• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Physics and chemistry of the earth >Estimation of flood losses to agricultural crops using remote sensing
【24h】

Estimation of flood losses to agricultural crops using remote sensing

机译：利用遥感估算农作物的洪灾损失

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The estimation of flood damage is an important component of risk-oriented flood design, risk mapping, financial appraisals and comparative risk analyses. However, research on flood loss modelling, especially in the agricultural sector, has not yet gained much attention. Agricultural losses strongly depend on the crops affected, which need to be predicted accurately. Therefore, three different methods to predict flood-affected crops using remote sensing and ancillary data were developed, applied and validated. These methods are: (a) a hierarchical classification based on standard curves of spectral response using satellite images, (b) disaggregation of crop statistics using a Monte Carlo simulation and probabilities of crops to be cultivated on specific soils and (c) analysis of crop rotation with data mining Net Bayesian Classifiers (NBC) using soil data and crop data derived from a multi-year satellite image analysis. A flood loss estimation model for crops was applied and validated in flood detention areas (polders) at the Havel River (Untere Havelniederung) in Germany. The polders were used for temporary storage of flood water during the extreme flood event in August 2002. The flood loss to crops during the extreme flood event in August 2002 was estimated based on the results of the three crop prediction methods. The loss estimates were then compared with official loss data for validation purposes. The analysis of crop rotation with NBC obtained the best result, with 66% of crops correctly classified. The accuracy of the other methods reached 34% with identification using Normalized Difference Vegetation Index (NDVI) standard curves and 19% using disaggregation of crop statistics. The results were confirmed by evaluating the loss estimation procedure, in which the damage model using affected crops estimated by NBC showed the smallest overall deviation (1%) when compared to the official losses. Remote sensing offers various possibilities for the improvement of agricultural flood loss estimation. However, crop prediction and loss modelling are still quite uncertain and further research is needed.
机译：洪水破坏的估计是面向风险的洪水设计，风险制图，财务评估和比较风险分析的重要组成部分。但是，关于洪水损失建模的研究，尤其是在农业部门，尚未引起足够的重视。农业损失在很大程度上取决于受影响的农作物，需要对其进行准确的预测。因此，开发，应用和验证了三种使用遥感和辅助数据预测受洪灾影响的作物的方法。这些方法是：（a）基于使用卫星图像的光谱响应的标准曲线的分级分类，（b）使用蒙特卡洛模拟的农作物统计数据分解以及在特定土壤上种植农作物的概率，以及（c）农作物分析使用土壤数据和多年卫星图像分析得出的农作物数据进行数据贝叶斯分类器（NBC）的旋转。在德国哈维尔河（Untere Havelniederung）的洪水滞留区（pol田）中应用并验证了农作物的洪灾损失估算模型。在2002年8月的极端洪水事件中，将田用于临时存储洪水。在2002年8月的极端洪水事件中，对农作物的洪灾损失是根据三种作物预测方法的结果进行估算的。然后将损失估计值与官方损失数据进行比较以进行验证。使用NBC进行的轮作分析获得了最佳结果，正确分类的农作物占66％。使用归一化植被指数（NDVI）标准曲线进行鉴定，其他方法的准确性达到34％，而使用农作物统计数据的分类则达到19％。结果通过评估损失估算程序得到了证实，其中与官方损失相比，使用由NBC估算的受影响作物的损害模型显示出最小的总体偏差（1％）。遥感为改善农业洪灾损失估算提供了各种可能性。但是，作物的预测和损失模型仍很不确定，需要进一步研究。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号