Increased access to reliable data and computationally efficient systems has created more collaborative potential between remote sensing and machine learning for species habitat prediction. Exploiting the integrative opportunities will require a deeper understanding of methods remote sensing instruments use to capture biophysical variables and the extent that data science can model ecological relationships. In this article, we provide the first systemic review of the integration of remote sensing and machine learning to predict habitat for the highly destructive desert locust and explore deep learning as a new method for increased classification. We evaluated the performance of six machine learning algorithms in two study regions (Niger and Sudan), using locust observations, multiple pseudoabsence data sets, and remotely sensed habitat data. In both regions, the k-nearest neighbor (kNN) and a deep neural network (DNN) were the best-preforming models. In Niger, the kNN average accuracy score was 88%, and the F-1 score was 89% for the Present (1) class. The DNN average accuracy score was 88%, and the F-1 score was 89%. In Sudan, the kNN average accuracy score was 88%, and the F-1 score was 88% for the Present (1) class. The DNN average accuracy score was 88%, and the F-1 score was 89%. Additionally, we outline a process for robustly modeling habitat through remote sensing data and highlight important limitations. We propose that the DNN model has the best potential for constructing a transferable representation and discuss novel methods to meet future challenges in desert locust management in a sustainable way.

中文翻译：

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整合遥感和机器学习进行区域尺度栖息地制图：沙漠蝗虫监测的进展和未来挑战


更多地获取可靠数据和计算高效的系统，为遥感和机器学习之间的物种栖息地预测创造了更多的协作潜力。利用整合机会需要更深入地了解遥感仪器用于捕获生物物理变量的方法以及数据科学可以模拟生态关系的程度。在本文中，我们首次系统回顾了遥感和机器学习相结合来预测具有高度破坏性的沙漠蝗虫的栖息地，并探索深度学习作为增加分类的新方法。我们使用蝗虫观测、多个伪缺失数据集和遥感栖息地数据评估了两个研究区域（尼日尔和苏丹）的六种机器学习算法的性能。在这两个区域中，k 最近邻 (kNN) 和深度神经网络 (DNN) 是表现最好的模型。在尼日尔，Present (1) 类的 kNN 平均准确度分数为 88%，F-1 分数为 89%。 DNN 平均准确度得分为 88%，F-1 得分为 89%。在苏丹，Present (1) 类的 kNN 平均准确度分数为 88%，F-1 分数为 88%。 DNN 平均准确度得分为 88%，F-1 得分为 89%。此外，我们概述了通过遥感数据对栖息地进行稳健建模的过程，并强调了重要的局限性。我们认为 DNN 模型最有潜力构建可转移表示，并讨论以可持续方式应对沙漠蝗虫管理未来挑战的新方法。