Monitoring forest cover loss using multiple data streams, a case study of a tropical dry forest in Bolivia

Dutrieux, L.P.; Verbesselt, Jan; Kooistra, Lammert; Herold, Martin

doi:10.1016/j.isprsjprs.2015.03.015

Cited by 101 publications

(80 citation statements)

References 60 publications

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“…A promising alternative might be to account for seasonal variation in the model by using external regressors. Dutrieux et al [65] take such an approach in a recent Landsat-based change detection study in tropical dry forests by adjusting the seasonal NDVI model using MODIS based measures of inter-annual variability, with encouraging results. They also tested a measure of rainfall variability as an external regressor but with poorer results.…”

Section: Model Sensitivity To Forest Type and Seasonalitymentioning

confidence: 99%

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Mapping Clearances in Tropical Dry Forests Using Breakpoints, Trend, and Seasonal Components from MODIS Time Series: Does Forest Type Matter?

et al. 2016

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Tropical environments present a unique challenge for optical time series analysis, primarily owing to fragmented data availability, persistent cloud cover and atmospheric aerosols. Additionally, little is known of whether the performance of time series change detection is affected by diverse forest types found in tropical dry regions. In this paper, we develop a methodology for mapping forest clearing in Southeast Asia using a study region characterised by heterogeneous forest types. Moderate Resolution Imaging Spectroradiometer (MODIS) time series are decomposed using Breaks For Additive Season and Trend (BFAST) and breakpoints, trend, and seasonal components are combined in a binomial probability model to distinguish between cleared and stable forest. We found that the addition of seasonality and trend information improves the change model performance compared to using breakpoints alone. We also demonstrate the value of considering forest type in disturbance mapping in comparison to the more common approach that combines all forest types into a single generalised forest class. By taking a generalised forest approach, there is less control over the error distribution in each forest type. Dry-deciduous and evergreen forests are especially sensitive to error imbalances using a generalised forest model i.e., clearances were underestimated in evergreen forest, and overestimated in dry-deciduous forest. This suggests that forest type needs to be considered in time series change mapping, especially in heterogeneous forest regions. Our approach builds towards improving large-area monitoring of forest-diverse regions such as Southeast Asia. The findings of this study should also be transferable across optical sensors and are therefore relevant for the future availability of dense time series for the tropics at higher spatial resolutions.

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Section: Model Sensitivity To Forest Type and Seasonalitymentioning

confidence: 99%

“…Previous forest disturbance based studies using BFAST have mapped on the Landsat scale using breakpoints alone (i.e., no magnitude thresholding) to detect change [65,71]. However, DeVries et al [22] showed that thresholding the break magnitude can be an important step distinguishing change from stable forest.…”

Section: Forest Disturbance Mapping Using Bfastmentioning

confidence: 99%

Mapping Clearances in Tropical Dry Forests Using Breakpoints, Trend, and Seasonal Components from MODIS Time Series: Does Forest Type Matter?

et al. 2016

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“…However, forest monitoring systems which detect deforestation events at sub-annual scales based on a bi-temporal change detection approach may face challenges in areas where forest has strong seasonality. To address this challenge, methods that detect deforestation at sub-annual scales from satellite image time series while accounting for seasonal variations have been developed in recent years [2][3][4][5][6][7][8]. These methods detect deforestation events by testing if a newly acquired observation at a particular pixel is abnormally low when compared to historical temporal dynamics of forest at such pixel [2,3,6,9].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Deforestation at Sub-Annual Scales as Extreme Events in Landsat Data Cubes

