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

Grogan, Kenneth; Pflugmacher, Dirk; Hostert, Patrick; Verbesselt, Jan; Fensholt, Rasmus

doi:10.3390/rs8080657

Cited by 39 publications

(39 citation statements)

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“…using only a selection of MODIS bands that are most relevant for a specific classification problem). Grogan et al (2016) showed that such a reduction of the featurespace affects classification accuracies only marginally but improves computational time, while other researchers obtained similar accuracies even by reducing the number of sample points (Pal, 2006).…”

Section: Discussionmentioning

confidence: 89%

“…shifting cultivation, rubber) and more recent studies, focusing on a broader range of dynamics and processes to determine vegetation and land-use phenology, have emphasized the importance of including band 7 (Shortwave Infrared (SWIR)) to improve the mapping accuracy of forest dynamics (Grogan, Pflugmacher, Hostert, Verbesselt, & Fensholt, 2016;Hurni, Hett, Heinimann, Messerli, & Wiesmann, 2013;Z. Li & Fox, 2012;Suepa et al, 2016).…”

Section: Methodsmentioning

confidence: 99%

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The expansion of tree-based boom crops in mainland Southeast Asia: 2001 to 2014

Hurni

Fox

2018

Journal of Land Use Science

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Over the past half century, countries of Mainland Southeast Asia (MSEA) -Cambodia, Laos, Myanmar, Thailand, and Vietnamhave witnessed increases in commercialized agriculture with rapid expansions of boom-crop plantations. We used MODIS EVI and SWIR time-series from 2001-2014 to classify tree-cover changes across MSEA and performed a supervised change detection using an upscaling approach by deriving samples from existing Landsat classifications. We used the random forest classifier and distinguished 24 classes (16 representing boom-crops) with an accuracy of 82.2%. Boom-crops occupy about 18% of the landscape (8% of which is rubber). Since 2003 74,960 km 2 of rubber have been planted; 70% of rubber is planted on former forest land, and 30% on low vegetation area (mainly former croplands). Timing, patterns of change, and deforestation rates, however, differ among the MSEA countries and the high spatial and temporal detail of our classification allowed us to quantify dynamics and discuss political and socio-economic drivers of change.

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Section: Discussionmentioning

confidence: 89%

Section: Methodsmentioning

confidence: 99%

The expansion of tree-based boom crops in mainland Southeast Asia: 2001 to 2014

Hurni

Fox

2018

Journal of Land Use Science

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“…BFAST is a widely used method for detecting trend and seasonal breaks in time series. It has mainly been applied to monitoring forest disturbance (e.g., [13,19,20]) but has also been applied to more general land cover monitoring scenarios (e.g., [21][22][23]). BFAST uses an iterative process to find both trend and seasonal changes across a whole time series [15].…”

Section: Bfastmentioning

confidence: 99%

“…(2) 15 −30 20 −37 25 −43 30 −49 Here, the SOS has been moved forward by 37 days by changing the c 1 parameter from 5 to 25.…”

Section: Simulating Seasonal Time Seriesmentioning

confidence: 99%

An Evaluation and Comparison of Four Dense Time Series Change Detection Methods Using Simulated Data

et al. 2019

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Access to temporally dense time series such as data from the Landsat and Sentinel-2 missions has lead to an increase in methods which aim to monitor land cover change on a per-acquisition rather than a yearly basis. Evaluating the accuracy and limitations of these methods can be difficult because validation data are limited and often rely on human interpretation. Simulated time series offer an objective method for evaluating and comparing between change detection algorithms. A set of simulated time series was used to evaluate four change detection methods: (1) Breaks for Additive and Seasonal Trend (BFAST); (2) BFAST Monitor; (3) Continuous Change Detection and Classification (CCDC); and (4) Exponentially Weighted Moving Average Change Detection (EWMACD). In total, 151,200 simulations were generated to represent a range of abrupt, gradual, and seasonal changes. EWMACD was found to give the best performance overall, correctly identifying the true date of change in 76.6% of cases. CCDC performed worst (51.8%). BFAST performed well overall but correctly identified less than 10% of seasonal changes (changes in amplitude, length of season, or number of seasons). All methods showed some decrease in performance with increased noise and missing data, apart from BFAST Monitor which improved when data were removed. The following recommendations are made as a starting point for future studies: EWMACD should be used for detection of lower magnitude changes and changes in seasonality; CCDC should be used for robust detection of complete land cover class changes; EWMACD and BFAST are suitable for noisy datasets, depending on the application; and CCDC should be used where there are high quantities of missing data. The simulated datasets have been made freely available online as a foundation for future work.

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“…Since the first overviews regarding change detection in the 1960s [3], many works in this discipline have been conducted using remote sensing data. Among the recent works, we can cite the monitoring of arid environments [4], shorelines [5] and forests or woodlands [6][7][8][9] as well as detection of urban expansion [10], changes in buildings structure [11], changes in submerged sea grass biomass [12], mapping of landslides [13], damage to cultural heritage sites [14] and the proposition of new methodologies [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Examining Multi-Legend Change Detection in Amazon with Pixel and Region Based Methods

et al. 2017

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Post-classification comparison is one of the most widely used change detection methods. However, it presents several operational problems that are often ignored, such as the occurrence of impossible transitions, difficulties in accuracy assessment and results not accurate enough for the purpose. This work aims to evaluate post-classification comparison change detection results obtained from LANDSAT5/TM data in a region of the Brazilian Amazon, using three legends in different levels of detail and both pixel wise and region based classifiers. A distinctive characteristic of the used approach is that each change mapping is the result of the combination of 100 land cover classifications for each date, obtained using varied training samples. This approach allowed to account for the training samples choice into the methodology, as well as the construction of confidence mappings. We presented and discussed different approaches for evaluating change results, such as the likelihood of land cover transitions occurring within the study area and time gap, the use of rectangular matrices to incorporate the occurrence of impossible or non evaluable changes and classification uncertainty. In general, change mappings obtained from region based classifications showed better results than the ones obtained from pixel based classifications. Globally, the use of region based approaches, in contrast to pixel based ones, led to an increase in accuracy of 15.5% for the change mapping from the most detailed legend, 7.8% for the one with the legend with intermediate level of detail and 3.6% for the less detailed one. In addition, individual transitions between land cover classes were better identified using region based approaches, with the exception of transitions from a non agriculture class to an agricultural one. The proposed quality mappings are useful to help to evaluate the change mappings, mainly in legend levels with higher level of detail and if reference samples are unreliable or unavailable. It was possible to access, in a spatially explicit way, that at least 29.0% of the pixel based change mapping and 21.9% of the region based one from the most detailed legend were erroneous classified, without ground truth information on the evaluated date. These values decreased to 0.5% and 1.4% (respectively the pixel and region based approaches) for results with the legend with the intermediate level of detail and are non existent in the results from the less detailed legend. The more generalized the legend (lower number of classes), the most similar are the accuracy of region and pixel based change mappings. These accuracy values also increase as fewer classes are considered in the legend, since similar classes are assembled during clustering, which reduces the overlap between groups. However, this accuracy is still low for operational purposes in areas with few changes, even considering the very high accuracy of the land cover classifications used to generate the change mappings (land cover classification with Overall Accur...

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

Cited by 39 publications

References 84 publications

The expansion of tree-based boom crops in mainland Southeast Asia: 2001 to 2014

The expansion of tree-based boom crops in mainland Southeast Asia: 2001 to 2014

An Evaluation and Comparison of Four Dense Time Series Change Detection Methods Using Simulated Data

Examining Multi-Legend Change Detection in Amazon with Pixel and Region Based Methods

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