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Suitability Assessment of Satellite-Derived Drought Indices for Mongolian Grassland

机译:卫星草原干旱指数对蒙古草原的适宜性评估

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In Mongolia, drought is a major natural disaster that can influence and devastate large regions, reduce livestock production, cause economic damage, and accelerate desertification in association with destructive human activities. The objective of this article is to determine the optimal satellite-derived drought indices for accurate and real-time expression of grassland drought in Mongolia. Firstly, an adaptability analysis was performed by comparing nine remote sensing-derived drought indices with reference indicators obtained from field observations using several methods (correlation, consistency percentage (CP), and time-space analysis). The reference information included environmental data, vegetation growth status, and region drought-affected (RDA) information at diverse scales (pixel, county, and region) for three types of land cover (forest steppe, steppe, and desert steppe). Second, a meteorological index (PED), a normalized biomass (NorBio) reference indicator, and the RDA-based drought CP method were adopted for describing Mongolian drought. Our results show that in forest steppe regions the normalized difference water index (NDWI) is most sensitive to NorBio (maximum correlation coefficient (MAX_R): up to 0.92) and RDA (maximum CP is 87%), and is most consistent with RDA spatial distribution. The vegetation health index (VHI) and temperature condition index (TCI) are most correlated with the PED index (MAX_R: 0.75) and soil moisture (MAX_R: 0.58), respectively. In steppe regions, the NDWI is most closely related to soil moisture (MAX_R: 0.69) and the VHI is most related to the PED (MAX_R: 0.76), NorBio (MCC: 0.95), and RDA data (maximum CP is 89%), exhibiting the most consistency with RDA spatial distribution. In desert steppe areas, the vegetation condition index (VCI) correlates best with NorBio (MAX_R: 0.92), soil moisture (MAX_R: 0.61), and RDA spatial distribution, while TCI correlates best with the PED (MAX_R: 0.75) and the RDA data (maximum CP is 79%). The VHI is a combination of constructed VCI and TCI, and can be used instead of them. Finally, the mode method was adopted to identify appropriate drought indices. The best two indices (VHI and NDWI) can be utilized to develop a combination drought model for accurately monitoring and quantifying drought in the future. Additionally, the new framework can be adopted to investigate and analyze the suitability of satellite-derived drought indices and determine the most appropriate index/indices for other countries or areas.
机译:在蒙古,干旱是一种重大自然灾害,会与破坏性的人类活动相关联,对大片地区造成影响和破坏,导致牲畜减产,造成经济损失并加速荒漠化。本文的目的是确定最佳的卫星衍生干旱指数,以准确,实时地表达蒙古的草原干旱。首先,通过比较九种遥感干旱指数与使用多种方法(相关性,一致性百分比(CP)和时空分析)从实地观察获得的参考指标进行适应性分析。参考信息包括三种土地覆盖类型(森林草原,草原和沙漠草原)的环境数据,植被生长状况以及受不同尺度(像素,县和地区)影响的干旱地区(RDA)信息。其次,采用气象指数(PED),归一化生物量(NorBio)参考指标和基于RDA的干旱CP方法来描述蒙古干旱。我们的结果表明,在森林草原地区,归一化差异水指数(NDWI)对NorBio(最大相关系数(MAX_R):高达0.92)和RDA(最大CP为87%)最敏感,并且与RDA空间最一致分配。植被健康指数(VHI)和温度状况指数(TCI)分别与PED指数(MAX_R:0.75)和土壤水分(MAX_R:0.58)最为相关。在草原地区,NDWI与土壤水分最密切相关(MAX_R:0.69),VHI与PED最密切相关(MAX_R:0.76),NorBio(MCC:0.95)和RDA数据(最大CP为89%) ,与RDA空间分布保持最一致。在沙漠草原地区,植被状况指数(VCI)与NorBio(MAX_R:0.92),土壤水分(MAX_R:0.61)和RDA空间分布最相关,而TCI与PED(MAX_R:0.75)和RDA最相关。数据(最大CP为79%)。 VHI是已构建的VCI和TCI的组合,可以代替它们使用。最后,采用模式方法确定合适的干旱指数。最好的两个指数(VHI和NDWI)可用于开发组合干旱模型,以在未来准确监测和量化干旱。此外,可以采用新框架来调查和分析由卫星得出的干旱指数的适用性,并确定其他国家或地区最合适的指数/指数。

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