scholarly journals The Novel Microwave Temperature Vegetation Drought Index (MTVDI) Captures Canopy Seasonality across Amazonian Tropical Evergreen Forests

2021 ◽  
Vol 13 (3) ◽  
pp. 339
Author(s):  
Liyang Liu ◽  
Xueqin Yang ◽  
Fanxi Gong ◽  
Yongxian Su ◽  
Guangqing Huang ◽  
...  
Keyword(s):  
Tropical Forests ◽  
Drought Index ◽  
Vegetation Index ◽  
Severity Index ◽  
Evergreen Forest ◽  
Mean Annual Precipitation ◽  
Drought Severity ◽  
Earth Observing System ◽  
Tropical Evergreen Forest ◽  
Terrestrial Water

Despite its perennial canopy, the Amazonian tropical evergreen forest shows significant canopy growth seasonality, which has been represented by optical satellite-based observations. In this paper, a new Microwave Temperature–Vegetation Drought Index (MTVDI) based on Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) sensors was used to capture the canopy seasonality from 2003 to 2010 in comparison with four climatic dryness indicators (Palmer Drought Severity Index (PDSI), Climatological Water Deficit (CWD), Terrestrial Water Storage (TWS), Vapor Pressure Deficit (VPD)) and two photosynthesis proxies (Enhanced Vegetation Index (EVI) and Solar-Induced chlorophyll Fluorescence (SIF)), respectively. Our results suggest that the MTVDI shows opposite seasonal variability with two photosynthesis proxies and performs better than the four climatic dryness indicators in reflecting the canopy photosynthesis seasonality of tropical forests in the Amazon. Besides, the MTVDI captures wet regions that show green-up during the dry season with mean annual precipitation higher than 2000 mm per year. The MTVDI provides a new way for monitoring the canopy seasonality of tropical forests from microwave signals.

scholarly journals Performance of Various Forms of the Palmer Drought Severity Index in China from 1961 to 2013

2019 ◽  
Vol 20 (9) ◽  
pp. 1867-1885 ◽  
Author(s):  
Ziqian Zhong ◽  
Bin He ◽  
Lanlan Guo ◽  
Yafeng Zhang
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Potential Evapotranspiration ◽  
Southern China ◽  
Northern China ◽  
Severity Index ◽  
Drought Severity ◽  
Average Rainfall ◽  
Terrestrial Water ◽  
Better Than

Abstract A topic of ongoing debate on the application of PDSI is whether to use the original version of the PDSI or a self-calibrating form, as well as which method to use for calculating potential evapotranspiration (PET). In this study, the performances of four forms of the PDSI, including the original PDSI based on the Penman–Monteith method for calculating PET (ETp), the PDSI based on the crop reference evapotranspiration method for calculating PET (ET0), the self-calibrating PDSI (scPDSI) based on ETp, and the scPDSI based on ET0, were evaluated in China using the normalized difference vegetation index (NDVI), modeled soil moisture anomalies (SMA), and the terrestrial water storage deficit index (WSDI). The interannual variations of all forms of PDSI agreed well with each other and presented a weak increasing trend, suggesting a climate wetting in China from 1961 to 2013. PDSI-ET0 correlated more closely with NDVI anomalies, SMA, and WSDI than did PDSI-ETp in northern China, especially in northeastern China, while PDSI-ETp correlated more closely with SMA and WSDI in southern China. PDSI-ET0 performed better than PDSI-ETp in regions where the annual average rainfall is between 350 and 750 mm yr−1. The spatial comparability of scPDSI was better than that of PDSI, while the PDSI correlated more closely with NDVI anomalies, SMA, and WSDI than did scPDSI in most regions of China. Knowledge from this study provides important information for the choice of PDSI forms when it is applied for different practices.

