Temperature Vegetation Drought Index Research Articles

Understanding the spatiotemporal dynamics of vegetation drought and its time-lag link with teleconnection factors on the Loess Plateau

Study regionLoess Plateau (LP), China Study focusThis study investigated the spatiotemporal dynamics of vegetation drought on the LP using an improved temperature vegetation drought index (ITVDI) and explored its time-lag link with teleconnection factors. New hydrological insights for the regionFindings indicated that: (1) ITVDI is an effective tool for vegetation drought monitoring; (2) the vegetation droughts were primarily characterized by mild to moderate intensity, with spring manifesting the most expansive drought-stressed areas (about 61.89% of pixels); (3) approximately 71.69% of the region exhibited an increasing trend in vegetation drought, particularly in the Ordos Plateau and typical agricultural zones; (4) long-term dependence in vegetation drought patterns was observed in 70.65% of the region, characterized by forest-covered areas becoming wetter, crop-covered areas getting drier, and grass-covered areas shifting from dryness to wetness; (5) the lag time between ITVDI and Southern Oscillation Index (SOI) was generally less than four months (approximately 71.01% of pixels), while ITVDI lagged behind Pacific Decadal Oscillation (PDO) mostly between 6 and 8 months (around 75.77% of pixels); (6) extremum correlation coefficients effectively determined the lag time for vegetation drought behind large-scale circulation factors, considering both monotonic and non-monotonic relationships. These insights contribute to drought monitoring, risk assessment, and early warning system, thereby providing valuable support for drought mitigation and management strategies in the LP and similar regions worldwide.

Study loss of vegetative cover and increased land surface temperature through remote sensing strategies under the inter-annual climate variability in Jinhua-Quzhou basin, China.

The Jinhua-Quzhou basin in China is one of the most susceptible areas to drought. Due to the loss of vegetation and great fluctuations in rainfall and surface temperature, global warming occurs. Timely, accurate, and effective drought monitoring is crucial for protecting local vegetation and determining which vegetation is most vulnerable to increased LST during the period 1982-2019. It assumes a strong correlation between loss of vegetation cover, changes in monsoon climate, drought, and increases in land surface temperature (LST).Due to significantly increased in LST, low precipitation and vegetation cover, NDVI, TVDI, VCI, and NAP are useful in characterizing drought mitigation strategies.The temperature vegetation drought index (TVDI), normalized difference vegetation index (NDVI), vegetation condition index (VCI), and monthly precipitation anomaly percentage (NAP) can be helped to characterize drought reduction strategies. Monthly NDVI, NAP, VCI, TVDI, normalized vegetation supply water index (NVSWI), temperature condition index (TCI), vegetation health index (VHI), and heat map analysis indicate that the Jinhua-Quzhou basin experienced drought during 1984, 1993, 2000, and 2011. Seasonal SR, WVP, WS, NDVI, VCI, and NAP charts confirm that the Jinhua-Quzhou basin was affected by severe drought in 1984, which continued and led to severe droughts in 1993, 2000, and 2011. Regression analysis showed a significant positive correlation between NDVI, TVDI, VCI, and NAP values, while NVSWI, TVDI, and VHI showed positive signs of good drought monitoring strategies. The research results confirm the correlation between loss of vegetation cover and LST, which is one of the causes of global warming. The distribution of drought changed a trend indicating that compared with the Jinhua region; the Quzhou region has more droughts. The changing trend of drought has characteristics from 1982 to 2019, and there are significant differences in drought changing trends between different Jinhua-Quzhou basin areas. Overall, from 1982 to 2019, the frequency of drought showed a downward trend. We believe that these results will provide useful tools for drought management plans and play a relevant role in mitigating the effects of drought and protecting humanity from climate hazards.

Optimization of multi-dimensional indices for kiwifruit orchard soil moisture content estimation using UAV and ground multi-sensors

Accurately estimating of soil moisture content (SMC) is essential for effective irrigation water management and optimizing plant water productivity. Recent advancements in multi-sensor platforms and ensemble learning (EL) algorithms such as the use of UAV-based multispectral (MS) and thermal infrared (TIR) images, have enabled precise mapping of SMC at the field level. The purpose of this study is to optimize the multi-dimensional (n-D) index set and EL model to achieve accurate inversion mapping of SMC in the 0–60 cm root zone at a kiwifruit orchard during the fruit growth period (May-Sep 2022). The n-D index set was computed using kiwifruit canopy MS and TIR data from UAV along with in-field temperature data (air temperature [Ta], kiwifruit canopy temperature [Tc], soil temperature [Ts], and Ta-Tc, Ta-Ts, Tc-Ts). The primary findings of this study are as follows: (1) The Three-Dimensional Drought Index (TDDI), Temperature Vegetation Drought Index (TVDI), and Crop Water Stress Index (CWSI) demonstrated superior performance in capturing temporal variations of root zone SMC compared to Normalized Difference Vegetation Index (NDVI) and Enhance Vegetation Index (EVI). (2) The optimal Categorical Boosting (Catboost) model with two optimal TDDIs (TDDI22: (Tc-Ts)-EVI10,6,2-CWSI space and TDDI7: Tc-NDVI7,3-CWSI space), exhibited exceptional performance for SMC estimation, with determination coefficient (R2) and root-mean-square error (RMSE) of 0.966 ± 0.010 and 0.046 ± 0.003%, respectively. (3) The planted-by-planted mapping performed well in estimating root zone SMC at different growth stages and under different irrigation treatments, with a correlation coefficient (R) of 0.936 ± 0.074 (p < 0.001). This study demonstrated that the integration of multi-sensor indicators and EL models can improve the accuracy of SMC estimation due to its robust adaptability to various field conditions. Furthermore, the planted-grid-based SMC mapping framework can be considered as a valuable approach for monitoring dynamic SMC, facilitating informed irrigation decisions at the individual planting grid.

