Assessing the Application of a Geographic Presence-Only Model for Land Suitability Mapping.
Assessing the Application of a Geographic Presence-Only Model for Land Suitability Mapping.
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DOI：
10.1016/j.ecoinf.2011.04.004
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发表时间：
2011-09
影响因子：
5.1
通讯作者：
McDaniel, Phillip M.
中科院分区：
文献类型：
--
作者：
Heumann, Benjamin W.;Walsh, Stephen J.;McDaniel, Phillip M.
生态建模的最新进展集中在表征环境的新方法上，这些方法使用仅存在数据和机器学习算法来预测物种发生的可能性。这些新方法在发展中国家的土地适宜性应用方面可能具有巨大潜力，因为在这些国家，详细的土地覆盖信息往往无法获得或不完整。本文评估了适应和应用的存在唯一的地理物种分布模型，MaxEnt，农业作物适宜性映射在泰国农村地区的低地水稻和高地作物占主导地位。为了评估这种建模方法，使用了三个独立的作物存在数据集，包括农户的社会人口调查，土地利用/土地覆盖的遥感分类，以及地面控制点，用于大地测量和专题参考，其地理分布和样本大小各不相同。不同的环境数据被整合，以描述Nang Rong区的环境设置，该地区面积约为1，300平方米。公里大小。结果表明，MaxEnt模型是能够模拟作物适合高地和低地作物，包括水稻品种，虽然模型结果不同的数据集之间的高度敏感性的模型观察到的作物位置在地理和环境空间的分布。准确性评估表明，模型结果受到样本大小以及样本点在地理和环境空间分布的影响。需要进一步研究的准确性评估的存在，只有缺乏真正的缺席数据的模型进行了讨论。我们的结论是，Maxent模型可以提供很好的作物适宜性的估计，但许多领域需要仔细审查，包括地理分布的输入数据和评估方法，以确保现实的建模结果。
Recent advances in ecological modeling have focused on novel methods for characterizing the environment that use presence-only data and machine-learning algorithms to predict the likelihood of species occurrence. These novel methods may have great potential for land suitability applications in the developing world where detailed land cover information is often unavailable or incomplete. This paper assesses the adaptation and application of the presence-only geographic species distribution model, MaxEnt, for agricultural crop suitability mapping in a rural Thailand where lowland paddy rice and upland field crops predominant. To assess this modeling approach, three independent crop presence datasets were used including a social-demographic survey of farm households, a remote sensing classification of land use/land cover, and ground control points, used for geodetic and thematic reference that vary in their geographic distribution and sample size. Disparate environmental data were integrated to characterize environmental settings across Nang Rong District, a region of approximately 1,300 sq. km in size. Results indicate that the MaxEnt model is capable of modeling crop suitability for upland and lowland crops, including rice varieties, although model results varied between datasets due to the high sensitivity of the model to the distribution of observed crop locations in geographic and environmental space. Accuracy assessments indicate that model outcomes were influenced by the sample size and the distribution of sample points in geographic and environmental space. The need for further research into accuracy assessments of presence-only models lacking true absence data is discussed. We conclude that the Maxent model can provide good estimates of crop suitability, but many areas need to be carefully scrutinized including geographic distribution of input data and assessment methods to ensure realistic modeling results.
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影响因子：
6.4
作者：
Lobo, Jorge M.;Jimenez-Valverde, Alberto;Real, Raimundo
通讯作者：
Real, Raimundo
DOI：
10.1007/978-3-540-27819-1_33
发表时间：
2004-01-01
期刊：
LEARNING THEORY, PROCEEDINGS
影响因子：
--
作者：
Dudík, M;Phillips, SJ;Schapire, RE
通讯作者：
Schapire, RE
影响因子：
6.1
作者：
Groenemans, R;VanRanst, E;Kerre, E
通讯作者：
Kerre, E
影响因子：
4.6
作者：
Jimenez-Valverde, Alberto;Lobo, Jorge M.;Hortal, Joaquin
通讯作者：
Hortal, Joaquin
影响因子：
3.9
作者：
Araujo, Miguel B.;Guisan, Antoine
通讯作者：
Guisan, Antoine