A Comparison Between Optical and Radar Satellite Images for Detecting Tropical Forest Fires in South Sumatra, Indonesia

Sunuprapto, Heri; Hussin, Y.A.

doi:10.21831/gm.v11i2.3446

Cited by 3 publications

(4 citation statements)

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“…The performance gain from using shallower incidence angles was predominantly due to improved sensitivity. This agrees with earlier research that reported that a shallower incidence allows for a better discrimination of urban areas [57][58][59][60]. This is usually due to a combination of higher spatial resolutions achieved at larger incidence angles, as well as the increasing sensitivity to differences in surface roughness that occurs at larger incidence angles [61].…”

Section: Robustness Of Acf-based Classificationsupporting

confidence: 92%

Temporal Autocorrelation of Sentinel-1 SAR Imagery for Detecting Settlement Expansion

Kapp,

Kemp

2023

Geomatics

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Urban areas are rapidly expanding globally. The detection of settlement expansion can, however, be challenging due to the rapid rate of expansion, especially for informal settlements. This paper presents a solution in the form of an unsupervised autocorrelation-based approach. Temporal autocorrelation function (ACF) values derived from hyper-temporal Sentinel-1 imagery were calculated for all time lags using VV backscatter values. Various thresholds were applied to these ACF values in order to create urban change maps. Two different orbital combinations were tested over four informal settlement areas in South Africa. Promising results were achieved in the two of the study areas with mean normalized Matthews Correlation Coefficients (MCCn) of 0.79 and 0.78. A lower performance was obtained in the remaining two areas (mean MCCn of 0.61 and 0.65) due to unfavorable building orientations and low building densities. The first results also indicate that the most stable and optimal ACF-based threshold of 95 was achieved when using images from both relative orbits, thereby incorporating more incidence angles. The results demonstrate the capacity of ACF-based methods for detecting settlement expansion. Practically, this ACF-based method could be used to reduce the time and labor costs of detecting and mapping newly built settlements in developing regions.

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Section: Robustness Of Acf-based Classificationsupporting

confidence: 92%

Temporal Autocorrelation of Sentinel-1 SAR Imagery for Detecting Settlement Expansion

Kapp,

Kemp

2023

Geomatics

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“…Each burst includes all individually focused complex bursts into a single subswath image. Hence deburst is needed to separate out the subswath products (Sunuprapto & Hussin, 2000). This is followed by calibration which is a useful tool since it directly relates the pixel values with the RADAR backscatter and makes the SAR imagery useful for quantitative interpretation (Shirvany, 2012).…”

Section: Pre-processingmentioning

confidence: 99%

“…In coregistration, the slave image usually of the later time frame is aligned pixel wise with respect to the master image of the earlier time frame (Mccormack, Thomas, & Solomon, n.d.). This way, the changes that have taken place over the time frame are delineated and can be studied with great ease with respect to the variations in the pixel level (Chung, Liu, Cheng, Lee, & Shieh, 2015;Keydel, 2007;Sunuprapto & Hussin, 2000). Thereafter, an interferogram was generated from the coregistered image product, to see the changes that have occurred on the surface due to deformations if any and the change in phase that occurs as the waves return to the RADAR sensor after interaction with the target.…”

Section: Interferometric Processingmentioning

confidence: 99%

Utilization of Spaceborne C-Band Sar Data for Analysis of Flood Impact on Agriculture and Its Management

Tripathi

Tiwari

2019

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

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<p><strong>Abstract.</strong> North India, since time immemorial remained the cradle of agricultural development. Every year during the monsoons, there is a high upsurge in the river Rapti. This leads to heavy flooding in the area and takes a toll on life and property besides submerging acres of precious agricultural land. Till now, such floods have been mapped using satellite data from optical sensors, but in monsoons due to cloud cover the task becomes difficult. Spaceborne SAR (Synthetic Aperture RADAR) data solves this problem by offering all-weather availability. This study aims to utilize the potential of C-band dual PolSAR data from Sentinel-1A for flood mapping and analysis of its impact on agriculture along with suggesting mitigation measures. Sentinel-1A data of January and September 2018 was taken and pre-processed. Thereafter, an interferogram was prepared and was phase filtered using modified Goldstein phase filter to remove phase distortions and a DEM (Digital Elevation Model) was prepared. The phase maps generated were analyzed for phase variations after removing the topographic phase and it was found that there was very less phase variation from urban and agricultural lands surrounding the river which clearly indicated heavy flooding and submergence of land in water. Using the DEM map generated, hydrological processing was done to delineate flow lines and calculation of catchment area at point of maximum submergence of agricultural land. This was followed by the generation of contours in the area and finally, the flood impact analysis was done by calculating the water volume in watershed during the flood.</p>

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“…Synthetic Aperture Radar (SAR) on the other hand provides surface information with the advantage by the capability to penetrate clouds cover, penetrate rain in some intensity, and also can be operating in the nighttime. These all capabilities are important to complement the optical disability [9] and give strong reason to employed SAR data in this research.…”

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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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A Comparison Between Optical and Radar Satellite Images for Detecting Tropical Forest Fires in South Sumatra, Indonesia

Cited by 3 publications

References 0 publications

Temporal Autocorrelation of Sentinel-1 SAR Imagery for Detecting Settlement Expansion

Temporal Autocorrelation of Sentinel-1 SAR Imagery for Detecting Settlement Expansion

Utilization of Spaceborne C-Band Sar Data for Analysis of Flood Impact on Agriculture and Its Management

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

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