The research of common drought indexes for the application to the drought monitoring in the region of Jin Sha river

Abstract Based on MODIS data from 2010 to 2020 and precipitation, air temperature, and soil moisture data of 33 meteorological stations in Jinsha River Basin from 1990 to 2020, the applicability of different remote sensing drought indexes in Jinsha River Basin was studied. These indexes include temperature condition index (TCI) and temperature vegetation drought index (TVDI), the results of vegetation condition index (VCI), vegetation supply water index (VSWI), standardized precipitation evapotranspiration index (SPEI), and standardized precipitation index (SPI) showed that TCI and TVDI, VSWI and TCI, VSWI and TVDI, VSWI and TVDI, VSWI and TVDI, VSWI and TVDI, VSWI and TVDI, SPEI and SPI, respectively. The correlation between VSWI and VCI was significant. VCI had the lowest correlation with SPEI and SPI. The average correlation coefficient between TCI and VSWI was similar. The correlation between VSWI, SPEI, and SPI was low in January, March, and October and reached significant or above levels in other months. TVDI had the highest correlation with SPEI and SPI. TVDI was significantly correlated with soil moisture every month of the year, indicating that TVDI can be effectively used for remote sensing drought monitoring in Jinsha River Basin and has strong adaptability. According to the temporal and spatial analysis of drought monitoring in the Jinsha River Basin by TVDI, the drought areas in December and January are mainly located in the middle reaches of the Jinsha River Basin, while the light drought areas are mainly located in the upper and lower reaches of the Jinsha River Basin. From March to June, the risk of severe drought increased in the middle reaches of the Jinsha River, and the moderate drought area in the Jinsha River Basin also increased. The drought from July to November was weaker than in the previous months. The moderate drought area is mainly located in the middle and lower reaches of the Jinsha River, and the mild drought area is mainly distributed in the upper reaches of the Jinsha River Basin.

A comprehensive drought index based on spatial principal component analysis and its application in northern China.

In the background of the greenhouse effect, drought events occurred more frequently. How to monitor drought events scientifically and efficiently is very urgent at present. In this study, we employed the Vegetation Water Supply Index (VSWI), Temperature Vegetation Drought Index (TVDI), and Crop Water Stress Index (CWSI) as individual variables to construct a composite drought index (CDI) using spatial principal component analysis (SPCA). The validity of CDI was assessed using gross primary productivity (GPP), soil moisture (SM), Standardized Precipitation Evapotranspiration Index (SPEI), and Vegetation Condition Index (VCI). CDI was subsequently used for drought monitoring in northern China from 2011 to 2020. The results showed that (1) at a 99% confidence level, the Pearson correlation coefficients between CDI and GPP was 0.72, while the value between CDI and SM was 0.69, which indicated the relationship between SM, GPP, and CDI was significant. (2) We compared CDI with other variables such as Standardized Precipitation Evapotranspiration Index (SPEI) and Crop Drought Index (CDI) and found that the monitoring result of CDI was more sensitive, which indicated that the proposed CDI had a better effect in local drought monitoring. (3) The results of CDI showed that the drought status in the northern region during 2011-2020 lasted from March to October, and the high severe drought period generally occurs in March-May and September-October, with low severe drought in June-August.

