Near real-time tropical forest disturbance monitoring using Landsat time series and local expert monitoring data

2013 ◽  
Author(s):  
Ben DeVries ◽  
Arun Kumar Pratihast ◽  
Jan Verbesselt ◽  
Lammert Kooistra ◽  
Sytze de Bruin ◽  
...  
Keyword(s):  
Time Series ◽  
Tropical Forest ◽  
Real Time ◽  
Forest Disturbance ◽  
Monitoring Data ◽  
Local Expert

Can VIIRS continue the legacy of MODIS for near real-time monitoring of tropical forest disturbance?

2020 ◽  
Vol 249 ◽  
pp. 112024 ◽  
Author(s):  
Xiaojing Tang ◽  
Eric L. Bullock ◽  
Pontus Olofsson ◽  
Curtis E. Woodcock
Keyword(s):  
Tropical Forest ◽  
Real Time ◽  
Forest Disturbance ◽  
Real Time Monitoring

scholarly journals ON-LINE CHANGE MONITORING WITH TRANSFORMED MULTI-SPECTRAL TIME SERIES, A STUDY CASE IN TROPICAL FOREST

2016 ◽  
Vol XLI-B7 ◽  
pp. 987-989 ◽  
Author(s):  
Meng Lu ◽  
Eliakim Hamunyela
Keyword(s):  
Time Series ◽  
Tropical Forest ◽  
Satellite Data ◽  
Vegetation Index ◽  
Forest Disturbance ◽  
Spectral Band ◽  
Satellite Image ◽  
Spectral Bands ◽  
Normalised Difference Vegetation Index ◽  
Change Monitoring

In recent years, the methods for detecting structural changes in time series have been adapted for forest disturbance monitoring using satellite data. The BFAST (Breaks For Additive Season and Trend) Monitor framework, which detects forest cover disturbances from satellite image time series based on empirical fluctuation tests, is particularly used for near real-time deforestation monitoring, and it has been shown to be robust in detecting forest disturbances. Typically, a vegetation index that is transformed from spectral bands into feature space (e.g. normalised difference vegetation index (NDVI)) is used as input for BFAST Monitor. However, using a vegetation index for deforestation monitoring is a major limitation because it is difficult to separate deforestation from multiple seasonality effects, noise, and other forest disturbance. In this study, we address such limitation by exploiting the multi-spectral band of satellite data. To demonstrate our approach, we carried out a case study in a deciduous tropical forest in Bolivia, South America. We reduce the dimensionality from spectral bands, space and time with projective methods particularly the Principal Component Analysis (PCA), resulting in a new index that is more suitable for change monitoring. Our results show significantly improved temporal delay in deforestation detection. With our approach, we achieved a median temporal lag of 6 observations, which was significantly shorter than the temporal lags from conventional approaches (14 to 21 observations).

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

2020 ◽  
Vol 12 (18) ◽  
pp. 2953 ◽  
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.

Near real-time monitoring of tropical forest disturbance: New algorithms and assessment framework

2019 ◽  
Vol 224 ◽  
pp. 202-218 ◽  
Author(s):  
Xiaojing Tang ◽  
Eric L. Bullock ◽  
Pontus Olofsson ◽  
Stephan Estel ◽  
Curtis E. Woodcock
Keyword(s):  
Tropical Forest ◽  
Real Time ◽  
Forest Disturbance ◽  
Assessment Framework ◽  
Real Time Monitoring ◽  
New Algorithms

Model of multicomponent narrow-band periodical non-stationary random signal

2020 ◽  
Vol 2020 (48) ◽  
pp. 17-24
Author(s):  
I.M. Javorskyj ◽  
◽  
R.M. Yuzefovych ◽  
P.R. Kurapov ◽  
◽  
...  
Keyword(s):  
Time Series ◽  
Real Time ◽  
Hilbert Transform ◽  
Spectral Properties ◽  
Narrow Band ◽  
Analytic Signal ◽  
Random Signal ◽  
Hilbert Transformation ◽  
The Hilbert Transform

The correlation and spectral properties of a multicomponent narrowband periodical non-stationary random signal (PNRS) and its Hilbert transformation are considered. It is shown that multicomponent narrowband PNRS differ from the monocomponent signal. This difference is caused by correlation of the quadratures for the different carrier harmonics. Such features of the analytic signal must be taken into account when we use the Hilbert transform for the analysis of real time series.

860-P: Benefits of Real-Time Sharing and Following of Continuous Glucose Monitoring Data from Youth

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 860-P
Author(s):  
ANDREW PARKER ◽  
MARK DERDZINSKI ◽  
SARAH PUHR ◽  
JOHN WELSH ◽  
TOMAS C. WALKER ◽  
...  
Keyword(s):  
Real Time ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Monitoring Data ◽  
Time Sharing
Sign in / Sign up

Export Citation Format

Share Document