Temporal and spatial heterogeneity of drought impact on vegetation growth on the Inner Mongolian Plateau

2018 ◽  
Vol 40 (2) ◽  
pp. 113 ◽  
Author(s):  
Miao Bailing ◽  
Li Zhiyong ◽  
Liang Cunzhu ◽  
Wang Lixin ◽  
Jia Chengzhen ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Severity Index ◽  
Drought Severity ◽  
Summer Drought ◽  
Desert Steppe ◽  
Vegetation Types ◽  
Mongolian Plateau ◽  
Typical Steppe ◽  
Recent Climate ◽  
Normalised Difference Vegetation Index

Drought frequency and intensity have increased in recent decades, with consequences for the structure and function of ecosystems of the Inner Mongolian Plateau. In this study, the Palmer drought severity index (PDSI) was chosen to assess the extent and severity of drought between 1982 and 2011. The normalised difference vegetation index (NDVI) was used to analyse the responses of five different vegetation types (forest, meadow steppe, typical steppe, desert steppe and desert) to drought. Our results show that during the last 30 years, the frequency and intensity of droughts have increased significantly, especially in summer and autumn. The greatest decline in NDVI in response to drought was observed in typical steppe and desert steppe vegetation types. Compared with other seasons, maximum decline in NDVI was observed in summer. In addition, we found that NDVI in the five vegetation types showed a lag time of 1–2 months from drought in the spring and summer. Ancillary soil moisture conditions influenced the drought response, with desert steppe showing a stronger lag effect to spring and summer drought than the other vegetation types. Our results show that drought explains a high proportion of changes in NDVI, and suggest that recent climate change has been an important factor affecting vegetation productivity in the area.

A novel index for ecological drought monitoring based on ecological water deficit: a case study of Northwestern China

2021 ◽  
Author(s):  
Tianliang Jiang ◽  
Xiaoling Su
Keyword(s):  
Water Deficit ◽  
Time Scales ◽  
Drought Index ◽  
Vegetation Index ◽  
Standardized Precipitation Index ◽  
Northwestern China ◽  
Drought Monitoring ◽  
Drought Indices ◽  
Drought Severity ◽  
Different Time Scales

<p>Although the concept of ecological drought was first defined by the Science for Nature and People Partnership (SNAPP) in 2016, there remains no widely accepted drought index for monitoring ecological drought. Therefore, this study constructed a new ecological drought monitoring index, the standardized ecological water deficit index (SEWDI). The SEWDI is based on the difference between ecological water requirements and consumption, referred to as the standardized precipitation index (SPI) method, which was used to monitor ecological drought in Northwestern China (NWRC). The performances of the SEWDI and four widely-used drought indices [standardized root soil moisture index (SSI), self-calibrated Palmer drought index (scPDSI), standardized precipitation-evaporation drought index (SPEI), and SPI) in monitoring ecological drought were evaluated through comparing the Pearson correlations between these indices and the standardized normalized difference vegetation index (SNDVI) under different time scales, wetness, and water use efficiencies (WUEs) of vegetation. Finally, the rotational empirical orthogonal function (REOF) was used to decompose the SEWDI at a 12-month scale in the NWRC during 1982–2015 to obtain five ecological drought regions. The characteristics of ecological drought in the NWRC, including intensity, duration, and frequency, were extracted using run theory. The results showed that the performance of the SEWDI in monitoring ecological drought was highest among the commonly-used drought indices evaluated under different time scales [average correlation coefficient values (r) between SNDVI and drought indices: SEWDI<sub></sub>= 0.34, SSI<sub></sub>= 0.24, scPDSI<sub></sub>= 0.23, SPI<sub></sub>= 0.20, SPEI<sub></sub>= 0.18), and the 12-month-scale SEWDI was largely unaffected by wetness and WUE. In addition, the results of the monitoring indicated that serious ecological droughts in the NWRC mainly occurred in 1982–1986, 1990–1996, and 2005–2010, primarily in regions I, II, and V, regions II, and IV, and in region III, IV, and V, respectively. This study provides a robust approach for quantifying ecological drought severity across natural vegetation areas and scientific evidence for governmental decision makers.</p>

scholarly journals Dynamical delimitation of the Central American Dry Corridor (CADC) using drought indices and aridity values

