Digital mapping for cost-effective and accurate prediction of the depth and carbon stocks in Indonesian peatlands

Rudiyanto, .; Minasny, Budiman; Setiawan, Budi Indra; Arif, Chusnul; Saptomo, Satyanto Krido; Chadirin, Yudi

doi:10.1016/j.geoderma.2016.02.026

Cited by 68 publications

(35 citation statements)

References 62 publications

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“…Previous studies 4,20,21 have found "distance to river" to be an important topographic feature in estimating the peat depth as well as in discriminating spatial distributions of tropical peatlands.…”

Section: Image Classificationmentioning

confidence: 97%

“…Therefore, it is challenging to map their spatial distributions. 4 Remote sensing is the most effective tool for mapping spatial distributions of tropical peatlands at various spatial and temporal scales, especially when combined with ground truth data. 3 Thus, the remote sensing technique serves as an advantageous tool due to its periodic monitoring system at a wide-scale synoptic view, particularly in remote sites.…”

Section: Introductionmentioning

confidence: 99%

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Polarimetric synthetic aperture radar application for tropical peatlands classification: a case study in Siak River Transect, Riau Province, Indonesia

Novresiandi

Nagasawa

2017

J. Appl. Remote Sens

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Abstract. Mapping spatial distributions of tropical peatlands is important for properly estimating carbon emissions and for providing information that aids in the sustainable management of tropical peatlands, particularly in Indonesia. This study evaluated the performance of phased array type L-band synthetic aperture radar (SAR) (PALSAR) dual-polarization and fully polarimetric data for tropical peatlands classification. The study area was in Siak River Transect, Riau Province, Indonesia, a rapidly developing region, where the peatland has been intensively converted mostly into oil palm plantations over the last two decades. Thus, polarimetric features derived after polarimetric decompositions, backscatter coefficients measurements, and the radar vegetation index were evaluated to classify tropical peatlands using the decision tree classifier. Overall, polarimetric features generated by the combination of dual-polarization and fully polarimetric data yielded an overall accuracy (OA) of 69% and a kappa coefficient (K) of 0.57. The integration of an additional feature, "distance to river," to the algorithm increased the OA to 76% and K to 0.66. These results indicated that the methodology in this study might serve as an efficient tool in tropical peatlands classification, especially when involving the use of L-band SAR dual-polarization and fully polarimetric data. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Image Classificationmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Polarimetric synthetic aperture radar application for tropical peatlands classification: a case study in Siak River Transect, Riau Province, Indonesia

Novresiandi

Nagasawa

2017

J. Appl. Remote Sens

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“…The predicted value is then the mean across the regression tree ensemble [43]. RF is robust to the presence of correlated or unhelpful predictors [44] and has been successfully used for many earth science applications, generally comparing favorably with linear and other nonlinear methods [45][46][47][48][49][50][51][52]. The implementation in the R randomForest package [53] was used.…”

Section: Nonlinear Model: Random Forest (Rf)mentioning

confidence: 99%

Year-ahead predictability of South Asian Summer Monsoon precipitation

Krakauer

2019

Environ. Res. Lett.

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Since the South Asia Summer Monsoon is the main source of water for a densely cultivated and climate-sensitive region, its predictability has long been the target of research. This work estimates the predictability horizon of monsoon precipitation amount by systematically comparing statistical forecasts made using information from different lead times before the monsoon start. Linear and nonlinear prediction methods are considered that use the leading modes of the global sea surface temperature field to forecast monsoon-season (June-September) total precipitation on a 0.5°grid over South Asia, where each method is trained on data from 1901 to 1996 and evaluated on data from 1997 to 2017. Forecasts were found to outperform a climatology baseline up to at least 1 year ahead, with a nonlinear method (random forest) on average outperforming linear regression with group lasso, although with greater variability in skill across locations and years. Forecast performance measures (fractional reduction in root mean square error and information skill score) decreased with increasing lead time following exponential decay timescales of 5-12 months, depending on the performance measure and forecast method. Even at lead times of several years, there was some forecast skill compared to climatology, as a result of the impact of long-term climate change on monsoon precipitation. The results suggest that monsoon prediction is possible with longer lead times than generally attempted now.

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“…The EVI patterns indicated different types of ecosystems and/or different response of ecosystems to the changing environment in the Sumatera. Peat depth modelled was developed as a function topography (Rudiyanto et al 2015), and also as a function topography and spatial position (Rudiyanto et al 2016) for Sumatera and Kalimantan Islands. The spatial models were calibrated with the ground observations, and the models of the peat depth prediction were 0.67 to 0.92 of coefficient determination.…”

Section: International Journal Of Remotementioning

confidence: 99%

Can the Peat Thickness Classes Be Estimated From Land Cover Type Approach?

Trisakti

Julzarika²,

Nugroho³

et al. 2018

IJReSES

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Abstract. Indonesia has been known as a home of the tropical peatlands. The peatlands are mainly in Sumatera, Kalimantan and Papua Islands. Spatial information on peatland depth is needed for the planning of agricultural land extensification. The research objective was to develop a preliminary estimation model of peat thickness classes based on land cover approach and analyse its applicability using Landsat 8 image. Ground data, including land cover, location and thickness of peat, were obtained from various surveys and peatlands potential map (Geology Map and Wetlands Peat Map).The land cover types were derived from Landsat 8 image. All data were used to build an initial model for estimating peat thickness classes in Merauke Regency. A table of relationships among land cover types, peat potential areas and peat thickness classes were made using ground survey data and peatlands potential maps of that were best suited to ground survey data. Furthermore, the table was used to determine peat thickness classes using land cover information produced from Landsat 8 image. The results showed that the estimated peat thickness classes in Merauke Regency consist of two classes, i.e., very shallow peatlands and shallow peatlands. Shallow peatlands were distributed at the upper part of Merauke Regency with mainly covered by forest. In comparison with Indonesia Peatlands Map, the number of classes was the two classes. The spatial distribution of shallow peatlands was relatively similar for its precision and accuracy, but the estimated area of shallow peatlands was greater than the area of shallow peatlands from Indonesia Peatlands Map. This research answered the question that peat thickness classes could be estimated by the land cover approach qualitatively. The precise estimation of peat thickness could not be done due to the limitation of insitu data.

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Digital mapping for cost-effective and accurate prediction of the depth and carbon stocks in Indonesian peatlands

Cited by 68 publications

References 62 publications

Polarimetric synthetic aperture radar application for tropical peatlands classification: a case study in Siak River Transect, Riau Province, Indonesia

Polarimetric synthetic aperture radar application for tropical peatlands classification: a case study in Siak River Transect, Riau Province, Indonesia

Year-ahead predictability of South Asian Summer Monsoon precipitation

Can the Peat Thickness Classes Be Estimated From Land Cover Type Approach?

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