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首页> 外文期刊>ISPRS Journal of Photogrammetry and Remote Sensing >Optimising the spatial resolution of WorldView-2 pan-sharpened imagery for predicting levels of Gonipterus scutellatus defoliation in KwaZulu-Natal, South Africa
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Optimising the spatial resolution of WorldView-2 pan-sharpened imagery for predicting levels of Gonipterus scutellatus defoliation in KwaZulu-Natal, South Africa

机译:优化WorldView-2全锐化图像的空间分辨率,以预测南非夸祖鲁-纳塔尔省的Gocutterus scutellatus落叶水平

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摘要

Gonipterus scutellatus Gyllenhal is a leaf feeding weevil that is a major defoliator of the genus Eucalyptus. Understanding the relationship between levels of weevil induced vegetation defoliation and the optimal spatial resolution of satellite images is essential for effective management of plantation resources. The objective of this study was to identify appropriate spatial resolutions for predicting levels of weevil induced defoliation. We resampled the Normalized Difference Vegetation Index (NDVI), Simple Ratio (SR) and Enhanced Vegetation Index (EVI) images computed from a WorldView-2 pan-sharpened image, which is characterised with a 0.5 m spatial resolution and 8 spectral bands. Within each plantation compartment 30 x 30 m plots were established, representing different levels of defoliation. From the centre of each plot, the spatial resolution of the original image was progressively resampled from 1.5 to 8.5 m, with 1 m increments. The minimal variance for each level of defoliation was then established and used as an indicator for quantitatively selecting the optimal spatial resolution. Results indicate that an appropriate spatial resolution was established at 1.25, 1.25, 1.75 and 2.25 m for low, medium, high and severe levels of defoliation, respectively. In addition, an Artificial Neural Network was run to determine the relationship between the appropriate spatial resolution and levels of Gonipterus scutellatus induced defoliation. The model yielded an R-2 of 0.80, with an RMSE of 1.28 (2.45% of the mean measured defoliation) based on an independent test dataset. We then compared this model to a model developed using the original 0.5 m image spatial resolution. Our results suggest that optimising the spatial resolution of remotely sensed imagery essentially improves the prediction of vegetation defoliation. In essence, this study provides the foundation for multi-scale defoliation mapping using high spatial resolution imagery. (C) 2015 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
机译:Gonipterus scutellatus Gyllenhal是一种食叶象鼻虫,是桉树属的主要落叶者。了解象鼻虫引起的植物落叶水平与卫星图像的最佳空间分辨率之间的关系对于有效管理人工林资源至关重要。这项研究的目的是确定合适的空间分辨率,以预测象鼻虫引起的脱叶水平。我们对从WorldView-2平锐图像中计算出的归一化差异植被指数(NDVI),简单比率(SR)和增强植被指数(EVI)图像进行了重新采样,其特征是空间分辨率为0.5 m,具有8个光谱带。在每个种植室内,建立了30 x 30 m的地块,代表了不同程度的落叶。从每个图的中心开始,原始图像的空间分辨率从1.5到8.5 m逐步采样,增量为1 m。然后建立每个落叶级别的最小方差,并将其用作定量选择最佳空间分辨率的指标。结果表明,分别为低,中,高和严重的落叶水平分别建立了适当的空间分辨率,分别为1.25、1.25、1.75和2.25 m。另外,运行人工神经网络来确定适当的空间分辨率与盾形小刀诱发的落叶水平之间的关系。基于独立的测试数据集,该模型的R-2为0.80,RMSE为1.28(平均测得的落叶量为2.45%)。然后,我们将该模型与使用原始0.5 m图像空间分辨率开发的模型进行了比较。我们的研究结果表明,优化遥感影像的空间分辨率可以从根本上改善植被脱叶的预测。从本质上讲,该研究为使用高空间分辨率影像的多尺度落叶映射提供了基础。 (C)2015国际摄影测量与遥感学会(ISPRS)。由Elsevier B.V.发布。保留所有权利。

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