Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series

DeVries, Ben; Verbesselt, Jan; Kooistra, Lammert; Herold, Martin

doi:10.1016/j.rse.2015.02.012

Cited by 240 publications

(312 citation statements)

References 76 publications

(115 reference 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%

Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data

Liu

Wei

et al. 2017

Remote Sensing

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Detection of forest disturbance and recovery has received much attention during the last two decades due to its important influence on forest carbon budget estimation. This research used Landsat time-series data from 1984 to 2015 to examine forest disturbance and recovery in a subtropical region of eastern Zhejiang Province, China, through the LandTrendr algorithm. Field inventory data and high spatial resolution images were used to evaluate the disturbance and recovery results. This research indicates that high producer and user accuracies for both disturbance and recovery classes were obtained and three levels of disturbance and recovery each can be detected. Through incorporation of climate data and disturbance results, drought events also can be successfully detected. More research is needed to incorporate multisource data for detection of forest disturbance types in subtropical regions.

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Section: Introductionmentioning

confidence: 99%

Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data

Liu

Wei

et al. 2017

Remote Sensing

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“…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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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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“…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%

A Weight-Based Clustering Method

2017

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This paper proposes a weight-based self-constructing clustering method for time series data. Selfconstructing clustering processes all the data points incrementally. If a data point is not similar enough to an existing cluster, then (1) if the point currently does not belong to any cluster, it forms a new cluster of its own; (2) otherwise, the point is removed from the cluster it currently belongs to before a new cluster is formed. However, if a data point is similar enough to an existing cluster, then (1) if the point currently does not belong to any cluster, it is added to the most similar cluster; (2) otherwise, it is removed from the cluster it currently belongs to and added to the most similar cluster. During the clustering process, weights are learned and considered in the calculations of similarity between data points and clusters. Experimental results show that our proposed approach performs more effectively than other methods for real world time series datasets.

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Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series

Cited by 240 publications

References 76 publications

Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data

Examining Forest Disturbance and Recovery in the Subtropical Forest Region of Zhejiang Province Using Landsat Time-Series Data

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

A Weight-Based Clustering Method

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