Time-series analysis of multi-resolution optical imagery for quantifying forest cover loss in Sumatra and Kalimantan, Indonesia

Broich, Mark; Hansen, Matthew C.; Potapov, Peter; Adusei, Bernard; Lindquist, Erik; Stehman, Stephen V.

doi:10.1016/j.jag.2010.11.004

Cited by 164 publications

(123 citation statements)

References 52 publications

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“…However, satellite-based medium-resolution optical (Landsat), as well as C-(ERS-1, ENVISAT ASAR) and L-band SAR imagery (JERS-1, ALOS PALSAR) is available for most areas of the world. While Broich et al [83] demonstrated the derivation of time series of forest probabilities from Landsat time series for Sumatra and Kalimantan, Shimada et al [37] successfully demonstrated the extraction of F and NF distributions for PALSAR across global biomes using globally-distributed F and NF reference areas. An approach to automatically extract the required F and NF reference areas on the basis of Landsat imagery has been introduced by Huang et al [84].…”

Section: Discussionmentioning

confidence: 99%

A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

et al. 2015

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Abstract:To address the need for timely information on newly deforested areas at medium resolution scale, we introduce a Bayesian approach to combine SAR and optical time series for near real-time deforestation detection. Once a new image of either of the input time series is available, the conditional probability of deforestation is computed using Bayesian updating, and deforestation events are indicated. Future observations are used to update the conditional probability of deforestation and, thus, to confirm or reject an indicated deforestation event. A proof of concept was demonstrated using Landsat NDVI and ALOS PALSAR time series acquired at an evergreen forest plantation in Fiji. We emulated a near real-time scenario and assessed the deforestation detection accuracies using three-monthly reference data covering the entire study site. Spatial and temporal accuracies for the fused Landsat-PALSAR case (overall accuracy = 87.4%; mean time lag of detected deforestation = 1.3 months) were consistently higher than those of the Landsat-and PALSAR-only cases. The improvement maintained even for increasing missing data in the Landsat time series.

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Section: Discussionmentioning

confidence: 99%

A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

et al. 2015

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“…As with many other disturbance analyses in tropical regions, our lack of Landsat coverage during the 1990s affects our ability to continuously monitor long-term changes [14,75]. In our case, zero Landsat images are available between 1992 and 1995.…”

Section: Other Limitationsmentioning

confidence: 99%

Leveraging Multi-Sensor Time Series Datasets to Map Short- and Long-Term Tropical Forest Disturbances in the Colombian Andes

2017

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Abstract:The spatial distribution of disturbances in Andean tropical forests and protected areas has commonly been calculated using bi or tri-temporal analysis because of persistent cloud cover and complex topography. Long-term trends of vegetative decline (browning) or improvement (greening) have thus not been evaluated despite their importance for assessing conservation strategy implementation in regions where field-based monitoring by environmental authorities is limited. Using Colombia's Cordillera de los Picachos National Natural Park as a case study, we provide a temporally rigorous assessment of regional vegetation change from 2001-2015 with two remote sensing-based approaches using the Breaks For Additive Season and Trend (BFAST) algorithm. First, we measured long-term vegetation trends using a Moderate Resolution Imaging Spectroradiometer (MODIS)-based Multi-Angle Implementation of Atmospheric Correction (MAIAC) time series, and, second, we mapped short-term disturbances using all available Landsat images. MAIAC-derived trends indicate a net greening in 6% of the park, but in the surrounding 10 km area outside of the park, a net browning trend prevails at 2.5%. We also identified a 12,500 ha area within Picachos (4% of the park's total area) that has shown at least 13 years of consecutive browning, a result that was corroborated with our Landsat-based approach that recorded a 12,642 ha (±1440 ha) area of disturbed forest within the park. Landsat vegetation disturbance results had user's and producer's accuracies of 0.95 ± 0.02 and 0.83 ± 0.18, respectively, and 75% of Landsat-detected dates of disturbance events were accurate within ±6 months. This study provides new insights into the contribution of short-term disturbance to long-term trends of vegetation change, and offers an unprecedented perspective on the distribution of small-scale disturbances over a 15-year period in one of the most inaccessible national parks in the Andes.

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“…Hence, change products derived at sparse temporal intervals cannot capture such temporal dynamics, especially when forest cover change is caused by harvest and other land management practices [21]. In areas where forests can re-establish within a few years after having been cleared, coarse-interval change detection may also miss significant portions of the changes that are followed by rapid regrowth [14,22,23].…”

Section: Introductionmentioning

confidence: 99%

“…In the tropics, where most carbon emissions from deforestation are located, cloud and shadow contamination is another limiting factor for large-area land cover change mapping with Landsat [33]. Coarse-resolution sensors, such as MODIS, with a daily revisit frequency, have a greater probability of obtaining cloud-free observations annually [22,33].…”

Section: Introductionmentioning

confidence: 99%

Annual Detection of Forest Cover Loss Using Time Series Satellite Measurements of Percent Tree Cover

et al. 2014

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Abstract:We introduce and test a new method to detect annual forest cover loss from time series estimates of percent tree cover. Our approach is founded on two realistic assumptions: (1) land cover disturbances are rare events over large geographic areas that occur within a short time frame; and (2) spatially discrete land cover disturbances are continuous processes over time. Applying statistically rigorous algorithms, we first detect disturbance pixels as outliers of an underlying chi-square distribution. Then, we fit nonlinear, logistic curves for each identified change pixel to simultaneously characterize the magnitude and timing of the disturbance. Our method is applied using the yearly Vegetation Continuous Fields (VCF) tree cover product from Moderate Resolution Imaging Spectroradiometer (MODIS), and the resulting disturbance-year estimates are evaluated using a large sample of Landsat-based forest disturbance data. Temporal accuracy is ~65% at 250-m, annual resolution and increases to >85% when temporal resolution is relaxed to ±1 yr. The r 2 of MODIS VCF-based disturbance rates against Landsat ranges from 0.7 to 0.9 at 5-km spatial resolution. The general approach developed in this study can be potentially applied at a global scale and to other land cover types characterized as continuous variables from satellite data.

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Time-series analysis of multi-resolution optical imagery for quantifying forest cover loss in Sumatra and Kalimantan, Indonesia

Cited by 164 publications

References 52 publications

A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

Leveraging Multi-Sensor Time Series Datasets to Map Short- and Long-Term Tropical Forest Disturbances in the Colombian Andes

Annual Detection of Forest Cover Loss Using Time Series Satellite Measurements of Percent Tree Cover

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