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

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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The NDVI algorithm utilization on the google earth engine platform to monitor changes in forest density in mining area

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/886/1/012100 ◽

2021 ◽

Vol 886 (1) ◽

pp. 012100

Author(s):

Munajat Nursaputra ◽

Siti Halimah Larekeng ◽

Nasri ◽

Andi Siady Hamzah

Keyword(s):

Remote Sensing ◽

Time Series ◽

Large Scale ◽

Vegetation Index ◽

Forest Degradation ◽

Google Earth ◽

Aerial Images ◽

Forest Monitoring ◽

Forest Density ◽

Google Earth Engine

Abstract Periodic forest monitoring needs to be done to avoid forest degradation. In general, forest monitoring can be conducted manually (field surveys) or using technological innovations such as remote sensing data derived from aerial images (drone results) or cloud computing-based image processing. Currently, remote sensing technology provides large-scale forest monitoring using multispectral sensors and various vegetation index processing algorithms. This study aimed to evaluate the use of the Google Earth Engine (GEE) platform, a geospatial dataset platform, in the Vale Indonesia mining concession area to improve accountable forest monitoring. This platform integrates a set of programming methods with a publicly accessible time-series database of satellite imaging services. The method used is NDVI processing on Landsat multispectral images in time series format, which allows for the description of changes in forest density levels over time. The results of this NDVI study conducted on the GEE platform have the potential to be used as a tool and additional supporting data for monitoring forest conditions and improvement in mining regions.

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Mapping Forest Degradation due to Selective Logging by Means of Time Series Analysis: Case Studies in Central Africa

Remote Sensing ◽

10.3390/rs6010756 ◽

2014 ◽

Vol 6 (1) ◽

pp. 756-775 ◽

Cited By ~ 28

Author(s):

Manuela Hirschmugl ◽

Martin Steinegger ◽

Heinz Gallaun ◽

Mathias Schardt

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Case Studies ◽

Central Africa ◽

Forest Degradation ◽

Selective Logging ◽

Series Analysis

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Monitoring tropical forest degradation using spectral unmixing and Landsat time series analysis

Remote Sensing of Environment ◽

10.1016/j.rse.2018.11.011 ◽

2020 ◽

Vol 238 ◽

pp. 110968 ◽

Cited By ~ 35

Author(s):

Eric L. Bullock ◽

Curtis E. Woodcock ◽

Pontus Olofsson

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Tropical Forest ◽

Forest Degradation ◽

Spectral Unmixing ◽

Series Analysis

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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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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.

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