A novel composite drought index combining precipitation, temperature and evapotranspiration used for drought monitoring in the Huang-Huai-Hai Plain

Single drought index such as Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Evapotranspiration Condition Index (ETCI) and so on, has a limited capability to indicate drought state. It has been proved that the composite drought index proposed by integrating multiple single drought indices can indicate and predict drought state more efficiently and accurately. This paper presents the Precipitation Temperature Evapotranspiration Drought Index (PTEDI) combining the PCI, TCI and ETCI. The three-dimensional Euclidean PCI-TCI-ETCI space is built, and the PTEDI is defined as the magnitude of the vector in the PCI-TCI-ETCI space. To verify the rationality and effectiveness of the PTEDI as a desired drought index in the Huang-Huai-Hai Plain, it is compared with the PCI, TCI, ETCI and PCACDI (Principal Component Analysis Combined Drought Index), and is evaluated with the Drought Affected/Disaster Area (DAA/DDA) and Net Primary Productivity (NPP). The results indicate that the PTEDI outperforms the PCI, TCI, ETCI and PCACDI, and is highly correlated with the Standardized Precipitation Index (SPI-1) (R = 0.61), Soil Moisture Condition Index (SMCI) (R= 0.56), and NPP (R = 0.56). The time series of the PTEDI and four indices (SPI-1, SMCI, Crop Water Stress Index (CWSI) and Temperature Vegetation Drought Index (TVDI)) exhibits good spatial consistency. The PTEDI has a negative correlation with the DAA/DDA (R < −0.6). Furthermore, to illustrate the applicability of the PTEDI, the drought spatiotemporal variation is analyzed by taking it as a drought index. At the monthly scale, moderate drought occurs most frequently in March and July. At the annual scale, the PTEDI fluctuates between 0.34 and 0.60 without obvious periodicity, and the study area is mainly characterized by mild drought. Moreover, the drought evolution is analyzed by using the timing analysis methods. In conclusion, the PTEDI can be applied to monitor drought effectively.

Ecological drought monitoring of Inner Mongolia vegetation growing season based on kernel temperature vegetation drought index (kTVDI).

Ecological drought monitoring is important for regional status assessment and protection of water resources. In this study, we constructed a new ecological drought index, the kernel temperature vegetation drought index (kTVDI), by using the kernel normalized vegetation index (kNDVI) to improve the temperature vegetation drought index (TVDI) in Inner Mongolia. We further analyzed the spatial and temporal distribution of ecological drought in Inner Mongolia during 2000-2022 and the future trend of ecological drought by using segmented linear regression model, Theil-Sen median, Mann-Kendall test, and Hurst index. The results showed that kTVDI performed better in monitoring ecological drought than TVDI. From 2000 to 2022, kTVDI showed a decreasing trend in the growing season in Inner Mongolia, but the change was not significant, and a sudden change occurred in 2016, and the wetting trend after the sudden change was more obvious. During the study period, ecological drought in 23.6% of the areas of Inner Mongolia showed an aggravating trend, and ecological drought was alleviated in 46.5% of the area. In the future, ecological drought would be exacerbated in the eastern part but alleviated in the central and western parts of Inner Mongolia.

Spatiotemporal Variation and Future Predictions of Soil Salinization in the Werigan–Kuqa River Delta Oasis of China

Soil salinization is a serious global issue; by 2050, without intervention, 50% of the cultivated land area will be affected by salinization. Therefore, estimating and predicting future soil salinity is crucial for preventing soil salinization and investigating potential arable land resources. In this study, several machine learning methods (random forest (RF), Light Gradient Boosting Machine (LightGBM), Gradient Boosting Decision Tree (GBDT), and eXtreme Gradient Boosting (XGBoost)) were used to estimate the soil salinity in the Werigan–Kuqa River Delta Oasis region of China from 2001 to 2021. The cellular automata (CA)–Markov model was used to predict soil salinity types from 2020 to 2050. The LightGBM method exhibited the highest accuracy, and the overall prediction accuracy of the methods had the following order: LightGBM &gt; RF &gt; GBRT &gt; XGBoost. Moderately saline, severely saline, and saline soils were dominant in the east and south of the research area, while non-saline and mildly saline soils were widely distributed in the inner oasis area. A marked decreasing trend in the soil salt content was observed from 2001 to 2021, with a decreasing rate of 4.28 g/kg·10 a−1. The primary change included the conversion of mildly and severely saline soil types to non-saline soil. The generalized difference vegetation index (51%), Bio (30%), and temperature vegetation drought index (27%) had the greatest influence, followed by variables associated with soil attributes (soil organic carbon and soil organic carbon stock) and terrain (topographic wetness index, slope, aspect, curvature, and topographic relief index). Overall, the CA–Markov simulation resulted exhibited suitable accuracy (kappa = 0.6736). Furthermore, areas with non-saline and mildly saline soils will increase while areas with other salinity levels will continue to decrease from 2020 to 2050. From 2046 to 2050, numerous areas with saline soil will be converted to non-saline soil. These results can provide support for salinization control, agricultural production, and soil investigations in the future. The gradual decline in soil salinization in the research area in the past 20 years may have resulted from large-scale land reclamation, which has turned saline alkali land into arable land and is also related to effective measures taken by the local government to control salinization.

