2015
DOI: 10.1016/j.rse.2015.02.012
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Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series

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Cited by 240 publications
(312 citation statements)
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“…Field surveys are the most accurate approach to examine forest In recent years, the two algorithms LandTrendr [24,[41][42][43][44][45] and BFAST [16,33,46,47] often have been used to detect forest disturbance and recovery in North America. There are also other algorithms such as Continuous Change Detection and Classification (CCDC) [48] and Continuous Monitoring of Forest Disturbance Algorithm (CMFDA) [9].…”
Section: Introductionmentioning
confidence: 99%
“…Field surveys are the most accurate approach to examine forest In recent years, the two algorithms LandTrendr [24,[41][42][43][44][45] and BFAST [16,33,46,47] often have been used to detect forest disturbance and recovery in North America. There are also other algorithms such as Continuous Change Detection and Classification (CCDC) [48] and Continuous Monitoring of Forest Disturbance Algorithm (CMFDA) [9].…”
Section: Introductionmentioning
confidence: 99%
“…First, we only fit a first-order harmonic curve to the Landsat time series using BFAST Monitor, whereas we used a third-order harmonic for the MAIAC time series. This decision lies in the irregular distribution of Landsat observations that can result in model over-fitting [14]. Second, we omitted the trend component when modeling the Landsat time series but used a seasonal-and-trend model with MAIAC.…”
Section: Landsat-based Disturbance Detection With Bfast Monitormentioning
confidence: 99%
“…With the historical model in place, we applied BFAST Monitor sequentially from 2001 to 2015, following the approach by DeVries at al. [14].…”
Section: Landsat-based Disturbance Detection With Bfast Monitormentioning
confidence: 99%
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“…Clustering techniques play a very important role in the field of artificial intelligence [1] [2][3] [4]. In particular, they are widely applied in times series data analysis in a variety of areas, such as bioengineering [5], environmental monitoring [6], economic applications, and so on. In the process of clustering time series data, using the same weight for each dimension may cause bad effects.…”
Section: Introductionmentioning
confidence: 99%