首页> 外文会议>Asian conference on remote sensingACRS >REVERSE THINKING THE NEGATIVE EFFECTS OF TOPOGRAPHIC SHELTERS ON TAIWAN RED CYPRESS DISTRIBUTION BY GEOSPATIAL INFORMATION TECHNOLOGY
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REVERSE THINKING THE NEGATIVE EFFECTS OF TOPOGRAPHIC SHELTERS ON TAIWAN RED CYPRESS DISTRIBUTION BY GEOSPATIAL INFORMATION TECHNOLOGY

机译:逆转地理空间信息技术对台湾红色赛普纹分布的地形避难所的负面影响

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Ecological niche modeling (ENM) coupled with 3S provides useful information on large spatial scales. Chamaecypahs formosensis (Taiwan red cypress, TRC) grows on Chilung Mountain and Paiku Mountain in fog-forest belt, but almost do not grow in most Huisun area, except Shou-Cheng Mt. Ultimately, we will attempt to find out northeastern season wind blocked by topographic shelters is a key factor by reverse casting from the case of Taiwan firs in Hohuan Mountains. We developed ENMs by generalized linear model (GLM), back propagation neural network (BPNN), and maximum likelihood (ML). The mean kappa of the models (0.96) developed by samples taken only from Shou-Cheng Mountain was significantly greater than that of models (0.54) samples taken from entire study area. Topographic variables currently used except elevation were almost useless. This result indicated that our ENMs could have left out critical predictor variables. We will incorporate surrogates of wind or humidity, topographic sheltering index, topographic sheltering index (wind or humidity), into ENMs to improve model accuracy.
机译:与3S相结合的生态利基造型(ENM)提供了关于大型空间尺度的有用信息。 Chamaecypahs formosensis(台湾红桧,TRC)生长在Chilung山和Paiku山雾林带,但几乎就在最惠荪面积不增长,除了守城山最终,我们将试图找出东北季节风受阻通过地形避难所是通过在Hohuan Mountains的台湾冷杉的情况下逆转来的关键因素。我们通过广义线性模型(GLM),回传播神经网络(BPNN)和最大可能性(ML)开发了enm。由Shou-Cheng Mountain拍摄的样品开发的模型(0.96)的平均κ显着大于整个研究区域所采取的模型(0.54)样本。目前使用的地形变量除外几乎没用。结果表明,我们的enm可能会遗漏关键预测因子变量。我们将加入风或湿度的特色,地形避风指数,地形避风指数(风或湿度),以提高模型准确性。

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