Validation Analysis of Drought Monitoring Based on FY-4 Satellite

Droughts are natural disasters that have significant implications for agricultural production and human livelihood. Under climate change, the drought process is accelerating, such as the intensification of flash droughts. The efficient and quick monitoring of droughts has increasingly become a crucial measure in responding to extreme drought events. We utilized multi-imagery data from the geostationary meteorological satellite FY-4A within one day; implemented the daily Maximum Value Composite (MVC) method to minimize interference from the clouds, atmosphere, and anomalies; and developed a method for calculating the daily-scale Temperature Vegetation Drought Index (TVDI), which is a dryness index. Three representative drought events (Yunnan Province, Guangdong Province, and the Huanghuai region) from 2021 to 2022 were selected for validation, respectively. We evaluated the spatial and temporal effects of the TVDI with the Soil Relative Humidity Index (SRHI) and the Meteorological Drought Composite Index (MCI). The results show that the TVDI has stronger negative correlations with the MCI and SRHI in moderate and severe drought events. Meanwhile, the TVDI and SRHI exhibited similar trends. The trends of drought areas identified by the TVDI, SRHI, and MCI were consistent, while the drought area identified by the TVDI was slightly higher than the SRHI. Yunnan Province has the most concentrated distribution, which is mostly between 16.93 and 25.22%. The spatial distribution of the TVDI by FY-4A and MODIS is generally consistent, and the differences in severe drought areas may be attributed to disparities in the NDVI. Furthermore, the TVDI based on FY-4A provides a higher number of valid pixels (437 more pixels in the Huanghuai region) than that based on MODIS, yielding better overall drought detection. The spatial distribution of the TVDI between FY-4A and Landsat-8 is also consistent. FY-4A has the advantage of acquiring a complete image on a daily basis, and lower computational cost in regional drought monitoring. The results indicate the effectiveness of the FY-4A TVDI in achieving daily-scale drought monitoring, with a larger number of valid pixels and better spatial consistency with station indices. This study provides a new solution for drought monitoring using a geostationary meteorological satellite from different spatial–temporal perspectives to facilitate comprehensive drought monitoring.

Normalized Temperature Drought Index (NTDI) for Soil Moisture Monitoring Using MODIS and Landsat-8 Data

As the fundamental regulator of energy exchange in the vegetation–soil–atmosphere circulation system, soil moisture is a key parameter for drought monitoring and is indispensable to the land surface hydrological processes. In order to overcome the constraints of the Perpendicular Drought Index, PDI (performs poorly over the fields with dense vegetation and hard to construct the soil line), and the Temperature Vegetation Drought Index, TVDI (requires similar atmospheric forcing and large enough dimension of mapping area), in soil moisture monitoring, a new drought index (Normalized Temperature Drought Index, NTDI) is proposed to explore the spatiotemporal changes of soil moisture by substituting red and near-infrared reflectances with vegetation index and normalized land surface temperature on the basis of the PDI framework. Victoria, Australia, was selected as the study area as it experiences many severe droughts and has been affected for more than ten years. Time series of satellite-based data were applied to evaluate the effectiveness and applicability of the NTDI at the regional scale. Results indicated that the expression of the soil line representing the water condition of the bare soil is easier to obtain in the new trapezoid framework and has good fits with the coefficients of determination (R2) of more than 0.8. Compared with PDI, TVDI and Modified PDI (MPDI) at the cropping sites, NTDI exhibits a relatively better performance in soil moisture monitoring for most days where the R2 achieved can reach to more than 0.7 on DOY 242, 254 and 272. Meanwhile, spatial–temporal mappings of the four drought indices from satellite data were conducted, and the NTDI presented the slightly seasonal variation and effectively described the real spatial characteristics of regional drought. Overall, the NTDI seems to a viable approach and can provide insight into spatial and temporal soil moisture monitoring at different scales.

Spatio-temporal variations in trends of vegetation and drought changes in relation to climate variability from 1982 to 2019 based on remote sensing data from East Asia

Studying the significant impacts on vegetation of drought due to global warming is crucial in order to understand its dynamics and interrelationships with temperature, rainfall, and normalized difference vegetation index (NDVI). These factors are linked to excesses drought frequency and severity on the regional scale, and their effect on vegetation remains an important topic for climate change study. East Asia is very sensitive and susceptible to climate change. In this study, we examined the effect of drought on the seasonal variations of vegetation in relation to climate variability and determined which growing seasons are most vulnerable to drought risk; and then explored the spatio-temporal evolution of the trend in drought changes in East Asia from 1982 to 2019. The data were studied using a series of several drought indexes, and the data were then classified using a heat map, box and whisker plot analysis, and principal component analysis. The various drought indexes from January to August improved rapidly, except for vegetation health index (VHI) and temperature condition index (TCI). While these indices were constant in September, they increased again in October, but in December, they showed a descending trend. The seasonal and monthly analysis of the drought indexes and the heat map confirmed that the East Asian region suffered from extreme droughts in 1984, 1993, 2007, and 2012 among the study years. The distribution of the trend in drought changes indicated that more severe drought occurred in the northwestern region than in the southeastern area of East Asia. The drought tendency slope was used to describe the changes in drought events during 1982–2019 in the study region. The correlations among monthly precipitation anomaly percentage (NAP), NDVI, TCI, vegetation condition index (VCI), temperature vegetation drought index (TVDI), and VHI indicated considerably positive correlations, while considerably negative correlations were found among the three pairs of NDVI and VHI, TVDI and VHI, and NDVI and TCI. This ecological and climatic mechanism provides a good basis for the assessment of vegetation and drought-change variations within the East Asian region. This study is a step forward in monitoring the seasonal variation of vegetation and variations in drought dynamics within the East Asian region, which will serve and contribute to the better management of vegetation, disaster risk, and drought in the East Asian region.

