首页> 外文会议>25th Asian conference on remote sensing (ACRS Silver Jubilee) >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:LANDSAT数据对ENSO对泰国南部素叻他尼Ban Don海岸资源和环境的影响

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The Landsat data in 3 periods: which are during the El Nino years (1993, 1994, 1997 andrn2002); the La Nina years (1988,1998 and 2000); and Normal year (1989, 1996 and 1999), arernclassified for landuse types and by NDVI (Normalised Difference Vegetation Index). We foundrnthat during El Nino years, the rainfall anomalies are minus (less than normal), the airrntemperature anomalies are plus (hotter than normal), Tapi river discharge anomalies are minus,rnand NDVI's are lower than normal (less growing vegetations). During La Nina years, thernrainfall anomalies are plus (higher than normal), the air temperature anomalies are minus (colderrnthan normal), the Tapi river discharge anomalies are plus, and NDVI's are higher than normalrn(much growing vegetations). In 1998, first half of the year was El Nino, and last half of the yearrnwas La Nina. However, we used the data on 27 April 1998, which is in the El Nino period, so itrnreceived the impact of El Nino.rnFor fisheries statistics at Surat Thani during El Ni?o years, the quantity of fish catch andrnthe yields of aquacultures (shrimp and fish) were higher than average. During the La Ni?a yearsrnthe same parameters were lower than average The areas of shrimp ponds have been increasedrnwith the increasing population. The population of Surat Thani has been increased every year.rnDuring the El Ni?o years, the areas of natural and mangroves were decreased, while during the La Ni?arnyears, the areas were increased.rnEl Ni?o caused higher frequency of tropical cyclone. La Ni?a also caused tropical cyclone butrnlesser than El Ni?o linkage. The linkage of ENSO on MSL is not very clear owing to the small quantityrnof data. During La Ni?a years, MSLs show plus and minus anomalies for one of each year. However,rnwe may conclude that El Ni?o causes higher MSL than mean value. El Ni?o caused lower river runoffrnthan normal. La Ni?a caused higher runoff than normal.rnSTUDY AREA Bandon Bay (9.20 0 N and 99.67 0 E) is located in Surat Thani Province, southernrnThailand. The watershed of the bay is 12,220 km 2, of which an important part consists ofrnagricultural lands and aquaculture. The population living within the watershed numbersrnapproximately 830,000 with fisheries, aquaculture and tourism as their main activities. Ban DonrnBay is an inlet of the biggest river on the east coast of the Southern Peninsula, which is the Tapi-Phumduangrnriver to the Gulf of Thailand, which lies to the east of Surat Thani province. Mangrovernforest plays an important role in the economies of Ban Don Bay, particularly as a source of energyrnand food protein. However, population and economic development pressures have beenrnresponsible in part for the conversion mangroves to various land uses in the past years, I.e.,rnagricultural, residential and industrial sites, salt ponds, ports and harbors, and aquaculture.
机译:Landsat的数据分为3个时期:分别是厄尔尼诺时代(1993、1994、1997和2002年);拉尼娜年(1988、1998和2000);根据土地利用类型和NDVI(归一化植被指数)对正常年份(1989、1996和1999)进行分类。我们发现,在厄尔尼诺现象期间,降雨异常为负(低于正常),气温异常为正(比正常高),塔皮河流量异常为负,NDVI低于正常值(生长较少的植被)。在拉尼娜(La Nina)年,降雨异常为正(高于正常值),气温异常为负(比正常值低),塔皮河流量异常为正,而NDVI高于正常值(生长着很多植被)。 1998年,上半年是厄尔尼诺现象,下半年是拉尼娜现象。但是,我们使用的是1998年4月27日厄尔尼诺时期的数据,因此它收到了厄尔尼诺现象的影响。对于厄尔尼诺年期间素叻他尼的渔业统计,鱼的捕获量和水产养殖的产量(虾和鱼)高于平均水平。在拉尼娜年期间,相同的参数低于平均水平。虾塘的面积随着人口的增加而增加。素叻他尼(Surat Thani)的人口每年都在增加。rn在厄尔尼诺现象期间,天然和红树林的面积减少了,而在拉尼翁时期,则增加了面积。气旋。拉尼娜还造成了比艾尔尼奥连锁更强的热带气旋。由于少量的rnof数据,ENSO在MSL上的联系不是很清楚。在拉尼娜(La Ni?a)年中,MSL每年出现一次正负异常。但是,我们可能得出结论,厄尔尼诺现象引起的MSL高于平均值。厄尔尼诺造成的河水径流比正常低。 La Ni?a造成了更高的径流量。研究区域Bandon Bay(9.20 0 N和99.67 0 E)位于泰国南部的素叻他尼府。海湾的分水岭为12,220 km 2,其中重要的部分包括耕地和水产养殖。流域内的人口约有830,000,主要活动是渔业,水产养殖和旅游业。班·唐恩湾(Ban DonrnBay)是南部半岛东海岸最大的河流的进水口,它是素叻他尼省东部的泰国湾的塔皮-蓬登河。红树林在班登湾的经济中起着重要作用,尤其是作为能量和食物蛋白的来源。然而,人口和经济发展压力在一定程度上是由于过去几年将红树林转变为各种土地用途而造成的,即农业,住宅和工业用地,盐水池,港口和港口以及水产养殖。

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