To counteract land degradation in Ethiopia, initiatives such as the Sustainable Land Management Programme (SLMP) have been implemented. As vegetation condition is a key indicator of land degradation, this study used satellite remote sensing spatiotemporal trend analysis to examine patterns of vegetation between 2002 and 2018 in degraded land areas and studied the associated climate-related and human-induced factors, potentially through interventions of the SLMP. Due to the heterogeneity of the landscapes of the highlands of the Ethiopian Plateau and the small spatial scale at which human-induced changes take place, this study explored the value of using 30 m resolution Landsat data as the basis for time series analysis. The analysis combined Landsat derived Normalised Difference Vegetation Index (NDVI) data with Climate Hazards group Infrared Precipitation with Stations (CHIRPS) derived rainfall estimates and used Theil-Sen regression, Mann-Kendall trend test and LandTrendr to detect changes in NDVI, rainfall and rain-use efficiency. Ordinary Least Squares (OLS) regression analysis was used to relate changes in vegetation directly to SLMP infrastructure. The key findings of the study are a general trend shift from browning between 2002 and 2010 to greening between 2011 and 2018 along with an overall greening trend between 2002 and 2018. Significant improvements in vegetation condition due to human interventions were found only at a small scale, mainly on degraded hillside locations, along streams or in areas affected by gully erosion. Visual inspections (based on Google Earth) and OLS regression results provide evidence that these can partly be attributed to SLMP interventions. Even from the use of detailed Landsat time series analysis, this study underlines the challenge and limitations to remotely sensed detection of changes in vegetation condition caused by land management interventions aiming at countering land degradation.

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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.

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