Drought Vulnerability Curves Based on Remote Sensing and Historical Disaster Dataset

As drought vulnerability assessment is fundamental to risk management, it is urgent to develop scientific and reasonable assessment models to determine such vulnerability. A vulnerability curve is the key to risk assessment of various disasters, connecting analysis of hazard and risk. To date, the research on vulnerability curves of earthquakes, floods and typhoons is relatively mature. However, there are few studies on the drought vulnerability curve, and its application value needs to be further confirmed and popularized. In this study, on the basis of collecting historical disaster data from 52 drought events in China from 2009 to 2013, three drought remote sensing indexes were selected as disaster-causing factors; the affected population was selected to reflect the overall disaster situation, and five typical regional drought vulnerability curves were constructed. The results showed that (1) in general, according to the statistics of probability distribution, most of the normalized difference vegetation index (NDVI) and the temperature vegetation drought index (TVDI) variance ratios were concentrated between 0 and ~0.15, and most of the enhanced vegetation index (EVI) variance ratios were concentrated between 0.15 and ~0.6. From a regional perspective, the NDVI and EVI variance ratio values of the northwest inland perennial arid area (NW), the southwest mountainous area with successive years of drought (SW), and the Hunan Hubei Jiangxi area with sudden change from drought to waterlogging (HJ) regions were close and significantly higher than the TVDI variance ratio values. (2) Most of the losses (drought at-risk populations, DRP) were concentrated in 0~0.3, with a cumulative proportion of about 90.19%. At the significance level, DRP obeys the Weibull distribution through hypothesis testing, and the parameters are optimal. (3) The drought vulnerability curve conformed to the distribution rule of the logistic curve, and the line shape was the growth of the loss rate from 0 to 1. It was found that the arid and ecologically fragile area in the farming pastoral ecotone (AP) region was always a high-risk area with high vulnerability, which should be the focus of drought risk prevention and reduction. The study reduces the difficulty of developing the vulnerability curve, indicating that the method can be widely used to other regions in the future. Furthermore, the research results are of great significance to the accurate drought risk early warning or whether to implement the national drought disaster emergency rescue response.

Is land degradation worsening in Northern China? Quantitative evidence and enlightenment from satellites

AbstractLand degradation has become an urgent environmental issue globally. The complexity of land degradation hinders its quantification and monitoring, which is needed to realize the sustainable development of land resources. This study constructed a comprehensive index—land degradation status index (LDSI), which integrates in fractional vegetation cover (FVC), net primary productivity (NPP), albedo, and modified temperature vegetation drought index (MTVDI) based on spatial principal component analysis (SPCA). Land degradation was then identified by the dynamics of land degradation status during 2001–2018. This study investigated the spatio‐temporal process and driving mechanism of land degradation in Northern China. The result indicates that: (1) LDSI had a better monitoring performance compared with normalized difference vegetation index (NDVI), (2) although some degraded land had been effectively rehabilitated (17.11%), a highly clustered spatial distribution of land degradation status remained, challenging to break, (3) localized land degradation had expanded (1.063%), mainly in semiarid (0.481%), dry sub‐humid (0.289%), and humid (0.187%) regions, and (4) differences in climate, environmental backgrounds, and human activities were driven land degradation status and process. This study also assessed the effectiveness of ecological projects implemented by the Chinese government. The in‐depth understanding of the change regularity and influencing mechanism in land degradation status and process can provide a scientific basis for formulating ecological policies based on local conditions.

Soil moisture monitoring by downscaling of remote sensing products using LST/VI space derived from MODIS products

Abstract Soil moisture (SM) has an important role in the earth's water cycle and is a key variable in water resources management. Considering the critical state of water resources in the Urmia Lake basin, northwest Iran, this study examined the potential for utilizing a variety of remote sensing data and products, in conjunction with a promising downscaling method, to monitor soil moisture with a reasonable spatial and temporal resolution, as a novel and effective tool for agricultural and water resource management. Accordingly, remote sensing products of surface soil moisture were scaled to MODIS's image scale (∼1 km) using the UCLA downscaling method and Temperature, Vegetation, Drought Index (TVDI) values obtained from the scattering space method. Results showed that the LPRM, ESA-CCI, and GLDAS downscaled images had the highest inverse correlation with the TVDI (best results) accordingly equal to −0.600, −0.787, and −0.630. Also, based on the evaluation of the obtained results with the ground stations data, the LPRM and the ESA-CCI downscaled images had the best statistical indices values accordingly in 2010 and 2014 that confirm the promising application of remote sensing soil moisture data (rLPRM (2010) = 0.92, MAELPRM (2010) = 4.14%, RMSELPRM (2010) = 6.39% and rESA-CCI (2014) = 0.7, MAEESA-CCI (2014) = 2.23%, RMSEESA-CCI (2014) = 2.59%).