et al. 2016

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Current methods for monitoring deforestation from satellite data at sub-annual scales require pixel time series to have many historical observations in the reference period to model normal forest dynamics before detecting deforestation. However, in some areas, pixel time series often do not have many historical observations. Detecting deforestation at a pixel with scarce historical observations can be improved by complementing the pixel time series with spatial context information. In this work, we propose a data-driven space-time change detection method that detects deforestation events at sub-annual scales in data cubes of satellite image time series. First we spatially normalised observations in the local space-time data cube to reduce seasonality. Subsequently, we detected deforestation by assessing whether a newly acquired observation in the monitoring period is an extreme when compared against spatially normalised values in a local space-time data cube defined over reference period. We demonstrated our method at two sites, a dry tropical Bolivian forest and a humid tropical Brazilian forest, by varying the spatial and temporal extent of data cube. We emulated a "near real-time" monitoring scenario, implying that observations in the monitoring period were sequentially rather than simultaneously assessed for deforestation. Using Landsat normalised difference vegetation index (NDVI) time series, we achieved a median temporal detection delay of less than three observations, a producer's accuracy above 70%, a user's accuracy above 65%, and an overall accuracy above 80% at both sites, even when the reference period of the data cube only contained one year of data. Our results also show that large percentile thresholds (e.g., 5th percentile) achieve higher producer's accuracy and shorter temporal detection delay, whereas smaller percentiles (e.g., 0.1 percentile) achieve higher user's accuracy, but longer temporal detection delay. The method is data-driven, not based on statistical assumption on the data distribution, and can be applied on different forest types. However, it may face challenges in mixed forests where, for example, deciduous and evergreen forests coexist within short distances. A pixel to be assessed for deforestation should have a minimum of three temporal observations, the first of which must be known to represent forest. Such short time series allow rapid deployment of newly launched sensors (e.g., Sentinel-2) for detecting deforestation events at sub-annual scales.

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“…The both tests verified that the algorithm is able to detect and characterize abrupt changes in trend component with robustness against noise and seasonal changes. Furthermore, Dutrieux et al [54] successfully applied this algorithm on MODIS NDVI data to monitor forest cover loss in a tropical dry forest of Bolivia (overall accuracy of 87%).…”

Section: Change Detection Methods Of Ndvi Time Seriesmentioning

confidence: 99%

Improved Multi-Sensor Satellite-Based Aboveground Biomass Estimation by Selecting Temporally Stable Forest Inventory Plots Using NDVI Time Series

Urbazaev

Thiel

Migliavacca

et al. 2016

Forests

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Accurate estimates of aboveground biomass (AGB) are crucial to assess terrestrial C-stocks and C-emissions as well as to develop sustainable forest management strategies. In this study we used Synthetic Aperture Radar (SAR) data acquired at L-band and the Landsat tree cover product together with Moderate Resolution Image Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series data to improve AGB estimations over two study areas in southern Mexico. We used Mexican National Forest Inventory (INFyS) data collected between 2005 and 2011 to calibrate AGB models as well as to validate the derived AGB products. We applied MODIS NDVI time series data analysis to exclude field plots in which abrupt changes were detected. For this, we used Breaks For Additive Seasonal and Trend analysis (BFAST). We modelled AGB using an original field dataset and BFAST-filtered data. The results show higher accuracies of AGB estimations using BFAST-filtered data than using original field data in terms of R 2 and root mean square error (RMSE) for both dry and humid tropical forests of southern Mexico. The best results were found in areas with high deforestation rates where the AGB models based on the BFAST-filtered data substantially outperformed those based on original field data (R 2 BFAST = 0.62 vs. R 2 orig = 0.45; RMSE BFAST = 28.4 t/ha vs. RMSE orig = 33.8 t/ha). We conclude that the presented method shows great potential to improve AGB estimations and can be easily and automatically implemented over large areas.

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Monitoring forest cover loss using multiple data streams, a case study of a tropical dry forest in Bolivia

Cited by 101 publications

References 60 publications

Mapping Clearances in Tropical Dry Forests Using Breakpoints, Trend, and Seasonal Components from MODIS Time Series: Does Forest Type Matter?

Mapping Clearances in Tropical Dry Forests Using Breakpoints, Trend, and Seasonal Components from MODIS Time Series: Does Forest Type Matter?

Monitoring Deforestation at Sub-Annual Scales as Extreme Events in Landsat Data Cubes

Improved Multi-Sensor Satellite-Based Aboveground Biomass Estimation by Selecting Temporally Stable Forest Inventory Plots Using NDVI Time Series

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