Attribution of Disturbance Agents to Forest Change Using a Landsat Time Series in Tropical Seasonal Forests in the Bago Mountains, Myanmar

Shimizu, Katsuto; Ahmed, Oumer S.; Poncé-Hernandez, Raul; Ota, Tetsuji; Win, Zar Chi; Mizoue, Nobuya; Yoshida, Shigejiro

doi:10.3390/f8060218

Cited by 41 publications

(31 citation statements)

References 62 publications

(88 reference statements)

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“…In the face of rapid and multiple forest changes, the stand-and landscape-level information that reveals disturbance patterns is particularly relevant for countries, such as South Africa, whose forestry resources contribute greatly to the economy. The planning and execution of timber harvesting to sustain or increase forest ecosystem services (that is, carbon stocks) prevail in the context of escalating forest disturbances [14] in order to meet forest management objectives [15]. While harvest operations are generally well known in advance by forest authorities, independent means for mapping are often pursued after the event [16].…”

Section: Introductionmentioning

confidence: 99%

“…However, the use of SAR in forest applications has not developed to the extent of optical platforms, partly because of the hitherto limited data processing capability [31]. The policy of making data freely available, which was introduced in 2008, unlocked access to records of satellite data from several space agencies and data suppliers such as NASA, the US Geological Survey and, more recently, the European Space Agency [15,32]. In order to support these resources, the Google Earth Engine (GEE) provides a cloud-computing platform to access massive, freely available, and pre-processed remotely sensed imagery [33], including the Sentinel-1 ground range detected (GRD) SAR and Sentinel-2 archive.…”

Section: Introductionmentioning

confidence: 99%

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Detecting Harvest Events in Plantation Forest Using Sentinel-1 and -2 Data via Google Earth Engine

Xulu

Mbatha

Peerbhay

et al. 2020

Forests

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South Africa is reported to experience timber shortages as a result of growing timber demands and pulp production, coupled with the government’s reluctance to grant new forestry permits. Rampant timber theft in the country makes these circumstances worse. The emergence of cloud-based platforms, such as Google Earth Engine (GEE), has greatly improved the accessibility and usability of high spatial and temporal Sentinel-1 and -2 data, especially in data-poor countries that lack high-performance computing systems for forest monitoring. Here, we demonstrate the potential of these resources for forest harvest detection. The results showed that Sentinel-1 data are efficient in detecting clear-cut events; both VH and VV backscatter signals decline sharply in accordance with clear-cutting and increase again when forest biomass increases. When correlated with highly responsive NDII, the VH and VV signals reached the best accuracies of 0.79 and 0.83, whereas the SWIR1 achieved –0.91. A Random Forest (RF) algorithm based on Sentinel-2 data also achieved over 90% accuracies for classifying harvested and forested areas. Overall, our study presents a cost-effective method for mapping clear-cut events in an economically important forestry area of South Africa while using GEE resources.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detecting Harvest Events in Plantation Forest Using Sentinel-1 and -2 Data via Google Earth Engine

Xulu

Mbatha

Peerbhay

et al. 2020

Forests

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“…Stratified random sampling was employed to collect samples. We used forest disturbance maps from 2016 and 2017 in this study area that were derived from time series analysis of annual Landsat data [75]. These disturbance maps were originally developed for the period 2000-2014, but additional Landsat data from 2000-2019 were used to generate the disturbance maps used in this study.…”

Section: Collection Of Reference Samplesmentioning

confidence: 99%

Detecting Forest Changes Using Dense Landsat 8 and Sentinel-1 Time Series Data in Tropical Seasonal Forests