Improving drought mitigation strategies and disaster risk reduction through MODIS and TRMM-based data in relation to climate change over Pakistan.

Drought is an extreme climatic event that mostly occurs as a result of low rainfall, which leads to lack of water in various agro-ecological conditions of Pakistan. The condition could be further exacerbated by the prevailing dry weather. Therefore, accurate, timely, and efficient drought monitoring is crucial to ensure that its adverse effects are mitigated. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) and TRMM-based data were used together with remote sensing techniques to improve drought mitigation and disaster risk reduction strategies. In order to monitor drought mitigation and disaster risk reduction strategies in Pakistan, the crop water stress index (CWSI), vegetation condition index (VCI), normalized vegetation supply water index (NVSWI), vegetation health index (VHI), and temperature vegetation drought index (TVDI) were chosen as the instrument. Due to low rainfall and significantly low vegetation, CWSI, NDVI, TVDI, and VHI are useful in characterizing drought mitigation strategies in Pakistan. Monthly NDVI, NAP, NVSWI, TVDI, VCI, and VHI values and heat map analysis show that Pakistan suffered from drought in years 2001, 2002, and 2006. Seasonal CWSI, NDVI, VHI, and TVDI confirmed that Pakistan was affected by severe drought in 2001, which continued and led to severe drought in 2002 and 2006. We generate spatial correlation coefficients between NDVI and NVSWI, VCI, and VHI, and NVSWI and VCI and VHI, while the VCI and VHI values are significantly positively correlated. CWSI, NDVI, VHI, and TVDI show positive signs of effective climate change drought mitigation and disaster risk reduction strategies in Pakistan. Thus, these drought indices have been confirmed to be a complete drought monitoring indicator and reduce the risk of drought in Pakistan.

秦巴山地植被绿度特征及其对地表水热的响应

PDF HTML阅读 XML下载 导出引用 引用提醒 秦巴山地植被绿度特征及其对地表水热的响应 DOI: 10.5846/stxb202107272040 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 科技基础资源调查专项-中国南北过渡带综合科学考察（2017FY100902） Vegetation greenness characteristics in Qinling-Daba Mountains and its response to surface hydrothermal Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:地表水热通过影响土壤和大气间的水分交换以及植被生长用水对植被格局起关键作用。使用2000-2020年MODIS叶面积指数（Leaf area index，LAI）作为秦巴山地植被绿度的评价指标，基于遥感技术获取同时段地表温度（Land surface temperature，LST）和温度植被干旱指数（Temperature vegetation drought index，TVDI），用于指示地表水热状况。结合坡向和海拔等地形因素，使用均值和趋势法分析了秦巴山地植被绿度的时空特征；采用时滞相关性分析、主导因素分析探究了植被绿度与地表水热的相关关系，以及影响植被绿度变化的主导因素；借助广义可加模型，结合植被类型探讨了植被绿度与地表水热的非线性关系。结果表明：（1）秦巴山地LAI集中在1-5之间，LAI均值随海拔先增后减，不同坡向LAI差异较大，表现为大巴山北坡 > 大巴山南坡 > 秦岭南坡 > 秦岭北坡 > 西秦岭。秦巴山地LAI高值区与地表水热较好的地区在空间分布上具有一致性，LAI低值区与LST低值区或TVDI高值区较为一致。（2）秦巴山地LAI以由增变减趋势为主，变化趋势的海拔分异大于坡向分异。在LAI由增变减区，地表水热以由增变减为主，其次是持续增加。（3）秦巴山地LAI与LST主要为显著正相关，与TVDI主要为显著负相关。LAI对地表水热的时滞响应不明显，LAI受TVDI的影响较受LST的影响面积广。（4）除LST增加导致落叶阔叶林和混交林LAI单调递增外，地表水热对各植被类型LAI均为非线性影响，即存在一定阈值，阈值两侧LAI的响应规律不同，不同植被类型对地表水热响应的阈值差异较大。 Abstract:Surface hydrothermal plays a key role in vegetation pattern by affecting soil-atmosphere water exchange and vegetation growth water use. MODIS Leaf area index (LAI) from 2000 to 2020 was used as the evaluation index of vegetation greenness in Qinling-Daba Mountains. Land surface temperature (LST) and temperature vegetation drought index (TVDI) in the same period were obtained based on remote sensing technology to indicate surface hydrothermal conditions. Combined with terrain factors such as aspect and altitude, the temporal and spatial characteristics of vegetation greenness in Qinling-Daba Mountains were analyzed by mean and trend methods. The correlation between vegetation greenness and surface hydrothermal, as well as the leading factors affecting the change of vegetation greenness were explored by time-delay correlation analysis and dominant factor analysis. The nonlinear relationship between vegetation greenness and surface hydrothermal was discussed with the help of the generalized additive model and vegetation types. The results showed that:(1) the LAI of Qinling-Daba Mountains was concentrated in the range of 1-5. The average value of LAI increased first and then decreased with altitude, while the LAI varied greatly in different aspects, which was shown as the north slope of Daba Mountains > the south slope of Daba Mountains > the south slope of Qinling Mountains > the north slope of Qinling Mountains > the west Qinling Mountains. The LAI high value area in the Qinling-Daba Mountains was consistent with the area with good surface hydrothermal in terms of spatial distribution. The LAI low value area was consistent with LST low value area or TVDI high value area. (2) The LAI of the Qinling-Daba Mountains mainly changed from increase to decrease, and the change trend of altitude differentiation was larger than aspect differentiation. In the area of LAI from increase to decrease, surface hydrothermal was mainly from increase to decrease, followed by continuous increase. (3) The LAI of Qinling-Daba mountains was significantly positive correlation with LST, but significantly negative correlation with TVDI. The time lag effect of surface hydrothermal on LAI was not obvious, and LAI was more affected by TVDI than LST. (4) Except for the increase of LST which led to monotonous increase in LAI of deciduous broad-leaved forest and mixed forest, the surface hydrothermal had nonlinear effects on the LAI of various vegetation types. There was a certain threshold, and the response law of LAI on both sides of the threshold was different, and the thresholds of different vegetation's response to surface hydrothermal were quite different. 参考文献 相似文献 引证文献

