Tropical peat subsidence rates are related to decadal LULC changes: Insights from InSAR analysis

Umarhadi, Deha Agus; Widyatmanti, Wirastuti; Kumar, Pankaj; Yunus, Ali P.; Khedher, Khaled Mohamed; Kharrazi, Ali; Avtar, Ram

doi:10.1016/j.scitotenv.2021.151561

Cited by 27 publications

(20 citation statements)

References 73 publications

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“…Canopy cover might be well correlated with peat thickness where peatlands are still in pristine condition without any disturbance. Peatlands in our study area have been degraded massively, reportedly since the 1970s, consisting of land drainage and conversion to plantations (Umarhadi et al, 2022). This led to the remaining forests not being in the ideal condition of peat swamp forests.…”

Section: Identification Of Peat Thickness Through Wetness Indexmentioning

confidence: 96%

“…Carbon emission of drained peatland in Indonesia has occurred, releasing approximately 632 Tg/yr CO2 (Jauhiainen et al, 2008). The original pristine peatlands were converted to other land uses, widely for agriculture and plantations to support food and economic needs (Umarhadi et al, 2022). However, the use for plantations is not completely appropriate for peatlands, taking an example of the failure of the Mega Rice Project in 1996 that established one million ha of rice fields over peatlands, ended up being abandoned and caused severe environmental issues (e.g., land fires) (Hergoualc'h et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Remote Sensing Image Analysis for Identification of Peat Thickness Using Spectral Transformation Approach: Case Study of Bengkalis Island, Riau, Indonesia

2022

IJG

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Peatland plays an important role in the global climate. Balancing economic, social and conservation needs on peatland utilization become an obligation in developing sustainable peatland regulation. To identify the appropriate land function in the peatland environment, the depth of peat is the main property to manage those balance needs. On the other hand, vast areas of peatland changing hinder rapid peat depth mapping method to have high accuracy. Multi-temporal remote sensing data were used to identify peatland-related land-use changes. The vegetation and wetness indices spectral transformations had been analyzed. The method used for the accuracy test in this study was correlation and regression analysis for modeling and the Standard Error of Estimate (SEE). The results of this study showed the vegetation indices and NDSI was not able to obtain peat thickness model due to the unstable vegetation and land cover changes. However, the NDWI was satisfied with the statistical assessment and was able to obtain the peat thickness with 41.96% accuracy. The determination of a sample design, the number and distribution of samples in preserved land covers, and missed variables and external factors in this study need to be considered in further research. The vegetation indices and wetness indices potentially can be the alternative variables to construct the peat depth map.

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Section: Identification Of Peat Thickness Through Wetness Indexmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Analysis for Identification of Peat Thickness Using Spectral Transformation Approach: Case Study of Bengkalis Island, Riau, Indonesia

2022

IJG

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“…Tropical peatlands are widely distributed in three major Indonesian islands including Sumatra, Kalimantan, and Papua, with a total area of 13.43 Mha (Anda et al, 2021). This area undoubtedly provides carbon storage in a huge quantity (Mitra et al, 2005), while on the other hand, it has a large potential as a carbon emission source due to the conversion to agricultural and plantation purposes (Umarhadi et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Mapping Spatial Distribution Of Thickness and Carbon Stock of Tropical Peatland Based on Remote Sensing Data: A Case Study in Lake Sentarum National Park, Indonesia

MUSTHOFA¹,

Widyatmanti²,

Arjasakusuma³

et al. 2022

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Indonesia is one of the countries with the largest area of tropical peatlands in the world. These wide peatlands have a vital role in the carbon cycle and carbon storage in huge quantities, thus strict conservation in the area is necessary. One effort to carry out conservation is to understand the spatial distribution of carbon stock in peatlands. This study aims to map the spatial distribution of carbon stock based on peat thickness modeling using machine learning algorithms, i.e., Random Forest (RF), Quantile Regression Forests (QRF), and Cubist. A case study was conducted in a part of Lake Sentarum National Park, Indonesia where human interference is still limited. Digital elevation model (DEM) and synthetic aperture radar (SAR) data were included as the input variables. The results showed that RF performed the best among other models to estimate peat thickness (mean = 1.877 m) with an RMSE (root mean square error) of 0.483 m and an R 2 of 0.786. DEMs are the most important parameters in our analysis compared to SAR data. Based on the best model, we estimated the total volume of 5,112,687 m 3 in the study area, produced at 12.5 m resolution, which was then converted to a total of carbon stock at 0.337 ± 0.106 Mt carbon.

