Change in tropical forest cover of Southeast Asia from 1990 to 2010

Stibig, Hans-Jürgen; Achard, Frédéric; Carboni, Silvia; Raši, Rastislav; Miettinen, Jukka

doi:10.5194/bgd-10-12625-2013

Cited by 78 publications

(99 citation statements)

References 18 publications

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“…Threatened by the negative consequences of forest resources depletion [106], Vietnam has made considerable efforts to increase its forest cover during the last few decades, and expanded it to 39.7% of land area (13.5 million ha) in 2011 [23]. However, both forest gain and forest loss processes were identified in Vietnam during the 1990-2010 period [24]. In particular, the increase of forest area was mainly due to new plantations (2.9 million ha) and the expansion of natural forests on grasslands and shrublands, while deforestation and extensive degradation still remained prevalent with the result that over two thirds of Vietnam's natural forests are considered poor or regenerating [25,26].…”

Section: Introductionmentioning

confidence: 99%

Carbon emissions from land cover change in Central Vietnam

et al. 2016

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The carbon emissions and removals due to land cover changes between 2001 and 2010 in the Vu Gia Thu Bon River Basin, Central Vietnam, were estimated using Landsat satellite images and 3083 forest inventory plots. The net emissions from above-and belowground vegetation biomass were equal to 1.76 § 0.12 Tg CO 2 , about 1.1% of the existing stocks. The vast majority of carbon emissions were due to forest loss, with the conversion of forest to cropland accounting for 67% of net emissions. Forest regrowth had a substantial impact on net carbon changes, removing 22% of emissions from deforestation. Most deforestation occurred in regrowth forest (60%) and plantations (29%), characterized by low carbon stock density. Thus identifying the type of forest where deforestation occurred and using local field data were critical with net emissions being 4 times larger when considering only one forest class with average carbon stock, and 5-7 times higher when using literature default values or global emission maps. Carbon emissions from soil (up to 30 cm) were estimated for the main land change class. Due to the low emission factors from biomass, soils proved a key emission category, accounting for 30% of total land emissions that occurred during the monitoring period.

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Section: Introductionmentioning

confidence: 99%

Carbon emissions from land cover change in Central Vietnam

et al. 2016

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“…This suggests an association between model accuracy and the gradient of forest seasonality, or degree of deciduousness, ranging from dry-deciduous to evergreen forest. Dry season leaf senescence can cause confusion in forest change analysis which can lead to higher error frequently reported in deciduous classes [57][58][59]. Besides seasonal variation in reflectance increasing with forest deciduousness [60], there can also be considerable natural inter-annual variation linked to changing climate and variable rainfall patterns [61][62][63].…”

Section: Model Sensitivity To Forest Type and Seasonalitymentioning

confidence: 99%

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

et al. 2016

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Tropical environments present a unique challenge for optical time series analysis, primarily owing to fragmented data availability, persistent cloud cover and atmospheric aerosols. Additionally, little is known of whether the performance of time series change detection is affected by diverse forest types found in tropical dry regions. In this paper, we develop a methodology for mapping forest clearing in Southeast Asia using a study region characterised by heterogeneous forest types. Moderate Resolution Imaging Spectroradiometer (MODIS) time series are decomposed using Breaks For Additive Season and Trend (BFAST) and breakpoints, trend, and seasonal components are combined in a binomial probability model to distinguish between cleared and stable forest. We found that the addition of seasonality and trend information improves the change model performance compared to using breakpoints alone. We also demonstrate the value of considering forest type in disturbance mapping in comparison to the more common approach that combines all forest types into a single generalised forest class. By taking a generalised forest approach, there is less control over the error distribution in each forest type. Dry-deciduous and evergreen forests are especially sensitive to error imbalances using a generalised forest model i.e., clearances were underestimated in evergreen forest, and overestimated in dry-deciduous forest. This suggests that forest type needs to be considered in time series change mapping, especially in heterogeneous forest regions. Our approach builds towards improving large-area monitoring of forest-diverse regions such as Southeast Asia. The findings of this study should also be transferable across optical sensors and are therefore relevant for the future availability of dense time series for the tropics at higher spatial resolutions.

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“…It is confirmed in a recent forest cover research that shows the main cause of forest loss in South East Asia is the conversion of the forest to cash crop plantation (Stibig et al, 2014). Extensive deforestation and conversion have significant impacts from local to regional scales on wide ranges of ecosystem offered by forest such as hydrological and habitat provision (Blackburn et al, 2014).…”

Section: Aboveground Biomass and Carbon Stocks In Relation To Climatementioning

confidence: 56%

Modelling the Carbon Stocks Estimation of the Tropical Lowland Dipterocarp Forest Using Lidar and Remotely Sensed Data

Zaki¹,

Latif²,

Suratman³

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Tropical forest embraces a large stock of carbon in the global carbon cycle and contributes to the enormous amount of above and below ground biomass. The carbon kept in the aboveground living biomass of trees is typically the largest pool and the most directly impacted by the anthropogenic factor such as deforestation and forest degradation. However, fewer studies had been proposed to model the carbon for tropical rain forest and the quantification still remain uncertainties. A multiple linear re gression (MLR) is one of the methods to define the relationship between the field inventory measurements and the statistical extracted from the remotely sensed data which is LiDAR and WorldView-3 imagery (WV-3). This paper highlight the model development from fusion of multispectral WV-3 with the LIDAR metrics to model the carbon estimation of the tropical lowland Dipterocarp forest of the study area. The result shown the over segmentation and under segmentation value for this output is 0.19 and 0.11 respecti vely, thus D-value for the classification is 0.19 which is 81%. Overall, this study produce a significant correlation coefficient (r) between Crown projection area (CPA) and Carbon stocks (CS); height from LiDAR (H_LDR) and Carbon stocks (CS); and Crown projection area (CPA) and height from LiDAR (H_LDR) were shown 0.671, 0.709 and 0.549 respectively. The CPA of the segmentation found to be representative spatially with higher correlation of relationship between diameter at the breast height (DBH) and carbon stocks which is Pearson Correlation p = 0.000 (p < 0.01) with correlation coefficient (r) is 0.909 which shown that there a good relationship between carbon and DBH predictors to improve the inventory estimates of carbon using multiple linear regression method. The study concluded that the integration of WV-3 imagery with the CHM raster based LiDAR were useful in order to quantify the AGB and carbon stocks for a larger sample area of the Lowland Dipterocarp forest.

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Change in tropical forest cover of Southeast Asia from 1990 to 2010

Cited by 78 publications

References 18 publications

Carbon emissions from land cover change in Central Vietnam

Carbon emissions from land cover change in Central Vietnam

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

Modelling the Carbon Stocks Estimation of the Tropical Lowland Dipterocarp Forest Using Lidar and Remotely Sensed Data

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