2019 ◽  
Vol 43 (5) ◽  
pp. 627-642 ◽  
Author(s):  
Luis Eduardo Quesada-Hernández ◽  
Oscar David Calvo-Solano ◽  
Hugo G Hidalgo ◽  
Paula M Pérez-Briceño ◽  
Eric J Alfaro
Keyword(s):  
Drought Index ◽  
Pacific Coast ◽  
Potential Evapotranspiration ◽  
Standardized Precipitation Index ◽  
Severity Index ◽  
Rainfall Anomaly ◽  
Central American ◽  
Drought Indices ◽  
Drought Severity ◽  
Southern Mexico

The Central American Dry Corridor (CADC) is a sub-region in the isthmus that is relatively drier than the rest of the territory. Traditional delineations of the CADC’s boundaries start at the Pacific coast of southern Mexico, stretching south through Central America’s Pacific coast down to northwestern Costa Rica (Guanacaste province). Using drought indices (Standardized Precipitation Index, Modified Rainfall Anomaly Index, Palmer Drought Severity Index, Palmer Hydrological Drought Index, Palmer Drought Z-Index and the Reconnaissance Drought Index) along with a definition of aridity as the ratio of potential evapotranspiration (representing demand of water from the atmosphere) over precipitation (representing the supply of water), we proposed a CADC delineation that changes for normal, dry and wet years. The identification of areas that change their classification during extremely dry conditions is important because these areas may indicate the location of future expansion of aridity associated with climate change. In the same way, the delineation of the CADC during wet extremes allows the identification of locations that remain part of the CADC even during the wettest years and that may require special attention from the authorities.

scholarly journals A New Global 0.5° Gridded Dataset (1901–2006) of a Multiscalar Drought Index: Comparison with Current Drought Index Datasets Based on the Palmer Drought Severity Index

2010 ◽  
Vol 11 (4) ◽  
pp. 1033-1043 ◽  
Author(s):  
S. M. Vicente-Serrano ◽  
S. Beguería ◽  
J. I. López-Moreno ◽  
M. Angulo ◽  
A. El Kenawy
Keyword(s):  
Spatial Resolution ◽  
Drought Index ◽  
Palmer Drought Severity Index ◽  
National Research Council ◽  
Severity Index ◽  
Research Unit ◽  
Climatic Research Unit ◽  
Drought Severity ◽  
Spatial And Temporal Variability ◽  
Drought Severity Index

Abstract A monthly global dataset of a multiscalar drought index is presented and compared in terms of spatial and temporal variability with the existing continental and global drought datasets based on the Palmer drought severity index (PDSI). The presented dataset is based on the standardized precipitation evapotranspiration index (SPEI). The index was obtained using the Climatic Research Unit (CRU) TS3.0 dataset at a spatial resolution of 0.5°. The advantages of the new dataset are that (i) it improves the spatial resolution of the unique global drought dataset at a global scale; (ii) it is spatially and temporally comparable to other datasets, given the probabilistic nature of the SPEI; and, in particular, (iii) it enables the identification of various drought types, given the multiscalar character of the SPEI. The dataset is freely available on the Web page of the Spanish National Research Council (CSIC) in three different formats [network Common Data Form (netCDF), binary raster, and plain text].

scholarly journals Identification of Drought and Performance Evaluation of MODIS and TRMM through Remote Sensing: A Case Study in North and West Africa during 2002–2018

2020 ◽  
Author(s):  
Malak Henchiri ◽  
Qi Liu ◽  
Bouajila Essifi ◽  
Shahzad Ali ◽  
Wilson Kalisa ◽  
...  
Keyword(s):  
Remote Sensing ◽  
West Africa ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Severity Index ◽  
Precipitation Anomaly ◽  
Drought Severity ◽  
Monthly Precipitation ◽  
Water Index ◽  
And Performance

