| 基于环境减灾卫星CCD数据的海南岛洪涝灾害监测 |
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| 引用本文： | 李海亮,汪秀华,戴声佩,田光辉.基于环境减灾卫星CCD数据的海南岛洪涝灾害监测[J].农业工程学报,2015,31(17):191-198. |
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| 作者姓名： | 李海亮 汪秀华 戴声佩 田光辉 |
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| 作者单位： | 1. 中国热带农业科学院科技信息研究所/海南省热带作物信息技术应用研究重点实验室，儋州 571737;,2. 中国热带农业科学院科技处，海口 571101;,1. 中国热带农业科学院科技信息研究所/海南省热带作物信息技术应用研究重点实验室，儋州 571737;,3. 海南省气象科学研究所，海口 570203; |
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| 基金项目： | 海南省自然科学基金项目（612119）；中央级公益性科研院所基本科研业务专项资金（中国热带农业科学院院本级）资助项目（1630012015019） |
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| 摘 要： | 洪涝灾害监测是农情监测的主要任务之一，遥感监测可以弥补地面观测耗人、耗财、信息滞后等诸多不足，已成为洪涝灾害研究领域的重要发展方向。该文基于HJ-1A/1B-CCD数据，以海南岛为研究区，选取研究区内400个训练样本，利用区分度（division degree，DD）对归一化差异水体指数（normalized difference water index, NDWI）、基于蓝光的归一化差异水体指数（normalized difference water index based on blue light, NDWI-B）和混合水体指数（combined index of NDVI and NIR for water body identification, CIWI）3种水体指数进行比较分析。分析结果显示，在应用HJ-1CCD数据进行纯水体、湿地识别时，NDWI-B模型效果最好（综合区分度分别为31.30%、28.13%），是海南岛洪涝灾害监测的最优模型。经验证，NDWI-B模型的水体识别总体精度达91.50%。通过对采样点的水体指数值与地物类型的反复对比确定NDWI-B模型的水体识别阈值为－0.015。利用NDWI-B模型对海南岛2010年9月25日至10月25日的洪涝灾情进行监测。结果表明，10月12日的灾情最为严重，全岛洪水淹没面积达到监测期内最高值，为120.22km2，除东方、昌江、乐东外所有市县均出现新增水体，新增水体主要分布于村庄、耕地、道路、城镇居民地等。从区域上看，东部的文昌、琼海、海口、定安为洪涝重灾区，西部的东方、昌江、乐东为洪涝轻灾区。全岛洪涝影响最大的土地利用类型为水田，其次为旱地。10月12日，水田、旱地的淹没面积分别为61.46和29.59 km2，耕地（水田和旱地）淹没面积占总淹没面积的比例分别为75.73%。NDWI-B模型具有水陆区分度较大和水体面积提取精度较高的优点外，还能够识别小范围水体和湿地，是海南岛洪涝灾害监测较为理想的模型。该文为海南岛水资源管理、洪涝灾害动态监测及防灾减灾提供参考。
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| 关 键 词： | 卫星 洪涝 灾害 监测 环境减灾卫星 归一化差异水体指数 海南岛 |
| 收稿时间： | 5/4/2015 12:00:00 AM |
| 修稿时间： | 2015/8/31 0:00:00 |
Flood monitoring in Hainan Island based on HJ-CCD data |
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| Li Hailiang,Wang Xiuhu,Dai Shengpei and Tian Guanghui.Flood monitoring in Hainan Island based on HJ-CCD data[J].Transactions of the Chinese Society of Agricultural Engineering,2015,31(17):191-198. |
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| Authors: | Li Hailiang Wang Xiuhu Dai Shengpei and Tian Guanghui |
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| Institution: | 1. Institute of Scientific and Technical Information, Chinese Academy of Tropical Agricultural Sciences/Key Laboratory of Practical Research on Tropical Crops Information Technology In Hainan, Danzhou 571737, China;,2. Science and Technology Department, Chinese Academy of Tropical Agricultural Sciences, Haikou 571101, China;,1. Institute of Scientific and Technical Information, Chinese Academy of Tropical Agricultural Sciences/Key Laboratory of Practical Research on Tropical Crops Information Technology In Hainan, Danzhou 571737, China; and 3. Hainan Institute of Meteorological Sciences, Haikou 570203, China |
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| Abstract: | Abstract: Flood monitoring is one of the main tasks of agricultural monitoring. For surface observation, it consumes many manpower, resources as well as delayed information, all of those can be overcome by use of remote sensing monitoring flood disaster. The spatial and temporal resolution of HJ-1A/1B-CCD data is 30 m and 2 d. HJ-1A/1B-CCD data are desired remote sensing data of flood monitoring. We chose Hainan Island as research area and selected 400 training samples. Of them, 100 samples were from surface waters and wetlands, respectively, and 50 samples were respectively for forest lands, cultivated lands, roads and settlement places. In this paper, we analyzed and compared three water indexes: normalized difference water index (NDWI), normalized difference water index based on blue light (NDWI-B) and combined index of NDVI and NIR for water body identification (CIWI) by using the division