Detection of Dry-Flammable Peatland Area by Using Backscattering Coefficient Information of ALOS-2 Data L-Band Frequency

Widodo, Joko; Izumi, Yuta; Takahashi, Ayaka; Kausarian, Husnul; Kuze, Hiroaki; Sumantyo, Josaphat Tetuko Sri

doi:10.23919/piers.2018.8598099

Cited by 6 publications

(6 citation statements)

References 3 publications

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“…Low severity is yellow, moderate-low severity is orange, moderate-high severity is red, high severity is purple, and the unburned area is green (Figure 5). The burned area mainly was found at Sungai Apit and Mempura Districts, as found by other studies (Nurjanah and Sakir, 2021;Widodo et al, 2018). Moreover, Siak Regency has 13 Disaster-prone Districts and 71 Disaster-prone Villages.…”

Section: Forest and Land Fire Severitysupporting

confidence: 67%

Forest and Peatland Fire Severity Assessment at Siak Regency, Riau Province using Sentinel-2 Imagery

Afina¹,

Syaufina

Sitanggang

2022

JPSL

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Siak Regency, Riau Province is one of the most forest and land fire-prone regencies in Indonesia. Most of the fires occur in peatland areas which contributes to the transboundary haze pollution in the region. Despite limited studies, fire severity assessment is an essential step in post-fire activities to estimate ecological impacts and economic impacts and law enforcement. This study aims to estimate fire severity using Sentinel-2 imagery at Siak Regency, Riau Province. The methods applied Normalized Burn Ratio on Sentinel-2 Imagery as an identification model based on reflectance value for 2019 imagery. The study revealed that burned areas in Siak Regency could be classified into four fire severity classes: low fire severity, moderate-low fire severity, moderate-high fire severity, and high fire severity. High fire severity was found mainly at Sungai Apit and Mempura Districts.

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Section: Forest and Land Fire Severitysupporting

confidence: 67%

Forest and Peatland Fire Severity Assessment at Siak Regency, Riau Province using Sentinel-2 Imagery

Afina¹,

Syaufina

Sitanggang

2022

JPSL

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“…Normal baseline Bn is the relative position between master and slave images in term of distance laterally the direction normal to the reference slant range, with a relationship with incidence angle θ as θ = Bn R k (16) where the master range of reference point O is R k = S k O and the relative of interferometric is thus…”

Section: ) Differential Interferometry Synthetic Aperture Radar (Dinmentioning

confidence: 99%

“…PALSAR and PALSAR-2 are popular sensors of SAR L-Band Frequency as a part of Advanced Land Observation (ALOS) Satellite with ownership by Japan Exploration Space Agency (JAXA). These sensors have possibility as an input data to monitor various conditions in peatland area such as forest biomass change, soil moisture, water level, peat dome detection, peat thickness, and peat subsidence [15], and groundwater table [16].…”

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confidence: 99%

Detection of Peat Fire Risk Area Based on Impedance Model and DInSAR Approaches Using ALOS-2 PALSAR-2 Data

Widodo¹,

Izumi

Takahashi

et al. 2019

IEEE Access

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Forest fire in Indonesia occurs mostly in peatland area. Dry peatland areas with groundwater table (GWT) more than 40 cm from the soil surface have become degradation areas with high potentials to fire. This paper presents a new novel to detect a peat fire risk area by incorporating two methods: the impedance model and the differential interferometric SAR (DInSAR) technique which is based on the knowledge of annual subsidence rate associated with the GWT. The previous impedance model is modified in this paper by integrating the surface roughness information in the model as a part of novelty. The proposed method was then validated with ground truth data of GWT. By using an impedance model, this paper successfully detected peat fire risk area based on the backscattering coefficient simulation of dry peatland. Based on the simulation model, the average, minimum, and maximum of backscattering coefficient of dry peat are −13.97, −11.5, and −17.29 dB, respectively. The correlation coefficient between the simulated backscattering coefficient and backscattering from ALOS-2/PALSAR-2 data is 0.8 with root mean square error of 1.4. By using the DInSAR method, detection of dry peatland area was successful. The significant relationships confirmed between GWT measurement and model are 0.71 for Pair A and 0.85 for Pair B. Both methods showed that peat fire risk areas were identified successfully. The dielectric constant of the peat soil also revealed that the soil condition of the area of interest is very dry indicating the potential to peat fire risk. Employing two models, respectively, were recommended to get precision of detection analysis. INDEX TERMS Detection of peat fire risk area, impedance model, DInSAR, ALOS-2 PALSAR-2 data. I. INTRODUCTION Indonesia has the largest peatland area in Southeast Asia (47%), besides Malaysia (6%), Papua New Guinea (3%), and smaller area amounting 1% spreading in Brunei, Myanmar, Thailand, and Vietnam. Conservation and rehabilitation of peat-forest area in Indonesia have been conducted however The associate editor coordinating the review of this manuscript and approving it for publication was Weimin Huang. it remains the largest area because of (1) human impact such as plantations of oil palms, rubber, pulp trees, and food production, and (2) the great impact of climate changes including El Nino, La Nina, and ENSO [1]. Forest fire is the main problem in Indonesia starting in 1982 when 75% of the forest fire is occurred in peatland area, mainly in open area [2]. Between 1990 and 2015, almost 27.5 million ha of forest had changed into logging, fires, timber, pulpwood, and palm oil plantations, and now of the 75%,

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“…Subsequently, Tanase et al, (2015) assessed the relationship between fire severity and SAR at both P-band and L-band in Australian forests, finding that in low biomass forests, SAR and optical imagery provided comparable results, but general optical data provided a better estimation of (post-fire) severity. Widodo et al, (2018) used a Yamaguchi Decomposition detect tropical peatlands areas from fullypolarimetric L-band ALOS-2 data, and then related InSAR displacement to changes in ground water, concluding that if ground water was less than 40 cm from the surface, the peatlands were potentially combustible. (Kong et al, 2014) assessed the differences between wet and dry images, using Radarsat-2, as a way to indicate changes in fuel moisture in the South African savannah.…”

Section: Introductionmentioning

confidence: 99%

Seasonally-decomposed Sentinel-1 backscatter time-series are useful indicators of peatland wildfire vulnerability

Millard

Darling

Pelletier

et al. 2022

Remote Sensing of Environment

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Detection of Dry-Flammable Peatland Area by Using Backscattering Coefficient Information of ALOS-2 Data L-Band Frequency

Cited by 6 publications

References 3 publications

Forest and Peatland Fire Severity Assessment at Siak Regency, Riau Province using Sentinel-2 Imagery

Forest and Peatland Fire Severity Assessment at Siak Regency, Riau Province using Sentinel-2 Imagery

Detection of Peat Fire Risk Area Based on Impedance Model and DInSAR Approaches Using ALOS-2 PALSAR-2 Data

Seasonally-decomposed Sentinel-1 backscatter time-series are useful indicators of peatland wildfire vulnerability

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