2019

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The accurate and timely detection of forest disturbances can provide valuable information for effective forest management. Combining dense time series observations from optical and synthetic aperture radar satellites has the potential to improve large-area forest monitoring. For various disturbances, machine learning algorithms might accurately characterize forest changes. However, there is limited knowledge especially on the use of machine learning algorithms to detect forest disturbances through hybrid approaches that combine different data sources. This study investigated the use of dense Landsat 8 and Sentinel-1 time series data for detecting disturbances in tropical seasonal forests based on a machine learning algorithm. The random forest algorithm was used to predict the disturbance probability of each Landsat 8 and Sentinel-1 observation using variables derived from a harmonic regression model, which characterized seasonality and disturbance-related changes. The time series disturbance probabilities of both sensors were then combined to detect forest disturbances in each pixel. The results showed that the combination of Landsat 8 and Sentinel-1 achieved an overall accuracy of 83.6% for disturbance detection, which was higher than the disturbance detection using only Landsat 8 (78.3%) or Sentinel-1 (75.5%). Additionally, more timely disturbance detection was achieved by combining Landsat 8 and Sentinel-1. Small-scale disturbances caused by logging led to large omissions of disturbances; however, other disturbances were detected with relatively high accuracy. Although disturbance detection using only Sentinel-1 data had low accuracy in this study, the combination with Landsat 8 data improved the accuracy of detection, indicating the value of dense Landsat 8 and Sentinel-1 time series data for timely and accurate disturbance detection.

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“…Interestingly, accuracy was higher when information about the timing (year) of disturbance was excluded from the model. Shimizu et al (2017) also used Random Forest to classify patches of contemporaneously disturbed pixels to discriminate anthropogenic forest changes, such as logging, plantation conversion, and urbanization in Myanmar. Finally, Shimizu et al (2019) evaluated the relative effectiveness of several different approaches to disturbance-agent classification in a South Asian tropical forest: threshold-based detection using one spectral index, machine-learning methods trained on temporally segmented vegetation index values, and one machine-learning method trained directly on the Landsat time series without prior temporal segmentation.…”

Section: Introductionmentioning

confidence: 99%

An Examination of an Enhanced Remote Sensing Method for Agent Attribution of Forest Disturbance

Hm¹

2020

Preprint

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Patterns of disturbance in Sierra Nevada forests are shifting as a result of changing climate and land uses. These changes have underscored the need for a monitoring system that both detects disturbances and attributes them to different agents. Addressing this need will aid forest management and conservation decision-making, potentially enhancing forests’ resilience to changing climatic conditions. In addition, it will advance understanding of the patterns, drivers, and consequences of forest disturbance in space and time. This study proposed and evaluated an enhanced method for disturbance agent attribution. Specifically, it tested the extent to which textural information could improve the performance of an ensemble learning method in predicting the agents of disturbance from remote sensing observations. Random Forest (RF) models were developed to attribute disturbance to three primary agents (fire, harvest, and drought) in Stanislaus National Forest, California, U.S.A., between 1999 and 2015. To account for spectral behavior and topographical characteristics that regulate vegetation and disturbance dynamics, the models were trained on predictors derived from both the Landsat record and from a digital elevation model. The predictors included measurements of spectral change acquired through temporal segmentation of Landsat data; measurements of patch geometry; and a series of landscape texture metrics. The texture metrics were generated using the Grey-Level Co-Occurrence Matrix (GLCM). Two models were produced: one with GLCM texture metrics and one without. The per-class and overall accuracies of each model were evaluated with out-of-bag (OOB) observations and compared statistically to quantify the contribution of texture metrics to classification skill. Overall OOB accuracy was 72.0% for the texture-free model and 72.2% for the texture-dependent model, with no significant accuracy difference between them. Spatial patterns in prediction maps cohered with expectations, with most harvest concentrated in mid-elevation forests and fire and stress co-occurring at lower elevations. Altogether, the method yielded adequate identification of disturbance and moderate attribution accuracy for multiple disturbance agents. While textures did not contribute meaningfully to model skill, the study offers a strong foundation for future development, which should focus on improving the efficacy of the model and generalizing it for systems beyond the Central Sierra Nevada.

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Attribution of Disturbance Agents to Forest Change Using a Landsat Time Series in Tropical Seasonal Forests in the Bago Mountains, Myanmar

Cited by 41 publications

References 62 publications

Detecting Harvest Events in Plantation Forest Using Sentinel-1 and -2 Data via Google Earth Engine

Detecting Harvest Events in Plantation Forest Using Sentinel-1 and -2 Data via Google Earth Engine

Detecting Forest Changes Using Dense Landsat 8 and Sentinel-1 Time Series Data in Tropical Seasonal Forests

An Examination of an Enhanced Remote Sensing Method for Agent Attribution of Forest Disturbance

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