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“…InSAR is a technique in radar remote sensing to measure deformation in the earth's surface which is calculated based on the change in phase difference between two SAR image acquisitions (Zebker and Werner, 1994). Some previous research has been proved InSAR as an effective technique to monitor subsidence not only in urban areas (Aobpaet et al, 2013) but also in peatlands (Hoyt et al, 2020;Khakim et al, 2020;Umarhadi et al, 2022). Peatlands in neighbor regions were monitored by measuring differences in water table depth (Hooijer et al, 2012;Nusantara et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

PARAMETERS TO ESTIMATE CO₂ EMISSION IN PEATLAND AREA BASED ON CARBON CONTENT AND SUBSIDENCE RATE FROM SAR INTERFEROMETRY

Hayati

Sari²,

Arief³

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Peatlands are one of the important parameters to determine environmental quality because peatlands can store carbon stocks in their biomass. Based on the official page pantaugambut.id, Kalimantan Island is the second largest area that has peatlands. Land subsidence (subsidence) on peatland is one of the physical environmental impacts due to the shrinking volume of peat due to the drought process at a certain level in peatland processing, but also due to the decomposition process, erosion, and peat fires. This subsidence phenomenon will be measured using the InSAR technique, which is one of the existing methods in active remote sensing technology to measure changes in the earth’s surface which is calculated based on changes in the phase difference between two SAR image acquisitions. Our research used the time series InSAR technique with the incorporation of Persistent Scatterer (PS) and small baseline approach to accurately calculate the value of soil subsidence rate to the cm-mm level. Furthermore, the estimation of CO2 emission was calculated from the carbon parameters observed by the in-situ measurement, the affected peatland area, and its subsidence obtained by satellite remote sensing. We found that the highest CO2 emission was located in the southern part of the study area. Since years ago, the region has been affected by deforestation and land expansion due to the need for fiscal revenues from palm oil and urbanization.

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Tropical peat subsidence rates are related to decadal LULC changes: Insights from InSAR analysis

Cited by 27 publications

References 73 publications

Remote Sensing Image Analysis for Identification of Peat Thickness Using Spectral Transformation Approach: Case Study of Bengkalis Island, Riau, Indonesia

Remote Sensing Image Analysis for Identification of Peat Thickness Using Spectral Transformation Approach: Case Study of Bengkalis Island, Riau, Indonesia

Machine Learning for Mapping Spatial Distribution Of Thickness and Carbon Stock of Tropical Peatland Based on Remote Sensing Data: A Case Study in Lake Sentarum National Park, Indonesia

PARAMETERS TO ESTIMATE CO₂ EMISSION IN PEATLAND AREA BASED ON CARBON CONTENT AND SUBSIDENCE RATE FROM SAR INTERFEROMETRY

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Tropical peat subsidence rates are related to decadal LULC changes: Insights from InSAR analysis

Cited by 27 publications

References 73 publications

Remote Sensing Image Analysis for Identification of Peat Thickness Using Spectral Transformation Approach: Case Study of Bengkalis Island, Riau, Indonesia

Remote Sensing Image Analysis for Identification of Peat Thickness Using Spectral Transformation Approach: Case Study of Bengkalis Island, Riau, Indonesia

Machine Learning for Mapping Spatial Distribution Of Thickness and Carbon Stock of Tropical Peatland Based on Remote Sensing Data: A Case Study in Lake Sentarum National Park, Indonesia

PARAMETERS TO ESTIMATE CO2 EMISSION IN PEATLAND AREA BASED ON CARBON CONTENT AND SUBSIDENCE RATE FROM SAR INTERFEROMETRY

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PARAMETERS TO ESTIMATE CO₂ EMISSION IN PEATLAND AREA BASED ON CARBON CONTENT AND SUBSIDENCE RATE FROM SAR INTERFEROMETRY