K. V. Suresh Babu, Swati Singh, G. Kabdulova, Kabzhanova Gulnara, G. Baktybekov
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This study examined three spectral indices, namely AVI, BAI, and GEMI, for mapping the burnt area based on the four spectral bands NIR, blue, red, and green of the KazEOSat-1 satellite datasets. The DN values for each band are used to determine TOA reflectance, which is then used as a basis for deriving the aforementioned spectral indices. The results of spectral indices, AVI, BAI, and GEMI are compared based on a discriminative index (M) for quantifying the effectiveness of each index based on burned area derived from KazEOSat-1 datasets. The spectral index BAI shows higher M values than other indices; therefore, the index BAI has the higher capability to extract the burned area as compared with AVI and GEMI. Accuracy was calculated based on the number of forest fire incidents that fell in burned and unburned areas, and the results indicate that BAI shows the highest accuracy, whereas AVI shows the lowest accuracy among them. Therefore, the BAI has the highest ability for extracting the burned area using the KazEOSat-1 satellite datasets. As the revisit time period of KazEOSat is 3 days, this study will be useful to map the burnt area and fire progression in Kazakhstan.","PeriodicalId":507254,"journal":{"name":"Frontiers in Forests and Global Change","volume":"122 22","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Geospatial assessment of forest fire impacts utilizing high-resolution KazEOSat-1 satellite data\",\"authors\":\"K. V. Suresh Babu, Swati Singh, G. Kabdulova, Kabzhanova Gulnara, G. Baktybekov\",\"doi\":\"10.3389/ffgc.2024.1296100\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Forest fires or wildfires frequently occur in Kazakhstan, especially in the months from June to September, damaging the forest resources. 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The results of spectral indices, AVI, BAI, and GEMI are compared based on a discriminative index (M) for quantifying the effectiveness of each index based on burned area derived from KazEOSat-1 datasets. The spectral index BAI shows higher M values than other indices; therefore, the index BAI has the higher capability to extract the burned area as compared with AVI and GEMI. Accuracy was calculated based on the number of forest fire incidents that fell in burned and unburned areas, and the results indicate that BAI shows the highest accuracy, whereas AVI shows the lowest accuracy among them. Therefore, the BAI has the highest ability for extracting the burned area using the KazEOSat-1 satellite datasets. 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引用次数: 0
摘要
哈萨克斯坦经常发生森林火灾或野火,尤其是在 6 月至 9 月,对森林资源造成破坏。绘制烧毁面积图对于火灾管理者在火灾发生后采取适当的缓解措施和开展恢复活动非常重要。本研究利用 KazEOSat-1 高分辨率卫星数据集绘制哈萨克斯坦各地区的烧毁面积图。KazEOSat-1 卫星位于太阳同步轨道,由四个波段组成,即蓝光、绿光、红光和近红外多光谱波段,空间分辨率为 4 米,全色数据的空间分辨率为 1 米。本研究根据 KazEOSat-1 号卫星数据集的近红外、蓝、红和绿四个光谱波段,研究了绘制烧毁区域图的三个光谱指数,即 AVI、BAI 和 GEMI。每个波段的 DN 值用于确定 TOA 反射率,然后以此为基础得出上述光谱指数。光谱指数、AVI、BAI 和 GEMI 的结果根据判别指数(M)进行比较,以量化每个指数在从 KazEOSat-1 数据集得出的烧毁面积基础上的有效性。光谱指数 BAI 的 M 值高于其他指数;因此,与 AVI 和 GEMI 相比,BAI 指数提取烧毁面积的能力更强。精确度根据发生在燃烧区和未燃烧区的森林火灾事故数量进行计算,结果表明 BAI 的精确度最高,而 AVI 的精确度最低。因此,利用 KazEOSat-1 卫星数据集提取烧毁面积的能力最强的是 BAI。由于 KazEOSat 的重访周期为 3 天,这项研究将有助于绘制哈萨克斯坦的烧毁面积和火灾进程图。
Geospatial assessment of forest fire impacts utilizing high-resolution KazEOSat-1 satellite data
Forest fires or wildfires frequently occur in Kazakhstan, especially in the months from June to September, damaging the forest resources. Burnt area mapping is important for fire managers to take appropriate mitigation steps and carry out restoration activities after the fire event. In this study, KazEOSat-1 high-resolution satellite datasets are used to map the burnt area in the regions of Kazakhstan. KazEOSat-1 satellite is in a Sun-synchronous orbit, consisting of four bands, namely blue, green, red, and NIR multispectral bands, in 4 m spatial resolution, while panchromatic data are in 1 m spatial resolution. This study examined three spectral indices, namely AVI, BAI, and GEMI, for mapping the burnt area based on the four spectral bands NIR, blue, red, and green of the KazEOSat-1 satellite datasets. The DN values for each band are used to determine TOA reflectance, which is then used as a basis for deriving the aforementioned spectral indices. The results of spectral indices, AVI, BAI, and GEMI are compared based on a discriminative index (M) for quantifying the effectiveness of each index based on burned area derived from KazEOSat-1 datasets. The spectral index BAI shows higher M values than other indices; therefore, the index BAI has the higher capability to extract the burned area as compared with AVI and GEMI. Accuracy was calculated based on the number of forest fire incidents that fell in burned and unburned areas, and the results indicate that BAI shows the highest accuracy, whereas AVI shows the lowest accuracy among them. Therefore, the BAI has the highest ability for extracting the burned area using the KazEOSat-1 satellite datasets. As the revisit time period of KazEOSat is 3 days, this study will be useful to map the burnt area and fire progression in Kazakhstan.