North and West Africa are the most vulnerable regions to drought, due to the high variation in monthly precipitation. An accurate and efficient monitoring of drought is essential. In this study, we use TRMM data with remote sensing tools for effective monitoring of drought. The Drought Severity Index (DSI), Temperature Vegetation Drought Index (TVDI), Normalized Difference Vegetation Index (NDVI), and Normalized Vegetation Supply Water Index (NVSWI) are more useful for monitoring the drought over North and West Africa. To classify the areas affected by drought, we used the TRMM spatial maps to verify the TVDI, DSI and NVSWI indexes derived from MODIS. The DSI, TVDI, NVSWI and Monthly Precipitation Anomaly (NPA) indexes with the employ of MODIS-derived ET/PET and NDVI were chosen for monitoring the drought in the study area. The seasonal spatial correlation between the DSI, NPA, NVWSI, NDVI, TVDI and TCI indicates that NVSWI, NDVI and DSI present an excellent monitor of drought indexes. The change trend of drought from 2002 to 2018 was also characterized. The frequency of drought showed a decrease during this period.

scholarly journals ANALISIS KEKERINGAN DI KABUPATEN SRAGEN DENGAN METODE PALMER, THORNTHWAITE, DAN STANDARDIZED PRECIPITATION INDEX

2021 ◽  
Vol 17 (2) ◽  
pp. 111-124
Author(s):  
Safrudin Nor Aripbilah ◽  
Heri Suprapto
Keyword(s):  
Drought Index ◽  
Southern Oscillation ◽  
Standardized Precipitation Index ◽  
Severity Index ◽  
Precipitation Index ◽  
Suitable Method ◽  
Drought Severity ◽  
Severe Drought ◽  
Drought Hazard ◽  
Dry Conditions

El Nino and La Nina in Indonesia are one of the reasons that caused climate changes, which has possibility of drought and flood disasters. Sragen Regency wherethe dry season occurs, drought happened meanwhile other areas experience floods and landslides. A study on drought needs to be carried out so as to reduce the risk of losses due to the drought hazard. This study is to determine the drought index in Sragen Regency based on several methods and the correlation of each methods and its suitability to the Southern Oscillation Index (SOI) and rainfall. Drought was analyzed using several methods such as Palmer Drought Severity Index (PDSI), Thornthwaite-Matter, and Standardized Precipitation Index (SPI) then correlated with SOI to determine the most suitable method for SOI. The variables are applied in this method are rainfall, temperature, and evapotranspiration. The results showed that the drought potential of the Palmer method is only in Near Normal conditions, which is 1%, Severe drought conditions are 29% for the Thornthwaite-Matter method, and Extreme Dry conditions only reach 1,11% for the SPI method. The PDSI and SPI methods are inversely proportional to the Thornthwaite-Matter method and the most suitable method for SOI values or rainfall is the SPI method. These three methods can be identified the potential for drought with only a few variables so that they could be applied if they only have those data.Keywords: Drought, PDSI, Thornthwaite-Matter, SPI, SOI

scholarly journals Assessing vegetation response to drought in the Laohahe catchment, North China

2012 ◽  
Vol 43 (1-2) ◽  
pp. 91-101 ◽  
Author(s):  
Xiaofan Liu ◽  
Liliang Ren ◽  
Fei Yuan ◽  
Jing Xu ◽  
Wei Liu
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Correlation Coefficients ◽  
Meteorological Drought ◽  
Growing Season ◽  
Severity Index ◽  
Drought Severity ◽  
Seasonal Effect ◽  
Vegetation Types ◽  
The Relationship

In order to better understand the relationship between vegetation vigour and moisture availability, a correlation analysis based on different vegetation types was conducted between time series of monthly Normalized Difference Vegetation Index (NDVI) and Palmer Drought Severity Index (PDSI) during the growing season from April to October within the Laohahe catchment. It was found that NDVI had good correlation with PDSI, especially for shrub and grass. The correlation between NDVI and PDSI varies significantly from one month to another. The highest value of correlation coefficients appears in June when the vegetation is growing; lower correlations are noted at the end of growing season for all vegetation types. The influence of meteorological drought on vegetation vigour is stronger in the first half of the growing season, before the vegetation reaches the peak greenness. In order to take the seasonal effect into consideration, a regression model with seasonal dummy variables was used to simulate the relationship between NDVI and PDSI. The results showed that the NDVI–PDSI relationship is significant (α = 0.05) within the growing season, and that NDVI is an effective indicator to monitor and detect droughts if seasonal timing is taken into account.