degree (DD). Result showed that when NDWI-B was used to extract pure water, the division degrees of pure water to forest land, cultivated land, road and habitation were all between NDWI and CIWI. The division degrees of NDWI-B in these aspects were obviously higher than NDWI and CIWI when extracting wetland, which meant that when applying HJ-1A/1B CCD data to extract lack branch, small water bodies and wetland, the NDWI-B was better than NDWI and CIWI. The comprehensive discriminations of NDWI-B model were 31.30% and 28.13%, respectively. Thus, NDWI-B model was the optimal model when carrying out flood monitoring in Hainan Island. Based on the error matrix theory in combination with the actual measurement of sample data, the precision of water (pure water and wetland) in Hainan Island extracted by the NDWI-B was verified. Result showed that the user accuracy of water and non-water were 90.29% and 92.78%, respectively, and the overall accuracy was 91.50%. Through repeated comparison between the water index value and the surface feature type in the sampling point, when using NDWI-B model to extract water, the water scope of threshold value -0.015 was the largest, the broken figure spot was relatively less, and it was easy to exclude road and habitation. As such, the water identification's threshold value of NDWI-B was ensured, which was -0.015. Remote sensing images with better data qualities during the monitoring period (Sep. 26th, Oct. 6th, Oct. 12th, Oct. 20th, 2010, time series data) were chosen and the flood disaster conditions in Hainan Island from Sep. 25th, 2010 to Oct. 25th, 2010 were monitored by the NDWI-B. Result showed that the disastrous situation on Oct. 12th was the severest. The inundated area of the whole island reached 120.22 km2, the ceiling value during the monitoring period. Newly-emerged water bodies, mainly spreading over villages, farmlands and roads, were in all cities and counties except Dongfang, Changjiang and Ledong. Regionally, Wenchang, Qionghai, Haikou and Ding'an in the east were severely flood affected areas; Dongfang, Changjiang and Ledong in the west were less affected. The most affected land-use type by the flood in Hainan Island was the paddy field, whose inundated areas were 54.31 km2, 61.46 km2, and 42.58 km2 on Oct. 6th, Oct. 12th and Oct. 20th, respectively. The secondly affected land-use type was the dry land. Its inundated areas were 22.56 km2, 29.59 km2 and 18.36 km2 on Oct. 6th, Oct. 12th and Oct. 20th, respectively; the inundated areas accounted for 73.13%, 75.73% and 71.37%, respectively in the whole inundated area. Besides, taking Sanjiang farm in Meilan District, Haikou City as an example, we calculated the drowned water depth in the farm at October 6th, 2010. The result showed the drowned water depth of some fishpond and cultivated lands in Sanjiang farm was more than three meters. The flooding and water logging disaster situation was quite serious. Study showed that when recognizing the pure water the distinction degree of NDWI-B was a little lower than that of the CIWI, but high when recognizing the wetland. Besides advantages of larger land and water distinction degree and higher extracting precision of water area, the NDWI-B can also identify the water and wetland in small area. It was a relatively ideal model in monitoring flood disasters in Hainan Island. This paper provided references for the water resource management, the dynamic monitoring of flood disaster and disasters prevention as well as damages reduction in Hainan Island. |
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| Keywords: | satellites floods disasters monitoring HJ satellite normalized difference water index based on blue light (NDWI-B) Hainan Island |
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