2016
DOI: 10.5539/jas.v8n10p107
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Mapping Soil Moisture as an Indicator of Wildfire Risk Using Landsat 8 Images in Sri Lanna National Park, Northern Thailand

Abstract: Severely dry climate plays an important role in the occurrence of wildfires in Thailand. Soil water deficits increase dry conditions, resulting in more intense and longer burning wildfires. The temperature vegetation dryness index (TVDI) and the normalized difference drought index (NDDI) were used to estimate soil moisture during the dry season to explore its use for wildfire risk assessment. The results reveal that the normalized difference wet index (NDWI) and land surface temperature (LST) can be used for T… Show more

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Cited by 11 publications
(11 citation statements)
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“…The temporal soil moisture change due to CSA practices was determined using satellite images. The time series Normalized Difference Water Index (NDWI) was used as a proxy to assess soil moisture stress risk [22][23][24]. NDWI is derived from the Near-Infrared (NIR) and Short-Wave Infrared (SWIR) reflectance [25].…”
Section: Data Sourcesmentioning
confidence: 99%
“…The temporal soil moisture change due to CSA practices was determined using satellite images. The time series Normalized Difference Water Index (NDWI) was used as a proxy to assess soil moisture stress risk [22][23][24]. NDWI is derived from the Near-Infrared (NIR) and Short-Wave Infrared (SWIR) reflectance [25].…”
Section: Data Sourcesmentioning
confidence: 99%
“…NDDI merupakan metode untuk memperkirakan kelembapan tanah selama musim kemarau berdasarkan kondisi kekeringan lahan. Metode NDDI juga dapat digunakan untuk pemetaan kelembapan tanah sebagai indikator kebakaran hutan (Burapapol dan Nagasawa, 2016 (Rahman.dkk, 2017). Penelitian lain yang telah dilakukan di Indonesia mengenai analisis pola kekeringan lahan pertanian di Kabupaten Kendal dengan menggunakan algoritma TVI dari citra satelit Modis Terra.…”
Section: Pendahuluanunclassified
“…For soil moisture monitoring purposes, Landsat 5 and 7 have been rarely used to estimate SMC directly from their spectral bands or band ratios [9,[12][13][14], but they are more widely estimated through the use of a combination of vegetation-based indices, such as the Normalized Difference Vegetation Index (NDVI), the Temperature Vegetation Dryness Index (TVDI), with the Land Surface Temperature (LST), retrieved from these sensors' signals [15][16][17][18]. The newest Landsat, Landsat 8 (L8), which was launched recently in 2013, was also explored for SMC estimation [19][20][21]. As with the use of previous Landsats for SMC measurement purposes, the SMC estimation models using L8 were developed in these studies, mostly based on the empirical linear relationship between in situ SMC with vegetation indices, such as the Normalized Difference Tillage Index (NDTI) and TDVI [10], and/or the Normalized Difference Water Index (NDWI) [20] and/or LST [19] and/or NDVI [22].…”
Section: Introductionmentioning
confidence: 99%
“…The newest Landsat, Landsat 8 (L8), which was launched recently in 2013, was also explored for SMC estimation [19][20][21]. As with the use of previous Landsats for SMC measurement purposes, the SMC estimation models using L8 were developed in these studies, mostly based on the empirical linear relationship between in situ SMC with vegetation indices, such as the Normalized Difference Tillage Index (NDTI) and TDVI [10], and/or the Normalized Difference Water Index (NDWI) [20] and/or LST [19] and/or NDVI [22]. The spectral response of various SMCs has not been considered and integrated in the model development process, making it difficult to interpret and evaluate the performance of the proposed models.…”
Section: Introductionmentioning
confidence: 99%