首页> 外文会议>Asian conference on remote sensing >ENSO IMPACTS ON COASTAL RESOURCES AND ENVIRONMENT AT BAN DON, SURAT THANI, SOUTHERN THAILAND BY LANDSAT DATA
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ENSO IMPACTS ON COASTAL RESOURCES AND ENVIRONMENT AT BAN DON, SURAT THANI, SOUTHERN THAILAND BY LANDSAT DATA

机译:ENSO对南唐,泰国南部的沿海资源和环境的影响由Landsat数据

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The Landsat data in 3 periods: which are during the El Nino years (1993, 1994, 1997 and 2002); the La Nina years (1988,1998 and 2000); and Normal year (1989, 1996 and 1999), are classified for landuse types and by NDVI (Normalised Difference Vegetation Index). We found that during El Nino years, the rainfall anomalies are minus (less than normal), the air temperature anomalies are plus (hotter than normal), Tapi river discharge anomalies are minus, and NDVI's are lower than normal (less growing vegetations). During La Nina years, the rainfall anomalies are plus (higher than normal), the air temperature anomalies are minus (colder than normal), the Tapi river discharge anomalies are plus, and NDVI's are higher than normal (much growing vegetations). In 1998, first half of the year was El Nino, and last half of the year was La Nina. However, we used the data on 27 April 1998, which is in the El Nino period, so it received the impact of El Nino. For fisheries statistics at Surat Thani during El Ni?o years, the quantity of fish catch and the yields of aquacultures (shrimp and fish) were higher than average. During the La Ni?a years the same parameters were lower than average The areas of shrimp ponds have been increased with the increasing population. The population of Surat Thani has been increased every year. During the El Ni?o years, the areas of natural and mangroves were decreased, while during the La Ni?a years, the areas were increased. El Ni?o caused higher frequency of tropical cyclone. La Ni?a also caused tropical cyclone but lesser than El Ni?o linkage. The linkage of ENSO on MSL is not very clear owing to the small quantity of data. During La Ni?a years, MSLs show plus and minus anomalies for one of each year. However, we may conclude that El Ni?o causes higher MSL than mean value. El Ni?o caused lower river runoff than normal. La Ni?a caused higher runoff than normal. STUDY AREA Bandon Bay (9.20 0 N and 99.67 0 E) is located in Surat Thani Province, southern Thailand. The watershed of the bay is 12,220 km 2, of which an important part consists of agricultural lands and aquaculture. The population living within the watershed numbers approximately 830,000 with fisheries, aquaculture and tourism as their main activities. Ban Don Bay is an inlet of the biggest river on the east coast of the Southern Peninsula, which is the Tapi-Phumduang river to the Gulf of Thailand, which lies to the east of Surat Thani province. Mangrove forest plays an important role in the economies of Ban Don Bay, particularly as a source of energy and food protein. However, population and economic development pressures have been responsible in part for the conversion mangroves to various land uses in the past years, I.e., agricultural, residential and industrial sites, salt ponds, ports and harbors, and aquaculture.
机译:3个时期的LANDSAT数据:在El Nino岁月内(1993,1994,1997和2002); La Nina岁(1988,1998和2000);和正常年(1989,1996和1999年),分类为土地使用类型和NDVI(归一化差异植被指数)。我们发现在El Nino岁期间,降雨异常是减去(小于正常),空气温度异常是加号(比正常)更热),Tapi河放电异常是减去,NDVI低于正常(植被不那么种植)。在La Nina岁期间,降雨异常是加(高于正常),气温异常是减去(比正常较冷),Tapi河放电异常是加号,并且NDVI高于正常(植被种植较大)。 1998年,上半年是El Nino,最后一半的时间是La Nina。但是,我们在1998年4月27日使用的数据,即在El Nino期间,所以它得到了El Nino的影响。对于El Ni期间Surat Thani的渔业统计数据,鱼类捕获量和水产养殖产量(虾和鱼类)的数量高于平均水平。在La Ni期间?多年来,相同的参数低于平均水平,虾池的区域随着人口的增加而增加。每年都有比苏丹人口增加。在El Ni?O年期间,自然和红树林的区域下降,而在La Ni期间?多年来,这些地区增加了。 El Ni?O引起了较高的热带气旋频率。 La Ni?A也导致热带气旋,但比El NiΔO连锁。由于少量数据,ENSO对MSL的联动不是很清楚。在La Ni期间?多年来,MSLS为每年之一的展示加号和减去异常。然而,我们可以得出结论,El NiΔO会导致更高的MSL而不是平均值。 El Ni?O引起了低河径流而不是正常。 la ni?造成的径流高于正常。学习区Bandon Bay(9.20 0 N和99.67 0 e)位于泰国南部的苏比省。海湾的流域是12,220公里2,其中一个重要的部分由农业土地和水产养殖组成。生活在分水岭中的人口约830,000,渔业,水产养殖和旅游作为主要活动。 Ban Don Bay是南半岛东海岸最大河流的入口,这是泰国湾的Tapi-Phumdiang河,位于苏叻岛省的东部。红树林森林在班唐湾经济中发挥着重要作用,特别是作为能量和食物蛋白质的来源。然而,人口和经济发展压力部分负责,部分原因是过去几年的各种土地使用的转换红树林,即农业,住宅和工业场地,盐池,港口和港口和水产养殖。

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