Reconstruction and application of the temperature-vegetation-precipitation drought index in mainland China based on remote sensing datasets and a spatial distance model

In recent years, remote sensing drought monitoring indices have been gradually developed and have been widely used for global or regional drought monitoring due to their strong drought-monitoring capabilities and easy implementation advantages. However, some defects of remote sensing drought indices stand to be improved due to certain errors in the inversion of surface characteristics by remote sensing datasets. The temperature-vegetation-precipitation drought index (TVPDI) was taken as the research object, and the leaf area index (LAI), the difference between the land surface temperature (LST) and monthly average temperature, and Global Precipitation Measurement (GPM) precipitation data were selected instead of the normalized difference vegetation index (NDVI), LST and tropical rainfall measuring mission (TRMM) data to improve TVPDI. The improved remote sensing drought index was named the improved temperature-vegetation-precipitation drought index (iTVPDI). The drought-monitoring accuracy of iTVPDI was verified by the gross primary productivity (GPP), soil moisture, and crop yield per unit. The drought-monitoring ability of iTVPDI was verified with traditional drought indices, including the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), Palmer drought severity index (PDSI), temperature-vegetation drought index (TVDI), drought severity index (DSI) and crop water stress index (CWSI). The drought-monitoring accuracy of iTVPDI was verified by selecting sample areas. iTVPDI was applied to monitor drought in mainland China over the 2001–2020 period and obtained four main results. First, the correlation analyses of iTVPDI and TVPDI with GPP, crop yield per unit area, and soil moisture showed that iTVPDI had a stronger monitoring ability in Northeast, North, and Southwest China; the R2 value obtained with soil moisture was 0.62 (p < 0.05), and this value was higher than that of TVPDI. Then, the correlation analyses of iTVPDI and TVPDI with SPI, SPEI, PDSI, CWSI, DSI and TVDI showed that the correlation coefficients of iTVPDI and TVPDI with these six indicators were basically consistent, which indicated that the drought-monitoring capability of iTVPDI was consistent with that of TVPDI. In local areas such as the Qinghai-Tibet Plateau in China, the monitoring ability of iTVPDI was stronger than that of TVPDI. Third, through the sample area analysis, iTVPDI was found to moderate the NDVI-characterized vegetation factors in TVPDI in low-vegetation-cover areas affected by soil disturbances and in high-vegetation-cover areas affected by oversaturation. Finally, the results obtained from the application of iTVPDI in mainland China showed that during the warm-dry to warm-wet climate transition between 2001 and 2021, in 2010 and 2018, and in other special drought years, iTVPDI had the best response.

Assessment of the spatiotemporal characteristics of vegetation water use efficiency in response to drought in Inner Mongolia, China.

