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首页> 外文会议>Imaging a Sustainable Future >A MAXIMUM ENTROPY MODEL OF THE BEARDED CAPUCHIN MONKEY HABITAT INCORPORATING TOPOGRAPHY AND SPECTRAL UNMIXING ANALYSIS
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A MAXIMUM ENTROPY MODEL OF THE BEARDED CAPUCHIN MONKEY HABITAT INCORPORATING TOPOGRAPHY AND SPECTRAL UNMIXING ANALYSIS

机译：大胡子Capuchin猴栖息地的最大熵模型纳入地形和光谱解密分析

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Movement patterns of bearded capuchin monkeys (Cebus (Sapajus) libidinosus) in northeastern Brazil are likely impacted by environmental features such as elevation, vegetation density, or vegetation type. Habitat preferences of these monkeys provide insights regarding the impact of environmental features on species ecology and the degree to which they incorporate these features in movement decisions. In order to evaluate environmental features influencing movement patterns and predict areas suitable for movement, we employed a maximum entropy modelling approach, using observation points along capuchin monkey daily routes as species presence points. We combined these presence points with spatial data on important environmental features from remotely sensed data on land cover and topography. A spectral mixing analysis procedure was used to generate fraction images that represent green vegetation, shade and soil of the study area. A Landsat Thematic Mapper scene of the area of study was geometrically and atmospherically corrected and used as input in a Minimum Noise Fraction (MNF) procedure and a linear spectral unmixing approach was used to generate the fraction images. These fraction images and elevation were the environmental layer inputs for our logistic MaxEnt model of capuchin movement. Our models' predictive power (test AUC) was 0.775. Areas of high elevation (>450 m) showed low probabilities of presence, and percent green vegetation was the greatest overall contributor to model AUC. This work has implications for predicting daily movement patterns of capuchins in our field site, as suitability values from our model may relate to habitat preference and facility of movement.
机译：巴西东北巴西的胡须卡普宁猴（Cebus（Sapajus）libidinosus）的运动模式可能会受到海拔，植被密度或植被类型等环境特征的影响。这些猴子的栖息地偏好为环境特征对物种生态学的影响以及它们在运动决策中纳入这些特征的程度提供了洞察力。为了评估影响运动模式的环境特征和预测适合运动的区域，我们采用了最大的熵建模方法，使用沿着Capuchin Monkey日常路线的观察点作为物种存在点。我们将这些存在点与空间数据组合在陆地覆盖和地形上的远程感测数据中的重要环境特征。光谱混合分析程序用于产生代表研究区域的绿色植被，阴影和土壤的分数图像。研究区域的Landsat主题映射器场景在几何和大气校正并用作最小噪声分数（MNF）过程的输入，并且使用线性光谱解密方法来产生分数图像。这些分数图像和高度是我们斗篷运动的逻辑膨胀模型的环境层输入。我们的模型'预测电源（测试AUC）为0.775。高海拔地区（> 450米）显示出低的存在概率，绿色植被百分比是模型AUC的最大贡献者。这项工作对我们的现场网站中的Capuchins的日常运动模式预测，因为我们模型的适用性值可能与栖息地偏好和运动设施有关。

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