Remote sensing monitoring and analysis of influencing factors of drought in Inner Mongolia growing season since 2000

Qin, Yi; Zhang, Tingbin; Yi, Guihua; Wei, Pengtao; Yang, Da

doi:10.31497/zrzyxb.20210215

Cited by 6 publications

(4 citation statements)

References 14 publications

(9 reference statements)

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“…The climate is arid and semi-arid, making it one of the most sensitive regions to global climate change. From east to west, the vegetation types are coniferous forest, coniferous broad-leaved mixed forest, broad-leaved forest, meadow steppe, typical steppe, desert steppe, and desert [29]. The main landscape type is natural steppe.…”

Section: Study Areamentioning

confidence: 99%

Impact of Extreme Climate on the NDVI of Different Steppe Areas in Inner Mongolia, China

Chen

Bao

et al. 2022

Remote Sensing

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The frequency of extreme climate events has increased resulting in major changes to vegetation in arid and semi-arid areas. We selected 12 extreme climate indices and used trend analysis and multiple linear regression models to analyze extreme climate trends in steppe areas of Inner Mongolia and their impact on the normalized difference vegetation index (NDVI). From 1998 to 2017, the NDVI of the Inner Mongolia steppe increased overall; however, there was a small area of decrease. Extreme climate indices related to warming exhibited increasing trends, particularly in the desert steppe. Although the extreme precipitation index did not change significantly overall, it increased in the northeastern and southwestern regions of the study area and decreased in the central region. The established model showed that the extreme climate explained the highest NDVI variation in desert steppe (R2 = 0.413), followed by typical steppe (R2 = 0.229), and meadow steppe (R2 = 0.109). In desert steppe, TX90P (warm days index) had the greatest impact; in typical steppe, R10 (number of heavy precipitation days index) had the greatest impact; in meadow steppe, R95P (very wet days index) had the greatest impact. This study offered new insights into dynamic vegetation changes in steppe areas of Inner Mongolia and provided a scientific basis for implementing environmental protection strategies.

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Section: Study Areamentioning

confidence: 99%

Impact of Extreme Climate on the NDVI of Different Steppe Areas in Inner Mongolia, China

Chen

Bao

et al. 2022

Remote Sensing

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“…In this study, the LDN levels in FVC, NPP, and land cover declined from 2000 to 2010, which may be caused by climate change and overgrazing. According to the research conducted by Qin et al, 2007 and 2010 were the most severe drought years in Inner Mongolia since 2000, which largely led to the decline of the aforementioned indicators, especially FVC [47]. However, from 2010 to 2020, those indicators show an upward trend due to the proactive efforts of the local government to implement various ecological protection and restoration projects, such as the Implementation Plan for the New Round of Grain for Green Project and the construction of the "Three North" shelter forest system.…”

Section: Discussionmentioning

confidence: 99%

Assessment of Land Degradation at the Local Level in Response to SDG 15.3: A Case Study of the Inner Mongolia Region from 2000 to 2020

Wang

Dong

et al. 2023

Sustainability

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SDG15.3 aims to achieve “Land Degradation Neutrality (LDN)”, but its only indicator (SDG15.3.1) is designed for national-level assessment and monitoring, and is not suitable for local-level applications. Thus, taking Inner Mongolia as the study area, this paper provides a localized reform of SDG15.3.1 based on the local context, and assesses the progress of SDG15.3 in the study area (2000-2020) at multiple levels (indicator, specific, and overall). The Moran’I and Standard Deviation Ellipse (SDE) are also utilized to analyze the spatial–temporal change of land degradation. The results show that as of 2020, the proportion of land degradation and improvement areas to the total area was 7.51% and 9.42%, respectively. Inner Mongolia had generally met the goal of SDG15.3, but on the indicator level, water erosion still falls far below the standard of SDG15.3. Additionally, at the spatial level, 3 out of 12 municipalities and 71 out of 103 counties had not achieved LDN, with a pattern of low LDN levels in the southeast and high in the northwest at the county scale. This indicates that the progress of SDG15.3 is extremely uneven both at the indicator and spatial levels. Thus, it is essential to continue to promote land degradation management in Inner Mongolia to achieve LDN in the entire area and across all aspects.

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“…When the vegetation is under drought stress, the NDVI and EVI will decrease accordingly [35,36]. The NDVI is influenced by soil background values, and its sensitivity is low in areas with high vegetation cover [37]. In contrast, the EVI can effectively correct the influence of the background values (soil and aerosol) of the vegetation by adding blue light bands to adjust the atmospheric effect on vegetation.…”

Section: Vegetation Condition Indexmentioning

confidence: 99%

A Random Forest Model for Drought: Monitoring and Validation for Grassland Drought Based on Multi-Source Remote Sensing Data

et al. 2022

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The accuracy of drought monitoring models is crucial for drought monitoring and early warning. Random forest (RF) is being used widely in the field of artificial intelligence. Nonetheless, the application of a random forest model in grassland drought monitoring research is yet to be further explored. In this study, various drought hazard factors were integrated based on remote sensing data, including from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Precipitation Measurement (GPM), as multisource remote sensing data. Based on the RF, a comprehensive grassland drought monitoring model was constructed and tested in Inner Mongolia, China, as an example. The critical issue addressed is the construction of a grassland drought disaster monitoring model based on meteorological data and multisource remote sensing data by using an RF model, and the verification of the accuracy and reliability of its monitoring results. The results show that the grassland drought monitoring model could quantitatively monitor the drought situation in Inner Mongolia grasslands. There was a significantly positive correlation between the drought indicators output by the model and the standardized precipitation evapotranspiration index (SPEI) measured in the field. The correlation coefficients (R) between the drought degree were 0.9706 and 0.6387 for the training set and test set, respectively. The consistent rate between the model drought index and the SPEI reached 87.90%. Drought events in Inner Mongolia were monitored from April to September in wet years, normal years, and dry years using the constructed model. The monitoring results of the model constructed in this study were in accordance with the actual drought conditions, reflecting the development and spatial evolution of drought conditions. This study provides a new application method for the comprehensive assessment of grassland drought.

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Remote sensing monitoring and analysis of influencing factors of drought in Inner Mongolia growing season since 2000

Cited by 6 publications

References 14 publications

Impact of Extreme Climate on the NDVI of Different Steppe Areas in Inner Mongolia, China

Impact of Extreme Climate on the NDVI of Different Steppe Areas in Inner Mongolia, China

Assessment of Land Degradation at the Local Level in Response to SDG 15.3: A Case Study of the Inner Mongolia Region from 2000 to 2020

A Random Forest Model for Drought: Monitoring and Validation for Grassland Drought Based on Multi-Source Remote Sensing Data

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