Ecosystem water use efficiency (eWUE) can be used to obtain a better comprehension of the ecosystem water-carbon cycle. This study aimed to characterize the regional-scale responses and adaptations of different vegetation categories to drought changes and the spatiotemporal characteristics of WUE and associated drought factors for nine vegetation categories in Inner Mongolia, China, from 2000 to 2020. This study estimated drought, the association between drought and eWUE among varying vegetation categories, and the differences in eWUE between the drought stage and the post-drought stage by analyzing the spatiotemporal variations in eWUE of different vegetation categories based on MODIS ET (evapotranspiration), GPP (gross primary productivity), and temperature vegetation drought index data. The results illustrated the following: (1) the multi-year mean eWUE from 2000 to 2020 was 1.03g·m-2·mm-1, with an overall significantly increasing trend of 0.008g·m-2·mm-1 and eWUE decreasing from northeast to southwest. (2) The rank of vegetation types in Inner Mongolia according to multi-year mean eWUE was evergreen coniferous forest > savanna > evergreen broadleaf forest > forested grassland > farmland > deciduous broadleaf forest > mixed forest > closed scrub > grassland. All vegetation categories illustrated an increasing trend in eWUE over time. (3) eWUE was inversely associated with drought in the drought stage and a clear effect of drought legacy was identified in which harsh drought impacted the eWUE of the ecosystem, whereas eWUE was positively associated with drought. (4) The eWUE values of ecosystems increased significantly after drought, indicating that ecosystems that are adapted to drought show high capacity to recovery from drought stress.

Spatiotemporal Variability in Water-Use Efficiency in Tianshan Mountains (Xinjiang, China) and the Influencing Factors

Water-use efficiency (WUE) is a crucial physiological index in carbon–water interactions and is defined as the ratio of vegetation productivity to water loss. The variation in climatic variables and drought have the most significant effects on WUE and evapotranspiration (ET). Nevertheless, how WUE varies with climate factors and drought processes in the Tianshan Mountains (TMS) is still poorly understood. In the present work, we analyzed the spatiotemporal variations in WUE, and investigated the correlations between WUE, climate factors, and drought, in the study area. The results showed that, in the TMS during 2000–2020, annual net primary productivity (NPP) ranged from 147.9 to 189.4 gC·m−2, annual ET was in the range of 212.5–285.8 mm, and annual WUE ranged from 0.66 to 0.78 gC·kg−1·H2O. Both NPP and ET exhibited an increasing trend with some fluctuation, whereas WUE showed the opposite tendency during the study period. The obtained results demonstrated that the decrease in WUE was primarily because of the increase in ET. There were obvious differences in WUE, under different land-use types, caused by NPP and ET. However, the interannual variation in WUE showed small fluctuations and the dynamic process of WUE in each land-use type showed good consistency. Temperature and wind speed had a positive influence on WUE in the middle and eastern regions of the TMS. Precipitation also played a mainly positive role in enhancing WUE, especially on the northern slope of the TMS. There was strong spatial heterogeneity of the correlation coefficient (0.68, p &lt; 0.05) between WUE and the temperature vegetation drought index (TVDI). Moreover, the slopes of WUE and TVDI showed good consistency in terms of spatial distribution, suggesting that drought had a significant impact on ecosystem WUE. This work will enhance the understanding of WUE variation, and provide scientific evidence for water resource management and sustainable utilization in the study area.

Spatial-Temporal Evolution and Driving Forces of Drying Trends on the Qinghai-Tibet Plateau Based on Geomorphological Division.

The Qinghai–Tibet Plateau (QTP) is a sensor of global climate change and regional human activities, and drought monitoring will help to achieve its ecological protection and sustainable development. In order to effectively control the geospatial scale effect, we divided the study area into eight geomorphological sub-regions, and calculated the Temperature-Vegetation Drought Index (TVDI) of each geomorphological sub-region based on MODIS Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data, and synthesized the TVDI of the whole region. We employed partial and multiple correlation analyses to identify the relationship between TVDI and temperature and precipitation. The random forest model was further used to study the driving mechanism of TVDI in each geomorphological division. The results of the study were as follows: (1) From 2000 to 2019, the QTP showed a drought trend, with the most significant drought trend in the central region. The spatial pattern of TVDI changes of QTP was consistent with the gradient changes of precipitation and temperature, both showing a gradual trend from southeast to northwest. (2) There was a risk of drought in the four seasons of the QTP, and the seasonal variation of TVDI was significant, which was characterized by being relatively dry in spring and summer and relatively humid in autumn and winter. (3) Drought in the QTP was mainly driven by natural factors, supplemented by human factors. The driving effect of temperature and precipitation factors on TVDI was stable and significant, which mainly determined the spatial distribution and variation of TVDI of the QTP. Geomorphological factors led to regional intensification and local differentiation effects of drought, especially in high mountains, flat slopes, sunny slopes and other places, which had a more significant impact on TVDI. Human activities had local point-like and linear impacts, and grass-land and cultivated land that were closely related to the relatively high impacts on TVDI of human grazing and farming activities. In view of the spatial-temporal patterns of change in TVDI in the study area, it is important to strengthen the monitoring and early warning of changes in natural factors, optimize the spatial distribution of human activities, and scientifically promote ecological protection and restoration.