scholarly journals A Comparison of Weekly Monitoring Methods of the Palmer Drought Index

2007 ◽  
Vol 20 (24) ◽  
pp. 6033-6044 ◽  
Author(s):  
Jinyoung Rhee ◽  
Gregory J. Carbone
Keyword(s):  
South Carolina ◽  
Drought Index ◽  
Severity Index ◽  
Drought Severity ◽  
Climatic Data ◽  
Monitoring Methods ◽  
The South ◽  
Index Method ◽  
Monitoring Method ◽  
Climate Division

Abstract A method for weekly monitoring of the Palmer Drought Index (PDI) by using four parallel month-long calculation chains in rotation (“ROLLING” method) was tested for the Kansas Northwest Climate Division and the South Carolina Southern Climate Division and compared to two other methods, a modified version of the Climate Prediction Center’s weekly Palmer Drought Index monitoring method with a modified set of coefficients (“WEEKLY” method) and the National Climatic Data Center’s (NCDC’s) projected monthly Palmer Drought Index method using long-term historical daily normal temperature and precipitation (“NORMALS” method). The results for the Kansas Northwest Climate Division and the South Carolina Southern Climate Division generally agreed. The weekly method produced drought severity values that differ most from standard monthly PDI values despite using a modified set of coefficients. The method recently adopted by NCDC successfully estimated Palmer Modified Drought Index (PMDI) values late in the month, but often presented a misleading trend early in the month. The method used in this paper produced PMDI and Z Index values that approximate those found using the standard monthly PMDI code. It also preserves approximately the same length of memory found in that code, provides a tool for progressive drought monitoring allowing users to assess current drought conditions, produces a weekly historical archive of the Palmer Drought Severity Index (PDSI) and Palmer Hydrological Drought Index (PHDI), and enables users to identify the onset of drought early and more clearly.

scholarly journals Selection Drought Index Calculation Methods Using Electre, Topsis, and Analytic Hierarchy Process

2018 ◽  
Vol 7 (4.44) ◽  
pp. 188
Author(s):  
Hadisuwito A.S ◽  
Hassan F.H
Keyword(s):  
Analytic Hierarchy Process ◽  
Forest Fires ◽  
Drought Index ◽  
Palmer Drought Severity Index ◽  
Severity Index ◽  
Drought Severity ◽  
Analytic Hierarchy ◽  
Forest Fire Danger ◽  
The Right ◽  
Hierarchy Process

The drought index is an essential indicator for calculating forest fires’ potential. Many methods are developed to maintain the drought index. However, they provide less suitable at many places. Every area has their own character, and each of methods has their own specification. The spot problem is how to find the right method for those places. The forest of Bukit Suharto, has particular character as one of the rain tropical forests, and it needs suitable method. Furthermore, this study is conducted to examine the right methods that compatible for the forest. They are: Palmer Drought Severity Index (PDSI), Keetch Byram Drought Index (KBDI), Reconnaissance Drought Index (RDI), Standard Precipitation Index (SPI), Effective Drought Index (EDI), McArthur Forest Fire Danger Index (MFFDI), and Standard Precipitation Evapotranspiration Index (SPEI). Every method has specific variables for the calculation, namely, the period, the data’s type, the formula’s complexity, the usability, and scale results’ type. On processing the seven methods, the researcher uses other techniques to asses them, namely, ELECTRE, TOPSIS, and Analytic Hierarchy Process. In final process, the conclusion is compared through the result. In summary, the results show that KBDI’s method is the most recommended, and TOPSIS is the best technique for recommendations. 

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